Intelligent Production Machines and Systems 2ndI*PROMS Virtual Conference 3-14 July 2006
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Intelligent Production Machines and Systems 2nd I'PROMS Virtual Conference 3-14 July 2006 Organized by FP61 I*PROMS Network of Excellence Sponsored by the European Commission
Editors
D T Pham E E Eldukhri A J Soroka
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First edition 2006 Copyright 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier 2006. All rights reserved
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Contents
Sponsors Preface Introduction by Mr A. Gentili, European Commission Programme and Organising Committees Special Session Organisers Special Session Chairs and Co-Chairs Referees I'PROMS Central Coordination and MEC Teams
xiii xv
xvii xix xix xx xx
xxii
Advanced Machine Tool Technologies Advanced signal processing in acoustic emission monitoring systems for machining technology E.M. Rubio, R.. Teti, I.L. Baciu Innovative signal processing for cutting force based chip form prediction K. Jemielniak, R. Teti, J. Kossakowska, T. Segreto Monitoring of slowly progressing deterioration of CNC-machine axes 13 E. Uhlmann, E. Hohwieler, C. Geisert The monitoring of the turning tool wear process using an artificial neural network G.C. Balan, A. Epureanu Use of interprocess communication in parallel neural networks for monitoring complex systems H. Marzi
20
26
Collaborative and Responsive Manufacturing Systems Collaborative research on new manufacturing technologies organization X. Maidagan, N. Ortega, L.N. L6pez de Lacalle, A. Lamikiz, J.A. S6nchez
33
Six sigma training programmes to help SME's improve T. Fouweather, S. Coleman, A. Thomas
39
The alignment of collaboration and the importance of integrated performance measurement 114. Sarana, R.J. Mason The cultural and trust aspects of collaborative supply chains G. Aryee
Concurrent Engineering BM_Virtual enterprise architecture reference model for concurrent engineering and
45
52
product improvement: An experiment A.J.C. Pithon, G.D. Putnik Collaborative design review in a distributed environment M. Sharma, V. Raja, T. Fernando
59
Implementing manufacturing feature based design in CAD/CAM T. Szecsi
71
Learning and reasoning techniques for automatic feature recognition from CAD model E.B. Brousseau, S.S. Dimov, R.M. Setchi Machining of large dies based on the prediction of the press/die deformation D. del Pozo, L.N. L6pez de Lacalle, J.M. L6pez, A. Herndndez
65
77
83
Digital Manufacturing A model-based graphical user-interface for process control systems in manufacturing X.J. Li, T. Schlegel, M. Rotard, T. Ertl
89
Product lifecycle management and information tracking using smart embedded systems applied to machine tools 95 E Meo, D. Panarese Product support knowledge N Lagos, R. Setchi
101
Visual simulation of grinding process M. Sakakura, S. Tsukamoto, T. Fujiwara, I. Inasaki
107
E-manufacturing E-business and Virtual Enterprises Collaborative analysis among virtual teams: an experience A.C. Pithon, M.R. Brochado, E E Sandonato, B.M. Teixeira
113
Collaborative virtual research environment to support integration & steering of multi-site experiments D.K. Tsaneva, K.T.W. Tan, M. W. Daley, N.J. Avis, P.J. Withers
120
e-Cat- Members profiling and competency management tool for virtual organization breeding environment J. Hadik, P. Be&vgtr, J. Vokoinek, J. Biba, E. Semsch
126
E-collaboration: a literature analysis Y. Wang
132
Learning the users view: information retrieval in a semantic network S. Thiel, S. Dalakakis
138
Leonardo da Vinci programme supports the development of e-learning methods in application to the vocational training in automation and robotics W. Klimasara, Z. Pilat, S. Sawwa, M. S3owikowski, J. Zieli~ski
144
User-interface architectures for VE dynamic reconfiguration: an initial analysis
150
vi
P. Gon(alves, G.D. Putnik, M. Cunha, R. Sousa
Using semantic web technologies to discover resources within the intranet of an organization S.C. Buraga, T. Rusu
158
Innovative Production Machines and Systems Design of precision desktop machine tools for meso-machining
165
A. Khalid, S. Mekid
Designing agent-based household appliances K. Steblovnik, D. Zazula
171
Governance, innovation and performance D. Wilson, C. Herron, S. Coleman
179
KOBAS: Integration of applications in machines EJ. Diez, R. Arana
185
On-line modal identification of a CNC machining system based on surface roughness laser scattering: theoretical perspectives Z.M. Hussin, K. Cheng, D. Huo
191
Selective laser sintering of metal and ceramic compound structures D. Trenke, N. Miiller, W. Rolshofen
198
The effect of the punch radius in dieless incremental forming L. Carrino, G. Giuliano, M. Stano
204
Intelligent and Competitive Manufacturing Air bearings based on porous ceramic composites E. Uhlmann, C. Neumann CBN grinding wheel inventory sizing through non-shortsighted flexible tool management strategies D. D 'Addona, R. Teti Flow front analysis in resin infusion process L Crivelli Visconti, M. Durante, A. Langella, U. Morano
211
217
223
Forces analysis in sheet incremental forming and comparison of experimental and simulation results F. Capece Minutolo, M. Durante, A. Formisano, A. Langella
229
Neural network based system for decision making support in orthodontic extraction R. Martina, R. Teti, D. D 'Addona, G Iodice
235
Optimization of a hydroforming process to realize asymmetrical aeronautical
vii
components by FE analysis F. Capece Minutolo, M. Durante, A. Form&ano, A. Langella
241
Optimization of friction stir welds of aluminium alloys A. Squillace, T. Segreto, U. Prisco, R. Teti, G. Campanile
247
Personalized ankle-foot orthoses design based on reverse engineering S.M. Milusheva, E.Y. Tosheva, L.C. Hieu, L. V. Kouzmanov, N. Zlatov, Y.E. Toshev
253
Quality evaluation of thermoplastic composite material single-lap joints 1.L. Baciu, 1. Crivelli ViscontL A. Langella, V. Luprano, R. Teti
258
Springback prediction with FEM analysis of advanced high strength steel stamping process S. Al Azraq, R. Teti, J. Costa
264
Intelligent Automation Systems A distributed stand-in agent based algorithm for opportunistic resource allocation P Benda, P. Jisl
271
A low-cost 3-d laser imaging system B.P. Horan, S. Nahavandi
277
Filter selection for multi-spectral image acquisition using the feature vector analysis methods 1.S. Chatzis, V.A. Kappatos, E.S. Dermatas
283
Global sensor feedback for automatic nanohandling inside a scanning electron microscope T. Sievers
289
Print-through prediction using ANNs K. Wang, B. Lorentzen
295
Visual servoing controller for robot handling fabrics of curved edges P. Th. Zacharia, 1. G. Mariolis, N.A. Aspragathos, E.S. Dermatas
301
Intelligent Decision Support Systems A novel self-organised learning model with temporal coding for spiking neural networks D.T. Pham, M.S. Packianather, E.Y.A. Charles
307
An algorithm based on the immune system for extracting fault classes from instance histories D.T. Pham, A.J. Soroka
313
Control Chart Pattern Recognition using Spiking Neural Networks D.T. Pham, S. Sahran
319
Engineering applications of clustering techniques D.T. Pham, A.A. Afify
326
viii
Fusing neural networks, genetic algorithms and fuzzy logic for diagnosis of cracks in shafts
332
K.M. Saridakis, A.C. Chasalevris, A.J. Dentsoras, C.A. Papadopoulos
Optimization of assembly lines with transportation delay using IPA
338
I. Mourani, S. Hennequin, X. Xie
Prediction of workpiece surface roughness using soft computing
344
B. Samanta, W. Erevelles, Y.. Omurtag
Service orientation in production control
350
W. Beinhauer, T. Schlegel
Statistical approach to numerical databases: clustering using normalised Minkowski metrics
356
D.T. Pham, Y.L Prostov, M.M. Suarez-Alvarez
Technology readiness model for enterprises
362
E. Oztemel, T.K. Polat
Intelligent Design Systems A critical analysis of current engineering design methodologies from a decision making perspective
369
K.W. Ng
A novel method of measuring the similarity of designs D.T. Pham, Y. Wu, S. Dimov
An I-Ching-TRIZ inspired tool for retrieving conceptual design solutions D.T. Pham, H. Liu, S. Dimov
Design for Rapid Manufacturing, functional SLS parts
375 381 389
W. Kruf B. van de Vorst, H. Maalderink, N. Kamperman
Life cycle and unit cost analysis for modular re-configurable flexible light assembly systems J. Heilala, J. Montonen, K. Helin, T. Salonen, O. Vdgitgiinen
Material-driven solution finding- functional materials in the design process P. Dietz, A. Guthmann, T. Korte
Neuro-fuzzy case-based design: An application in structural design K.M. Saridakis, A.J. Dentsoras, P.E. Radel, V.G. Saridakis, N. V. Exintari
Process planning support for intermediate steps design of axisymmetric hot close-die forging parts R.H. Radev
Smart design for assembly using the simulated annealing approach H. Shen, A. Subic
Virtual environment auditory feedback techniques to aid manual material handling Tasks D.T. Pham, S. Dimov, F. Abdul Aziz, I.A. Nicholas
395 401 407
413 419
425
ix
Intelligent Optimisation Techniques for Production Machines and Systems An efficient meta-heuristic for the single machine common due date scheduling problem A. C. Nearchou Evolutionary approach to measure production performance B. Denkena, C. Liedtke Feature selection for SPC chart pattern recognition using fractional factorial experimental design A. Hassan, M.S.N. Baksh, A.M. Shaharoun, H. Jamaluddin
431 436
442
Optimization of fixture layout by means of the genetic algorithm T. A oyama, Y. Kakinuma, I. Inasaki
448
The bees algorithm- a novel tool for complex optimisation problems D.T. Pham, A. Ghanbarzadeh, E. KoG S. Otri, S. Rahim, M. Zaidi
454
Intelligent Supply Chains Agents in the supply chain: lessons from the life sciences J. Efstathiou, A. Calinescu
461
Coordination model in the supply chain R. Affonso, E Marcotte, B. Grabot
468
Incorporating delay and feedback in intelligent manufacturing strategy selection D.T. Pham, Y. Wang, S. Dimov
474
Towards a reconfigurable supply network model T. Kelepouris, C.Y. Wong, A.M. Farid, A.K. Parlikad, D.C. McFarlane
481
Reconfigurable Manufacturing Systems A novel adaptive process planning framework B. Denkena, A. Battino An advanced engineering environment for distributed & reconfigurable industrial automation & control systems based on IEC 6149 T. Strasser, L Miiller, M. Schiipany, G. Ebenhofer, R. Mungenast, C. Siinder, A. Zoitl, O. Hummer, S. Thomas, H. Steininger
487
493
Analysis of wireless technologies for automation networking C. Cardeira, A. Colombo, R. Schoop
499
Engineering modular automation systems R. Harrison, A.A. West, S.M. Lee
505
Linking production paradigms and organizational approaches to production systems S. Carmo-Silva, A.C. Alves, F. Moreira Responsive system based on a reconfigurable structure B. Hu, J. Efstathiou
511
517
Towards reconfiguration applications as basis for control system evolution in zerodoufntime automation systems C. Siinder, A. Zoitl, B. Favre-Bulle, T. Strasser, H. Steininger, S. Thomas
523
Novel H u m a n M a c h i n e Interfaces - Tangible Acoustic Interfaces (Tai Chi) Acoustic source localization for design of tangible acoustic interfaces L. Xiao, T. Collins, Y Sun
529
Ambient intelligence in manufacturing I. Maurtua, M.A. POrez, L. Susperregi, C. Tubio, A. Ibarguren
535
Localisation of impacts on solid objects using wavelet transform and maximum likelihood estimation
541
D.T. Pham, Z. Ji, O. Peyroutet, M. Yang, Z. Wang, M. Al-Kutubi
Modelling elastic wave propagation in thin plates D. Rovetta, A. Sarti, S. Tubaro, G. Colombo
548
Pattern Matching for Tangible Acoustic Interfaces D. T. Pham, M. Al-Kutubi, M. Yang, Z. Wang, Z Ji
556
Tracking Target Using Wideband Cox Comb Signals for Human Computer Interaction Y. Sun, T. Collins, L.Xiao
562
Robotics An Intuitive Teaching Method for Small and Medium Enterprises C. Meyer, R.D. Schraft
568
From robotic arms to mobile manipulation: on coordinated motion schemes t~ Padois, J- Y Fourquet, P. Chiron
572
Fuzzy and neuro-fuzzy based co-operative mobile robots D. T. Pham, M.H. Awadalla, E.E. Eldukhri
578
Multi-agent snake-like motion with reactive obstacle avoidance
584
GL Birbilis, N.A. Aspragathos
Path planning in weighted regions using the Bump-Surface concept E.K. Xidias, N.A. Aspragathos Self-organising Locally Interpolating Map for the control of mobile microrobots H. Hiilsen, S. Fatikow, D. T. Pham, Z Wang
590
596
Spectral characterization of digital cameras using genetic algorithms I. Chatzis, D. Gavrilis, E. Dermatas
602
Towards more agility in robot painting through 3d object recognition A. Pichler, H. Bauer, C. Eberst, C. Heindl, J. Minichberger
608
xi
Sustainable Manufacturing Systems Key technologies and strategies for creating sustainable manufacturing organisations A.J. Thomas, B. Grabot
614
An integrated approach to TPM and six sigma development in the castings industry A.J. Thomas, G.R. Jones, P. Vidales
620
Characterising SME attitudes to technological innovation A.J. Thomas, R. Barton
626
Maximising the effectiveness of introducing advanced technologies R. Barton, A.J. Thomas
632
On the importance of maintenance costing H. Wong, N. Rich
638
Roadmapping as a strategic manufacturing tool A.J. Thomas, G Weichhart
644
The design of a sustainable manufacturing system: A case study of its importance to product variety manufacturing R. Jayachandran, S. Singh, J. Goodyer, K. Popplewell Traceability requirements in electronics assembly M. Ford, J.D. Triggs
xii
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Preface Intelligent Production Machines and Systems (IPROMS) employ advanced IT and computational techniques to provide competitive advantage. The 2006 Virtual International Conference on IPROMS took place on the Internet between 3 and 14 July 2006. IPROMS 2006 was an outstanding success. During the Conference, some 3600 registered delegates and guests from 69 countries participated in the Conference, making it a truly global phenomenon. This book contains the Proceedings of IPROMS 2006. The 107 peer-reviewed technical papers presented at the Conference have been grouped into sixteen sections, the last two featuring contributions selected for IPROMS 2006 by Special Sessions chairmen: 9
Advanced Machine Tool Technologies
9
Collaborative and Responsive Manufacturing Systems
9
Concurrent Engineering
9
Digital Manufacturing
9
E-manufacturing, E-business and Virtual Enterprises
9
Innovative Production Machines and Systems
9
Intelligent Automation Systems
9
Intelligent Decision Support Systems
9
Intelligent Design Systems
9
Intelligent Optimisation Techniques for Production Machines and Systems
9
Intelligent Supply Chains
9
Robotics and Micro Electromechanical Machines and Systems
9
Reconfigurable Manufacturing Systems
9
Sustainable Manufacturing Systems
9
Intelligent and Competitive Manufacturing Engineering
9
Novel Human Machine Interfaces - Tangible Acoustic Interfaces (Tai Chi)
Many of the IPROMS 2006 papers were written by partners and associate partners in the I'PROMS EU-funded FP6 Network of Excellence for Innovative Production Machines and Systems, but there were also large numbers of authors external to the Network. In total, IPROMS 2006 authors were from 28 countries across five continents. By attracting contributors and participants globally, IPROMS 2006 has made another step towards establishing the I'PROMS Network as the world's pre-eminent forum for the discussion of research issues in the field of Innovative Manufacturing. Numerous people and organisations have helped make IPROMS 2006 a reality. We are most grateful to the IPROMS 2006 sponsors, I'PROMS partners, Conference Programme and Organising Committees, Special Session Organisers, Session Chairmen, Authors, Referees, and the I'PROMS Central Coordination Team. The names of contributors to the success of IPROMS 2006 can be found elsewhere in the Proceedings. Here, we would highlight the much appreciated efforts of the Special Session Organisers, Professor R Teti of the University of Naples Federico II, and Dr M Yang of Cardiff University. Finally, our strongest vote of thanks must go to our colleague Vladimir Zlatanov, the technical coordinator of IPROMS 2006, who implemented the entire IT infrastructure for our Virtual Conference. Without his expertise and dedication, IPROMS 2006 would have forever remained virtual. D.T. Pham, E.E. Eldukhri and A.J. Soroka MEC, Cardiff University XV
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Introduction by Mr A Gentili, European Commission IPROMS 2006 is the 2nd online web-based conference on Intelligent Production Machines and Systems organised by the EU-funded FP6 I'PROMS Network of Excellence. It built on the outstanding success of its predecessor IPROMS 2005 which attracted over 4000 registered delegates and guests from 71 countries. During IPROMS 2006, interested researchers and industrial practitioners worldwide took part free of charge. They had the opportunity to view presentations, view/download full papers and contribute to the online discussions. As a sponsor of I'PROMS Network of Excellence, the European Commission strongly supports the organisation of this annual event. This will enable the Network to disseminate the results of its work globally for the benefit of the wider community. Moreover, it will contribute to the integration of research resources in Europe for an efficient spending of R&D budget, avoiding overlaps in European research activities and exploiting synergies. This event, inline with the Manufuture Platform initiative, will help to create an effective and cooperative research manufacturing "society" which is a necessary condition for the establishment of a European Manufacturing and Innovation Research Area. Andrea Gentili Manufuture Platform European Commission
xvii
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Programme and Organising Committees Prof. D. Pham (Chair), MEC, Cardiff University, UK Dr. E. Eldukhri (Organising Committee Chair), MEC, ibid Dr. A. Soroka (Programme Committee Chair), MEC, ibid Mr. V. Zlatanov (Technical Co-ordinator), MEC, ibid Prof. S. Dimov, MEC, ibid Dr. M. Packianather, MEC, ibid Dr. A. Thomas, MEC, ibid Dr. B. Peat, MEC, ibid Mrs. P. Pham, MEC, ibid Dr. A. Glanfield, Cardiff University, UK Prof. R Hines, Cardiff University, UK Prof. M. Naim, Cardiff University, UK Dr. N. Rich, Cardiff University, UK Dr. R. Setchi, Cardiff University, UK Prof. N. Aspragathos, University of Patras, Greece Prof. K. Cheng, Leeds Metropolitan University, UK Dr. A. Colombo, Schneider Electric, Germany Prof. B. Denkena, IFW, University of Hannover, Germany Prof. P. Dietz, Clausthal University of Technology, Germany Dr. J. Efstathiou, University of Oxford, UK Mr. F. Feenstra, TNO, The Netherlands Prof. B. Grabot, ENIT, France Dr. R. Harrison, Loughborough University, UK Prof. S. Hinduja, University of Manchester, UK Mr. M. Hoepf, Fraunhofer IPA, Germany Mr. E. Hohwieler, Fraunhofer IPK, Germany Prof. A. Kusiak, The University of Iowa, USA. Prof. R. La Brooy, RMIT University, Australia Prof. A. Labib, University of Portsmouth, UK Prof. V. Marik, Czech Technical Univ., Czech Republic Dr. F. Meo, Fidia S.p.A, Italy Prof. G. Putnik, University of Minho, Portugal Prof. E. Oztemel, Sakarya University, Turkey Prof. V. Raja, University of Warwick, UK Mr. T. Schlegel, Fraunhofer IAO, Germany Prof. R. Teti, University of Naples Federico II, Italy Dr. A. Thatcher, University of the Witwatersrand, S. Africa Dr M H Wu, University of Derby, UK Dr. Xiaolan Xie, INRIA, France Prof. X. Yu, RMIT University, Australia
Special Session Organisers R. Teti (Intelligent and Competitive Manufacturing Engineering) M. Yang and Z. Wang (Human Machine Interfaces - Tai Chi)
Session Chairs and Co-Chairs F. Abdul Aziz, MEC, Cardiff University, UK A. Afify, MEC, Cardiff University, UK xix
M. A1-Kutubi, MEC, Cardiff University, UK N. Aspragathos, University of Patras, Greece E. Brousseau, MEC, Cardiff University, UK E. Charles, MEC, Cardiff University, UK A. Colombo, Schneider Electric, Germany T. Fouweather, University of Newcastle-upon-Tyne, UK A. Ghanbarzadeh, MEC, Cardiff University, UK B. Grabot, ENIT, France R. van Heek, TNO, The Netherlands E. Hohwieler, Fraunhofer IPK, Germany Z. Ji, MEC, Cardiff University, UK G. Putnik, University of Minho, Portugal V. Raja, University of Warwick, UK M.Ridley MEC, Cardiff University, UK S. Sahran MEC, Cardiff University, UK T. Shamsuddin, MEC, Cardiff University, UK A. Soroka, MEC, Cardiff University, UK R. Teti, University of Naples Federico II, Italy A. Thomas, MEC, Cardiff University, UK D. Tsaneva, Cardiff University, UK Z. Wang, MEC, Cardiff University, UK O. Williams, MEC, Cardiff University, UK Y. Wu, MEC, Cardiff University, UK M. Yang, MEC, Cardiff University, UK V. Zlatanov, MEC, Cardiff University, UK
Referees R. Alfonso, ENIT, France R. Arana, Tekniker, Spain G. Aryee, Cardiff University, UK M. Awadalla, MEC, Cardiff University, UK S. Badiyani, University of Warwick, UK G. Balan, University Dunarea de Jos of Galati, Romania A. Battino, IFW, University of Hannover, Germany W. Beinhauer, Fraunhofer IAO, Germany G. Birbilis, University of Patras, Greece E. Brousseau, MEC, Cardiff University, UK C. Cardeira, Instituto Superior Tecnico, Portugal S. Carmo-Silva, University of Minho, Portugal S. Coleman, University of Newcastle-upon-Tyne, UK M. Cortina, Universidad de Guanajuato, Mexico K. Dotchev, MEC, Cardiff University, UK D. del Pozo, Fundaci6n ROBOTIKER, Spain A. Dentsoras, University of Patras, Greece E. Eldukhri, MEC, Cardiff University, UK F. Feenstra, TNO, The Netherlands J-Y, Fourquet, ENIT, France XX
T. Fouweather, University of Newcastle-upon-Tyne, UK C. Geisert, Fraunhofer IPK, Germany R. Harrison, Loughborough University, UK A. Hassan, Universiti Teknologi Malaysia, Malaysia R. van Heek, TNO, The Netherlands J. Heilala, VTT, Finland J. Ramakumar, Coventry University, UK R Jisl, Czech Technical University in Prague, Czech Republic T. Kelepouris, Cambridge University, UK T. Korkusuz Polat, Sakarya University, Turkey F. Lacan, MEC, Cardiff University, UK N. Lagos, MEC, Cardiff University, UK C. Liedtke, IFW, University of Hannover, Germany H. Liu, Guangdong Ocean University, China V. Majstorovic, University of Belgrade, Serbia H. Marzi, St. Francis Xavier University, Canada 1. Maurtua, Fundacidn TEKNIKER S. Mekid, University of Manchester F. Meo, Fidia S.p.A, Italy C. Meyer, Fraunhofer IPA, Germany Z. Mohd Hussin, Leeds Metropolitan University, UK S. Nahavandi, Deakin University, Australia D. Pham, MEC, Cardiff University, UK A. Pichler, PROFACTOR Produktionsforschungs GmbH, Austria Z. Pilat, PIAP, Poland A. Pithon, CEFET-RJ, Brazil G. Putnik, University of Minho, Portugal RJ. Radcliffe, RMIT University, Australia W. Rolshofen, Clausthal University of Technology, Germany T. Rusu, Petru Poni Institute of Macromolecular Chemistry, Romania M. Sakakura, Daido Institute of Technology, Japan T. Schlegel, Fraunhofer IAO, Germany R. Setchi, Cardiff University, UK M. Sharma, University of Warwick, UK H. Shen, RMIT University, Australia M. Sorli, Foundation LABEIN, Spain A. Soroka, MEC, Cardiff University, UK M. Strano, Universit/t di Cassino, Italy T. Strasser, PROFACTOR Produktionsforschungs GmbH, Austria C. Stinder, Technical University of Vienna, Austria T. Szecsi, Dublin City University, Ireland A. Thatcher, University of the Witwatersrand, South Africa S. Yhiel, Fraunhofer IAO, Germany K. Thramboulidis, University of Patras, Greece Y. Toshev, Bulgarian Academy of Sciences, Bulgaria D. Tsaneva, Cardiff University, UK
xxi
J. Vokrinek, Czech Technical University in Prague, Czech Republic Y. Wang, Cardiff University, UK H. Wong, Cardiff University, UK Y. Wu, MEC, Cardiff University, UK M. Zarrabeitia, CIC marGUNE, Spain
I ' P R O M S Central Coordination and MEC Teams A. Glanfield F.D. Marsh M. Matthews C. Rees M. Takala
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Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All rights reserved.
Advanced signal processing in acoustic emission monitoring systems for machining technology E.M. Rubio ~, R. Teti b and I.L. Baciu b aDept, of Manufacturing Engineering, University of Spain (UNED), Juan del Rosal, 12, Madrid, Spain bDept, of Materials &Production Engineering, University of Naples Federico II, P.le Tecchio 80, Naples, Italy
Abstract
This work is focused on the application of acoustic emission (AE) based monitoring systems to machining processes. It describes the most common advanced signal processing methods used in this type of systems such as continuous and discrete transforms (Fourier, Gabor and Wavelet) and statistical analysis methods (amplitude distribution method and the entropic distance method). Besides, some of the most relevant papers illustrating the mentioned signal processing methods have been discussed. The principal machining technology aspects considered for AE based sensor catastrophic tool failure and chip formation. K e y w o r d s : Acoustic emission, Monitoring systems, Advanced signal processing
1. I n t r o d u c t i o n
Acoustic emission (AE) can be described as a set of elastic pressure waves generated bythe rapid release of energy stored within a material. This energy dissipation is basically due to: dislocation motion, phase transformations, friction, and crack formation and growth. One feature of AE waves is that they do not travel through air but only through a solid material [1]. AE signals can be classified as continuous and burst type. Continuous type signals are associated with plastic deformation in ductile materials and the burst type signals are observed during crack growth within a material, impact and breakage [1-3]. Figure 1 shows the main sources of AE during cutting processes [ 1-4]. Cutting processes are not easy to manage due to the great number of effects involved. However, AE provides for the possibility of identifying, by means of signal changes between continuous and burst types, the tool wear state, which is essential for predicting tool life, and detecting malfunctions in the cutting process such as
chip tangling, chatter vibrations and cutting edge breakage. Thus, by adequate exploitation of AE signals, monitoring systems can be developed for different the aspects involved in machining processes. AE is usually detected by measurement chains like the one shown in Figure 2 [2].
Fig. 1. Main sources of AE stress waves associated with chip formation: 1) primary shear zone, 2) secondary shear zone and craterization by friction, 3) tertiary shear zone and flank wear by friction, 4) crack growth by tool tip-workpiece contact, 5) chip plastic deformation, and 6) chip-tool collision.
2. Signal processing
F(co) - ~ f (t)e -~~dt
The aim of AE signal processing is detecting and characterising the bursts that evidence the abrupt emissions of elastic energy produced inside the material, estimating their time localizations, oscillation frequencies, amplitudes and phases and, possibly, describing appropriately their overlapping structure. The extraction of such physical parameters from an AE signal is one of the most common problems in its processing. This is due to the fact that these signals are non-stationary and often comprise overlapping transients, whose waveforms and arrival times are unknown and involve variations in both time and frequency. Often, such events are partially overlapping in the time domain or affected by noise, i.e. they are interfered with secondary events that are not significant but affect their structure [4]. Different signal processing methods have been developed to analyse AE signals and extract from them features that allow testing and monitoring of machining processes. Some of them are presented and discussed in the following [5-8]. AE sensor
Pre-amplifier
BB
where t is time and co is angular frequency. Both functions, known as Fourier Transform (FT) pair, contain exactly the same information about the signal, but from different and complementary focuses. This type of functions is adequate to represent stationary and e i a t harmonic waves. Then, taking into account that AE signals are essentially non-stationary, it is possible to affirm that, in general, the FT pair does not represent this kind of signals correctly. However, some studies have been carried out successfully using FT for the processing of AE signals from cutting tools with different wear levels.
2.1.2. Gabor Transform Gabor Transform, also called short-time Fourier Transform (STFT), is a time-frequency technique used to deal with non-stationary signals. A Gabor Transform has a short data window centered on time. Spectral coefficients are calculated for this short length of data; the window is then moved to a new position and the calculation repeated. Assuming an energy-limited signal,f(t) can be decomposed by:
1
I
High-pass filter
/
I
Low-pass filter
/
Signal processing
I
Amplifier
f(t)--2--~x ~ ~ F(r'c~176176
(3)
Fg(Z',(o)- ~ f(t)g(t-'c)e-iCadt
(4)
where g(t-I:) is called window function. If the length of the window is represented by time duration T, its frequency bandwidth is approximately 1/T. Using a short data window means that the bandwidth of each spectral coefficient is of the order
1/T.
and recording
Fig. 2. Typical measurement chain based on AE sensors.
2.1. Continuous Transforms 2.1.1Fourier Transform A physical signal is usually represented by a time
functionf(t) or, alternatively, in the frequency domain by its Fourier Transform (FT), F(CO). Assuming an energy-limited and non-periodic signalf(t), this can be decomposed by its Fourier Transform F(co), namely:
f (t) - ~ 1 ~ F(co)e iOXdo)
(2)
(1)
Gabor Transform implementation for AE signal processing is efficient when it is used to locate and characterise events with very defined frequency patterns, not overlapping and long relatively to the window function. Again, it is completely inappropriate to detect details of short duration, long oscillations associated to the low frequencies, or to characterise similar patterns in different scales.
2.1.3. Continuous Wavelet Transform Continuous Wavelet Transform is an alternative to Gabor Trasform that uses modulated window functions, i.e. with variable dimension adjusted to the oscilation frequency. In particular, windows with the same number of oscillations in its domain. This is achieved by
generating a complete family of elementary functions by dilations or contractions and shifts in time, from a unique modulated window:
1
(t-b')
(5)
where ~t(t) is the mother wavelet function and ~ta,b(O a wavelet function, being a ~: 0 and b the scale and shift parameters. The function gt(t) must be located in time, of null average and its function transform T((o) has to be a continuous bandwidth filter and strongly falling when o)---)oo and o ) ~ 0 . Then, given a limited-energy signal s(t), its Continuous Wavelet Transform can be define by:
W~,s(a,b) - ~oos(t)~ta.b (t)dt
(6)
If the mother wavelet funtion is real, s(t) can be written: dbda a
2
(7)
where C~, is a positive constant. Signal processing with wavelets consists of decomposing the signal into its elemental components by using basic functions. Such basic functions consist of the wavelet scale function and scaled and shifted versions of the mother wavelet. The accuracy in time is inversely proportional to the acuracy in frequency, staying constant the relationship At Am 9 This is the fundamental difference with the Gabor Transform. Besides, for each value of a, the wavelets family, shitted by b, behaves like a mobile window, of constant bandwidth, with the same oscillations number of those elementary waves within the actual window flame.
2.2. Discrete Transforms The previous transforms belong to the class called Continuous Integral Transforms. The implementation of this type of transforms is expensive from the numeric and computational point of view. In general, the integral calculations could be approached by sums made over reasonably fine discrete nets. Both the parameters and the reconstruction points have to be discrete. In very special cases, it is possible to take the discrete parameters, so the values of the discrete transform can be effectively computed and the information contained in them reasonably well
represented by a numerically stable expression based on a set of sums. Such expression is a Discrete Transform of the function. It is not a simple approach of the continuous one, but rather a new way of analysing and synthesising the information. Then, it is possible to obtain different Discrete Transforms such as local Fourier series, Gabor discrete transform or the Wavelet one. Given a T-periodic signal s(t), from its Fourier series:
(8)
~(t) - Z s(cok )e ~~ 2
where o~= 2 N / T are the angular frequencies ands(o~) the Fourier coefficients, then the local Fourier series can be written as: 1
s(o)k ) - -~ ~'+Vs(t)e-i~
dt
(9)
like a signal limited to the interval [to, to+T], in the frequencies discrete net co and multiplied for a constant. Given a non-periodic signal s(t), the idea is to segment it using a window function g(t) of width T and that is shifted to regular intervals along all the domain. Selecting the window function g(t) appropriately and the displacement step to, the next representation will be obtained:
n
k
cn.k being the Fourier coefficients of the modulated segment s(t)g(t-nro). Those coefficients contain the information in frequency for each time segment. Eq. (10) represents a time-frequency discrete transform called Local Fourier Series that can be considered as a Gabor Discrete Transform. The design of a Wavelet Discrete Transform version consists of defining an appropiate set of parameters{(aj; bjk)}. Different types of sets exist. Among them, it is possible to remark the orthogonal wavelets bases given by:
aj -2-J;bjk =2-Jk
j,k~ Z
(11)
With this parameters selection, the usual expression for the wavelets is: gtjk (t) = 2J/2~(2Jt-k)
j,k ~ Z
(12)
Then, assuming a real mother wavelet and a limitedenergy signal s(t), the Wavelet Discrete Transform is
defined by:
kurtosis value indicates essentially flat characteristics [10].
DW~(j',k)=<s,~k >=s
j,k~ Z (13)
The synthesis formula will be:
~(t)= ZZ~j,,ej,, (t)-- Z Z < ~,~,.,, ~:,,(t) jk
(14)
jk
2.3.2. Entropic distance method The Entropic distance method is based on the comparison of the obtained signal with a pattern signal used as reference. To do this, the signals are adjusted to an Auto Regresive (AR) pattern of orderp: p
for appropriate coefficients cj~.
aix,_ i = e t
2.3. Statistical analysis
(17)
where {~;} is a gaussian random variable with E[~:, ] = 0,
2.3.1. Amplitude distribution method The amplitude distribution method is based on the results obtained by Kannatey-Asibu and Dornfeld [9]. It tries to recognise differences among signals through the study of the distribution of amplitudes. Basically, such distribution is obtained by making a plot of the frequency where the different amplitudes of the signal are given. A set ofparallel lines to thex axis is traced and the number of crossings of such lines by the signal is counted. If a part of the signal plot has a low slope, the value of the relative frequency in the interval corresponding to its ordinates will increase. In this way, the "aspect" ofthe curve will be reflected in the aspect of the distribution. There are two aspects to consider: the range of the distribution and its shape. The study of the problem through the characterisation of textures, similar to the study of surface profiles has proven that it is in the shape of the distribution where the most important aspects appear. The most comprehensive classification of the distribution shape can be achieved by means of the central moments of the distribution function. In particular, by the third and fourth central moments calledskew and kurtosis respectively and given by:
E[CsCj]=foo'~and a/the pattern coefficients. All roots/4 ofthe polynomial a(z)= 1 + a,z +...+ akz k satisfy [fli [ > 1. Once the parameters have been identified with the coefficients a,., < i < p and q, it is possible to write the function of probability of the sampling. A sampling of reference {XR} of length NR is compared with the test sampling {xr} oflengthNr. Then, adjusting both to a pattem of the same order p, it is possible to calcule the combined probability as well. Under the hypothesis Ho = "Both samplings fit the same pattern", the parameters % and ap will be obtained and the probability Lo will be maximum.
=
1
Exp{_I(N,R+N,r)}
(18)
Under the hypothesis H1 = "Both samplings fit different patterns", two sets of parameters (o-R, aR) and (o'r, at), will be obtained and then probability L1 will be: L1 -
1 S = --~ ~oo Ix- E(x)]3f(x)dx
ao = 1
i=0
^N'R ^N'T(X]~V'R+N'T Exp -
(N'R+N' r
crR o r
(19)
(15) Therefore, the coefficient of verisimilitude is"
1 K = - ~ ~ [x- E(x)]4f(x)dx
(16)
where f is the function of the probability density of variable x and o" the standard deviation. The skew measures the symmetry of the distribution about its mean value while the kurtosis represents a measure of the sharpeness of the peaks. A positive value of the skew generally indicates a shift ofthe bulk of the distribution to the right of the mean, and a negative one, a shitt to the left. A high kurtosis value implies a sharp distribution peak, i.e. a concentration in a small area, while a low
2,- L~ L1
.. 'R~ uj'T (7R• +N'T
(20)
Then, the entropic distance is defined as:
d=-21n2=(N'R+N'r)ln((~2)
-
(21)
_
Under normal conditions, d is a non negative number and is zero only if d R = 6-T and f i r - hT, i.e. if the patterns are the same.
Variations in the amplitudes of the signal will modify the value of d- but without modifying the polynomial coefficients. However, variations in the frequencies will affect the whole pattern.
3. Main studies 3.1. Studies using continuous and discrete transforms Kamarthi and his collaborators [10] applied the wavelet representation of the AE signals to study the flank wear estimation problem. The accuracy obtained was good to indicate that the wavelet transform is very effective in extracting the AE features and sensitive to gradually increasing flank wear. Li [ 11 ] showed that a wavelet transform can be used to decompose AE signals into different frequency bands in the time domain. The AEaMs values extracted from the decomposed signal for each frequency band can be used as the monitoring feature for tool wear. Then, the extracted features were classified by using different methods as, for example, a fuzzy neural network to classifk the tool wear [12, 13], a fuzzy ISODATA algorithm [14], and a parallel multi-ART2 neural network [ 15]. In this last one, Li presented, in addition, a real-time tool breakage detection method for smalldiameter drills using AE and current signals. The tool breakage features were obtained from the AE signal using continuous and discrete wavelet transforms. Xiaoqi [ 16] analysed the ~ signal detected during a milling process by wavelet transform and also with the short time Fourier transform in order to develop an inprocess machining monitoring system. Haili [ 17] used ~ and motor power signals to develop an on-line breakage monitoring in turning. Time-frequency analysis was used for the AE signals processing and a neural network based on adaptative resonance theory (ART2) for signal classification. Chen and his team [18] developed a signal processing scheme utilizing a wavelet transform to identify the possible dominant cutting mechanism for a given cutting state. 3.2. Studies using statistical analysis methods Penalva and Fernandez [19] studied tool wear and surface roughness by statistical methods, finding some accurate relationships between AERMs signal and different aspects of tool wear, such as crater appearance, cracks formation, and plastic deformation of the tool
edge. Emel and Kannatey-Asibu [20, 21] developed a linear discriminant function based on techniques for the detection of tool wear, tool fracture, or chip disturbance events by using the spectra of AE signals. Jemielniak and Otman [22] presented a method based on kurtosis and on parameters r and s of an assumed fl distribution of the AERMs signal to detect the catastrophic tool breakage. Besides, an interpretation of common AE signal distortions and possible solutions to avoid them were given by Jemielniak some years later [23]. Susic and Grabec [24, 25] proposed a statistical estimation theory based on non-parametric regression for ALEprocessing and a self-organizing neural network for the description of ground surfaces [26] and the characterization of the processes [27]. Penalva and Fernandez [19] studied, as well, the surface roughness by applying statistical methods to the signals detected by AE sensors. Tolosa and Fern/mdez [3 ] studied the fragmentation of the chip by comparison between a signal simulated by a personal computer and one obtained from the AE sensor. It is similar to the entropic distance method but, in this case, the reference signal is created and not taken from a real case. Gradiek and his collaborators [28] proposed two methods based on entropy for automatic chatter detection in outer diameter plunge feed grinding.
3.3. Other studies Teti [29] reported an interesting work on tool wear where different laboratories analysed the same AE signals using different processing methods. Besides, Teti has other relevant studies for the development of in-process monitoring of cutting conditions and tool wear using AE [30 - 33].
4. Conclusions
This paper describes some of the most diffused advanced signal processing methods utilised in AE sensor monitoring systems for machining technology. In particular, it is focused on continuous and discrete transforms by Fourier, Gabor and Wavelet, and on statistical analysis methods such as the amplitude distribution method and the entropic distance method. Besides, some studies showing the mentioned signal processing techniques have been shown as well.
Acknowledgements Funding for this work was provided in part by the Spanish Ministry of Education and Science (Directorate General of Research), Project DPI2005-09325-C02-02, Italian MIUR PRIN 2005 "ASMIM" Project, and the European Commission FP6 EC NoE on I'PROMS.
References [ 1] Byrne G, Dornfeld D, Inasaki I, Ketteler, G, K6nig W, Teti R., Tool Condition Monitoring ( T C M ) - The Status of Research and Industrial Application. Annals of the CIRP 44/2, 1995:541-567 [2] Rubio E.M., Teti R., Baciu I.L., Critical aspects in the application of acoustic emission sensing techniques for robust and reliable tool condition monitoring, 1st I'PROMS Virtual Intl. Cone on Intelligent Production Machines and Systems, Elsevier, 2005 [3] Tolosa I, Femfindez J., Carecterizacirn de la fragrnentacirn de la viruta en operaciones de tomeado a partir de la serial de emisirn acflstica. Actas del Congreso de M~quinasHerramienta y Tecnologias de Fabricaci6n, 1996:1-15 [4] Li X., A brief review: acoustic emission method for tool wear monitoring during turning. Int. J. Mach. Tools Man. 42, 2002:157-165 [5] Papoulis A., Signal Analysis, McGraw-Hill, 1977 [6] Firth JM., Discrete transforms, (Ed.) Chapman & Hall, London, 1992 [7] Denbigh P., System analysis and signal processing. AddisonWesley, London, 1998 [8] Shiavi R., Introduction to applied statistical signal analysis, 2ndEd. Academic Press, San Diego, 1999 [9] Kannatey-Asibu E Jr., Domfeld D.A., A study of tool wear using statistical analysis of metal- cutting acoustic emission, Wear, 76, 1982:247-261 [10] Kamarthi S, Kumara S, Cohen P., Wavelet representation of acoustic emission in turning process. Intelligent Engineering Systems of Artificial Neural Networks 5, 1995:861-866 [ 11] Li X., Intelligent tool condition monitoring using wavelet and fuzzy neural network. PhD Thesis, Harbin Institute of Technology, 1997 [12] Li X., Yao Y., Yaun Z., On-line tool condition monitoring system with wavelet fuzzy neural network. J. Int. Man. 8/4, 1997:271-276 [13] Yao Y.X., Li X., Yuan Z.J., Tool wear detection with fuzzy classification and wavelet fuzzy neural network. Int. J. Mach. Tools & Man. 39, 1999:1525-1538 [14] Li X., Yuan Z.., Tool wear monitoring with wavelet packet transform-fuzzy clustering method. Wear219/2,1998:145-154 [15] Li X.Q., Wong Y., Nee A.Y.C., Comprehensive identification of tool failure and chatter using a parallel multi-ART2 neural network. Trans. ofASME, J. Man. Sci. Eng. 120/2,1998: 433-
442 [ 16] Xiaoqi C., Hao Z., Wildermuth D., In-process tool Monitoring trough acoustic emission sensing. Automated Material Processing Group, Automation Technology Division,2001" 1-8 [17] Haili W, Hua S, Ming C, Dejing H., On-line tool breakage monitoring in tuming, J. Mat. Proc. Tech. 139, 2003" 237-242 [18] Chen X., Tang J., Domfeld D., Monitoring and analysis of ultraprecision metal cutting with acoustic emission. Proceedings of the ASME Dynamic Systems and Control ASME, New York, 1996, 387-393 [ 19] Penalva M.L., Femfindez J., Caracterizacirn del desgaste de la herramienta en procesos de tomeado duro de acabado a travrs de la serial de emisirn acflstica, Actas del Congreso de Mfiquinas-Herramienta y Tecnologias de Fabricacirn 2000" 383-396 [20] Emel E., Kannatey-Asibu E., Tool failure monitoring in tuming by pattern recognition analysis of AE signals. Trans. of ASME, J. Man. Sci. Eng. 110/2, 1988" 137-145 [21] Emel E., Kannatey-Asibu E., Acoustic emission and force sensor fusion for monitoring the cutting process. Int. J. Mech. Sci. 31/11-12, 1989:795-809 [22] Jemielniak K., Otman O., Catastrophic tool failure detection based on acoustic Emission signal analysis, Annals oflhe CIRP 47/1, 1998"31-34 [23] Jemielniak K., Some aspects of AE application in tool condition monitoring. Ultrasonics 38, 2000:604-608 [24] Susic E., Grabec I., Analysis of grinding process acoustic emission by a neural network. Faculty of Mechanical Engineering, University of Ljubljana, Slovenia, 1994 [25] Susic E., Grabec I., Application of a neural network to the estimation of surface roughness from AE signals generated by friction process. Int. J. Mach. Tools Man. 35/8, 1995:1077-1086 [26] Susic E., Mui P., Grabec I., Description of ground surfaces based upon AE analysis by a neural network. Ultrasonics 35/7, 1997:547-549 [27] Susic E., Grabec I., Characterization of the grinding process by acoustic emission, Int. J. Mach. Tools Man. 40/2, 2000:225-238 [28] Gradiek J., Baus A., Govekar E., Klocke F., Grabec I., Automatic chatter detection in grinding. Int. J. Mach. Tools Man. 43/14,2003:1397-1403 [29] Teti R., Buonadonna P., Round robin on acoustic emission monitoring of machining, Annals of the CIRP 48/3, 1999:47-69 [30] Teti R., Tool wear monitoring through acoustic emission, Annals ofthe CIRP 38/1, 1989:99-102 [31 ] Teti R., In-process monitoring of curing conditions and tool wear using acoustic emission, XV Review of progress in quantitative NDE, Univ. of California - San Diego, CA, 1988 [32] Lee D.E., Hwang I., Valente C.M.O., Oliveira J.F.G. and Domfeld D.A. Precision manufacturing process monitoring with acoustic emission. Int. J. Mach. Tools Man. 46/2, 2006 176-188 [33] Arul S., Vijayaraghavan L., Malhotra S.K. Online monitoring of acoustic emission for quality control in drilling of polymeric composites, On-line J. Mat. Proc. Tech. Available 4 May 2006
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Innovative signal processing for cutting force based chip form prediction K. Jemielniak a, R. Teti b, J. Kossakowska ~, T. Segreto b hTstitute of Manufacturing Technology, Warsaw University of Technology, Narbutta 86, Warsaw, Poland b Dept. of Materials & Production Engineering, University of Naples Federico II, P.le Tecchio 80, Naples, Italy a
Abstract
This paper reports on the activities of a joint research project work carried out by two Laboratories at the Warsaw University of Technology, Poland, and the University ofNaples Federico II, Italy. The joint research work comprised the following main activities: (a) generation, detection, and storage of cutting force sensor signals obtained during sensor-based monitoring of machining processes with variable cutting conditions generating different chip forms, and (b) cutting force signal (CFS) characterization and feature extraction through advanced processing methodologies, aimed at comparing chip form monitoring results achieved on the basis of innovative signal analysis and processing. Keywords: Chip form monitoring, Cutting force sensor, Advanced signal processing
1. I n t r o d u c t i o n
In this paper, the main activities of a collaborative research on chip form sensor monitoring based on cutting force signal analysis carried out jointly by two Laboratories, K. Jemielniak's Lab at Warsaw University of Technology (WUT) and R. Teti's Lab at the University of Naples Federico II (UN), Italy, are presented. These activities consist of: (i) generation, detection, storage of cutting force signals (CFS) obtained during sensor-based monitoring of machining processes with variable cutting conditions yielding different chip forms; (ii)examination and characterization of the CFS specimens with the aim of comparing chip form monitoring results achieved with diverse advanced signal processing and analysis methodologies. The WUT volunteered in providing CFS specimens from turning tests under variable cutting conditions, using commercial instrumentation for
cutting force detection and storage. The CFS specimens were utilized by the WUT and UN Labs to perform investigations through advanced analysis procedures for CFS processing, characterization and feature extraction to achieve reliable chip form identification and monitoring. This paper reports the characteristics of the CFS specimens and the investigation results obtained by the cooperating Labs, and presents the capabilities of the different advanced signal processing and data analysis methods for chip form prediction.
2. E x p e r i m e n t a l p r o c e d u r e
Cutting tests were performed at the WUT Lab through longitudinal turning ofC45 (AISI 1045) steel with coated carbide inserts and variable cutting parameters, yielding different chip forms: - cutting speed = 150,250 m/min - feed rate = 0.08, 0.13, 0.20, 0.30 mm/rev - depth of cut = 1.0, 1.5, 2.0, 3.0 mm
Three cutting force components (Fc, Fr and Fp) were measured using Kistler laboratory dynamometer 9263, digitised at sampling frequency 2500 for 3 s (data sequence 7500 points). Each test was repeated three times. Chip form types (ISO 3685) [ 1] obtained during the test are (see Fig. 1): 2.3 snarled tubular (unacceptable) 5.2 short, spiral helical (acceptable) 6.2 short, loose arc (acceptable)
1 level
2 level 3 level
Illlllllllll
oo oo
lillllllllllIllll
Fig. 2. Three level wavelet packet; blacked fields indicate the frequency band of the original signal. 3. S i g n a l p r o c e s s i n g
methodology
3.1. WUT Laboratory At the WUT Lab, a particular form of wavelet analysis, Wavelet Packet Transform, was applied. In this method, each of the cutting force component signals (Fc, Ff, Fp) was split into a low frequency component, called approximation A, and a high frequency component, called detail D, both at a coarser scale. Then, the approximation and detail are further split into a second-level approximation and detail, and the process is repeated (see Fig. 2). The vectors of approximation coefficients and detail coefficients are called packets. Calculations were performed up to the fourth level yielding 30 packets for each of the 3 cutting force signals. A Debauchies 2 (db2) was used as mother wavelet. The analysis started at the first level of decomposition. Except for the direct packets (approximation A and detail D), their relative values were calculated as the ratio ofthe packet over the average approximation value gA. v~= 250 m/min
Vc= 150 m/min
.
.......
1
1.5 2.0
3.0
1
.
.
.
.
.
.
1.5 2.0
;%':~?:..
0.2
,,
.......
,
0.0
t~ %
2.3 snarled tubular (unacceptable)
~)
5.2 short, spiral helical (acceptable)
~'::::~ 6.2 short, loose arc (acceptable) Fig. 1. Chip form obtained in the experiments.
3.0
For each packet, several features were calculated: standard deviation ((y), variance (o'2), moment of 3 rd degree ((~3), moment of 4 th degree (o4), energy (E = Zlog(xi)2). Then, all the values of each feature, obtained from all tests, were sorted according to the observed chip forms to identify the features that presented separate value ranges for different chip forms. If there was no such feature, the next level of decomposition, up to the third, was performed, followed by the same packet feature calculation. If, on any level, there was still no such feature, the best one (i.e. the one with the least overlapping range) and further four features were selected, each separating chip forms in different sets of test. Then, for each given test, the chip form was identified on the basis of features with values outside of the overlapping range.
3.2. UN Laboratory At the UN Lab, CFS specimens were processed to achieve their spectral estimation through a parametric method [2]. In this procedure, the signal spectrum is assumed to take on a specific functional form, the parameters of which are unknown. Thus, the spectral estimation problem becomes one of estimating these unknown parameters of the spectrum model rather than the spectrum itself. From each signal specimen (measurement vector), p features or predictor coefficients {al, ..., ap} (feature vector), characteristic of the spectrum model, are obtained through linear predictive analysis (LPA) [2]. Feature extraction was implemented through the application of Durbin's algorithm [2] with p = 4, 8, 16. Neural network (NN) based pattern recognition was carried out in high dimensions feature spaces [3] using the 4-, 8-, 16-elements feature vectors extracted from the CFS specimens through LPA. Three-layer feed-forward back-propagation NNs were built with the following architecture: the input layer nodes were equal to the number of input feature vector elements:
4, 8 or 16 (single cutting force component chip form classification), and 12 or 24 (combination ofthe three cutting force components chip form classification). The hidden layer nodes ranged from 4 to 64, depending on the number of input nodes. The output layer had only one node, yielding a coded value related to the chip form: 0 - {2.3} -- snarled; 1 = {6.2 } = short; 2 = {5.2 } - short spiral. NN training and testing was performed using training sets made of the 4-, 8-, 16- (single cutting force component) and 12-, 24- (integration of 3 force components) elements feature vectors, respectively. The leave-k-out method [2] was used: one homogeneous group of k patterns (here, k = 1), extracted from the training set, was held back in turn for testing and the rest of the patterns was used for training.
I
A
'
i
i.
i
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10
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1000
,i I i
t0
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i
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i
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chip form chip form Fig. 3. Example of packet features at the first level of decomposition, not enabling chip form separation.
4. Results and discussion
1000000
4.1. WUT Laboratory At the first level of decomposition, no signal feature enabled separation of single chip forms or, at least, acceptable (5.2 and 6.2) from unacceptable (2.3) chip forms. In Figure 3, example packet features at the first level are presented: variance of packet D (left) and variance of the ratio D/pA (right) for force component Ft. The second level of decomposition resulted in unambiguous recognition of unacceptable from acceptable chip forms that is critical for industrial applications. In Figure 4 two features are presented: standard deviation and variance of the relative packet AD for force component Ft. In both cases, the feature values for snarled tubular chip (2.3) are lower than for short spiral helical (5.2) and loose arc (6.2). Similarly, clear recognition was achieved at the third level of decomposition, shown in Figure 5. Separation of loose arc from spiral helical chips seemed much more difficult. The ranges of all packet features up to the third level of approximation were overlapping (see Figs. 4 and 5). Thus, the five best features with the least overlapping range that enabled chip form separation in different tests were selected and presented in Table 1 and Figure 6. In Figure 6, the method of feature integration is explained using three cutting conditions designated as X, Y and Z with cutting speed Vc2- 250 m/min. Dotted bars indicate the feature value range for spiral helical chips, while hatched bars indicate the range for loose arc chips.
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, 0 , , , 04 co 04 04 u5 ~5 ~ u~ ~5 chip form chip form Fig. 4. Packet features at the second level of , co
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Fig. 5. Packet features at the third level of decomposition, enabling separation of unacceptable (2.3) from acceptable (5.2 and 6.2) chip forms.
Dotted horizontal lines designate the feature value obtained in a specific test. If the line crosses one bar only, the feature recognizes the corresponding chip form. E.g., the energy of packet DDD for force component Fp, E[DDD(Fp)], can recognize chip form 6.2 in test Y, whereas it is inconclusive for tests X and Z. Chip form 6.2 in test Y is also recognized by 3 other features: cr (Y3[ADA/lkI,AAA(Ff)] and o-3[ADD(Fr)], and only a[DDA/gAAA(Fc)] is inconclusive. Thus, in test Y chip form 6.2 receives 4 "yes votes" and one "vote" can be considered as "abstaining". The last mentioned feature is the only one pointing for chip form 6.2 in test Z, while the other features are inconclusive. Test X is an example where all feature values were in the overlapping range, i.e. inconclusive. The summary of chip recognition results is shown in Figure 7. Numbers in squares corresponding to particular cutting parameters designate signal features "voting" for the recognized chip form. It is worth mentioning that separation of acceptable from unacceptable chip forms was 100% successful.
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4.2. UN Laboratory Cutting force sensor signal processing for feature extraction and NN pattern recognition analysis was carried out on the datasets to classify single chip forms based on cutting force sensor measurements. Experimental sensor data were respectively subdivided into 1500 points CFS specimens to construct full-bodied training sets comprising a total of 420 training cases. NN chip form identification was performed by inputting feature vectors from cutting tests with (a) fixed cutting speed (150 - 250 m/min) and variable feed rate and depth of cut, and (b) variable cutting speed, feed rate and depth of cut.
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Table 1 Packet features selected for separation of loose arc (6.2) from spiral helical (5.2) chip forms. Feature
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# of training pairs Fig. 8. Snarled/short/short spiral chip form identification for fixed (vc2) or variable cutting speed (Vcl+Vc2) training sets, containing 210 or 420 training cases respectively, using a single cutting force component (Fc) or the integration of 3 cutting force components (F~+F/+Fp). NN error vs. number of training pairs for: (a) Vce and Fc with SR = 93%; (b) Vc2 and (F~+F/+-Fp)with SR = 100%; (c) (Vcl+Vce) and F,, with SR = 87%; (d) (vcl+Vce) and (F~+F/+Fp) SR = 98%. Furthermore, chip form identification was carried out through sensor data processing of single cutting force components (Fc, FU, or Fp) and sensor data integration of the 3 force components (F~+F/+Fp). The NN output is correct if the actual output, O,, is equal to the desired output, Od, + 0.50 % of the difference between adjacent chip form numerical codes, which was 1. By setting error E - (0, - Od), the chip form identification is correct if-0.5 < E < +0.5; otherwise, a misclassification case occurs. The ratio of correct
classifications over total training cases yields the N N success rate (SR). NN processing results can be displayed as in Figure 8, where error E is plotted vs. training cases for fixed (Vc~, Vc2) and variable cutting speed (Vc~+Vc2) training sets using single cutting force component (Fe, If, Fp) and the integration of the 3 cutting force components (Fe+F/+Fp). The figure shows that chip form prediction: - does not improve for variable cutting speed (Vc~+Vc2) instead of fixed cutting speed conditions (Vcl, Vc2): in
11
the former case, misclassifications are not reduced in comparison with fixed cutting speed conditions for both single and integrated cutting force components; -improves by integrating the 3 force components (Fc+Ff+-Fp) instead of single components: in the former case, misclassifications are reduced in comparison with single cutting force component for both fixed and variable cutting speed conditions. These results were verified for all NN data processing trials. In Figure 9, the NN SR is reported for fixed and variable cutting speed training sets processing for both single cutting force components and integration of the 3 components with reference to all chip forms considered together. It can be seen that the SR is higher when integrating the 3 components instead of using single components for both fixed and variable cutting speed training sets. This comes from a synergistic effect of the 3 force components that, if integrated, yield a SR higher than the maximum SR for each single force component. It is worth observing that the use of training sets comprising cases for all process conditions (variable cutting speed training sets) reduces the SR for both single force components and their integration (see Fig. 9). In fact, although variable cutting speed training sets are larger (420 vs. 210 training cases), the increase in number of variables has a stronger negative influence on the SR. In Table 2, the NN SR for single chip form identification is reported, showing that chip form prediction SR based on single chip forms separately is characterized by the same behaviour as the one for all chip forms together.
4. Conclusions This paper presents the results of a joint collaborative work involving the analysis of cutting force signals, monitoring techniques and processing methods used by two Labs to predict the likely chip forms. This activity is carried out in view of the development of robust and reliable on-line, real time sensor monitoring procedures for chip form prediction of particular interest for practical industrial applications.
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-
20
Vc 2
6O 80 100 SR % Fig. 9. NN SR for fixed and variable cutting speeds using single cutting force components and integration of the 3 components for all chip forms.
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Table 2 NN SR for fixed and variable cutting speeds using single cutting force components and their integration for all chip forms together and single chip forms separately. Chip Form NN Force Component Snarled Short F~ Short Spiral All Forms Snarled Short Yj Short Spiral All Forms Snarled Short F~ Short Spiral All Forms Snarled Short F~+FAF,, Short Spiral All Forms
SR (%) 'NN SR (%) NN SR (%) Vc 1
Vc2
Wc 1+Vc2
95 82 85 87 100 98 96 98 95 98 92 95 100 98 97 99
93 93 92 93 100 100 98 99 96 97 95 96 100 100 100 100
89 92 84 88 99 98 96 97 94 96 94 94 100 99 96 98
References [1]
Acknowledgements [3]
12
: Vcl
-
0
[2]
This research work was carried out with support from the EC FP6 NoE on Innovative Productions Machines and S y s t e m s - I'PROMS and the Italian MIUR PRIN 2005 Project "ASMIM".
Vcl + Vc2
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ISO 3685, 1993, Tool-Life Testing with Single-Point Turning Tools, Annex G: 41. Teti, R., Buonadonna, P., 1999, Round Robin on Acoustic Emission Monitoring of Machining, Annals of the CIRP, 48/3, Int. Doc. & Rep.: 47-69. Segreto, T., Andreasen, J.L., De Chiffre, L., Teti, R., 2005, Chip Form Monitoring in Turning Based on NN Processing of Cutting Force Sensor Data, 1st Int. Virt. Conf. on IPROMS, 4-15 July: 609-614.
Intelligent ProductionMachines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka(eds) 9 2006 CardiffUniversity, Manufacturing EngineeringCentre, Cardiff, UK. Publishedby ElsevierLtd. All fights reserved.
Monitoring of Slowly Progressing Deterioration of CNC-Machine Axes E. Uhlmann a'b, E. Hohwieler a, C. Geisert a a
Fraunhofer-Institutefor Production Systems andDesign Technology (IPK), Pascalstr. 8-9, 10587Berlin, Germany b Technische Universitiit Berlin, IWF, Pascalstr. 8-9, 10587 Berlin, Germany
Abstract
Feed axes of CNC-grinding machine tools belong to the most mechanical stressed components ofmachine tools due to high process forces and the rough manufacturing environment. The resulting wear and tear depends strongly on users' product range and the manner of machine operation. To counteract a functional deficiency of these central machine units preventive maintenance activities have to be done. Manual inspection of feed axes is complex and time-consuming. An aggravating fact is that the deterioration normally progresses very slowly and its characteristic depends on the physical location at the axis. Existing approaches for automated estimation of feed axis' "health status" do not take this factor into account. In this paper a procedure that closes this gap is presented. During the execution of a simple test routine drive current, axis position, and feed rate are recorded. With the help of additional machine data characteristic values are computed directly at the computer of the human machine interface (HMI). The results are then transferred to and stored on a database server at the machine manufacturer. This approach enables the service technicians to trace the progression of the axis' "health status" over a long time. Using this approach, it will be possible to detect trends within the characteristic values at a very early point in time.
1. Introduction
Applications of centerless external cylindrical grinding are exceedingly appropriate to mass production. Central components of such CNCmachine tools which underlie wear and tear are the feed axes. How fast and at which physical location at the axis deterioration occurs depends on factors like 9 tool-workpiece combination, 9 kind of grinding method, 9 condition of lubrication, 9 quality of maintenance activities. Especially grinding methods such as plunge cut grinding and throughfeed grinding (see Fig. 1)
possess the characteristic that the effective work space is very small. This leads to highly position depending stress of the feed axis. At these locations at the axis the appearance of wearout has to be expected greater and faster. Another great influence to the degradation progress is the contamination of the guidance by grinding debris. This happens, if the wiper does not work correctly. The result is a loss of accuracy or, in the worst case, a total breakdown of the feed axis [2]. As is denoted in [3] non working wipers are responsible for 90% of all failures in linear guidance. Due to the axis controller's influence, the deterioration of a feed axis first becomes obvious, when its functional capability is delimited and the
13
PLC generates an alarm message.
Fig. 1. Plunge grinding (lett) and throughfeed grinding (right) [ 1] In this article, a procedure for tracing changes in the dynamic behaviour of feed axes is described. With statistic signal processing methods characteristic values are computed. They can be used to derivate the axis "health status". A position depending history of characteristic values is generated and stored in a relational database.
parameters that result from the "Universal axis test" are determined as friction characteristic quantities [12]. For the computation of the characteristic quantities complex mathematic models were implemented [ 13,14]. The dynamic system has to be stimulated with a special motion profile. To get a deeper understanding of this test, drive signals were sampled during a test (see Fig. 2). Trend curves are provided from the characteristic quantities, if repetitions of single measurements have been made. It is suggested to use the interpretation of these series of measurements as basis for the statusoriented maintenance. x 106
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2. State of the Art
Much effort concerning condition monitoring systems focus on the development of IT-frameworks and their ability to integrate external sensor signals respectively to use control integrated signals [5,6,7,8,9,10]. But, even though most of these systems provide various methods for signal processing and data analysis the adaptation to the concrete technical system to be monitored is very difficult [4]. For the correct assignment of generated patterns to a special degradation status, expert's know-how and a lot of case studies are needed. Topic of this paper is monitoring of CNCmachine axes. Therefore in this chapter only a selection of commercial products and current research activities in the field of condition monitoring of feed axes is given.
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2.1. ePS Network Services
The Siemens AG offers with its ePS Network Services a tool that provides amongst other services several tests for the acquisition and documentation of the current machine axis state [11 ]. This special application is called "Machine Performance" and includes the following tests: 9 Circularity test, 9 Following axis test, 9 Universal axis test. During the tests control internal data is sampled and used for feature extraction. The calculated
14
One objective within the research project " L o e W e - life cycle orientated machine tool" [ 15] is to monitor the degradation status of components with functional relevance like the ball screw of the feed axis. The knowledge about the current wear and tear condition shall then be used to calculate the remaining life time of this component. A model based approach is chosen to reach this goal. As input for the model it is suggested to use control integrated signals (drive current and speed) and additional external sensor signals [16]. At this moment, no further public information
about the concrete functionality and achieved results is available. The project is funded by the German Federal Ministry of Education and Research (BMBF) within the framework concept "Research for Tomorrow's Production".
2.3. REACH- Development of a method to improve the reliability and availability of machine tools Within the European Brite / EuRam project REACH (1998-2001) a modular system for the monitoring of machine tool component state should be developed [17,18]. To describe the component behaviour current control internal signals of open CNCs were used. If necessary, external sensors should be integrated into the system. With the aid of long-time tests, it could be shown that the usage of the drive current instead of displacement force signal is sufficient to detect mechanical disturbances [19]. Validation of the realized monitoring system was done at a test bench for two typical feed axis' failures: 9 backlash, 9 pitting on guideways. The backlash detection was analysed by building the difference between the direct and the motor encoder [20]. In context of this project a doctoral thesis should be mentioned [21]. A detailed enumeration of analytical methods for the detection of various feed axes disturbances is given there.
development of electronic services for the analysis and prediction of machine health status using enhanced diagnostic algorithms [24]. In this approach data recording is carried out during a specifically designed test-NC-program under defined conditions. The recorded signals used are drive current and rotation speed of axis or spindle drives. To estimate the condition of a feed axis with controlled rotation speed a mathematical linear model of this electromechanical unit was built. From this system of differential equations, which describes the process, characteristic diagnostic features are generated. These features represent the physical parameters static and sliding friction and the moment of inertia. Drive current, rotation speed and acceleration derived from the rotation speed are used as input for the least squares method. This approach is used for parameter identification from an overdetermined set of linear equations [25]. In addition to the diagnostic tests the load profile of the machine tool is continuously logged during normal machine operation. From this load profile experience based statements about the "health state" to be expected can be made. Fig. 4 shows an example of the estimation of the point in time where service activities are expected to be needed [26]. 9
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In an ongoing project at the Institute for Control Engineering of Machine Tools and Manufacturing Units (ISW) in Stuttgart, the usability of control integrated signals for the condition monitoring of feed axes is part of exploration [22]. The project is funded by the VDW Verein Deutscher Werkzeugmaschinenfabriken e.V. With the help of adapted test signals the "Stribeck Curve" is estimated and backlash error of a gear unit and other moveable machine components is detected [23].
2.5. e-Industrial Services One aspect of the Fraunhofer research project "eIndustrial Services" (2000-2003) was the
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3. Monitoring Concept As mentioned in chapter one in the case under consideration the machine tool axes underlay a special load profile that is specific for this kind of machining. The challenge is to develop a monitoring system that has on the one hand a sufficient sensitiveness to detect progressive, position depending deterioration. On the other hand it has to be robust enough to meet the rough manufacturing environment. Last but not least it has to fulfil the
15
requirement that additional auxiliary process time shall be avoided.
3.1. Prem&es The grinding machines are mainly implemented in the sector of mass production for the automotive industry and its subcontractors. The machine tools are characterised by a great number of machine axes due to the technology of centerless grinding. A typical axes plan is shown in Fig. 4. The grinding wheel is made up of corundum and mounted on the Xl-axis. The weight of a new one is about 400 kg (without mounting device) and will be reduced in consequence of abrasion.
Fig. 4. Axes plan of a MIKROSA centerless grinding machine tool of type KRONOS M. The machine tools are equipped with an open CNC system of type SINUMERIK 840D made by Siemens. This open CNC architecture enables data acquisition of control internal sensor signals and machine data via OPC (OLE for Process Control).
pre-processing, and data compression is done at the machine tool, storage, analysis, and visualisation is done at the location of the machine manufacturer. This has the advantage that a huge amount of data, even from distributed machine tools at different places, can easily be used for comparative analyses. The aspect of data transmission is left out in this paper. It belongs to the thematic area of information and communication technologies and how to build up a secure IT infrastructure (cf. [27]).
3.3. Axis Test and Life Cycle Data If wear and tear of a feed axis increases the dynamic behaviour of the feed drive system changes. E.g., in consequence of modifications within the tribological system the friction rises and sluggishness occurs. Along with the increased friction the drive current rises too [28]. By moving the axis with constant feed rate along the whole traversing range (both directions) it is possible to detect changes in the dynamical behaviour. During the test drive current, position, and feed are sampled. Fig. 6 exemplifies the sampled position and drive current data of an axis test. The feed signal is used for an automated detection of the both intervals, where the positive respectively negative axis motion is at a constant level. The phases of acceleration are masked because they would falsify the analysis. 6oo
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3.2. Structure of the Monitoring System The proposed monitoring system includes the following principal tasks: 9 data logging, 9 signal pre-processing (sensor signals only), 9 data compression, 9 data transmission, 9 storage of data, and 9 analysis of data. Fig. 5 presents the schematic structure of the system. Regarding the different tasks, a decentralised architecture was chosen. While data logging, signal
16
Fig. 6. Axis position (left) and drive current (right). Auxiliary data, containing information about the life cycle of the machine tool is logged continuously. Amongst others the alarm-log history and information about executed maintenance activities. These data support failure analysis because they represent the order of events that have happened during machining operation before a failure occurs [29].
Pre.Processing
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Fig. 5. Global structure of the monitoring system.
3.4. Pre-Processing The following statistical moments are calculated from the drive current signal and are used to generate key indicators for condition monitoring: 9 mean value, 9 variance, 9 skewness. The meaning of significant changes of mean value and variance is evident. Drive current is proportional to the driving torque. Therefore trend monitoring of the mean value indicates operation difficulties of the feed axis. The variance of the signal characterises the smoothness of the system.
Fig. 7 and 8 show exemplarily the chronological development of position depending variance and mean value of feed drive current signals. The traversing direction is from software limit switch plus to minus. The monitored axis is the X 1-axis (cp. Fig. 4) of a KRONOS L. The trends in the characteristic values are easy to see. More power is needed to traverse the feed axis and the variance is rising. The machine is still working and no maintenance activities were necessary until now. This example shows the sensitiveness of the characteristic values.
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Fig. 8. Chronological development of position depending mean value of feed drive current signal. The skewness defines at which side of the mean value there are more data. It can be used to detect beginning failures that depend on the direction of
17
traversing. As mentioned before the deterioration process does not proceed homogeneously at all positions on the axis. For this reason the analysis of the signal is divided into small windows. Every window belongs to a certain position on the axis. To avoid additional auxiliary process time the axis test is combined with a periodically recurring traversing of the axes that are used to disperse the lubricant on the guidance.
3.3. Data Management To detect trends in the chronological history of the stored characteristic values, the existence of an initial state is required. The computed characteristic values indicate the "good" health state of a feed axis and provide the basis for trend detection. XML (eXtensible Markup Language) was chosen as data interface between the different components of the monitoring system. XML is a universal data exchange format that can be used for machine to machine communication. In the presented work the XML-logfiles are parsed by a XSLT-processor. The processor generates files in SQL-format (Structured Query Language) to import the data into a relational database (see Fig. 7). The database scheme maps the relations between the data, the components and the individual machine tool.
fii! i t
monitoring of feed axes it is possible to detect trends of slowly progressing changes of the dynamic system. Information about the machine tool's load history that is additionally logged during the machine tool's life cycle can be used to support searching for failure causes. The database based approach enables comparative analyses and long term investigations. With the development of new axis tests and algorithms for the analysis further key indicators for degradation monitoring of feed axes will be provided. The implementation of intelligent machine tool components that are able to analyse and store their wear status will enlarge the field of condition monitoring applications.
Acknowledgement The presented work is part of the research project DYNAPRO which is funded by "Stiftung Deutsche Industrieforschung". It was done in cooperation with Studer Mikrosa GmbH. The Studer Mikrosa GmbH is manufacturer of centerless external cylindrical grinding machine tools. Fraunhofer Institute for Production Systems and Design Technology is partner of the EU-funded FP6 Innovative Production Machines and Systems (I'PROMS) Network of Excellence.
References [1] [2] [3]
[4] Fig. 7. Data transformation from XML to SQL. Visualisation of the data is provided by predefined reports. It is possible to have a look at the stored information from different views and within arbitrary time intervals.
[5]
[6] [7]
4. Conclusion and Outlook Using the proposed method for the condition
18
[8]
N.N.: KRONOS -Centreless surface cylindrical grinding. Product brochure, 2005. N.N.: www.ima.uni-stuttgart.de/dichtungstechnik/ aktuelle_projekte/wzm/wzm.en.html. Institute of Machine Components, University of Stuttgart, 2002. Jansen, M.: Abstreifer ffir Werkzeugmaschinenfi~hrungen. Dissertation, Universit~t Stuttgart, URN: urn:nbn:de:bsz:93-opus-24403, URL: http://elib.unistuttgart.de/opus/volltexte/2005/2440/, 2005. Walter,K.-D.: Wireless fi~rPr~ventiv-Aufgaben. In: Computer & Automation, 01 -2006, pp. 36-39. Lee,J. et al.: An integrated platform for diagnostics, prognostics and maintenance optimization, Proceedings of the Intelligent Maintenance Systems 2004, July 15-27, 2004-Aries, France. Lee,J, Ni, J.: Smart Prediction to Prevent Downtime. In: INNOVATION, Vol. 4 No. 3, 2004, pp. 41-42. Djurdjanovic,D.; Lee, J.; Ni, J.: Watchdog Agentan infotronics-based prognostics approach for product performance degradation assessment and prediction. In: Advanced Engineering Informatics, Volume 17, Issues 3-4, July-October 2003, pp. 109-125. Grudzien,W.; Seliger, G.: Life Cycle Unit in Product
[9]
[ 10]
[11]
[12] [13]
[ 14] [15] [16]
[17]
[18]
[19]
[20]
[21 ]
[22]
[23]
Life Cycle - Tool for improved maintenance, repair and recycling. In: Proc. 33rd CIRP Intern. Sem. Manufacturing Syst., 2001, pp. 121-125. Hirschmann, J.: Fault detection and diagnosis of the electromechanical drive units in the automation technology. Conference Proceedings: EManufacturing and E-Business Integration, Milwaukee, Wisconsin, USA, September 9-11,2002, pp. 179-181. Middendorf, A. et al.: Life-cycle information units for monitoring and identification of product use conditions. Proceedings: Global Conference on Sustainable Product Development and Life Cycle Engineering 2004, September 29 - October 1, 2004, Berlin, pp. 91-96. N.N.: More Productive with Telemaintenance Condition Monitoring reduces plant downtimes. In: motion world- Systems and Solutions for Machines and Plants, September 2005, pp. 18-19. N.N.: ePS Network Services 3.2 - Description of Functions. 9. August 2004. N.N.: Vorhandene Technologie noch bessernutzenExperteninterview zu aktuellen Entwicklungen im Bereich des Condition Monitoring. In: elektro Automation 7/2005, pp. 16-21. Barth, R.: Produktiver mit >Condition Monitoring<. In: WB Werkstatt + Betrieb 09/2005, pp. 183-185. N.N.: www.projekt-loewe.de Denkena, B. et al.: K6nnen teure Werkzeugmaschinen auf l~ingere Sicht gfinstiger sein? In: wt Werkstattstechnik online Jahrgang 95 (2005) H. 7/8, pp. 519-523. N.N.: http://www.cordis.lu/data/PROJ_BRITE/ ACTIONeqDndSE S SIONeq23541200595ndDOCeq 1 15ndTBLeqEN_PROJ.htm. Last updated: 2004-0908, last visited: 26 January 2006. N.N.: http://www.cordis.lu/data/RESU_BRITE/ ACTIONeqDndSES SIONeq23864200598ndDOCeq 1 52ndTBLeqEN_RESU.htm. Last updated: 2004-1123, last visited: 26 January 2006. Weck, M., P lapper, V., Groth, A.: Sensorlose Maschinenzustandsfiberwachung. VDI-Z 06/2000, Seite 53. Plapper, V., Weck, M.: Sensorless Machine Tool Condition Monitoring based on open NCs. Proceedings of the 2001 IEEE International Conference on Robotics & Automation, Seoul, Korea - May 21-26,2001, pp. 3104-3108. Plapper, V.: Steuerungsintegrierte Oberwachung von Werkzeugmaschinen. Doctoral thesis, Aachen, RWTH, 2004. Geschgftsbericht: 2002-2005 - Forschungsvereinigung Werkzeugmaschinen und Fertigungstechnik anlfisslich der Mitgliederversammlung am 13. September 2005 in Hannover. Herausgeber: Forschungsvereinigung Werkzeugmaschinen und Fertigungstechnik e. V. (FWF), 2005. Dietmair, A., Walther, M., Pritschow, G.:
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Antriebsbasierte Maschinendiagnose- Fortschritte bei der Nutzung von Antriebssignalen zur Maschinendiagnose. In: wt Werkstattstechnik online, Jahrgang 95, 2005, Heft 5, pp. 351-356. Uhlmann, E., Hohwieler, E., Berger, R.: Providing online services for machine operation and maintenance. International IMS Forum 2004. Vol.2: Global challenges in manufacturing. Villa Erba, Cernobbio, Italy, 17 - 19 May 2004, pp. 1120-1127. Hohwieler, E.; Berger, R.; Geisert, C.: Condition Monitoring Services for e-Maintenance. In: Proceedings of the 7th IFAC Symposium on Cost Oriented Automation, June 7 - 9, 2004, Gatineau/Ottawa, Canada. Hohwieler, E., Geisert, C.: Intelligent Machines Offer Condition Monitoring and Maintenance Prediction Services. In: Roberto Teti (Editor), Proceedings of the 4th CIRP International Seminar on Intelligent Computation in Manufacturing Engineering (CIRP ICME '04), 30 J u n e - 2 July 2004, Sorrento, Italy, pp. 599-604. Berger, R.: Sicherheitszentrierte Architektur far Internet-basierte Dienste im Maschinen und Anlagenbau. Doctoral thesis, Berlin, T echnische Universit~it, 2005. Krfiger J.: Methoden zur Verbesserung der Fehlererkennung an Antriebsstrecken. Doctoral thesis, Berlin, Technische Universitgt, 1998. Hohwieler, E., Geisert, C.: Holistic Approach for Condition Monitoring and Prediction of Machine Axis. In: Intelligent Production Machines and Systems, First I'PROMS Virtual Conference, 4 - 15 July 2005, D. T. Pham, E. E. Eldukhri and A. J. Soroka (eds), Elsevier Ltd., 2005, pp. 67-72.
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Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
The monitoring of the turning tool wear process using an artificial neural network G. C. Balan, A. Epureanu U n i v e r s i t a t e a Dunarea de Jos din Galati, Str. D o m n e a s c a nr. 47, G a l a t i - 8 0 0 008, R o m a n i a
Abstract The study of machine tool dynamics is performed here as "monitoring", which involves the checking and improving of machine functioning. Signals collected from certain sensors are processed by a computer. These data then lead to the monitoring decision, which is to associate the current state of operation with one ofthe classes from a set of known classes. For monitoring in turning, the classes (tool conditions) are shown. The experimental setup, experimental results and data processing are presented. For the monitoring of the tool wear, an artificial neural network (ANN) is used.
Keywords: monitoring, turning, ANN (= artificial-neural-network)
1. Introduction In turning, tool flank wear is one of the major factors contributing to the geometric error and thermal damage in a machined work-piece. Tool wear not only directly reduces the part geometry accuracy but also increases the cutting forces drastically. By changing the worn tool before or just at the time it fails, the loss caused by defect product can be reduced greatly and thus product quality and reliability is improved. To accomplish these objectives, artificial intelligence methods are the most modem means. The study of machine-tool dynamics is realised here as "monitoring", which means the checking and improving of machine functioning. Signals collected from certain sensors are processed by a computer. These data then lead to the monitoring decision, which is to associate the current state of operation with one of the classes from a set of known classes (process conditions) c = [ c 1, c : . . . . . . c n ] , according to: if t i . f < x _< t sup then c = c i , (1) ,
20
where x is the set (vector)of monitoring indices x - [ x 1, x 2 . . . . . x m] , and t - the admissible limit values. The samples can be registered as in table 1, in which m is the number of monitoring indices, n is the number of classes, and N - the number of samples. So x k =[x (k,1), x ( k , 2 ) . . . . . x (k,m)]. represents "the vector k", and c ( x k) ~- [Cl, c : ..... c . ] indicates the fact that in this recording the result was one of the classes: C 1 , C 2 . . . . . C n 9 The function can be introduced Q.'c--~ x , (2) which is "obscure" because on it you can do only indirect measurements which are, or which can be bound to the function. If for Q no theoretical relation can be obtained, a two phases data interpretation method involving l e a r n i n g and c l a s s i f i c a t i o n can be used. For a set of samples in which both x and c are known (a part of data from the table 1), in the learning phase an empirical relation between x and c is formed. In the classification phase the other part of the table 1 is used to predicting of c, thus testing and adjusting
Table 1 The samples Samples
Monitoring indices x
1
x 2
.....
x
i
xt
x(1,1)
x(1,2)
.........
x(1,i)
x 2
x(2,1)
x(2,2)
.........
x(2,i)
.
.
x x
.
.
x ( N , 1)
.
.
x(X,2)
.
.........
.
.
x(X,i)
the empirical relation. Thus, function Q is reversed: Q-1.x--) c. (3) Now the empirical relation is able to classify a new sample x in a certain class c k. So Q can have different aspects: an analytical one (with a little probability), an artificial neural network (ANN), a pattern recognition, a fuzzy system, etc. Du, et al. [4] represent the main source of inspiration for this paper, our contribution being the improvement of the classes and of the monitoring indices.
2. M o n i t o r i n g
in t u r n i n g
Table 2 [4] shows and defines the classes (tool conditions) for monitoring in turning. A chip on the tool inserts bigger than 0.05 mm 2 identified "Tool breakage". "Chatter" is identified by the high frequency noise and the chatter marks on the machined surface. "Transient cutting" (or intermittent cutting) is produced by machining a work piece that has a slot along the feed direction. The conditions in table 2 will be worked in order to coordinate them with [5,6] from which we quote: "According to figure 1, the usual criteria for the wear V B B mQx
VB ff
The z o n e
.-
Vg B
C
~.
b ~C
VS'lq
Ii il N
_1 -'1
Fig. 1 The cutting-tool wear of the rapid steel cutting-tools and of the cutting-tools with hard-cutting alloy plates are: - the average breadth of the wear by the separation from the main
......
x
......... .
.
.
m
(
CLASSES Process conditions )
x(1,m)
c(xl
x(2,m)
c ( x 2 ) E [ c 1, c 2...... c ~ ]
.
)E[Ct,
C2 ...... c n ]
.
x(N,m)
c ( x x ) E [ c 1, c 2...... c ~ ]
back edge in B zone is VB B = 0.3 m m if this has a regular form ; - the maximum breadth of the same wear V B ~ O. 6 m m , if this has an irregular form". C - r~ = the top radius of the tool = max. 2 mm. The classes c~, c 3 , c4 and c5 in table 2 will be adapted in accordance with these prescriptions and they will be r e a r r a n g e d as in table 3. [6] shows, in connection with class c2 in table 2, that "the advanced (catastrophic) deterioration means the intense deterioration of the cutting-tool edges after a period of normal cutting, under the combined action of all the factors involved in the processing". For the quantitative evaluation of this state, an overfulfilment with more than 0.1 m m of the wear criteria, and the overtaking till O. 1 m m showing a severe w e a r are proposed. Class c7 in table 2 will be eliminated, so that continual cutting (which might occur if keyway exits) does not occur on the lathe, the making of the keyway being the last operation in the shaft working process. In conclusion, the n = 7 classes referring to the working conditions are those in table 3, where to t h e f i r s t three m a x
--
classes the w o r k i n g conditions are n o r m a l a n d the others are abnormal.
In order to obtain the monitoring indices the following will be used: - strain gauges glued on the cutting-tool, which measure the components of the cutting force (Fy - the repelling force, Fz- the main force); - accelerations of cutter holder vibrations (a x, a y, a z). The signals of the sensors are registered simultaneously by means of device SPIDER 8 (H.B.M.). It is 4.8 carrier-frequency technology for S/G (strain gauges) or inductive transducers. Spider 8 is an electronic measuring system for PC, for electric measurement of mechanical variables such as strain, force pressure, path, acceleration and for temperatures. Each channel works with a separate A/D converter which allows measuring rates from 1/s to 9600/s. In [2] we made "the monitoring simulation". The vibrograms representing: - the component variations of the cutting force; - the relative displacement between tool and piece, on the repelling direction; - the power furnished by the electric engine, are realized with the
21
Table 2 The classes (tool conditions) Class Tool conditions c1 Normal c2 Tool breakage c3 Slight wear C4 Medium wear c5 Severe wear c6 Chatter c7 Transient cutting c8 Air cutting Table 3 The classes in turning Class
Identification on cutter wear < 0 . 1 mm chipping > 0.05 mm 2 0.11 < wear < 0.15 mm 0.16 < wear < 0.30 mm 0.31 mm < wear Fresh tool Fresh tool
Tool conditions
c i
Normal
c:
Slight wear
c3
Medium wear
c4
Severe wear
C5
Tool breakage Chatter Air cutting
Identification on workpiece
Number of samples M 1 = 144 M 2 = 49 M 3 = 114 M 4 114 M 5 = 114 M 6 61 M 7-- 15 M 8 = 13 --
Chatter marks An axial slot
Identification on cutter V B < 0.1 m m , or V B m a x < 0.2 mm 0.11 < V B < 0.2 m m , or 0.21 < V B m a x < 0.4 mm 0.21 < V B < 0.3 m m , or 0.41 < V B < 0.6 mm 0.31 < V B < 0.4 ram, or 0.61 < VB m a x < 0.7 mm V B > 0.41 m m , or V B ma x > 0.71 mm Fresh tool
=
Identification on work-piece
max
c6 c7
functions R A N D N and R A N D (from M A T L A B ) . Based on them, 11 monitoring indices are calculated. The A N N with l l inputs (the n u m b e r o f monitoring indices) and 8 outputs (the n u m b e r o f classes) is realized with 3 layers. In [3] the experimental setup and experimental results are presented: - C o m p o n e n t s o f cutting force were calculated on the basis o f the experimental study o f the lathe cutting-tool bending and with the help o f two strain gauges, stuck on the lathe cutting-tool and connected to S P I D E R . The recordings were made during the longitudinal turning o f a O L C 45 cylinder ( ~ 113, L = 1000), with a lathe cutting-tool with metal carbide P20 and 9t = 45 ~ It results: F z : 1 1 3 6 ( e 2 ~nr_ C1 1 , r ) [daN], (4)
Chatter marks
looked irregular (like a triangle), so the wear criterion was used.
VBmax
3. E x p e r i m e n t a l
results
191 recordings were m a d e and the p a r a m e t e r s o f the Spider device were set on: sampling frequency = 9 6 0 0 / s , no. o f periods - 1, samples / period - 4800; i. e. the device samples the received signals with a frequency o f 9600 Hz, but it can send to PC a recording with 4800 samples, which corresponds to 0.5 sec. Each working session lasted nearly 30 sec., and by h a l f this time, the Spider device was connected for one second. The cutting working conditions were: piece diameters D = 113 - 93.4 ram, the cutting depths t =
are the registered relative
0 . 5 - 3 m m , rotations n = 63 +500 rot/min., longitudinal
deformations o f the strain gauges. - Cutter-holder accelerations (3 Bruel&Kjaer 4329 type accelerometers were m o u n t e d on a plate solidary with cutter holder). - Tool w e a r (after each passing, the tool wear was m e a s u r e d with the help o f a Brinell lens. The w e a r spot
advances s = 0.024 - 0 . 5 mm/rot.; cutting speeds v =
where c1 i
22
n
r
and c2 i
n
r
d n/lO00 = 2 2 . 3 - 1 7 7 . 4 m/min. On each passing on the whole piece length (L = 1000 mm) the t constant was preserved, while s or n varied. 12 monitoring indices were calculated: 7s
Z1 - v ---)cutting speed; Z3 - s ~ longitudinal advance; Z4 - F z --+ average value of the main cutting force;
Z5 ---) Fz variation range (recording which has 960 samples was split into 4 equal parts - 240 samples each - and the maximum and minimum values were calculated for each part; X5 is the difference between the maximum and minimum average values); Z6 --+ number of intersections ofoscillogram Fz with its average value F ; Z7 --+ the average of Fz power spectral density in the frequencies range 1 - 2 4 0 0 Hz ; Zs --9 the average of Fz power spectral density in the frequencies range 2 4 0 1 - 4 8 0 0 Hz ; Z9 ---) the average of F~ power spectral density in the frequencies range 4801 - 9 6 0 0 Hz ; Zlo --+ the average of az inr power spectral density in the
frequencies range 1 - 2 4 0 0 Hz ; Z~ --+ the average of az inr power spectral density in the frequencies range 2401 - 4 8 0 0 Hz ; ZI: --~ the average ofazi"r power spectral density in the frequencies range 4801 - 9 6 0 0 Hz.
4. U s e o f A N N
on monitoring
layer s2 2 7. The input matrixp has the dimensions 12 (monitoring indices) x 655 (recordings), and the output matrix y has dimensions 7 (classes) x 655. The training functions are: tfl = p u r e l i n , tf2 = tansig, tf3 = logsig, therefore the output vectors have 7 elements, with values in domain (0, 1). The first runs (with the training functions trainrp, trainscg, etc.) showed errors only in the positions corresponding to the recordings in classes c5 and c 7, i. e. in the case of those classes which have the fewest recordings. The increase of the number of recordingswithout making new experiments - may be performed by adding the same recordings several times to the same class, which may be eventually affected by a noise of an average value of 0.1. Consequently: - we fourfold the recordings in class c5: an average value noise of 0.1 is attached to the first set, the second set is identical with the original one, an average value noise of 0.15 will be attached to the third set; - we threefold the recordings in class c7, acting by a way of analogy with the foregoing (only with first and the second set). Therefore, c5 will consist of 60 recordings, c 7 - 75 recordings, and the number of columns in the above matrices grows from 655 to 750. Using the following instructions: ind = find(c = - 0); dim = length(ind); er = dim / 750, we find: the "c" elements indices which are null, the "ind" number of elements as well as the network error, respectively. The results of a run are presented in what follows: R=8 ; Q=750 There was a redundancy in the data set, since the p r i n c i p a l c o m p o n e n t a n a l y s i s has reduced the size of the input vectors from 12 to 8. TRAINRP, Epoch 0/300, MSE 1.53359/0, Gradient 0.292509/le-006 TRAINRP, Epoch 25/300, MSE 0.487294/0, Gradient 0.0150939/le-006 TRAINRP, Epoch 43/300, MSE 0.477332/0, Gradient 0.0148044/le-006 TRAINRP, Validation stop. ind = Columns 1 through 18 440 441 445 446 447 448 449 450 454 455 456 457 458 459 463 468 472 473 Columns 19 through 37 474 475 476 477 481 482 483 519 520 521 533 545 594 595 596 646 647 658 672 dim=37 ; er=dim/750-0.0493= 4.93 %. =
Z: - t ---)cutting depth;
of the tool wear
The recordings are divided in two sets, "Learning" and "Classification", the first set having 60% of the number of recordings (those noted with "a", "c" and "e"), and the second set having the recordings noted with "b" and "d". Columns "az" and "Fz" belonging to the recordings in the "Learning" set, will be transferred into MATLAB, where 7 tables corresponding to the 7 classes will be made up. The table analysis shows t h a t - except for two c a s e s - the recordings in one class have quite similar average values of Fz and, as expected, values which grow (as the wear grows) from one class to another. The training set will contain the recordings in the "Learning" set, therefore 60% of the total number of recordings. Recordings "b" in the "Classification" set will be allotted to the validation set. and recordings "d" will be input into the testin s ~ ; therefore each set has 20 % of the recordings. ANN consists of 3 layers, with 2 hidden layers, No. of Inputs - 12 (monitoring indices), No. Output Neurons ss = 7(classes), No. Neurons in the first hidden layer s l -- 23, No. Neurons in the second hidden
23
It is a useful diagnostic tool to plot the training, validation and test errors to check the progress of training. The result as shown in the figure 2 is reasonable, since the test set error and the validation set error has similar characteristics.
LATHE
training (down) Validation (middle) Test (uto)
/ uJ g 03
1.2 1 0.8
0.6 0.4
i
0
~ " - -
..... ~-_-===~_
10
20
Epoch
30
40
50
Fig. 2. The progress of training In other runs: - carried out under the same circumstances, results were twice as above, and once as follows: 51 epochs; d i m - 27 ; er = 3.6 %; - without "init" function (to reinitialize weights and biases), in two runs the errors were 4.67% and 13.3%; - with the trainscg training function without "init" the error was 5.33%, whereas with "init"- 25.6%.
5.
Monitoring
Monitoring the tool wear involves that during the continuous process of cutting (fig. 3) the "Spider" device should be connected into the system, and it
PC
\,
i SPIDER
Fig. 3. The experimental setup should transmit a recording to the PC, based on which with table 3). Then "Spider" connects (therefore itself again, and so on. In case the class exceeds 3 ANN will say the class the processing is into (in conformity "abnormal"), PC will produce a sonorous signal, or stop the processing. To detail" the recording has 4800 samples, being a table in EXCEL with 4800 rows and 3 columns (A---inreg inreg 1 , B = e 2 , C = a z). The transfer Spider ---> PC is carried out within nearly 1 min. Out of this table a new (smaller) table is selected and it will consists of 960 rows and 4 columns, the first element being selected at random, at a location higher than A500. Function Fz (= 1136"A-1136"B) is calculated in fourth column, according to formula (4). This table is transferred in MATLAB, where 12 monitoring indices will be calculated and then they are presented at the ANN input. To see how ANN responds we use some of the recordings; the results are presented in table 4. With the first 4 recordings, after the first run a second run was carried out, and it lasted 2 min., the class given by ANN coinciding with the one of the first run.
Table 4 The answer of ANN
No
Rec.
Class
v [m/min]
t [mm]
s [ram/rot]
Epochs
Error %
0 1 2 3 4 5 6 7
1 046 183 168 174 099 190 095
2 1 5 4 4 2 7 6
3 69 71 75.4 120.6 83.2 0 83.2
4 0.5 3 2 2 1 0 1
5 0.25 0.302 0.302 0.416 0.334 0 0.353
6 43 40 76 67 43 47 47
7 4.93 4.4 2.5 3.47 4.93 3.6 23.87
The coincidence of the values in columns 2 and 8 shows that ANN provides correct outputs.
24
Class from ANN 8 1 5 4 4 2 7 6
Time [mini 9 10 8 6 8 7 7
To asses the soft efficiency, the runs will be resumed, as in table 4, but for one third of the 44
Table 5 The answer of ANN for class 3 No. Recor. v [m/rain] 0 1 2 1 115 130.6 2 118 130.6 3 126 79.3 4 129 79.3 5 133 63.4 6 136 63.4 7 139 63.4 8 142 63.4 9 146 77.3 10 149 77.3 11 152 77.3 12 155 123.7 13 158 123.7 14 161 123.7
t [ram] 3 1.2 1.2 1.4 1.4 1.4 1.4 1.4 1.4 1.5 1.5 1.5 1.5 1.5 1.5
s [ram/rot] 4 0.334 0.416 0.292 0.353 0.353 0.292 0.212 0.167 0.375 0.302 0.25 0.177 0.146 0.118
recordings of class 3. Table 5 presents the results; "L" is the distance from the centre of the cut zone to universal. It is noticed that only the recordings under no. 1 and no. 5 did not give the correct class (3), but a neighboring class. Other two runs carried out for each of these recordings - with the modification of the location of the first element in the table (960 x 4 ) showed the correct class (3). Therefore, with 25 runs only two were erroneous, the error amounting to 8 %. We consider this error to decrease in the future if we take several recordings in tables 4 and 5.
6- Conclusions The algorithm to monitor the tool wear making use of ANN proved efficient, the error range being below 5 percent. In the case of real monitoring, when the cutting is continuous, to avoid "thermal no-compensation" a cooling of the knife should be provided. However, water can cause trouble in the circuits of strain gauges, although they are protected (with Poxipol). Consequently, for this step of the experiment, the strain gauges will be removed and the cutting force components will be measured by averages of a KISLER device (Austria). Moreover, column 9 in table 4 shows that the current hard provides delayed information, i. e. we know that the tool is - for example - in class 4 (Severe wear), when it may have reached class 5 (Breakage), or 6 (Chatters). Therefore, a highly specialized PC is required, to reduce the responding time as much as
L [mm] 5 245 90 770 630 425 300 200 120 780 615 480 340 230 130
Epochs 6 40 76 43 43 40 76 64 67 43 47 47 40 31 76
Error % 7 4.4 2.53 4.93 4.93 4.4 2.53 2.67 3.47 4.93 3.6 23.87 4.4 15.2 2.53
Class in ANN 8 2 3 3 3 4 3 3 3 3 3 3 3 3 3
possible.
7. Acknowledgement This research was supported through two grants by Ministry of Education of Romania [1, 7].
References [ 1] Balan, G, 2002, The monitoring of a lathe using an artificial neural network, Grant type A hr. 33 445, Theme 19, Cod CNCSIS 451 [2] Balan, G., Tarau, C., 2003, The monitoring simulation of a lathe, Mathematical & Computational Applications, an International Journal published by the Association for Scientific Research, Vol. 8, Nr. 3, pp. 279-286 [3] Balan G. and Epureanu A., 2005, The monitoring of a lathe using an artificial neural network (1-st part), Annals of DAAAM for 2005 & Proceedings of the 16th International DAAAM Symposium "Intelligent Manufacturing...", Croatia, p. 019-020. [4] Du, R., Elbestawi, M. A., Wu, S. M., 1995, Automated Monitoring of Manufacturing Processes, Part 1: Monitoring Methods, Part 2: Applications, ASME Journal of Engineering for Industry, may, vol. 117, Part 1-pp. 121 - 132, Part 2 - pp .133 - 141. [5] STAS 12046 / 1 - 81, Cutting life testing. Wear. General notions [6] STAS 12046 / 2 - 81, Cutting life testing. Tool life testing methods in turning tools. [7] A. Epureanu, Contract nr. 22CEEXI03/'05, MEdC.
25
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Use of Interprocess Communication in Parallel Neural Networks for Monitoring Complex Systems Hosein Marzi Department of Information Systems, St. Francis Xavier University Antigonish NS B2G 2W5 Canada Tel: + 1-902-867-5356, Fax: +1-902-867-3353, Email: hmarzi~stfx.ca
Abstract
Industrial machinery is made up of complex integrated subsystems which may have independent critical issues. Neural Networks (NN) capabilities in monitoring and identifying failures in single non-linear systems are well proven. This paper describes use of Interprocessor Communication mechanism (IPC) in parallel neural networks. It describes integration of multi-neural networks cells for monitoring of complex industrial machines. Each neural network cell is trained with critical status of a subsystem of the machine. IPC signals are able to activate a particular NN cell for identifying real-time status of each subsystem of the complex machine. Each NNs cell has independent topology. Experimental results indicate that use of IPC in parallel NNs architecture achieve a high degree of precision in real-time condition monitoring. K e y w o r d s : - Parallel Neural Networks, Fault Detection, Real-time Systems, Interprocess Communications
1. I n t r o d u c t i o n
An overview on fault diagnosis and condition monitoring techniques indicates that these techniques can be classified into two main categories of mathematical modeling and pattem recognition. In general, model-based fault diagnosis covers areas such as parameter estimation [ 1] and state estimation [2]. In the former, the parameters of the model elements are compared with those of the healthy model whereas, in the latter, the mathematical (i.e. healthy) model is used in parallel with the actual system to generate residuals representing the inconsistency between the actual system and the healthy model; these residuals can then be processed in order to define faults. The pattern recognition technique generates some sort of response by the system which is affected in different ways by different faults. By recognizing the pattern of the response it is possible to define the condition of the system and diagnose the fault; examples of the technique have been described in references [3] and [4]. The pattern recognition technique is regarded as generally simpler to set up but normally only deals with one fault at a time whereas the mathematical model-based techniques are more complex but can in principle define multiple faults simultaneously.
26
Neural networks are applied to many non-linear problems where finding solutions using traditional techniques are cumbersome or impossible. Examples of applied areas of NNs include robotics [5], control [6] and systems identification [7]. They have been used successfully in condition monitoring and fault diagnosis [8] to [10]. These applications have usually used the pattern recognition approach combined with the classification ability of NNs. Feature extraction are also combined with pattern recognition to draw new data from existing data for NN [11, 12]. NNs applications to fault diagnosis and condition monitoring mostly are not concerned with dynamic situations. Those that are [13, 14] have been applied to process dynamics which are relatively slow. Two methods of presenting variables were given in reference [13], one using raw time series values of measured variables and the other using a moving average value. It was reported that the two methods performed similarly in detecting faults but that the time series was able to detect failure earlier. The current research is concerned with a dynamic (transient) pattern which has duration of 1 second. The method in this case is to use a series of values determined
from the transient pattern, after a primary steady state measurement indicated a value out of healthy threshold. The present paper brings together the work in references [15] to [21]. It describes how the diagnostic system can decide whether a so-called trained fault or a 'novel' (i.e. unknown) fault is occurring. In this application the diagnostic system is trained to diagnose four faults, namely the trained faults which occur relatively frequently, but it is accepted that other 'novel' faults will occur from time to time. If the diagnostic system decides that a trained fault is occurring, it then decides the severity of the fault. The work presented claims originality in the application and necessary adjustment of a well-known methodology (pattern recognition with NNs) to a real physical system: the real-time monitoring of the condition of a coolant system which is a subsystem of a CNC machine tool system. This puts forward problems that include (a) which data to use for pattern recognition, (b) which faults to prioritize, (c) data collection, filtering and reduction in a fast dynamic situation and (d) which diagnostic system to use in order to recognize faults and their severity. These problems exist in a situation of the variability of the response of the coolant system owing to the lack of strict laboratory control. After considerable research the problems have been solved with solutions to (a) to (c) presenting data suitable for analysis by a specially designed diagnostic system. This new design has multiple modules of NNs which were chosen for their suitability after testing of different models. An initial module stands as the core and identifies any faults. If faulty, then, a separate module decides on the severity level of the fault.
application in software of a low-pass filter which ASYST implements using the inverse Fourier transforms of a Blackman window in order to avoid edge effects caused by the rapid cut-off of the spectrum. After considering the data and carrying out preliminary tests the cut-off frequency was set at 30 Hz and this proved to give good results. The program also reduced the data to 100 data points. Thus, after these techniques were applied the response became with a dimension of 100 data points. An example of a digitized transient pattern with 100 data points is illustrated in Fig. 2. The normalized values of these data points have magnitude within the range of 0 to 1, and are used as inputs to the neural networks for diagnosis. 186
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2.2. Developing fault database 2. Selection of failure modes
Review of statistical data of the machine tool coolant system [22] shows that the fault areas are, in order of lowering criticality, blockage of the filter or pipe, pump failure, relief valve malfunction, leakage, and the level in the coolant trough. The fault diagnosis system described here concentrates on four failure modes: 1). partial opening of the flow control valve or partial blockage of the outlet side of the pump, fault P; 2). Filter blockage or partial blockage of the inlet side of the pump, fault F, 3). malfunction of the relief valve, fault R, and 4). coolant leakage reflected in the coolant trough level, fault L.
2.1. Amplification, filtering &calibration of signals The current and pressure transducers used were analogue and, in order to produce digital values for use in the NN, the signals were fed to a digital storage scope. A typical untreated transient which contains noise and has a dimension of 4096 data points is shown in Fig. 1. This signal is then fed to a computer where the software package ASYST [23] was used for data smoothing and reduction. The main program used involved the
The failure database or fault dictionary should contain pump pressure transients taken during the generation of real faults in the system. These data are not available and it was necessary to simulate the four trained faults. This was done as follows: Fault P, by varying the closure of the manual shut-off valve 1. Fault F, by varying the closure of the manual shut-off valve 2. Fault R, by varying the pressure setting of the relief valve. Fault L, by gradual draining of the coolant liquid in the trough. Each of the faults was simulated at four different levels of severity. For example with the manual shut-off valves, these were varied from approximately 20 percent closed (denoted as level 1 or L1) to approximately 80 percent closed (level 4 or L4). For the healthy condition the system was in the standard condition (without fault) and data were recorded at four different times to present a range for the healthy condition. The transient response of the pump outlet pressure (as the flow valve was closed) was recorded for all four severity levels of the four trained faults and for four different states of the healthy condition. The Fault Dictionary as shown in Fig. 3, contains four modules defined by each fault and their corresponding four severity levels.
27
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These patterns correspond to Modules 2, 3, 4, and 5 of stage 2 depicted in Fig. 4. The Fault Dictionary also contains four different healthy patterns-not shown in Fig 3 which correspond to Module 1 in Stage 1, shown in Fig. 4. 3. Implementation of diagnostic NNs system
3.1. Requirements of the diagnostic system The diagnostic system is required to decide (a) whether the coolant system is healthy or faulty, (b) whether the coolant system is faulty and whether a trained fault or a novel fault is occurring and (c) if a trained fault is occurring, which fault it is, and (d) then assigns the level of severity to the fault. In this diagnostic process steps (a) through (c) is decided by stage 1 module 1 and step (d) will be decided at stage 2 using one of the modules 2 to 5, Fig. 4.
3.2. Development of NN-based diagnostic System To select the best model of the NN for use in this research a number of different types of NN were examined. These included competitive learning, learning vector quantization, recurrent model of NNs and one and two-stage back propagation NNs. The tests showed that the two-stage back propagation NNs when designed in multi-modules gave the best results in terms of accuracy of prediction and success in learning. For these reasons the diagnostic system was implemented in a modular two stages as shown schematically in Fig. 4. Stage 1 is an NN which has the task of differentiating between four different trained faults, a novel fault and a healthy system. At the conclusion of this stage a decision to reconfigure the neural network for either of the failures of Fault P, Fault F, Fault R, or Fault L is made. The reconfiguration and loading of the corresponding topology and weight function is made by the designed interprocess communication which is the function linking one stage to another stage and module of the neural network. At stage 2 the task is to decide the level of severity of the trained faults.
28
,
100 Imdez
Fig. 2. Fault F - 2 5 % is shown specified by 100 data points. At t = 0.52 sec is the 40 th point which corresponds to p=28 psi. This indicates that the value o f 40 th input neuron is 28 and aider scaling down by 125 to map all input values between a range o f 0 to 1, it is 0.224.
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3.3. The back propagation neural networks A multilayer back propagation NN with least-squares learning procedure and the logistic function as the activation rule has been developed for this research. The rule of propagation is the weighted sum of excitory and inhibitory connections to a unit and the output function is the identity function [24]. The input data to the stage 1 of NN are the patterns similar to that shown in Fig. 3 and are available in the fault dictionary. For presentation to the NN each pattern is defined by 100 data points as discussed in Subsection II.A and the values at these points are fed as activation values to the 100 input neurons. Aider a number of trials involving one to three hidden layers with between 10 and 100 neurons in each layer the specification of the stage 1 of NN was: number of input neurons: 100, number of output neurons: 8, number of hidden layers: 2, number of neurons in the first hidden layer: 30, number of neurons in the second hidden layer: 10. The specification of each stage 2 NN was: number of input neurons: 100, number of output neurons: 8, number of hidden layers: 1, number of neurons in the hidden layer: 10. Training the NNs was a major factor from which has emerged the finalized architecture of double stage with a number of hidden layers in each stage. However, in order to assess the best architecture for training the NNs, various numbers of output neurons as well as hidden neurons and layers were examined. Although the final number of output neurons is eight, initially the NNs were trained with two and then with four output neurons. The NNs with two output neurons were capable of distinguishing four different states but this design never learned; there was no evidence of local minima but training was never achieved. When the number of output neurons was increased to four, the NNs performed only
.0
observed that the learning improved with increasing number of output neurons, the degree of this dependence was not examined.
slightly better; they were able to learn but on the majority of occasions the learning stage did not succeed and the training of the NNs failed. The number of output neurons was then increased to eight and the networks always learned. Although it was
Transient SiKnal From A Sub-system MNN Stagel/Modulel IPC
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4. Training the neural network
Table 1. Training state of the NN For training the NNs, the error between the target values and the actual values of the output neurons (E) can be set to a particular value. Choosing this value depends upon the accuracy and requirement of the NN. The generalized delta rule (GDR) function, (1), was used in calculating this error value. "
2//2
Where: E." summed squared error, i : index for the output neurons, n : total number of output neurons, ti : target value of the ith neuron, S : output value of the ith n e u r o n . The learning iterations proceed until this limit is reached and therefore a greater number of epochs will be required for smaller error value E. For training these NNs, E was set to 0.1 x 10.9 and the number of epochs typically required was 35000. With the present architecture of the NNs in this work there were no instances when an NN failed to learn because of the minima. One reason for this is the size of the networks as pointed out in Ch. 5 of reference [24].
NN Stage
1
NN Module
Input Fault name and level Pattern
Target values t~ of output neuron
H P
4 Healthy patterns 1 1 0 0 1 1 0 0 20%, 40%, 60%, 80% 1 1 1 1 0 0 0 0
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5
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1
As already discussed, the diagnostic system is implemented in two stages as shown in Fig. 4. The stage 1 NN learns all the available information about the health of the coolant system, the different trained faults and their levels. However, all teachers for healthy and different levels of severity of a single trained fault have identical target values at stage 1. The objective of this stage is only to discriminate between a healthy system and a faulty system and, if faulty, whether a trained fault or a novel
29
fault is occurring. If it is a trained fault, it will decide which fault F, P, R or L is occurring but not the severity of the fault. Hence at stage 1 NN the target values of the eight output neurons for each of the four fault levels for fault P were assigned identically as 1, 1, 1, 1, 0, 0, 0, 0 as shown in Table 1, and similarly for the healthy system and faults F, R, and L. The stage 2 NN learns only about levels of severity in each individual trained fault. Thus the stage is unique for each fault, each having its own NN, and during learning the various levels of severity of a single fault are fed as teachers. For example the stage 2 NN module 2 for fault F is trained with severity levels 80, 60, 40 and 20 percent as shown in Fig. 3 and each level has its own individual set of target values, as shown in Table 1. Table 1 gives the target values ti for each input pattern q from the fault dictionary. After training all the trained patterns when tested resulted in a dev, value [see (2) for definition] of zero. The target values are given in the order of output neuron 1, 2, 3, 4, 5, 6, 7 and 8. With eight output neurons and the number of input patterns during training, it was possible to select the target values in a paired matching format. This did not affect training but, when the software is interrogated, it helps the user to distinguish between different fault groups or fault levels.
5. Failure identification of trained faults
During condition monitoring the signals are collected from the real machine and are tested firstly through stage 1. If the result shows that the operation of the system is taking place under healthy conditions or in the presence of a novel fault, there will be no need for any further monitoring. However, if this is not the case, the cause of the fault will be detected and then the relevant module of stage 2 evaluates the severity of the fault. The flow chart in Fig. 4 shows the procedure of detection by each module of the two-stage NNs. In order to find the most likely fault, both the fault identification and the fault severity procedures are set up as a series of comparisons between the transient pressure response obtained during testing and the patterns available in the fault dictionary. This detection takes place in terms of the NN by feeding the new pressure signal into the network, computing the actual values of the output neurons and then comparing them with the target values of the faults available in the fault dictionary. The most likely fault or severity level is that whose target values give the least deviation from the actual output values of the test pattern. If tiq is the target value of the ith output neuron for pattern q in the fault dictionary (values given in Table 1) a n d f is the actual output of the ith neuron during a test, then for each pattem q from the fault dictionary, the deviation (denoted dev) (noting that there are eight output neurons) is given by: 8 devq --Zl(liq - - Z ) [ (2) i=1
30
5.1. Trained fault identification The value of dev is calculated for all patterns in the fault dictionary and the smallest value of dev defines the fault or severity level according to whether the stage 1 NN or stage 2 NN is being used. 6. Results and discussion on detection precision
In total, 395 tests using 'unknown' faults were carried out on the coolant system including 30 novel faults. In the latter case there was one misclassification and the overall accuracy for all tests was 99.24% for classification of faults and 96.71% defining the severity level. Details of the tests are presented in Table 2. Table 2. Result of testing two stage Nature of pattern No. of patterns tested Healthy, (H)
MNN for its accuracy. No. of Mis-classifications Cause#/(%) Level#/(%~ 2 (2.2%) N/A (0%)
Novel Failures
30
1 (3.2%)
N/A (0%)
Pump outlet Blockage, (P) Filter Blockage, (F)
69
Nil (0%)
4 (5.8%)
67
Nil (0%)
2 (2.98%)
Reliefvalve malfunction, (R) Coolant leakage,(L)
92
Nil (9%)
5 (5.43%)
Nil (0%)
2 (4%)
Total
395
3 (0.75%)
13 (3.25%)
99.24%
96.71%
Accuracy (%)
The test information of Table 2 include the number of times the neural networks at stage 1 and 2 were tested with an unknown pattern from each categories of healthy, faulty, novel fault (not seen before) to the neural network. The correct or incorrect diagnosis at each stage or module is indicated as well.
7. Conclusion
An artificial Neural Networks diagnostic system has been designed to diagnose faults in a coolant system which is a subsystem of a machine tools system. The diagnostic system consists of two stages. The first stage contains a single module and the second stage consists a number of modules, one for each failure mode, each containing NNs. In this application it was found that the back propagation NNs at each stage gave good results but this does not infer that they will provide the best results in other applications. The diagnostic system was trained to recognize the healthy coolant system, four different (trained) faults each acting alone and whether a novel (i.e. previously unmet) fault was occurring. These decisions were made at stage 1 within the first module of the diagnostic system. If this stage decided that one of the trained faults was occurring, stage 2 was activated by an interprocess communication
and this determined the level of severity of that fault. The diagnostic system has been tested against 'unknown' faults and was able to classify the fault correctly on over 99 percent of occasions. The work presents a real-time method of condition monitoring of the coolant system and uses the capability of NNs in storing information. The designed system is reconfigurable in that each stages of the double stage neural networks posses its own topology. The network topology changes as IPC activates a module within the second stage. The ability of the NN has been strengthened in this new multi-module architecture and this has resulted in improved ability to learn and a higher accuracy of detection. As a result a condition monitoring system with over 99 percent accuracy and the capability of real-time monitoring was achieved.
References
[1] Frank, K., Schwarte, A. and Isermann, R., Fault detection for modern Diesel engines using signal- and process model-based methods, Automatica (Journal of IFAC) Special section on fault detection, supervision and safety for technical processes, pp. 189-203, Vol. 13, Iss. 2, Feb 2005. [2] Gertler, J., Residual Generation from Principal Component Models for Fault Diagnosis in Linear Systems - Part II: Extension to Optimal Residuals and Dynamic Systems, Proceedings of the 2005 1EEE International Symposium on Intelligent Control, Mediterrean Conference on Control and Automation, pp. 634-639, 2005 [3] Diallo, D.; Benbouzid, M.E.H.; Hamad, D.; Pierre, X.; Fault Detection and Diagnosis in an Induction Machine Drive: A Pattern Recognition Approach Based on Concordia Stator Mean Current Vector, IEEE Transactions on Energy Conversion, pp.:512 - 519, Vol.20, Issue 3, Sept. 2005. [4] Martin, K. F. and Thorpe, P. Coolant system health monitoring and fault diagnosis via health parameters and fault dictionary. Int. Z Advanced. Manufacturing Technology, 1990, 5, 66-85. [5] Lesewed, A.; Kurek, J., Calculation of robot parameters based on neural nets,, Proceedings of the 5th International Workshop on Robot Motion and Control, pp. 117-122, 23-25 June 2005. [6] Faa-Jeng Lin; Po-Hung Shen; Ying-Shieh Kung Adaptive wavelet neural network control for linear synchronous motor servo drive, IEEE Transactions on Magnetics, pp. 4401- 4412, Vol. 41, Iss. 12, Dec. 2005. [7] Becerra, V.M.; Garces, F.R.; Nasuto, S.J.; Holderbaum, W., An efficient parameterization of dynamic neural networks for nonlinear system identification, IEEE Transactions on Neural Networks, pp. 983 - 988, Vol. 16, Issue 4, July 2005.. [8] Nandi, S.; Toliyat, H.A.; Li, X., Condition Monitoring and Fault Diagnosis of Electrical Motors A Review; IEEE Transactions on Energy Conversion, pp. 719 - 729, Vol. 20, Issue 4, Dec. 2005. [9] Simani, S.; Identification and Fault Diagnosis of a Simulated Model of an Industrial Gas Turbine, IEEE Transactions on Industrial Informatics, pp.202 - 216, Vol. 1, Iss. 3, Aug. 2005.
[10] Jing Peng; Heisterkamp, D.R.; Dai, H.K., LDA/SVM driven nearest neighbor classifier. IEEE Transactions on Neural Networks, Vol. 14, Issue 4, pp. 940- 942, July 2003. [ll] Guo H., Jack, L.B., Nandi, A. K., Feature generation using genetic programming with application to fault classification. IEEE Trans. on Systems, Man and Cybernetics, Part B, Feb 2005, V.35 (1), pp 89-99 [12] Shing Chiang Tan, Chee Peng Lira, Application of an adaptive neural network sith symbolic rule extraction to fault detection and diagnosis in a power generation plant. IEEE/ASME Transaction on Mechatronics, Dec 2004, Vol 9(4), pp 711-714. [13] Vaidyanathan, R. and Venkatasubramanian, V. Representing and diagnosing dynamic process data using neural networks. Engng, Applic. Artificial Intelligence, 1992,5(1),11-21. [14] Vaidyanathan, R. and Venkatasubramanian, V., On the nature of fault space classification structure developed by neural networks. Engng Applic. Artif Intelligence, 1992, 5(4), 289-297. [15] Marzi, M. H. and Martin K. F., Artificial neural network in condition monitoring and fault diagnosis. In Proceedings of the Conference of the International Association for Advanced Modeling and Simulation Techniques in Enterprise, California, 29-31 May 1991, pp. 113-124. [16] Martin, K. F. and Marzi, M.H., Neural network solution to coolant system diagnostics. In Proceedings of the Fourth International Conference on Profitable Condition Monitoring, 8-10 December 1992, pp. 217-227 (Kluwer Academic, Dordrecht, The Netherlands). [17] Martin, K. F. and Marzi, M. H., Defining novel faults in a neural network fault diagnostic system. In Proceedings of the Fifth International Conference on Profitable Condition Monitoring, December 1996, pp. 257-273. [18] Martin K. F. and Marzi M. H., Diagnostics of a Coolant System via Neural Networks. Proceedings of Instn. Mech. Engs, Journal of Systems and Control Engineering- Part-l; June 1999, Vol.213 No.3 pp.229-241. [19] Marzi H., Real-Time Fault Detection and Isolation in Industrial Machines Using LVQ. Proceedings of lnstn. Mech. Engs, Journal of Engineering Manufacture- Part-B., August 2004, Vol. 218, No. 8, 949-959. [20] Martens, J.-P.; Weymaere, N., An equalized error backpropagation algorithm for the on-line training of multilayer perceptrons. IEEE Transactions on Neural Netwroks, May 2002, Vol. 13, No. 3, 532-541. [21] Xiaoli Li; Du, R.; Guan, X.P.; Utilization of information maximum for condition monitoring with applications in a Machining Process and a water pump. IEEE/ASME Transactions on Mechatronics, Vol.9, Issue 4, Dec. 2004, pp.711 - 714 [22] NCSR 1988, AMTA Reliability Publications 1, CN Machining Centre, National Centre for System Reliability, Risley, 1988. [23] ASYST 2.0 Manual Module 1, System Graphics, Statistics, (Macmillan Software Company, London). [24] McClelland, J. L. and Rumelhart, D. E. Exploration in parallel distributed processing A Handbook of Models, Programs and Exercises", 1987 (MIT Press, Cambridge, Massachusetts).
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Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Collaborative research on new manufacturing technologies organization X. Maidagan ~, N. Ortega a, L.N. Ldpez de Lacalle b, A. Lamikiz b, J.A. Sfinchez b aMARGUNE Center, Poligono Ibaitarte, 5, E-20870 Elgoibar,PO 25,Spain bDepartment of Mechanical Engineering, University of the Basque Country, ETSI Bilbao, Spain
Abstract This article presents a model for collaborative research and development of advanced manufacturing systems, being known as the CIC marGUNE (Cooperative Research Centre for High Performance Manufacturing), placed in the Basque Country and composed for universities, technological centres and machine-tool companies. The article describes its mission, main objectives, proposed paradigms, structure and modus operandi. The projects being carried out to date are explained. An overall assessment of results (industrial and scientific) so far is also given.
Keywords: cooperative research, manufacturing technologies
1.
Introduction
The last few years of the 20th-century saw the onset of globalization, which is already affecting many sectors of industry at a more local level. This situation also has a clear influence on scientific and technological knowledge management. Indeed, governments have come to understand just how necessary it is to associate and optimize the capacity to produce knowledge with new distribution mechanisms, and to understand the abilities of different people to absorb and utilize that knowledge. The intention, therefore, is to encourage the generation of value in the business sector and to replace the factor based economy by a knowledge based economy. To that end, players on the supply side of science, technology and innovation in each country must become the true promoters of the full overall development of a system of science, technology and innovation, and must offer all-round excellence that
can meet the demand for technology on the part of businesses (and indeed society as a whole), which is increasingly sophisticated. This problem is exacerbated in mainly industrial regions such as the Basque Country, where a total of 700 million E was invested in scientific research and technological development in 2003. This figure is 1.5% of the region's GDP and is 4.1% up on the previous year, when investment totalled 672 million euros. However, the increase was one percentage point less than the 5.1% rise in GDP from 2002 to 2003. Continuing the trend of previous years, most spending on research has continued to take place in the fields of engineering and technology (541 million t~, 77% of the total), and in manufacturing technologies. Country-wide investment in R&D ranges from more than 2.5% of GDP in Sweden, the USA,
33
Denmark and Germany, to intermediate levels of between 1.5 and 2.5% in France, Norway, and the U.K. and lower levels in the Czech Republic, Ireland, Italy, Spain, Portugal and Greece. The level in the Basque Country is slightly higher than that of Italy, but lags behind that of other countries with a similar per-capita income. Players in the field of technology must make more effort to develop research programs in strategic areas that can benefit the economic and social development of the country in the medium and long term. If this objective is to be attained, more attention must be paid to the culture of collaboration and to networking in order to find solutions to specific needs in areas where collaboration is considered necessary due to the volume of demand, its strategic nature or increased likelihood of providing a competitive offer.
t ~ : 0 ~ i. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 ~ i ~ i. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Fig. 1. Technological level of the products manufactured in the Basque Country. The products made in the Basque Autonomous Community are of medium level in terms of technology (see Fig. 1), so the risk of becoming less competitive if technology does not progress fast enough is high. This classification is subject to universal criteria [1]. Ways must be found to exploit knowledge and assess the results. There are success stories in collaborative research work between different players in the chain of science, technology and business which can be used as models for many other countries. Among them are CRC's in Australia, which are the result of a government program established in 1990 that has seen the creation of about 158 centres of this type [2]. Other longstanding examples include more than 100 associations in Germany, Kplus and Kind/Knet in Austria, RRIT's in France and Competition Centres in Sweden. As has happened
34
in the Basque Country, the Spanish government this year set up new projects for strategic national consortia on research and technology which will entail collaborative research. However, ensuring proper orientation of common alliance of Research and action requires a continual technological monitoring of databases on scientific articles [3] and patents [4], and of trade affairs such as the EMO in Hannover, the JIMTOFF in Japan and the IMTS in Chicago. In this study we present the experience of the Basque Country in the shape of CIC (collaborative research centre) marGUNE and its initial results. e
The concept of Collaborative Centres (CIC's)
Research
The Autonomous Community of the Basque Country is a region in Spain where high performance manufacturing is highly important, covering a wide range of sectors in both a vertical approach based on the value chain and a horizontal approach based on the diversity of end products produced. To achieve the desired structural goals the creation of collaborative research centres has been encouraged as a key element in bringing together and making use of the synergies that already exist in the main strategic lines of Industry on a collaborative basis. A Collaborative Research Centre (CIC) is a platform for multi-party collaboration with a view to developing capacity in strategic economic and social areas for the Basque Country in the medium and long term. However, its framework of action is not merely regional but must extend to national and European levels. The purpose of CIC's is to optimize scientific and technological capacity and generate economies of scale, ensuring sufficient critical mass to increase research capacity in the Basque network of science, technology and innovation. There is therefore a need to generate capacity with other players in the system, undertaking to develop a common strategy for progress in a specific field of science and technology within the fields of strategic research defined by the government under its Science and Technology Policy. CIC's are dual organizations comprising a little core physical component and a big virtual component, in the latter working researchers from the universities and technological centres partners of this collaborative approach.
2.1.
The CIC core
The core component of a CIC must be able from the outset to make use of an infrastructure of its own that will enable it to carry out management, administration and marketing tasks for optimum development. In addition, the CIC uses facilities and equipment available at member organizations that have undertaken to place them at its disposal for the furtherance of its goals. In general terms, the functions of the CIC core are the following: General co-ordination of the research activities laid down in the plan of action. Performance of part of the research. Performance or hosting of benchmark training activities in its specialist scientific and technological area. Direct management, transfer and exploitation of the results of activities in the common framework. Ownership of all equipment and infrastructures acquired for the performance of activities and projects. 2.2. The "virtual CIC"
The virtual CIC comprises those players which are active in the lines of work pursued by the CIC. The research done by the virtual CIC is networked through co-operation with other players and with the CIC core, so that the capacity of the system is optimized. In other words, this virtual component comprises the body of researchers through which the technology partners take part in projects. CIC marGUNE was set up on November 4, 2002 as a collaborative research centre in highperformance manufacturing, with the remit of making the firms in its area more competitive by introducing manufacturing processes that are competitive on a worldwide basis, and developing excellence in research. High-performance manufacturing is an especially significant process that covers a wide range of sectors from both a vertical approach based on the value chain and a horizontal approach based on the diversity of end products. In accordance with the strategic frameworks established by public organizations and the concepts of basic and oriented research described in the manual by Frascati [5], CIC marGUNE seeks to
carry out research in two areas: Basic research where the prevailing need is for a greater knowledge of the fundamentals of processes, making use of the research potential of the universities involved, supplemented by the work of the technology centres. Research in which the knowledge acquired is quickly applied. 3.
Members
of
the
CIC
m a r G U N E
One of the areas where CIC marGUNE has most potential is in bringing together research capacity of a more scientific nature, characteristic of universities, with approaches more closely linked to actual industrial practice, characteristic of technology centres. This, together with ongoing orientation towards the needs of industry assured by the presence of leading companies, guarantees a bright future for the centre. The participation of intermediate innovation organizations (such as the Foundation INVEMA, which is in charge of dissemination) insures that results will be properly transferred to the fabric of Industry in our country. The number of active members of CIC marGUNE has grown steadily since its founding in 2003. Membership now includes representatives of the leading players at the different levels of the value chain in manufacturing technologies (see Fig. 2): Universities: University of the Basque Country (High School of Engineering of Bilbao), School of Engineering of the University of Mondrag6n and the School of Engineering of the University of Navarra. Research centres: Ideko S. Coop., Foundation Fatronik, Foundation Labein, Foundation Tekniker, Koniker S. Coop., Aotek S. Coop. and Lortek. Corporations: Danobat S. Coop., Goratu Group, CIE Automotive and MCC Forming Group. Intermediate innovation organizations: Foundation Invema, focused in the support to research about machine-tools. It is significant that although only three years have passed since the founding of CIC marGUNE, and there has therefore been little time, membership is growing steadily (at the time of writing this paper more membership applications are waiting for approval pending).
35
S C I E N C E & T E C H N O L O G Y PLA I~TRS UNIVERSITIES [] UPV/EHU [] MGEP (MU) [] TECNUN
OTHER PLAYERS [] 1NVEMA (~nterrnediate Innovation Organization)
TECHNOLOGY & R&D CENTRES Ill AOTEK
[] LABEIN
[] IDEKO
[] LORTEK
[] FATRONIK
[] KONIKER
II TEKNIKER
Fig. 2. The CIC marGUNE in 2005.
4. Organisation of CIC marGUNE As mentioned above, the organizational structure of CIC marGUNE is based on a CIC Core and a Virtual component, seeking maximum flexibility. The virtual team comprises the researchers working on each project, who are provided by the various technological members (universities and technology centres) of marGUNE according to needs for each action. The number of researchers working on the activities of marGUNE can therefore vary from one year to the next without the system suffering as a result. Clearly, research work is concentrated in the hands of the virtual team of the CIC, but this does not mean that it is impossible to form new research teams in new areas not covered by the technological members and to integrate them into the CIC Core of marGUNE. Basically, the function of the CIC Core is to handle a small part of the actual research work, to coordinate activities and to take responsibility for
36
ensuring that the results are transferred and exploited. The use of the latest ICT's is fundamentally important for the coordination of these actions. This is handled by an useful extranet (www.margune.org). The coordinated, synergetic oriented basic and applied research work of the technological members is turned into usable knowledge, which is transferred through collaboration projects (see Figure 3) from those technological members to companies. The companies transform knowledge into new products and, in short, into greater wealth for the Basque Country.
5. Research projects Two periods can be distinguished in the CIC projects: those from the first phase of action (20032005), which are already at the exploitation stage, and those from the current phase of 2005-2007, being currently developed.
Ill
Fig. 3. General layout of research and its transfer. -
-
EXACMED: Advanced measurement systems applied to experimental studies of machining processes [7]. ULTRASON: New ultrasonic-based machining processes.
5.2. 2005-2007 phase These projects entail 2.9 million euros of investment: GEHITU: Study of supplied material processes based on mixed technologies (see a picture in Figure 5). SURFMAN: Measurement of tensile stress and structural changes due to the machining process applied, study of the mechanical behaviour of parts. CALGESUP: Assessment of integrity and accuracy during machining processes. DIAPASON: Advanced machining process from the point of view of process modelling, virtual simulation and monitoring (see Fig. 3), some results are presented in [8]. KONAUTO: New forming process to generate low rigidity new generation parts. -
Fig. 4. Virtual milling on five axes.
5.1. 2003-2005 phase These projects entail 3 million euros of investment: ARKUNE: Machining process monitoring, some results are in [6]. BEROTEK: New machining processes assisted by heating systems.
37
-
ULTRAMEC: Analysis of drilling, turning and dressing grinding wheels processes, assisted by ultrasonic systems. Some projects are already at the development stage, and industrial applications are being sought (see Fig. 5).
communities, regions and countries, and the organisation is currently undergoing moderate growth. In conclusion, this paper presents a new way of working in collaboration which brings together the efforts of competitors in order to improve their unique capabilities, make their efforts in oriented basic and applied research more effective and provide solutions for the fabric of Industry in the current globalised age. Acknowledgments
Fig. 5. Plasma assisted milling of Inconel 718, IR view and actual view (developed by UPV/EHU).
We would like to give thanks to the Basque Government's Director of Technology Policy, Mr. Joseba Jauregizar, for the support that he has given to the idea of CIC marGUNE. We would also like to thank the members of the Scientific Committee and the Governing Board of marGUNE.
6. R e s u l t s
References
The measurable results obtained by the CIC in the period 2005-2007 are as follows. Scientific results: - 40 scientific papers in the last three years. - 19 contributions to international conferences on manufacturing. - 5 philosophical dissertations. Industrial results: - 3 new patents, about plasma assisted milling, and drilling monitoring - 10 industrial development projects. These figures are expected to double in the stage from 2006 to 2008 as lines currently open reach completion.
[!]Hatzichronoglou T. Revision of the Hightechnology Sector and Product Classification. OECD, STI, Working Paper 1997/2, Paris, 1997. [2] Australian Govemment, Department of Education, Science and Training. CRC: success through innovation, issue 6, October 2005. [3] www.sciencedirect.com, by Elsevier [4] van Dulken, S. Free patent databases on the Intemet: a critical view. World Patent Information. vol. 21, n.4, 1999, 253-257. [5] Frascati Manual: Proposed standard practice for surveys research and experimental development. 9 OECD, 2002. [6] Pefia, A., Rivero, A., Aramendi, G. and L6pez de Lacalle, L.N. Monitoring of drilling for burr detection based on internal signals. Int. J. of Machine Tool and Manufacture, vol. 45, n.14, 2005, 1614-1621. [7] Arrazola, P.J., Villar, A., Ugarte, D., Meslin, F., Le Maitre, F. and Marya, S. Serrated Chip Prediction in Numerical Cutting Models. 8th CIRP Intemational Workshop on Modelling of Machining Operations, Chemnitz, Germany, 2005, 115-122. [8] Gonzalo, O., L6pez de Lacalle, L.N., Cerro, I. and Lamikiz, A. Prediction of milling forces from oblique cutting FEM model. 8th CIRP Int. Workshop on Modelling of Machining. Chemnitz, Germany, 2005, 235-243.
7. C o n c l u s i o n s
We have presented a new concept in research based on collaboration between players of different kinds (universities, technology centres, and corporations) that seeks to develop new processes on the basis of in-depth knowledge of manufacturing processes. These processes are intended to be taken up by the machine-tool sector and the manufacturing sector in general, thus effectively upgrading the technology of the market. A list of projects is given, along with the results obtained so far; they are twice that previous period of research. The experience and the concept of CIC marGUNE can be exported to other
38
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Six sigma training programmes to help SMEs improve Tony Fouweather / Shirley Coleman and Andrew Thomas2 1: ISRU (Industrial Statistics Research Unit), University of Newcastle upon Tyne, UK, NE1 7RU 2: Manufacturing Engineering Centre, Cardiff University, UK, CF24 3AA
Abstract
This paper shows how SMEs were able to improve key processes by using the six sigma approach during a training programme organised by ISRU and partly funded by the European Social Fund. Six sigma training can be of great benefit as it gives opportunities for any company to become more efficient and competitive. The cost of this training is often too much for SMEs however, leaving them at a severe disadvantage to their larger competitors. ISRU were able to address this problem directly by offering hugely discounted six sigma training to local SMEs. This paper describes, 2 case studies, showing how statistical techniques can be applied to facilitate improvements in efficiency, reduction in waste and rejects and the general improvement of processes and how this in turn can improve the competitiveness of a SME ~. A small local bakery sent a delegate on a training course to learn six sigma techniques. "The Six Sigma training gave us a set o f tools which allowed us to improve the efficiency o f our packing line f o r one o f our most difficult products. " Another case study shows how a local chemical company used modelling techniques to increase their profitability. The delegate was able to model their drying process with the tools learnt on the training course and through this predictive model the company was able to produce an extra batch each week representing s profit for the company and so this had the potential to dramatically increase revenue and improve profitability by s pa. The need for assistance to SMEs is well documented and in regions such as Cardiff (UK) where heavy industry has declined in recent years similar to what has happened in Newcastle there is a growing need for SMEs to be offered assistance to become more competitive in order to survive. European funding has been obtained by MEC in Cardiff and adapting the programme set up in Newcastle to improve the prospects of Cardiff local SMEs is to be implemented via the IPROMS network of excellence.
1. I n t r o d u c t i o n
In recent years industry has become increasingly competitive and companies wishing to compete in any sector have realised that it is important to carefully manage their resources and general operating procedures in order to be as efficient as possible. Many companies now employ consultants or seek to have their own in-house experts in order to ensure that their business is running at the optimum. These experts often use the range of statistical techniques known collectively as sixsigma. George [ 1] It can be argued that six-sigma training is of great benefit to companies wishing to gain intelligent control over their processes and so increase productivity, quality and profits, as it not only gives them opportunities to become more efficient and competitive, but also helps to
embed crucial statistical techniques into the culture of the company. This new outlook should lead to the company becoming more competitive and efficient in the short term, and with the continuous improvement philosophy that six-sigma promotes it should lead to the long term improvement of the company's position in the market place. The cost of this six-sigma training is often quite large and too much for some SMEs ~ to bear, leaving them at a severe disadvantage to their larger competitors. 1
SME is defined by the European Union as an independent company with fewer than 250 or fewer employees and either an annual turnover not exceeding f~40 million or a balance sheet not exceeding f~27million
39
Through funding gained from the European Social Fund 2 and Regional Development Fund 3, ISRU were able to address this problem directly by offering hugely discounted six-sigma training to local SMEs. This paper seeks to demonstrate, through case studies, how statistical techniques were applied to facilitate process improvements. One case study is a bakery and the other is a chemical manufacturer. In addition this training scheme can be tailored to other regions across Europe through the IPROMS network. It will be demonstrated how similar funding obtained by Cardiff University can be used to help SMEs in that region to attain similar successes. The training programmes used in Newcastle can be applied to the type of industry situated near to Cardiff University and then to other regions covered by other IPROMs parmers. The model can be adapted to implement other subjects vital to SME success in the relevant markets utilising expertise from right across the IPROMS Network of Excellence.
2 Six Sigma 2.1 Six sigma training at ISR U ISRUs six-sigma approach seeks to implement cutting edge industrial statistical methodologies via the six-sigma strategy into local companies and must be driven from a senior management level. SMEs may not have much spare capital to pay for training, and/or limited staff so it may be difficult to release key personnel for training programmes. Hence the funding and programme's flexibility, such as training on 1 day per week, are essential to allow the SME to fit the training into their schedule. A major advantage for SMEs of the training programme is that the delegate chooses a process from within their own company which they then work on throughout the training programme as their black belt project. After each stage of the training the delegates apply relevant techniques to their project, resulting in the completion of a black belt project by the end of the training programme. Finding solutions to such problems not only shows management the potential for these techniques but also inspires the delegate, as 2 European Social Fund- This Measure complements Measure 2.5 by inviting organisations to run customised training and development packages 3 European Regional Development Fund - Measure 2.5 (ERDF Capital & Revenue) This Measure provides specialised support to SMEs who are operating within defined clusters and sectors and provides intensive assistance to improve their competitiveness.
40
they can clearly see that they are capable of implementing the techniques and can also see the benefits their intervention means to the business which builds confidence to tackle future projects. 2.2 DMAIC methodology The Six-Sigma strategy was developed in the 1980's and is measurement based focusing on process or service improvement through variation reduction and Black Belt projects. Companies such as Motorola, General Electric, Black & Decker and Sony have claimed to have used the strategy with great success. It is a common misconception that the six-sigma tools are relatively new concepts. Although the sixsigma initiative itself is relatively new, the majority of tools and techniques are not, such as Design of Experiments which was developed in the 19th century as part of the Scientific Management concept widely credited to F.W. Taylor. Another misconception is that six-sigma always involves the use of complex statistical tools whereas many of the tools are easy to understand and apply, such as the Cause and Effect Diagram developed by Kaoru Ishikawa. A major strength of six-sigma methodology is the structured approach with which individual projects are tackled. The six-sigma quality improvement cycle is commonly divided into the five phases; Define, Measurement, Analysis, Improvement and Control, known as the DMAIC model 4 as described in George [1 ]
9 Define - Defining the problem, the project goals, the project scope and the overall strategy. 9 MeasurementDeciding which quality characteristics to measure to assess the process performance and verifying the accuracy, precision, repeatability and reproducibility of the measurement systems. 9 Analysis - Identifying and quantifying sources of variation. 9 I m p r o v e - Removing causes of variation, discovering variable relationships and establishing optimum operating levels. 9 Control - Implementing controls and holding the gains by ensuring that all the changes are fully documented and become part of the standard operating procedures. Giving the delegates a methodology to follow for all six-sigma projects is one of the fundamental aspects of the training programme. The program equips the organisation with methods that are easy to apply and result in visible benefits that can improve operational performance. Appropriate techniques of data 4 Alternatively a DMEDI (Define, Measure, Explore, Develop and Implement) approach may be adopted
acquisition and analysis are adopted. The practical orientation of the programme helps remove the fear of statistics that people often experience and helps to build a bridge between business and statistical theory. The ability to attain critical thinking skills to gain knowledge about appropriate tool usage is a major outcome from the training programme. Critical thinking develops the ability to ask questions on the critical path and then to select the correct tools to efficiently answer those questions. Critical thinking is far more important than simply following a prescribed methodology for every problem encountered. It develops flexible resources that are capable of reaching into all areas of the business. 3. Project deliverables The project ended in December 2005 with ISRU helping 25 local SMEs for at least 25 days each. In addition basic advice has been given to 120 companies, 25 SMEs implemented outcomes of the assistance given, 25 SMEs improved their environmental performance, 25 SMEs enhanced applications of ICT, 15 SMEs introduced new or improved product and 15 SMEs implemented process improvements as a result of the project.
4. Case study 1 A small local bakery sent a delegate to a training programme with the aim of implementing six-sigma techniques to allow them to improve their processes. The problem that the delegate selected for her project was with one of their most popular products, pineapple bars. The product flow varied considerably and the variation between machine operators in the amount of bars packed and numbers of rejects were both known to be a problem with the process. Anecdotal evidence suggested that the problem was with the weighing machine. However as investigations continued following the DMAIC methodology other causes were identified.
4.1 Definition of the problem "Product flow through the Ishidd machine varies considerably when packing pineapple and the reject rate is high. The aim of the sixsigma project is to improve the operating efficiency of the Ishida ". To establish the causes for the problems they utilised a simple statistical tool, the cause and effect diagram. Several of the identified 5 An integrated weighing and sealing machine designed for packaging a wide variety of high volume non fluid materials (Foodstuffs, fine chemicals, pharmaceuticals etc)
variables were found to be non-controllable such as temperature and humidity. It was felt that these had a significant effect on the product flow and reject rate due to variation in the stickiness of the raw material they caused.
4.2 Measurement Phase The delegate set up a data collection sheet to be used by all the operators which sought to collect as much information relating to as many of the identified variables as possible. Evaluation at the beginning of the project showed that an average of 137.1 bags per half hour was packed with an average reject rate of 37.1 bags during the same period. At the end of the project the average number of bags packed was 188 with a reject rate of 29.7 bags.
4.3 Analysis Phase Operating procedures were examined to establish if the operators varied their approach and see if it could be found why the reject rates and amounts packed varied. They found that the stickiness of the pineapple varied depending on method and time of storage. Standard operating procedures were implemented which had a positive effect on the amount packed and also reduced the reject rate. Each operator had their 'own' machine settings that they preferred to use. There were 5 different settings that had been used over the previous 6 months for core vibration, core vibration time, radial vibration and radial vibration time. These could all be set individually between 1 and 9. The fact that many variables were considered led to very detailed data being collected which gave a good overview of the process variables. Regression analysis was carried out on the data to identify significant variables.
4.4 Improve Phase The regression analysis led to a designed experiment using some of the suspected important factors. The experiment had to be relatively simple as there was a limit on how much production time could be used to run it. It was decided to test each of the Ishida settings at two levels. Core vibration (A) was set at 5 and 9, Core Vibration Time (B) was set at 7 and 9, Radial vibration (C) set at 5 and 9 and Radial Vibration Time (D) was set at 1 and 3. Using the number of bags packed as the response variable, a designed experiment was conducted to determine the best combination of the Ishida settings to maximise the amount packed in 30 minute intervals.
41
The design of the experiment was a 2 4 half fractional factorial with I=ABCD and 2 replications, i.e. 16 runs. The analysis identified 1 significant setting and 1 significant interaction. The 2-way interactions were confounded with other 2 way interactions in this design. From this best settings were found for all 4-vibration settings.
interactions were considered. Thus from figure 3, it can be seen that the best setting for Radial vibration (C) is 5 when Core Vibration Time (B) is set at 9 ~
?/?~?~
//~,?
7~?//??<
><,u~?
~j
Factorial Fit: Amount versus A, B, C, D Estimated
Effects
Term Constant A B C D A*B or C*D A*C or B*D A*D or B*C S
= 30.8140
and
Coefficients
Effect -6 41 -28 28 22 25 -38 R-Sq
00 00 50 50 50 00 00
Coef 105.75 -3.00 20.50 -14.25 14.25 11.25 12.50 -19.00
= 75.70%
for
Amount
SE C o e f 7.703 7.703 7.703 7.703 7.703 7.703 7 703 7.703
R-Sq(adj)
(coded
T 13.73 -0.39 2.66 -1.85 1.85 1.46 1.62 -2.47
0 0 0 0 0 0 0 0
units)
P 000 707 029 102 102 182 143 039
Figure 2 Interaction between Core vibration (A) and Radial Vibration time (D)
= 54.44%
The MINITAB output above shows which factors/interactions were significant by the small 'p-value' in the last column. These significant relationships are illustrated by the graphs in figures 1, 2 and 3. Figure 1 shows how varying the Ishida settings affect the amount packed. Core vibration (A) has little effect when changing between the settings of 5 and 7 when considered on its own, but this factor significantly interacts with Radial Vibration time (D) when the combined effects are considered (see figure 2). Core Vibration Time (B) has a significant effect on the amount packed. The effects of Radial vibration (C) and Radial Vibration time (D) although not significant in the analysis can still be seen to have some influence on the amount packed depending on their settings.
\ =
130~
\
........... \
\
:
: ......
x \
iI0~ I00~
\ \
.
. ' ~9
.
.
.
:
: ..... . . . . . ............... .... . . . . . .. .. . . . ...................
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Figure 3 Interaction between Core Vibration Time (B) and Radial vibration (C) 4.5 Control Phase The optimum settings suggested from the designed experiment were a combination not previously used at the bakery. Core vibration ( A ) = 5, Core Vibration Time (B) = 9, Radial vibration (C) = 5, Radial Vibration time (D)= 3 The settings were implemented and a dramatic improvement in the process was noted. 4. 6 Benefits The project led to a s per year direct saving, decreased reject rate and improved product flow. There was also improved awareness of quality and confidence and ability to apply the techniques to other processes.
Figure 1 Ishida setting main effects plots Figure 2 shows that when Core vibration (A) is set at 9 it does not make much difference what the setting of Radial Vibration time (D) is, but when Core vibration (A) is set at 5 there is a vast increase in the amount packed when Radial Vibration time (D) is changed from 1 to 3. The interaction AD was confounded with interaction BC so the significant effect could be from either (or both) sources so both
42
5. Case study 2 A local chemical company used modelling techniques to increase profit margins on one of their key processes. The chemical produced was sold to a single customer who would purchase as much as could be produced.
5.1 Define Phase The delegate initially wanted to predict the time taken for a batch of the chemical to dry depending on certain factors which varied from
batch to batch. The method previously used was not reliable as the production manager 'guessed' when the chemical had dried sufficiently before stopping the process to send a sample for analysis to determine the percentage of water in the wet-cake. This would take about 20 to 30 minutes and often the analysis would indicate that further drying was required to meet the 0.2% specification set by the customer. The factors which were thought to affect the drying time were the weight of the wetcake, the drying temperature and the % water in the wet-cake. During the define stage a problem with the boxing system was identified and an improved system was installed.
5.2 Measurement Phase The delegate collected data from a number of runs of the drying process. The scatterplot shown in figure 4 indicated that there was a strong linear relationship between the %water and the drying time. The other factors of drying temperature and weight of the wet-cake also had an effect as confirmed by the regression analysis in the analysis phase of the project. Scatter plot showing relationship b e t w e e n % water and d r y i n g time 4.
3.
1.
0. 0
50
100
150
200
250
Drying time (minutes)
Figure 4 Scatter plot 5.3 Analysis Phase Initially the delegate looked carefully at the process data and then performed a regression analysis to identify potentially important factors and interactions which had a significant effect on the drying time. The drying time (DT) in minutes can be described by the levels of 3 factors, namely % water in the wet-cake (%W), drying temperature (T) and wet-cake weight (W). The model described the behaviour of the process very well. DT = 431 - 60 % W + 0.08 W - 3.9T 5.4 Improve Phase The model could then be used to predict the drying time by setting %W at the specification of 0.2% and trials showed the predictions were very accurate.
Further trials were conducted on the drying temperature and the optimum was found to be 85 degrees centigrade. This led to a simpler model with a fixed drying temperature (T) of 85 and fixed percentage water of 0.2 leading to a direct relationship between the drying time in minutes and the weight of the wet-cake. DT = 114 + 0.07W
5.5 Control Phase The model was applied to the process and in the first few months of operation the company found that they were able to accurately predict the required drying time for each batch. They noted that occasionally the final %water was just above the 0.2% specified, so they added 10 minutes onto the predicted drying time. DT = 124 + 0.07 weight
5.6 Benefits The new control ensured that all batches (so far) were below the specified 0.2% water when the process was stopped for the sample to be analysed. The time saved allowed an extra batch worth s to be produced each week, which is a potential gain of s per year. This clearly indicated to the management how applications of six-sigma techniques could give an instant boost to their profits which in turn encouraged them to apply the strategy to other processes. 6. Conclusions and opportunities for extending the application of Six Sigma Two very different process problems illustrated in the case studies were both tackled using the DMAIC strategy. By following the methodology solutions were found to improve both processes. Different combinations of statistical tools were used in both cases but by following the six sigma methodology the choice of tools worked in both cases. With many SMEs it often just takes one success, such as a project from a training programme, for the management to realise that using ,the six-sigma structure can give huge benefits. Generally, in any sector, not many other SMEs successfully utilise statistical and quality tools so any SME that does will attain a definite competitive advantage. A total of 63 delegates attended the training programmes with one company in particular sending 20 delegates on the courses with 3 black, 2 green and 15 yellow belts trained. Inward investment has played a significant part in the development of the Welsh economy for some twenty years or more. Large multinational organisations have been attracted into Wales and in many cases these large industries
43
have now become essential lifelines to many SMEs who support them. The formation of the Welsh Development Agency (WDA) has done much to promote the influx of new industries into Wales. However, the funding provided to many inward investors has not always received unanimous support especially from indigenous industries who have traditionally struggled to survive since the collapse of heavy and nationalised industries in the south Wales. The heavy industries of South Wales created a multitude of small engineering companies whose main economic growth area was to support the larger industries by providing engineering services. However, the decline of these industries has meant that SMEs have had to radically change their strategic perspective in order to survive. SMEs have had to make the shit~ from operating in primarily a service orientated environment to functioning in a manufacturing environment where more stringent Quality, Cost and Delivery criteria are exerted and the Voice of the Customer is paramount. These companies have had to operate in a far more insecure environment ot~en having to meet the needs of many different customers rather than a single industry as previously experienced. It has become increasingly important to ensure that Welsh SMEs are fully equipped with the essential innovative tools and techniques to maintain and improve market share. With certain parts of Wales receiving EU Objective 1 grant funding, governmental organisations have had to focus more on the smaller, indigenous companies. SMEs are extremely important to the Welsh economy. A WDA sub-committee report stated that "If SMEs are defined as those with less than 250 employees, then the SME sector accounts for 99.8% of all businesses in Wales (the same as in the UK), but provides some 71% of all Welsh employment (compared with 57% in the UK) and 63% of business turnover (54% UK)". It is therefore essential that SMEs are helped to grow and develop their products so that a distinct market advantage is created. Ideally, for South Wales to see any appreciable benefit from its manufacturing industry there would need to be a greater development of the Micro-SME and SME category companies. One way of achieving this would be to develop the company's technological platform allowing them to compete on a greater performance level. There are however, a number of aspects that indicate that SME development in Wales is lagging behind the UK and the EU, and that Welsh SMEs are under-performing in several crucial respects.
44
SMEs almost by definition, reach into all parts of the Welsh economy, both spatially and by sector. Hence encouraging SME development offers the twin prospects of both enhancing and spreading prosperity. Research into SMEs has demonstrated that only a small proportion has high growth and hence significant economic development impact potential. The sub-group report concludes that the development of SMEs in Wales is constrained by the shortage of marketing skills, lack of information/weak networks, motivation and culture, demand for, and supply of, SME finance and industrial structure This suggests that radical solutions will be necessary if Wales is to rise to the challenges of SME development, enhancing the growth of existing SMEs and maximising the contribution of SMEs to Welsh economic development. By offering the six sigma training programme developed in Newcastle, Welsh SMEs can enjoy the competitive advantage already gained by those in Newcastle. Over the past four years the MEC has been successful in obtaining Objective 1 grants aimed at assisting Welsh SMEs to develop their business and technological infrastructure so that growth can be achieved in these companies. The MECs SUPERMAN project focuses upon SUPporting Innovative Product EngineERing and Responsive MANufacture in Welsh SMEs. It aims to introduce specific tools, techniques, systems and machines into SMEs in order for these companies to undergo a step change in their manufacturing performance. The MEC is a core partner on the Manufacturing Advisory Service MAS Wales contract. This service is aimed at assisting Welsh SMEs in resolving technical and managerial issues relating to their manufacturing operations.
Acknowledgement Newcastle University and Cardiff University are partners of the Innovative Production Machines and Systems (I'PROMS) Network of Excellence funded by the European Commission under the Sixth Framework Programme (Contract No. 500273). www.iproms.org
References [1] Lean six sigma for service Michael L. George (McGraw-Hill 2003
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All rights reserved.
The alignment of collaboration and the importance of integrated performance measurement M. Sarana and R. J. Mason Cardiff University - Innovative Manufacturing Research Centre, Cardiff CF10 3EU, UK
Abstract
The notion that supply chains compete with supply chains is encouraging companies to manage their entire supply chain so that optimum levels of efficiency and effectiveness can be realized for the successful running of business operations. In line with this concept, all the players within the supply chain must work together to ensure the achievement of this. Therefore, supply chain efficiency and collaborative working appear to be positively correlated. However, whilst this may be the case, companies need to be able to measure the performance of collaboration in the supply chain. Collaboration between companies may occur in many forms such as integrated manufacturing processes, integrated distribution processes and information sharing processes. Do we measure the performance based on these processes or the supply chain as a whole? This paper presents a literature review of research in this area focusing on the alignment of collaboration and the importance of integrated performance measurement, supported by case study examples. It argues that where there is a misalignment of measures, even in a minor way, this can undermine trust and mutual understanding vital to the sustainability of an integrated collaborative approach.
Keywords: Collaboration, Performance Measurement, Alignment, Supply Chain Management
1. Introduction
Collaboration can be defined as, "two or more independent companies, who work jointly to plan and execute supply chain operations with greater success than when acting in isolation" [1 ]. Research has shown that while technologies (e.g. information communication technology and automated manufacturing) provide benefits to an organization, these processes can not work in isolation. Collaboration is required across the supply chain as a pre-requisite for enabling it to function with coordination [2]. However, not only is collaboration important, we also need to be able to measure what companies are gaining from it. This paper uses current literature to discuss what the characteristics of collaborative metrics are, and why the alignment of
collaborative performance measures is important in supporting an integrated supply chain strategy. It argues that inappropriate performance measures are a key reason for collaboration strategy misalignment between supply chain members. In addition, examples of companies which illustrate the importance of alignment to support collaborative business relationships are provided. 2. Motivation to Collaborate
There are a range of reasons why firms chose to collaborate. Our focus will be on collaboration which is motivated by the desire to improve the coordination of the business of product and service supply. Traditionally, companies and functions within companies have incentivised
45
performance in a myopic and self-focused way. Consequently, it is perhaps not surprising that members have tended to focus on their internal logistics performance measuring systems rather than a holistic measure for the entire supply chain [3]. In supply chain management literature it is argued that there is tremendous potential to prodigiously improve a value chain's effectiveness and efficiency through adopting a more integrated approach [4]. This seeks to better optimise performance bytackling areas such as the duplication of activities, hedging and risk aversion strategies, lack of coordinated partnering flexibility, demand disconnected from supply leading to uncertainty, poor forecasting, amplified demand, large stockpiles of inventory as well as many other sub-optimising issues which are all symptoms of uncoordinated intercompany processes. Holweg et al. [5] assert that to optimise performance synchronisation as well as collaboration is important, which is illustrated in a table stating the benefits of supply chain collaboration and synchronization (Table 1). Holweg et al. [5] go on to present factors that need to be considered in choosing a supply chain collaboration strategy (Table 2). Depending which factors a company perceives to be important will result in a certain type of collaborative relationship (for the different types of relationships see [6]). This relationship will need to be planned to fit into the participating companies' strategies and then measured to see how well the different strategies have been aligned and operationalised in the supply chain. In order to do this, companies need to have collaboration performance metrics in place so they are able to evaluate their performance and assess the true value that collaborative activities are having on their supply chains.
Table 1: Benefits of collaboration [5] Additional benefits, Benefits typically typically not achieved achieved without supply chain through supply chain synchronization" collaboration:
1. Elimination of the bullwhip effect by linking the inventory and replenishment decisions.
1. Collaborative forecasting enables better customer service levels, or a reduction in inventory (but generally not both), In fact in many cases these are traded off against each other, or service levels are traded. 2. Reduce the rationing gaming by giving the supplier responsibility for replenishment. However, if there is a general shortage this collaboration can quickly break down.
2. A reduction of inventory levels by up to 50% without compromising customer service levels (Disney & Towill, 2003), and better utilization of production capacity as the extended visibility of the supply chain provides a certain additional flexibility to prioritize or delay customer replenishment without compromising service levels, thereby reducing the need for capacity buffers (Waller et al., 1999) 3. Better utilization of transport resources, because shared information allows for better load consolidation. 4. Controlling the risk for constrained components or materials. For example, monitoring key items with long-lead times can create an early warning system of future supply constraints.
Table 2: Factors that need to be considered in choosing a supply chain collaboration strategy [5] Factors
46
Why Important?
Geographical dispersion of customers and supplier plants
The closer, and more dedicated supply is, the easier it is to implement synchronized production and inventory control of other members of the supply chain
Demand pattern of
The more stable the product' s
the product
consumer demand, the greater the dynamic benefits of eliminating bullwhip and synchronizing demand and supply in the supply chain
Product characteristics, in particular selling periods and shelf life, as well as value
The longer the shelf life or selling period of the product, the more sensible it is to consider collaborative practices. Equally the more valuable the product, the more impact tighter inventory control yields
3. Importance of Performance Measurement
The basic principle of collaboration is for all companies that have engaged in a collaborative activity to improve their operations individually but also as a supply chain. Collaboration may not come naturally to some companies and therefore, they need to understand the true benefits and need to see some kind of feedback measure that has been derived from their hard work and effort. The supply chain management discipline is underpinned by systems theory [7]. Developed in physics and biology [8], systems theory can be summarised as envisioning that the whole may be greater than the sum of its parts. Supply chain management is theoretically rooted in this concept with the aim of better optimizing the end to end supply chain. Fawcett et al. [9] state that effective performance measurement should be characterised by: 9 providing the insight for understanding the system; 9 influencing the behaviour of the system and; 9 providing information regarding the results of the system. Stainer [in 10] state that: "A performance measure system, or a set of performance measures, is used to determine the efficiency and/or effectiveness of an existing system, or to compare competing alternative systems". Simatupang & Sridharan [1 ] support this, adding that the performance measurement system should focus on continual improvement for supply chain members, end customers and outside stakeholders. Thompson & Sanders [11] show the benefits of collaboration in Figure 1. They suggest a positive correlation could be expected between the strength of the relationship of companies and the benefits of the partnering to achieve specific business objectives.
However, whilst the relationship between two companies may be of a low degree of objective alignment (e.g. co-operation), time may prove to demonstrate the benefits of a stronger relationship. Table 3 shows the benefits of collaboration for the short-term- long term. This may suggest different performance metrics depending on the length of the relationship. Table 3" Time Benefits of Collaboration [ 1] Short-term Increased planning capability, improved customer service, shorter order cycles, reliable delivery, assets utilised, reduced inventory, cash flow increased Medium-term Increased product variety, effective product life cycles, time to market, reduced overhead cost, flexibility increased Long-term Increased market share, increased human resource capability, increased customer service, reduced overhead costs High Coalescence 9
CD
e,,.
~z
C o l l ~
~
ra~
9 ,CooperQ~tio~
0-Q Low
Competition
CDCD Low
High Degree of Qbjectiyes Alignment _ Figure 1: l-ypes and collaboration and benehts [ 11 ] 4. Performance Measurement System Procedure
The system for performance measurement should aim to align the incentives of each of the members in the supply chain, so that members are considering the penalties and rewards for the whole
47
Hierarchy of performance Global performance measures
~
Performance Areas upply chain " ~ ' ~ x c e l l e n c e im p r o v e m e n t / / ~ processes
"~'~ustomer //service
inancial erformance
Collaborative objectives, Primary Measures, Targets, Initiatives
g;
s
s
Secondary performance measures Individual performance measures
Functional performance measures
Supporting performance measures
S
S
S
Relevant Key Business Processes
Figure 2: A Performance Map [1] supply chain rather than themselves [ 1]. Simatupang and Sridharan [ 1] advise certain steps when devising a performance: a. Design Performance- this refers to devising a system that allows supply chain members to monitor and improve performance and involves three processes: 9 Performance m o d e l - choosing a framework which will link supply chain performance with the different levels of decision hierarchy in meeting supply chain objections of each supply chain member. For example Supply Chain Operations Reference (SCOR); 9 Performance metrics - choosing measures that indicate the degree to which mutual objectives of the chain members have been achieved e.g. inventory across the whole supply chain at a particular point in time; 9 Secondary measures- measures developed for individual members of the chain. When implemented across the supply chain, these measures must be collected, analysed for individual performance and how this affects the holistic supply chain performance. It is important that the performance measures are communicated across the supply chain and an effective way of doing this could
48
S
be through the pert0rmance map (l~lgure 2). The performance map allows us to see the links between mutual objectives, global performance, individual chain member's performance and functional performance in each member of the chain. In addition, the map may assist in highlighting problem areas within the supply chain and what the causes of those are. This performance map can be viewed similarly to the process ofpolicy deployment.
b. Facilitate Performance- developing an effective performance information sharing system. This would allow the communication, monitoring and control of how actual performance compares to target performance. For example; to see the inventory level at different locations in the supply chain via the internet. c. Encourage Consistent Performance- providing supply chain members with timely incentives that they value and which increase value in the supply chain.
d. Intensify the Performance Measurement System - comparing and modifying performance measures. This may be done internally or by external auditors.
5. Empirical Discussion The paper has explored the importance of having the correct performance measurement system for collaboration alignment between companies. The successful operation of a good collaborative performance system will also support other key ingredients for supply chain collaboration such as the cultural elements of collaboration [14], partnering trust and openness to freely exchanging information, as well as the continual development of the right support systems (e.g. technology). Research undertaken by us indicates that although there may be support for a collaborative relationship, in practice alignment of measuring may be inappropriate or poorly drawn up. This is illustrated by two mini-case study examples taken from the steel and grocery sectors. The approach adopted in conducting these two cases is the 'Quick Scan' method, which has been developed and employed to audit supply chains [2]. The cases form part of a three year active research project within the Cardiff University Innovative Manufacturing Research Centre. In the steel supply chain our research has shown that each member involved uses an individual performance measure. Figure 3 shows the physical flow of materials in the chain. The supplier provides rolled coils which are delivered by the contracted transport provider to the tube manufacturer. In this case the three members used the following delivery metrics: 9 Materials supplier- ready on time/tonnes 9 Transport p r o v i d e r - percentage of loads delivered on time 9 Tube m a n u f a c t u r e r - percentage of loads delivered on time. Materials ~ [ supplier
Transport provider
Tube manufacturer
Figure 3: Steel Supply Chain Whilst the performance measures used by the transport provide and tube manufacturer appear to be the same the values at both companies were typically found to have a 20% difference. This indicates that the companies are using slightly different ways of measuring delivery. This scenario not only creates confusion in the supply chain but also provides a lack of trust between the players. This supply chain needs to develop some joint performance measures and
examine how these jointly developed performance measures fit into the overall performance strategy of each of the companies. In a separate example in the steel sector we observed that the supplying steel producer whilst recognizing the importance of on time and in full delivery was actually measuring under this name departed on time in full. This again meant that confusion could exist between receiving and supplying partners especially in leaner supply chains where on time in full delivery was more critical. Similarly in the grocery sector we found that great importance was placed on suppliers delivering on time in full. This requirement has become even more exacting as retailers' have progressively lowered inventory levels in their distribution centres thus reducing their buffer of stock which may have protected them from sell outs if deliveries were received late or below the ordered level. Therefore, this measure was agreed by both parties as a fundamental measure of service success. However, the measure was derived essentially by two different measuring systems, one driven by the retailer and one driven by the supplier. The possibility that the supplier may believe that their on time in full measure was above target whilst the retailer's equivalent failure to arrive (FTA) measure was below target was clearly a possibility. Our discussions exposed that indeed that this scenario did occur and could lead to an undermining of relationship trust and status. The examples highlight the importance of supply chain members developing a performance measurement system jointly. Such measures could be developed using the guidelines suggested by Simatupang and Sridharan [ 1] in Section 4. Their top down approach ensures players are measuring the same thing throughout the entire supply chain. This not only encourages the supply chain to operate more efficiently but also strengthens relationships.
6. Conclusions Although the literature on collaborative performance measurement is fairly developed a number of questions arise from it and our empirical examples, which point to avenues of potential future research. In order to motivate collaborative behaviour do we need a supply chain performance measurement team (a group of individuals who promote and monitor the collaboration)? In theory this seems like a good idea but which member of the supply chain is
49
responsible for this? Which member bears the cost of this? Companies operate in networks (multiple suppliers and customers) rather than a chain. Therefore, how do we measure collaboration given the complexity of over-lapping supply chains? The network structure of supply chains requires companies to trust each other explicitly and requires the collaborating company to share information with some customers and supplier and not others. Lambert & Pohlen [3] assert that "there is no evidence that meaningful performance measures that span the supply chain exist". In addition to this, there appears to be no empirical research showing a holistic (endto-end) measure of supply chain collaboration. Even simulation studies measuring collaboration in the supply chain tend to focus on inventory costs [ 13]. Finally, whilst most studies tend to focus on a single measure for collaboration the measure is not related back to the strategic orientation or global performance measure of the collaborating companies. Collaboration aims to improve value for the customers and stakeholders of the supply chain. The vital question this literature review poses is whether we need a universal measure for supply chain collaboration? If so, what should it be? In light of this, should we be looking at input compared to output in the supply chain, suggesting we measure the benefit of collaboration against the time and money a supply chain spends on it. Our empirical work exposes that even at the most basic level of a single combined measure which partnering firms both identify as critically important a functional, myopic approach is still under-pinning the measurement calculation which can lead to conflicting calculation of the same performance, such as the percentage of deliveries, made on time and in full. This can undermine the partnering relationship rather than supporting the constructive alignment of process activity that would further motivate collaborative engagement. References
[1] Simatupang, T. M. & Sridharan, R. The Collaborative Supply Chain, International Journal of Logistics Management, (2002)Vol. 13 (1) pp. 15-30. [2] Naim, M. M., Childerhouse, P., Disney, S. M. & Towill, D. R. A supply chain diagnostic methodology: determining the vector of change, Computers & Industrial Engineering: An International Journal (2002) Vol. 43, No. 1-2, pp.135-
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157 [3] Lambert, D. M. & Pohlen, T. L. Supply Chain Metrics, International Journal of Logistics Management, (2001) Vol. 12 (1) pp. 1-19. [4] Ellram, L. M. Supply Chain Management. The Industrial Organisation Perspective. International Journal of Physical Distribution and Logistics Management, (1991) Volume 21, No. 1, pp. 13-22 [5] Holweg, M. Disney, S. Holstrum, J. and Smaros, J. Supply chain collaboration: making sense of the strategy continuum, European Management Journal (2005) Vol. 23, No. 2, pp. 170-181 [6] Mason, R Synthesising the way various ways business relationships are categorized. Submitted to the IPROMS conference, 2006 [7] Giannakis, M., Croom, S. and Slack, N. Supply Chain Paradigms, in S. New and R. Westbrook, (eds.), Understanding Supply Chains, Oxford: Oxford University Press, (2004) pp. 1-21 [8] Von Bertalanffy, L. Theory of Open Systems in Physics and Biology. Science, III: (1950) pp. 23-29 [9] Fawcett, S. E. & Clinton, S. R. Enhancing Logistics Performance to Improve the Competitiveness of Manufacturing Organizations, Production and Inventory Management Journal, (1996) Vol. 37 (1) pp. 40-46. [ 10] Chan, F. T. S., Qi, H. J., Chan, H. K., Lau, H. C. W. & Ip, R. W. L. A Conceptual Model of Performance Measurement for Supply Chains, Management Decision, (2003) Vol. 41 (7) pp. 635642. [11] Thompson, P. J. & Sanders, S. R. Partnering Continuum, Journal of Management in EngineeringAmerican Society of Civil Engineers/Engineering Management Division, (1998) Vol. 14 (5) pp. 73-78. [ 12] Naim, M., Disney, S. & Towill, D. Supply Chain Dynamics, Ed New, S. & Westbrook, Understanding Supply Chains: Concepts, Critiques & Futures, Oxford University Press: Oxford, (2005) pp. 109-132. [13] Huang, Z. & Gangopadhyay, A. A Simulation study of Supply Chain Management to Measure the
Impact of Information Sharing, Information Resources Management Journal, (2004) Vol. 17 (3) pp. 20-31. [14] Barratt, M. Understanding the meaning of Collaboration in the supply chain, Supply Chain Management: An International Journal, (2004) Vol. 9 (1) pp. 30-42
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Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (otis) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
The Cultural and Trust Aspects of Collaborative Supply Chains Gilbert Aryee Innovative Manufacturing Research Centre, Cardiff University, Cardiff Wales, U.K
Abstract
A review of the literature on inter-organisational relationships is undertaken with the issue of trust and organisational culture setting the boundaries to this inquiry. The reason why business relationships or alliances are formed are first put forward in the shape of forms of collaboration. Theories on intercompany relationships are examined to see which ones deal with trusts and culture and their associated elements. Transaction Cost Economics, Network Theory, and Agency Theory are examples of theories selected for their relevance to the paper. Organisational culture is initially discussed. Following that, trust in its various forms, antecedents, limits, failure, measures, and impact on collaboration is presented. Empirical work previously carried out with industrial partners from the steel sector in the UK, is then applied to the results of the literature review. The results show that trust is a major issue in business collaborations. The limits set for trust in these business relationships were found to be instrumental in sustaining the form of collaboration that the business relation was created to fulfil. Keywords: Trust, Collaboration, Organisational theory, Empirical research
1. Introduction
Organisations are increasingly focusing on their core competencies and outsourcing more of their functions and processes due to the pressures of globalisations and internationalisation. The focus is to deliver value to the customer at an acceptable cost. Successful execution and delivery of such outsourced activities is based on several factors such as costs, legislation, technology, skills, etc. When such collaborative arrangements are put in place partners tend to concentrate on getting a contractual agreement set up which is useful as it offers legal protection in case of conflicts. This position although logical pre-supposes that the relationship or collaboration is prone to failure. What is therefore required is a
52
development towards a relational form of doing business based on the "soft" factors one of which is trust. According to [1] trust has been found to be beneficial to organisations by helping to avoid costs which can be incurred due to the monitoring and searching for evidence of opportunism which can occur in the absence of trusting relations. This is the pr6cis of Transaction cost economics The paper is organised as follows. The reason why business relationships or alliances are formed are first put forward in the shape of forms of collaboration. Theories on intercompany relationships are examined to see which ones deal with trusts and culture and their associated elements. Transaction Cost Economics and Network Theory as examples of such theories are presented. Organisational culture is discussed next. Following that, trust
in its various forms, antecedents, limits, failure, measures, and impact on collaboration is presented. A discussion on empirical studies used to validate the findings from the literature review is then presented followed by conclusion and further research. 2. Forms of Collaboration The aims or goals for getting into business relations are linked to the types of collaboration on offer. [1] proposes configurations which are paradigm cases of successful collaboration in Table 1. This list contains most examples of types of collaboration and it will be shown later that identifying early on what form of collaboration the alliance is based on could help to set up limits of trust. The list is not exhaustive however it offers a broad selection of types of collaborative arrangements organisations are likely to engage in. Table 1 Paradigm Cases of Forms of Successful Collaboration [ 1] Form of Collaboration Technology-design collaboration
Description One side has the scientifictechnological and the application-design capability
Production-product collaboration
One side has a good product and the other the production technology
Product-market collaboration
One side has a competence in product and production and the other has access to a market
Collaboration in complementary knowhow Sharing
In R&D, production, service, marketing
Collaboration between a firm in an emerging industry and the one in the industry which it is substituting
This involves sharing among similar producers, costs, facilities, brand name, or the pooling of effort to achieve efficiency, spread risk or exert power. This allows the new comer to tap existing distributing channels, give the incumbent firm a stake in the future, and reduces resistance to substitution
3. Culture and Organizational Structure [2] espouses that "culture in any "business" may be defined then as the beliefs which pervade the organisation about how business should be conducted, and how employees should behave and should be treated". He further asserts that organisational culture is formed by: 9 Behaviours based on people interactions 9 Norms resulting from working groups 9 Dominant values adopted by the organisation 9 Rules of the game for getting on 9 The climate [3] confirms these components of culture in another way by also putting forward parts and components of culture: truth, beliefs, values, logic, rules, and actions. These are interconnected. For example beliefs are based on truths, decision rules are derived from beliefs and values by a process of logic, and finally decision rules are the bases for purposive action. The culture in an organisation therefore governs its actions.
4. Theories on inter-company relationships [4] provide an analysis of three perspectives on inter-enterprise collaboration. These three perspectives focus on the business processes, organisational/governance structures, and business environments. Within these three theoretical frameworks, Supply chain management and cost approaches deal with theories on the business processes perspective, agency theory and transaction cost economics focus on organisational or governance structures perspectives, and finally strategic management, network theory, and resource dependency theory relate to the business environment perspective. The basis for including a discussion on theoretical perspectives is to provide firstly some rigour and secondly position organisational culture and trust within collaborative research. Of the various theoretical perspectives listed, transaction cost economics, agency theory, and network theory are relevant to issues on trust and organisational culture. These are now discussed.
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4.1 Transaction Cost Economics (TCE) [5] defines transaction as "transfer across a technologically separable interface". Another definition by [1] describes a transaction as "an event which takes place during a process of exchange and it is the moment at which agreement is reached and ownership rights are transferred" According to [5], a transaction in TCE has the following components: frequency of exchange, asset specificity that support the exchange, and Uncertainty surrounding the exchange. The causes of uncertainty stem from bounded rationality due to the limitation of human interactions amongst others, and also tendencies of partners in the transaction to succumb to chances of opportunisms which might undermine trusting relationships. However, [1] makes some criticisms on TCE, citing the following: TCE is static and does not reflect the dynamic ongoing transactional relations across businesses; TCE does not address the role of trust but rather opportunism during business relations. He suggests improvements to transaction cost theory by including Complementary theoretical insights to understand inter-organisational exchange such as long-term supply relationships and the incorporation of trust and commitment. 4.2 Network Theory An explanation to network theory will involve dynamic relationships which includes innovation and learning, trusts as a pre-requisite for successful partnership within the network, and power which should be avoided as it works against trust when partners exploit their positions to gain an advantage. Further explanations include nodes which represent the members or organisations in the network, links which are bilateral or dyadic relationships in the network, and finally the market which represents the demands of the customer. 4.3 Agency Theory This focuses on contractual arrangements between the principal (the buyer) and the agent (the supplier) as ways to avert risk. It is selfseeking in that the principal always tries to have its goal satisfied and so conflict of interest can arise much to the detriment of the business relation. [6] sums up agency theory into the following propositions: Agents behave in the interest of the principal when contracts are outcome based and also when the principal has information to verify against the agent; information systems are positively related to
54
behaviour-based contracts and negatively related to outcome-based contracts; outcome uncertainty is positively related to behaviourbased contracts and negatively related to outcome-based contracts. At the core principal-agent theory according to [6], is the trade off between the cost of measuring behaviour and the cost of measuring outcomes and transferring risk to the agent.
5. Trust and its Forms
To trust is to accept or neglect the possibility that things will go wrong. When applied to individuals it becomes behavioural trust which consists of actions that: 1)Increase one's vulnerability, 2) to another whose behaviour is not under one's control, 3) in a situation where the penalty one suffers if the other abuses that vulnerability is greater than the benefit one gains if the other does not abuse that vulnerability ([7]. [8] mentions personal and impersonal trust. Impersonal trust is further broken into systems trust and institutional trust to describe the functional and social role of individuals within organisations. These descriptions relate behavioural trust to personal trust and organisational trust to impersonal trust. Therefore in the organisational context, behavioural trust becomes organisational trust. [9] propose three categories of organisational trust which represent an evolution of trust in inter-enterprise relationships. These defined below: 9 Goodwill t r u s t - where a partner is trusted to take decisions without unfairly exploiting the other partner 9 Contractual t r u s t - is keeping of promises such as delivering goods or making payments on time, or keeping confidentiality 9 Competence T r u s t - depends on the technical and managerial competence of the company to perform a function such as to deliver components within specification. Figure 1 shows the progression from goodwill through contractual to competence trust as organisations develop their relationships. Trust in the organisation and in the individuals who work within it may or may not be similar. [ 10] and [ 11 ] pose the question as to whether in dealing with organisations one has to contend with the reliability of both the organisation and the people within it. The governance structure and organisational culture in place all help to determine or mediate the basis for such trust.
Before trust can be established certain premises need to be present. These can be
step being preoccupied with monitoring and searching for evidence of untrustworthiness. Trust can fail and so in addition to setting workable limits, it need not be blind or unconditional but must occur within limits.
Evolution
Performance
acceptability
of Trust,
of the Partners Childe,
1998
Figure 1 Evolution of Trust [9] termed the elements of trust and should include the conveyance of appropriate information, mutuality of influence (or as put forward by [ 12], in the notion of equity or "fair dealing" or by [ 13 ] as "distributive justice"), encouragement of self-control and the avoidance of the abuse of vulnerability of others. Trust has implications with regard to the impact it creates on the businesses of partnerships. Trust eliminates the need for monitoring and control and hence lowers costs bounded rationality and opportunism the antecedents to monitoring and control are circumvented. Trust creates an atmosphere where people will deliberate and renegotiate on the basis of give and take rather walk out when conflicts arises. There are however limits to a trusting relationship. Trusts has upper and lower limits of tolerance beyond which events can trigger a mode into seeking self-interest and survival. [7] give the following definitions: 9 Upper limit : A test of loyalty at any cost where one may trust someone up to his resistance to temptation or pressure to take up a "golden opportunity". 9 Lower limit: Where one partner may not have the capacity or attention to prevent even the smallest errors or imperfections from arising. That small deceptions and pilferage will not be noticed Within the limits of trust one can get on with the business of collaboration without at every
Outside such tolerances betrayal of trust can occur. Particularly between organisations there may be the risk of overdependence when one party has no way of exit but to hope that the other party will be benevolent. This is one example of a case with the potential of failure of trust. Another example is the case of learning and innovation, where one may be stuck with a partner who is falling behind. To get a measure on trust it needs to be operationalised through some other variables since it is latent. Empirical studies by [1] propose the following variables for use in the design of survey questionnaires. 9 Habitualization Having been transacting business with this customer for so long, all procedures have become s e l f - evident and we understand each other well and quickly In all our dealings we have never had the feeling of being misled. 9 Institutionalization In this relations both sides are expected to refrain from demand which can seriously damage each other's interest. The stronger or more powerful partner is expected not to pursue its interest at all costs. -
-
55
-
Formal and informal agreements have the same significance. In summary trust takes place within an organisational culture and so the choice of the two theoretical perspectives of organisational (or governance) structures and the business environment are pertinent for a discussion on culture and trust in business relationships. Case studies are examined in the next section to determine how the discussion so far fit the real world.
approaches to data collection namely process mapping, semistructured interviews, attitudinal and qualitative questionnaire, and archival information were carried out. In this paper the data from the interviews and questionnaires are mostly utilised although in a few instances archival information proved useful. This stems from the fact that trust is latent and as mentioned earlier its measure is done effectively through operationalisation of certain variables. This is best done through interviews and questionnaires. This Quick Scan examined the bilateral or dyadic relations between each of the three relationships present. The results are presented in Table 2 with evidence of the source of information and implication for trust and collaboration given. The idea of stating the issues at the interface is to enable a 2-way approach to emerge where reciprocal or general views can be obtained from both sides of the dyad. Issues which involve trust or collaboration are first identified giving the source of data collection method used. Next the implications of trust with regards to its limits of operation are inferred.
6. Case Studies To put results of the literature review in the context of empirical evidence of trust and collaboration, case studies conducted on a steel supply chain within the UK are utilised. The case studies comprise a primary steel producer, a steel tubes manufacturer who purchases steel coils from the primary steel producer, and the third party transport provider which links up the two companies. We therefore have a triadic relationship in place. Using a methodology called "Quick Scan" developed in [14], four
Table 2 Results of the Quick Scan for the Steel Supply Chain
Issues
The Transport-Primary steel producer interface Evidence Steel Producer not communicating to The Transport Company in real-time of technical problems (e.g. crane breakdown)
Issues
The Primary Steel Producer gives The Interviews at The Primary Transport Company a delivery visibility of Steel Producer and The 24 hours but receives a 72 hours visibility Steel Tubes Manufacturer from The Steel Tubes Manufacturer citing mistrust of The Transport Company to deliver when required and not earlier The Transport -Steel tubes producer Interface Evidence
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A lower limit of trust levelled at The Transport Company by The Primary Steel Producer
Implication for Trust and collaboration
A lower limit of trust levelled at The Transport Company by The Steel Tubes Manufacturer The Primary steel producer- Steel tubes producer Interface Evidence Implication for Trust and collaboration Need to set limits of trust Questionnaire on IT, The Steel Tubes Manufacturer not sharing for information sharing Cross-interview with The forecasting information of customer demand with The Primary Steel Producer. (This will Steel Tubes Manufacturer require a joint sales and operations planning on rolling cycle and information sharing (SOP) with The Primary Steel Producer as done with its sister company.
The Transport Company not paying attention to detail (i.e. meeting delivery slots designed to space out delivery times)
Issues
Interview with The Transport Company cites a case where they would have preferred a bigger delivery window due to a crane breakdown
Implication for Trust and collaboration Need to set limits of trust for information sharing
Data analysis of delivery times, observation on site
The Steel Tubes Manufacturer not making effective use of information provided by The Primary Steel Producer.
Interview at The Primary Steel Producer
Customer complaints from The Steel Tubes Manufacturer on quality not addressed
Questionnaire on quality and cross-interview
A lower limit of trust levelled at The Steel Tubes Manufacturer by The Primary Steel Producer A lower limit of trust levelled at The Primary Steel Producer by The Steel Tubes Manufacturer
7. Conclusion and Further Work The elements of trust such as the mutuality of influence and the avoidance of vulnerability to others are enabled or frustrated by the organisation structure, process, or culture. Trust must occur within limits for successful collaboration to be sustained.
Based on the literature and the "Quick Scan" conducted, certain research questions could be formulated as a precis for future work. These include the following: 9 Is trust in an organisation and in its employees correlated? 9 Do who you collaborate with and on what form of collaboration indicate the limits of trust put in place?
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Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 CardiffUniversity, Manufacturing EngineeringCentre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
BM_Virtual Enterprise Architecture Reference Model for Concurrent Engineering and product improvement: An experiment A.J.C. Pithon a, G.D. Putnik b aDepartment of Production Engineering, Federal Center of Technological Education, Rio de Janeiro, Brazil b Department of Production System, University ofMinho, Guimarfies, Portugal
Abstract
In order to test the potential of Virtual Enterprise (VE) organizational principles for Concurrent Engineering (CE) team work organization, an experiment applying the BM_Virtual Enterprise Architecture Reference Model (BM_VEARM) is organized. Three CE teams are asked to create a web site. The teams have been organized as follows" 1) CE distributed team (virtual team according to literature); 2) CE agile team (agile organization according to BM_VEARM) and 3) CE virtual team (virtual organization according to BM_VEARM), in order to complete the reqired task. The principal objective of the experiment is to show that the three organization models of the CE teams work effectively and analyse the performance of each one according to previously defined criteria. In this paper the results of product quality are presented. The experiment has shown that better product quality, for the product quality aspects analysed, is achieved in cases when VE organization is applied. Keywords" Concurrent Engineering, Virtual Enterprise, Broker, BM_Virtual Enterprise Architecture Reference Model, product quality improvement
1. Introduction 1
To support the requirements for the new organizational forms of enterprises, the cooperative work and work groups approach has appeared. Cooperative work may be defined as one where a group of people, physically separated or not, articulate the accomplishment of a common task in a synchronous or asynchronous form. In order to 1 This paper is a continuation ofthe one [2], presented in the 1st I'PROMS International Conference in 2005 where the results on development lead time were presented. To make this paper auto-sufficient some of the text from [2] is repeated, namely the part that presents the BM_VEARM application for the CE team organization.
cooperate, a previous agreement should be considered. All should be committed to work to reach a common objective [1 ]. It is supposed that the agility, i.e. dynamics, with which these work groups may be created and reconfigurated, makes it possible to use the best "resources", i.e. the (best) individuals capable to add value to one defined task, independently of their (the individuals') (geographic) location and, consequently, contributes to the product and process quality. In that sense, it is supposed that application of Virtual Enterprise (VE) organizational principles contributes to the agility of the work teams, i.e. to the Concurrent Engineering (CE) work teams, or team work, organization. With the objective to test the potential of
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Enterprise (VE) organizational principles, in accordance with the BM_Virtual Enterprise Architecture Reference Model (BM_VEARM), for Concurrent Engineering (CE) team work organization, three different work groups had been created, i.e. organized: 1) CE distributed team (virtual team according to literature); 2) CE agile team (agile organization according to BM_VEARM) and 3) CE virtual team (virtual organization according to BM_VEARM), and to each group were equally attributed the same project: to create a site 2 called "Virtual University", making use of information technology to support the education organization and process [3]. This task, i.e. experiment, was carried out by computer sciences students in their last year and undertaken in the period of October 27th to the 30th, 2003. The experience has not been published in Brazil or in Portugal. It simultaneously involved three work groups, specially the agile and virtual groups of the BM_VEARM model, with the application of diverse software. The only asynchronous communication tool used among the members of the groups was e-mail. This paper is organized in three parts. In the first part, we present the basic principles of CE as well as the organizational model for CE work groups. In the second part, we present the concept of Virtual Enterprise in accordance with the BM_VEARM model and also present the integration of the CE work groups in the BM_VEARM model. In the last part, we present the CE concrete work group models that were tested in the exercise, as well as the experiment results.
2. Concurrent Engineering The concept of CE defines that various activities are developed in parallel, interactively, involving professionals from different specialties, covering the entire cycle of product development, in opposition to the traditional method of sequencing stages. Therefore, there is feed-back among the activities. This new form of working is very beneficial, since it avoids the possibility of wasting time and resources, originated from a lack of complete involvement of the different sectors in all the stages of the project. Besides improving the quality of the development process itself it also improves the quality of product. On the contrary, the time and resources wasted in the
2 A page or website that adds several links and services. It is an entrance gate, or starting point, for navigation.
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execution of tasks, that later would have to be redone, will never be recovered [4].
ManagerI
I memberof ~
memberof ~
memberof ~
ou 41
me~nberof
Figure 1 - ManagementModelof Concurrent EngineeringTeams
The model that is being developed in this study is guided by work groups, also designated as the "task force" (Figure 1), which has the group leader as: 1) a linking element between the members of the group and the company higher level management, and 2) the group, i.e. the CE process, management (or manager).
3. BM_VEARM Model of Virtual Enterprise A V E in accordance with BM_VEARM (Figure 2) is defined as a hierarchical model of multiple levels of the enterprise, with the broker inserted between two consecutive control, or management, levels (principal/broker/agent) of the enterprise, or manufacturing, process control system, which ensures integrability, distributivity, agility and virtuality [5]. Integrability is considered the capacity of an enterprise to access (interconnect) existing heterogeneous resources 3 inside and outside the organization. The integration of heterogeneous resources should occur at low cost. This is a characteristic of open system architectures. In the context of virtual enterprises, distributivity is considered as the capacity the enterprise has of integrating and operating needed resources at a distance, remotely. The concept of a competitive enterprise implies the ability to access the best resources" simply seeking the cooperation of other enterprises, purchasing components sub-contracting other companies or creating consortiums, as well as the capacity to manage all business and manufacturing
3 A resource is (from an enterprise point of view) an object that is used to conduct or support the execution of one or more processes (e.g. materials, machines, tools, computers, human operators, time, money, software, etc.). An enterprise is also a resource if it is contracted by another enterprise to render a service. In this work, a member of the group is a resource.
functions, independently of distance, using Wide Area Network (WAN) technologies and corresponding protocols, e.g. Internet. Therefore, the distributed manufacture/enterprise system is defined as a system in which performance does not depend on the physical distance between the elements of the enterprise. It is necessary for an (virtual) enterprise be agile, i.e. to have a capacity of rapid adaptability or rapid reconfigurability between two operations (off- line), in order to quickly respond and/or pro-actively act upon dynamic market changes. Virtuality is introduced with the objective of further improvement of the performance of an agile enterprise, i.e. virtuality should provide the system with the capacity of re-configurability during the undergoing operation (on-line reconfigurability) without the interruption of the operation, and in this way improving the "response" time. Virtuality, combined with agility, distributivity and integrability provides the enterprise with highest level of flexibility and pro-activity. The main agent of agility and virtuality is the broker and his role is to reconfigure the (VE) organizational structure during an operation in real time or between two operations. The broker acts as an interface between two hierarchical control layers, in a way that one layer is hidden from the other, and with the capability to reconfigure the VE architecture without interruption of the operation on the other control level, in order to avoid any loss of time due to the reconfiguration. The broker action between two operations, which obviously implies interruption of the process, is permitted by BM_VEARM but it is a relaxed case in terms of the VE by BM_VEARM. To resume the above, we can now better define what we think a virtual enterprise [5] is: "The Virtual Enterprise (VE) is an optimized and synthesized enterprise on a universal set of resources 4 with its physical structure substituted in real time. The project and control of the system is executed in an abstract or virtual environment". The expression "its physical structure which can be substituted in real time" combined with "in a virtual environment" provides the high level of flexibility and agility that the enterprise requires. The specific
architecture of the BM_VEARM, through the specific role of the broker, should provide the highest level of the VE reconfigurability dynamics (and, consequently, of the CE teams organization based on BM_VEARM). control level i [ integration mechanism ..~l
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Figure 2 - Elementary hierarchical structure of Virtual Enterprise by BM_VEARM
In Figure 1 we can see the CE group members and their relationship as VE partners applying to the VE organization model in conformance with the BM_VEARM reference model. In Figure 2, we can obtain an organizational structure of the CE group as in Figure 3. The new element is/are broker(s) whose role is to manage the CE team organization dynamic reconfiguration in order to keep the maximum performance of the CE team.
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Figure 3 - BM_VEARMModel for CE Group
4. The Experiment Plan A set of"universal resources" is understood to be any type of resource, primitive or complex, that can be distributed globally and can be located both inside and outside the frontiers of the enterprise. This implies VE as a network, or consortium, of enterprises, i.e. networked enterprise over globally distributed independent enterprises, i.e. partners, in the VE.
The experiment was carried out by the three CE teams described above. Thus, the main objective ofthis exercise is to observe and analyze the three CE teams working in the practical situation and to evaluate the performance of each group by following the predefined criteria (described in section 4.2).
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The members of the CE teams didn't have any face-to-face meeting. To manage the work flow (simple or complex) and the communication among the team members, the broker was introduced. NetOffice software was used in addition to e-mails. The advantage of using NetOffice is that it is accessible from the conventional browsers (Internet Explorer, Netscape and Opera). 4.1. Distributed CE team organization (virtual)
necessary to interrupt the activity that is on course. The reconfiguration will be performed on-line, i.e. the potential of this model consists of the substitution of a team member with another one, without that, this substitution affects the task and is not perceived by other members of the team or by another hierarchical superior level. The Broker in this model possesses the same attribution as the one defined for the agile model above. Since the reconfiguration is "hidden", we say that the group works with a "virtual" underlying organizational architecture.
The distributed CE team (Figure 4) was composed by the manager, leader, reviser, projector, designer, programmer/specialist in animation and the customer
I Manager[ Broker
representative, i.e. students and professors. The only difference between this group and the traditional group of CE is that the traditional group of CE works in the face-to-face form and the distributed one works through the Web [6]. Figure 4 - Distributed CE team organization ("virtual")
Figure 5 - Agile CE team organization 4.3. Virtual CE team organization in BM_ VEARM 5. Evaluation Criteria
4.2. Agile CE team organization by BM_ VEARM The main characteristic of this group is the insertion of the broker (element chosen by the manager or the leader in order to catch the necessary resources for the execution of the task, e.g. to enlist the members of the group) as the new member of CE team, to act as the agent of the reconfigurability ofthe CE, now agile, teams. In this way, the reconfigurability will be done off-line, i.e. when it will be the necessity to substitute a member of the group with another one, the broker will interrupt the execution of the task and perform the substitution. The members of the team that are "waiting" to substitute an acting member of the group, i.e. that will participate in the process of team reconfigurability, presented in the lower part of Fig. 5. This team also has as the main characteristic the insertion of the broker as the agent ofreconfigurability in the CE virtual teams (Figure 6). In this case, the performance of the broker is different. To reconfigure a member of the group into another one, it is not
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In the experiment, the following metrics, concerning the quality of the product (web site), were used: 1 - Load time: It is the time that each section of the site takes to be loaded on the customer's terminal (the anonymous user that accesses the site through the Internet) from the server. As the standard for the tests, a connection of 56Kbps was used (dialup or dialed) and the calculation of this test was carried through Microsoft FrontPage 2000 software. 2 - Quality o f the code: It is the evaluation of the conformity of the written code, based on international standards and agencies (ISO/OSI, W3C, PHP.NET). It verifies the syntax, straightness and skill in writing the code of the site, perfect use of the programming language, use of commentaries, identification and heading, as well as the optimizer demanded for item 1[7].
6. Results
To obtain more data, the site was divided into six screens/sub-pages (homepage, institutional, classroom, coffee, secretariat and library) and for each one of these screens/sub-pages, the defined metrics above were applied. These tasks and metrics organization were applied in each of the tested teams. In the following sections, the obtained results, for each of the CE teams, are presented.
6.1. Partial results of the Distributed CE team The tasks were distributed by the leader to each member of the group. As this model does not have a market of resources, i.e. available experts/professional by whom the changes in the team can be made, the substitution of a member for another one can delay the same project for days or for weeks. In our experiment, the communication mechanism used by the members of the group (with the leader) was e-mail. The experimental results, according with the metrics and defined task structure are presented in Tables 1 and 2. Table 1 - Load time for CE distributed model Screens/pages of Load time (seg.) the site
Homepage Institutional Classroom Coffee Secretary Library
41 20 15 18 14 14
Total time
122
Table 2 - Quality of code for CE distributed model Screens/pages of the site
Quality of code
Homepage Institutional Clasroon Coffee Secretary Library
19 19 27 30 22 25 142
Total
6.2 Partial results of Agile CE team by BM_ VEARM The two basic differences of this model against the "distributed team" model are: 1) the insertion of the broker between the manager and the members of the group, 2) the dynamic substitution of a member of the team with another one.
This substitution causes an interruption of the task that was being carried out and, because of this interruption, the time of the project needed to be extended, in that way, the same could be executed. However, the reason for the existence of the broker within the CE team is to make this interruption as short as possible. The broker is an expert in organizational "reconfiguration" and he has access to the "market of resources", i.e. to the market of experts/professionals that can efficiently join the EC team. Therefore, it is supposed to have a better "alignment" with the CE process objectives, including the project lead time as well as the product q u a l i t y - because there is a market of specialized resources and a selection of the best available resources available.The experimental results for this CE team, according with the metrics and task structure defined, are presented in Tables 3 and 4. Table 3 - Load time for CE agile model Screens/pages of the site
Load time (seg.)
Homepage Institutional Classroom Coffee Secretary Library
35 14 19 16 13 14
Total time
111
Table 4 - Quality of code for CE agile model Screens/pages of the site
Quality of code
Homepage Institutional Classroom Coffee Secretary Library
26 22 30 30 28 27 163
Total
6.3. Partial results of the Virtual CE team with BM VEARM This CE team organizational model uses the same mechanisms as the "agile" CE team model, but since the substitutions in this model can be made in a shorter time, the alignment with the CE process objectives is even better (including the project lead time as well as the product quality). In reality, the CE team reconfiguration, does not affect the total time of the task, due to the organizational architecture that implies virtuality. The experimental results for this CE team, according with the metrics and task structure defined,
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are presented in Tables 5 and 6. Table 5 - Load time for CE virtual model Screens/pages of the site
Load time (seg.)
Homepage Institutional Classroon Coffee Secretary Library
29 10 12 14 10 12
Total time
87
References
Table 6 - Quality of code for CE virtual model Screens/pages of the site
Quality of code
Homepage Institutional Classroon Coffee Secretary Library
29 10 12 14 10 12
Total
87
6.4. Total results o f each one tested team
Finally, in Table 7 we present the total values (the total values of Table 1 and Table 6) obtained for the load time and the quality of code of the three CE team organizational models that were tested. Table 7 - Total values obtained from the three models with metric load time and code quality Total Distributed Agile by BM Virtual by BM
I
Load time (s) 122 111 87
I
Quality of code 142 163 170
7. C o n c l u s i o n Based on the experimental results, we can conclude that the application of the VE organizational principles brings benefits to the CE objectives. This is realized not only for the project (CE process) lead time, already presented in [2], but for the product quality as well. The authors believe that the realized experiment is important as it confirms the theoretical expectations of the VE concept. Surely, it is necessary to perform more experiments in the industrial environment, as well as for different types of products, in order to make a positive and valid conclusion of the benefits of
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application of VE organizational principles. This work is under course. Also, it is necessary to refine the CE process management models in the environment of a VE.
[1] Borges, M.R.S. Suporte por Computador ao Trabalho Cooperativo. Jornada de Atualizag~o em Informfitica: Congresso Nacional da SBC. Canela, 1995. [2] Pithon A.J.C., Putnik G.D. (2005) BM_Virmal Enterprise Architecture Reference Model for Concurrent Engineering processes performance improvement: An experiment, in Pham D. T., Eldukhri E. E., Soroks A. J. (Eds.) Intelligent Production Machines and Systems (Proceedings of the 1st I'PROMS Virtual International Conference), Elsevier, Amsterdam, pp: 61-66, [3] Pithon, A., Putnik, G. Concurrent Engineering based in BM_VEARM for Development of Infrastructure (Portal) for Distance Learning. 10th ISPE International Conference on Concurrent Engineering: The Vision for the Future Generation in Research and Applications. R. Jardin Gongalves, J. Cha, A. Steiger-Garg~o (Ed.), A Balkema Publisher, V.2, 2003, pp 931-936. [4] Pithon. A., Putnik, G. Team Work for Concurrent Engineering in agili/Virtual Enterprise by BM_Virtual Enterprise Architecture Reference Model. Collaborative Business Ecosystem and Virtual Enterprise. Third Working Conference on Infrastructure for Virtual Enterprise (PRO-VE'02). L.M. Camarinha-Matos (ed), Kluwer Academic Publishers, 2002. [5] Putnik, G. BM_Virtual Enterprise Architecture Reference Model, In A. Gunasekaran (Ed) Agile Manufacturing: 21 st Century Manufacturing Strategy, Elsevier Science Publ,. 2000, pp 73-93. [6] Pithon, A. Projecto Organizacional para a Engenharia Concorrente no ~anbito das Empresas Virtuais, PhD Tesis, University ofMinho, Guimarges, Portugal, 2004, 225p. [7] Staa, A.V. Programaggo modular: desenvolvendo programas complexos de forma organizada e segura, Rio de Janeiro, Editora Campus, 2000.
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
C O L L A B O R A T I V E D E S I G N R E V I E W IN A D I S T R I B U T E D ENVIRONMENT Manglesh Sharma
Vinesh Raja ", Terrence Fernando b
aUniversity of Warwick, Warwick Manufacturing Group, UK, {manglesh.sharma,vinesh, raja}@warwick.ac,uk bUniversity of Salford, Future Workspaces Research Centre, UK,
[email protected]
Abstract
This paper investigates how collaborative environment technology can be used to integrate geographically dispersed design teams and other technical and non-technical members from within and outside the organisation to support collaborative product design reviews. The creation of such a collaborative environment for design will allow participants from different phases of the product development cycle to collaborate and form a virtual team. A new framework is proposed to extend the current advances in collaborative technologies to provide access to local and global resources within design teams and the deployment of private and public design review workspaces to provide an efficient collaborative design environment. Also, a prototype collaborative CAD environment has been developed and the results are discussed in this paper. Keywords: Collaborative Design, Shared Workspace, Design Review
1. Introduction
In the past few decades, the global financial market has seen a shifting trend of the leading industries in focussing their visions more towards the international market than just the home market. This 'globalisation factor' has urged them to span the growth of their business to other parts of the world and utilise the resources available there. At the same time it has raised the standard of competition among them. There are some other factors that arise with time and seeking for a better collaboration, like increasing the role of the component and service suppliers in the decision making policy of Original Equipment Manufacturer (OEM) in giving the final shape to the product [ 1]. The strategies are followed by industries in de-constructing and concentrating on the core competencies leading to the implementation
of the concept of distributed virtual enterprises. In such distributed virtual enterprises, the actors comprised of different cultures, different languages and concepts, using different tools and processes. A collaborative Product Commerce system is the dominant technology for managing inter-enterprise data and providing design teams with a virtual design space. Better collaboration during the design stage can help in reducing design time typically by 2540% [2]. To prevent inconsistency among subsolutions, designers must communicate and negotiate with each other sufficiently so that the needed information flows timely, knowledge about the design is shared, and design activities are well coordinated [3]. Product design review is a typical scenario of collaborative product development. A typical design team is comprised of members from multiple disciplines and also geographically
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dispersed. The design review and the engineering change approval can take place in a collaborative environment during the detailed design stage of the product development through the web site and the conferencing tools [4]. The author in [5] describes numerous ways of conducting design review. This paper has been structured in a way to cover the various aspects of the collaborative design review, starting from available commercial and research-based tools overview to the proposal of new system architecture and its implementation results. 2. Related work
In this widespread market world, many leading CAD industries have already begun in-house development and offer some kind of collaboration functionality within their CAD products/services. Similarly many third-party vendors have developed collaborative tools that can be used along with existing non-collaborative products.
2.1. Commercial solutions Although there are several tools currently available in the market, claiming to aid the design collaboration, only some of the common ones have been considered here because of their distinctive features and different approaches towards providing collaboration facility for design. For an off-line collaboration, email-based eDrawings Professional [6] serves as a strong candidate as any members can be approached for their feedback/review without the concern of registration or a special system set-up. It provides most of the tools necessary for reviewing the CAD models along with the ability to review Analysis files. Pro~COLLABORATE [7] is good for design members who want to share both CAD and non-CAD files and work in a project management environment. However it is more suitable to Pro/ENGINEER members as only they can initiate the whole process of sharing design information and only Pro/ENGINEER CAD files can be reviewed. CATweb Navigator [8] can initiate the process of collaboration with any standard Java-enabled browser with no extra plug-in to be downloaded. Sharing, viewing and editing of models with normal browser make the whole process easier and quicker on different platforms. OneSpace.net [9] works in the similar way but has additional facilities of collaboration: real-time text-based meeting, and editing of CAD models by sharing the application during the meeting. Additionally it supports several
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CAD file formats and also has better security for data-protection during the review meeting. Unigraphics NX [10] and CollabCAD [11] products best support the real-time collaboration as they provide different channels of collaboration like text/audio/video/whiteboard-based meeting for the design review. They also permit editing of CAD models by all the members present in the meeting. Unigraphics NX supports CAD/CAM/CAE compared to CollabCAD that supports CAD/CAM only. CollabCAD has Client/Server architecture; only restricted number of members can access the system collaboratively. With regards to maturity, CollabCAD is still in the process of development and quite new compared to the well established ones.
2.2. Distributed virtual reality based solutions The proposed system and its implementation are based on the distributed VR technology, it is important to discuss this domain as well. As stated in the Roadmap for Future Workspaces [12] the current state-of-the-art will allow real-time collaboration between distributed sites especially in aerospace, automotive and building construction sectors, but critical security issues and network bandwidth limitations and their associated costs restrict effectiveness. At present the distributed Virtual Environments are not tightly integrated with the useful engineering tools and they are less userfriendly. In commercial sectors, various toolkits are CAVELib, Division's dVS, Sense8's WorldToolkit and Panda3D and in research category the list includes MR toolkit, GIVEN++, DIVE, BrickNet, Alice/DIVER,, Aviary, Maverik/DEVA, VR Juggler, Bamboo and Dragon [13]. Some other popular distributed environment like Avango, MASSIVE, FAVE, DIVE, Ygdrasi, etc are highlighted by [14, 15]. DIVE [ 16] provides architecture for implementing distributed multi-user interactive virtual environments in a heterogeneous network environment. The framework is developed around the concept of shared distributed world database. FAVE is generic software Framework Architecture for Virtual Environment created for developing VR applications [17]. For creating synchronized distributive VR applications, the application must start up in the same state and receive only changed data at run-time providing remote distributivity with low network traffic. These applications have been used for developing applications in Urban modelling, Simulation and visualisation of gas explosions and
fire, and in Medicine field. It is found that none of the system/toolkits/ services mentioned so far discuss about setting private/public area within shared workspace or use of local/global resources in any noticeable way. As Hydra [18] is an in-house development and provides the necessary features along with needed technical assistance, it is used as the base for developing the prototype application for the proposed system in CAD field. It is similar to DIVE and FAVE, Hydra is a software framework for developing flexible distributed, multi-user interactive visualization and simulation applications [18, 19]. It can deploy and control many interconnected applications like CAD, CAE and Virtual Reality applications at geographically remote locations and can be manipulated by multiple users. The distribution module of the HYDRA uses CORBA with TAO providing distribution protocols for data distribution enabling flexible deployment of software components and multi-user interaction over different operating system. The developer of Hydra has also provided a visualization interface, called viSpaces. The next section will explore how current distributed system can be further extended to exploit the local and global resources of companies and support human-human collaboration through private and public shared workspaces in a business setting.
3. Proposed system design There is a need for better way of utilizing the shared workspace and also the resources available with some of the team members. Based on these requirements and the current trend in the industries a system design is being proposed here. Figure 1 shows an overview of the system architecture to illustrate how the overall system would shape to enable collaborative design review by geographically distant team members. Client D ]
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Client here is considered as a virtual team member situated geographically apart from other team members during the design review meeting. The local resource exists locally on the computer system of the clients. Only this client of that system can access this resource directly and manipulate it. This resource here can be a CAD system for 2D/3D modelling, a finite element analysis system for stress / displacement analysis of parts, or any simulation system like assembly simulation or motion simulation system. A global resource is similar to a local resource but is public in nature so provides unrestricted accessibility by any team member (client) during the design review meeting. Shared Workspace is a virtual workspace containing design information brought through individual resources and accessible by all the team members over the network.
3.1. System operation In this architecture, team members, referred here as clients, are geographically separated from each other. The clients connect to the shared workspace using the interface provided by the executable application on their computer system. This way shared design information becomes available to team members. On a request made by any team member, the client connects to the local resource available on his computer system. Depending upon the resources available, the respective information is pulled to the shared workspace through the application interface and which in turn updates all the clients' applications. Similarly other team members can make their design information available through their resources to this shared workspace. As this local resource is held by the client, only he/she can decide what design information needs to be shared. Other members access this local resource indirectly. As the connection with a resource is maintained, the member holding the respective resource can carry out the design changes needed during the review process itself. Every other team member can see the changes in the design from their own locations. Besides the local dedicated resources, a global resource is also maintained in this environment. This global resource can be residing anywhere geographically but can be accessed by all members over the network. The design data for global access/sharing by every team member can be put in this global resource. The team members using
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~
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Local Services Local Resource
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Client application Figure 2: Client-side System Architecture collaborative tools can provide feedback like resources where they can apply the design changes accepting or rejecting the design changes or raised during the review. requesting the further changes immediately. Here, clients can participate in the design review process 3.2.2. Local services from any computer with any operating system The client application would use this services to connected to the network. This will lead to an perform the requirements of the client such as efficient design review process. visualising and manipulating the design data, navigating to study it, and providing feedback for 3.2. Client-side system architecture communicating their views to others participants. Local resource service provides the way of sending Figure 2 shows detail architecture of this clientcommands to a local or global resource to do specific side system. The descriptions of its individual tasks like requesting to load a CAD model, components are described below. sectioning the model (in the case of CAD modelling), or changing the load pattern / its 3.2.1. Client application orientation (in case of FE analysis simulation). The A client is a member of virtual team scene data management service of Shared collaborating together to discuss the design related Workspace would manage the design data retrieved from resources so that all the clients can access it issues during the design review meeting. It is an external physical entity interacting with this system. from their terminals for visualisation purpose. The Each client has a front end (a visual interface) that rendering service retrieves the most current design would allow him/her to visualise and interact with information from the scene data management service of Shared Workspace and prepares a scene for the design data and also to collaborate with other visualisation. The GUI service allows clients to team members (clients) in real time. To initiate interact with the scene objects directly. It also allows progress and conclude the design review one of the clients to do specific operations for which team members will need to act as a team leader communication with the local or global resource is during the meeting. It is also possible that any client not required, e.g. moving the components in an can share the design information with other members assembly, viewing an exploded view, or measuring a of the team through the local resource that is part. Such operations will be performed locally at dedicated (private) to him/her. He would be the client-end without affecting what other team person responsible for editing and manipulating the members' view at that time. design information to apply the design changes if required by the members during the review. This 3.2.3. Shared workspace would be particularly in a case where the client Shared Workspace has a high-level objectrepresenting any organisation can share the design based description that is rendered by the user information for visualization purpose but needs to interface at each client's end participating in the restrict other members from manipulating or editing design review meeting so that they can visualize and it directly in his private resource. Indeed all the interact with shared design objects. All the clients clients can have direct control over the global
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share the design information through this virtual shared workspace. Scene Data Management Service manages the design information received through the local or global resource. This design data is stored in a highlevel object format so that it can be shared, distributed and rendered by all the client applications to form a scene for visualization purpose. Whenever a local service interacts with a local or global resource and looks for anything to be updated in the scene, it updates its dataset and the Distribution Management Service then updates it for each client. Team-management service is needed to take care of the administration related tasks that include marinating team-member's database, authentication, control passing mechanism, maintaining the privately shared workspace with the right members only. The real-time conferencing service will aid collaboration among the team members efficiently besides the sharing of design information using text, audio, video, whiteboard tools/services. This shared workspace can further be classified in two types namely publicly shared workspace and privately shared workspace. All the design information that is available in publicly shared workspace can be read and visualised by all the clients in the meeting. In case of privately shared workspace some part of the design information contained in shared workspace will be accessible to only few team members who are already in a mutual agreement. This could be important when some team members are working for the same organisation but located in different regions and want to exchange ideas or discuss design issues that are not meant for rest of the team members who belong to different organisations. Figure 3 illustrates a case of using these types of shared workspace. Client A, B and C all have equal access to Public area of shared workspace whereas the accessibility of Private area in the shared workspace is limited to Client A and B only.
3.2. 4. L o c a l r e s o u r c e
A Local Resource describes a part of the system that is dedicated and available only to the client accessing it directly. Only this client can modify/manipulate the design source for any design changes proposed and approved during the design review. Once the design changes are applied by the owner client, the refreshed data is sent back to the shared workspace. Global Resource is similar to a Local Resource but the only difference lies in that the local resource is dedicated to only one client who can access it directly whereas all the clients can access a global resource directly.
4. System implementation and results The primary author of this paper took Hydra framework as foundation and added functionalities to assist collaborative design review specifically in CAD modelling field. A CAD Plug-in module has been developed so that the design team member can connect, access and edit CAD models in a CAD system (local resource) and share updated design models with the other team members anytime during design review meeting. SolidWorks| CAD package is used here as a CAD System. The plug-in module creates the virtual reality model corresponding to the actual CAD model for display purpose. It directly communicates with the CAD system using different APIs provided by the CAD System. Figure 4 shows CAD plug-in work with Hydra System to give exposure to CAD System. Besides visualization of model, the system also displays CAD system alike components and featureshistory in tree-structure format. It also shows the
hared Workspace rivate~ Public I I Workspace I [ workspac,e
CAD System
ll
Figure 3: Clients' interaction with Private and Public Shared Workspace Collaborative System Figure 4: Connection of local resource CAD System and Collaborative System
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dynamic characteristics like mass related properties for this model. Client can modify CAD model in the CAD system and the design changes would be reflected immediately in the visual interface of the distributed review system. Design team members use text-based chat tool to give any feedback during the review. 5. Conclusion This paper proposed a framework which considers the integration of resources and methods of working in a collaborative product development setting. In particular, the integration of local and global resource with private and public shared workspace to provide a rich collaborative design environment was introduced. A part of the proposed system was implemented to help in conducting the design review process for CAD Modelling. The implemented CAD plug-in is now integrated within the HYDRA architecture. 6. Future research work It is possible to extend interfaces to other kind of CAD systems and create a heterogeneous CAD environment for reviewing purposes. Besides this, further research could be carried out to integrate other simulation software like kinematics and stress analysis, assembly, maintenance into the design review environment to support multi-functional teams. This will offer a powerful design review and problem solving environment for distributed design teams. Acknowledgements University of Warwick is partner of the EUfunded FP6 Innovative Production Machines and Systems (I'PROMS) Network of Excellence. http://www.iproms, org References [1] Steve Hanley: Value Chain Collaboration in a Speedto-Market World. Vice President- Systems Integration Group, Dana Corporation, August 2001. [2] Mike True, Carmine Izzi: Collaborative Product Commerce: Creating Value Across the Enterprise. ASCET Volume 4, May 2002. [3] Stephen Lu, Yan Jin: Engineering as Collaborative Negotiation. University of Southern California, Los Angeles, May 1997. [4] Huang G. Q., Mak K. L.: Internet Applications in
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Product Design and Manufacturing. Published by: Springer-Verlag Berlin Heidelberg, Germany, 2003. [5] Brusse-Gendre T.: Design Reviews. The University of Calgary, Canada, V I.1, 2002. WWW page http://www. ucalgary.ca/-design/toolbox/Design%20Reviews.pdf, accessed 05.01.2006 [6] SolidWorks: eDrawings Professional. SolidWorks Corporation. WWW page http://www.solidworks.com/ pages/products/edrawings/professional.html, accessed 05.01.2006 [7] PTC: Pro/COLLABORATE. Parametric Technology Corporation.WWW page. http://www.ptc.com/community/ procollaborate.htm, accessed 05.01.2006 [8] PDMIC: CATWEB Version 2 Release 2 offers mature and effective access to CAD and PDM data via the Web, PDM Information Center, March 1999. [9] CoCreate: OneSpace.net - a web collaboration tool. CoCreate Software, Inc.WWW Page http://www, cocreate. com/products.cfm?ProdFamilyID = 1&ProductID=31, accessed 05.01.2006 [10] Team-Engineering: Unigraphics NX- Integrated Collaboration, Team Engineering Ltd. WWW page http://www.team-eng.com/products/unigraphics/int_col/ index.htm, accessed 05.01.2006 [11] NIC: CollabCAD SOFTWARE. National Informatic Centre. WWW Page http://www.collabcad.com/, accessed 05.01.2006 [12] Terrence Fernando: A Strategic Roadmap for Defining Distributed Engineering Workspaces of the Future. Future_Workspaces IST-2001-38346. University of Salford. June 2002. [13] Russell M. Taylor II, Thomas C. Hudson, Adam Seeger, Hans Weber, Jeffrey Juliano, Aron T. Helser: VRPN- A Device-Independent, Network-Transparent VR Peripheral System. VRST Conference, Canada, 2001. [14] Martin Naef, Edouard Lamboray, Oliver Staadt, Markus Gross: The blue-c Distributed Scene Graph. Proceedings of IEEE Virtual Reality 2003, Pages 275-276, Los Angeles, California, 2003. [15] Wilson S., Sayers H., McNeill M. D. J.: Using CORBA Middleware to Support the Development of Distributed Virtual Environment Applications. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, WSCG, Czech Republic, 2001. [16] Emmanuel Frdcon, M~rten Stenius: DIVE: A scaleable network architecture for distributed virtual environments. Distributed Systems Engineering Journal, Vol. 5, No. 3, pp. 91-100, Sweden, Sept. 1998. [17] Christian Michelsen Research AS: FAVE Framework Architecture for Virtual Environment. Norway. WWW page http ://www.cmr.no/avd50/ avd50 services fave.shtml accessed 05.01.2006 [18] Bee Simon: H y d r a - An Overview. University of Salford, UK. To be published [19] Bee Simon, Ford R., Margetts L., Roy K.: Collaborative Virtual Design in Engineering. SC Global Showcase, Arizona, November 2003.
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Implementing manufacturing feature based design in CAD/CAM T. Szecsi School of Mechanical and Manufacturing Engineering, Materials Processing Research Centre, Dublin City University, Dublin 9, Ireland
Abstract
The paper presents the development of a new design system that allows the composition of a design from manufacturable features. The system contains several modules: hierarchical design-for-manufacture rule system, manufacturing feature library, manufacturing feature-based design module, designer advisory module, manufacturing feature recognition module, and design analysis module. Keywords: Manufacturing Feature Based Design, Feature Extraction, Design for Manufacture
1. Introduction
Conventional CAD/CAM systems normally compose designs using geometric primitives and Boolean operations performed on them. A 3D solid model, for example, can be composed using cubes, cylinders and other geometric elements, extrusions, rotational solids, etc, and operations like subtracting and intersecting volumes can be applied. More modem CAD systems implement a set of more advanced operations for creating 3D solid and surface geometry. These include surfaces passing through several generating profiles, smooth transition from one generating profile to another, volumes included between predefined surfaces, and others. However, whatever the set of such operations, the design is purely geometry-driven. The major problem using such technology is that the design does not include information for the subsequent phases of the product development cycle. Once the design is ready and is passed over to the next expert, the designer's intent is lost. Other experts can only obtain information from
the design relevant to them by interpreting the geometric elements in the design and map them into features of higher order.
Fig. 1. Part with pocket.
The part in Figure l, for example, contains a pocket. Although any process engineer would be able to identify the pocket in the design, the very concept of a pocket is not directly represented. It can only be obtained by mapping the combination of the existing geometric primitives to known concepts. Although this approach can be used easily with simpler designs, more complex ones make the process error prone. This is
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especially true when the process is automated, like in a Computer Aided Process Planning (CAPP) system. With traditional design approaches, most of the information included in the design is geometry-related. However, part geometry is not the major concern of most experts in the product development cycle. From a manufacturing point of view, for example, actual part dimensions are of secondary importance. Production volume, material, accuracy and surface finish are much more important. In order to include information for other experts in the design, a technology called Feature-Based Design can be implemented [1 ]. Although many modern CAD/CAM systems support feature-based design, virtually all features are limited to the functionality of the design, thus they are design features [2]. Features like slots, pockets, holes, fillets, bands, steps, used in most CAD/CAM systems, are pure design features, and do not contain actual manufacturing information. Most systems do not even have the capabilities of a full-scale validation of the functionality of the design features. These systems would allow the insertion of a design feature whose functionality is not consistent with the design. Although mapping a design feature to a manufacturing feature is easier than mapping geometric primitives, the fact that manufacturability is not considered at the design phase means that later design changes due to manufacturing problems are costly. There have been numerous attempts to deal with manufacturing information in features [3, 4, 5], but the consideration of manufacturability in feature-based design is still very limited despite the fact that there is a large collection of rules and guidelines available [6]. In order to reduce manufacturing costs, a fully implemented manufacturing feature based design system is being developed at Dublin City University.
2. Manufacturing feature based design system The aim of the project is to develop a software system that enables designers to appreciate manufacturing process capabilities and limitations during the design phase. The system will be linked to a commercial CAD/CAM package Pro/Engineer that supports parametric feature-based design. The system consists of several stand-alone, but interconnected modules:
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2.1. Hierarchical design-for-manufacture rule system For the last decades, a large number of design-formanufacture (DFM) rules (guidelines) have been developed [6]. In order to apply them, they have to be systematised and organised into a hierarchical rule system. Rules at the higher level of the hierarchical system are applied to more generic manufacturing features, and more specific rules are applied to more detailed features. Rules at lower levels of the hierarchy (children) inherit the characteristics of their parent rules (Fig. 2) For example, the specific feature 'centrehole' inherits all the parameters and DFM rules from its parent, more generic features that reside at higher levels of the hierarchy. In addition to the inherited rules and parameters, a feature may have its own set. The hierarchical structure enables to minimise the number of rules used and to avoid repetition when applying the rules. It also makes the extension and modification of the rule system easier, and gives a much clearer structure. Holes
le:'Avoid non-round holes if possible'
[
Machined holes
IRule'Entry : surface should be perpendicular to the spindle axis' [Rule'Avoid : non-round holes is possible' ?/-] Rough machined holes ~ ~/~
I
Centre hole
Fig. 2. Hierarchical design-for-manufacture rules. The hierarchical rule system is based upon a hierarchical process classification system. All manufacturing processes are arranged in a hierarchical, tree-like structure. Although the structure of this system is different from that of the DFM rules, the two systems show similarities. The link between the rule system and the process system is established using pointers (Fig. 3). Generic and specific rules can point to both process groups and individual processes. Pointing to process groups as opposed to specific processes facilitates inheritance. Since manufacturing processes can be classified into many different systems, the one that is closest to the hierarchical structure of the DFM rules results in the
simplest rule application. This means that the process classification system is developed with DFM in mind.
_~, L2
,__.
A1
A2
Fig. 4. Parameterised feature centre-hole.
Feature datum
Iling
de~
Fig. 3. Pointing DFM rules to processes.
Fig. 5. Feature data.
The extension of the hierarchical systems is relatively simple, provided the existing classifications are not altered, but only expanded. In this case, the extension means that new rules and processes are added to the hierarchical trees without changing the remaining structures.
Since a manufacturing feature contains information about cutting tools, too, limitations to feature dimensioning may apply due to the fact that the feature will have to be produced using standard tools. For example, a manufacturing feature centre-hole can only have dimensions that match one of the combinations in Table 1.
2.2. Manufacturing feature library Manufacturing features contain the following information: parameterised geometry (Fig. 4) of the feature, the description of the manufacturing process to produce the feature (including machine tool, tooling, possible fixtures, manufacturing conditions, production volume), relative cost information, design limitations, functionality rules, and links to design-for-manufacture rules. A t~ature also includes the description of the datum surfaces that link the feature to other, already inserted features (Fig. 5). This description also contains rules of what types of surfaces the feature can be linked to.
Table 1. Standard centre-drill parameters [7]
D1 (mm)
L1 (mm) A1 (deg)
A2 (deg)
5.56
5.56
120
60
6.35
6.35
120
60
7.94
7.94
120
60
Features are selected from a hierarchical feature library (Fig. 6). Since inserting a manufacturing feature into the design involves manufacturing process planning, the designer is guided in his/her selection.
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I
Drill
Forged
Pockets
Punched
Holes
Machined
Slots
Cast
...
~
Rough
\
Drill+Countersink Drill+Counterbore Centre
...
Design Functionality Rules
DFM Rules
Hole
... Cutting Tool Cutting Conditions Machine Tool Datum
~4
Cost
...
Fig. 6. Manufacturing Feature Library.
2.3. Manufacturing feature-based design module This module allows designers to compose a 3D solid model not from geometric primitives, but from a set of manufacturable features (Fig. 7), using feature libraries.
Keyway
checked and possible problems are identified. Features that may cause functionality and/or manufacturing problems are rejected. This ensures that a design remains consistent with the specifications and will not cause manufacturing problems. A possible reason for functionality and/or manufacturing problems is improper feature dimensioning. Consider the length L2 of the conical surface of the centre-hole feature in Figure 9. The other dimensions of the feature were limited to a certain combination of the dimensions based on the values in Table 1. But L2 could not be assigned an exact value because its length depends on the drilled depth. However, due to functionality and manufacturing problems, L2 should not exceed the effective length of the cutting edge of the centre-drill LT. Improper dimensioning of L2 will cause functionality and manufacturing problems. If L 2 > L T , the exit surface of the centre-hole feature becomes cylindrical, thus preventing a centre from having a proper contact with the hole. This is a feature functionality problem. At the same time, attempting to drill a centre hole deeper than the cutting edge of the drill would result in the cylindrical surface of the drill (which has no cutting edge and flute) blocking the outlet of the hole during drilling, thus preventing chips to escape the hole and cutting fluid to enter the cutting zone. L2
Manuf. Feature Rolled Bar
LT
Manuf.
Feature Centre hole
. . . . . . . .
t_.
Fig. 7. Designing with manufacturing features. The feature is positioned in the design by relating its datum surfaces to existing surfaces of other features in the design (Fig. 8). This link is maintained even if the design is modified and ensures its consistency.
v Fig. 9. Improper feature parameter.
Fig. 8. Linking of feature datum. By inserting a manufacturing feature, the linked functionality and design-for-manufacture rules are
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Since functionality and DFM rules are not linked, it might be the case that a feature is rejected by one of them only. The centre-hole features in Figure 10 are only rejected by the functionality rules. The one on the left because the axis of the centre-hole does not coincide with the axis of the rotational body, which is the functional violation of the concept of a centre-hole.
(It should be noted that if the machine tool specified is a lathe, this feature would also be rejected by the DFM rule system because it would be impossible to be machined). The feature on the right results in a too thin wall L5, thus causing functionality problems of the centre-hole.
2.4. Designer advisory module This module is linked to the Manufacturing featurebased design module. Its aim is to provide assistance to designers when selecting manufacturing features. Since functionality and DFM rules are applied in real time, during the actual design process, the designer is warned if they attempt to include features that are difficult to manufacture or violate functionality rules. Alternative solutions are suggested. The module also provides process engineering background for designers.
2.5. Manufacturing feature recognition module Fig. 10. Rejected features due to functionality. The drilled-hole feature in Figure 11 is only rejected by the DFM rule system by violating the rule that 'Exit surfaces of drilled holes should be perpendicular to the drill axis'. However, the feature does not violate any functionality rule (in fact the opening at the bottom of the hole would ensure that no air is trapped inside the hole during inserting a pin into it). / I
i
Although the major concern of the system is to compose a design using validated manufacturing features, in addition to the direct feature-based design system, a feature recognition module is being developed. It will be beneficial in cases where a design is not using features. The aim of this module is to translate a 3D solid model into manufacturable features. The geometric elements of the solid model are analysed and features that are recognised as manufacturing entities are extracted and organised in a topological manner. The extracted features are mapped to existing features in the feature library.
2.6. Design analysis module
Fig. 11. Rejected feature due to manufacturing. Many features may be rejected by both functionality and manufacturing reasons. The centrehole feature in Figure 12 violates the functionality of the feature and causes manufacturing problems due to the entry surface not being perpendicular to the drilling axis.
This module analyses a 3D solid model in terms of functionality and manufacturability. Based on manufacturing feature recognition (extraction), the functionality and manufacturability of the model is defined by applying the functionality and hierarchical design-for-manufacture rule set the same way as during the direct feature-based design process. Elements that violate the rules are highlighted and the corresponding rules are referred to. Since feature extraction lacks the benefits of exactly reflecting the designer's intent (as opposed to feature-based design), this type of analysis can not be conclusive and requires extensive manual interaction.
3. C o n c l u s i o n s
Fig. 12. Rejected feature due to functionality and manufacturing.
The manufacturing feature based design system allows the designer to compose a design using manufacturable entities. The inserted features are
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validated from functionality and manufacturing point of view. The functionality rules ensure that the design is consistent with the functionality requirements. The manufacturing rules enable early detection of manufacturing problems during the design phase. Solutions that are likely to cause manufacturing problems are rejected, or alternative solutions are suggested. This leads to reduced manufacturing costs.
Acknowledgements Dublin City University is a partner of the EUfunded FP6 Innovative production Machines and Systems (I'PROMS) Network of Excellence (www.iproms.org).
References [ 1] Shah JJ and Mantyla M. Parametric and Feature-Based CAD/CAM: Concepts, Techniques and Applications. John Wiley, 1995. [2] Salomons O.W. et al. Review of research in feature based design, Journal of Manufacturing Systems, 12(2), 1993, pp 113-132. [3] Brunetti G and Golob B. A feature-based approach towards an integrated product model including conceptual design information. Journal of ComputerAided Design. 32(14), 2000, pp 877-887. [4] Li WD et al. Feature-based design in a distributed and collaborative environment. Journal of Computer-Aided Design. 36(9), 2003, pp 775-797. [5] Basak H and Gulesim M. A feature based parametric design program and expert system for design. Journal of Mathematical and Computational Applications. 9(3), 2004, pp 359-370. [6] Bralla J. Design for Manufacturability Handbook. McGraw-Hill, 1999. [7] Green RE et al. Machinery's Handbook. Industrial Press Inc., New York, 1995.
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Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Learning and Reasoning Techniques for Automatic Feature Recognition from CAD Models E.B. Brousseau a, S.S. Dimov a, R.M. Setchi b a The Manufacturing Engineering Centre, Cardiff University, Cardiff CF24 3AA, UK b Cardiff School of Engineering, Cardiff University, Cardiff CF24 3AA, UK
Abstract
During the product development, Automatic Feature Recognition (AFR) techniques are an important tool for achieving a true integration of design and manufacturing stages. In particular, AFR systems offer capabilities for the identification in Computer-Aided Design (CAD) models of high-level geometrical entities, features that are semantically significant for manufacturing operations. However, the recognition performances ofmost ofthe existing AFR systems are limited to the requirements of specific manufacturing applications. This paper presents a new AFR method that facilitates the deployment of such systems in different application domains. In particular, the method provides a formal reasoning mechanism that combines the advantages of inductive and deductive techniques for feature recognition from Boundary Representation (B-Rep) part models. The proposed AFR method is implemented within a prototype feature recognition system and its capabilities are verified on a benchmarking part. Keywords: Feature recognition, CAD/CAM integration
1. Introduction
Market pressures force companies to reduce the lead time from the conceptual design of products to their serial production. In order to stay competitive, the companies also have to manufacture the products up to their technical specification at a minimum cost. Such market pressures have led to the development of concurrent engineering practices that require the design of products and processes to be integrated and carried out simultaneously. The realisation of a true integration between the product and process design stages is a challenging goal and it requires a consistent utilisation of product information at different levels of abstraction. To interface Computer-Aided Design (CAD) and Computer-Aided Manufacturing (CAM), the Boundary Representation (B-Rep) scheme is commonly preferred
to the Constructive Solid Geometry (CSG) scheme. In particular, CSG is non-unique as one solid object may have several different valid CSG representations. The geometrical data stored using such schemes cannot be utilised directly for process design because this data lacks high-level geometrical entities that are meaningful from a manufacturing point of view. To bridge this information gap between CAD and CAM, Automatic Feature Recognition (AFR) techniques are applied to identify geometrical entities, features in the CAD model, which are semantically significant in the context of specific downstream manufacturing activities. For the last twenty-five years, many AFR techniques have been proposed for recognising both simple and interacting features in CAD models. Such techniques implement approaches based on rules [ 1,2] or hints [3, 4] for example. However, the focus of the
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research efforts was on developing techniques for recognising features in the context of a particular manufacturing application. In order to develop more flexible AFR systems, their knowledge bases should be easily adaptable to changes in the application area and also extendable to cover other applications. In this context, the objective of this research is to develop a feature recognition method that employs knowledge acquisition techniques. To achieve this, the paper proposes a new AFR method that combines the 'learning from examples' concept with the rule-based and hint-based feature recognition approaches. In particular, the method includes two main processing stages, learning and feature recognition. During the learning stage, rules and feature hints are extracted from training data. Then, these hints and rule bases are utilised in the feature recognition stage to analyse B-Rep part models and identify their feature-based internal structure. The paper is organised as follows. Section 2 presents the learning process of the proposed AFR method. Section 3 describes the feature recognition process when it is applied to recognise both simple and interacting features. A prototype system implementing the proposed method is presented in Section 4 and its capabilities are verified on a benchmarking part. Finally, Section 5 presents the main conclusions ofthis research.
2. Learning process This process applies the knowledge acquisition techniques described in Dimov et al. [5] for generating feature recognition rules from training data. In particular, the learning process is composed of three consecutive sub-processes (Fig. 1): 9 Training data creation. This sub-process includes the design of B-Rep models that represent examples of simple features in accordance to a given feature taxonomy. Then, characteristic vectors are extracted from each model to code their topological and geometrical information. In this research, the feature concept proposed by Sakurai and Gossard [6] is adopted. A feature is defined as a single face or a set of contiguous faces, called a face set. Thus, two levels of abstraction are considered in extracting the characteristic vectors. In particular, the first level of abstraction represents feature faces as single entities. At the second level, the face set that defines a feature is analysed. As a result of this
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START) / A given featuretaxonomy/ Training data creation
] !
I Training set: the / characteristicvectors[ f single feature faces/
c Trainingset: the / haracteristic vectors of face sets
] Ruleformation ]
I ule ormation I
/ Firstsetofrules /
/ Secondset of rules/
] Automatichint definition] /A set of feature hints/
I
(END) Fig. 1. Learning process. process, two different data sets are created that represent features at the considered two levels of abstraction. Rule formation. In this sub-process, an inductive learning algorithm, RULES-5 [7], is applied on each of the two training sets of characteristic vectors to generate two sets of rules. These two rule bases define feature patterns found in the BRep models of feature examples. The rules generated from the training set that stores information about single feature faces are referred to as the first set of rules. The rules formed by applying RULES-5 on the training set composed of vectors coding information about face sets are referred to as the second set of rules. Automatic hint definition. Hints are extracted from the rules generated for every feature class present in a given taxonomy. Conceptually, a hint is an indication that a specific feature is present in a part model and also, it is an incomplete representation of a feature from an implementation point ofview. Therefore, the rules that define feature patterns at the first level of abstraction (partial geometrical representation of features) are utilised to define the hints. In particular, a statistical measure is employed to assess the importance of each condition in the first set of rules and thus to select those conditions that would be used as hints for the features classes of a given taxonomy.
3. Feature recognition process
START)
The rules together with the hints generated during the learning process are employed to recognise features in B-Rep part models. In particular, the proposed feature recognition method 'reconstructs' features in stages relying initially on an indicative information in the form of a hint. Fig. 2 shows the feature recognition process that includes the following four sub-processes: seed detection, leaf development, plant modification and feature validation.
/
3.1. Seed detection
] Plant modification ]
The aim of this sub-process is to detect individual faces in a B-Rep model that match the definition of feature hints. Every face identified in this way is an incomplete representation of a feature and is considered only a seed from which a feature might be constructed. It is possible for a face to match the descriptions of the hints of more than one feature class. This is due to the fact that a hint represents only a hypothesis for the presence of a feature in a part model. In particular, each hint in this research is only a partial definition of a given feature class. Therefore, a face could satisfy the conditions of more than one hint, hence this face could be considered a seed for more than one feature class.
3.2. Leaf development Once a seed has been identified, it is then utilised to build a face set that could form a feature. Such a face set is called a plant. This sub-process is performed by a geometric reasoning algorithm. In particular, using a seed as an input, this algorithm verifies whether some of the surrounding faces to a seed could be used to construct a plant. It is very important that the algorithm employed in this sub-process retrieves a face set that potentially represents a simple feature. Thus, it would be possible to use the rules that define patterns of simple features for the validation of this plant. Therefore, faces in a plant should be analysed in order to detect if they are affected by feature interactions. In particular, the classification of the possible types of feature interactions proposed by Gao and Shah [4] is adopted in this research for detecting faces that could be altered by such interactions. This classification defines six categories that reflect the following three topology variations caused by the interactions: merging offaces, loss of concave edges and splitting of faces. Table 1 describes these six types of interactions.
Seed detection
~
Featurehints /
/ Asetosees / ] Leafdevelopment I
/
Asetofplants
/
IlLearningprocess]l
1
A set of plants / Feature validation ~-//Second set of rule
/
1
i
i
Asetoffeatures /
Fig. 2. Feature recognition process The algorithm utilised in this sub-process stops when the face set under consideration is bounded by a closed sequence of concave or convex edges. For this reason, there are two possible outcomes when some of the faces in a plant are affected by interactions: 1. The algorithm could form a plant that does not include all faces belonging to a simple feature. This could result from interaction types II, III, V and VI because they lead to either the splitting of faces or the loss of concave edges. 2. The algorithm could retrieve a plant that includes faces belonging to different simple features. This could occur due to interaction types IV, V and VI because they result in face merging. Table 1 Classification of feature interactions (Gao and Shah [4]) Interaction type
Merged faces Lost concave Split face edges
I II III
IV
No No No Yes
No No Yes No
No Yes No No
V
Yes
Yes
No
VI
Yes
No
Yes
79
Thus, in the next sub-process, a plant is analysed in order to check whether it includes all relevant faces and also whether it is comprised only of faces defining a simple feature. If this is not the case, further processing is required to generate from a plant one or more different face sets that satisfy this condition. 3.3. Plant modification Faces in a plant are analysed in order to detect if they are affected by feature interactions. In particular, a geometric reasoning algorithm is employed to identify face attributes that could be associated with the considered interaction types. If this is the case for a given plant, its structure is then changed according to the interaction type detected. As a result, such a face set could be modified or split into more face sets that potentially represent simple features. 3.4. Feature validation The second set of rules, that includes patterns defining geometrical and topological relations between feature faces, is applied to validate the plants. One possible outcome of this validation could be that the face set defining a given plant satisfies all the conditions in a rule and thus, the feature constructed with the faces in this plant is considered recognised. This means that the plant meets all geometrical and topological constraints that are associated with a face set defining a valid feature of a given class. The other possible outcome could be that the plant is not validated by the rules. There are three possible reasons for this. The first is that each plant is only a potential feature, just a hypothesis constructed from a seed and thus, a plant may not always represent a valid feature. The second reason could be that the plant constitutes a valid feature but its class is not covered by a given taxonomy. Therefore, this taxonomy should be extended to cover such new features. Finally, it is possible that the plant constitutes a valid feature of one of the classes of a given taxonomy but, at the same time, the topological and geometrical configuration of this plant is not recognisable by the existing rules for that class. In such a case, the coverage of these rules should be extended. This could be achieved by adding a new feature model to the training set of the considered feature class. Then, by executing the learning process again, a new rule base could be automatically generated.
4. Implementation and testing A prototype system implementing the proposed AFR method was developed using the Java T M programming language. The primary objective of this system is to verify in one specific application domain that the performance of the method is similar to that of other methods. The system includes two modules, one for the learning process and one for the feature recognition process. Both modules use as input 3D CAD models in STEP format generated using the STEP Application Protocol 203 (AP203). The feature taxonomy adopted in this implementation is inspired by the classification of machining features proposed by Pham and Dimov [8] (see Table 2). This table also shows the B-Rep models that were used as an input to the learning module. The attributes included in the characteristic vectors at the first level of abstraction are given in Table 3. The first set of rules formed by applying the RULES-5 algorithm on the training set at this level of abstraction is shown in Table 4. The attributes selected in the characteristic vectors at the second level of abstraction together with the second set of rules are given in Dimov et al. [5]. The last step in the learning process resulted in extracting the feature hints (Table 5) from the first set of rules for each feature class in the adopted taxonomy. Table 2 Taxonomy of machining features Generic Featureclass feature type Depression Rectangular pocket (po_re)
~
Obround pocket (po_ob) ~ Blind hole (ho_bl) Through hole (ho_th) Through slot (slth) Non-through slot (sl_nt) ~ Step (st) Protrusion Circular protrusion
(prci)
Rectangular protrusion (prre)
80
B-Repfeature models
~
~
~
~
~
~ @ ?
~
@
~
@
Table 3 Attributes utilised at the first level of abstraction Attribute Symbol number faceTy nEd faceCv nP1Ad nC1Ad
Description
Table 5 Feature hints Feature class Condition ccAd = 3
A face whose ccAd measure equals three.
po_ob
nSccEd = 2
The face convexity: neutral (i.e. planar), concave or convex. The number of planar adjacent faces,
ho bl
ned = 2
hoth
nCcEd = 0
A face with two smooth concave edges. A face whose number of edges equals two. A face with no concave edge.
The number of cylindrical adjacent faces. The number of concave edges,
pr_ci pr_re sl th
nCvEd = 3 nCcEd = 2
The geometry of the surface defining a face. The number of edges for the face.
6
nCcEd
7
nCvEd
The number of convex edges,
8
nSccEd
The number of smooth concave edges.
9
nScvEd
The number of smooth convex edges,
sl_nt
l0
ccAd
The number of concave edges of the adjacent faces divided by the total number of such faces.
st
The feature recognition module was applied on the b e n c h m a r k i n g part shown in Fig. 3, w h i c h was used in similar studies b y other researchers [9]. This test part contains both simple and interacting features. The Table 4 First set of rules Rule Rule description IF ccAd=3 THEN featureClass = po_re IF 2<=nP1Ad<=4 AND nCvEd=l AND nSccEd=2 AND ccAd=2 THEN featureClass = po_ob IF nC1Ad=2 AND nCcEd=4 THEN featureClass = po_ob IF faceCv=cc AND nCcEd=0 THEN featureClass = ho th IF faceCv=cc AND nP1Ad=2 AND nCvEd=l THEN featureClass - ho bl IF nEd=2 AND nCcEd=2 THEN featureClass = ho bl IF nEd=2 AND nCcEd=0 THEN featureClass = pr_ci
11 12 13 14 15
IF nCcEd=l AND nCvEd-3 AND ccAd=2 THEN featureClass = pr_re IF nC1Ad=0 AND nCcEd=0 AND nSccEd=0 THEN featureClass = pr_re IF 2<=nCcEd<=3 AND ccAd=2 THEN featureClass = sl nt IF nEd=5 AND nCvEd=2 THEN featureClass = sl nt IF 5<=nEd<=6 AND 3<=nCvEd<=4 THEN featureClass = s l th IF faceCv=ne AND 2<=nPlAd<=4 AND l<=nCcEd<=2 AND ccAd=l THEN featureClass = sl th IF faceTy=pl AND l<=nP1Ad<=4 AND ccAd=0 THEN featureClass = st
faceCv = cv A convex face. nCvEd = 3
ccAd = 0
A face whose number of convex edges equals three. A face whose number of convex edges equals three. A face with two concave edges. A face whose ccAd measure equals zero.
STEP file o f this part was d o w n l o a d e d from the National D e s i g n R e p o s i t o r y [ 10]. H o w e v e r , this part was redesigned with respect to its original g e o m e t r y in order to generate a more up-to-date version o f this STEP file. The c o r r e s p o n d i n g face sets o f six features present in this part were retrieved and validated successfully b y the system. These features are the steps and the through holes that do not interact and also the step and the t h r o u g h slot affected b y interaction types I and III, respectively. For the other six features that were not recognised, the plants for five o f them were constructed successfully but t h e y were not validated as features. O n l y for one feature, the through slot with the missing face, its c o r r e s p o n d i n g plant was not retrieved at all. The p r o p o s e d A F R m e t h o d did not recognise these features because:
9
A plant is not covered by the rule set. This can be easily addressed b y adding examples representing such features in the training set.
IF' faceCv=cv THEN featureClass = pr_ci
10
Description
po_re
9
The faces affected by interactions are not identified. To address this issue, it is n e c e s s a r y to extend the range o f considered interaction types. The p r o p o s e d A F R system has an open architecture and could easily a c c o m m o d a t e additional cases o f feature interactions.
!~'.... i~~ ' - - 2 : : ; ..... Fig. 3. Test part.
81
5. Conclusions In this paper, a new AFR method is proposed. The distinguishing characteristics of this method are as follows: 9 The method applies knowledge acquisition techniques for generating feature recognition rules and feature hints automatically. This is a major advantage of the proposed method in comparison with other rule-based and hint-based AFR methods. 9 The feature recognition process employs sequentially, a set of hints, a set of rules and two geometric reasoning algorithms to construct valid features from the geometrical and topological information stored in B-Rep part models. 9 The proposed AFR method has built-in capabilities for recognising interacting features. A geometric reasoning mechanism is developed for detecting faces that could be affected by interactions. Then, depending on the interaction type detected, a face set is modified or split into more face sets that potentially could represent simple features. A prototype system was developed to verify the capabilities of the proposed AFR method to recognise simple and interacting features for one possible application domain. The recognition results obtained on a benchmarking part are similar to those achieved by applying other methods. However, the implementation of the proposed AFR method offers several advantages: 9 The knowledge base that determines the performance of the AFR systems could be easily updated to reflect changes in the application areas or to broaden it. In particular, the utilisation of an inductive learning algorithm during the learning process elevates the knowledge acquisition issues associated with the development of such systems. The application of the 'learning from examples' concept provides a formal and at the same time an automatic mechanism for refining existing rules or defining new rules. This brings significant time savings in developing AFR systems and also increases their robustness due to the consistency of the rule bases throughout the system lifecycle. 9 The method could be deployed easily in different application domains. This requires only representative training sets to be constructed for each of them. For this, the attributes used to code the B-Rep models of feature examples will differ to reflect the specific feature taxonomy of the new
82
application domain.
Acknowledgements The authors would like to thank the European Commission, the Welsh Assembly Government and the UK Engineering and Physical Sciences Research Council for funding this research under the ERDF Programme 52718 "Support Innovative Product Engineering and Responsive Manufacture" and the EPSRC Programme "The Cardiff Innovative Manufacturing Research Centre". Also, this work was carried out within the framework of the EC Networks of Excellence "Innovative Production Machines and Systems (I'PROMS)" and "Multi-Material Micro Manufacture: Technologies and Applications (4M)".
References [ 1] Henderson MR and Anderson DC. Computer recognition and extraction of form features: a CAD/CAM link. Comp. in Industry, 5(4), (1984), pp 329-339. [2] Dong J and Vijayan S. Feature extraction with the consideration of manufacturing processes. Int. J. Prod. Res. 35(8), (1997), pp 2135-2155. [3] Vandenbrande JH and Requicha AAG. Spatial reasoning for the automatic recognition of machinable features in solid models. IEEE Trans. Pattern Analysis and Machine Intelligence, 15(12), (1993), pp 1269-1285. [4] Gao S and Shah JJ. Automatic recognition of interacting machining features based on minimal condition subgraph. CAD, 30(9), (1998), pp 727-739. [5] Dimov SS, Brousseau EB and Setchi RM, Automatic formation of rules for feature recognition in solid models. 1st I'PROMS Virtual Int. Conf., (2005), pp 4954. [6] Sakurai H and Gossard DC. Recognizing shape features in solid models. IEEE Comp. Graph. & App., 10(5), (1990), pp 22-32. [7] Pham DT, Bigot S and Dimov SS. Rules-5: a rule induction algorithm for classification problems involving continuous attributes. Proc. ImechE, Part C: J. Mech. Eng. Science, 217(C12), (2003), pp 1273-1286. [8] Pham DT and Dimov SS. An approach to concurrent engineering. Proc. IMechE, Part B: J. Eng. Man., 212(B1), (1998), pp 13-27. [9] Gupta SK, Kramer TR, Nau DS, Regli WC and Zhang G. Building MRSEV models for CAM applications. Adv. Eng. Soft., 20(2-3), (1994), pp 121-139. [10]National Design Repository. Available from www.designrepository.org (accessed 24th April 2006).
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Machining of large dies based on the prediction of the press/die deformation D. del Pozo a, L.N.L6pez de Lacalle b, J.M. L6pez a, A.Hernfindez b aRobotiker Technology Center, Parque Tecnol6gico, Edificio 202, 48170 Zamudio, Spain,
[email protected] b University of the Basque Country, UPV/EHU. Faculty of Engineering, Department of Mechanical Engineering, Alameda de Urquo'o s/n, C.P. 48013, Bilbao, Spain.
Abstract
In this work a methodological scheme for a reduction of both the try-out and lead-time of complex dies is presented. The finite element simulation of the tool behaviour along the stamping process results in criteria for the best design of high-cost dies/punches. Using it, modifications of the main geometry, components and functional parameters are recommended. Examples are deeply explained as experimental evidences. Keywords: Stamping, Try-out, Bending, Dies, Simulation
1. Introduction
The problem analysed in this work is the extra deformation that appears in the die, which is necessary to polish manually, during the try-out phase. It is also necessary to perform this manual operation in the large presses for the final process. In this work the structural behaviour of the tool and the press is considered, and a methodology for their calculation is developed. Sheet metal forming is an important production process based on deep experience and involves several trial and error loops. Tekkaya [1], analyses the industrial requirements for simulating sheet metal forming, the various approaches in Finite Element methodologies, that is, element types, and commercial programs for sheet metal simulations, including expensive commercial software packages used by industry in the simulation of sheet metal forming processes. Nowadays the main software packages have specially oriented tools for die makers and automotive.
In these software packages the forming process with rigid dies is analysed, but the deformation of the dies and the forming presses are not taken into account [ 1]. To date, much of the research using FEM applied to the analysis of metal deformation has focused on the simulation of material flow sequence; however the die is assumed to be perfectly rigid to enable the computation [2]. Chodnikiewicz et al. [3] expressed a method and measurement system for the calibration of metalforming presses, since practitioners of metal-forming operations know that the necessary force to produce the parts additionally results in a deformation in the press; although this is relatively small compared to the size of the machine, it is bigger than the allowed tolerance for the final part. Jian Cao et al. [4] proposed another approach for sheet metal forming, by means of design and control of variable binder force for the next generation of stamping dies [5]. Rosochowski [6] proposed a procedure for taking into account the die
83
deflection and part springback, mainly in net-shape forming processes. R. Lingbeek et al. [7] proposed a method for springback compensation in the tools for sheet metal products, concluding that for industrial deep drawing products the accuracy of the results has not yet reached an acceptable level. Altan et al. [8] recommended procedures for obtaining accurate and reliable results from FE simulations for process simulation in stamping. Hernfindez et al. [9] proposed an approach combining the use of numerical simulation with the design-aid system CAETROK. Some experimental tests carried out in three local companies have verified that the tools deformation due to these effects can reach the size of tenths of millimetre. A process for producing mold is presented in the patent [ 10], considering the press deformation in an empirical way, with range of values of millimetres. In this work, a global methodology in order to take into account the structural behaviour of the tools in the mechanical group press-die is presented, assuming that both components are not rigid solids, and that the deformation of the press, although small, is in the order of magnitude of the manufactured part tolerance. This methodology is also tested with a sample in an industrial application, where the calculated deformation is proposed to machine the die surface. The patent [ 11] basically develops the procedure. This method has been proposed as a utility for the try-out of dies by die-makers [12]. Section 2 describes the experimental evidences. The methodology is developed in section 3. In section 4 an industrial example of application is shown. Section 5 explains how this deformation affects the machining of the dies. Finally the conclusions are explained in section 6.
2. Experimental evidences Due to the recent techniques for high speed machining (HSM), it is possible to reach a great precision on the manufacturing of dies. Nevertheless, due to the big loads in the stamping process (400-1500 tons), the deflection of dies is suffered during the bending process, but it is not taken into account. A part of that deflection results from the deformation experienced by the die material as a consequence of its characteristics; however, most of the deformation is due to the die structure and to its lack of rigidity. The combination of both factors is what defines the total deflection of the die.
84
Table 1 Variation of parameters for die design Die parameter / Operation hypothesis Diameter of the fasten screws Application form of the load Height of the punch Increase the width of the punch
...........................................................................................................................................................................................................................
...........................................................................................................................................................................................................................
...........................................................................................................................................................................................................................
...........................................................................................................................................................................................................................
Number of ribs in the inferior base die Ribs in lateral walls of the die Reinforcements in punch Curve forms in lateral walls
...........................................................................................................................................................................................................................
...........................................................................................................................................................................................................................
...........................................................................................................................................................................................................................
The magnitude of deformation experienced by the die is considerable. Therefore, it is necessary to proceed to manual operations with the purpose of modifying the shape of the die surface in order to compensate the deflection. This process of manual work-in-bench requires specialized manpower, resulting in higher prices for the die.
2.1. Preliminary studies In preliminary studies, the variation of parameters in die design has been considered to obtain useful design criteria, which allows the design of dies with maximum rigidity and minimum deformation from the initial conceptual design phases (see Table 1). mm.
0.t78
-1.121
-2.900
Fig. 1. 3D general view. Preliminary results. Results, shown in figure 1 and 2 are in the range of millimetre (see Fig. 1, Fig. 2).
mnl. 0.178
I 3D CAD model of draw die I I
3D CAD model of simplifted press
FE model of mechanical group die-press ! I Static structural analysis based on" I -1.121
FE model of applied 3 force, fixtures and support of press
3D Mesh of mechanical 2 group die-press
-2.900
Fig. 2. 3D bottom view. Preliminary results. 2.2. Deformation in press
The protocols of press geometric verifications used for sheet metal forming are: the parallelism between bolster and slider, the looseness in the guides of the slider, the deflection of bolster. This latter measuring protocol for a hydraulic press of 1200 tons, reaching to 75% of the load, gives values of 0.85 mm. This problem can be compared to the flexion of a doubly supported beam under the pressure of a load. This value has been calculated by the finite element (FE) method (see Fig. 3).
~:~f:
0~85 mmi:~th 900 Tons
Fig. 3. Doubly supported beam: press deformation.
3. Simulation methodology The proposed methodology assumes that, just been defined and validated the process for obtaining the desired stamped part, that is the method-plan, for the surface of the draw die, that is, the whole method-plan has been correctly done with good shape for all the elements: the punch, the die and the blankholder (see Fig. 4).
g Material Characteristic of die and press components" elastic, not rigid body
FE model of contact 4 elements between die and press Material
Solver calculations
6
Results: Deformation map7 of contact surface between die and punch
Deformation map of contact surface and CAD geometry of the die" corrected geometry
3; Machining of die surface 9 based on deformation map.
Fig. 4. Methodology. As initial point, the design of3D geometry of the whole die is performed: die, punch and blankholder, in a CAD model (CATIA V5, Ideas), positioned according to their operation. The 3D geometry of the simplified press is also performed in a CAD modeller. It includes bolster and slider. Based in the CAD geometry, a FE model is created, following the next steps: 1. The static structural analysis. 2.3D meshes of the total components of the mechanical group die-press: die, punch, blankholder, bolster and slider. 3. Applied force by the press and position, and supporting (fixture). 4. Definition of contact elements between: bolsterdie, die-punch, and punch-slider, based in 2D meshes of the contact surfaces. 5. Material characteristics of die and press components for FE model: modulus of elasticity, Poisson's ratio, density, etc. 6. The used solver for the computation and results was Ideas TMSolver; the stress, strain and deformations of the complete group are obtained.
85
7. By postprocesing the deformation results in the contacting surface between die and punch, the predicted deformation map of die surface is obtained. 8. By applying the deformation map of contacting surface to the CAD geometry of the die, the corrected geometry is obtained. 9. This deformation map will be applied to the machining process of the initial surface of the die, based on the die-press deformation.
4. Application of the methodology to an industrial case
Fi~. 6. 3D-down (bottom view) ~eometrv CAD of die. small deformation, but the central part of the group shows a big deformation, the contacting surface
Fig. 5. 3D-up (general view) geometry CAD of die. In order to validate the methodology this section shows an industrial sample case according to the previously described steps. The press was a hydraulic one with a nominal force of 1200 tons. The dimension of the bolster, 5000x2500 mm. The material was ASTM60 [ 13]. The applied force in the process, based in the plan-method, was: punch 500Tn, and holding 120Tn. The contacting surface (die-punch) was 0.84 m 2 (1275x850mm). 3D CAD model of the geometry ofthe die includes all the elements: ribs, holes, exact surface of the part modelled with CATIA V5 in solids (see Fig. 5, Fig. 6). The complete 3D mesh of the mechanical group is Ideas Master Modeler, includes the holes and ribs made in the die, punch and blankholder in their operational position (see Fig. 7). Once the analysis is completed the comparison of the deformation in the press can be seen: bolster and slider; and draw die components: die and punch. The upper part of the die (in this case the punch) shows a
~"~
.................: ...........~,i . ~:.....: , i ; ~'~'~'~+'~-::
9~
Fig. 7. Complete 3D mesh model.
Fi~. 8. Comparison of tools deformations.
86
~
.~:..~.~-~ < ~ z ~ :,:~..~ ~.,.:.,.~>
.......
,~.~. . . .
~ .:.~
:,~. . . . . . . . . . . . . .
..~ : ~ , , ~ . ,
deformation by machining, since generally the punch surface is machined reproducing exactly the manufactured part, and the die surface is machined to an offset equal to the thickness of the blank. The diepunch behaviour, due to the flexion of press, shows that the punch opposing to the close of the die (see Fig. 11). At the moment, the only way to avoid this problem of lack of matching is by manual polishing. This is why procedure is so labour intensive and expensive.
Fig. 9. Deformation of contacting surface and behaviour. between die and punch (see Fig. 8). In addition, the analysis results show the deformation of the contacting surface between die and punch and its behaviour during process: front, top and isometric view (see Fig. 9). ================================================ DISPLnCEMENT .~o:
0.6o4
.,~
Cagn~tudm u .......ged ..~:
,~.~1~
~,o
::=:
Top ~he11
Fig. 11. Die-punch behaviour. Some measurements can be performed on the diepunch, after the part is successfully obtained. In this way the distance between die and punch (part thickness plus tolerance) in its operating position can be obtained, this distance is called "deflection" (see Fig. 12). The deflection between die-punch shows that, although the part is symmetrical, its values are not constant and they are asymmetrical. This is due to the ribs structure applied to the die-punch in order to lightweight.
Fig. 10. Contacting surface deformation (z direction). Finally the deformations map shows that these deformations are not equals or proportional in the contacting surface. They depend on the location of ribs. The value range is in term of tenths of millimetre, 0.3 mm for this industrial case, calculated as the difference between the maximum (0.9 mm) and the minimum (0.6 mm) deformation in the contacting surface (see Fig. 10).
5 Machining The idea is to eliminate the previously explained
Fig. 12. Measurements of deflection between die-punch.
6. Conclusions A methodology for the machining of large dies
87
Table 2 Chart Load-Deformation.
References
Press Load (Tm)
Max Deformation z (mm)
100 200
0.053 0.105
300 400 500 600 700 800 900 1000
0.157 0.210 0.262 0.313 0.369 0.414 0.470 0.530
based on the prediction of the pres/die deformation has been presented. The methodology considers the press deformation due to the effect of the large force applied during the process and the components of the draw dies considered as not rigid bodies. The value of predicted deformation is in the range of tenths of millimetre, similar to the range of tolerance of the part to be produced by the dies. The deformation of press can be simulated and calculated by FE method, verifying the protocol of geometric arrow of bolster. The value of the total deformation depends directly on the force for the stamping process. Given a predefined press, a chart showing the relation between the force and the deformation, with a maximum deformation value can be expressed assuming: symmetrical distribution of ribs, relation of holes constant, the dimensions of holes and thickness of ribs are homogeneous, the sizes of dies are similar (see Table 2). Anyway, the value is the maximum, and it does not spread equally over the contacting surface. The methodology also considers the asymmetries in the die structure (ribs, holes, etc.).
Acknowledgements Thanks are addressed to the ROBOTIKER Technological Aula in the Faculty of Engineering in the University of the Basque Country for its support.
88
[ 1] Tekkaya A.. State-of-the-art of simulation of sheet metal forming. Journal of Materials Processing Technology, Volume 103, Issue 1, (2000) 14-22. [2] Balendra R., Qin Y. and Lu X. Analysis, evaluation and compensation of component-errors in the nett-forming of engineering components. Journal of Materials Processing Technology, Volume 106, Issues 1-3, 31 (2000) 204211. [3] Chodnikiewicz K. and Balendra R. The calibration of metal-forming presses. Journal of Materials Processing Technology, Volume 106, Issues 1-3, 31 (2000) 28-33. [4] Cao J., Kinsey B., Yao H., Viswanathan V. and Song N. Next generation stamping dies-controllability and flexibility. Robotics and Computer-Integrated Manufacturing, Volume 17, Issues 1-2, (2001) 49-56. [5] Martinez F. Optimisation du serre flan de son pilotage pour une amelioration de l'ecoulement du materiau en emboutissage. D. Phil. Thesis, Universit6 de Nantes, France, 2004. [6] Rosochowski A. Die compensation procedure to negate die deflection and component springback. Journal of Materials Processing Technology, Volume 115, Issue 2, (2001) 187-191. [7] Lingbeek R., Hu6tink J., Ohnimus S., Petzoldt M. and Weiher J. The development of a finite elements based springback compensation tool for sheet metal products. Journal of Materials Processing Technology, Volume 169, Issue 1, (2005) 115-125. [8] W. Thomas, T. Oenoki, T. Altan. Process simulation in stamping- recent applications for product and process design. Journal of Materials Processing Technology 98 (2000) 232-243. [9] Hemfindez A., Vallejo J., Canales J., Albizuri J. Stamping die design based on numerical simulation and knowledge systematisation. Internationa Journal of Computer Integrated Manufacturing (1999) 427-438. [10] Akatsu J. Process for producing mold, EP 0 417 311. 20-3-1991. [11] Del Pozo D. and L6pez JM. Procedimiento de fabricaci6n de troqueles, solicitud de PCT/ES02/00230. 14-5-2002. [12] Del Pozo D. and Renterfa A. Herramientas para la puesta a punto en la fabricaci6n de troqueles. IMHE Issue 285/286 (2003) 139-142. [ 13] Society of Manufacturing Engineers, Die Design Hand Book (third edition) Michigan 1990 (ISBN N~0-87263375-6) 28-20.
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
A Model-Based Graphical User-Interface for Process Control Systems in Manufacturing X.J. Li ~'b, T. Schlegel ~, M. Rotard b, T.
Ertl b
a Fraunhofer Institute for Industrial Engineering, Nobelstr. 12, 70569 Stuttgart, Germany b Institute for Visualization and Interactive Systems, University of Stuttgart, Universitaetsstr. 38, 70569 Stuttgart, Germany
Abstract
The communication of humans with manufacturing processes - respectively their representation in software is an integrated cooperation. It includes information acquisition via sensors, information processing by control systems and feedback information. This supports control personnel in supervising and operating the processes using information captured in real time. This paper proposes an approach of building an innovative graphical user-interface for intelligent process control systems based on the analysis of various requirements for process control of today's manufacturing. Information and presentation models are used for transforming real-time production data into a dynamic and easy-to-use graphical user interface using XML technology.
-
Keywords:
INT-MANUS, process control, presentation model, XML, transformation, user interface
1. I n t r o d u c t i o n
The manufacturing systems of today consist of various processes from planning to transport. Technical production processes are initialized by the human and accordingly must be monitored and controlled by the human. The human-process communication of manufacturing is an integrated cooperation that works together with information acquisition via sensors, information processing by control system and information feedback which supports control personnel to manipulate the processes with the captured information in real time [1 ]. Today's manufacturing requires real-time monitoring and control of the production process through innovative mechatronics. The EU project INTMANUS (Intelligent Networked Manufacturing System) addresses these problems by developing a
Smart-Connected-Control (SCC) Platform that will allow decentralized control of the production process in a new way. One important part of such an SCC platform is a user-friendly interface that visualizes and interacts with complex data of production processes. The user interface will be embedded into a display wall [2] that enables the supervisors to monitor the real-time process values and on their PDAs handled by the control personnel [3]. This paper proposes an approach of building an innovative graphical user-interface for intelligent process control systems based on the analysis of various requirements for process control of modern manufacturing. In order to distinctly model the functions of a user-interface the tasks of this work are divided into the information model [4] and the presentation model. In the information model, the process values are modeled according to data structures and data
89
types. The presentation model describes the graphical objects that present the data to the user in various manners. The bridge to connect the two function models are interactive elements. The objective of this work is to build information and presentation models which are used for transforming real-time production data into a dynamic and easy-to-use graphical user interface using XML technology.
2. Requirements Analysis of process control in different problem domains The analysis of requirements for a process control system is the foundation of building models for human-machine interfaces. The restriction of the requirements is the basis of choosing graphical user interface technologies. Process control engineering encompasses all technical means that assist humans to control a process according to previously captured requirements. In automation systems the processes are controlled by monitoring the process values and allow full access to measurement and configuration of execution data. In this work three problem domains in process control systems are discussed: realtime monitoring and control, statistical process control, and phase model of production processes. 2.1 Real-time monitoring and control One of the important functions of a user-interface for process control systems is to display current process information in real-time because the control personnel must acquire the process values for process management. General information on a production process, such as the order of the products, the model of the target product, the number of workers, etc. is essential information required by all production processes. Besides this general information the real-time process values include also the inner information of partial processes and atom elements, for example, the running state and controllability of a machine. For reliable process control the ability of response to exceptions and errors is important and necessary in case a value exceeds or falls below a limit. In order to avoid dangerous errors the alert range of a value should be considered by defining the data type of a value so that the control person can be warned when a value approaches the threshold value. Table 1 shows an example for a complex data type "machine temperature", which has a valid range
90
from 18~ to 27~ Lower than 15~ and higher than 32~ the alert will be started. Table 1 An example of complex data type - Temperature Unit Control Max Warning Min Warning Current Max Min Data Type string bool Example
~
false
float
float
float
float
float
32
27
15
18
25
2.2 Statistical process control For the quality control of a manufacturing system not only the current process values but also the trend and history or statistics of the values are of interest to the control personnel. In other words, what is happing currently and what has happened previously should be analyzed by the system. In the database for a production process the process values during a certain time should be stored in an archive. Normally the history and trend of a process are displayed in a diagram or a chart with an axis for value and an axis for time. Diagram representations for engineering data like histograms, check sheets, scatter diagrams, and control charts and more ways to visualize the process values are described and illustrated in [5]. 2.3 Phase Model A manufacturing process system can be described in different models. The model for the workflow of a process is called phase model [6]. Here, a process can be decomposed into more process elements and each process element can again be hierarchically composed of other process elements. A process can exist individually or serve as successor or predecessor of another process. A simple process contains process elements, input product as well as semi-finished and end products from process elements and other output elements. Fig.1 shows a simple phase model of the process for lacquering the door of car. The door of car is an output product produced from other processes and acts in this process as input product. The first process element (Mixing) accomplishes mixing of the raw materials. The output product from this process element works together with another input product (door) through the second process element (Lacquering).
I~acquer of two coiours
Car's door finished from other process
Mixing Mixed lacquer
La,
Fig. l. An example of phase model
3. Information models of process control systems Manufacturing a process is an integration of products, machines, and process elements. We attempted to model our user-interface for manufacturing systems step by step from simplicity to complexity. At first the models for the simplest entities such as products and machines are built with their attributes, each entity must have a unique identifier in the whole production process in order that it can be easily referenced by the other models without confusion. Based on the models of all entities it should be considered how the entities are combined for a complex function. Fig. 2 shows an entity model of a machine. In this work, the information model is built on Product Model, Machine Model and Process Model that will be explained later.
will lead to confused operations and false control. Therefore, the uniqueness of all components in the control system is necessary. In the information models one identifier indicates a unique component such as product and machine with specific functions. Product model A product model can be described in a class. For each special product an instance of this class or model is created. One product model can correspond to more than one product with variable parameters which are distinguished by unique ids. Because each id corresponds to one product for a certain usage the control person can monitor the states of all products by choosing the product id. Machine Model To model a tool machine the configuration and state information of the machine should be considered. The configuration of a machine is determined by the usage and functions of the machine.
t
EE] attributes [ ................
i
Machine_Group
id
controllable
i~il !iii
i ,iiii s HP,o.u- o.,
.......
Pro"u '
i
Fig. 3. Model of machine group in XML Schema
Fig. 2. An entity model of a machine
Identifier. In order to let a process control system run trouble-flee it must be avoided that a component of the system has different meanings or a notation corresponds to more than one component. In this case the ambiguous relationship of system components
For different functions the information is divided into two blocks, the inner information and the external information. The inner information of a machine exists free of context, that is, the information is independent of other machines and the position in a process. The inner information is mostly defined by the machine manufacturer, limited by sensors and specification. The external information of a machine is context sensitive that depends on cooperating machines and the function of the machine in a process. The external information determines the logical relationship between the machines and other cooperative compositions. In practical production processes it is possible
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that some machines are designed for multiple functions or more than one machine is used for one task/purpose. Such machines with complex functions are built based on the model of single machine with single function. Fig.3 shows a model of a machine group that is built with referenced models of single machine and products. Process model. A process element model can be built by combining the models of product and machines with additional process information. A process consists of process elements and products. These are connected according to their functions and their positions in the p r o c e s s - referenced to their unique identifiers.
4. Modeling with X M L Schema In this work we adopted XML Schema to build the information model by defining the data of process values with their data types in XML Schema which can be treated as a meta-model for the farther modeling. In XML Schema an arbitrary data type can be easily defined and referenced anytime when required. Besides, the XML Schema diagram enables the developer to have an entire concept of the model. Step 1: Building models from atom elements. Each atom element is defined in a separate model. Complex data types are defined by combination of simple data types and other complex data types. Step 2: Associating models of atom elements. The process model is built stepwise by associating product models, machine models and process element model. Each atom model can be referenced repeatedly. Step 3: Application-specific models. Based on the models defined in XML Schema the data of process values can be specified in XML format corresponding to different tasks.
5. Presentation Model The presentation model describes which graphical elements will be presented to the users. According to the functions of a user interface for process control we divided the presentation model into two major parts: navigation model and process information display. In the navigation window the graphical navigation flow chart, in which each process element is represented by an icon, is used to visualize the production phase for quick switch between processes [7]. The sequence and relationship of components in the flow chart should correspond to the real process
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phase model. In the information window various minor user interface elements such as process bar, control slider, diagram, etc. are employed to display the process information and receive user commands. The selection of suitable minor user interface elements is driven by the information models described in the previous sections.
6. Generation of User Interface based on Models
6.1 Data Transformation To find a consistent and appropriate representation for information elements, it must be considered how the data can be transformed into a presentation object. The concept of the two-block function model [8] is adopted for transforming data elements to graphical elements. With the two-block function model the task of a user-interface is divided into two blocks, one is the information model and the other is the presentation model, both are already described in the previous sections. The bridge to connect the two function model is interactive functions that determine how the data is transformed into graphical elements. On
Off ~ J ~ Data Acquisition [ [Interactive [ [Translation of [ [El . . . . t s ~ . U s e r C . . . . . ds I
'
'
I Server
2000 U/min
Data Acquisition Interactive I | Elements ~___.~Translation of [ I User Commands n
Fig. 4. Transformation of data and user commands After the instances of the information model have been created, the data types of the concrete process values influence directly their representation by the presentation objects. Interactive elements play an important role as management of data model and presentation objects. The data and the commands from users are translated by the interactive elements such that suitable presentation objects in the presentation model can be chosen. Fig. 4 shows the approach of transforming information structures and data into presentation objects. The interactive elements translate the process values that have been acquired from a central server and user commands via user interface. The suitable presentation objects are chosen according to the data types and user commands. For example, if the value of machine running state is defined as "controllable",
a controllable presentation object such as a switch should be chosen.
6.2 Generation of dynamic User Interface .for realtime controlling In our scenario, we have identified a two-dimensional graphical presentation as the best suited user interface form for a process control system, especially a control centre. In this work we declare the method of transformation with web application and SVG presentation which will be later explained in the following section. Requirements Analvsi~ Schema Specialization Frame
I
Machine l-)ata
I I
x~x~L I Js;Ja,',~ [ svr
D........... t] XML Pars~ I
DOM
I-q
svG
I
Rep ...... . . . . . tation[
Fig. 5. Workflow of generation of user interface The approach of generating a user interface based on the information models is illustrated in Fig. 5. As previously declared the information models are built at first in XML Schema and then specialized according to various applications. The data of process values, such as the state of a machine, is acquired via sensors and filled in the corresponding terms of the XML Frame that constructs a complete XML document. At the next step, the needed data can be read from the XML document in JavaScript or Java using an XML parser. According to the data types and values the corresponding SVG presentation objects will be selected from predefined templates and created with help of SVG DOM mechanisms [12]. As required a refresh time can be set in JavaScript that enables the XML document to be parsed at regular intervals, at the time, the presentation objects are changed dynamically to represent always the actual state of the processes. 7. Technologies of SVG user interface
In our case, the application control will be done using a web interface, so we have chosen SVG [1 l, 12] as the target for the user interface generation. This vector-based representation has the advantage of being an XML format and allowing for lossless
zooming, needed when scaling the control centre interface to a PDA-based version with conformity to user e x p e c t a t i o n - using the same elements. The specification SVG Tiny provides a solution for the generation of user interfaces consisting of SVG components for the PDAs and other mobile devices. The DOM mechanism of SVG enables full access to data and various event handlers. For all purposes in a process control system, the interfaces can be generated for different platforms. For desktop-oriented graphical interfaces the Batik SVG Toolkit [13], a Java based toolkit could be used. For web interfaces today's web browsers can render SVG natively or need a special plug-in. The XML code in Fig.6 describes an instance of the temperature model. Because the XML elements are stored a DO M tree structure by an XML parser, all elements can be traversed. First the element "temperature" is found and the value of the node attribute "controllable" is read for determining whether the e l e m e n t - here t e m p e r a t u r e - is controllable or not. If "controllable" is false, the predefined design pattern for a non-controllable (noninteractive) temperature element is called from resource. Similarly, the values of children nodes "MAX", "M1N" and "Actual" are interpreted and assigned to corresponding parameters. Aat Instance of Temperature Model
18
Actual: 18 Pres:
"c i ~ "~ i 4o ~o ;
t ~2
1200
Fig. 6. Presentation object from XML document Another method for transforming the information model into presentation objects for a web-based interface is using XSL Transformation (XSLT) [9]. XSL Transformations is a template language expressed in XML syntax. XSL was developed to add style to an XML document and XSLT was designed to be more general and to allow the transformation of documents into any documents of XML type like XHTML or and Scalable Vector Graphics (SVG) [10]. With different XSL Transformation an information model can be transformed to user-interfaces for different platforms and specific users.
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References: 8. Conclusions [1] Building a dynamic user-interface for production control centers it is important to consider that the essential of process control engineering is information exchange and visualization as well as the communication between human controllers and the production system. The basis for a model-based, dynamic user-interface is therefore the information models. To build an information infrastructure we chose to define the data of process values with their data types in XML Schema. Based on this XML Schema document, the defined models with adjacent data can be specialized in XML format corresponding to different tasks. The models built in this work prove that XML Schema can effectively define a metamodel of information structures even with complex data types. In our approach the generic information model plays the role as a meta-model consisting of a set of first-class modeling artifacts allowing for keeping it small and invariant. Compared to the metamodel the application-specific models are the lower layer of abstraction, they do not introduce new artifacts but are depicted using the meta-model artifacts. In order to better model and manage the user interface for information exchange, the functions of data exchange through user interface are separated into data acquisition and data representation. Both parts can be modeled individually and connected by interactive elements. The separation of functions benefits code separation and code reuse. Presentation models rely on the previous built information models, according to specific task only limited presentation objects are required. The data types in the metamodel give us an early perception of which presentation objects may be possibly used.
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10] Acknowledgments This work is related to INT-MANUS project, funded by the European Commission. Fraunhofer IAO is a member of I'PROMS network of excellence, supported by the European Commission.
[11]
[12] [13]
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Peters, B.; Epple, U.: A generic object model to build the human-process interface. 6 th IFAC Symposium on Automated Systems Based on Human Skill, September 1997. Wallace, G.: Tools and Applications for LargeScale Display Walls, IEEE Computer Graphics and Applications, vol. 25, no. 4, pp. 24-33, August 2005. Farella, E.; Brunelli, D.; Bonfigli, M. E.; Benini, L.; Gaiani, M.; Ricco, B.: Using Palmtop Computers and Immersive Virtual Reality for Cooperative archeological analysis: the Appian Way case study, In International Conference on Virtual Systems and Multimedia (VSMM) Gyeongju, Korea, 2002. Albrecht, H.: On Meta-Modeling for Communication in Operational Process Control Engineering, VDI Verlag, 2002. Engineering Statistics online Handbook http ://www.itl.nist.gov/div898/handbook/, last update, September 2005. Lauber, J.: Methode zur funktionalen Beschreibung und Analyse von Produktionsprozessen als Basis zur Realisierung leittechnischer L6sungen, Verlag Mainz, 1996. Constantine, L.L.; Lockwood, L.A.D.: Software for Use: A Practical Guide to the Models and Methods of Usage-Centered Design, AddisonWesley Professional, 1999. Peters, B.; Epple, U.: The Two-Block-Model to communicate with processes, 7 th IFAC Symposium on Automated Systems Based on Human Skill, June 2000. XSL Transformations (XSLT) Version 1.0 Specification, W3C Recommendation, November 1999. Froumentin, M.; Hardy, V.: Using XSLT and SVG together: a survey of case studies, SVG Open Conference 2002, July 2002. Fettes, A.; Mansfield, P.: SVG-Based User Interface Framework, SVG Open Conference 2004, September 2004. Scalable Vector Graphics (SVG) 1.1 Specification, W3C Recommendation, January 2003. The Apache Software Foundation: Batik SVG Toolkit, http://xmlgraphics.apache.org/batik/, May 2006.
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All rights reserved.
Product Lifecycle Management and Information Tracking using Smart Embedded Systems applied to Machine Tools Fabrizio M e o a
D a n i e l e Panarese a
Fidia S.p.A., Corso Lombardia 11, 10099 San Mauro Torinese, Italy
Abstract
This project is developing appropriate technology, aiming at enabling the exploitation of the seamless flow, tracing and updating of information about a product along its lifecycle: from the delivery to the customer up to its final destiny (deregistration, decommissioning) and back to the designer and producer. This technology includes product lifecycle models, Product Embedded Information Devices with associated firmware and software components, tools for decision making based on data gathered through a product lifecycle. The application to machine tools is foreseen in the field of condition-based predictive maintenance and traceability of components. K e y w o r d s : Product Lifecycle, Maintenance, RFID
1. I n t r o d u c t i o n
The project "Product Lifecycle Management and Information Tracking using Smart Embedded Systems" (PROMISE) is developing appropriate technology, including product lifecycle models, Product Embedded Information Devices with associated firmware and software components, and tools for decision making based on data gathered through a product lifecycle. This is done to enable and exploit the seamless flow, tracing and updating of information about a product, after its delivery to the customer and up to its final destiny (deregistration, decommissioning) and back to the designer and producer. The breakthrough, in the long term, is to allow information flow management to go beyond the customer, to close the product lifecycle information loops, and to enable the seamless e-Transformation of Product Lifecycle Information to Knowledge. The
implementation plan includes fundamental and applied research activities in the disciplines of information systems modelling, smart embedded systems, short and long distance wireless communication technologies, data management and modelling, statistical methods for predictive maintenance, End Of Life (EOL) planning, and adaptive production management. The objective of PROMISE is to develop a new generation of Product Information Tracking and Flow Management system. This system will allow all actors that play a role during the lifecycle of a product (managers, designers, service and maintenance operators, recyclers, etc.) to track, manage and control product information at any phase of its lifecycle (design, manufacturing, Middle of Life (MOL), EOL), at any time and any place in the world. The concept shown in Figure 1 below puts the requirements for the technologies to be investigated and developed.
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opportunity of dramatically reducing machine unavailability enhancing their diagnostic performances. According to these issues the main applications of the project foreseen at this stage are: 9 diagnosis of the machine (prediction of interventions for substitution of mechanical parts, self tuning); 9 traceability of components. 3. RFIDs
The above concepts and requirements compose what it could be defined as seamless e-Transformation of Information to Knowledge, which is planned to be realised.
The project also focuses on the use of RFIDs that may be used as smart memory tags on which different type of data could be written. RFIDs could be useful in order to store data of each component of a milling system from the beginning of its life to the end. In fact data like dimension, weight, material, etc.., can be stored and during the component life data reflecting how the component is working can be added. Reading the huge amount of information stored in the RFIDs it is so possible to understand the behavior of the component for a feedback to the design department. In order to allow diagnosis applications and traceability features on machine tools, RFIDs should gather information like: 9 who built the component and when; 9 if and when the component was installed on a machine, de-installed and installed on another machine...; 9 information to be used as a term for comparison in order to detect degradation and to perform condition diagnosis.
2. Application case: machine tools
4. Condition-based Predictive Maintenance
Fidia is a world leader in the design, construction and marketing of integrated systems for the machining of complex forms for the moulds and dies industry. Moulds and dies are used in the manufacturing of mass-produced products. Consequently, they find application in a very wide and increasing range of production sectors owing to the cost-effective pressing and moulding production process. Fidia manages all the technological areas, allowing for complete management of the milling process, from the postdesign phase to the finished product. In particular, Fidia produces and markets: 9 Numerical controls for milling systems; 9 High-speed milling systems; 9 Servo drives for milling systems.
4.1. State o f the art
PEID reader Declion
.~ing
Fig. 1. New scenario in Data and Data Flow
The main elements of the concept and requirements shown in Figure 1 are: 9 Local (short distance) connection mode for product data and information exchange, 9 Internet (long distance) product information and knowledge retrieval 9 Data and information flows 9 Envisaged decision support software
Modern
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Information Technologies offer the
The most popular current maintenance approaches are: Breakdown Maintenance (BDM) or Corrective maintenance is only performed when a failure occurs and no action is taken to detect the onset of failures or to prevent them. In this case the system is maintained on an "as-needed" basis, usually after a major breakdown. Corrective maintenance is very expensive because breakdowns usually occur in unexpected times, not allowing the user to exploit his machine tool; this usually forces a reorganisation of his schedule, but very often does not allow him to respect a contract for delivery of a job. This is a worse problem in cases when the user has a tight schedule, and has to pay in case he
is not able to respect a contract. Preventive maintenance (PM) aims at reducing the number of failures and their financial consequences by performing maintenance actions at a predetermined point of time (age or calendar time based), regardless of the condition of the component or equipment. The time to action is usually optimized in order to minimize maintenance costs. PM has some difficulties such as the need of decision support systems, insufficient historical failure data and inaccuracy in assessing the time to action especially when the spread in the time to failure of identical component is large. Although it increases system availability, it is expensive because of frequent replacement of costly parts before the end of their life; another disadvantage is that it is time-based, while, frequently, the wear out depends on the type of usage rather than the time of usage.
4.2. Objectives Condition-Based Preditive Maintenance strategy is based on deterministic and probabilistic models. Data about failure behaviour can be obtained via suitable condition monitoring parameters, which give information about the actual state of the system. The analysis of signals is a complex process that often requires a good knowledge of phenomena and the participation of very experienced engineers. It is very difficult, for instance, to make a distinction among vibrations associated to a normal way of functioning and those due to an abnormal condition. Relationships between the source of energy and the measurement position, and among the elements of the measured signal are extremely complex. Diagnostic systems are usually realized through a previous knowledge of the system mixed with instinct, which very often causes a subjective judgement to be used. As a consequence several methodologies have been born to help designers to extract from data the necessary information for the diagnosis of failures. The selection of parameters to be used as input for diagnostic and monitoring algorithms (the so called feature extraction) is one of the most critical phases in the whole process. Parameters must be selected that are able to provide all the necessary information, and that are strictly linked with failures that must be identified and diagnosed. The feature extraction includes: 9 The identification of unsignificant data (bad data or outliers), data linked to abnormal behaviour
(sensor malfunctioning .... ), or to uncontrolled functioning (start-up or shutdown..). 9 The selection of the set of data used for calibration and off line validation of the diagnostic system. The difficulties enclosed in the process of collecting data and extracting features make so that this kind of maintenance strategy, although its significant benefits, is not widely used in industrial environments in the area of machine tools. On the other hand, useful information about the health state of the components of a milling machine can be gained performing periodical checks on the machine. Storing this data enables statistical analysis of the components lifecycle, such as finding the relation between the wear and the failure rate. This can help to single out the causes of machine failures, allowing the optimization of the technical interventions and thus minimizing machine unavailability. The fulfilment of these objectives will improve the current product on reliability, availability and maintainability aspects having a big impact in industrial environment. A set of sensors are installed on the axes of the milling machine and allow to monitor different data (position, speed, power absorbed by the electric motor, etc...). The first and main difficulty stays in the translation of these data into values expressing the criticality of each component forming the axis.
4.3. Decision support system (DSS) This is a fundamental tool that is meant to somehow partially translate the experience and the knowledge of users and maintenance crew in order to support them in taking decisions concerning the maintenance to be performed on the milling machine. It works in four steps.
4.3.1. Step 1 The criticality of each axis is computed. The axes are monitored and data related to the accuracy of their movements, to their speed, to the power absorbed by the motor are collected. Historical data are then considered in order to "translate" these performances into a measure of the criticality of each axis. In particular, results obtained are used to perform a diagnosis of the possible causes of problems detected on the machine. In our scenario the methodology takes into consideration a set of possible alternative hardware failures affecting the monitored components: the most meaningful outcomes are classified as the main occurrences. Different patterns are then
97
constructed by putting into relation the sequences of events which (individually or in combination) could lead to the main problems. By ascribing probabilities to each event, the probability of a main problem can be calculated. Probabilities are calculated taking into consideration historical data (that is: translating maintenance crew and users experiences into figures), while measurements collected from sensors and tests are used as inputs for the basic causes. Going up and multiplying the registered values for the rates of occurrence, we are able to identify the most critical failure/problem.
4.3.2. Step 2 As a second step each axis criticality is desumed and these values are compared to pre-defined thresholds (threshold values are derived from historical data and past experiences pursuing the goals both to avoid useless intervention and, moreover, to avoid components breakdown or problems in the usage ofthe milling machine). If the criticalities of the axis are under the thresholds, the DSS flow stops and a proper message is displayed through the DSS Graphical User Interface (GUI). On the contrary, if one of the axis has a criticality that exceeds the threshold, further investigations are required. 4.3.3. Step 3 The goal of this third step is to identify which is the component of the critical axis that is the main responsible for the monitored deviations from standard values. The choice of basing the diagnosis on sensors that are already installed on the milling machine produces on one side a cheaper and less invasive solution but, on the opposite, a more difficult identification of the single component responsible of the monitored problems: with the monitored dimensions we are able to directly calculate the criticality of each axis as a whole, but not the criticality of each component forming the axis (more and more expensive sensors would be needed for directly measuring performances of each component). Relying on historical data and on users and manufacturers experiences, the relationship between the criticality of the axis and the criticality of each component forming the axis is here represented again through a fault tree.
The main input is the criticality of the most critical component computed in the previous step. Alternative options are here suggested depending on the registered values: 9 If the computed value is between (pre-defined) threshold T[1] and threshold T[2], the DSS gives the suggestion to simply increase the number of controls to be performed on the machine in a given time period. 9 If the value is between threshold T[2] and threshold T[3], some working parameters are partially relaxed. For example, the maximum speed and accelerationallowed are reduced in order to prevent increased inaccuracies. If, finally, the value is above T[3], the replacement of the worn component is recommended.
4.4. Benefits
9
9
The main addressees of this tool are: The customers (i.e.: the final user of the milling machine): they should be able on one side to avoid useless maintenance interventions and, on the other side, to prevent unexpected stops of the machine using monitored performance variations. The machine tool builder service: the maintenance crew can both avoid useless interventions and be supported in identifying the best solution for facing the situation.
Fig. 2. Maintenance
Application
scenario
for Predictive
5. Traceability
4.3.4. Step 4 The criticality of each component computed in the step above is then compared with pre-defined thresholds and, if someone exceeds, the most critical component is identified and displayed This is the final step of this module of the DSS.
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5.1. State of the art The components of a milling machine do not reside on the same machine throughout their lifecycle.
Often after a repair intervention an electronic component taken out from the machine is brought to the laboratory; in case it can be repaired, it may be reinstalled later on a different machine. In fact it often happens that when a service technician performs an intervention he takes out several electronic cards from the slots and substitutes all of them. When these cards are checked in the laboratory, in most cases no failure is identified, and they are re-used. But in some cases the tests performed in the laboratory are not able to identify an existing problem, that presents again after reuse of the card. As a consequence, when a fault occurs on the machine tool, the service department doesn't know which components are installed on that machine and the "history" of those components. It would be highly desirable to keep track of the "history" and the characteristics of the components installed on each machine because knowing their exact features makes the technical interventions easier. Moreover other important information are not available, such as the firmware version installed on each card.
5.2. Objectives A backend system that will be a database SW application, installed on Central PC, able to: 9 provide detailed information about the history of each component (represented by a set of suitable parameters); 9 perform statistical analysis on aforementioned parameters; 9 identify the presence of defective stocks, etc...
depending on the requests and queries carried out by the DSS users. The tool will provide some pre-defined (but customisable) queries and, as a result, tables, charts and interactive sensitivity-analysis features. The tool is expected to be used every time needed by its users: when the technical assistance service receives a call for maintenance or when the Design department is interested in examining performances of the installed machines. When a request for support is fulfilled through the proper GUI of the DSS module, after a validation of the consistency of the requested analysis and the availability of the needed data, the necessary information is collected in the DSS database and the requested analysis performed. The output of this analysis (charts, spreadsheets, etc...) is then displayed to the user through the GUI.
5.4. Benefits The main addressees of this tool are: The machine tool builder service: (i.e.: the maintenance crew) can use the recorded data in order to better plan the needed interventions when problems on the machines occur. Handling, for example, all the different interventions performed during its lifecycle on a specific machine and on each component of this machine, would allow the maintenance crew to restrict the field of failure causes (thus making easier the assistance Technical Assistence Department
l Pointto Point modem / conned~on/ lnterne~
1
5.3. Decision support system (DSS) This second case of the DSS differs from the previous one for various reasons: it's going to be installed in the central PC of the milling machine manufacturer (and not on the NC of the milling machine users); it is a Data Analysis System and it is not expected to perform an optimisation or to give direct suggestions to its users. Inputs to this module come from the RFID installed on the main mechanical and electronic components: interventions performed on each monitored component of each machine, lifespan, replacements or repairs performed are registered and then stored in a proper central database. The output of the module is quite heterogeneous
Fig. 3. Application scenario for Traceability The designers and engineers of the milling machine manufacturer. This DSS module supports them in gathering data on the state of each machine critical component. Performing statistical analysis on each component and keeping track of the overall components performance would allow, for example, to improve the design of future machines or, moreover, to select the most reliable manufacturers of components.
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]
6. Conclusions
The application ofproject results to machine tools is foreseen in the field of condition-based predictive maintenance and traceability of components. Product field data can be used to generate knowledge about the working conditions of the machine in order to be able to avoid production stoppages and sudden interruptions that could have big economical impact on the customer site. The former could be reduced using smart IT systems within a predictive maintenance framework. Furthermore it will allow the traceability of the components that will simplify the work of the technical personnel and make their job more effective. Consequently, this application will improve the present Product Lifecycle Management providing information and feedback to the design division. The improvements of the interaction level among the actors of the value chain are listed below:
9 9
Customers Higher quality of Technical Assistance. Lower Technical Assistance fares. Service Lower travelling and manpower costs for each intervention. Design department Better comprehension of malfunctions and breaks (in collaboration with Service). Improvement of reliability and technical quality of the product with satisfaction of customers.
Predictive maintenance strategies and improved traceability can greatly increase the productivity, improve the reliability of the product and in long term the optimisation of product design. Acknowledgements
The project "Product Lifecycle Management and Information Tracking using Smart Embedded Systems" (PROMISE) is a project funded by the European Commission under the Joint Call between "Information Society Technologies" (IST) and "Nanotechnology and nanosciences, knowledge-based multifunctional materials, new production processes and devices" (NMP). The contract number is 507100.
lO0
The author wish to thank the other partners of the consortium: Sintef (N), BIBA, Cognidata, Indyon, Infineon, InMediasP, SAP (D), Bombardier Transportation, Enotrac, Ecole Polytechnique Federale de Lausanne (CH), Cambridge University (UK), Caterpillar (FR), CIMRU (Ireland), CR Fiat, ITIACNR, MTS, Politecnico di Milano, Wrap (I), Helsinki University of Technology, Stockway (FI), Intracom (GR). Fidia S.p.A., Cambridge University and CR Fiat are partners of the EU-funded FP6 Innovative Production Machines and Systems (I'PROMS) Network of Excellence. http://www, iproms, org References
[1] Project homepage: http://www.promise.eu.
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 CardiffUniversity, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Product Support Knowledge N. Lagos, R. Setchi School of Engineering, Cardiff University, Cardiff CF24 3AA, UK
Abstract
This paper aims to discuss and define product support knowledge. The paper analyses product support from a task-based point of view and accordingly introduces the concept of a PRoduct Support System (PRSS). Next it outlines the constituents that form product support knowledge and illustrates their definitions. It is concluded that semantically rich models are needed in order to express the multidimensionality of product support knowledge. Keywords: Product support, knowledge, semantics
1. Introduction
Intelligent product manuals, interactive electronic technical manuals, and electronic performance support systems are some of the forms with which product support systems have been delivered to the end users. These systems share two basic characteristics when involved in the product support field. 1. Utilisation of electronic means and technologies. 2. Provision of information to the user. However nowadays, the electronic-based provision of information alone is not considered adequate for effective support. Cliff [ 1] claims that "information is not power but organised information is strength, accurate information is essential and up-to-date/new information is valuable". In accordance, Pham et al. [2] have defined several requirements for an IPM to be effective including the ability of supporting different categories of users in different activities and the availability of highly accurate information. Furthermore, in a visionary paper on EPSSs in the 21 st century, Raybould [3] argues that the convergence of knowledge engineering and support should be imminent, as a necessity of the new knowledge intensive era, where information overload is apparent.
The first step towards merging knowledge engineering and product support is the identification and definition of the knowledge contained within a product support system. The paper is organised as follows. Section 2 briefly analyses product support. Section 3 delineates the notion of product support systems and section 4 defines product support knowledge. Conclusions and directions for future research are discussed in the final section of the paper.
2. Analysis of product support
Product support could be analysed in terms of the tasks involved. A task is defined as a strategy, which is followed in order to achieve a specific goal [4] and could be decomposed into several subtasks, and actions. An action is a primitive operation needed for a task to take place. For example, repairing a product is considered as a task that includes subtasks like locate, remove, and examine the faulty part and actions such as unscrew a bolt. A task can be represented as follows. G = J r (Rr, Or, Y)
(1)
where G is the goal of the task, fT denotes the transformation that takes place within a task, R y is the
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input to the task, and C r are the constraint(s) that are encountered at different levels of granularity Y for reaching a specific goal. Granularity is the degree to which a task can be decomposed in different subtasks, for achieving a goal. In general, the largest the granularity the more flexible becomes the operation of completing a task. Equation (1) illustrates that a task' s goal relates to the input that the process receives and to the constraints that control it. The input of a task includes available resources. The optimal situation is to have all required resources available just-in-time and just-inplace. In product support, these include tools, equipment, data, information, personnel, facilities, computers, supply support, spare components, experience, skills and knowledge. The input therefore can be represented as a number of sets {R1,...,Rp }, where each set stands for a different type of resource needed to achieve the goal. R r -{R1,...,Rp} (2) where p is the number of the resources' sets, for example, RI= tools, R 2 - - - - e q u i p m e n t , R 3 -knowledge, and so on. Each of these sets contains several members that are needed to complete the task. Consequently, if a 1 - screwdriver, a 2 = hammer, a 3 = spanner, etc., then R 1 = {a 1,..., a i }, where i denotes the number of tools that are included in R 1 . A task can be redefined as follows.
G = f T {(R1....
(3>
where C r is a set representing the constraints ck 9
C T --{c1,...,Ck} (4) Requirements related to cost, quality and safety, as well as conformance to standards and regulations are typical constraints that need to be satisfied in product support. Statement 1: Product support is needed when there is lack of resources for completing a task and~or when the existing resources do not satisfy specific control constraint(s). 3. Product support systems Today's product support systems should include the following feature. 1. Provision of accurate and up-to-date information to the user in a coherent and personalised manner. Therefore, the resource that a product support system should have is knowledge. A product support
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system can be defined as follows. Definition 1: A PRoduct Support System (PRSS) is an electronic medium that aims to alleviate the lack of knowledge of the user in a particular subject or situation related to a product by providing accurate and up-to-date information in a coherent and personalised manner. The repercussions of the third characteristic on the design of PRSSs are numerous. 1. Up-to-date information in a dynamic environment like that ofproduct development and exploitation means that the product support system has to be integrated into the product lifecycle. 2. Accurate information can be provided only if the knowledge underlying them is formally defined, rigidly structured, and semantically analysed. 3. Coherency can be achieved if the domain knowledge is consistently represented and its relation with product support analysed. 4. Personalisation of delivery and presentation indicates that knowledge about users and tasks should be modelled and included within the product support system. Nevertheless, although current research addresses the use of knowledge engineering practices in product support, requirements (1), (2), and (3) have been only partially considered. The rest of this paper identifies the knowledge contained within a knowledge-based PRSS and defines product support knowledge as an aggregation of the knowledge retrieved from product, task, user, and documentation modelling application areas.
4. Definition of product support knowledge A PRSS should be able to process and analyse thorough, detailed and contemporary knowledge ofthe domain of interest. Consider the example of a novice user having to change a tyre. The system should have knowledge about the products, tasks, and users supported. 9 Product. In this case, knowledge about both the vehicle and its tyres' characteristics and specifications is needed. Knowledge about the vehicle refines the search for a tyre to a specific type (e.g. 21" tyre), while knowledge about the tyre helps in refining the search even more (e.g. according to rubber roughness). 9 Task. The series of actions that should be
followed for inserting the tyre can be designed, as long as the initial problematic and the goal states are known and knowledge about each action exists. 9 User. The representation of the solution that the product support system delivers is based on the knowledge it has about the user. If the user is novice, technical jargon can be replaced or supported by multimedia (e.g. images) and examples. If the user is more experienced, a textual description of the main steps is sufficient. As illustrated, the product support knowledge base should contain relevant knowledge about the products, users, their tasks, and the way in which these are linked to each other (i.e. product documentation) (Figure 1).
k.... , e d ~
know,ed~>/ know,ed~"
suppor~ knowledge
Product support virtual documentation
Fig. 1. Product support knowledge So, if the knowledge available in a product support system is Kpss, product knowledge is Kp, user knowledge is Ku and task knowledge is Kt, then for the product support system to be able to deliver optimal support, the following formal requirement must be satisfied. Kp wK u ~K t cKps S
(5)
Recent research has developed approaches for modelling product support systems (i.e. user-centred, task-centred, and performance-centred design). However, there are currently no uniform definitions of product, task and user knowledge within this application area. This section attempts to fill this gap.
4.1. Definition of knowledge According to Webster, Oxford and Cambridge dictionaries, the word 'knowledge' has the following meanings: 9 Perception; clear perception of fact, truth or duty. 9 Apprehension, awareness, experience; Familiarity, awareness, or understanding gained through experience or study. 9 Learning; a branch of learning, a science. 9 Information; the body of facts accumulated
by mankind, specific information about something, information acquired. Plato's definition of knowledge as "justified true belief' [5] and Ackoff's and Emory's [6] as "awareness of the efficiency and effectiveness of different actions in producing outcomes based on experience" emphasizes the highly subjective nature of knowledge, the fact that it is normally based on individual perceptions. On the other hand, the study of Kakabadse et al. [7], where knowledge is defined as "information put to productive use", highlights that knowledge is created and applied within specific application context. Other researchers [7-11] concentrate on the transformation of data into information, and then knowledge. Data is viewed as raw elements, which if organized in explicit way, form information. Knowledge is created when the information is structured according to certain purpose, context or perception. Accordingly, Nonaka [12] argues that Western management sees knowledge as formal and systematic, captured in codified procedures [13]. Strengthening that opinion, Stefik [14] states that "knowledge in terms of the knowledge systems refers to the codified experience of agents". Based on the aforementioned analysis, information is viewed as the building block of knowledge, whether it is derived from direct or indirect experience, study, or learning. However, the information acquired cannot be transformed into knowledge unless its meaning is apprehended. This understanding is tightly related to the purpose, context and beliefs within which knowledge is interpreted. Furthermore, the transformation of information in knowledge depends on the cognitive abilities of the individual users. The following working definition of knowledge is adopted in this work.
Definition 1. Knowledge is a specific semantic interpretation of information. In the terminology of logic, "interpretation" is a mapping from statements to conceptualization. In this definition, "specific interpretation" means that knowledge is context-dependent and therefore inherently different for each individual. "Semantic interpretation" denotes that the mapping to conceptualization is carried out using semantics.
4.2. Product knowledge Kaposi and Myers [15] define a product in terms of its attributes and processes and the interrelations between them, while others (e.g. Oxford dictionary) concentrate on its property of "being produced".
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Ahn and Chang [ 16] distinguish between product and process and state that "in a knowledge intensive firm, product is the explicit output of the value-adding activities or production", describing product as the explicit outcome of a process. In the product support area, products are both tangible (e.g. vehicle) and intangible (e.g. software). Additionally, a product support system refers to things for which there is immediate interest (either from the user or the system itself). According to the above discussion, a product within product support is defined as follows. Definition 2. Product is an entity of interest created by a process [15]. In the above definition the meaning of the word "entity" is adopted by ISO 8402 [17] and is "that which can be individually defined or considered". In accord with ISO 10303-1 [18], product data is "a representation of information about a product in a formal manner suitable for communication, interpretation, or processing". In addition, Petiot and Yannou [19] claim that product semantics is "the study of the symbolic qualities of man-made forms in the context of their use, and application of this knowledge to industrial knowledge". Ahn and Chang [16] analyse product knowledge from the perspective of business performance and classify product knowledge into tacit and explicit, claiming that "tacit product knowledge is productspecific know-how that cannot be easily expressed and it resides on the human brain. Explicit product knowledge is the knowledge accumulated in a knowledge repository...product knowledge tends to be object-oriented, focused on a specific product". Product knowledge merges and extends the notions of product data and semantics as it includes and relates both of them. Definition 3. Product knowledge is a formal, temporal representation of the specific semantic interpretation of information, associated with an entity of interest created by a process. The representation of information should be formal, as it has to be suitable for communication, interpretation, or processing, as required by ISO 10303-1 [18]. Moreover, it should be temporal, because it is valid only for a specific instance or period of time during which the information remains unchanged. Valid means that the information is within certain specified boundaries.
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4.3. Task knowledge A product support system should be able to advise the user on the sequence of actions or the strategy that should be followed to reach a specific goal. The definition of a task given by Wielinga et al. [4] is adopted in this study as it reflects the above description. Definition 4. Task is a strategy, which is followed in order to achieve a specific goal [4]. Liebowitz and Megbolugbe [11] also describe tasks in terms of their goals and sequence of actions. So, they claim that "task knowledge describes which goal(s) an application pursues and how these goals can be realised through decomposition into tasks and inferences". In the same manner, task knowledge for a product support system is defined as follows. Definition 5. Task knowledge is a formal, temporal representation of the specific semantic interpretation of information, which defines a strategy followed to achieve a specific goal.
4.4. User knowledge Several definitions for "user" exist in the literature, sharing the characteristic of system orientation. This means that they are formed according to a reference system and its expected utilisation. For a product system, user and user knowledge are defined as follows Definition 6. User refers to any person, group or functional unit that directly interacts with a system. Definition 7. User knowledge is a formal, temporal representation of the specific semantic interpretation of information, associated with aperson, group or functional unit that directly interacts with a system.
4.5. Product support virtual documentation Product support knowledge is defined as the composition of product, task, and user knowledge and the understanding of the way in which these are integrated with each other in a product support system. The integration is achieved through product support electronic-based documentation. One of the main reasons for electronic-based documentation success is the ability of re-purposing its components according to the requirements. For example in many cases a paragraph or a sentence are reused by copying and pasting in different documents. However, it is possible to reuse documentation elements in a more sophisticated way if accessing and
processing them at run-time is possible. In order to do that, a flexible and dynamic but also rigid and formal underlying model of product support virtual documents is needed. Towards this attainment a working definition for product support virtual document has to be provided. The Oxford dictionary describes a document as "a piece of written, printed, or electronic matter that provides information or evidence", which means that there are two important aspects in a document. 9 The substance from which it is created (i.e. written, printed, or electronic). 9 The purpose of its existence (i.e. it provides information or evidence). In addition Gruber [20] has defined a virtual document (VD) as "a hypermedia document that is generated on demand from underlying information sources, in response to user (reader) input" [20]. Gruber therefore, defines a VD as a specialisation of a document by elaborating on the matter utilised (i.e. hypermedia or virtual) and on the generation approach (i.e. on demand from underlying information resources). Furthermore, a Product Support Virtual Document (PSVD) is a VD that has the constraint of providing information related to a product. A PSVD is defined therefore, as follows. Definition 8. A product support virtual document is a piece of hypermedia that is generated on demand from underlying information sources, in response to user O'eader) input, and provides information or evidence related to a product. The definitions given in this section are summarised in Table 1.
Table 1 Product support knowledge related definitions.
Knowledge
Knowledge is a specific semantic interpretation of information.
Product
Product is an entity of interest created by a process (Kaposi and Myers 2001)
Product knowledge
Product knowledge is a formal, temporal representation of the specific semantic interpretation of information, associated with an entity of interest created by a process.
Task
Task is a strategy, which is followed in order to achieve a specific goal (Wielinga 1993).
Task knowledge
Task knowledge is a formal, temporal representation of the specific semantic interpretation of information, which defines a strategy followed to achieve a specific goal.
User
User refers to any person, group or functional unit that directly interacts with a system.
User knowledge
User knowledge is a formal, temporal representation of the specific semantic interpretation of information, associated with a person, group or functional unit that directly interacts with a system.
Product support virtual document
A product support virtual document is a piece of hypermedia that is generated on demand from underlying information sources, in response to user (reader) input, and provides information or evidence related to a product.
5. Summary and conclusions
5.1. Summary This paper suggests that the first step towards developing product support that is up-to-date, accurate, coherent, and personalised is to define the knowledge contained within a product support system. Product support knowledge is identified as the synthesis of product, task, and user knowledge. The paper presents the key characteristics of each of the aforementioned class of entities and their working definitions. Product support virtual documentation forms the link between the different product support knowledge constituents and is the medium that enables provision of usertailored product support related information to the user.
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5.2. Conclusions The paper started with a task-based analysis of product support that revealed the direct relationship of product support with the lack of resources for performing a task. Having that as a starting point a product support system has been defined as a medium that aims to alleviate the lack of user's knowledge. This viewpoint transforms the complex problem of creating a product support system (PRSS) into the more manageable aim of developing a knowledgebased platform for product support. The analysis and design of a knowledge-based system follows the natural order of defining the knowledge required, modelling it with a platformindependent way, and identifying and applying appropriate reasoning techniques. As a result the rest of this paper has examined product support knowledge related definitions and has unified them by identifying their key characteristics for a product support system. Precisely stating the essential nature of all product support knowledge constituents forms a solid basis for following the modular way of knowledge-based systems construction. The investigation of the definitions found in the literature illustrates that knowledge is captured in terms of specific semantics. Therefore, semantically rich modelling and representation of product support knowledge is deemed as an essential part of product support system creation.
Acknowledgements
[4]
[5] [6] [7]
[8] [9]
[ 10]
[ 11]
[12] [13] [ 14] [15]
The research described in this paper was supported by Cardiff University and performed within the I ' P R O M S Network of Excellence and ISAR project sponsored by FP6 of the European Community.
References [1]
[2] [3]
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Cliff S. Information is Power? Envisioning the Minnesota Public Internet- Public service and community information and interaction in the public interest. In: Information for Change conference, St. Paul, Minnesota, 1999. Pham DT, Setchi RM and Dimov SS. Enhanced Product Support through Intelligent Product Manuals, International J. Sys. Sc. 6-33 (2002)433-449. Raybould B. Building performance-centered web-based
[16] [17] [ 18]
[19]
[20]
systems, information systems and knowledge management systems, in the 21 st century. Performance Improvement. 6-39 (2000) 32-39. Wielinga B, Schreiber G and Breuker J. Modelling Expertise. In: G. Schreiber, B. Wielinga, J. Breuker, (Ed) KADS: A principled approach to knowledgebased system development, vol. XI. Academic, New York. 1993, pp. 21-47. Plato. "Phaedo", Plato I, trans. By Gowler, H.N., Harvard University Press/The Loeb Classical Library. Cambridge, MA. 1953 117-124. Ackoff RL and Emery FE. On purposeful systems, Chicago IL-Aldine-Atherton, 1972. Kakabadse NK. Kakabadse A and Kouzmin A. Reviewing the knowledge management literature: towards a taxonomy, J. Knowl. Manag., 4-7, (2003) 7591. Nonaka I. A dynamic theory of organisational knowledge creation. Organisation Sc. 1-5 (1994) 14-37. Gunnlaugsdottir J. Seek and you will find, share and you will benefit: organising knowledge using groupware systems. Int. J. Inform. Manag. 5-23 (2003) 363-380. Bose R. Knowledge management-enabled health care management systems: capabilities, infrastructure, and decision support. Expert Sys. With Appl. 1-24 (2003) 59-71. Liebowitz J and Megbolugbe I. A set of frameworks to aid the project manager in conceptualizing and implementing knowledge management initiatives. Int. J. Project. Manag. 3-21 (2003) 189-198. Nonaka I. The knowledge creating company. Harvard Business Review, 1991, pp 96-104. Belogun J. and Jenkins M. Re-conceiving Change Management: A Knowledge-Based Perspective. European Manag. J. 2-21 (2003) 247-257. Stefik M. Introduction to Knowledge Systems. Morgan Kaufmann Publishers, London, UK, 1995, Chap. 3. Kaposi A. and Myers M. Systems for All. Imperial College Press, London, UK, 2001, pp.76-79. Ahn JH. and Chang JG. Assessing the contribution of knowledge to business performance: the K P 3 methodology. Dec. Support Sys. 4-36 (2004) 403-416. ISO 8402: 1994. Quality Management and Quality Assurance. Vocabulary of the ISO 9000 Quality Standard, 1994. ISO 10303-1 : 1994.Industrial Automation Systems and Integration-Product Data Representation and Exchange. Part 1: Overview and Fundamental Principles, TC 184/ SC 4, ISO, 1994 Petiot JF. and Yannou B. Measuring consumer perceptions for a better comprehension, specification, and assessment of product semantics. Int. J. Ind. Erg. In Press, 2004. Gruber TR, Vemuri S and Rice J. Model-based virtual document generation. Int. J. Hum.-Comp. Stud. 46 (1997) 687-706.
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka(eds) 9 2006 CardiffUniversity,ManufacturingEngineeringCentre, Cardiff,UK. Publishedby ElsevierLtd. All fights reserved.
Visual Simulation of Grinding Process M. Sakakura S. Tsukamoto b, T. Fujiwara I. Inasaki ~ aDepartment of Robotics, Daido Institute of Technology, 10-3, Takiharu-cho, Minami-ku, Nagoya, Japan b Graduate School of Natural Science and Technology, Okayama University, 1-1, Tsushima-Naka, 1-Chome, Okayama, Japan c Cooperative Research Center, Okayama University, 5302,Haga, Okayama, Japan o Faculty of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Japan
Abstract
Grinding is one of the machining methods for finishing which is performed by using a large number of abrasive grains with irregular shapes, and random distribution. While this feature enables accurate and high quality machining, it complicates analysis of the grinding process. In order to solve this problem, several computer simulations have been carried out using the Monte Carlo Method. Most of them, however, statically calculate geometric interference between a grain and a workpiece, and have not provided enough achievements for practical applications. In this study, taking the background into account, a simulation program has been developed based on the elastic behaviour model of a grain which has been previously investigated by the authors. The program focuses on the generation process of a workpiece surface, and simulates the interaction of grains with a workpiece, which includes the elastic and plastic deformation and the removal of workpiece material. The simulation result is visualized using a three-dimensional graphics technique. An example of the simulation shown in this study verifies that the simulation program makes it easy to analyze the microscopic grinding phenomena, and can be used as a practical tool for predicting the grinding results and for optimizing grinding parameters. Keywords: Grinding, Simulation, Surface Generation
1. Introduction
Grinding is performed as the aggregation of microscopic material removal by a large number of abrasive grains. This feature enables accurate and high quality machining. However, different from a turning tool, a grain has stochastic geometry and is distributed at random in the surface layer of a grinding wheel. This characteristic makes analysis of the grinding process difficult, and could be an obstacle to prediction of grinding results and optimization of grinding parameters. In order to
solve this problem, several computer simulations have been carried out using the Monte Carlo Method [1][2]. Most of them, however, statically calculate geometric interference between a grain and a workpiece, and have not provided enough achievements for practical applications. The fact that a grain is connected with adjacent grains via elastic bond-bridges must not be neglected in a real simulation [3][4]. Some papers have reported that the amount of elastic deflection of a grain is on the same order as the cutting depth of the grain [5][6]. In this study, taking the background mentioned
107
above into account, a simulation program has been developed based on the elastic behaviour model of a grain which was previously investigated by the authors [7]. The program focuses on the generation process of a workpiece surface, and simulates the interaction of grains and a workpiece which includes the elastic and plastic deformation and the removal of workpiece material. The simulation results are visualized as an animated image using a threedimensional graphics technique. The simulation makes it easy to understand the microscopic grinding phenomena, and makes it possible to analyze the surface generation process quantitatively. Consequently, it could be used as a practical tool for predicting the grinding results such as surface roughness, and for optimizing grinding parameters such as infeed rate.
2.
Simulation Model
2.1 Characteristics of Simulation Model
A lot of studies have pointed out that an abrasive grain in a grinding wheel is supported elastically and displaced during grinding. Some in-depth investigations have reported that the amount of elastic displacement of a grain is the same as the cutting depth of a grain and must not be neglected. They, however, have dealt with only the outermost grain of a grinding wheel and have assumed that all grains behave with the same elasticity. The authors, on the other hand, considered that the elasticity of grain support depends on the distributed location of a grain. The relation between three-dimensional distribution and elastic displacement of a grain measured by the contact stylus method was investigated. The results made it clear that elasticity of a grain support located in a surface layer is relatively small, and that the deeper the location of a grain becomes, the further the elasticity ranges from small to large. Based on the above experimental result, an elastic support model of a grain was constructed in which the elasticity of support changes according to the height of a grain. Where, the height of a grain is defined as the length from the reference layer which is located in constant depth from the wheel surface. In other words, a high grain corresponds to a grain located in the shallow position from the wheel surface. As the model was incorporated into the
simulation program, it could be expected to simulate a grinding process more real than any other simulation program developed so far. 2.2 Standard Grain Data
The primary purpose of the simulation is analyzing a surface generation process. Since the surface profile is generated as a continuous transcription of grain shapes, the two-dimensional contour in the perpendicular plane to the grinding direction was applied as a grain shape instead of a three-dimensional shape which causes a heavy computational load. Although simple twodimensional shapes such as a circle, a triangle, and a trapezium have been used in many simulations, measured grain shapes were applied for a real simulation. Grain shapes were extracted from the topography measured by the contact stylus method, and the height and support elasticity of grains were also measured [8]. Fifty grain data were prepared for simulation and they were labelled "standard grain data", some of which are illustrated in Table 1. The wheel surface model was constructed from these standard grain data. After the number of grains was determined according to the given distribution density, each grain was selected from the standard grain data and located in the wheel surface layer at random. Table 1" Samples of standard grain data. Stiffness
h c [~m] ks[N/lum ]
Length [ gm ] 0
50
100 150 200 250 300
===/
0.00 5.00 0.00 5.00 ~0.00 !5.00 ;0.00 ;5.00 bO.O0
0.00
0
Length [ gm ] 50 100 150
38.85
0.30
18.30
1.18
200
5.00 10.00 15.00 20.00
0.00 5.00 0.00 5.0{3 !0.00
108
Height
Shape
0
\
Length [ gm ] 50
'
k. [
_/
100 17.54
0.71
~l~W , \\
Elastic Plastic Detbrmation Deformation
~ ~
Cut
Plastic Elastic Deibrmation Deformation
I ~"
\
Workpiece
~~~~
'.
.~~
_~
i - - " -- . . . . . . I I
" ~ "
"
I
,~ ~
i
"'"
! IElastic Deformatio~ "-- / -~
~
~
"i"
--
~
""
""
""
1
c it ti)max
+
~!N~|@~ I t c
~|
I" I(
t i)max
\i.-
Fig. 1 : Three modes of interactions between a grain and a workpiece. Table 2: Symbols.
Si
s
sectional area of interaction between grain and workpiece normal grinding force acting on a grain tangential grinding force acting on a grain elastic deflection of grain
ks
elastic coefficient of grain support
(//)max
maximum depth of interaction
tc
depth of cut
te
Sl,Sr
limit depth of interaction for elastic deformation limit depth of interaction for plastic deformation sectional area of pile-up
Sg
sectional area of removed groove
Op
angle of pile-up
hc
height of grain
(2
force coefficient coefficient for surface generation pile-up coefficient ratio of normal to tangential force
tp
7 2 P
2.3 Equations When a grain interacts with a workpiece, not only the elastic deflection of the grain but also the elastic and plastic deformation of the workpiece must be considered. It has been reported that a small depth of interaction does not result in cutting but rather, results in elastic or plastic deformation as shown in Fig. 1 [9]. Equations for the interaction are as follows and related symbols are listed in Tables 2.
S/ - S~ -
)cSg/2 Sg/ 2
t c -- (1-~')((ti)rnax
3.
-te)
(Iv < (ti)max)
((//)max "~/p)
(1)
(2)
q =aSi P : Pq
(3) (4)
d2s = p / k s
(5)
Simulation Program
3.1 Calculation Method Cylindrical plunge grinding is selected as the target process of the simulation. As shown in Fig. 2, a square area on the workpiece surface having the dimension of l mm x l mm is defined as the simulation area. It is composed of a hundred
109
sectional profile lines which are set vertical to the grinding direction and arranged at equal intervals of 101am. Each line consists of a thousand points, whose height represents the surface profile. Interactions between a grain and a workpiece are judged at all points, and deformation of the surface is calculated. In the beginning of the simulation, the grinding wheel is set to contact with the workpiece, and then fed into the workpiece by a constant amount at every rotation of the workpiece. Once the position of the grinding wheel is set, the simulation area of the workpiece is rotated for a very short time, and the interaction of the area with the grains in the grinding wheel surface is checked. When an interaction is detected, the profile line of the simulation area is modified according to the interaction model shown in Fig. 1. Calculations are repeatedly conducted until the simulation area passes through the zone of interaction with the grinding wheel. 3.2 Functions
visualized by animated display using a threedimensional graphics technique. Processing, including calculation and display, takes about 2 minutes per revolution of the workpiece using a Pentium 4 (2.4GHz) processor. Another function of the program is to display the interaction in different colours for four conditions. In Fig. 3, red, blue, cyan and white represent cutting, plastic deformation, elastic deformation and non-interaction, respectively. The grains are also displayed in the same colour according to the interaction status. As an additional function, the shape of any sectional profile line can be displayed along with the roughness value.
4.
Example of Simulation
The developed simulation program was utilized to analyze the generation process of a ground surface, especially focusing on the behaviour of grains. The grinding force coefficient, ~, which
The examples of the execution screen of the developed simulation program are shown in Fig. 3. As shown in the figure, the shape change and the interaction with the grains of the simulation area are L
i
Wor~
~ /
X,,,,
o aN
Ma
iGrwih~igX
"
i
Simulation area i~
I.AI
.......
~ . AA
x
1000
. A A
w
:
!
1000 lam
Fig.2: Coordinate system for the simulation.
110
Fig. 3: Examples of the execution screen of the simulation program.
100
100
I
(b) a =0.4
Ca) a --0.0
80
._~ 60 0
o 40 O ",~ c~
O
40-~
=
~ 20
0
~2~__~
I
.
.
~
~o~o~o
.
~
.
32.5
i,, lilHII
.
,
..
,
o Oo dr
, 9 ..oAf
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oo~o~
0
I
0
I
I
I
100 200 300 400 Peripheral position of grain [mini
500
0
0
--I
' ' ' ' 100 200 300 400 Peripheral position of grain [ram] Content Diameter of grinding wheel
370 [mm]
Diameter of workpiece Rotational speed of grinding
90 [mm] 30.0 [m/s]
wheel
Vw
Rotational speed of
= 40
Ad
Infeed of grinding wheel
~D
dg
20
Density of grain in grinding
workpiece] 11.8 [/pro 2]
wheel surface i
9
9
9
9
9
9
ii
OooOS.o.%...o. 9
Illl
,
r .................. Z ~_, ~ ~_ :_ o . . m no oo o o 0 7 , ~ ,
O O0
I
~o
, , , ?o _o oooo
At
Step time of simulation Force coefficient
7
Removal coefficient
I l
I
Pile-up coefficient
10 0
2.0 [pm / rev. of
20
30
0.3 [m/s]
workpiece
0 .,-.
0 40
500
Value [Unit]
Dw Vg
9
~2
21.5
DS
60 O
o
~--
I
00
G :~ .,..,
Symbol
80
.,
o ~o o
N 10
100
B
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dominates the elasticity of a grain support was set to three different values of 0.0, 0.4 and 2.1. The case of a=0.0 corresponds to applying a rigid grinding wheel. The elastic displacement of a grain to the same interaction depth grows large as a grows large. The case of a large a corresponds to applying a soft grinding wheel such as a rubber grinding wheel. The area of a grinding wheel surface which has a possibility of interacting with the simulation area can be calculated according to the rotational speed ratio of the workpiece and the grinding wheel. For all grains in the calculated area of the grinding wheel, temporal changes in their interaction mode with the simulation area were recorded and analyzed. Fig. 9 shows the record when the 25th infeed, that is, a total infeed of 50gm was given. In the lower graph of each set of diagrams, the grains having a possibility to interact with the simulation area were plotted according to the coordinate in the peripheral direction and its height. The black circles in the figure show the grains which interacted with the simulation area, and are called "active grains." On the other hand, the white circles show the grains which did not interact, and are called "inactive grains." The horizontal axis in the upper graph of each set shows the coordinate of grains in the peripheral direction, and the vertical axis shows the sequence of the sectional profile lines which compose the simulation area. The line inside the graph represents an interaction record of a grain from the bottom part to the top part of the graph. The active grain enters the elastic deformation at the point indicated by the red cross mark and then plastic deformation starts at the point marked by the red triangle mark. As the interaction depth increases, cutting occurs between the red circle mark and the blue circle mark. Further, the plastic deformation finishes at the blue triangle mark, entering into an elastic deformation until the interaction terminates at the blue cross mark. The thin dotted line shows an elastic or plastic deformation part, while the thick solid line shows a cutting part. It is observed from the figure that when ~ is equal to 0.0, in other words, a grinding wheel without any elastic deformation is applied, only nineteen grains with larger height are active. This fact indicates that only a few grains located closer to the outermost periphery of the grinding wheel interact with the simulation area. It is observed that when a increases to 0.4 and 2.1, the number of active grains also increases in the directions of both periphery and height.
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5.
Conclusions
The elastic behaviour of an abrasive grain was modelled based on the knowledge regarding the elastic grain support, and a simulation program was developed. The program calculates the interaction between the grains and the workpiece, and visualizes the results in animation using a three-dimensional graphic technique. The program was applied for analyzing the interaction between individual grain with a workpiece surface, and the effect of elastic grain support was clarified. The program could be used as a practical tool for predicting the grinding results such as surface roughness, and for optimizing grinding parameters such as infeed rate. References
[1] T6nshoff, H. K., Peters, J., Inasaki, I., Paul, T., 1992, Modeling and Simulation of Grinding Processes, Annals of the CIRP, 41/2:677-688. [2] Chen, X., Rowe, W. B., 1996, Analysis and Simulation of the Grinding Process. Part II: Mechanics of Grinding, International Journal of Machine Tools and Manufacture, 36/8:883-896. [3] Hahn, R. S., 1955, The Effect of Wheel-Work Conformity in Precision Grinding, Trans. ASME, 77:1325-1329. [4] Nakayama, K., Brecker, J., and Show, M. C., 1971, Grinding Wheel Elasticity, trans. ASME, Series B, Journal of Engineering for Industry, 93:609-614. [5] Saini, D. P., Wager, J. G., Brown, R. H., 1980, Elastic Deflections in Grinding, Annals of the CIRP, 29/1:215-219. [6] Saini, D. P., Wager, J. G., Brown, R. H., 1982, Practical Significance of Contact Deflection in Grinding, Annals of the CIRP, 31/1:215-219. [7] Nakajima, T., Tsukamoto, S., Yoshikawa, M., Takehara, K., Yokomizo, S., 1994, Distribution of Spring Constant of Grain Mounting and Displacement Behaviour of Abrasive Grain on Vitrified Wheel, Japan Society for Precision Engineering, 60/10:14901494. [8] Nakajima, T., Tsukamoto, S., Odani, O., 1993, Formation Mechanism of Ground Surface Profile under Considering Grinding Wheel Surface Topography, Japan Society for Precision Engineering, 59/3:491-496. [9] Nakajima, T., Yoshikawa, M., Tsukamoto, S., Takehara, K., 1998, Simulation of Ground Surface Profile Generation with Measured Distribution of Mounting Spring Constant, Dimensional Position and Shape of Abrasive Grains, Japan Society for Precision Engineering, 64/7:1072-1077.
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
C o l l a b o r a t i v e A n a l y s i s a m o n g v i r t u a l t e a m s : an e x p e r i e n c e A. C. Pithon , M. R. Brochado F. S. Sandonato a, B. M. Teixeira a Department of Pos graduation, Federal Center of Technological Education Celso Suckow da FonsecaCEFET/RJ, Av. Maracan~t, 229, Rio de Janeiro, Brazil, BRA
Abstract The virtual work modifies establish habits of teamwork, therefore the experience is lived deeply not to be physically together in workstation while tasks are carried through. This new form to work extends the concepts of space and time. Nowadays, innovations in communication area and computer science generate new behaviours and new organization styles resultant by new kinds of dissemination of knowledge and new social interactions. Thus these innovations in the services of communications" nets come reinforce cooperative work, especially the based one on CSCW (Computer Supported Cooperative Work). This article presents an analysis of application boarding of CSCW in a virtual environment developed by two separate work groups for the distance.The objectives of each one of the groups were distinct. While the group "A" would have to mount a team based on cooperative work, group "B" would have to analyze the functioning of a small company and to search in the group "A", through only virtual interactions, subsidies for the elaboration of an improvement proposal. Keywords: Virtual Teams, CSCW, Groupware
1. Introduction The contemporary age is characterized by rapid and deep changes in the various areas of society. The technological development and the velocity of communication leads to a real interdependency between the social groups and the companies; the globalization of economical market breaks the physical frontiers; every day, relationship between companies are much more conducted by virtual means, making it difficult to catch up with its influence and power. Inside this complex web of relationships, the human being, as a user, becomes more demanding and integrated and, as a participant, needs to be qualified in using new technologies as a way for keeping relationships. Every day, social groups are more and more affected by the quantity of information and the new ways of relationships, in which the possibilities to
interact by means of technology are much more accessible and converging. The organizations are affected by the increasing level of competition and the fast changes of this competitive environment. Thus, they bind themselves in producing innovating products and services through projects developed by a teamwork of qualified professionals, ready to deal with multidisciplinary knowledge in flexible ways of working and supported by modern means of communication, interaction and project managing. Virtual groups are being used more frequently to generate projects rapidly and under lower costs, enabling the companies to have a better view of the changes imposed by the new economy. It is, therefore, important to study the behavior and the impact in using the virtual teams as they bring about habit changes, introduce new ways of communication and broaden the dynamics in
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exchanging information. Besides, it is apparent that they establish new meaning for the old values in social communication, affecting the level of perception, reliance and truth, as it makes use of a virtual environment which presents a paradoxical idea between the greatest physical environment and the greatest virtual proximity, as communication through the net is instantaneous. This article relates the continuity of the experiences in the development of collaborative work between the two virtual teams, through the following subject, Collaborative Work in Projects of Technological Innovation, required by the Mastering Degree Course in Technology from C E F E T - RJ, where the groups were able to interact with each other for the development of a project by using groupware tools. 2. The project
The virtual teams are now what companies seek, since they make use of a wider source of exchanging information- the electronic means, available due to the Technology of Information, which makes it possible to have some tasks done without the necessity of the physical presence in the workplace. This changing in habits together with the tendency in grouping people of different kinds of education, culture, value and experience to develop the same project demand that good conditions of work be established so that the familiarization with the concepts of organization, treatment of information and domain of new technologies become basic conditions for one to participate actively in this new scenery most part of relationships is built in a virtual environment. The projects of virtual collaboration broaden the positive and negative effects of the ways of communication based on electronic means, in which a contact with each one in the teamwork is not always possible - or even never established [ 1]. These teams, which differ in education and pointsof-view, must get together to perform requirements in the project by integrating their differences through a cooperative attitude of exchanging information and sharing experiences[2], in which reliance, perception and negotiation practice are essential for the fulfillment of the goals. 3. Groupware, CSCW and Cooperative work
The tools that enable the teamwork to develop
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their projects interactively- even if they are not together in place and t i m e - are categorized as Groupware. They are responsible for the technological support through specific software, used in the computers of the group members, leading to a more cooperative environment. The research area of projects supported by Groupware tools is called CSCW. It is the subject that studies the techniques and methodologies of group work and the supporting technologies [3]. Its goal is to study the necessary environment for the increasing level of collaboration and the potential in communication of the virtual teams. [4]. Every activity which is developed by many people, as a group, with cooperation and interaction to get to a common goal, can be defined as Cooperative Work [5]. Cooperating is a social act and it depends on human interactions such as speaking, body language, writing and face expressions. Projects developed under CSCW depend on the exchanging of information between the participants of the communication, in an individual way or in group. Communication is the key-word for the existence of cooperation. A management which warrants its sharing, accessibility and quality will be essential for the success in projects based on Cooperative Work. The way the virtual groups behave is directly affected by the quality of communication of the group. Communication may be affected by many elements related to personal differences- ego, power relations, low self-esteem, differences in feelings and opinionsand also related to management f a u l t s - lack of leadership and knowledge, disability to promote higher levels of reliance. These aspects interfere in communication and weaken the virtual teams, as it affects their quality and hinder them from concluding their projects. 4. Instructions for the success of CSCW projects
Some steps are necessary to be taken for the success of projects based on cooperative work, especially the ones based on CSCW: 4.1. Establishing trust
Confidence must not only be established within the teams but also in the relationship between teams. If mutual confidence is not reached, communications will undergo a lack of quality and tasks will not be efficiently concluded.
4.2. Defining the roles and responsibilities When it is not clear everyone's role in the virtual teams, disturbances in communication and lack of cooperation occur between them, as well as overlapping of tasks, ignorance of activities, nonfulfillment of tasks and wrong use of human resources and materials [6]
multidisciplinary characteristic of the students from the Mastering Degree Course from CEFET-RJ was also present in the arrangement of group "A", compounded by 1 student of Law, 3 company administrators, 3 engineers of production, 1 computer science engineer and 1 mechanical engineer. All the components of group "B" were graduate students in Engineering of Production.
5.2. Stimulate goals for the research 4.3. Adequate technology An appropriate infra-structure is necessary to support the virtual communication between the teams which enables the clear distribution of tasks, interaction between members, administration of the environment by the administrators of the project, the control of performance indicators, the ease in using and accessing the appropriate levels of safety.
According to instructions from the coordinators of this subject, group "A" chose a leader and joined in groups at random, according to their needs to fulfill the tasks. The interactions between groups "A" and "B" could be made considering the groups' criteria, by using any freeware tools which were accepted under agreement between integrants of both groups.
5.3. Used tools 4.4. Integration and physical proximity Although technologies can make it easy to exchange and administrate the information and the organization of the group can control and align the activities to one single objective, it is true that the settlement and maintenance of a level of friendship which permits an increasing degree of reliance require face-to-face interaction between the members of the groups. Whenever possible, it must be found a way to establish this personal contact, so as to maintain or raise the degree of reliance between people as well as to fill in the gap brought by the isolation of the individuals, which in general, focus their groups on the schedule of tasks, but not engaged with the common goal.
For the diversity of tools and the convergence of functions of software that could be used to fulfill the CSCW functions, the same tools already in use by group "B" were chosen, since they have already carried through experiences in other projects using the structure of groups from Yahoo! to create a virtual environment, the one of instantaneous MSN messages and the multimedia software of communication- voice and image - Skype (Figure 1).
5. Description of the experiment
5.1. Creation and description of the teams The experiment was carried through with two groups: group "A", compounded by eight students from the Mastering Degree Course in Technology, and group "B", compounded by eight undergraduate students. Each group had distinct goals. Group "A" should set up a team based on Cooperative Work while group "B" should analyze the operation of a small company and look for subsidies for the elaboration of an improvement proposal which would come from group "A", through virtual interactions. The
Fig. 1. Groupware tools.
5.4. Standardization of the information There has been an initial difficulty to access the registers of the activities being developed by the group. The diversity of desktops made it difficult to find the documents, the lack of standardization of the names of the electronic files hindered the association of the objects to the project, and the distribution of objects for
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the groups conveyed to a multiplicity of copies and it produced different versions of documents among the members of the groups. It was assigned to one of the members of group "A" the task of registering all the information generated by the project, excusing him from interacting directly with group "B", so as he could concentrate himself in creating and managing the virtual environment, collect data of the synchronous (chat, voice) and asynchronous communications (mail, papers, presentations). To facilitate the localization of electronic documents, a folder in common for the stations of group "A" was designed. For the identification of these documents, a rule for the nomenclature of objects was established, so that it allowed, in a simplified way, not only the identification, but also the establishment of a chronology of objects and a controlled version (It appears 2). All messages from the group had been converted into electronic files as soon as they were produced and the software of synchronous communication had been configured in a way to store the registers of the communications. By following this, the registered object number in the end of the papers - 253 files - was very superior to the one from the previous project- 38 files. It was possible to create statistics and controllers which allowed the identification of the fall in the cooperation process, by the perception of the brusque reduction in the amount of information exchanged between the groups - which motivated an intervention in group "B", and enabled the maintenance of a chronological description of all the ways of communication, synchronous and asynchronous, in a single line of time.
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Fig. 2. Pattern for electronic files of project. 5.5. Communication process
The initial communication models defined for both groups was based in a trusted and free interaction. All members of the groups could be able to send and receive - with no moderation or intervention by brokers all kind of information- files, mails, chat, voice (figure 3 ).
-
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orientation
Fig. 3. Initial model of cooperation. Initially, the model considered had made it possible to keep a synchronous interaction with any one of the components of the groups. The communications between the groups were free and independent, mostly based on chat tools - whose low capacity of reflection produced the first noises in communication between the groups. In order to diminish these impacts, group "A" decided to modify the function of its coordinator, that started to center the exchanging of information, calling him "Broker", and assigning him the whole responsibility for the interactions between the groups. At the same time, there was a change in the structure of group "B", which had the coordinator substituted. The Broker of group "A" started to concentrate the communications, the making of the documents and the administration of the virtual environment of Yahoo, and the direct access between the collaborators from group "A" and group "B" was blocked. Days after the change in the communication model, the difficulties to promote the internal interactions in group "A" persisted- for the little virtual participation of the c o m p o n e n t s - perhaps for the existence of an element, the Broker, who was responsible for the communications and concentrator of functions. The cooperation of group "B" supposedly for the impact of the gap in the first communications- also persisted low. The Broker's role has been consolidated, and he found himself overloaded, not only for the difficulty in getting the correct description of the problem from group "B" and trying to express to group "A" the real necessity of group "B", but also for the innumerable tasks that were centered and which depended on his intervention- administration of the internal group, interest in the interaction with the external group, documentation of the interactions, distribution of the
tasks and administration of the conflicts of relationship between the groups. As the end of the project was getting close, new gap in communication came about for the lack of clearness about the distribution of tasks and agreement of the current functioning of the g r o u p , - although minutes containing the rules of the functioning of the group were created and distributed. It is due to the instructions sent by group "A" for the resolution of problems, some of them completely out of focus and others still trying to fill gaps of knowledge already expressed in the first report of group "B". After internal meeting, the virtual environment of the Yahoo! was modified so that it started to be moderated by the Brokers, with the messages, associations and writing of files being accomplished only after the authorization of the moderator of the groups, which increases more and more the concentration and volume of activities of the Brokers. In the half of the stated period for the conclusion of the project, the number of deposited files and the number of messages sent by group "A" were about 100 units, being that in its majority, originated from the Broker, while only four participants of this group they had sent some message or file to the virtual environment of Yahoo. New gap occurred because one of the members of group "A" sent an e-mail message, whose content was considered inadequate by group "B", which started to ignore any attempt of communication with group "A". The reduction of the interactions between the groups demanded the interference of the coordinator of the research to be in charge of integrating them, since group "B" refused keeping interaction with the other group. The final model of communication started to count with only two elements for the exchanging of information (Fig. 4), where the Broker from group "A" centered the communications and activities of group "A" and the Broker from group "B" centered the interactions between the two groups.
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6. Comparative analysis of 2004-2005 The possibility of free interaction between the groups not only stirred up a bigger amount of initial information, but also led to a series of gaps in the communication between the groups, due to the intrinsic characteristics in the synchronous tools, which led to a less pondered content. The inexistence of a leadership clearly defined, the lack of knowledge on the considered subject, and the lack of specification of each type of activity expected from each component of group "A" who would have to solve the questions proposed by group "B", caused scattering in the inquiry of the problem. The choice of a centralizing model - the Broker - for group "A", caused an internal discomfort in relationship of the group, since the other members did not receive the information produced by the Broker in time. Besides, the Broker found himself overloaded of tasks, and without having a formal mechanism of distribution and accompaniment of tasks, it led to a low participation of the other components and, consequently, to idleness. The management and classification of the information, which only happened in 2005, increased the number of information compared to the one in 2004, however, the Broker's participation, only in the experiment of 2005, brought about quality of the information since the gap was reduced, although the quantity of activities that would have to be fulfilled by this centralizing element was greater than before. The low integration of the tools, although fully functional, delayed the acquisition and treatment of the information, which would have to be catalogued and exported to a database of consolidation out of each one of the environments. In this way, the products created through chat rooms, e-mail and virtual environment have been conveyed to an independent environment, where analysis graphs have been made. The lack of qualified pointers which have not been generated during the accomplishment of the experience
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probably made it difficult to identify the gap in the communication and intercept the non-appropriate messages that have been exchanged between the groups. The experience of the year 2005 produced a greater number of files than the one in 2004 (Fig. 5). The main reasons for that can be seen in the great concern in cataloging and registering the information, with the establishment of specific routines in group "A", such as the minutes, configuration of the virtual environment, development of parameters of the chat programs for storage of messages and recording of emails, so as to generate information. The analysis of figure 6 which shows the distribution of messages in months in which the experience occurred, presents an abrupt reduction of messages between November and December (there were no activities in October/2005). Although the pattern of distribution is similar to the one in 2004, the initial occurrences have favored the completion of the task in the first month this year, which justifies the gradual decrease in the number of messages. In 2005, even with all the available ways to generate and catalogue the information, there was a decrease in the number of files during the period they were looking for a solution for group "B" - which was not carried through completely by group "A" - due to the gap mentioned in item 5 of this article.
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One of the basic aspects for the good development of a work in group is the contribution between its members. In the collaborative work, it is basic that the activities are argued in the group, even if the tasks are divided by subgroups or by individuals. The whole group will only be coherent if the parts are sharpened, that is, all the members must have knowledge and "collaborate" in the development of each part of the project. True partnerships are formed if everything that they carry through has a common goal. Collaboration favors growth and positive valuation for the individuals. Therefore, besides getting distinct results compared to those gotten by means of individual effort, the relation between the members of the group creates a constructive dependence in terms of valuation of the other, which induces to a certain care and a collective identification of a distributed net of worldwide dimensions. Although the technologies can facilitate the exchanging and the management of the information, and despite the organization and management of the group in keeping all activities in a controlled and aligned common goal, it was not observed during the experiment the establishment and the maintenance of a level of friendship in group "B" to get to a satisfactory reliance between the teams. This fact can be observed in the description of item 5.5. The registers generated during the execution of the experience (accomplishment times, notes, comments, interactions and documents), together with the stories of the members of the group, must be the raw material for the next experience to be carried through in the spring of 2007.
References
[1] Pithon, A.J.C., 2004, "Projeto Organizacional para a Engenharia Concorrente no fimbito das Empresas Virtuais", Ph.D. Thesis, University of Minho, Portugal [2] Moraes, I., Zorzo, A., 2000, "Uma Arquitetura Gendrica para Aplica96es Colaborativas", Relat6rio T6cnico n ~ 6. [3] Greenberg, S., 1991, "Personalizable groupware: Accommodating Individual Roles and Group Differences". In: Proceedings of 2nd European Conference on Computer Supported Cooperative Work, p. 17-31 [4] Ellis, C.A.; Gibbs, S.J.; Rein, G.L., 1991, "Groupware: Some Issues and Experiences". Communications of the ACM, 34(1), p. 38-58. [5] Borges, M.R.S., 1995, "Suporte por Computador ao Trabalho Cooperativo. Jornada de Atualizagfio: Congresso Nacional da SBC". Canela, Brazil. [6] Lipnack, J.; Stamps, J.; 2000, "Virtual Teams: People Working Across Boudaries weith Technology". John Wiley & Sons, Imc.
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Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Collaborative Virtual Research Environment To Support Integration & Steering of Multi-site Experiments Daniela K. Tsaneva a, Kevin T. W. Tanb, Michael W. Daley a, Nick J. Avis ~, Philip J. Withers b aSchool of Computer Science, Cardiff University, Queen's Buildings, Newport Road, P.O. Box 916, Cardiff CF24 3XF, UK Corresponding Fax: +44(0)29-20874598 bManchester Material Science, University of Manchester, Grosvenor Street, Manchester M1 7HS, UK,
Abstract
This paper presents a prototype Virtual Research Environment (VRE) which orchestrates and exposes a set of collaborative tools to support multidisciplinary and geographically distributed teams. Our focus in this study is to support and enable teams of material scientists, academic and industrial engineers, as well as instrument scientists to work together in undertaking, compiling, analysing, interrogating and visualizing multiple experiments on components of high complexity at different sites. The VRE harnessed tools will facilitate interactive steering of 24hour experiments between post-doctoral and PhD students located on-site with senior researchers at the host institution or even at home. The developed VRE aims to enhance the student learning/training experience and to identify exciting opportunities that arise during the experiment that are currently sometimes missed. The VRE deployment is also expected to reduce the number of experimental errors. It is based on a Collaborative Web Portal providing a number of Web Services for the material scientists. One of these, the "Shared Workspace" is be based on the JSR- 168 standard to allow extra portability between our developed web portlets and other web portal framework within the VRE community. The enabled features of Web Services are to be consumed via Web Services for Remote Portlets (WSRP) by any JSR 168 compliant or non JSR 168 compliant, Java or .NET-based Web Portal. In this paper we report our initial developments and findings in the deployment and analysis of the prototype VRE.
K e y w o r d s : Virtual Research Environment (VRE), Web Portals, Geographically Distributed Teams
1. B a c k g r o u n d
During the past decade, a number of complementary material characterisation techniques have been developed, which provide maps of structure and stress inside three dimensional engineering components. Some of them are available in specialised laboratory sites, such as the Stress & Damage Unit in Manchester University, while others rely on specialised neutron and synchrotron X-ray beams and are available at International User Facilities such as
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ISIS (Rutherford Appleton Lab), SRS (Daresbury), ESRF & ILL (Grenoble). Taken together, the information provided by these techniques becomes very powerful giving a picture of the state of the structure including any defects and internal stresses. This allows the integrity and lifetime of the structure to be predicted. The experimental methods are noninvasive, which means that the evolution of structure in response to service conditions (static loads, fatigue, stress corrosion environments, etc) can be followed in real time.
The experimenters at International User Facilities must work at speed throughout 24 hours/day experiments (Fig. 1). The beam time is very precious and it may be months before another time slot is available to the team. Often post-doctoral researchers and/or PhD students travel to the site and must work in shifts alongside resident instrument scientists. Key decision points are often encountered late at night and without the benefit of a preliminary analysis of the data, support from home (university) site is often required. Due to inexperience, simple mistakes are sometimes made, but these are often only evident upon detailed off-site analysis by which time it is impossible to rectif}r the situation. Currently, this community has little experience of VREs, with telephone calls and emails being the primary method for remote discussions between the experimental site and the home site to explain the problems encountered and to utilise the expertise available. The use of the telephone has obvious limitations in visualising problem situations. Email is an asynchronous communication medium, which can often result in large delays between the exchange of ideas and makes interactive brainstorming impossible. While a supervisor may be prepared to log on to a computer at home in the middle of the night to give assistance, or receive a telephone call - a trip to the University to use the proposed VRE is not practicable! Whilst the commonly available two party face-to-face video conferencing systems coupled with limited drawing board capabilities (via MSN or Yahoo! Messenger) may allow certain aspects of the experiment to be discussed, high quality video from more than one video feed (camera) is often required allowing the remote site (i.e. supervisor) to clearly appreciate the experiments going on inside the experimental hutch. Besides, discussions centred on the experimental results ideally require shared customised analysis applications (2D and 3D visualisation) and must be capable of handling the transmission of reasonably large file sizes. It is therefore, preferable to allow the various participants to log-into a shared server which hosts the required applications to support common analysis and discussions. Experimental thinking time is precious and so interaction infrastructures must be lightweight, natural and unobtrusive to gain wide-spread acceptance.
Fig. 1 An instrument at an international facility (ESRF, France) 2. Need for Advanced Collaborative Tools
It is common that measurements are often undertaken using the laboratory frame, and the coregistration of data from different scans or instruments is very difficult [ 1]. This is especially important when data sets need to be accurately combined, for example, at least 3 strain measurements are required to calculate stress at a given point. Furthermore, only rarely can a single experiment provide the complete picture (Fig. 2). Carrying out experiments and measurements at different sites and combining them is thus a difficult task. Various software packages are often used, which can result in different coordinate systems being employed and information being stored in different formats. Data fusion requires the cooperative actions of engineers, materials scientists and instrument scientists at different sites. Ideally all team members should be able to steer the experimental strategy; to identify and focus on regions of interest, to modify the conditions (loads, temperatures, etc), or to compare the results currently being acquired with complementary archived data. An earlier EPSRC-funded Engineering Body Scanner project (GR/R38774/01) developed a suite for site-specific sample registration, compilation, reconstruction and co-visualisation tools. We have previously presented progress towards a grid enabled Engineering Body Scanner (EBS) project [2]. This project, Integrated and Steering of Multi-site Experiments for Engineering Body Scanner (ISME) is now funded as part of the JISC-VRE Programme with the aim of integrating and refining these tools into a VRE to make them deployable by teams of instrument scientists, material scientists and engineers in a transparent and robust manner. It is helping to extend the culture and functionality of collaborative multi-site experiments.
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predictions and discuss and develop an evolving experimental strategy with Grid middleware. The "shared workspace" is a repository developed for group members. It allows them to post documents, ideas and discuss processes in the purpose of sharing, storing and organising knowledge, to communicate visually and collaboratively manipulate data. Discussions would involve instrument scientists, the experimental team, project supervisor, modellers, owners of complementary data collected previously on the same component and industrialists with applied interests in the behaviour of the component.
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4. Data Management Function (User to Hub) Fig. 2 A complete experimental process on a single sample The project targets two problems, 9 the need for a mechanism/medium for experiment steering, to discuss progress, modify strategies, and to train and instruct students 9 the need for a mechanism/medium for collaboratively analysing data and making available archival data collected elsewhere for immediate sideby-side comparisons. These two themes require separate, but connected, approaches. The latter can be viewed as "software interaction" under a Data Management Function, and the former "human interaction" to achieve a Strategic Experimental Steering Function. The "human interaction" aspects are being pursued via the provision of Access Grid (AG) functionality at the remote sites while the "software interaction" activities involve embedding our previous developed EBS software within a portal service framework using toolsets such as uPortal [3].
3. Experimental Steering Function (User to User) Stress measurement often takes place at a remote experiment site and expert advice is often needed outof-office hours. Intelligent discussion, training and steering requires a combination of three modality streams on screen: 9 Group-based face to face contact, or at least voice to voice (via Access Grid) 9A shared view of the experimental set-up (using Access Grid) 9 A common shared 'tablet' or 'workspace' to visualise results from the Data Management Function Interactions via the multimedia resource should be at the experimental level, whereby the whole team can 'meet' together bringing their own data, modelling
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Once the experiment has begun the software required to assimilate the data can often not be run at the workcell, or remote facility(Fig. 1) usuallybecause of computing, software or time constraints. Onlyrarely is it possible to compare results with those previously collected elsewhere. As a consequence a picture of the quality and significance of the data is often not available until the scientist returns to their home institution and post-processes the collected data. Discussions at this point, with project supervisors, and potentially industrial engineers using live data, as well as archived data, will add real value to the experiments. The availability of the whole database to members of the team along with all the analysis tools of the EBS project allows effective strategies to be devised. Within the previous EBS project the appropriate software tools for material scientists and engineers have already been developed to assist them conduct physical experiments on large scale instruments, numerical modelling and to gain efficiencies and scientific insight. Although these tools have been disseminated to other groups at present they operate in a discrete unconnected manner. We will grid enable: 9Virtual running of experiment prior to travel 9 Automated set-up and component mounting Access to data processing suite (local or remote, using a variety of in-house and commercial codes) 9Data recording and archiving 9Download & uploading of data & analysis 9 Visualization (either using local or remote resources and collaborative, 1D, 2D, 3D and 4D) 9Data co-registration & co-visualisation 9Presentation and interrogation of assimilated 3D data at remote sites (includes industry sites) 5. Collaborative Tools to Use For the Experimental Steering Function we have
trialled Access Grid, focusing primarily on how best to configure it to optimise HCI and usability. To this end we have established our own 'virtual venue'. Due to the nature of our experiments and cost implications it is deemed more appropriate to use Access Grid predominantly on a computer with good quality webcam rather than non-portable traditional Access Grid studios. This is because firstly, for the experimenter, involvement must be seamless with the practical experimental function and secondly, because academics may need to enter into dialogue at home at unsociable hours. Connectivity between the two Functionalities will be achieved through the use of a shared virtual screen ("Shared Workspace") on which data analysed in the Data Functionality can be viewed on the web portal through the use of a standard web browser. To establish what web services are required bythe material scientists, a questionnaire for the Material Science Centre, University of Manchester has been prepared consisting of 13 questions. The interviews have been conducted with 6 members of the research group - a supervisor and project manager, a lecturer, an instrument scientist and three PhD students. The following outcomes regarding the required web services were summarised: 9 A proper communication with visualisation is required, which will help to show the problem or the data. 9
9
9
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There is a need for shared desktop/workspace for better collaboration, especially when necessary to communicate problems and share data. A data archive is required, so that the users would be able to retrieve the documents and the data, to have an easy access to previous work, with the experiments recorded, the data and reports. A log book of the experiment is useful, very simple and easy to use, including only pictures and text. To be able to analyse the data at the facilities would be also very useful. To maintain a framework to store all the data at the same format, like metadata, XML, and have access to it. A catalogue tool to organise the data you transfer. A tool to ensure you have the latest version of the data you are using is also required. The problem to send big data (GBytes) back home should be overcome - may be by using a very fast Internet connection. Would be nice to have Access Grid their, but it
9 9 9
9
should be already set up, easy to use, portable, as a package, not to require additional time to be used. To have access to a very powerful computer via Internet and have results quickly. A project scheduling tool would be also useful to plan the experiment and to keep a diary during it To simulate the experiment in advance, like a virtual experiment, so the new students can get used to the procedure and the facilities. To have more staff at the facility to support the
material scientists at their experiments. Currently, we are working on deploying initially three of the required services on the web p o r t a l - the shared desktop, the virtual log book and the project scheduler.
5.1. JSR-168 Compliant Web Portals Whilst the web portal concept can acts as an efficient medium for our "Shared Workspace" (discussion), to download/upload information (achieving/restore) or even to retrieve previous experiment strategic (playback virtual running experiments), it is imperative that our web portal conform to a standard to allow efficient ways to deploy, share and even reuse portlets from various other VRE projects. It came to our attention that JSR 168 is a Java Specification Request (JSR) that establishes a standard API for creating portlets. One of our web portal choices, uPortal is based on previous experience, has been developed in close conformance to the JSR 168 standards. This brings benefits of interoperability achieved by standardisation between various web portal services. It means that portlets developed from our web portal efforts are portable and could be deployed by other web portals associated with other VRE projects, and this standardization furthermore simplifies upgrading existing systems, as well as developing new ones.
5.2. Non-JSR 168 Compliant Web Portlets With JSR 168 compliant web portlets able to plug directly into any Java-based portal framework, developers still need to source the portlet and run it locally on their web portal framework. In addition the 'plug-n-play' concept means that the portlets between web portals should also conform towards a non- Java-
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While XML-based web services have been used in different API platforms to transfer data between them, a new concept, Web Service for Remote Portlets (WSRP) allows portlets to be exposed as Web services [5]. The resulting Web service will be user-facing and interactive among different web portals. Unlike traditional Web services, WSRP will carry both application and presentation logic that can be displayed by a consuming portal. To the end user, remote portlets will look like and interact with the user just as local portlets would. Our "Shared Workspace" JSR- 168 portlets can be exposed as pluggable Web services for other portals to consume. The overall design architecture of the ISME web portal is shown in Fig. 3. The consuming portal interacts with the remote portal service through a firewall proxy since it uses as a HTTP-based XML Web Services. Since we deploy it as Web services, we get the added benefit of being able to deploy our remote portlets in any programming language (.NET or any not JSR-168 compliant Web Portal), given that the interface laws are laid down by the XML-based Web Service Description Language (WSDL) interface description. It is therefore, the job of a remote portlet
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Web service to deliver HTML, XML, or any content an end portal client might expect. 6. Access Grid Trial
For the Experimental Steering Function we have begun to trial Access Grid (AG) between Manchester Materials, The Daresbury Laboratory (Fig. 4) and Cardiff University, focusing primarily on how best to configure it to optimise HCI and usability. The connectivity between the above two aspects of the problem mentioned in section 2, will be achieved through the use of a shared virtual screen on which data analysed in the Data Functionality can be viewed on the AG portal. During this first phase of the AG configuration, the project has compared two AG software toolkits; inSORS and AGToolkit. inSORS [6] is commercialware having better usability to initiate from the webbased interface, whilst AGToolkit [7], an open source toolkit, presently lacks user friendly features in its Windows-Python based interface which also can prolong the training for new users and impacts negatively on the familiarity and usability of the AG software. Both software toolkits have been developed assuming normal office working environments. However, when Personal Interface to the Access Grid (PIG) is deployed in experimental hutches there is often considerable background noise. In our first experiments conducted in Station 16.3 at Daresbury Laboratories, the inSORs toolkit failed to filter this background noise to an acceptable level, whereas the AGToolkit using RAT (Robust Audio Tool) appeared to cope well. In these experimental settings we are therefore presented with a greater challenge when
choosing AG software capable of delivering good quality of video and audio in noisy science laboratory environments.
work, which has been developed at ISIS using the XML file format. We are looking into the potential of working with other VRE projects to embed certain advanced features (as part of our WSRP pluggable web portlet) such as Access Grid Recording Sections, Memetic [8] from Manchester Computing. In the future we will perform usability studies using different experimental case studies to determine the potential of the project.
Acknowledgement
Fig. 4 Daresbury experimental hutch viewed from an AG node. We have just started to deploy a new Access Grid Node at ISIS with the same specifications as that at Daresbury. In addition we have also experimented with the use of a wearable access grid as depicted in Fig 5. The wearable node consists of a mini laptop (Toshiba Libretto), Headphones and boom microphone, mouse and mini web camera. In addition there are a pair of LCD Display glasses that can be used to display extra data such as a 3D images or data sheets. The whole unit is completely wireless so that the operator can move around freely.
Fig. 5 Wearable Access Grid
7. Future Work At this stage of the project, we have developed a preliminary version of our "Shared Workspace" based on WSRP (shown in Fig. 3) to be consumed in .NETbased web portals. VREs are a relatively new technology especially in materials sciences field and we will collaborate closely with a wide range of material scientists to determine an ontology and a workflow model. Our present work in this area has highlighted different terminology used at different experimental centres and we had planned to extend the
The authors gratefully acknowledge the funding from the JISC-VRE to support this second year of the ISME JISC project. (http://www.jisc.ac.uk/index.cfm?name=programmevre).
References [ 1] G.A. Webster, 2000, Neutron Diffraction Measurements of Residual Stress in Ring & Plug, Versailles Project on Advanced Materials & Structures TWA20, Tech Report No. 38 ISSN 1016-2186, p. 64 [2] K.T.W. Tan, N.J. Avis, G. Johnson and P.J. Withers, 2004, Towards a grid enabled engineering body scanner. UK e-Science Programme All Hands Meeting 2004, Nottingham [3] uPortal - http://www.uportal.org [4] R. Allan et. al., Sakai: new Collaboration and Learning Environment (CLE). http://tyne.dl.ac.uk/Sakai, 2005 [5] Web Services for Remote Portlets http ://ww w. oas isopen, org/c om m ittee s/w srp [6] inSORS- Multimedia Conferencing & Collaboration Software, http://www.insors.com [7] The Access Grid P r o j e c t - a grid community, http://www.accessgrid.org [8] M. Daw et. al., Meeting Memory Technologies Informing Collaboration, Manchester Computing, University of Manchester. http://www.memetic-vre.net, 2005 [9] M. Baker and R. Lakhoo, Narada Brokering Video and Chat Services, Distributed Systems Group, University of Portsmouth, 2005, http://dsg.port.ac.uk/projects/ VRE/reports/NB_Chat_and_Video_report.pdf [10] G. B. Wills et. al., Towards Grid Services for a Virtual Research Environment, Fifth IEEE International Conference on Advanced Learning Technologies (ICALT'05), 2005, pp. 863-867 [11] M. Hayes et. al., GROWL: A Lightweight Grid Services Toolkit and Applications, UK e-Science Programme All Hands Meeting 2005, Nottingham [12] Virtual Research Environments Programme, http://www.jisc.ac.uk/index.cfm?name=programme vre
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Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
e-Cat- Members Profiling and Competency Management Tool for Virtual Organization Breeding Environment Jifi Hodik a, Petr Be~vfi~ b, Jifi Vokfinek a, Jifi Biba a, Eduard Semsch a a
Gerstner Laboratory, Department of Cybernetics, Czech Technical University in Prague, Technickd 2, 166 27 Prague, Czech Republic b CertiCon, a.s., Applied Research, Vdclavskd 12, 120 O0 Prague, Czech Republic
Abstract
The e-Cat is a research prototype of a tool for maintenance of members' profiles and competencies in a Virtual Organisation Breeding Environment. The system combines peer-to-peer approach and centralized architecture. Distribution enables members to maintain their profiles locally and to work in the off-line mode when needed. Centralized components of the system ensure coherence in the common schema of competency and support common understanding. They also prevent anonymous users from advertising incorrect data via the catalogue and allow full control over members entering or leaving the community. Keywords: Virtual organization breeding environment, virtual organization, competency management
1. Introduction
The clustering and integration of Small and Medium Enterprises (SME) is a natural evolution that reflects acceleration and increasing complexity of the business opportunities [ 1]. Most of the forms of virtual integration created for improving cooperation among independent entities [2] are covered by Collaborative Network Organizations (CNO). To work effectively, CNO needs to be supported with appropriate technologies that would provide effective partners searching, social knowledge management, negotiation support and other tasks. The existing support tools (based on web portals, emails, databases, etc.) work well, however, they work separately without any possibility to ensure consistent view of CNO. In the area of CNO, terminology is not unified. Terminology used in this work is based on the research done by Camarinha-Matos and Afsarmanesh e.g. in [3]. Their research is oriented toward Virtual
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Organizations (VO) and Virtual Organization Breeding Environments (VBE). VO introduces a temporary coalition of organisations, which utilizes pre-prepared and pre-negotiated general parts of the contract. VBE introduces a pool (alliance) of entities that is established in order to facilitate exploitation of possible and profitable collaborations by means of VOs creation and management. Operation of VBE and its institution is financed by membership fees. One of the missions of VBE is to facilitate sharing information of its members' profiles and competencies. This work introduces e - C a t - a VBE members profiling and competency management system.
2. Theoretical Framework
This part briefly summarizes competency management terminology used in this work since the most important terms like competency and profile are
used in various publications with a slightly different meaning. We have also found useful to strictly differentiate between competency class and competency instance. For the purpose of e-Cat the following definition is used: Competency is an ability to perform business processes, which is supported by necessary available resources, practices and activities, allowing the organization to offer products~services. Competency class defines existence of the competency in the world, and tries to define it and distinguish it from other existing competencies. Competency class can also define means that can be used to measure the level and robustness of the competency. According to HR-XML [4] schema, the specifying attribute of competencies is called Competency Evidence (HR-XML is focused on human resources management but it is also easily applicable to other CNO domains). Competency Evidence approach is used to describe features of a competency class (e.g. capacity, resources and others). Competency class does not relate to any particular subject (person or company) and its Competency Evidences are not bound to any particular values. Ifthe class is not specific enough, it can be divided into subclasses - specializing classes. Thus, every competency class can have its generalizing and specializing class(es). Taxonomy structure is used to organize larger sets of competency classes and to comprehend the relations among competencies. Different sets of competency classes may use different description systems for the same competency. Competency instance always refers exactly to one competency class and to one subject (company, person, VBE, etc). If the competency class defines Competency Evidences, the competency instance can optionally assign values to them. One competency class can be instantiated multiple times by different subjects. Instances usually vary in values of Competency Evidences. In the e-Cat system, each subject instantiates as many competency classes as many competencies they offer, and each subject can instantiate any of the competency classes only once. In this work we also use partner's profile, which is based on two main blocks: (i) general information about the partner, and (ii) a set of instantiated competencies derived from the competency classes.
3. e-Cat Design
and private knowledge that is not intended to be shared. Presented technology takes this constraint into account and therefore it is based on distributed elements organized in peer-to-peer network. On the other hand, the power of VBE is the support of its members in VO creation process by various centralized components provided by VBE supporting institutions. So the e-Cat system utilizes naturally centralized elements too. Such a solution enables effective cooperation in a distributed environment as well as support provided by VBE. VBE members profiling and competency management tool should provide three main services: 9 9 9
Management of VBE members' profiles Management of competency classes Management of access rights to the information provided within VBE.
In e-Cat, these services are provided by specialized components. According to the requirements of the system, e-Cat consists of distributed as well as centralized components. Distributed elements ensure maximal independence of VBE members and facilitate storing sensitive information on their local servers. Local copy of data allows each member to use the system, even if it is totally disconnected from the rest of the world. On the other hand, a "master copy" of published data is managed by each member so it is fully controlled by them. Centralized elements ensure common understanding of competencies in the whole VBE and maintaining identifying information about VBE members. They can also restrict the access to the community only to authorized members. All centrally maintained data should be supervised only by the responsible expert. Each authorized distributed element creates a local copy of the centrally maintained data, so even if central element is temporary inaccessible, the system works. The e-Cat consists of following subsystems (each part is discussed in detail later):
Distributed Profile Catalogue, which keeps, manages and distributes profiles of VBE members. Because of the distribution the members can maintain their profiles individually. Each member of VBE has a read-write access to its profile and a read-only access to other members' profiles.
A VBE is a naturally distributed environment of independent SMEs having their own business goals
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Catalogue of C o m p e t e n c y Classes, which defines the competencies available in the VBE and their exact description, taxonomy and attributes. It ensures coherence in the common schema of competency. M e m b e r s Registration Authority, which allows full control over members entering community, and maintains data for identification of partners.
Members Registration Authority and Catalogue of Competency Classes are intended to be deployed on VBE management servers maintained by VBE support institutions. The Profile Catalogue can be distributed and in such case it is deployed on each member's server. Users can share servers (does not matter, where the physical equipment is located) to install their parts of Profile Catalogue. The VBE management server can also contain specialized part of Profile Catalogue with web interface to summarize data from all members and represent the profile of VBE as a whole. As an extreme case, the distributed part of the catalogue may be omitted and the whole system can be deployed on one server. The Fig. 1 presents the use cases of e-Cat system. The Fig. 3b presents the e-Cat architecture.
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3.1. Members Registration Authority The Members Registration Authority enables members to join the community. It also maintains the basic information about them. This part is designed to be centralized to allow the VBE management to control the members entering the community. The authority also maintains the contact and identification information, which is used to authorize each VBE member. Therefore it prevents anonymous users from advertising data via the catalogue and anybody from pretending to act as an existing VBE member. Each member's record contains information to be used by a human user and by the e-Cat system. The record is divided into two parts: (i) Exact identification of a member. Identification consists of the name ofthe company, the postal address and the e-Cat contact information. It can be edited only by the VBE management. (ii) Additional contact information (phone and FAX numbers, addresses of web-sites and e-mails) that can be edited by each user.
3.2. Catalogue of Competency Classes This catalogue contains competency classes and the relations among them. It is hierarchically organized in tree-like structures to enable defining generalizations and specializations of individual competencies. The catalogue defines the schema that is used by all VBE
members. It is centralized in order to ensure existence of one common schema in VBE and to support common understanding of this schema. This also facilitates management of commonly understandable member's profiles within the whole VBE community. The catalogue is edited by a "competency expert" of the VBE, who is responsible for clear definition of classes. The competency expert can create, edit and remove any competency class and also search and navigate through the database of competency classes. VBE members can download whole database of classes, search and navigate through it, and instantiate competency classes in their profiles. The competency expert is also responsible for contacting the members who have instantiated a class before modification. Competency expert has to ask them to actualize the competency instance because only the competencies and their details defined in the actual version of the catalogue can be searched. This may cause a profles consistency problem. The valid version of competency class is the actual one presented in the catalogue. The initial set of competency classes and taxonomy is supposed to exist in the beginning of VBE operation phase. During the VBE creation phase, Catalogue of Competency Classes includes this predefined catalogue. Data can be consequently modified during operation phase of VBE without any limitations. The initial set of competency classes is given by VBE members, or adopted from any reputable source.
member can add (instantiate), edit and remove a competency in its profile using an existing competency class. This operation may contain several steps, depending on competency description model used. When working with profiles of other VBE members, a VBE member can search and navigate through the other members' profiles. Each member can make local backup copy of remotely stored data and thus outlast a period of their inaccessibility. In the case of competency class modification, the member is informed by a competency expert. Member is responsible for updating the profile as soon as possible. It can happen that some other partner performs a search between the competency modification and the profile updating. In this case, consistency is not assured and searching mechanism may provide incorrect results. Distribution is a native feature of Profile Catalogue but sometimes it is not applied: (i) Components of Profile Catalogue are mainly deployed on servers of VBE members; one or more agents can also be installed on one server, maintained e.g. byVBE management. When necessary, multiple components of Profile Catalogue can be installed on one server, sharing the same user interface. (ii) Some members want to use the e-Cat for searching for partners but they are not able or they do not want to maintain the master copy of their profile. In such case an external expert hired by the member or provided by the VBE management maintains their profiles.
3.3. Profile Catalogue
4. Technology Used
The main task of profiles management system is to keep, manage and distribute profiles of VBE members. This system is designed as distributed to allow the members to maintain their profiles individually- this is a very important feature because of the requirements for information privacy. Each component of Competency Profile represents one VBE member and manages a master copy of its profile. It also communicates with Members Registration Authority in order to keep social knowledge (identification and communication details of other VBE members) up to date and with Catalogue of Competency Classes to keep the actual competency database. Whenever the local profile is updated, it is distributed to all known VBE members. If some information (member database, competency database, profile of some member) is expected to be out of date, the particular partner is queried for the data. When working with its own profile, the VBE
The e-Cat system is a distributed system that uses multi-agent technology as the ICT. Multi-agent technology is not only a distributed technology that could support this research prototype; it was chosen because of existing suitable components and easy implementation of the whole system. Multi-agent part of the system is implemented using JADE multi-agent platform [5, 6]. Each component of the e-Cat system (Catalogue of Competency Classes, Members Registration Authority and Profile Catalogue) consists of agents specialized to perform various services (Web GUI, communication with other e-Cat components, etc.). For communication among agents within one e-Cat component, JADE native intra-platform messaging technology is used. For communication among e-Cat components (centralized
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Fig. 2. Expert's interface of Members Registration Authority to create and update profile of VBE member
servers and distributed parts of Profile Catalogue), HTTP protocol based inter-platform messaging is used. This interaction is performed using standard FIPA protocols [7]. XML format is used as the Message Content Language. For all user interfaces the web-based thin clients are employed. This technology allows the end users to use the system without installing any special software on their computers. The e-Cat is based on E2E technology that was developed for projects ExPlanTech [8] and ExtraPlanT [9] as a core technology for supporting extra enterprise cooperation. The server side applications of the web interfaces are based on EEAgents also developed within the ExtraPlanT. To implement the server side of the application, Apache Jakarta Tomcat Servlet Container [10] has been used. The application combines Servlet and JSP (JavaServer Pages) [11] technology.
5. Scenarios
5.1. Joining the e-Cat community and creating a new profile New member of VBE installs Profile Catalogue Component of e-Cat on a server. Following configuration includes adding addresses of Members Registration Authority and Catalogue of Competency Classes, which are provided to the company during the
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process ofjoining the VBE. Then an expert of the Members Registration Authority creates a new record in the register including basic member's contact information, and the name and the address of the new e-Cat member. After this procedure the other members are notified that a new partner has joined the community. The expert's interface of Members Registration Authority for creating, updating and viewing profiles of VBE members is presented on the Fig. 2. If the new member decides to offer some services to other VBE members, the competency class for such services must be found in Catalogue of Competency Classes. If the proper class does not exist in the catalogue, it can be added in cooperation with catalogue expert management, or the generalizing competency is used. Selected competency classes are instantiated in the profile and the user may assign values to their competency evidences in order to quantify and qualify them. When they are set, the profile is automatically distributed to all known VBE members.
5.2. Lookingforprovider of competency The search engine of e-Cat offers various attributes for finding potential partners. Local copy of profiles of other members is searched for the competency. If the local copy of profiles is lost or outdated, partners are asked for data dynamically (if obsolescence of data is not recognized and thus update is not done, only an intersection of former and actual versions is utilized). If the search result is unsatisfactory, user can decide to use taxonomy to find generalizing or specializing competency and search profiles for them. Sequence diagram of data exchanges showing one member (represented by E2E agent responsible for negotiation within e-Cat system) searching for a competency is presented on Fig. 3b.
6. Conclusion
The e-Cat system is a research prototype of VBE partners profiling and competency management tool. It combines peer-to-peer approach with centralized architecture. The e-Cat consists of three main parts. The first one is the Members Registration Authority, which is the gate to the VBE community. It maintains static information about all the VBE members. The second
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one is the Catalogue of Competency Classes, which ensures common understanding ofcompetencies in the whole VBE. The third part is the Profiles Catalogue, the task of which is the maintenance of profiles of the individual VBE members. This component is distributed in order to enable VBE members to maintain their profiles locally and to work in the offline mode when needed. The e-Cat is developed as a members profiling and competency management tool for one VBE only. Ifthe Catalogue of Competency Classes is shared among multiple VBEs then the instances of competency classes are ensured to be correctly visible and understandable across the VBEs sharing this catalogue. The Members Registration Authority is always unique for each VBE.
Acknowledgements
This research work has been supported by the EU Integrated Project, European Collaborative Networked Organizations Leadership (ECOLEAD), I'PROMS Network of Excellence and the Ministry of Education, Youth and Sports of the Czech Republic grant No. MSM 6840770013. References
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2002. Kluwer Academic / Plenum Publishers. [2] Hagel III J and Armstrong AG. Net Gain: Expanding Markets Through Virtual Communities. HBS Press, 1997. [3] Camarinha-Matos L and Afsarmanesh H. Collaborative Networked Organizations / A research agenda for emerging business models. Kluwer Academic Publishers, Norwell, MA, 2004. [4] HR-XML. HR-XML Consortium homepage [online]. http://ns.hr-xml.org, 08 2004. [5] Bellifemine F, Rimassa G, and. Poggi A. JADE- A FIPA-compliant agent framework. In Proceedings of 4th International Conference on the Practical Applicationsof Intelligent Agents and Multi-Agent Technology, London, 1999. [6] JADE. Java Agent Development Framework TILAB homepage [online]. http://jade.cselt.it, 09 2005. [7] FIPA. The Foundation for Intelligent Physical Agents Homepage [online]. http://www.fipa.org, 12 2003. [8] P6chou~ek M, Riha A, Vokfinek J, Mafik V and Pra~ma V. Explantech: applying multi-agent systems in production planning.In International Journal of Production Research, 40(15):3681-3692, 2002. [9] Hodik J, Be~vfi~ P, Pechoueek M, Vokfinek J and Pospigil J. ExPlanTech and ExtraPlant: multi-agent technology for production planning, simulation and extra-enterprise collaboration. In International Journal of Computer Systems Science and Engineering, vol. 20, no. 5. 2005, p. 357-367 [10] Tomcat. Tomcat homepage [online]. http://jakarta.apache.org/tomcat/, 08 2005. [11] JSP. JavaServer Pages Technology Sun Microsystems homepage [online]. http://java.sun.com/products/jsp/, 08 2005.
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Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UIC Published by Elsevier Ltd. All fights reserved.
E-collaboration- a literature analysis Yingli Wang a a
CUIMRC, Cardiff Business School, Cardiff University, Cardiff CFIO 3EU, UK
Abstract Recent advances in inter-enterprise software and communication technologies, along with the trends towards globalization, networking, mass customization, and digitization in the context of the supply chain, have led to the development of'e-collaboration' concept. E-collaboration has been seen as a new way doing business and a strategic weapon which could fundamentally change the traditional business relationships. However emerging in the late1990s, it is still relatively embryonic. There is a confusing assortment in both academic and practical areas of what ecollaboration really implies and how it differs from traditional collaboration. The purpose of this paper is to provide a basic appreciation of the current literature. It examines altematives of definitions & practices, the evolution of ecollaboration, and the supporting systems and tools. By looking at different well-documented case studies, it investigates how e-collaboration alters the way of doing business and its impact on business relationships. The paper concludes by summarizing the evolution of e-collaboration and highlighting future research opportunities.
Keywords: e-collaboration, literature review, e-business system, networking, e-supply chain
1. Introduction Collaboration in the supply chain has been widely discussed, and a wealth of concepts is at hand. The origin of supply chain collaboration could trace back to the emergence and promotion of supply chain management philosophy over the last two dedicates where it is realized that competition no long takes place between individual businesses, but between entire supply chains. Collaboration can provide the competitive edge that enables all the business partners in the supply chain act as one in order to achieve synchronised and seamless supply chain[I]. Collaboration means for improving a supply chain by increasing the intensity and scope of co-operative behaviour between two or more independent decisionmaking units. Enabled and supported by advanced information and communication technology (ICT), it is argued that the value and importance of collaboration has
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changed, as we migrate from traditional SCM approach to the eSCM perspective [2]. Recent advances in inter-enterprise software and communication technologies, along with the trends towards globalization, networking, mass customization, and digitization in the supply chain, have led to the development of 'e-collaboration' concept [3]. E-collaboration has been seen as a new way doing business and a strategic weapon which could fundamentally change the traditional business relationships. However emerging in the late-1990s, it is still relatively embryonic. There is a confusing assortment in both academic and practical areas of what e-collaboration really implies and how it differs from traditional collaboration. In order to fully appreciate and utilize the potential of e-collaboration, there is a need for a deeper understanding of the full significance of a number of issues, such as what does e-collaboration really imply? Why do organisations need e-
collaboration? How are the developments in ICT fostering collaboration? This paper aims to investigate the answers to the above questions by a synthesis of the available literature. First, it examines alternatives of definitions & practices, the development of e-collaboration, and the supporting systems and tools. Second, by highlighting several case studies, it investigates how e-collaboration alters the way of doing business and its impact on business relationships. The paper concludes by summarizing the evolution of e-collaboration and highlighting future research opportunities. 2. What is e-collaboration?
E-collaboration in the context of supply chain is an amorphous meta-concept that has been interpreted in many different ways by both organisations and individuals. The academic definitions of 'e' of ecollaboration mainly focus on B2B internet-based technologies, while practical definitions have wider scope referring to any electronic technologies. For examples, Johnson and Whang [4] defines ecollaboration as "business-to-business interactions facilitated by the Internet. These interactions go beyond simple buy/sell transactions and may be better described as relationships. These include such activities as information sharing and integration, decision sharing, process sharing, and resource sharing". McDonnell [5] considers e-collaboration as internet-based collaboration which integrates people and processes giving flexibility to supply and service chains. In practice, the concept of e-collaboration is actively promoted by leading software and hardware providers e.g. IBM, Oracle, SAP, etc., and has been discussed in more loosely defined ways. ECollaboration, according to Grocery Manufacturers of America Association, is the use of Internet based technologies to facilitate continuous automated exchange of information between supply chain partners. They claim that "E-Collaboration is about companies working together to integrate their operations and eliminate barriers that impact their ability to satisfy consumers and drive out unnecessary cost. It is being used to integrate previously separate aspects of the supply chain and to enhance the value delivered to the consumer by providing a series of practical improvement concepts to unlock this value" [6]. For IBM, e-collaboration means "anything that allows people to collaborate - or work together-
more easily using electronic tools"[7]. They emphasize that "E-collaboration requires complex connections among a multitude of existing systems and sophisticated middleware to translate data from one system to another in a way that will make sense to the user based on his or her job function... information won't flow and facilitate e-collaboration unless it's attached to a good foundation of tightly linked enterprise systems". There are other streams in defining ecollaboration as virtual teaming of structured communication activities by using electronic tools e.g. blogs, groupware, discussion boards, portals and instant messaging [8]. For instance, Ned Kock and John Nosek [9] simply think e-collaboration is collaboration among individuals engaged in a common task using electronic technologies. Under this condition, e-collaboration is referred to as ecommunication, which seems to be too narrow focused in supply chain context. The recent invent of web-based technologies e.g. XML and sophisticated middleware have made it more flexible and less costly in intra- and interenterprise systems connection. This resulted in timely information sharing, process automation, reduction of lead time and inventory, and increased responsiveness to the demand. Therefore internet technologies serve as key driver in triggering the fast development of ecollaboration between business partners. But that is not to say that e-collaboration can only happen based purely on internet technology. The using ofintranet or extranet to exchange information e.g. EDI obviously fall in e-collaboration category as well. Hence a combination of both academic and practical definitions seems more adequate to define ecollaboration i.e. it is the collaboration between two or more independent decision-making units facilitated by electronic technologies in order to share risks and rewards that result in higher business performance than would be achieved by the units individually. It has found that the taxonomy of e-collaboration i.e. where and what activities we can e-collaborate is also confusing. With wide spread interest in the bullwhip effect, information sharing has seen the most research. Process sharing like collaborative planning and product design is also attracting increase attention. Johnson and Whang [4] mentioned that ecollaboration goes beyond simply e-buy and e-sell activities and includes activities such as information sharing and integration, decision sharing, process sharing and resource sharing (see Figure 1). Though
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Fig. 1: E-business forms and their impact (Source: [4]) Johnson and Whang depict a clear boundary of ecollaboration, other authors argue that collaboration is not limited only with upstream suppliers and downstream customers. It shall include both vertical and horizontal collaboration. The latter involves collaboration with third party logistics providers, financial service providers and even with the competitors [10]. Enabled by advanced interenterprise systems, the collaboration no long exists just in the linear supply chain and it migrate towards the dynamic interaction in the supply networks between virtual organisations. L. R. Williams et al [2] provide a much wider picture of e-collaboration including almost every group of stakeholders of an organisation. In line with the extension of e-collaboration boundary, similar terms like virtual/extended enterprise, adaptive supply chain network (ASN), and collaborative commerce (c-commerce) are developed and widely discussed in the literature, which may lead to more confusion [11-13]. Nevertheless, apart from the differences, those terms to some extent all address similar attributes of e-collaboration. Overall it is demonstrated that e-collaboration is a very broad and encompassing term in supply chain management. Up to date, there is no single silver definition of what it really implies. It has different interpretations under different contexts. Future research which aims to address inter-organisation process-oriented interactions should explore to incorporate third party logistics, system integrators, etc. into Johnson and Whang [4]'s framework. Meanwhile it seems that the intra-organisation ecollaboration is less difficulty to implement with the wide adoption of Enterprise Resource Planning (ERP) systems. But little research has been done to clarify and investigate in depth on how companies utilise information technology to achieve supply chain effectiveness through collaboration across internal processes.
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3. Why do we need e-collaboration?
There are many driving forces well documented in the literature behind collaboration, for example [3, 14, 15]. The reasons why companies adopt ecollaboration are mainly driven by the increasing need of information visibility and sharing along the supply chain, the efficient communication in a distributed network, the cost reduction, JIT and time compression philosophy, process automation, increased potential opportunities on partnership, and the flexibility and adaptability. Without the advances of enabling ICT technologies, all above could not be realistically achieved. SCM initiatives in recent years such as VMI, CPFR, Cross docking, Continuous replenishment have proved that e-collaboration can fundamentally change the inter-organisational processes, reshapes business relationships and bring competitive advantages into the organisation. L. R. Williams, et al.[2] point that because ecollaboration is created via electronic linkages, thereby providing low switching cost, it allows for the supply chain configuration to be very adaptable to changing trends, consumer preferences and competitive pressures. It can be used as a balancing act for the companies to seek equilibrium between the costs associated with arm's length relationships and the structural benefits of traditional supply chain management. This argument is also supported by [ 16, 17]. The ability of e-collaboration to rotate and re-link is changing the underlying philosophy about business relationship. Some authors (e.g. [ 18]) suggest that ecollaboration is no longer a source of competitive advantages and it becomes a 'must'. Whiles others (e.g. [19]) claim that it will provide competitive advantages if an organisation leverages the 'intelligence' inherent in the SCM network and transform existing business processes. Nevertheless there is no doubt that the collaboration fostered by ebusiness has shifted to the coordination of the activities of a dynamic supply chain network. 4. How Are Developments in ICT Fostering Collaboration?
Internet and e-business applications have significantly influenced the operation of SCM, and increasingly separate the flow of information from the flow of physical goods. Hence it transformed the traditional supply chain into more advanced so-called
'e-supply chain' which by definition means the supply chain mediated by e-business technology. The evolution of e-supply chain also represents the evolution of e-collaboration, which could be demonstrated through a road map developed by[20]. Similar with Stevens's four stage integration model [21 ], four types of e-SCM are proposed, indicating that e-collaboration evolves from the reduction of waste in the supply chain towards increased responsiveness, customer satisfaction, and competitiveness among all members of the partnership. It is argued that collaborative supply chain management systems allow organisations to progress beyond operational-level information exchange and optimization and can transform a business and its partners into more competitive organisations. Enterprises falling into different eSCM types have different properties and characteristics of e-collaboration, as well as different means of implementation and utilisation of e-business resources. T. McLaren et a1.,[22] further clarified ecollaboration systems into three major types: message-based systems, electronic procurement hubs, portals, or marketplaces and shared collaborative systems (mainly one-to-one inter-organisational information systems). By putting those systems into classification (Figure 2), the authors provide a firstcut approximation of which situations each system is most appropriate for, and thus lay foundation for future inter-organisational e-collaboration research. Strictly speaking, the systems like offline auctioning which are out of dotted line circle in Figure 2 should be excluded from e-collaboration system, because little electronic linkage is actually in place. The future research should concentrate on those systems which are within the circle. It needs to point out that that a new business model termed e-network has emerged in the late 1990s and has not been explicitly incorporated into the classification in Figure 2 yet. The major difference between e-network and e-marketplace is that the former mainly uses a web-based platform for strategic alignment, while the latter normally for spot trading[23]. Few empirical studies have looked at the impact of fostering supply chain collaboration by means of electronic tools, though lOSs provides ample evidence of the benefits of electronic integration. Luc Cassivi, et al. [24] did a detailed multiple-case study combined with an electronic survey in electronics
industry to investigate further the impact of ecollaboration tools on firms' performance. The study contributes remarkably to the e-collaboration literature, and provides in-depth insight on the identification of e-collaboration tools, the assessment of their relative efficiency throughout the entire supply chain, and mapping out the tools' potential to enhance the firms' performance. Two different types of e-collaboration tools are classified; supply chain planning (SCP) tools and supply chain execution (SCE) tools. The former is to plan the optimal (minimal) use of inputs (human and monetary resources) into production process and the latter handles day-to-day activities to maximize productivity (output and flexibility).
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Fig. 2: Interorganisational systems for supply chain collaboration (Source: [22]) Although the case study conducted by [24] is about a specific telecommunications equipment supply chain, the e-collaboration tools being identified can be observed in other industries as well. Hence they could be the representative examples in general. The study particularly discovers a top-down approach to the use of e-collaboration tools where the flow of decisions originates with the customers' customer and moves up the suppliers' supplier. To gain more insights on the e-collaboration implementation implications, two more case examples are examined by the author. One case conducted by [3] confirms the application of e-collaboration technologies have enabled the company being studied to enhance the monitoring of the partner network in virtual enterprise, reduce business transaction lead time and increase the responsiveness to the market. Another case is conducted by [25] of IBM's Magirus project in exploring B2B (application-
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application) online ordering with one of its distributors. The study finds that the E-marketplace E-network actual implementation of web-based ecollaboration is more difficult than of o ERP~ "'", i i B2B human-application interaction, as it ~:, requires skilful change management over / 1960s 1970s 1980s ! 1990s 1 ', Present J a long period of time. It also argues that large companies usually have the power to bring other participants into e'~MRPI One-to-one collaboration model. > ,-J' lOS To successfully implement e~TradRiona, collaboration, organisations need to have applications Operational = Strategic a deep understanding of when and how MRPI: Material Requirement planning; MRPIh Manufacturing Resource Planning; ERP: Enterprise resource Planning; DSS: Decision Support System; lOS: inter-organisational system to utilize the e-collaboration systems and tools being discussed above. The case examples Fig. 3: Evolution of e-collaboration (Source: demonstrate that it is still a long way to go before full author) e-collaboration potential can be realized. Though L. Horvath [26] outlined a list of attributes for a example), successful e-collaboration, further research needs to -Monetary issues in terms of justification of be done to explore how organisations should design investment and bond of old EDI systems, and implement e-collaboration in a structural way. - Human resource issues i.e. lack of skilled manager and staff, and 5. C o n c l u s i o n - Technical issues in terms of scalability, compatibility, and security problems. The synthesis of the literature leads to the As can be observed from Figure 3, recent webdevelopment of an evolution path (Figure 3) of etechnology has now triggered two emergent models: collaboration. Each stage is represented by the most e-marketplace @ & e-network (~). Model @ has typical supply chain software system at that time. In a been widely adopted in some industries like nutshell, e-collaboration evolves from intra- to interautomotive and electronics and well discussed in the organisational collaboration, from vertical to literature. E-network (model (~) is still at its infancy horizontal collaboration and from operational to stage but has shown the great potential to satisfy the strategic level collaboration. With the proliferation of dual challenges in supply chain operation: 'speed and tools, systems and platforms, organisations can now 9 flexibility' and 'low-cost and efficiency' [30]. The collaborate in a more flexible and portable way with duality is yet to be proved. This is one of the most different partners, comparing with traditional promising areas to which the future research should collaboration. For examples, a tightly integrated ebe directed to. Possible future research agenda may collaboration could be implementation of VMI also include answers to the following questions: between a manufacturer and a retailer which fosters 1) How will inter-organisational e-collaboration the relationship into strategic alliance[27] , and a impact upon the collaboration at the intraloosely coupled e-collaboration could be online organisational level? auctioning of commodity-like materials. Under this 2) Is it necessary that the organisations have to case, e-collaboration might turn the traditional follow the 'the evolutionary path' proposed in consolidated relationship between manufacturer and Figure 3? For example, can inter eits suppliers into an arm-length one [28]. collaboration be achieved before close intra eThough supported by above discussion, it seems collaboration is in place? that e-collaboration is a more hybrid and flexible 3) How can a generic structural framework be model which could bring tangle benefits to the developed in order to guide an organisation the organisations, the literature also shows there are many design and implementation of e-collaboration practical challenges to overcome during with different partners? implementation. Major issues include: 4) Consequently, what information architecture - Information 'leakage' issue (see [29] for an should be deployed to accommodate different u
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prerequisites in implementing various types of e-collaboration? Finally, studies are also required to measure and quantity the benefits of e-collaboration in SCM practices. References
[ 1] Towill, D.R., The seamless supply chain: the predator's strategic advantage. International Journal of Technology Management, 1997. 13(1): pp. 37-56. [2] Williams, L.R., T.L. Esper, and J. Ozment, The electronic supply chain: its impact on the current and future structure of strategic alliances, partnerships and logistics leadership. International Journal of Physical Distribution and Logistics Management, 2002.32(8): pp. 703-719. [3] Tatsiopoulos, I.P., et al., Realization of the virtual enterprise paradigm in the clothing industry through ebusiness technology. Production and Operations Management, 2002. 11(4): pp. 516-530. [4] Johnson, M.E. and S. Whang, E-business and supply chain management: an overview and framework. Production and Operations management, 2002. 11 (4): pp. 413-422. [5] McDonnell, M., E-Collaboration: transforming your supply chain into a dynamic trading community. Supply Chain Practice, 2001.3(2): pp. 80-89. [6] GMAA,
http ://www. gmabrands, com/indust~affairs/ecollab oration.cfm. 2005. [7] IBM, IBM Product Lifecycle Management (PLM). June 2001. [8] Rutkowski, A.F., et al., E-collaboration: The reality of virtuality, IEEE Transactions on professional communication. 2002.45(4): pp. 219-230. [9] Kock, N. and J. Nosek, Expanding the boundaries of e-collaboration. IEEE Transactions on Professional Communication, 2005.48(1): pp. 1-9. [ 10] Barratt, M., Understanding the meaning of collaboration in the supply chain. Supply Chain Management: An International Journal, 2004.9(1): pp. 30-42. [ 11] Byrne, J.A., The Horizontal Corporation. Business Week, 1993. December 20, 1993: pp. 76-81. [ 12] Dunstall, S. E-sourcing, Procurement and the Adaptive Supply Network. in Strategic E-sourcing and Procurement Conference. 2004. [ 13] Hunt, I., Applying the concepts of extended products and extended enterprises to support the activities of dynamic supply networks in the agri-food industry. Journal of Food Engineering, 2005.70(3): pp. 393-402. [ 14] Malhotra, A., S. Gosain, and O.A.E. Sawy, Absorptive capacity configurations in supply chains: gearing for partner enabled market knowledge creation.
MIS Quarterly, 2005.29(1): pp. 145-187. [15] Xie, F.T. and W.J. Johnston, Strategic alliances: incorporating the impact of e-business technological innovations. Journal of Business & Industrial Marketing, 2004. 19(3): pp. 208-222. [16] Bask, A.H. and J. Juga, Semi-integrated supply chains: towards the new era of supply chain management. International Journal of Logistics: Research and Applications, 2001.4(2): pp. 137-152. [17] Clemons, E.K., S.P. Reddi, and M.C. Row, The impact of information technology on the organisation of economic activity: the 'move to the middle' hypothesis. Journal of Management Informaiton Systems, 1993. 10(2): pp. 9-35. [18] Cart, N.G., IT doesn't matter. Harvard Business Review, 2003.81 (5): pp. 41-49. [19] Lee, H., Simple theories for complex logistics. Optimize, 2004. July(22). [20] Folinas, D., et al., E-volution of a supply chain: Cases and best practices. Internet Research, 2004. 14(4): pp. 274-283. [21] Stevens, G.C., Integrating the supply chain. International Journal of Physical Distribution and Materials Management, 1989. 19(8): pp. 3-8. [22] McLaren, T., M. Head, and Y. Yuan, Supply chain collaboration alternatives: understanding the expected costs and benefits. Internet Research, 2002. 12(4): pp. 348-364. [23] Howard, M., R. Vidgen, and P. Powell, Automotive e-hubs: Exploring motivations and barriers to collaboration and interaction. Journal of Strategic Information Systems, 2006. 15: pp. 51-57. [24] Cassivi, L., et al., The impact of e-collaboration tools on firms' performance. International Journal of Logistics Management, 2004. 15(1): pp. 91-110. [25] McAfee, A., Will web services really transform collaboration. MIT Sloan Management Review, 2005. 46(2): pp. 78-84. [26] Horvath, L., Collaboration: the key to value creation in supply chain management. Supply Chain Management: An International Journal, 2001.6(5): pp. 205-207. [27] Disney, S.M. and D.R. Towill, The effect of VMI dynamics on the bullwhip effect in the supply chains. International Journal of Production Economics, 2003.85: pp. 199-215. [28] Bartezzaghi, E. and S. Ronchi, Internet supporting the procurment process: lessons from four case studies. Integrated Manufacturing Systems, 2003. 14(8): pp. 632641. [29] Zhang, H., Vertical information exchange in a supply chain with Duopoly retailers. Production and Operations management, 2002. 11(4): pp. 531-546. [30] Grieger, M., et al., Electronic marketplaces and supply chain relationships. Industrial Marketing Management, 2003.32: pp. 199-210.
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Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Learning the Users View: Information Retrieval in a Semantic Network S. Thiela, S. Dalakakisb a Fraunhofer Institute for Industrial Engineering, Nobelstr. 12, 70569 Stuttgart, Germany b Institute of Computer-aided Product Development Systems, University of Stuttgart, Germany
Abstract
In flexible production environments, technically supported knowledge becomes more and more a key factor for success. Therefore, this paper presents the development of a learning retrieval agent for knowledge extraction from the Active Semantic Network with respect to user-requests. Based on a reinforcement learning approach, the agent learns to interpret the user's intention and learn his mental models. Especially, the learning algorithm focuses on the retrieval of complex long distant relations. Increasing its learnt knowledge with every request-result-evaluation sequence, the agent enhances his capability in finding the intended information.
K e y w o r d s : machine learning, information retrieval, rapid product development
1. INTRODUCTION Knowledge is referred to be the capital of today's economy. Accessing knowledge just in time means to enterprises the chance to use knowledge as a capital investment which is necessary to gain innovation. The focus of this paper is accessing knowledge in the Rapid Product Development (RPD) scenario. The fast development cycles RPD stands for, are heavily depending on an easy to maintain and highly adaptable knowledge storing and retrieval system. This includes requirements like application independence and multi-platform availability. The Active Semantic Network was designed to answer these requirements. It provides a knowledge representation and an agent based interface granting distributed access. Thus it is the task of specialized agents to do the retrieval part.
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In a first attempt simple full text searching agents where built. Simple agents turned out to be quite good in finding single entries but act very poor in the retrieval of relations. This significant restrictions lead to a different approach: The Learning Retrieval Agent. In a semantic network concepts are connected by relations. Even if it is very difficult to find common terms to describe certain concepts, it is nearly impossible to design all appropriate relations between them. Therefore a single person's view of the facts represented in the system will always differ from the actual design. So it is very hard for a user to formulate requests the right way, because he cannot know the actual design of a large and complex semantic network. The Learning Retrieval Agent pursues a casebased learning strategy to learn each user's view of the relations. This enables the agent to answer requests about the correlation of concepts, even if they are
actually designed as indirect connections. These will further on be called "long distant relations". 1.1 The Basic Framework
The Active Semantic Network (ASN) is designed to provide a global and central knowledge base and information structure for enterprises to represent the rapid prototyping process and the knowledge around the product. On the one hand the ASN can handle data of different applications, enrich them with meta information and provide status and process control. On the other hand all kind of information can be modeled primary as a combination of ASN concepts and relations. While the first mentioned data does not provide any semantic information about the RPD domain, however the semantics of ASN concepts and relations can be defined in various ways. The meaning of concepts and relations is not absolutely determined and has to be interpreted by humans or artificial intelligence methods. Since facts and their semantics can be represented in several ways, leading to a high complexity, adding more facts blows up the ASN to a big network, too large to be overviewed by a human user. A search engine that regards semantically information becomes indispensable. The basic framework of ASN consists of three independent layers: The database system, the application server and the multi agent system (MAS). While the database system guarantees fault save and transaction oriented storage of ASN nodes and edges, the application server ~provides a location transparent ASN access. The MAS layer consists of an agent framework providing access to the application server, agent communication and coordination for request oriented agent generation as well as agent outage handling mechanisms. The agent framework realizes a simple agent privilege management, supporting different agents having special ASN access. Thus a client-server concept is applied, with low-privileged client agents, integrated in RPD applications and ASNaccessing server agents. Following server agents for RPD dependent tasks are available:
3. Transaction Agent: supports transactions protected processes and transaction protected execution of other agents within the MAS. 4. Aggregation Agent: prepares in appropriate format retrieved knowledge. 5. Retrieval Agent: retrieves ASN knowledge. In our aim to search for knowledge with more intelligent tools we involved the learning retrieval agent. The design of the ASN specifies a layer of server agents to access the stored data. Therefore the claimed search engine realizes a specialized search agent. The approach of this search agent is to model an interpretation of the semantics of the ASN that corresponds with the user view of the ASN. Thus a request can be handled in the way that is meant by the user. The only method to generate such a model of the user view is continuous learning which results are rated good for a certain request. The described learning algorithm is of the kind Reinforcement Learning. Its main task is to classify user requests according to their interpretation. This enables a generalized learning, that doesn't only regard specialized requests but request classes with appropriate interpretation. 2. Motivation
Having a closer look on the interaction process between user and agent, the retrieval situation is analyzed in a first step without a learning component. 2.1 Interaction Process
The sequence starts with the user posing a request to the agent. The agent retrieves information from the ASN and returns the result to the user. The user may now be happy with the received information. If he isn't, he will modify the request according to an assumed functionality of the agent hoping for a better result in the second run. In this way the user tries to learn the behavior of the agent. This is comprehensible observing any Google user.
1. Monitoring Agent: monitors the ASN and notifies changes. 2. Coordination Agent: supports coordination within the RPD by a finite state machine.
1 The current approach runs a JBOSS server.
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explicit defined and what is more, semantics for every possible combination of relations have to be computable. Since these preconditions are generally not satisfied, the interpretation of long distant relations remains a human task.
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Fig. 1 U s e r - Agent: interaction process The idea of the LR-Agent is to add learning capabilities to the agent's side of communication. This tends to a sensitive interaction between user and agent. The agent learns to memorize the user's intention. This means: remember request sequences and their final result and derive a retrieval method for further comparable requests. To afford that, the agent needs information about which results have been helpful for the user. Therefore an evaluation step was added into the interaction process. This is illustrated in figure 1.
2.2 Request
Table 1 Classification of Requests
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Result Set of concepts Set of concept pairs Set of paths
A path is defined as a sequence, beginning with a concept called "start", followed by relation-concept pairs. Which means that, beginning at "start", every following concept is connected to its predecessor by the given relation. This tends to a string like: "concept - relation- concept - ... - relation- concept". While it is easy to find algorithms to answer the first and second request types, finding fitting results containing long distant relations is much more difficult. This is because the semantics of those relations is not designed explicit and has to be derived by the semantics of relations and concepts located on the route between "start" and "target". An automatically derivation of such semantics would only be possible, if every potential kind of relation is
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Fig. 2 Relation: User's view Although the user may be able to interpret complex relations in a just way, he doesn't have an actual and complete view of the ASN. Because of its complexity even the designer may not be able to keep track of the ASN as a whole. Since the user does not know the exact design of the ASN, his assumed view of the modeled knowledge will very likely differ from the real design, which poses a problem even for users asking type two questions, according to table 1.
2.3 Example
The input for the LR-Agent is the user request. Three different kinds of requests can be classified. These are denoted in the table 1.
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Suppose that a user wants to know who is responsible for the development of product "b". He will request "product b" as "start concept" and "isresponsible-for" as the relation, that leads to the demanded target. Obviously the user imagines an ASN design as shown in figure 2. Though the actual ASN design may not have an "is-responsible-for" relation at all, but the same semantics could be represented by a structure as shown in figure 3. It is the task of the learning retrieval-agent to map the user's view of the ASN to a search algorithm working on the real structure of the ASN, thus retrieving results satisfying the user's intention.
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2.4 Learning Methods" Learning in this context is regarded as part of the communication process between user and agent. As shown in figure 1 this process consists of three steps: request, result and evaluation. In the agents view these steps represent the interface to the user. In [3],[4],[5] two basically different learning methods are distinguished: Learning from examples and Reinforcement Learning (RL). The latter means learning by experience which is not restricted to a temporarily terminated learning phase, but can be continued for the whole operation time. Thus RL provides an appropriate solution for the requested features: Adapting of the agents behavior to the alternating ASN model and the mutating user reaction as a consequence of the communication with the agent [6].
but it involves the trouble of natural language processing, which includes as a sub problem, the problem of understanding the user's intention, called pragmatics. Therefore the request interface provides concept specifications for start and target and the specification of the linking relation. This structure is expressed in a DTD defined XML file. Therefore user requests can easily be submitted by the communication protocol of the given agent framework. To show the functionality we built a GUI prototype to express requests in an easy way. A snapshot of the agent's request generator can be found in figure 4.
3. Approach However the LR-Agent is considered to learn the user's intention with the restricted view of the actual request. That means to interpret each request and map it to an adequate search method. (See figure 5 for the agent's design.)
3.1 Preliminary Considerations This interpretation of user requests raises two questions: 9How can the request space be classified for comparable requests? 9In which way can the interpretation of user requests be learned by the agent from the user? Concerning the first question, the classification of user requests is limited by the number of search algorithms leading to different results. Furthermore, it would be reasonable to handle analog requests with the same search algorithm. The search algorithm, assigned to a class of user requests, transforms the user's view of the ASN to its real structure and content. Therefore it will be called in the following, interpretation of the request.
Fig. 4 LR-Agent: GUI Prototype
3.3 Characteristic The classification of user requests is done by a profile, called characteristic, assigned to each interpretation. The characteristic describes the significant properties of requests sharing the same interpretation. Characteristics are generated out of former generalized requests. They are modified in the learning process adapted to the changing user behavior and state of the knowledge base. In this context, knowledge base means, a list of such request characteristics, each assigned to a structure called request interpretation.
3.2 Requests For the LR-Agent we decided to restrict the request formalism to a fix request structure. For ergonomic purpose a natural language interface would be better,
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J LR"ClientI User
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multi agent framework. These are related with synchronization of various agent instances sharing a single KB. Other aspects apply to problems regarding the KB administration which needs special algorithms to delete irrelevant request models and join equal ones. But in this paper we will not further discuss them.
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3.6 Learning Techniques
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Fig. 5 LR-Agent: Accessing dependencies
3.4 Interpretation The interpretation of a request means a description how to adapt the search algorithm in respect to the concrete assigned user request. Therefore the interpretation is separated in two parts. The first part is called relation processor; it defines which ASN relations should be followed to find long distant relations. This implies to have a set of start concepts, from which the algorithm begins to search. Those start concepts are found with regard to the criteria given by the user. In some cases it can be useful to add fix start concepts to an interpretation. This is done for concepts that have proven relevant in the context of an interpretation. Such interpretations are assigned to their fitting characteristic. Both will further on be called a request model. This is illustrated in figure 6. request
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Fig. 6 request model: interpretation function
3.5 Knowledge Base A knowledge base (KB) contains a list of request models, which consists of a characteristic and an interpretation. This KB module as shown in figure 5, represents the information needed for the mapping "user view" on "ASN model". The KB may be excluded from the LR-Agent which leads to a bunch of interesting problems regarding the
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Technically, it is the knowledge base that represents the learnt data. Therefore learning techniques for the aspects, characteristic and interpretation have to be found. The learning process depends on former information and the three components of the particular request: The request, the result and the evaluation of the result. While the request and the result are important to deliver boundary conditions, the evaluation provides the information to consider the quality of the assignment on the one hand and that of the used interpretation on the other. While results can be represented as a list of paths, an evaluation can simply be given by rating the result paths. The prototype client provides a user review of the relevance of each result path on a scale between zero and ten. Then the evaluation is sent back to the LR-Agent as shown in figure 2. The learning algorithm for the interpretation part of the request model regards every single path and readjusts the relation processor according to the relations along the path and the paths rating. The characteristic part regards a global evaluation that is computed out of the single path evaluations. The learning algorithm follows the idea to generalize the characteristic if the " r e q u e s t - request model" assignment has proven successful and to specify it otherwise. 4. Conclusions
The challenge to learn the user's view of the ASN requires the classification of the request space. In our approach this is done by request models, where the characteristic part assigns each request to an interpretation. The used request models are summarized in the agent's knowledge base, which helps to interpret further requests. The interpretation steers the searching process on the ASN. The biggest benefit from our approach is the ability to provide a flexible and independent retrieval method. The user is supported in the searching process. The LR-Agent was implemented as a learning communications counterpart that has the ability to
memorize previously posed requests and even more, derives and assigns similar requests.
Acknowledgments This work is related to 1NT-MANUS project (Contract Number NMP2-CT-2005-016550), funded by the European Commission. Fraunhofer IAO is a member of I ' P R O M S network of excellence, supported by the European Commission. Special thanks to Dieter Roller, Institute of Computer-aided Product Development Systems at the University of Stuttgart, for his support. We also thank the developers of the ASN, who have designed the stage for this work.
References [1] Dalakakis, S., Stoyanov, E., Roller, D.: A Retrieval Agent Architecture for Rapid Product Development. In: Perspectives from Europe and Asia on Engineering Design and Manufacture, EASED 2004, X.-T. Yah, ChY. Jiang, N. P. Juster, (eds.), Kluwer Academic Publishers, 2004, pp. 41-58 [2] Dalakakis, S.; Diederich, M.; Roller, D.; Warschat, J.: Multiagentensystem zur Wissenskommunikation im Bereich der Produktentstehung-Rapid Product Development. ISBN 3-7908-1574-8 pp. 1621-1640. [3 ] Stuart J. Russell and Peter Norvig. Artificial Intelligence: a modern approach. Prentice Hall, 1995. [4] Richard S. Sutton und Andrew G. Barto. Reinforcement Learning: An Introduction. Bradford Books, 1998. [5] Tom M. Mitchel. Machine Learning. McGraw-Hill, 1997. [6] I. Kreuz, D. Roller: Reinforcement Learning and Forgetting for knowledge based Configuration, Artificial Intelligence and Computer Science, 83-121, Nova Science Publishers, Inc., 2005. [7] A. Agnar and P. Enric: Case-based reasoning : Foundational issues, methodological variations, and system approaches, AI Communications, 7(1), March 1994.
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Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Leonardo da Vinci Programme supports the Development of e-Learning Methods in Application to the Vocational Training in Automation and Robotics W. Klimasara, Z. Pilat, R. Sawwa, M. SIowikowski, J. Zielifiski Industrial Research Institute for Automation and Measurement PIAP Al. Jerozolimskie 202, 02-486, Warsaw, Poland
Abstract
The development of presentation techniques that involve multimedia tools - and especially the enhanced quality and availability of IT communication technologies - has enabled the introduction of new teaching methods. One of them is e-Learning - distance learning supported with electronic presentation and communication media. This approach can be applied both in school-based teaching and in vocational training for adults. The latter area is particularly important in the light of accelerating changes in labour market preferences, e-Learning facilitates retraining as well as gaining new and raising existing qualifications. The advantages of this method are especially significant in technical professions. It makes significant improvement of the trainings effects possible. This paper presents the application of e-Learning in the area of automation and robotics. Keywords: e-Learning, Vocational Training, Automation & Robotics
1. I n t r o d u c t i o n
The global economy entered the 21 st century with a backlog of unsolved problems. Natural disasters, armed conflicts and unstable resource prices have aggravated this unfavourable situation. It is commonly believed today that many problems can be solved only with the cooperation of multiple states or their organisations. From the European Union point of view, one of the key challenges is shaping the labour market. The shortage of jobs in traditional sectors and simultaneously the inadequacy of employees' qualifications and competences that do not meet the requirements in new sectors create a paradox. On the one hand, unemploy-
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ment in the EU is very high. On the other hand, the lack of personnel in new technology areas (IT, communication technologies, modern manufacturing) is more and more perceptible. At the same time, Europe continues to import poorly qualified workforce, mostly for jobs that require lower qualifications, are less profitable, are done in more difficult or even harmful conditions, and have a lower social status. With all that in mind, one ofthe most urgent problems that EU countries need to solve is the creation of a modern adult training system that will allow gaining new qualifications and raising existing ones. As a result, participants will become attractive on the labour market and able to find employment more easily. The system will also help to reduce personnel shortages in new technology areas. Training profiles can be shaped
to allow adjusting the acquired competences to the current and anticipated needs of specific economic sectors in given regions. The traditional vocational training system cannot meet the challenges of current economic conditions or the expectations of both the trainees and employers who need new personnel. The main disadvantage of classroom-based school courses is that they are costly. They are unavailable to many of those who need them: people living in distant locations, disabled or busy with household errands. In addition, they are not flexible enough as far as tailoring the content and methods to new needs is concerned. No such constraints apply to e-Learning--distance learning supported with electronic presentation and communication media [ 1]. This approach can be applied both in school-based teaching and in vocational training, including technical professions. Developing such a modern training system for automation and robotics using e-Learning methods is the objective of the "Integrated Set of vocational trainings in the field of most advanced solutions in technology, organization and safety in Automated and Robotized manufacturing systems" (ISAR) project conducted as part of the Leonardo da Vinci (LdV) EU programme.
2. The Main Features of e-Learning e-Learning is commonly regarded as a type of distance learning [4], which has a long tradition dating back to correspondence courses in the United States. The first advertisements appeared in US press in the early 18th century [3]. The main features that set e-Learning apart from other distance learning methods are: [] teaching materials are used mainly in electronic form together with modern IT means and media (multimedia presentation techniques, links to auxiliary materials and other resources), [] the use of the Internet as the main channel for knowledge and information exchange, [] the use oflnternet-basedmutual communication mechanisms: o e-mail, o chat, o discussion groups. e-Learning is not only about preparing and distributing teaching materials. It also includes knowledge creation and management as well as communication among the participants in the educational process. The participants can be categorised into four groups [2]:
[] observers - seek a suitable
course, take part in "demo" classes, provide information about their expectations, [] students - are the audience of specific training courses, [] trainers - manage courses, i.e. they conduct them according to schedules, prepare and distribute materials, handle information sent in by students, [] administrators of IT systems- ensure the technical operation of the system, provide and supervise tools for knowledge creation and management as well as for managing students and their communication with trainers etc. All the participants of an educational process use the resources of an IT centre that is the heart of an e-
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Fig. 1. The general structure of an e-Learning system Learning system (Fig. 1). It contains typical IT system mechanisms that ensure proper hardware and software resource management, communication with users, peripherals and the world outside. The software component of an IT centre includes, among other things: [] knowledge bases, [] teaching material databases, [] databases of educational process participants [] trainer databases [] teaching aids, e.g. a virtual library, virtual labs, simulation games, etc. [] supporting utilities, e.g. glossaries, online help, organisers, etc. e-Learning has a number of advantages, for example: [] it is much cheaper than conventional teaching - no classrooms are necessary, teachers and students do not need to travel but work at home or office, [] it can reach much larger student audiences, [] a trainer can quickly develop a new course by customising a set of existing materials,
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O [] []
students can adjust their learning pace to their individual preferences, students can take part in a training course at the most convenient time of the day, a single course can be reused for other student groups and by individual students for their own purposes.
3. e-Learning in the EU Countries Until recently, the possibilities for a practical use of an e-Learning system were very limited due to the low transfer rates of IT networks and high telecommunications costs (with regard to the software, hardware and current costs). Today, these limitations are much weaker. Internet service prices have plummeted, while data transfer performance in networks has improved. New multimedia tools have also brought about significant improvement in the content-related quality and attractiveness of teaching materials and courses as a whole. Currently, three approaches to e-Learning can be observed on the market [3]: [] A company organises its own e-Learning centre by creating independently or buying complete software and hardware. This approach is used by large companies with many employees located at geographically distant sites, e.g. telecommunications operators, retail chains or large consulting corporations. Such companies usually have efficient intranets that are used for training, which lowers the costs and enhances the security of transferred data. [] A company uses rented or leased, ready-made e-Learning systems where training courses can be selected from an offered set, and proprietary courses can be implemented as well. This approach is much cheaper than the previous one but still enables tailoring the training scope and content to the requirements of the audience. [] Students use ready-made e-Learning courses offered on the Internet by professional training companies. With this approach, students have very limited options for customising the syllabus. Instead, they need to compare a number of available courses (e.g. by viewing demo lessons) and select the one that matches their preferences. It is characteristic of the first two approaches that the trainees are mostly employees of the companies that pay the training costs. They do so because such
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investment is very profitable and eventually results in employees raising or gaining new qualifications and skills that are useful for the company. Such training courses are mainly vocational. The third approach is mostly used by individuals and employees of Small and Medium-sized Enterprises (SME) who cannot afford more expensive solutions. In this approach, it is often the student who bears the cost. Students usually study at home, using their own hardware, software and Internet connection. The portfolio of public training courses on the market is currently quite extensive, but it does not cover individual thematic areas of the education system to an equal extent. The dominating topic is IT, especially office suites (editors, spreadsheets). Linguistic courses, business counselling and hobby-related (playing instruments, cooking, sewing) courses are also widespread. However, vocational training is very poorly represented in this group while potential audiences for courses in this area could be numerous unemployed people who could have a chance to gain new qualifications, people who are temporarily out of the labour market for other reasons (women on maternity leave, people undergoing rehabilitation following an illness or accident), employees of SME's that would like to implement new, more advanced manufacturing, organisation and management methods. They all need such vocational training the most. Such a situation is characteristic in many EU countries. Expanding e-Learning in vocational training is not only needed by EU economy, but it is also a chance for a rise in the number of active participants of the labour market from many social groups and, consequently, for reducing unemployment. At the same time, the market for such training is--according to recent estimates--not very broad and, in particular, not rich due to the financial situation of the potential audience. Since the EU understands the significance of the problem and objective difficulties related to solving it solely with market mechanisms, it is determined to support the development of such training using various mechanisms, including financial ones. This policy is implemented for example through EU development programmes, such as the Leonardo da Vinci educational programme. The training project ISAR is an example of that support.
4. The Idea, General Principles and Objective of the ISAR Project The scope of implementation of the ISAR project
in-
cludes the development of an integrated vocational training system dedicated to advanced technological, organisational and safety solutions in automated and robotised manufacturing systems. Training courses related to the project will be interactive and will use the latest e-Learning technological solutions combined with group teaching. These training courses will aim at improving students' personal skills and competences as well as motivating participants to implement advanced solutions in technology, organisation and occupational safety in automated and robotised (A&R) manufacturing systems,. 4.1. The target sectors
Training courses in the project will be addressed primarily to SME' s from those industrial sectors that need to be automated and robotised the most, where high quality and product repetitiveness are of key importance, and where controlling the manufacturing process is complicated and difficult. ISAR will focus on the following target sectors: [] Manufacturing SME's (A&R system users), and in particular: o companies cooperating with car manufacturers, o suppliers for household appliance manufacturers, o electronic and electric equipment manufacturers. According to cautious estimates, at least 4,500 SME's from the three above sectors in the EU need a new training system for A&R systems. At later stages, ISAR will expand the training system to other sectors (e.g. the traditional metallurgical and timber, aerospace, medical equipment and food industries). [] Schools and training institutions that provide training on A&R subjects: o technical secondary schools and universities, o institutions that organise vocational training. [] Organisations that develop and implement advanced A&R solutions: o consulting companies, o professional associations, o engineering groups. 4.2. The target groups
The anticipated training participants can be categorised into target groups whose interest focuses on similar
topics. It is expected that the training audiences in the ISAR project will be: [] Group 1 directors of industrial SME's. In order to make decisions that are technically and economically justified, enterprise directors need to have appropriate knowledge. This is particularly important with regard to investments associated with A&R, because the purchase and integration of such systems entail significant costs, and the choice should be based on a thorough calculation of all costs associated with this process. Directors should know what could be improved in the organisation of their enterprises and how the production performance and quality as well as occupational safety could be enhanced. They also need to know what HR, organisational and logistic changes should be made when implementing A&R. 21 Group 2 technical personnel of industrial SME's. These employees need to know how to implement, integrate and operate A&R equipment effectively and safely. They also need to understand the benefits for the personnel and enterprise (many employees are afraid of changes). In addition, technical employees need to improve their skills and expand their knowledge of new and advanced A&R solutions on an ongoing basis. [] Group 3 other personnel, especially less qualified employees ofSME's in the industrial sector, and the unemployed. Training will help participants from this group to raise their qualifications and improve their chances on the labour market. The project envisages promoting cooperation with local institutions (local employment agencies) that will help the unemployed to participate in training. With e-Learning, unemployed participants from remote locations will find it much easier to prepare for employment as their access to other means of raising qualifications is often limited. [] Group 4 - trainers and consultants. Demand for training services in Europe is growing. Enterprises need qualified trainers who can follow closely the specialisation of the enterprise and sector, and who can satisfy the educational needs of the employees. There is also increasing demand for consulting services related to A&R system implementation. With ISAR training, those people will be able to maintain their training competences in line with the latest trends and ensure a consistently high quality of
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their training offering. El Group 5 - - students of technical schools and highly qualified unemployed participants (engineers). One of the key needs in this group is supplementing their theoretical knowledge gained at university with information on practical aspects of A&R as well as information on the latest solutions from leading A&R system providers that is often unavailable to them due to a lack of professional contacts. Trained students will improve their chances for employment. Engineers who have remained unemployed for a longer period need to update and round out their knowledge on the latest A&R technical solutions and trends as well as their practical application in order to improve their chances for new employment. 4.3. The ISAR project consortium
ISAR, like all projects in the Leonardo da Vinci programme, is an international undertaking. The project consortium groups institutions that represent four European countries: El P o l a n d - Industrial Research Institute for Automation and Measurement PIAP is responsible for project coordination, implementing a website, developing qualification assessment procedures, and preparing lessons devoted to selected A&R systems as well as the benefits of using A&R systems and their software. 121 Germany - Institute for Applied Systems Technology GmbH ATB, is responsible for developing and implementing the e-Learning solution (online training) and the evaluation of the system. In the preparation of the training content, ATB will focus on topics that combine elements of system intelligence with A&R installations. Cl Slovakia - Technical University of Kogice TUKE, as a research and educational institution with extensive technical experience, is responsible for launching and conducting training courses after the project is completed. TUKE takes part in developing, testing and implementing e-Learning solutions related to lessons in efficient production management methods, Web-CIM issues, integration and implementation of intelligent tools and manufacturing systems. cl The United Kingdom - Cardiff University
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Manufacturing Engineering Centre - MEC, as an educational institution and a research centre, will provide teaching support that is necessary to develop training courses and will conduct them after the project is completed. MEC will also contribute the results of analyses and research from several A&R areas, especially those that focus on adjusting and expanding A&R systems to satisfy the specific needs of SME's with regard to technology and occupational safety. MEC has thorough expertise in multi- and hypermedia systems, which will be helpful in developing the e-Learning solution. The training syllabus will be developed and validated jointly by specialists from all the institutions that form the consortium. This will allow including the experience of the individual countries and enable the transparency of qualifications gained by trainees in the system so that they can be recognised in different EU countries. Training courses will be available in the four languages of the project partners. International virtual classes are to be organised where training courses in a given language will be available to participants from other countries. This approach to the organisation of courses requires a specific structure and organisation of the IT centre. Physically, it will be installed on computers in four centres, thus creating a single virtual organism.
5. The Training C o n t e n t and O r g a n i s a t i o n The ISAR training system is planned to be developed in stages. In the first phase, a basic course will be provided that includes 10 lessons in subjects like: a) introduction to A&R systems, b) A&R needs in SME's, c) combining A&R implementation with a company's objectives, d) specifying requirements for A&R systems, e) integrating new A&R systems with the existing manufacturing environment, f) A&R market offer and suppliers, g) safety in automated and robotised systems. The basic course will be common to all the target groups. It will be utilize for the test learning cycle realization. Using obtained results various methods for assessment the quality of the course will be investigated. At a later stage, new lessons will be developed and
basic lessons will be expanded (into detailed lessons, so-called sub-lessons) for different groups. They will focus on subjects like: a) an overview of A&R systems and their application, b) selecting the optimal A&R systems tbr various technological processes, c) assessing potential benefits of A&R systems, d) estimating the implementation and operation costs of A&R systems, e) customising/upgrading A&R systems to satisfy the specific needs of an SME, f) integrating A&R systems with various ICT systems in enterprises, g) organisational principles for working with A&R systems, h) reorganising the manufacturing environment for A&R systems, i) A&R systems operating and programming, j) development trends in A&R systems. Future courses will be created by combining and selecting different lessons for specific syllabuses. When signing up for a course, a participant will be given access rights to the appropriate Web page of the IT centre where he or she will be able to download materials for consecutive lessons, communicate with the trainer and other participants, submit homework, and complete tests to check the learning progress. Lessons will be illustrated with video footage. The project also envisages a library of multimedia presentations of A&R system implementations. The library will be based on the results of application work by the project partners.
across the United Europe because the content will be based on widely applied knowledge integrated with the latest products and documentation from leading European A&R system manufacturers, suppliers and designers. Therefore, information about the project is to be distributed not only in the partners' countries but also elsewhere. During the course of the project, the basic mechanisms of the training system will be implemented, and trial courses in a selected thematic scope will be conducted. The results will be used for evaluating and verifying the adopted solutions and in developing a working prototype of the system. After the project is completed (in September 2007), the members of the consortium will implement and operate the e-Learning training system. Its further development will depend on the actual market demand and feedback from the first trainees.
6. Concluding Remarks
[1]
The integrated vocational training system delivered as a result of the ISAR project will offer benefits for the target sectors (especially for SME's) mainly by enhancing the competitiveness of them on the market. This will be achieved by increasing competences thanks to the implementation of advanced solutions in technology, organisation and safety in A&R manufacturing systems on an ongoing basis. It is expected that participation in training courses offered by the system will also be beneficial to users from all the five target groups. The training will provide them with new, more comprehensive and usable competences, and it will improve their professional position. Participants will be able to use their qualifications gained from training in the ISAR project system
Acknowledgements This paper presents the results of work conducted as part of the Leonardo da Vinci programme, in project LDV PL/05/B/F/PP/174007 "Integrated Set of vocational trainings in the field: most advanced solutions in technology, organization and safety in Automated and Robotized manufacturing systems" (ISAR). Industrial Research Institute for Automation and Measurement PIAP is partner of the EU-funded FP6 Innovative Production Machines and Systems (I'PROMS) Network of Excellence http://www, iproms, org
References
[2]
[3] [4]
Bielawski L., Metcalf D. Blended eLearning. Integrating Knowledge, Performance Support and Online Learning. HRD Press Inc., Amherst, Massachusetts, USA, 2005 Jodtowska A. E-Learning. Global village of knowledge. Technologies and Industry, summer 2002, Warsaw Poland (in Polish) Jodtowska A. E-Learning. The newface of education. Technologies and Industry, autumn 2002, Warsaw Poland (in Polish) W. Klimasara, Z. Pilat, R. Sawwa, M. Stowikowski, J. Zielifiski. e-Learning - a Modern Educational and Vocational Training Method in the Context of Automation and Robotics. AUTO MATION 2006, Warsaw Poland (in Polish)
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Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
User-interface Architectures for VE Dynamic Reconfiguration" An Initial Analysis P. Gongalves a, G.D. Putnik b, M. Cunha a, R.Sousa b a
Department of Systems and Information Technologies, Polytechnic Institute ofCdvado andAve, Barcelos, Portugal bDepartment of Production Systems, University ofMinhol, Guimar6es, Portugal
Abstract In order to achieve and to manage the VE dynamic reconfiguration, it is necessary to minimize the VE reconfiguration time, as one of the dynamic reconfiguration factors. The paper presents the concept of the two userinterface architectures, based on the BM_Virtual Enterprise Architecture Reference Model (BM-VEARM), and their role in VE operations: the "direct" communication architecture (DCA) and the "virtual" communication architecture (VCA) as well as an implementation. The developed implementation is based on basic available technologies capable of representing the phenomena. In the case of our study, the DCA implementation consists of face-to-face communication through the well known videoconferencing software MSN Messenger. The end-user can hear and see an interlocutor (human, person) on the computer screen. The VCA is based on a MS Agent technology interface.The end-user can not see or hear the human interlocutor because he is "masked" by one of the chosen software agents. In the application, called E-Mask, the end-user always sees the same software agent/mask and hears the same voice (mask' s). It is expected that the set-up time, i.e. the end-user adaptation time, will be similar with or without a change in the interlocutor during a conversation when using the VCA. A comparison of the two proposed architectures is made.
Keywords: User-interface, Direct Communication Architecture, Virtual Communication Architecture, Virtual Enterprise, Virtual Enterprise Dynamic Reconfiguration, Virtual Enterprise Integration, BM_VEARM
1. Introduction Even though there are many communication tools used to collaborate, not many studies have suggested their global efficiency, therefore, the new challenge to cope with the business requirements is how to apply the philosophy of effective and efficient virtual communication in a VE network. Computers extend what people want to communicate through wide area network (WAN) technologies in a short period of time, however, even though written, verbal and body
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language generally enhance human interaction, sometimes one may be momentarily misinterpreted causing the communication process to be inefficient or delayed. In this context, this paper is organized into three parts: in the first part we present the basic userinterface roles in VE operations; in the second part, two user-interface architectures are proposed, the first is the "direct" communication architecture (DCA) and the second is the "virtual" communication architecture (VCA). Finally, in the third part, a comparison
between the two architectures is made.
2. Virtual Enterprise Reconfiguration Dynamics and BM_Virtual Enterprise Architecture Reference Model (BM-VEARM) To achieve the desired competitiveness in order to fully exploit the business opportunities, enterprises are required to achieve permanent alignment with business environment. Some organizational approaches, that aim at satisfy these functional requirements (the permanent alignment with the business environment) rely on dynamically reconfigurable partnerships in permanent alignment with the market, strongly supported by information and communication technology, dictating a paradigm shift face to the traditional organizational models. The leading organizational model incorporating these characteristics is the Virtual Enterprise (VE) organizational model, characterized as a dynamic (inter-enterprise) networked organizational model. In other words, the VE organizational model is characterized as a dynamically reconfigurable (interenterprise) networked organizational model. Reconfiguration, meaning substitution of resource providers, generating a new instantiation of the network, can mainly happen for three reasons: 1. Reconfiguration during the network company life c y c l e - a consequence of product redesign in the product life cycle in order to keep the network aligned with the market requirements. 2. Reconfiguration as a consequence of the nature of the particular product life cycle phase (evolutionary phases). 3. Reconfiguration can also happen as a consequence of the evaluation of the resources performance during one instantiation of the network, or voluntarily by rescission of participating resources, wiling to disentail from the network. VE dynamics considers a succession of network's states (physical configurations of the VE) along time, i.e. network reconfiguration dynamics. Dynamics means precisely the intensity of change the VE is subject to. A VE is defined as a reconfigurable network to assure permanent business alignment, in transition between states or instantiations (configurations) along time, (Figure 1). In [1], the authors propose two parameters of Reconfigurability Dynamics: the number of requested reconfigurations per unit of time (Reconfiguration Request Frequency) and the time to reconfigure (Reconfiguration Time). Reconfigurability dynamics is
directly proportional to the number of requests and inversely proportional to the time to make the reconfiguration operational (selection, negotiation and integration of resources in the VE). Ideally, reconfiguration time should tend to zero, and stable configuration durations should be dictated by business alignment needs, to keep VE performance at its maximum level. Due to reconfiguration cost and time, most of the times, dynamics is sacrificed by increasing the duration of stable and in less performing configurations.
Network ~~d~ "~ Netw~ k
N e t w o r k state 1
N e t w o r k state 2
Network state n
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Figure 1 - Networking dynamics considers a succession of network' s states along time [ 1] The implementation of the VE model should assure the required reconfiguration dynamics. The BM_ Virtual Enterprise Architecture Reference Model (BM_VEARM), proposed by Putnik (2000a, 2000b), is defined as a hierarchical structure of multiple levels of inter-enterprise processes. The elementary structural pattern is presented in Figure 1. It (BM_VEARM) is conceived to satisfy four fundamental requirements: integrab ility, distributivity, agility and virtuality. We will extend the consideration of the latest since it is crucial to fully comprehend the concept of virtuality in order to grasp the importance of user-interface roles in VE operations. Integrability is one of the most important requirements for the virtual enterprise. It is the capability for efficient access to heterogeneous resources inside and outside the organization. In order to be efficient, the integration of the resources should occur at a low cost. In the BM_VEARM, distributivity relates to the capability of the enterprise to integrate and operate needed resources at a distance, in other words, it has to access the best resources by cooperating with other enterprises, purchasing components, sub-contracting other companies and/or making partnerships. All of these business and manufacturing operations must be
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managed through wide area network (WAN) technologies and protocols, e.g. Internet. Agility should be achieved through the capability offast adaptability orfast reconfigurability in order to rapidly respond to the market (or customer demand) changes. The reference model provides the specific architecture that facilitates it. The broker is the key architecture element and the agent for agility. As for virtuality "it provides the system with the capability of on-line system reconfigurability without the interruption of any process" (Putnik, 2000a). To implement virtuality in the enterprise, the introduction of an interface layer between the Control level i (principal, manager, "client") and the Control level i+l (agent, worker, "server") is proposed. It now becomes Control level i+2. The role of this level is management of the underlying physical structure, i.e., management of resources, which will carry on the process ordered by the upper level. Therefore, the VE agility must be carried on by some organization configuration manager, i.e., resource manager or broker, similarly as for the concept of agility. In BM_VEARM the organization configuration management, i.e. the function that provides virtuality is presented through the Resource Management2 (see Figure 2).
~~ Controlleveli~_~
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---~ Controlleveli+2 ~-i~ Figure 2. Elementary structure for a virtual hierarchical multilevel system control [2]. The model is represented as a three-level hierarchical system with a rigorous hierarchy. During the operation the Control level i (principal, manager) and the Control level i+2 (agent, worker)communicate through the Resource Management level i+l, i.e., through the resource manager, or broker. During the operation, the manager (the principal, the "client") does not have direct contact with the worker (the operator or agent, the "server"), who provides the service (or production). Figure 3. presents an informal scheme of the virtual enterprise elementary structure operation (including agility as well). It is important to notice that the proposed structure provides virtuality. The enterprise reconfigurability occurs during the
152
principal's, or client's, single operation, at run time. The resource manager, or broker, can reconsider the organization structure during the operation at the run time, as well as between two operations, and act with the objective to adapt it (reconfigure it). In this way, the resource manager or broker is the principal agent of virtuality and agility.
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2 This is the main principle of concurrent or simultaneous engineering.
language and verbal language barriers. 3. User-interface Role in VE Operations
4.1 A Model of Communication Architecture Lambert [4], states that evaluating a user-interface involves assessing how effectively and efficiently information is retrieved. In our opinion, the information retrieval referred by the author may be translated into how effectively and efficiently people actually communicate with each other in different architectures. The main role of the architecture, that we call "direct" communication architecture (DCA), is to be able to see and hear the person on the other side, physical appearance; age, race, and tone of voice are all subject to the user's perception. When it comes to communication in VE, as dynamic reconfiguration of VE occurs and the interlocutor changes, the end-user must have the capability to quickly adapt to these changes and continue to trust the person who appears on the screen. In order to minimize these time wasting barriers, the "virtual" communication architecture (VCA) could be one of the solutions since there is an increased concern in reducing the adaptation time of the end-users to the new interlocutors and to eliminate physical appearance and language as barriers, maintaining at the same time a good level of trust based on authentication of the interlocutors. This authentication deals with the end-user's knowledge that the interlocutor is registered in the company as a trustworthy employee and regular control is made. Even though the interlocutor can not be seen by the end-user he should be trusted and the decision making process could be shortened, therefore the agility in the VE network may respond quicker to the customer' s/market's need. The role of User-interface in VE operations is to further improve the dynamics of the VE reconfiguration towards an "ideal" alignment with the market. In this sense, a specific interface architecture may be a factor, or enabler, of VE reconfiguration dynamics.
In Figure 4. a logical structure scheme of the Userinterface in VE is presented, both informally and formally. It is structured in three levels: The level "Interface 2" provides a graphical interface to the operator; the level "Interface 1" provides the data and protocol interface to the communication network and, finally, the level "Logical Control" provides, the required functionality mode of the particular interface architecture in terms of "direct" or "virtual" communication, i.e. interface architecture. In Figure 5. the elementary VE organizational structure in conformance with BM_VEARM is presented. Depending on the solution implemented for the interface's level "Logical Control", we will have the "direct" or "virtual" communication, i.e. interface, architecture. Human Operator! RemoteCo~k ~.2n~fface21
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Our main goal is to identify and test interface models in order to obtain a user-friendly interface in distributed agile and virtual environments. Our hope is that the "virtual" communication architecture (VCA) can be more efficient, in some ways, than the "direct" communication architecture (DCA) in the case of dynamic reconfiguration of the VE network structure. We believe that the VCA may help eliminate body
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4.2 Direct Communication Architecture- An Implementation In the case of our study, the "direct" communication architecture (DCA), which consists of face-to-face communication, is implemented through the well-known videoconferencing software MSN Messenger. It is possible for the end-user to hear and to see an interlocutor (human, person) on the computer screen. In a VE dynamic network the "interlocutor" will, in the limit, constantly change, requiring a constant adaptation of the end-user to the new interlocutor. Figure 6 shows the MSN Messenger Interface.
Figure 8a. illustrates the set-up time between the end of a question and the beginning of the answer given by the end-user when there is no change in the interlocutor using MSN Messenger (DCA). This "model" could be considered as a "reference" model, characteristic of communication in "traditional" enterprises or in VE that do not consider the reconfiguration dynamics but the "long-term" stable partnerships, i.e. mainly "static" organization structure (e.g. supply chains). It is our belief that the set-up time, when using the DCA, will be longer if there is a change in the interlocutor during a conversation, which is the case of VE as dynamically reconfigurable structures. Figure 8b illustrates the set-up time between the end of a question and the beginning of the answer given by the end-user when there is a change of the interlocutor. 4.3 Virtual Communication Architecture- An Implementation
Figure 6. The MSN Messenger Interface ("Direct" Communication Architecture). According to the BM-VEARM, represents the dynamics of the DCA.
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In the "virtual" communication architecture (VCA) we conceived, the end-user can not see or hear the human interlocutor because he is "masked" by an interface element that hide the interlocutors from each other, providing a "virtual" environment for them. The "virtual" communication architecture (VCA) is based on a MS Agent technology interface. The enduser cannot see or hear the human interlocutor because he is "masked" by one of the chosen software agents. In this application, called E-Mask, the end-user always O
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154
where:
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Figure 8. Set-up time in DCA, using MSN Messenger: a) without a change in the interlocutor; b) with a change in the interlocutor
sees the same software agent/mask and hears the same (mask' s) voice. Conceptually, there is no need to adapt to a new face or to a new voice and the agents may be chosen. If the end-user does not enjoy hearing one of the agent's voices he/she may chose another one. There is a server program and a client program in the application. On the server side there is a person who types what he/she wants the agent to say to the enduser in a text box. The end-user is on the client side and may speak to the agent because he/she will be heard on the server side. Figure 9 illustrates the EMask Interface. The E-Mask (virtual mask) application was developed according to the paradigm Client-Server in C# on the .NET platform. It is integrated in MSN Messenger in order to use the same audio features, however, its function is to mask the interlocutor, or in other words, the end-user is not able to see a person as the interlocutor but rather a MS Agent character. The user has to choose one of the three agents he wishes to speak to. In Figure 10 it is possible to understand the client application and the server application functions of E-Mask. In this program three MS Agents are used: Robby, E-Man and E-Woman. Their role is to represent "virtual people" and facilitate communication and decision-making by end-users in distributed environments. Suppose the end-user clicked on E-Man, the agent would fly into the centre of the screen and automatically introduce itself, this applies to the other characters as well (Robby and E-Woman).
agent's own voice) to the end-user, after listening to the end-users' responses. The end-user will have a sense that he is talking to the agent and that the agent is replying. Figure 11 demonstrates how the person behind the server program selects a sentence or question he/she wants the agent to say to the end-user and then pastes it into the dialog box of the server program.
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Figure 11. The Server Interface of the E-Mask Application. The server interacts with the client through text that is converted into sound as the chosen agent reads
155
out loud what the server wrote in the appropriate text box. In Figure 12 you can see an example of an MS Agent introducing himself to the client (end-user). The text that is written in the balloon is actually read bythe agent.
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Figure 12. MS Agent introduces itself to the client.
According to the BM-VEARM model, Figure 13 represents the dynamics of the VCA. Figure 14a illustrates the set-up time between the end of a question and the beginning of an answer given by the end-user when there is no change in the interlocutor using E-Mask (VCA). Fig. 14b illustrates the set-up time between the end of a question and the beginning of an answer given by the end-user when there is a change in the interlocutor using E-Mask (VCA). CONTROL LEVEL i
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5. A Comparison In Table 1. a comparison of some characteristics of the "Direct" Communication Architecture (DCA) and the "Virtual" Communication Architecture (VCA) is presented.
6. Conclusions The purpose of this paper was to present two userinterface architectures for the VE dynamics by explaining their role in the BM_VEARM Reference Model and illustrating their potentials in terms of time efficiency. The "direct" communication architecture (DCA) was compared with the "virtual" communication architecture (VCA). Table 1 - A comparison between the two architectures. "Direct" Communication Architecture
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It is expected to prove that the "virtual"
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156
communication architecture (VCA) may be more efficient than the "direct" communication architecture (DCA) when it comes to the dynamic reconfiguration of the VE network structure. The interface of the E-Mask application (VCA) was designed in order to assure that the interaction with the client was through a Microsoft Agent. This technology allows end-users to have both a visual (they can see the agents) and a sound interaction (they can hear one agent speak at a time). The objective of this interaction is to enable a practical and friendly interface for the client (end-user) during a future experiment. Our experimental goals are to determine which architecture has a better performance with end-users who are knowledgeable about the Internet (use of a browser and MSN Messenger) and have Windows XP experience. We will assess "better performance" by listening to and timing audio recordings of the endusers who participate in a pre-defined conversation (using scripts) in both architectures. We will also compare end-users audio responding time when new interlocutors are introduced into a conversation. The timings will not show whether the end-users are satisfied with the interface or not, they will only reflect the dynamic communication process. In order to determine which architectural environment end-users perceive to better adapt to, some questionnaires will be developed. End-users will be expected to rate questions about their feelings towards the "tutor' s" voice and appearance (in both the DCA and the VCA), their feelings towards a change of the "tutor" during a conversation in the DCA, and their feelings towards "trusting" the tutor (in both the DCA and the VCA). In our study, we want to assure that although the use of videoconferencing software such as MSN Messenger may have its advantages in VE communication environments, the use of Microsoft Agent software may shorten the adaptation of endusers to new people, during a conversation, since these are represented by the same agent characters who have a user-friendly appearance and a user-friendly voice. The agents have interactive characteristics and programmable personalities in order to better correspond to the requested communication needs of the end-user therefore, the response to the market need may also be more efficient.
This research work was carried out with the support of the "PRODEP III Programme", Ministry of Education and Science of Portugal.
References [1] Cunha, M. M., & Putnik, G. D. (2005a). Business Alignment Requirements and Dynamic Organizations. In G. D. Putnik & M. M. Cunha (Eds.), Virtual Enterprise
Integration." Technological and Organizational Perspectives (pp. 78-101). London: Idea Group Publishing. [2] Putnik, G. D. (2000a). BM-VEARM Enterprise Architecture Reference Model (Technical Report RTCESP-GIS-2000-
). Portugal: University of Minho. [3] Putnik, G. D. (2000b). BM-VEARM Enterprise Architecture Reference Model. In A. Gunasekaren (Ed.), Agile Manufacturing: 21st Century Manufacturing Strategy (pp. 73-93). UK: Elsevier Science Publ. [4] Lambert, J. (1996). Information Resources Selection. London: Aslib, The Association for Information Management. p.27.
Acknowledgements The University of Minho is a partner of the EU FP6 Network of Excellence I'PROMS.
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Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufaetttring Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Using Semantic Web Technologies to Discover Resources within the Intranet of an Organization S. C. Buragaa, T. Rusub a
Faculty of Computer Science, "A.I. Cuza "' University ofla~L Berthelot St., 16, Ia~i, Romania, [email protected] b "P.Poni" Institute of Macromolecular Chemistry, Ghica Voda St., 41A, lar Romania, [email protected]
Abstract
The paper presents a semantic Web-based architecture used to discover Web resources within the Intranet of an enterprise. This platform is based on Web services and agents, exploiting the spatial/temporal relations related to Web resources. All involved information within the system is XML-based, by using special languages for expressing metadata and temporal relationships between Web hypermedia documents. Keywords: Semantic Web, resource, platform
1. Introduction
Framework) [4] syntactic constructs.
Computers are used principally to render this hypermedia information, not to reason about it. Information retrieval has become ubiquitous with the WWW space' s development and information needs no longer to be intended for human readers only, but also for machine processing. As an expected result, intelligent information services, personalized Web sites, and semantically empowered search engines are going to be designed and d e v e l o p e d - this is the seminal idea of the Semantic Web [1, 2]. This paper will present I T W - a semantic Webbased distributed platform for hypermedia resource discovery within an Intranet of an organization (e.g., enterprise). The general architecture of the ITW system uses software agents, Web services, and other software entities. The platform takes advantage of the temporal relations established between Web sites' resources and uses a semantic Web-based model for the representation of metadata and additional information that involves time, by using XML (Extensible Markup Language) [3] and RDF (Resource Description
2. I T W system - the general architecture
158
2.1. General presentation The main purpose of ITW system is to offer a heterogeneous interoperable infrastructure, based on Web components, in order to discover hypermedia resources [5]. Using a Web interface, the user is able to make complex queries that involve time. The information and the associated RDF (Resource Description Framework) [4] metadata generated bythe ITW system is stored on independent Web servers. Even if one server is shutdown, the system is able to continue its execution, providing the same capabilities. In fact, ITW can be considered as a multi-language and multi-platform architecture for (hypermedia) resource discovery based on semantic information related to the Web resources. In the resource discovery process, one of the important issues is to deal with time. Using our defined RDF-based model, the ITW software agents are able to
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Fig. 1. General architecture of the ITW system. reason about the spatial and/or temporal relations established between certain resources localized in different Web sites. The general architecture of the ITW system consists of two main entities (see Fig. 1): 9 I T W W e b s e r v i c e s - their role is to give preferred information about the resources and the access to these resources; the Web services can be invoked by diverse software applications (e.g., user a g e n t s - such as Web browsers or special clients-, certain agents or Web services). For these Web services, we could associate semantic (ontological) descriptions specified in OWL (Web Ontology Language) [6]; 9 ITW agentstheir role is to effectively discover distributed multimedia resources stored on different sites; these agents are intended to be implemented within a multiagent system, such as Omega or ADF (Agent Development Framework) [7, 8]. 2.2. I T W s e r v i c e s
The Web services part of the ITW application consists of: 9 L o c a l Web s e r v i c e s to give information about the resources stored on the local Web (i.e. the intranet or the public Web site of an organization), 9 E x t e r n a l Web s e r v i c e s to be invoked in order to have access to certain Web resources; these services are offered, for example, by Google or other organizations. Also, ITW includes a local storage system for storing metadata and relations between found
resources. We can think these Web services as one entity (in a similar way provided by the Grid architectures). These services can be independently invoked by other external entities. The information exchanged by Web services and their clients is stored as Simple Object Access Protocol (SOAP) messages [7, 2]. For each local design Web services, the ITW system provides Web Services Description Language (WSDL) [7, 2] descriptions. In order to invoke external Web services, the system uses available WSDL documents provided by third-party organizations. To add semantic descriptions for each ITW service, we suggest the use of OWL-S specification (see [5]). The ITW services employ RDF documents about the discovered hypermedia resources. These documents are automatically produced by the ITW agents (presented in section 2.3) and are stored within the storage system. 2.3. I T W a g e n t s
The ITW Web agents are intended to be developed within a multi-agent platform. We choose to deploy two agent systems: Omega [10, 11] and ADF [7, 8]. To enable the flexible querying and accessing mechanisms about the distributed Web resources, Omega offers a facility for serialization- in an independent m a n n e r - the data and metadata (objects) processed by the system. The serialization method is detailed in and uses XML Schema and SOAP. Additionally, for each object, different metadata constructs are attached to specify several semantic properties. These descriptions are using RDF model and consists of certain XML vocabularies that describe properties of the resources - see below. The agents of the Omega system have the following tasks to be accomplished in their activity of discovering multimedia resources on Web: 9 Using different XML constructs expressed in our defined XFiles and TRSL (Temporal Relation Specification Language) [12,13] languages, for each Web resource an RDF document is produced (details about this process are given in [11]). The XFiles documents are used to keep all significant metadata that can be associated with a Web resource: location, type (e.g., XHTML document, PNG image, JavaScript program etc.), access way, timestamp of last modification and others - for details, see [ 13]. The TRSL constructs are used to store
159
temporal information regarding the relationship between resources. TRSL language is based on ITL (Interval Temporal Logic) [ 14-16] formal model. 9 Using RDF constructs regarding the temporal relations, one key aim is to preserve these relations (e.g., ifa Web resource is in an ITL relation Before with another one, the agent will try to maintain this relation, by checking regularly the metadata associated with the involved resources). For this relation, the user could specify certain actions to be executed by using TRS L constructs. 9 The internal behavior of the multi-agent environment can be modeled by BDIKcTL logic [17, 18], often used in the context of multi-agent systems. For a suitable communication between agents, the ITW system uses our XML-based agentcommunication language over SOAP messages, presented in detail by [ 10] and [ 11 ]. The second choice is to use ADF (Agent Developing Framework) [7, 8], an open source agent platform, with a stated emphasis on collaboration. The purpose of ADF is to build an interoperable, flexible, scalable and extensible framework that will allow realworld multi-agent systems to be developed with less effort. ADF system is built as a Service-Oriented Architecture (SOA) [9], which assures loose coupling between the interacting SOl,ware agents. The ADF architecture is also compatible with the FIPA (Foundation for Intelligent Physical Agents) [19] abstract reference model, which ensures interoperability with other FIPA compliant agent platforms, such as JADE and FIPA-OS. Agent communication is message-oriented and is compatible with the FIPA model. ADF agents communicate by asynchronously exchanging messages. Currently, two transport protocols are supported: first approach is relying on HTTP (HyperText Transfer Protocol), and the second one uses Java Message Service (JMS).All messages exchanged by agents are XML encoded. Additionally, ADF provides a SOAP encoding via SAAJ (SOAP with Attachments API for Java). Other details are provided by [7].
provide a flexible query user-interface within the Mozilla/Firefox Web browser. To describe user interactions on different controls of the Web interface, a protocol must be adopted. One of the best solutions is to use the XUP (Extensible User-interface Protocol) [2]. An alternative solution uses the standard XHTML language, in conjunction to ECMAScript (JavaScript) constructs. The user queries are stored into a customized version of our defined WQFL (Web Query Formulating Language) [21] language, to indicate supplementary information about the search (e.g., relation with another resource, method of access, resource type, etc.). An example of the WQFL document is the following: <webquery timestamp="2005 - 11-20T10:33:00" maxpages=" 15"> <engine url="http://itw, org?linux"> ITW Linux WS <engine url="http://itw, org ?windows"> ITW Windows WS <engine url="http://www.google.com/"> Google Search Engine temporal Petri nets <structure> <element name="p" order="0"> <element name= "title " appear="no" order="l ">
2.4. ITW user-interface
This query is expressing "temporal Petri nets" +with < 7 paragraphs on top +without links +without tables. Using WQFL, the user can specify a certain structure of the searched Web resources (in this case, without tables and hyper-links and maximum 7 paragraphs to be placed on top of each Web page). Also, the list of search engine is provided- in our case, the ITW services and Google.
The user-interface consists of one script that uses Extensible Markup Language (XUL) [20] in order to
After the search activity is accomplished, a possible result can be the following:
160
<webquery timestamp-"2005-11-20T10:35:10"> <engine url="http://itw, org?linux"> ITW Linux WS temporal Petri nets <structure> <element name="p"> <xf:Type xf:mime =''text/html''>ordinary <xf: Timestamp xf:type="modification"> 2005-09-01T12:00:33 <xf:Location xf:ip-" 193.231.30.225" xf:port="80"> www.site.org
References [ 1] Davies J., Fensel D., van Harmelen, F. (Eds.). Towards the Semantic Web. John Wiley & Sons, England, 2003 [2] World Wide Consortium's Technical Reports,. Boston, 2006: http ://www.w3. org/TR/ [3] Bray T. (Ed.). Extensible Markup Language (XML) 1.0 (Third Edition). W3C Recommendation, Boston, 2004: http ://www.w3. org/TR/REC-xml [4] Manola F., Miller E. (Eds.). RDF Primer, W3C Recommendation, Boston, 2004:
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http ://www.w3. org/TR/rdf-primer/ [5] Buraga S. C., Ggbureanu P. "A Distributed Platform based on Web Services for Multimedia Resource Discovery". In Paprzycki M. (Ed.). Proceedings of the 2nd International Symposium on Parallel and Distributed Computing. IEEE Computer Society Press, 2003 [6] Dean M., Schereiber G. (Eds.). OWL Web Ontology Language Reference. W3C Recommendation, Boston, 2004: http ://www.w3.org/TR/owl-ref/ [7] Hri,tcu C., Buraga S. C. "A Reference Implementation of ADF (Agent Developing Framework): Semantic WebBased Agent Communication". In: Petcu D. et al. (Eds.) Proceedings of the 7th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing- SYNASC 2005. IEEE Computer Society Press, 2005 [8] Nichifor O., Buraga S. C. "ADF - Abstract Framework for Developing Mobile Agents". In: Petcu D. et al. (Eds.) Proceedings of the 6th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing - SYNASC 2004. Mirton Publishing House, Timi~oara, 2004 [9] Krafzig D., Banke K., Slama D. Enterprise SOA: Service-Oriented Architecture Best Practices. Prentice Hall PTR, 2004 [ 10] Alboaie S., Buraga S. C., Alboaie L. "An XML-based Serialization of Information Exchanged by Software Agents". In: International Informatica Journal, vol.28, no. 1, April 2004 [ 11] Buraga S. C., Alboaie S., Alboaie L. "An XML/RDFbased Proposal to Exchange Information within a MultiAgent System". In: Grigora~ D. et al. (Eds.) Proceedings of NATO Advanced Research Workshop on Concurrent Information Processing and Computing. IOS Press, 2005 [12] Buraga S. C., Ciobanu G. "A RDF-based Model for Expressing Spatio-Temporal Relations between Web Sites". In: Proceedings of the 3rd International Conference on Web Information Systems EngineeringWISE 2002. IEEE Computer Society Press, 2002 [ 13 ] Buraga S. C. "A Model for Accessing Resources of the Distributed File Systems". In: Grigora~ S. et al. (Eds.) Advanced Environments, Tools, and Applications for Cluster Computing. NATO Advanced Research Workshop. Lecture Notes in Computer Science- LNCS 2326, Springer-Verlag, Berlin, 2002 [14] Allen J. "Time and Time Again: The Many Ways to Represent Time". In: International Journal of Intelligent Systems, 6 (4), 1991 [15] Allen J. "Maintaining Knowledge about Temporal Intervals". Communications of the ACM, 26 (11), 1983 [ 16] Allen J., Hayes P. "Moments and Points in an Intervalbased Temporal Logic". Computational Intelligence, 5 (4), 1989 [ 17] Rao A. et al. Formal Methods and Decision Procedures for Multi-Agent Systems. Technical Report No. 61, Australian Artificial Intelligence Institute, 1995 [18] Wooldridge M., Jennings N. "Intelligent Agents: Theory and Practice". Knowledge Engineering Review,
1995 [ 19] The Foundation for Intelligent Physical Agents (FIPA): http://www.fipa.org/ [20] Oeschger I. XUL Programmer's Reference Manual: h ttp ://www. xu lp lan et. org/ [21 ] Buraga S. C., Rusu T. "An XML-based Query Language Used in Structural Search Activity on Web". In: Transactions on Automatic Control and Computer Science, Vol. 45 (59), No.3. Politehnica Press, Timisoara, 2000 [22] Buraga S. C., Brut M. "Different XML-based Search Techniques on Web". In: Transactions on Automatic Control and Computer Science, vol. 47 (61), No. 2. Politehnica Press, Timi~oara, 2002 [23] Cioca M., Buraga S. C. "New Tools for Human Resource Management in e-Business: Combining UML Language, Reference Architectures and Web Programming". In: IEEE International Conference on Industrial Informatics- IND1N'03 Proceedings. IEEE Press, 2003 [24] Cioca M., Buraga S. C. "Instruments and Web Technologies for Implementing Architectures and Integration Informatics Systems in Virtual Enterprises" Proceedings of the 3rd International Conference on Research and Development in Mechanical IndustryRaDM12003, Herceg Novi, Montenegro Adriatic, 2003 [25] Adler S. et al. (Eds.). Extensible Stylesheet Language (XSL) Version 1.0, W3C Recommendation, Boston, 200 I: http://www.w3.org/TR/xsl/ [26] Alboaie L., Buraga S. C., Alboaie S. "tuB i G - A Layered Infrastructure to Provide Support for Grid Functionalities". In: Paprzycki M. (Ed.) Proceedings of the 2rid International Symposium on Parallel and Distributed Computing. IEEE Computer Society Press, 2003
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Intelligent ProductionMachines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka(eds) 9 2006 CardiffUniversity, ManufacturingEngineeringCentre, Cardiff,UK. Publishedby ElsevierLtd. All fights reserved.
Design of precision desktop machine tools for meso-machining A. Khalid and S. Mekid School of Mechanical, Aerospace & Civil Engineering, The University of Manchester, Manchester M60 1QD, UK
Abstract
The paper describes the classification of non-lithography based meso-manufacturing. Desktop machine tools and standard machine tools are the current manufacturing equipment for such machining scale, however desktop machines are the most economic and should achieve better accuracies. Various inherent problems and challenges in the development of highly precise desktop machine tools are discussed in this paper. A design strategy is proposed in the end for the development of a miniaturized machine tool with the aim of a very high precision. Keywords: Desktop machine tools, Micro machining, Miniaturization
1. Introduction
The idea of desktop machines was initiated for the concept of 'micro factories' in the previous decade. Presently, the meso scale parts are manufiactured with various processes like electrolytic in-line dressing (ELID), Electro-chemical machining (ECM), die sinking electro discharge machining (EDM), wire cut electro discharge machining (WEDM), milling, turning etc. However, lithography based techniques are the most common for micro manufiacturing. As the size of the products becomes increasingly smaller and the market demand for meso scale parts are on the increase, the previous non-lithographic processes are required to be employed at the micro and meso scale. Many researchers in Japan have already developed micro machines that can be mounted on a table top [2, 5]. This paper classifies the non-lithography based micro manufacturing on the basis of machine tool
size. Several instances are provided for the use of both standard and desktop size machines in micro manufacturing. Several benefits are identified for the use of miniaturized machines in micro manufacturing processes. Challenges and foreseeable problems for the design of desktop size machines are also discussed. A design strategy is proposed based on the robust design and optimization technique for a desktop machine tool. The mesoscale lies between the molecular or atomistic scale (where it is convenient to describe molecules in terms of a collection of bonded atoms) and the continuum or macroscale (where it is convenient to describe systems as continuous). 2. Non-lithography based micro manufacturing classifications
Non-lithography based meso scale parts are manufactured with different processes and from
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different size of machines ranging from small to large scale. Classification can be made in nonlithography based meso scale parts manufacturing, based on the size of machines employed for the purpose. Currently, two major groups exist for nonlithography based micro manufacturing; Small scale machines often called desktop machines and the standard size machines. Size of machine is identified based on the total volume of the machine tool.
configuration. MTS3 has the base size of 200 x 300 mm 2, feed drive positioning accuracy of 0.5 lam and a work piece surface roughness, Ra 0.02 ~tm achieved by turning brass C3604. MTS5 is a small CNC precision milling machine having a bed size of 320 x 260 mm 2. Table for each axis is supported by a set of crossed roller ways and drives through a lead screw. Machine uses a G8 controller and the positioning accuracy of feed drive is 1 lam.
2.1. Micro manufacturing through small machines
2.2. Micro machines
Mishima et al. [11] have provided a brief development history of Japanese micro factory and the benefits of utilizing it in the industry. Kitahara et al. [5] have developed a micro lathe in 1996. The lathe has a size of 32 x 25 x 30.5 mm3and weights only 0.1 Kg. The machine has a feed drive resolution of 0.05 lam, positioning accuracy of 0.5 lam and can hold a maximum workpiece diameter of 2 mm. It comprises of an X-Y driving unit driven by laminated piezoactuators, a main shaft device driven by a micro motor that incorporates ball bearings with rotating accuracy of less than 1 ~tm. The machine has achieved a surface roughness of 1.5 lain and roundness of 2.5 lam in the workpiece turning operation of a brass rod. Lu et al. [7] have build a micro lathe turning system of overall size 200 x 200 x 200 mm 3 and weights about 10 Kg. The machine consists of X-Y and Z driving tables with an axis resolution of 4nm. A work material of brass, O 0.3 mm is cut to a minimum diameter of 10 lam achieving a surface roughness under 1 lain. Kussul et al. [6] have developed micro machine tool of overall size 130 x 160 x 85 mm 3 and a travel range of 20 x 35 x 20 mm 3 with a resolution of 1.87 ~tm. Test pieces manufactured with this machine have dimensions from 50 lam to 5 mm with a tolerance range of 20 lam. KERN [17] (Germany): KERN offers a 5-axis table top version (KERN M M T ) o f a large machining centre with the smallest travel range option of 160/100/200 mm 3 (X/Y/Z). Heidenhain TNC controller is applied and a feed drive resolution of 0.1 lam, positioning accuracy of i l lam and the work piece accuracy of +2.5~tm is achieved. NANOWAVE [19] (Japan): Many desktop models are offered by this company. MTS2, MTS3 and MTS4 are the CNC precision micro lathe systems with cross roller slide ways arranged in a 'T'
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manufacturing
through
standard
Many standard size machine tool developers have equipped standard machines for micro machining. Many researchers have also used standard machines for the fabrication of meso scale parts. [ 12, 14] AGIE [15] (Switzerland): This manufacturer is a leading supplier of ultra precision Wire Cut EDM and Die Sinking EDM. In addition to the different model series for WEDM like AGIECUT VERTEX, AGIECUT CLASSIC and for Die sinking EDM like AGIETRON SPIRIT and AGIETRON HYPERSPARK, it also offers EDM for micro and nano scale application. 'Agietron Micro-Nano' is a die sinking machine that can perform micro drilling to machining of micro structures with the addition of nano module. Machine has a travel range of 220/160/100 mm 3 (X/Y/Z), positioning accuracy of + 1 lam, resolution, 0.1 gm and the surface roughness of work piece, Ra 0.1 gm. The nano module has a travel range of 6/6/4 mm 3 (X/Y/Z) and can fit on the same machine by replacing the rotary axis of the AGIETRON micro. The nano module uses voice coil linear motors and can achieve a positioning accuracy of +0.1 gm, resolution 0.02 gm and the surface roughness of work piece, Ra 0.05 lam. Nano module has used parallel kinematics for axes movements. PRIMACON [20] (Germany): It has developed a 5-axis vertical machining centre (Model # PFM 4024-5D) for the manufacturing of small components. The machine has a travel range of 400/240/350 mm 3 (X/Y/Z) with a positioning accuracy, under 1 gm and a rotational repeatability of 1 second arc. The machine uses Heidenhain iTNC 530 controller for the CNC. FANUC [16] (Japan): The model of this company for micro manufacturing is ROBONANO a-0iB, 5 axis CNC precision machining centre. It is a multi-purpose machine used for milling, turning,
grinding and shaping with a linear axes resolution of l nm. FANUC series 30i controller is applied for the CNC. Static air bearings are selected for the movement of slides, feed screws and direct drive motors. The machine has an overall size of 1500/1380/1500 mm 3 and the stroke length of 280 x 150 mm 2 in the horizontal direction and 40 mm in the vertical direction. Surface roughness of Ra 1 nm is achieved in the turning operation on aspherical lens core of material Ni-P plate. MOORE NANOTECHNOLOGY SYSTEMS [ 18] (USA): The company manufactures many medium size lathe machine models like Nanotech 250 UPL, 350UPL and 450UPL whereas Nanotech 350FG and 500FG are 3-axis micro milling and 5axis grinding machines respectively. 350 UPL is a 4 axis lathe using oil hydrostatic slide ways. Delta Tau PC based CNC motion controller is applied. Linear feed drives use frameless, brushless DC motors having a resolution of l nm. Surface roughness of a cubic phase plate of Zinc sulphide material as machined on the Nanotech 350 UPL is 4.112 nm Ra. Meso and micro scale manufacturing form a middle-scale stepping stone by which the benefits of nanotechnology may be accessed. In the past 5-10 years, these meso and micro scale parts have seen increased use in medical applications, consumer products, defence applications and several other areas [3]. A generalized approach is required for the robust miniaturization of standard size machines and manufacturing processes. Miniaturization of conventional machine tools has become a potential research area due to the high demand of the meso scale components. The cluster for Advanced Production Machines (APM) at IPROMS is investigating related research in micro machining as well as the future roadmap for micro engineering.
amount of energy consumption may be reduced to approximately 30 percent of the conventional factory by the half-miniaturization of the production systems. [4] 2. Vibration amplitude will be minimized due to the reduction in mass of the moving components. Large natural frequencies will be obtained for the micro system. 3. Cutting forces will also be reduced in micro manufacturing processes that may increase the achievable accuracy of machine tool. 4. Thermal drifts that are generated by the machining process causing deformations that effect directly the accuracy of standard machines. These effects are reduced in micro machines due to the miniature nature of the components, and can often be regarded as negligible. [9] 5. Small machines will be capable of providing high acceleration. The next generation of machine tools, will require axes to have acceleration capabilities in excess of 1g. 6. Micro machine tool's accuracy will improve by the inherent reductions of machine component's inertia, negligible thermal drift and larger eigen frequencies. [9] 7. Consumption of raw material will be dramatically reduced in micro manufacturing. Due to the low consumption of raw material, costly materials can be utilized. Even machining of nonconventional materials like ceramics may also be possible. 8. No new research is required for the materials to be specifically used for micro manufacturing [3]. Almost all the efforts carried out so far in micro manufacturing have made use of the same materials that are used in the macro manufacturing machines and processes.
3. Benefits & Challenges in micro manufacturing
3.2. Challenges in Miniaturization
Overall, there are plenty of benefits in miniaturization of machine tools where as there are some hidden challenges to overcome as well
1. The dominant hurdle for the development of micro manufacturing machines capable of machining with a very high accuracy is the identification and evaluation of the micro physical phenomena. At the micro scale, the laws of macro scale physics, no longer prevails. As different forces behave differently at the macro, micro and nano scales, some of them are more influential at a particular scale. For example, surface forces become very important at the micro scale but their influence is negligible at the macro scale.
3.1. Benefits of Miniaturization 1. Miniature machines will bring economical space utilization and energy saving. Individual micro machines in the Japanese micro factory take 1/50 of the space that the standard size machine tool, occupy on the shop floor. In watch manufacturing, the
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2. Another important issue is the assembly of the micro parts. Micro scale and multi scale products may have different challenging issues for assembling and packaging. Even the micro and meso scale parts which will be manufactured with micro machines will have their unique requirements for fabrication and assembling. Human handling of micro parts is certainly impossible and special robotic manipulators are essentially required. All the micro machine tool developers have addressed this issue by developing micro manipulators like transfer arm and a twofingered hand in the case of Japanese micro factory [10]. With micro-scale components, interactive forces (e.g. van der Waals, surface tension, electrostatic) will exist between components, which instigate difficulties in manipulation and control. To overcome these problems, contact type manipulators such as ultrasonic travelling waves, or mechanical grippers; or non-contact type manipulators for example magnetic fields, aerostatic levitation, or optical trapping could be used in place of the conventional solutions [ 1]. 3. When multi scale parts may assemble together, the multi scale physics will play its role in design, packaging and assembly of the products. Normal design and modelling tools are not capable of handling multi scale physics and modelling. The need to interface and integrate micro scale parts with parts of a different scale may require multi-scale modelling tools to predict system-level behaviour. These implications point to a need for a departure from traditional macro scale design models and simulation tools. [3] 4. In the micro turning process, the rigidity or strength of the shaft decreases as the diameter of the shaft reduces. There will be a restriction of achievable minimum diameter of the shaft which can withstand the magnitude of the cutting forces for the acceptable deflection in the shaft. Lu et al. [7] have measured the deflection of work piece shaft, 50 lam in length with its reducing diameter in the micro turning operation (See Fig. 1). 5. There are many design techniques available in the literature and in practice as well for the macro scale parts. But these design techniques will not work for the micro scale parts unless the dominant physical phenomena will be addressed fully and incorporated at the design stage. Modelling tools will also be required to acquire multi scale physics. Micro manufacturing standards have not been established so far and no work is done for micro
168
metrology and inspection of micro and meso scale parts [3]. Being the nascent stage of this technology, there is a need to limit the gap of the state of the art micro manufacturing technology and the required knowledge. At this stage, uncertainty and risk of utilizing micro manufacturing technology for the commercial manufacturing of meso scale parts is higher. Due to the lack of modelling tools and standards for micro manufacturing and micro metrology, researchers are using their own means of modelling and simulation tools to design meso scale parts. [3] 1.0
i
E" 0.8
1= 50~ m F = 1mN
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0.4
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b
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" i'o'io
..................... io4"o
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Fig. 1. Deflection estimation of work piece under cutting force [7] 3. Design Tentative
The expected problems in micro manufacturing and the issues related to miniaturization are discussed above and the purpose was to highlight them before planning the strategy for the mechanical design and control of the state of the art micro machine tool.
3.1. Design strategy Design strategy is developed with the aim of getting a very high accuracy in micro machining. The difference in this design strategy from the strategy of standard size machine lies in the introduction of second order phenomena that includes the micro physics involved between the components of the micro machine tool. Once the micro physics will be modelled and verified, the study can be extended to the multi scale physics to secure a full modelling of the major effects available at this scale. The development of highly precise micro machine should be designed based on the
usual precision engineering rules including robust design strategy with the static and dynamic analysis of the machine tool. The strategy can be started with the problem
i
iiiiii!
..... i ............................
.......
N!
tli
Fig. 2 Design variables of micro machine tool definition or the specifications of the micro machine followed by the generation of basic conceptual ideas. A thorough concept analysis will be carried out for the selection of a final model out of the pool of the initial concepts. Micro scale physics will be modelled to evaluate the influential physical forces at the micro scale. Once the physical phenomena model will be developed and verified experimentally, the model will be further used in the design and optimization analysis. The optimization analysis of machine components will be carried out using volumetric error as the objective function. Finite Element Analysis based static, dynamic and thermal models of machine tool will be integrated with the optimization model and the optimized values of design variables will be found. The optimised design will be further used to find the tolerance budget of each design variable. The comprehensive optimization analysis will be continuously carried out unless a robust design of micro machine tool will be achieved. A robust design in static analysis of a micro machine, under development at the University of Manchester has revealed the sensitivity in terms of tolerance for the design variables (See Fig. 2), where lth - Thickness of X and Y carriages ly- Length of Y-Carriage ld- Spindle-Column Distance x - X-Carriage travel
~error- Minimizing Objective function based on volumetric error. 3.2. Control
An important aspect to be considered is the system of control, which is increasingly being required to perform a wide variety of complicated tasks under varying operating conditions and in different environments, while at the same time achieving higher levels of precision, accuracy, repeatability and reliability [13]. The control design will be a selection according to the two different design philosophies opted in precision machine design to achieve high precision as follows. 1. Design of precise mechanical structures with most of the phenomena considered as second order error sources addressed. An adjusted servo-controller will animate the system to satisfy the specifications. 2. Design of a mechanical structure with an overall satisfaction with the implementation of an expert dedicated servo controller to compensate for all errors. [8] Servo control should be able to handle the complete process control. Process control is extremely important for such machines to fit machine kinematics with machining process. Spline movements should be taken into account by the controller to secure full trajectory of the tool and protect against frequent breakage. An intelligent controller will be applied to control the machining process through CAM (Computer aided machining). CAM will include complete process planning, and NC programming. Integration of the PC-based control system into a CAD/CAM manufacturing system is a fascinating area of research. The CAD/CAM system incorporates a feature recognition program which links directly to a computer-aided process planning (CAPP) software tool; hence the manufacturing process can become totally automatic thus improving efficiency.[9] Modern CNC approaches employ PC-based solutions to incorporate extensive functionality in order to combine high quality and flexibility, with reduced processing time. One must also consider the processing power of the controller hardware, as too great a modularity can result in deterioration in the real-time performance of the system. Finally, the success of the machine servo control depends on the type of control applied and the interaction agility between sub-systems. Actuators and sensors will
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also be selected before the fabrication and assembling of machine tool. Micro metrology standards will be needed at this stage. The remaining unmodelled phenomena will be compensated through error compensation techniques. 4. Conclusion
The paper classifies the non-lithography based meso and micro manufacturing according to the size of the machine. An overview of the small size machines has been presented. Accuracies achieved by standard size machines are found to be better than the small size machines. The benefits of the use of micro machines for the manufacturing of meso scale parts are discussed. Challenges for the development of highly precise micro machines are revealed. It is found that a little knowledge is available for the modeling of micro machines. Also, a lot of effort is required to investigate the micro and multi scale physical phenomena that is inherently degrading the accuracy of micro machine tools. A design strategy is proposed with the aim of developing a highly precise micro machine tool. References
[1] Alting L., Kimura F., Hansen H. N. and Bissacco G. Micro Engineering. Annals of the CIRP. 52(2) (2003) 635-657. [2] Ashida K., Mishima N., Maekawa H., Tanikawa T., Kaneko K. and Tanaka M. Development of Desktop Machining MicrofactoryTrial Production of Miniature Machine Products. 2000 Japan USA Flexible Automation Conference. Ann Arbor, Michigan. [3] Ehmann K. F., Bourell D., Culpepper M. L., Hodgson T. J., Kurfess T. R., Madou M., Rajurkar K. and DeVor R. E. WTEC Panel Report on INTERNATIONAL ASSESSMENT OF RESEARCH AND DEVELOPMENT IN MICROMANUFACTURING. World Technology Evaluation Center (WTEC), Inc., 2005. [4] Kawahara N., Suto T., Hirano T., Ishikawa Y., Kitahara T., Ooyama N. and Ataka T. Microfactories; new applications of micromachine technology to the manufacture of small products. Microsystem Technologies (1997) 37-41. [5] Kitahara T., Ishikawa Y., Terada T., Nakajima N. and Furuta K. Development of Micro-Lathe.
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Journal of Mechanical Engineering Laboratory (1996). [6] Kussul E., Baidyk T., Ruiz-Huerta L., Caballero-Ruiz A., Velasco G. and Kasatkina L. Development of micromachine tool prototypes for microfactories. Journal of Micromechanics and Microengineering. 12 (2002) 795-812. [7] Lu Z. and Yoneyama T. Micro cutting in the micro lathe turning system. International Jounal of Machine Tools & Manufacture. 39 (1999) 1171-1183. [8] Mekid S. Design Strategy for Precision Engineering: Second Order Phenomena. Engineering Design. 16(1) (2005). [9] Mekid S., Gordon A. and Nicholson P. Challenges and Rationale in the Design of Miniaturised Machine Tool. International MATADOR Conference. Manchester. 465-471. [10] Mishima N., Ashida K., Tanikawa T. and Maekawa H. Design of a Microfactory. Institute of Mechanical Systems Engineering, National Institute of Advanced Industrial Science and Technology, Tsukuba, Ibaraki, 305-8564, JAPAN. [l l] Okazaki Y., Mishima N. and Ashida K. Microfactory-Concept, History and Developments. Journal of Manufacturing Science and Engineering. 126 (2004) 837-844. [12]Rahman M. A., Rahman M., Kumar A. S. and Lim H. S. CNC microturning: an application to miniaturization. International Jounal of Machine Tools & Manufacture. 45 (2004) 631-639. [13] Smith M. H., Annaswamy A. M. and Slocum A. H. Adaptive control strategies for a precision machine tool axis. Precision Engineering. 17(03) (1995). [14]Takeuchi Y., Yonekura H. and Sawada K. Creation of 3-D tiny statue by 5-axis control ultraprecision machining. Computer Aided Design. 35 (2003)403-409. [ 15] www.agie.com. [ 16] www. fanuc.co.jp. [ 17] www.kern-microtechnic, com. [ 18] www.nanotechsys.com. [ 19] www.nanowave, co.jp. [20] www.primacon.de.
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldttkhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Designing Agent-Based Household Appliances K. Steblovnik a, D. Zazula b aGorenje d. d., Partizanska 12, 3320 Velenje, Slovenia buniversity of Maribor, Faculty of EE and CS, Smetanova 17, 2000 Maribor, Slovenia
Abstract
As agents gain acceptance as a technology there is a growing need for practical methods for developing agent applications. Our paper introduces new technologies which are driven by fast new development of embedded systems and becoming very attractive for the design of intelligent household appliances. We assess the tools, design and development environments which have to support the implementation ideas in this field, we briefly focus on necessary agent structures, and reveal the parallels of the selected Prometheus agent design methodology and agent execution environment called Jadex. Finally a practical design example of an agentbased household appliance, namely Multiagent Washing Assistant as a special instance of Rational Home Assistant, is demonstrated. The applied steps have proven effective in assisting all the phases from the development, design, and documentation, to the system implementation and simulation. K e y w o r d s : Multiagent systems, household appliances, rational agent, BDI model, agent design methodology
1.
Introduction
Ambient intelligence, ubiquitous computing, intelligent multiagent systems, all combined with the Internet capabilities they bode significant changes of human dwelling environments in a very near future [1]. At the same time, the idea of intelligent home has matured, embedded computer technologies have reached the price/performance ratio acceptable for wide consumer community, while l~ast communication infrastructure spread out to all levels of today's society. This is bringing forth a new type of consumers that expect the way of usage and operation of technological devices will draw as close to the natural as possible. Consequently, new families of household appliances will have to follow these trends. In the first place, this applies to home entertainment facilities and the products of white-ware. Some of
such high-end appliances can already be found on the market: washing machines, refrigerators, or ovens are controlled by 16-bit microprocessor technologies, touch-sensitive screens, and built-in speech-recognition interfaces. Higher levels of integration and more computing performance are becoming imperatives. Trend-setting companies are developing embedded systems with 32-bit Arm RISC technology, along with more powerful image and speech processing algorithms. These developments are going to mitigate the most frequent household activities, such as laundry, cooking, baking, refrigeration, etc. New appliances will show a certain degree of intelligence, they will be able to act autonomously, to plan and induce actions in order to achieve the requested goals, and even to learn. Communication with them is going to be user-friendly, mainly based on visual and speech acts in both directions. A variety of new sensors is
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assumed to enable this kind of devices to obtain a wide spectrum of crucial pieces of information, such as the quality of washing powder, water turbidity, the level of liquid in the refrigerated bottles, etc. The machine will be able to complement the user's knowledge and wishes with its technical aspects, so that any action will be optimized through an initial user-appliance dialogue and hand-shaking procedure. On the other hand, agent-based and multiagent theory [2,3,4] and computer-so~ware technologies [5] have also achieved the stage, where different technical and technological solutions successfully implement them [6] and convenient design methods [7] and execution engines [8] became available. The fundamental features of agents are aimed at autonomous, collaborative, and intelligent operation [9]. Thus, combined with powerful computer systems they can serve as a firm basis for the implementation of intelligent household appliances. In the continuation, we are going to assess the tools which have to support the implementation ideas in the field of intelligent household appliances. In Section 2, a brief overview of necessary agent structures is given. Section 3 reveals the parallels of a selected agent design methodology and agent execution environment, whereas a practical design example is demonstrated in Section 4. Section 5 concludes the paper. 2.
Rational agents and BDI architecture
From the generic point of view, an agent is supposed to be able to perform adapted and autonomous actions within a dynamic, unpredictable and open environment [3 ]. Considering today's computer systems, they certainly meet this definition. By looking in the reverse order, this means that a computer installation may qualify as an agent. What properties do they have to show? Agents sense the stimuli from their environment and they react accordingly. They also are proactive, persistently pursuing their goals, they adapt to unpredictable situations, and they are robust in the sense they detect and try to correct their own faults and faults of their collaborative agents. Another important property of agents is their ability to interact and build up multiagent communities [9]. Yet, the multiagent systems need more humanlike characteristics in order to interact in a rational
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and deliberative manner. They have to mimic mental states, such as to proceed from believes, have their own wishes and goals, and be intentionally oriented to achieving them. By attributing these kinds of states to the machine, we can describe it as a rational agent with believes, desires, and intentions. This defines it as the so called BDI agent or BDI agent model [10].
2.1.
Generic BDI model
Fig. 1 shows typical elements of generic BDI agent model. + ,opot
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Fig. 1: Generic BDI agent model Assume a bit more formal description of Fig. 1. If T stands for a set type, then ?(T) represents the set's cardinality. If further x means Cartesian product, the process of updating the agent's believes may be derived as the believe revision function (brJ):
?(Believes) x Perceptions --~ 7(Believes). Current believes and perceptions generate a new set of believes. The agent's deliberation is divided in two separate functions: create options and filtering. The former creates a set of alternatives, while the latter chooses among competitive alternatives. Creation of new options and filtering out one of them is formally written as:
?(Believes) X ?(Intentions) --+ ?(Desires) and ?(Believes) X ?(Desires) X ?(Intentions) ?(Intentions). This actually means the process of the agent's deliberation. Agents achieve their goals by applying the appropriate plans:
?(Believes) x ?(Intentions) -> Plan.
When implementing an agent, it is crucial it's believes update promptly to make the agent reconsider its goals, revise them if necessary and create new plans accordingly. The model from Fig. 1 corresponds to the structure which was adopted in the design of our intelligent multiagent household assistant. The design process was based on the methodology supported by the Prometheus environment. The resulting structures are ready to be implemented in the BDI architecture simulators or execution engines. Next two sections are devoted to a case study researching the design possibilities in the filed of agent-based household appliances.
0
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for
systems
Multiagent systems most often control complex environments and perform a variety of rational acts. It is therefore of great importance to have a powerful design methodology at one's disposal. We want to construct a rational system of a Multiagent Washing Assistant which will be apt of simple induction and learning. The BDI agent architecture turned out to be the most suitable one. This decision was alleviated by finding several parallels and good matching between the Prometheus design methodology and the Jadex simulation environment.
3.1.
Agent-oriented design methodologies
The Prometheus methodology covers the main steps in a multiagent system design. However, using it in combination with the Jadex simulation/execution environment, certain precautions must be taken. Jadex adopts classical BDI architecture and terminology, as it deals with perceptions, believes, desires, goals, intentions and plans. Prometheus generates all structures connected to goals and plans, it helps building agent internal structures, verifies the correctness of all communication links and messages, and prints valuable reports as exemplified in Section 4. It naturally suggests commencing an agent-based design by setting up the goals first. Different ways how these goals can be achieved are designated use case scenarios in Prometheus. Each scenario might include new goals and/or roles, and these roles are played by collaborating agents. So, the definition of scenarios and roles dictates the agent structure and
interconnections, the input perceptions, and plans for their acting. The Prometheus design methodology is based on the following development phases [ 11]: 1. System specifications: -Recognize external influences, such as other applications and users who interact with the system - Define system goals - Conceive system scenarios - Identify basic system functionalities, i.e. system roles - F o r each scenario, select input perceptions and output acts - Link each scenario to a specific goal -Follow iteratively the hierarchy of goals (by asking How and why?) and scenarios (by inserting goals, acts, and partial scenarios) - Combine goals and roles when the overall system architecture and organization become accurate enough - Incorporate perceptions and acts in the roles 2. System architecture design - Define types of agent in the system - Connect believes (data in the databases) and roles with agents - Determine agent interlinks and system architecture - Set communication protocols between cooperating agents 3.
Detailed design of the agents Define a detailed internal structure of each agent - Incorporate roles they are supposed to act -Implement agent plans, which means software algorithms, e.g. in Java; the plans are triggered by the agent believes, goals and internal events, whereas the execution of a plan can generate new events and goals - Connect each agent to its environment by making it consider, and respond to, the messages according to the implemented protocols - Finally, determine the overall hierarchy of goals to induce the sequences of plan executions. -
This general Prometheus framework is always adapted to the concrete problem under consideration. The abovementioned steps are applied iteratively, so that the obtained hierarchy of goals, interconnections of goals and roles, scenario structures, agents, and eventually the overall system architecture suit our target system most effective.
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3.2. Development and simulation environment for multiagent systems-Jadex Jadex is called a BDI-based reasoning machine [12]. Jadex resides on the JADE platform [13]. It integrates BDI agent features by using objectoriented approach and XML-based descriptions. JADE respects the FIPA-introduced standards and takes care of all the system communications. Jadex is actually a virtual machine which interprets a user-defined hierarchy and structure of multiple interconnected BD] agents. It thoroughly supports the internal structures of believes, plans and goals. The agents are intrinsicly assumed rational, as they share the properties described in Section 2. Any Jadex-based system is linked to its environment via a built-in messaging. Input messages trigger internal events, such as updating believes or commencing a plan execution, while the output messages communicate system actions. This mechanism applies equally to all agents whose interaction is based on message exchange as well. Jadex offers a well structured framework for all steps for a multiagent system construction. Flexible and open XML data structures are available for declaring the agents' believes and goals. At the same time, plans are supposed to be coded in Java and symbolically linked to corresponding data structures (believes and goals). Jadex interprets such user-defined systems, takes care of the agents' deliberation and updates their believes according to the external events or perceptions, register all events and corresponding arguments, and equips the user with a variety of useful statistics and graphical interpretation tools, such as an on-line display of message passing or current believes of selected agents. Jadex is thus not only a multiagent system development tool, but it is also a sophisticated testing tool, model simulation environment, and agent-oriented execution engine.
0
Prometheus methodology and Multiagent Washing Assistant design
This section exemplifies the conceptual design of a Multiagent Washing Assistant (MWA)-a household appliance whose intelligent behavior and environmental perception is meant to make laundry easy and effective. As we are designing an agentoriented system, we resort to the agent-oriented Prometheus design methodology [ 11]. Eventually,
174
it is important to consider which parts of the system should be treated as agents. We described the approach of Prometheus in Subsection 2.1. In our example of the MWA design we made a preliminary decision in favour of a multiagent system structure. We specified one supervisory agent that is helped by the agent assistants, because of two reasons: firstly, suitable physical/hardware structure of the target system, and secondly, the agents in such organization correspond to the basic tasks of an MWA. Our desired structure of MWA is shown in Fig. 2.
He~t up, Temperature
Pumpin, PumpOut, Water level
Waterturbidity
Add powder,Quaeity
2
Fig. 2: The MWA organization as induced by today's physical/hardware appliance structure An important issue linked to our preliminary decisions must be paid a special attention. We are using Prometheus for the system specification, design and consistency test phases, whereas we plan the implemention phase of an agent-oriented system using the Jadex development tool [ 12]. This has a significant impact to the way Prometheus is used. A general approach in Jadex considers the BDI agent model as ideally suited for describing an agent's mental state and desires (goals) representating its motivational stance as the main source for agent's actions. This strongly emphesizes Jadex as goal-oriented agent development tool. Actually, the goals are playing a central role in both tools, Prometheus and Jadex.
Jadex supports four types of goals: 'achieve', 'perform', 'maitain', and 'query' [12]. This is why
general structure assumed for an MWA depicted in Fig. 2. The next step must identify the basic functionalities of the system. Prometheus specifies separate roles to perform a scenario. A simple role of'Water heating in the drum' is shown in Fig. 4.
we decided to adopt the same type of goals in our Prometheus design of MWA. Our design is therefore strongly goal-oriented. Each goal can have a number of plans that can be used to achieve it. Each plan can have a number of sub-goals that themselves can have multiple applicable plans. This can then naturally be depicted in a goal-plan tree. The children of each goal are alternative ways of achieving that goal (an OR structure), whereas the children of each plan are sub-goals that must all be achieved in order for the plan to succeed (an AND structure) [ 11]. Generally, the basic step suggested by Prometheus is to define system goals and then use them to develop use case scenarios illustrating the system's operation. An example of such scenarios is shown in part in Fig. 3.
Fig. 4. The water heating role The system top goal of our MWA is 'achieve_ mosteffective_laundry_washed'. The scenario 'Perform washing procedure scenario' describes
;>Loood,,,o~o~, ........ ,,o;,-
how this top level goal can be achieved. All other goals represent its sub-goals and are its subordinates. Describing this subordinate structure of goals, Prometheus guides the user through an iterative goal design, and provides a harmonized and verified final structure of goals. The example of our MWA is depicted in Fig. 5. Only a part of the whole system goal structure is clipped. The path marked by a thicker solid line through the goals hierarchy exemplifies which sequence of goals and plans reaches the 'maintain_water_temperature' sub-goal. This sub-goal is one of several that must be materialized within the tree-hierarchical manner to obtain the highest level goal 'achieve_
Fig. 3. MWA scenarios
mosteff ective_laundry_washed'.
Table 1 concentrates only on a single, very simple scenario of the agent for water heating (Agent Waterheating). This agent originates in the Table l" The water heating scenario 9 Name [ Description
Trigger
I
J . . . .
I S~tem ! RWA
': i n i ~ t e d b y i Steps:::
I Water heating seenario I Wa~r heating system ,
:.
#
:.
.
.
.
.
.
.
.... t
Type
Name
Goal
achieve recognition command water temperature
Percept
Temperature measurement
3
Goal
maintain water temperature
4
Goal
query_ if water in drum
5
Goal
achieve water temperature
Role Water heating in the drum Water heating the drum Water heating the drum Water heating the drum Water heating the drum
Description
Data used
TOP GOAL (system SUB-GOAL)
Data produced Waterheatersystembelief
in in
SUB-GOAL
in
SUB-GOAL
in
SUB-GOAL
Waterheatersystembelief
Waterheatersystembelief Waterheatersystembelief
Waterheatersystembelief
Waterheatersystembelief
175
Action
Heater ON
iii!iiiiiiii!il: Action
Heater OFF
Goal
perform_heatersupervision
Percept
Heater check
Water heating in the drum Water heating in the drum Water heating in the drum
.....
SUB-GOAL
Waterheatersystembelief
Waterheatersystembelief
J .........
Fig. 5. A segment of the MWA goal structure The next phase of the system design by Prometheus instructs the user to the architectural design, going through agent types, agent descriptors, interaction diagrams and interaction protocols to the system overview diagram as depicted in Fig. 6. From this figure, we can see the system diagram mimics our desired organization structure suggested in Fig. 2 perfectly. There are also some other steps in this basic design, such as
agent acquaintance diagram, inter-agent messages definition, shared data repositories, but because of limited space we are not going to describe them in detail. The Prometheus detailed design leads to a progressive refinement of each agent with its
capabilities, internal events, detailed data structure, and process specification. Fig. 7 depicts the
aforementioned
simple
agent
Agent_Waterheating, whose structure exemplifies a simple AND/OR goal-plan tree. It is also obvious
176
the agent's placement in its environment introduces perceptions, actions and messages to other agents. Internal agent messages take care of its goals which basically maintain the water in the drum heated and also perform the system check and report failure conditions.
5.
Conclusion
We have briefly described the key aspects of using the Prometheus methodology in designing agentbased household appliances. The methodology has been in use for several years and we found it very appropriate for the specification and design phase of an agent-based system development, in combination with Jadex as an implementation and simulation environment. Because the both tools are strongly goal-oriented, the results of using them in BDI agent-based systems proved to be synergetic
A~nt_Laundryload~n~
\\ Agent_WaterHeating
Ag~_p~rsoft~ng
Fig. 6. The MWA system overview diagram as designed by Prometheus
1
/
J
t
/
,l
] .'
.....................................................
.................
i
,t
~
li:~ ~
:
:~1
Fig. 7: Detailed design of the Agent_Waterheater agent
177
and gave promising results. One of the advantages of this approach is also the number of cases where the automated tools can be used for the consistency checking against various artifacts of the design process. For example, the input and output events for an agent must coincide when checked in the system overview diagram and the agent overview diagram. The final detailed design as an AND/OR goal plan tree for a particular agent, which is obtained by Prometheus, is ready to be directly coded in Jadex environment. Although Prometheus is sometimes not as user friendly as it could be, and it does not provide goal items in the detailed agent design phase (instead the inter-plan messages are used), our overall experience is very positive and we seriously believe that Prometheus is a useful and very appropriate design tool for goal-oriented BDI agent-based systems, especially in combination with the Jadex implementation and simulation environment.
[6]
[7]
[8]
Acknowledgement System Sol[ware Laboratory from the Faculty of EE and CS of Maribor, headed by Prof. D. Zazula, is an Associate Partner of the I'PROMS Network of Excellence.
[9] [10]
References [1]
[2]
[3] [4] [5]
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J. Ahola, Ambient Intelligence, Internet publication, 2001. Accessible: http ://www.ercim.org/publication/Ercim News/ enw47/intro.html. N.R. Jennings, M. Wooldridge, "Application of Intelligent Agents", In N. R. Jennings and M. Wooldridge (Eds.), Agent Technology,: Foundations, Applications, and Markets, Springer-Verlag, New York, 1998. Accessible: http ://agents.umbc.edu/introduction/jennings98. pdf S. Russel, P. Norvig, Artificial intelligence: A Modern Approach, Second Edition, Prentice Hall, 2003. M. J. Wooldridge, An Introduction to Multiagent Systems, John Wiley & Sons, Ltd., 2002. B. Hermans, Intelligent Software Agents on the
[11] [12]
[13]
Internet: an inventory of current offered functionality in the information society & a prediction of (near-)future development, Tilburg University, Internet reference, Tilburg, The Netherlands, 1996. Accessible: http ://www.hermans.org/agents/index.html D. Kazakov, D. Kudenko, Multi-Agent Systems and Application, chapter Machine Learning and Inductive Logic Programming for Multi-Agent Systems, Springer-Verlag, New York, pp. 246270, 2001. Accessible: http://www-users.cs.york.ac.uk/%7 Ekazakov/papers/acai01 .htm P. Massonet, Y. Deville, C.Neve, "From AOSE Methodology to Agent Implementation", Proceedings of the first international joint conference on Autonomous agents and multiagent systems, Bologna, pp 27-34, 2002. Accessible: http://portal.acm.org/citation.cfm?id=544747 A. Pokahr, L. Braubach, W. Lamersdorf, Jadex." A BDI Reasoning Engine, University of Hamburg, Chapter: Multi-Agent Programming, Springer Science and Business Media Inc., USA, 2005. M. J. Wooldridge, Reasoning about Rational Agents, The MIT P r e s s Cambridge, Massachusetts, 1998. A. S. Rao, M. P. Georgeff, BDI Agents: From Theory to Practice, Technical Note 56, Australian Artificial Intelligence Institute, 1995. Accessible: http ://www.agent.ai/doc/upload/ 200302/rao95.pdf L. Padgham, M. Winikoff, Developing Intelligent Agent Systems, John Wiley & Sons, Ltd, 2004. L. Braubach, A. Pokahr, D. Moldt, W. Lamersdorf, "Goal Representation for BDI Agent Systems", Second International Workshop on Programming Multiagent Systems: Languages and Tools, New York, pp. 9-20, 2004. Accessible: http ://vsis-www.informatik.unihamburg.de/publications/view.php/208 F.,Bellifemine, G. Rimassa, A. Poggi, "JADEA FIPA-compliant agent framework", 4th International Conference on the Practical Applications of Agents and Multi-Agent System, London, pp. 97-108, 1999.
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Governance, innovation and performance David Wilson 1, Colin Herron2, Shirley Coleman 3 tPhD Research student, Industrial Statistics Research Unit, Newcastle University, Newcastle-upon- Tyne, United Kingdom, NE1 7R U. :Manager One North East/Nepa Best Practice Programme, ONE NorthEast, Riverside House, Goldcrest Way, Newburn Riverside, Newcastle upon Tyne, NE1 4EP 3,Shirley Coleman, Technical Director, Industrial Statistics Research Unit, Stephenson Centre, Stephenson Building, Newcastle University, Newcastle-upon-Tyne, United Kingdom. NE1 7R U [email protected] * Correspondance author.
Abstract
This paper describes a new method for investigating the role of workplace culture in innovation and performance. A manager intuitively recognises the need to invest in the workforce; however it is difficult to measure any expected gains. Spending limited resources on employees is therefore often considered risky, resulting in underinvestment. The locked-in value in a workforce, which economists call human capital, is now established as having a key role in growth but the mechanism generating this growth is still unknown. Indicators of cultural factors have been demonstrated to be significant to growth; however the lack of a consistent definition of culture means that the concepts cannot be applied practically. It is likely that measurement of influence techniques actually captures unconsidered underlying factors which really cause the growth. Influence techniques are a likely candidate to provide a useful mechanism and framework. This paper describes an industrial case study testing the hypotheses that influence and authority structures both improve and include standard measures of cultural factors. The most effective influence factors are considered and the implications and uses are included. The investigation suggests that understanding the influence structure in a company provides a sound basis for improving management investment strategies to increase innovation and growth. K e y w o r d s : governance, innovation, culture, performance, influence techniques
1 Background
Companies spend enormous resources to improve their competitive position. Creative thought goes into business process re-engineering, performance analysis and quality improvement. However, the processes are all run by the workforce and it is their motivation and commitment that is vital to the success of the company. Companies are well aware of how much physical capital they have but less aware of their human and social capital. Training a workforce and thereby increasing human capital, is a waste of resources unless the environment is created to utilise the new skills acquired. During the 1990's economists tried to incorporate culture into their growth theories. This
topic of research was known as social capital. Research into the effects of governance or culture amongst the workforce in a company has found that 'trust' Coleman [1] and 'network factors' Putnam [2] the two leading frameworks for social capital, are relevant to growth. Social capital can be defined as the relative ability of the workforce or management to make decisions. In this way social capital is related to human capital, because the incentive to train a workforce decreases if there is no intention to allow the workforce to use that training. The ability to govern depends on who makes the decisions in the workplace. The influence structure is an integral feature of culture and governance. It forms the basis for
179
understanding the role of social capital in innovation and growth Hippel [3]. Stanley Milgram [4] investigated responses to authority. His research showed that individuals will follow an authority figure significantly further than their common sense or morality would usually allow. Such responses occur throughout society, see for example Cialdini [5]. Cialdini [5] defines 6 distinct influence techniques; reciprocation, commitment and consistency, social proof, liking, authority and scarcity. Influence techniques are most flagrantly abused by advertising practises, but the same techniques are applicable in many other arenas. Reciprocation is the desire to return favours. If you have been given something there is strong pressure to return the favour. Often small offerings induce people to more than repay the kindness. Commitment is the desire to remain consistent with prior actions. After 'shocking' an actor to the level of intolerable pain, participants in Milgram's obedience experiments would state that the actor, an unknown stranger, had somehow deserved the punishment. Social proof represents social pressure. Liking someone makes it easier for them to influence you. Scarcity is also a widely used method of influence. This research examines the relationship between the influence techniques and social capital. If a relationship can be shown, then this leads to an alternative method of investigating culture based on influence structures. The paper reports an investigation into the influence structure within a group of 7 interested companies. The analysis also explores the relationship between the different influence techniques and the effects on company performance. Outputs based on strategic interventions performed by One North East, responsible for regional development to improve productivity, were available for comparison. In particular, this paper explores the effects of the 4 influence techniques authority, commitment and consistency (CC), social proof and liking in companies. Output results include productivity rates, quality and value measurements. The propensity of companies to incorporate new ideas into their processes is the key aim before the success of the new ideas can be included. The aim is to determine whether common patterns of influence emerge from companies which perform successfully. 2 Survey
2.1 Questionnaire A questionnaire was designed and included questions relating to social capital, the influence techniques (see Figure 1), unused qualifications and
180
opinions towards change. There was insufficient data to test the potentially complex reciprocation influence therefore it was excluded during the analysis. The scarcity influence technique is not included as it was not considered relevant to the production processes in the companies involved. Question number 4
Question
Technique
If I am given an instruction A by my supervisor which I know to be incorrect I would still follow the instruction. 13 I have in the past often SP changed decisions (within the company) because of peer disapproval. 31 I feel personally committed to CC the company. 19 I like my workplace. L Figure 1 Example questions for each influence technique
The social capital questions were separated into 2 sections. The first section included questions based on trust indices using techniques based on the research of Coleman [1], for example "/regularly participate in social activities with other members o f the company" and "most people at my workplace can be trusted" Braverman [6]. The second was based on the Social Network Analysis (SNA) style of Putnam [2] which represents knowledge networks such as "/ understand the work related skills of my colleagues and the areas in which they are knowledgeable", Bishop and Trout [7]. This way the full spectrum of economic cultural indicators and social capital measures were covered. Questions requested either yes/no responses or reactions on a Likert scale (between 1 and 5). Some questions overlap, for example, "Decisions made within the company are fair and transparent" is often included in social capital questionnaires as well as SNA analyses. It was also included as a commitment and consistency question. The questionnaire was piloted and then distributed to 4 companies. Analysis of this first set of questionnaires was aimed at understanding the relationship between social capital and the influence techniques. Then a second shorter questionnaire containing a subset of the questions in the first questionnaire was prepared for use with a wider range of companies to focus on the relationships between the influence techniques. The combined results from both questionnaires were then used to compare influence techniques and company performance. The key problem in the development of the questionnaires was to judge whether responses would be representative of actions rather than opinions. This is a common problem in related research and certain techniques have been
demonstrated to have a greater degree of accuracy. A recent example by Anderson [8], shows experimentally that familiar questions, such as "most people can be trusted", accurately reflect actions. 2.2 R e s p o n d e n t s
Companies receiving regional funding from One North East, neither sector nor size dependent, were asked if they were prepared to participate in the research. Companies were selected on the basis of availability and their participation in NEPA (North East Productivity Alliance) activities which allowed reliable impartial outputs of the whole company performance. This meant that 16 companies were potentially available depending on their agreement and the logistics. It was important to get as many companies as possible, however, in the end only the results from 7 companies could be made available. The 7 companies volunteered and agreed to hand out questionnaires to 50 employees under the agreement that the research would not significantly disrupt production. Summary measures of company characteristics and performance were requested from the collaborating companies. Usable results were obtained for the ratio of direct to total labour in the company, absence rates and scrap rates. One company was unable to furnish absence and scrap rates. Direct labour is the people directly involved in producing the goods. The ratio of direct to total labour reflects the size of management. The employees given the questionnaire within each company were selected at random based on their logistical availability by managers who were not aware of the purpose of the research. All questionnaires were completed confidentially. All workers were rotated around shift patterns so even where only a single shift was available it is reasonable to assume that the responses are representative. 3 Results
3.1 Overview The response rates are shown in Figure 2. First the combined questionnaires from the first 4 companies were investigated to understand the general relationships between the influence techniques, and to compare them to social capital questions. This allowed the shortening of the questionnaire. The results from all 7 companies for the subset of questions were then used to investigate the relationships between the influence techniques and the general questions.
Company
1
2 3 4 5 6 7 Total
Total employees 164 286 174 600 236 630 336 2090
Number of responses 32 32 43 32 49 18 50 256
% Response to Total 19.5% 11.2% 24.7% 5.3% 20.7% 2.9% 14.8% 9.8%
% Response rate 64% 64% 86% 64% 98% 36% 100% 73%
Figure 2 - Responses per company Results were investigated using regression analysis, Principal Components Analysis, Pearson's product moment correlation coefficient and Kendall's tau. For all significant relationships, the raw data were studied to ensure the relationships found were meaningful. The shortened questionnaire contained 10 authority questions, 3 social proof questions, 11 commitment and consistency questions and 4 liking questions. The large number of questions being analysed means that a p-value considerably lower than 0.05 was required in order to achieve statistical significance at the 5% significance level ~. 3.2 G e n e r a l results
The first principal component is dominated by the social capital questions, liking questions and commitment and consistency influence (CC) questions which are contrasted against the authority questions and social proof. The second principal component was strongly influenced by the authority and social proof questions which were based on the respondent; these were contrasted again with the social capital questions. The third principal component was most strongly influenced by the authority and social proof questions which related to other members of the company than the respondent, these contrasted most with liking questions, commitment questions and social capital questions. This suggested that there may be a relationship between authority and social proof. The social capital questions seemed likely to be best related to the CC and liking influences. None of the scatter plots gave evidence to suggest that
1
Let ~ ' be the significance of the individual result. Then for a questionnaire wide 5% significance level, 5% overall - 1-(1-a" )" - 0 . 0 5 . If, for example there are 30 combinations of possible questions, a p-value of 0.0017 would be required for statistical significance. However, this assumes that all questions are independent when in fact they are highly correlated so the result is a conservative estimate.
181
any particular company was excessively swaying the results. The social capital questions were most strongly linked to the liking influences. This is shown well by the relationship between the questions '7 like my workplace" and "most people at my workplace can be trusted". The p-value was 0.000 for Kendall's tau (see Figure 3). The liking and CC influences were positively related to the social capital measures, while the authority influences were found to be negatively related to social capital.
| Liking
I
0
I
t
I
2
4
6
Social Capital
Company Number
Authority index 0.501 0.505 0.497 0.522 0.533 0.471 0.507
Social Proof
Commitment Consistency
0.55 0.50 0.48 0.49 0.57 0.51 0.51
0.65 0.74 0.70 0.58 0.67 0.82 0.67
Liking
Direct/Total
Absence
Scrap
0.68 0.72 0.74 0.61 0.68 0.81 0.70
0.67 0.63 0.71 0.64 0.36 0.79 0.58
1.3 4.4 3.1
2.0 0.8 5.2
10.6 3.6 7.0
5.7 22.0 1.4
-k
Figure 3 Relationship between social capital and liking
Questions for the 4 influence techniques, (authority, social proof, commitment and consistency, and the liking influences) were compared to each other in order to find underlying relationships. The authority and social proof influences were found to be positively related to each other. The commitment and consistency influence (CC) and the liking influence were also positively related. However each of the two was negatively related to the other. The relationships are summarised in Figure 4.
~
~
Because only 7 companies participated, there was insufficient data to make generalised statements. However some evidence of a positive relationship between the authority index and the direct/total labour values could be found. This relationship is represented in Figure 6. It is an important relationship as it provides support of both the validity of the authority index and the effect on management control. Greater levels of authority require more non productive personnel to administer. ........ il;i U;I ;;i(: 84184 .... ! !; i i!~ii?ii!?iiii!A~?!iiiiiiiiii!i~iiiiil;iliiiiiii:;~ii 84i; ;iliC;!i~ii!iiV~il ; :(ii?iiiiiiil;iiiiiii i : :.g~'..a~ ~Direct/Total ~ r ~riW
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Figure 5 - Influence indices and outputs per company
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Figure 4 Relationship between influence techniques Questions were combined to provide a single index for each influence technique. The indices were related to the companies as shown in Figure 5. Absence or scrap rates were not available for Company number 4.
Figure 6 Relationship between authority and proportion of direct labour.
The relationship between the liking index and direct/total labour was in the positive direction as shown in Figure 7.
182
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S c a ~ r p t o t o f Di rect/Total Labour vs Liking with L=low CC or H=high CC
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Figure 7 Relationship between liking and proportion of direct labour, also indicating the level of commitment and consistency (CC). The scatterplot shows that higher liking scores go with greater commitment and consistency. 4 Discussion
Measures of social capital have been shown to be related to the influence techniques. The influence structure within a company is related to governance and the way that employees are able to express themselves, make decisions and offer ideas. The relationships between the influence techniques give more insight into how social capital relates to company performance and innovation. Authority reflects the extent to which individuals will respond to the commands of those higher in the management chain than themselves. It was expected that individuals would receive authority from either managerial or social pressures. Therefore the authority technique was expected to be negatively related to social proof which represented the amount social pressure influenced individuals not to stand up for their own judgements against the perceived social will of the rest of the workforce. However, the authority technique was found to be positively related to the social proof technique which supports the hypothesis that individuals would take their authority from either management, in the form of the authority technique or society, through social proof. Where authority can be imposed absolutely all ideas will conform to the beliefs of the management, which will be implemented at the expense of all others. Both the authority and the social proof influence techniques were negatively related to the commitment and consistency technique. The liking technique was positively related to the commitment and consistency technique but negatively related to authority and social proof. This suggests that individuals like to be committed to their work, and those who are committed will resist the
authoritative influences of management and social pressures to perform the actions which they think are correct. The authority influence, being negatively related to the commitment influences, implies lethargy towards the implementation of new technologies. This implies higher costs associated with the need to control employees more carefully with implications for company performance. Conversely, greater levels of employee commitment tend to be associated with employees being keen to improve the workings of the company. Where employees wish to be a part of a more successful company they choose to suggest process improvements. Where their suggestions are treated well their levels of commitment also increase. These levels of commitment will be reduced if they feel that the atmosphere in the company does not allow them to express their opinions, either as a result of overpowering management or excessive social pressure. The level of direct/total labour is closely related to the authority index. If a company wishes to exert a higher level of authority they create a larger authority structure so that the ratio of direct labour to total decreases. There is evidence that the absence rates are positively related to the authority and social proof indices. This suggests that increasing the amount of authority will cause an extra cost associated with greater absences. No relationship was found between scrap rates and any of the four indices. 5. Conclusions
In summary, companies with greater authority influence are less likely to see the commitment and consistency or the liking influence in their workforce. The implication is that investment in training and development of employees is less likely to be rewarded by innovation and improved company performance in an authoritative company than in one with a more relaxed atmosphere. The size of the case study was limited by logistical issues; it would be preferable to have more company output measures and more companies taking part. Also, ideally the survey data would include psychological analysis of each employee as well as the exact competence and set of qualifications of each. The results of the study were provocative and should pave the way for further investigation. They imply that governance and culture may be reasonably viewed in terms of influence techniques. This vantage interestingly engulfs all the existing frameworks of social capital. Network theories may be retranslated into systems of influence, thus affecting knowledge indirectly, and trust measures are relevant because they increase the potential for permitting influence.
183
The aim of the research was to find whether the distribution of innovative workers in a company could be reflected by comparing influence techniques. If so then the value of training workers in a situation with given influences present is easier to estimate. Companies may then use the presented indices to optimise their output. The methodology is cheap, simple and, for a cultural measure, specific. Importantly it can be used by managers to see clearly when investment in the workforce can be expected to be a robust investment that will lead to better company performance and growth. It is anticipated that this methodology could be generalised to any size of firm, network or even economy. Intelligent production systems are more likely to succeed if the workforce is committed, not only to the job, but also to innovation and continual improvement. This research suggests, at the very least, that careful consideration should be given to the extent of authority in the company culture. Features of the influence structure affecting the workforce may explain any lack of benefit from training and provide a way to release the inner inspiration and enthusiasm of personnel throughout the whole company.
Acknowledgement Newcastle University is a partner of the Innovative Production Machines and Systems (I'PROMS) Network of Excellence funded by the European Commission under the Sixth Framework Programme (Contract No. 500273). www.iproms.org
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References [1 ] Coleman J.S. (1990) Foundations of Social Theory. Cambridge, MA. [2] Putnam R.D., Leonardi R., Nanetti R.Y. (1993) Making Democracy Work: Civic Traditions in Modern Italy. Princeton University Press, Princeton, NJ. [3] Hippel E.V. (1988) The sources of innovation. Oxford University Press, New York. [4] Milgram, S., Obedience to authority; an experimental view. [ 1st ] ed. 1974, New York: Harper & Row. [5] Cialdini R.B. (2001) Influence : science and practice. Allyn and Bacon, Boston, MA. [6] Braverman, H., (1975) Labor and monopoly capital," the degradation of work in the twentieth century. New York: Monthly Review Press. xiii, 465 p. 21 cm. [7] Bishop, M.A. and J.D. Trout, (2005) Epistemology and the Psychology of Human Judgement. Oxford: Oxford University Press. [8] Anderson L.R., Mellor J.M., Milyo J. (2004) Realism in Experimental Economics: Integrating the Real World into Experiments - Social Capital and Contributions in a Public-Goods Experiment. The American economic review. 94, no. 2
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldttkhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
KOBAS" Integration of applications in machines F.J. Diez a, R. A r a n a a a
Ingenieria de Producci6n, Fundaci6n Tekniker, Avda. Otaola n ~20, 20600 Eibar, Spain
Abstract This article examines a solution to a complex problem of integration, with two differentiating features: first, the great variety of the different applications and second their integration into a single production machine. The content has been extracted from the work that is being done in the European Project named Kobas, that was approved in a call focused on solving problems faced by SMEs. In this project the companies are specialized in the construction of capital goods. The article structure first starts showing the problem to be solved, will then describe the framework of the project in which the solution has been developed and finally addresses the solution itself in greater detail.
Keywords: Interoperability, Open platform
Introduction Integrating different components is a complex task, especially when these components cover wide range of applications as diverse as in the project presented here (Finite Elements, Maintenance, Training, Rule-Based Knowledge, etc.). In addition, there is the need of defining a scalable framework which will in turn enable new components to be integrated in the future. Another characteristic aspect of the system is the fact that the applications may coincide on the same machine, meaning that potentially problems of incompatibility may arise between different running environments. At the same time, a multiplatform solution should be able to run on the most commonly used operating systems, namely the Microsoft Windows family and the several variants of Linux. As we show in the next paragraph, a specific project characteristic is that it is being developed by groups of different profiles and nationalities.
Also conditioning factor is the need to achieve a certain minimum level of performance. This is not a real-time system per se, but some operations must run quickly in order for the operation with the machine they serve to be functional. The proposed solution takes into account the need for them to function cooperatively [4]. Of the three main types of integration architectures applicable-point to point, middleware and broker--the architecture finally selected lies somewhere between the second and the third, due to the fact that given the machine operating restrictions, it is not feasible for the entire information traffic to be centralised on a central node. In order to achieve a valid solution, the information that is handled by the various applications needs to be defined consistently. XML has been chosen for exchanging information. However, this does not entirely solve the problem; a set of common metadata also needs to be defined. There is a lack of standardization [1] in the system area, requiring a special effort to achieve standardization based on
185
existing arrangements that can be adapted to our solution [2] and in turn can subsequently be used in the area of the machines. 1
General Information
The developments done in the research of the integration approach presented in this document are being carried out as part of the KOBAS project [10] granted by the European Commission.
1.1
Framework of the project
KOBAS: is the acronym for "Knowledge Based Customized Services for Traditional Manufacturing Sectors Provided by a Network of High Tech SMEs" project, approved under the Sixth Framework Program FP6 within the call 2002-NMP-2 - 3.4.3.1-4, "Development of new processes and flexible & intelligent manufacturing systems: Support to the development of new knowledge-based added value products and ser-vices in traditional less RTD intensive industries - IP dedicated to SMEs".
1.2
Objectives
The purpose of KOBAS is to create a network of SMEs consisting of high-tech companies that, using the tools developed during the project, will enable the machines made by industrial equipment manufacturers to be fitted with personalised and innovative software valid both for process planning and for carrying out advanced tasks (maintenance, remote assistance, tequezt.sforthe "~
in which industrial equipment is used at present. To this end, a series of new generation tools are being developed, designed to supply manufacturers with specific software suitable for each machine. This is schematically shown in the Fig 1. 2
Operating environment
The production machine is the environment for which the components are being developed. This point is itself a very significant drawback to be taken into account. The components are customised to meet the requirements of the manufacturer or user of the equipment so that they can be used on the factory floor. The final goal is to have the integrated solution on the control of the machine itself, although as a second option (if it might interfere with the machine process) the solution could be provided on a PC working together with the machine.
2.1
Components
The components that have been developed to be used by the network reflect the different aspects in the approach set out by Kobas for the use of a manufacturing machine:
2.1.1
User Interface
Using this component, the network will be able to build and configure a low-cost man-machine interface based on virtual reality, so that all functions and services of the solution integrated by Kobas are easily and naturally accessible for the final user.
KoBaS~olLtions
2.1.2
Rule-based knowledge
This component is designed to provide the network with a rule-based knowledge (related to the process and geometry), to support programming, configuration, maintenance and training ofthe machine by means of rules. Serviceprovided: customizedsol,waresolution
I~elligent I~achine
Fig. 1. Service by the Network programming, etc.). This service will be carried out for manufacturers of machines who henceforth will be able to sell machines with a high added value. The project seeks to substantially modify the way
186
2.1.3
Experienced-based knowledge
This component is used by the network to build a database of knowledge based on past experiences related to the processes carried out. Once the cases have been acquired and processed, this module provides the methods for accessing and reusing this knowledge. This module operates in direct connection
with the previous component (rule-based knowledge component).
file Machine Builder ha.~ nsked tbr a KoB&S service to be provided
The general solution provided: fi'om the virtual desired object..,
help in the maintenance procedures. For all of these purposes it will use the rules- and experience-based knowledge bases. 2.1.8
Training
The network will use this component to develop a training module for the machine under study, to provide training support for the end user of the machine. The component will in turn use the capacities of other components (such as simulation) to offer a virtual training environment.
ii~,~!;ii~i~,ii~i~ i i~~:i.........~~,~i~]
2.1.9
Ttie 8MF.s Netw,~rk, thaa~ks to these Components, proVide tile 8olutiot~
Maaagein~nt a M Training suppo~
Fig. 2. Personalized solution. 2.1.4
Partprogram creator
Using this component, the network will offer to the machine operator a fast customised solution for programming the machine offline, making it possible to generate the programs to make the parts in very short periods of time. This module takes into account the information supplied by the experience and rule based knowledge base. 2.1.5
3D-simulation
This module is used to generate a personalised simulation environment. It is designed to place the machine in a virtual environment which recreates the real world, offering a working environment in which machine tasks and the maintenance and training modes can be demonstrated. 2.1.6
Finite element analysis
Using this component, the network builds a finite elements analyser, tailor-made for the machine in question. 2.1.7
Maintenance
Using this component, the network will be able to build a personalized maintenance service for the machine under analysis. The solution offered seeks to prevent faults, propose intelligent maintenance and
Mechatronic Configuration
This component will allow the network to develop solutions to configure the machine under analysis, based on a modular machine architecture that meets the end user's requirements. 2.1.10
Production Planner
This component is designed to allow the network the possibility of developing personalised arrangements of production management functions on the machine (cost planning, scheduling and optimisation). 2.1.11
Integration
This component is used to build the framework of the proposed KOBAS solution, ensuring interoperability between all components. 2.1.12
Web Services Enabler
This component provides the way to access the KOBAS machine functions, already developed by the other components, to be invoked using standard Internet-based protocols. This component is integrated but is independent of the other components. Its advantages are as follows: broad and remote access over the network, integration with company or plant applications, external integration with the customer, partner or supplier. 3
C o m p o n e n t vs. BlackBox
Kobas' philosophy is based on the idea that each machine manufacturer, or even that each machine user, is being able to use the components that interest them. This means that each component must be able to
187
operate independently from the others. Obviously, some components have a greater importance or degree of usefulness than others: for example, non-use of components such as the GUI or Simulation would involve a great loss of functionality or the need to implement this functionality outside the project. This last possibility is granted due to the selected type of architecture and the use of standards. The Kobas solution included clearly two distinguishable phases: Configuration and execution. The configuration phase plays an important role, more than in other systems because the information needed is required from the machine and its environment and therefore a correct parameterization is very important for the integrated functioning of the components. At the same time the robustness of the system must be maintained in the case of unavailability of one or more components and it is in this phase when this functionality needs to be configured. In the execution phase, the runtime of the component is what is known as the "Black Box". This "Black Box" acts as a service offering different functionalities, both to the end user and to other Black Boxes or services. To summarize, the different components developed for each production machine includes all the tools and applications that will serve to configure the different "Black-Boxes", based on the characteristics of the machine and on the other services it will supply. Its installation, of each component, as a service on the final machine is what it is known as a "Black-Box". 4
Interfaces
The characteristics of the interfaces vary greatly. One of the main tasks of the project involves the definition and performance of these interfaces. Two of the Black Boxes with the greatest number of interactions are the experience database manager and the rule-based knowledge manager, since they provide support to the others in improving their functions. The mechatronic component could change the description of the machine. This description is used as a base for many operations in the maintenance and diagnosis components or for creating the parts programs, and a change in this description may therefore involve reconfiguring both Black Boxes. The interaction between the Part Program Creation Black Box and the Simulation Black Box is also shown in the diagram: this information is essential for the simulation component and will therefore also involve reconfiguring its performance.
188
Although most interfaces do not have critical time requirements (given that the operations are performed outside machine operation and do not interfere with it), there are some of them that must meet a minimum response time, such as the status change of certain variables detected by the maintenance Black Box which are transmitted to the user through the GUI Black Box. One very important aspect of the definition of the interfaces is to ensure a common meaning for all the entities in the system. When a Black Box requests a complex datum (object) from another Black Box, both must be in agreement as to what is being requested. This interaction is simple to solve when there are only two Black Boxes but becomes more complicated when the number of actors involved is increased. We have considered the use of ontology to allow semantic contents to be given to the different terms with which each component identifies a concept. The problem arises when it is required to map those concepts in runtime. This terrain is not considered to be entirely mature and given that it is a crucial point for fulfilling the project, we have opted for a more pragmatic approach. This approach consists of achieving a data model in the design stage of the system that will ensure definition of all the entities common to different Black Boxes. Achieving this model has required a great effort by all the participants in terms of unifying concepts and terminology, and in some cases has necessitated redefining or adapting designs or developments already underway. The final result will be a set of XML-Schemas that form a single data model thanks to the characteristics of the namespaces and which allow each component to use the files it requires without needing to use the complete data model, which could result in an excess of resources. 5
Architecture
The result of having so many components is that the architecture chosen needs to have a large degree of interoperability. One of the pillars of the project for achieving a high degree ofinteroperability is the adoption of XML [9] as the data exchange system, due to its standardisation. At the same time, XML thanks to the definition of the standard XML Schema, also allows definition of meta data and all kinds of complex structures
necessary for defining the parameters, both of the machine and of the environment that surrounds it. Once it is known what the operating philosophy is and the way of defining the data to be exchanged, the means of exchanging these data are defined. With a requirement of a large degree of interoperability and greatest possible standardisation two options of architecture have been considered. One option involves a SOA architecture, using SOAP [9] as a communication protocol. These types of architecture are currently considered to have the greatest future, headed by the web services. As well as being a very extensive protocol, SOAP allows different transport levels to be used. This means that it is possible to make critical communications using protocols such as IIOP or to implement web characteristics using HTTP[9]. Another option is to use the dynamic link (reflection) capacities together with the multithread capacities of Java to generate a model that enables components to be inserted with the plug-in philosophy. This type of model is proving very successful and is being widely developed in applications such as Eclipse or Protdg6. One aspect which needs to be emphasised is the integration of the user interface of all services in another service. This makes it possible to have a very homogenous user interface, with the same look and feel and behaviour, which would consequently be easy to use and learn. In both architectures, implementation of this philosophy involves a great deal of complexity, but it is considered to be greater in SOAP. Another critical aspect involves the interactions, which require very short response times. The SOAP protocol performs several different transformations on the data exchanged and one result is that these short response times may not be achieved.
6
Integration strategy
The integration strategy in either of the two options will be the same. There will be an integration component which will allow the dynamic link of the various components in the system. When it comes to defining the system integration strategy, we need to take into account the differentiation previously made between component and Black Box. In order to perform the task of configuring and building the black boxes, many components use the same information. It is advisable for them to use the
same sources and formats. To achieve this, the network has been based on the power of XML through the XML Schema and the use of standards in industrial software such as STEP NC and VRML. All the Black Boxes must be capable of functioning on a single computer linked to the machine. In the case of some manufacturers, this computer only operates on Microsoft operating systems but in the case of others it can also run on some Linux distribution. As a result, the software has been developed in a language independent of the operating system, namely Java. The java code is run on a virtual Java machine: this means that the entire development has to be carried out in the same version of Java in order to avoid the use of several different virtual machines on the same computer.
Fig. 3. Integration architecture. As well as the virtual machine there needs to be an operating environment (framework) which makes it possible to resolve the level of transport between the different black-boxes and manage its activation, configuration, safety, transactions, etc. In terms of execution for defining the process of communication between the different Black Boxes there have been two chief differential restrictions; 9 The system must respond to certain critical events in as short a time as possible. 9 The system must be robust in the event of unavailability of any Black Box. The integration component appears for the purposes of saving these restrictions by performing the tasks ofa UDDI server in web services, but adapted to the defined architecture. WSDL files are generated in the configuration for defining the interfaces of each Black Box. These files are read by the integration component by filling the necessary data structure for the use of the corresponding Black Box in execution. In execution, if a Black Box (A) needs something from a Black Box (B), A contacts the integration Black
189
Box to obtain the URI of B. The integration Black Box returns this URI. With the URI returned A makes the communication with B without the mediation of the integration Black Box. This direct communication is meant to eliminate possible delays that might be introduced by this intermediary step, thus making the response time shorter. At the same time the integration component makes it possible to carry out system-adapted actions in the event of unavailability of another black-box, ensuring the robustness of the entire set.
7
Conclusions
The results of the Kobas project, that is still running, will allow us to know if the SOA architecture is valid for systems close to real time systems. The wok done until know confirms that such an architecture is valid to increase the flexibility and reutilization of software, needed for every type of machine, but this still is an open point. The introduction of XML and SOAP implies construction and translation actions on messages. This will introduce delays referring to traditional systems. It will be analysed in future phases of the project if such a delay it is admissible by the machine builder. The independence of the different black boxes is somehow lost with the introduction of the integration black box. It is need to define the best strategy to follow when this black box fails.
8
Aknowledgment
The Tekniker Foundation is partner of the EUfunded FP6 Innovative Production Machines and Systems (I'PROMS). Kobas Knowledge Based
Customized Services for Traditional Manufacturing Sectors Provided by a Network of High Tech SMEs (www.kobasproject.com) is funded by the European Community.
References 1. D. Richard Kuhn. On the Effective Use of Software Standards in Systems Integration. Proceedings, First Intl. Conference on Systems Integration, IEEE Computer Society Press, 1990. 2. European Industry Association. Eicta Interoperability White paper, June 2004. http://www.agoria.be/ICT-TICFlash/n 1/88/88-4-white%20paper[ 1].pdf 3. R. Depke, G. Engels, M. Langham, B. Ltitkemeier, S. Th6ne. Process-Oriented, Consistent Integration of Software
190
Components. Proc. of the 26th Int. Computer Software and Applica-tions Conference (COMPSAC) 2002, Oxford, UK, IEEE, Aug. 2002, pp. 13-18. 4. S. Larsson, Towards an Efficient and Effective Process for Integration of Component-Based Software Systems. Proceedings of the 3rd Conference on Software Engineering Research and Practise in Sweden, Lund, Sweden, October 23-24, 2003. 5. R. Land, I. Crnkovic, C. Wallin. Integration of Software Systems - Process Challenges. 29th Euromicro Conference(EUROMICRO'03), Antalya, Turkey, September 01 - 06, 2003, pp 413-417 7. Rikard Land, Ivica Crnkovic. Existing Approaches to Software Integration- and a Challenge for the Future. Proceedings of 4th Conference on Software Engineering Research and Prac-tice in Sweden (SERPS), Lin. 8. M. Jersak and K. Richter R. Ernst and J.-C. Braam and Z.Y. Jiang and F. Wolf.. Formal Methods for Integration of Automotive Software. In Designers Forum at Design, Automation and Test in Europe Conference, pages 45-50. Munich, Germany, March 2003. 9. http://www.w3.org/ 10. http:/www.kobasproject.com
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhfi and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
On-line modal identification of a CNC machining system based on surface roughness laser scattering" theoretical perspectives Zarina Mohd Hussin, Kai Cheng*, Dehong Huo School of Engineering and Design, Brunel University, Middlesex UB8 3PH, UK *Correspondence to: Professor Kai Cheng Email: [email protected]
Abstract
On-line modal identification of a Computer Numerically Controlled (CNC) machining system is essential and much needed for understanding the system dynamics and so as to control the system' s performance in the process in the light of the dynamic variables identified. In this paper, an on-line modal identification approach is proposed by using surface roughness laser scattering in the process. By on-line identifying the surface roughness (Rz) which is a collective signature resulted from the machining process, the dynamic modal variables of the machining system are derived based on the algorithms developed by the authors. Furthermore, the theoretical aspects of the approach are explored including measurement of Rz, correlation between Rz and vertical vibrations in the system, dominant equation and solutions, and algorithms and their implementation with MATLAB, etc. The experimental set up and trials are also explored albeit more experimental cutting trials are planned and to be carried out in the near future. Keywords: Surface roughness; specular reflection; machining dynamics; modal identification
1. Introduction
Real time monitoring and control during the machining process should be adopted for accuracy, productivity, and efficiency enhancement purposes. Furthermore, it can help characterisation of the machining system and control of chatter vibrations during the process, which constitute to the system stability and its productivity. This is important especially for a high speed machining system to produce high quality machined components and products. In order to maintain its stability and precision, the vibrations within the machining system need to be monitored and controlled so as to achieve normally the minimum vibration displacement between the tool and a workpiece if possible. On-line
modal identification of the machining dynamics and system is essential and much needed for the process optimization and intelligent manufacturing in particular. This paper presents an investigation on on-line modal identification of CNC machining system in order to optimally control the system based on the system dynamics modal parameters identified. The inspiration of the work came from the extension of the experimental studies carried out by Liu and Cheng [1]. With respect to this, a study has been undertaken on the correlation relationship between the surface texture (peak-valley) and the vibration displacement with a model as shown in Figure 1. Jang, et al [2] have also studied the relationship in hard turning both theoretically and experimentally.
191
N o m e n c l a t u r e s
Ra
- average roughness (pm)
Rq Rp
- root mean square roughness (pm)
Co
maximum profile peak height (pm)
-
maximum profile valley depth (IJm)
Rv
-
Rt
- maximum height of the profile (pm)
Rz
- average maximum height of the profile or surface peak-valley (pm)
Rzyg~ - average roughness measured by Zygo 3D Surface Profiler (IJm)
N Zj
- no. of surface height variations - surface height variations (pm)
i(o) 0o
intensity distribution with angle of scattering given by Gaussian distribution - angle of light incidence (degree)
0o
- angle of light reflectance (degree)
L M c K
F
F. Film
Zn
z.(t)
d
-
G
-
Kd
-
-
-
coefficient (N/pm/s) stiffness (N/pm) dynamic mass (kg) a
m
p
i
n
g
Znm
dynamic damping coefficient (N/pm/s) d
y
n
a
m
i
c
stiffness (N/pm)
matrix representation of
Zn
Cd
Kd applicable variables of the relationship
coefficient of the relationship
- no. of occurrences time varying cutting force applied in the system (vibratory direction) (N) time varying cutting force applied in the system (vibratory direction) with no. of occurrences (N) - cutting force (N) amplitude of cutting force with no. of occurrences (N) -
-
-
-
matrix representation of
F2
F~
(N)
- angular frequency (rad/s) time (s) - phase angle (rad) amplitude of vibration displacement (gm) amplitude of vibration displacement with no. of occurrences (pro) time varying of vibration displacement (pm) time varying of vibration displacement with no. of occurrences (pm) -
- mass (kg) -
-
-
z(t)
The experiment envisaged in the paper is comprised of the machined workpiece surface roughness measurement by laser scattering method and mathematical manipulations in predicting the machining system dynamics characteristics from the measurement and physical relationships. The measurement system consists of a laser light emitting source, an optical track and an electro-optical sensing for capturing reflected light. The reflected light is
192
L(t)
-evaluation length (Iam)
-
k.
f(t)
- l i g h t
Md
Xdm
n
-
-
-
-
matrix representation of Z e
(pm)
Z3 vibration displacement with no. of occurrences (~m) -
velocity of vibration displacement with no. of occurrences (Iam/s) acceleration of vibration displacement with no. of occurrences (pm/s 2) -
ZKI
-
Zn
detected and captured by a photosensor, which converts light intensities to voltage levels. The concept of the experiment obeys the law ofreflection where the angle of incidence, 0/o is equal to the angle of reflectance Or o in magnitude with reference to the normal of the surface. This actually carries information about the surface roughness in terms of surface peak-valley measurement (Rz) where it correlates with the vibrational displacement within
the machining system [2]. Ra is the most used parameter in the industry as a measurement of the surface finish and Rz is used more on the theoretical analysis to study the behaviour of the surface as described in the American National Standard AS ME B46.11995 [3]. By on-line measurement of the surface roughness and vibrational characteristics which relate to the machining system, the system dynamics modal parameters M, C and K can be automatically identified by using the algorithms developed by the authors, which are implemented with MATLAB/SIMULINK.
M d z(t)+C d z ( t ) + K d z ( t ) - f ( t )
(2)
Summarizing equation (2) in a matrix form with matrix dimensions: = [F]3byj
[Z]3by 3 C d
(3)
Kd 3byl
~
Identific~ Algorithms .~
/'Z//,//f
D/A
R~
Photosensor
('2
Ix
>1
w~,O~
Optical fibre cable >
Fig. 1. The spring-damper vibratory model of milling operation.
The overall set up of the experiment is described as a second order system as shown in Figure 2. The system is designed to identify the dynamics parameters of tool-workpiece surface loop (from Figure 2) during machining trials. The dynamics parameters are labelled as the dynamic mass (Md), damping coefficient (Cd) and stiffness (Kd). With reference to the applied force exerted, the sensitive movement of the system vibrations is in the z direction (vertically up and down). The system is translated in terms of forces and by applying Newton' s second law of motion the physical relationship of the system is formulated. That is [4]:
d2z(t) dz(t) +C d +Kdz(t)- f(t ) dt 2 dt
Cutting force
Tool-workpiece surface loop dynamics variables: Md, Cd and Ka
Laser <
2. Theoretical framework
Md
,
Dynamics of the machining system { Md, Ca and Ka }
Cutting tool
Md (workpiece) ]tf.
[
Kd
+
~ /
Cd
Fig.2. On-line modal identification system set up. As the machined surface roughness [Rz] is correlated proportionally with the vibrations [Z], that is
]3by3
(4)
[Zl3by 3 -- co[knez]3by 3
(5)
[Z]3by 3 oc kn [R z
(1) and so
Equation (1) can also be written as:
193
By substituting equation (5) into (3) and there is:
IMd)
[coknez]3by3 Cd = [F]3by1 Kd 3byl
(6)
and therefore:
dJ [F]3byl Cd = [coknRz]3by3 Kd
(7)
Thus the values of the system dynamic mass (Md), damping coefficient (Cd) and stiffness (Kd) can be obtained in the light of equation (7). The mathematical derivations and process above outline the theoretical framework on which the investigation is based.
3. Further analysis of the theoretical issues
3.1. Measurement of Rz In principle, all optical methods have common background. It includes light emitting, optical track and electro-optical sensing. The approach works by initially transmitting a laser beam to the specimen and scatters when it hits a surface. This will produce light reflection which can be reflected in two ways, specular or diffuse reflection depending on either the surface is smooth or rough respectively. Normal line Ipeak for the ~rc/2 ~ I(0) specularly ~v_.,---'-" ~ reflected beam N ~ / /
Laser beam
N /
pecularly \
Oi~ Or~ I/~" % 7[/2 ~
~
/
~
f
l
reflectedbeam
Fig. 3. Light intensity distribution on a textured surface. Whether the surface is microscopically smooth or
194
rough, it has a tremendous impact upon the following reflection of a light beam. Every ray which strikes a rough microscopic surface in a different way will have a ray ofreflection. The beam transmitted can be referred to as a bundle of individual light rays which are travelling parallel to each other. The angle of incidence, 0i~ and reflectance, Or~ are equal in magnitude with reference to the normal of the surface in accordance to the law of reflection. Figure 3 shows a light intensity distribution, I (0), of the reflected speckle pattern from a rough surface, which consists of specularly and diffused reflected beam. Angle 0 is the angle of scattering given by a Gaussian distribution from Tay and Quan [5]. The main components used in the experiment are laser and photosensor. The type of laser chosen is widely used in metrology applications where with this kind of laser, it can predict the behaviour of the beam with a good degree of accuracy as claimed by Williams [6]. Also, based on other resources, the laser is selected on the fact that it is possible to be used in the experiment whereby the surface roughness is much smaller than the wavelength ofthe incident light beam [7]. The reflected light is detected and captured by a photosensor which converts light intensities to voltage levels. It actually carries information about the surface roughness where it correlates with the surface roughness. The voltage measurements from the reflected light are quantified and presented by sampling techniques in the American National Standard ASME B46.11995 [3] for correlation purposes. The related surface parameters are defined by referring to the surface sample as illustrated in Figure 4.
ra-x,~ [-,,, ~ ,~a , " ~"^v/ ' - a~'v ^ / " l "w-/"~^ vk/ /~~"r'3/~ v] Mean level Evaluation length, L - - ~ / (
Traversinglength
l
>]
Fig. 4. Surface profile measurement lengths. Analytically, the related surface parameters used for profiling methods in this paper are as follows: # Ra is the average of the absolute values of the surface height variations, Zj measured from the mean level along a line of profile data.
N
Z./
./=1
(8)
9 Maximum profile peak height (Re) - the distance between the highest point of the profile and the mean line within the evaluation length. 9 Maximum profile valley depth (Rv) - the distance between the lowest point of the profile and the mean line within the evaluation length. 9 Maximum height of the profile (Rt) - the vertical distance between the highest and lowest points of the profile within the evaluation length. R, - Rp + R,,
are manipulated by using mathematical techniques as referred to Jordan and Smith [10]. The simplest forms ofsinusoidal functions are used to resemble the periodic force and vibration displacement in the system. The periodic force which is the input of the system is given by, f.. ( t ) - F n sin rot
The machining process is excited by the cutting force where forced vibrations are periodic within the system and will oscillate at the force frequency. Thus vibration is defined in terms of a regular periodic motion, for which the vibration displacement is:
(9) zn(t) = Z n sin(ox- 0)
, Average maximum height of the profile or surface peak-valley (R:) - the average of the successive values of Rtcalculated over the evaluation length. This parameter is the same as R~ (DIN) [8] when there are 5 sampling lengths within an evaluation length. 5
=
(13)
Equation (13) represents the vibration displacement to be used in solving the equations representing the system. Equation (2) is converted to a matrix form: [Mdl{Zn}+[Cg]{Zn}+[Kd]{Z,,}-[Fnlsino)t
(14)
5
Y~Rp + Z R v Rz
(12)
1
1
5
(lo)
3.2. Correlation of Rz and vibrations (vertical)
Surface peak-valley (Rz) of a workpiece changes with vibration during the machining process which has been stated earlier in [2]. Hassui and Diniz [9] have presented this idea by using surface root mean square roughness (Rq). This paper introduces Rz which is obtained from Ra and relates to the system modal parameters. Surface peak-valley measurement is correlated with vibration displacement in their relationships as: Z-CoR ~
(11)
Ideally, Co represents factors that collectively affect the correlation relationship such as damping (force), cutting fluid (lubrication), tooling variations (size, type, etc), heat, friction (drag), hardness and thickness of specimen and cutting debris, etc.
Initially, the velocity ( Z n ) and acceleration ( Z, ) in equation (14) are evaluated in terms of vibration displacement (Z n ). These are carried out by trigonometric manipulations and in matrix forms. The three matrix equations generated are: z, - {Z, }~cos 0n ]sin rot-[sin 0n ]cos cot]
(15)
zn - {Znw}[[c~ r ]cos o)t + [sin 0n ]sin rot] (16) ..
z, - {-z~(o 2 }[[cos O, ]sin ox-[sin O, ]cos 0~] (17) Substituting equations (15), (16) and (17) into (14) will produce equation (18). {[Mj ]{-Zn w2 }[[cosr n ]sinox- [sinCe. ]cos~]}+ {[Cj ]{Zn w}[[cosr . ]cos~ + [sing}..]sino)t]}+
{[X~ ]{Z. }[[cos0. ]sin~- [sin0, ]cosox]}[F, ]sino, Further expansion, turn out to be:
3.3. Solving the equations and algorithms
Analytically, solutions attained from this paper
195
[Md ]{-2=0) 2 }[cOS~n]sine.at- [M a ]{-2=0) 2 }[sin0. ] coscat+ [Ca ]{Z. 0)}[cos@.]coscat + [Ca ]{Z=0)}[sin@. ] sin~ + [Kd ]{Z, }[cos0= ] s i n ~ - [Xd ]{Z=}[sin0= ] cosc_.a = [F=]singa (19t By grouping the coefficients of sin mt terms of the left and right hand sides of equation (19), it is simplified as: [Md ][{-Z,0)2}cos@=]+ [Ca ][{Z=0)}sin0= ] +
[K,][{z.}cos0.]= [e=]
(20)
The interfacing of experimental data is applied into equation (20) to find out the values of the dynamic modal parameters as on-line measurement technique. Further mathematical manipulations are carried out by using matrix method within MATLAB environment.
3.4. Implementation with MATLAB Experimental trials without machining process are carried out in a dynamic situation as on-line measurements to obtain Rz through surface average roughness Ra. The experiment is initially set up in a static situation and later extended to on-line measurement [11, 12]. The experiment is programmed by using LabVIEW graphical data logging system and MATLAB software. The experimental data obtained will be displayed roughly as shown in Figure 5. Figure 5 shows the correlation between reference readings (Rzygo) and experimental readings (Ra). Samples of correlated R, and Rz are tabulated in Table 1 and are then used to obtain online values of the system dynamic mass (Aid), damping coefficient (Ca) and stiffness (Ka) as in Table 2.
Therefore, if:
[Znm]=
I i Zl0)2c~
Z1~176
glc~
1
/20) 2cOs~2 /20)sin~02 /2cos~ , Z30)2 COS~13 Z3 ~ i n #s Zs cos @3 (21)
I]
Ix,=]= c,
(22)
Ka
Fig. 5. Correlation between reference readings (R~go) and experimental readings (Ra).
and Table 1 The correlated data of Ra and Rz
IF.m]= F2
(23)
F3 then
iiiiii iiiiiiiii ii ii!i iiii iiiiiiiiiNNiii i, iiiii!i iiiiiiii. [Xam]=[Z=m~'[F=m]
(24)
Thus the values of the system dynamic mass (Ma), damping coefficient (Ca) and stiffness (Ka) can be calculated from matrix equation (24).
196
1 2 3 4 5 6
Ral Ra2 Ra3 Rzl Rz2 Rz3
0.412 0.404 0.419 0.816 0.807 0.834
0.412 0.425 0.398 0.818 0.848 0.794
0.400 0.500 0.400 0.797 0.871 0.794
0.397 0.410 0.407 0.789 0.809 0.803
Table 2 The on-line data of modal identification parameters.
" :ii!i!!:~:~:~::~,~!4
" 0.081
2.34
329
64
The discrepancies of the system designed can be gauged from the predicted results and the percentage errors obtained from the experiment. Thus these can be used to evaluate the physical dynamics behaviour of the system designed.
4. Concluding remarks In this paper, the theoretical framework and associated detail issues are investigated for on-line modal identification of a CNC machining system by using surface roughness laser scattering. Understanding of these issues and their mathematical derivations and algorithms are essential for later on experimental trials and implementation on shop floor machining applications. The authors have been undertaking the experimental trials which preliminarily prove the presented approach being very promising, although more trials need to be undertaken and results to be critically reviewed.
[4] Altintas Y. Manufacturing Automation: Metal Cutting Mechanics, Machine Tool Vibrations and CNC Design. Cambridge University Press, Cambridge, UK (2000). [5] Tay C J and Quan C. A parametric study on surface roughness evaluation of semi-conductor wafers by laser scattering, Optik, 114 (2003), pp. 1-6. [6] Williams D C. Optical Methods in Engineering Metrology. Chapman & Hall, London (1993). [7] R Silvennoinen, et al.. Specular reflectance of coldrolled aluminum surfaces. Optics and Lasers in Engineering, 17 (1992), pp. 103-109. [8] Deutsche Normen DIN 4768. Determination of Surface Roughness Values Ra, Rz, R. . . . with Electric Stylus Instruments-Basic Data (Berlin: Beuth Verlag, GmbH (1974). [9] Hassui A and Diniz A E. Correlating surface roughness and vibration on plunge cylindrical grinding of steel. Int. Journal of Machine Tools and Manufacture, 43 (2003), pp. 855-862. [10] Jordan D W and Smith P. Mathematical Techniques: An Introduction for the Engineering, Physical, and Mathematical Sciences, Oxford University Press Inc., New York (2002). [ 11 ] Mohd Hussin Z, Cheng K, Crispin A and Ward R. An optical fibre sensor based system approach for the inspection and measurement of engineering surfaces. Proceedings o f 4 th Int. Conference of the European Society for Precision Engineering and Nanotechnology (2004), pp. 175-176. [12] Mohd Hussin Z, Cheng K, Crispin A and Ward R. Online measurement of workpiece surface topography and texture by using laser scattering. Proceedings of 7 th Int. Conference and Exhibition on Laser Metrology, Machine Tool, CMM & Robotic Performance-Laser Metrology and Machine Performance VII (2005), pp. 574-583.
References [ 1] Liu X and Cheng K. Modelling the machining dynamics of peripheral milling, Int. Journal of Machine Tools and Manufacture, 45 (2005), pp. 1301-1320. [2] Jang D Y, et al. Study of the correlation between surface roughness and cutting vibrations to develop an on-line roughness measuring technique in hard turning. Int. Journal of Machine Tools and Manufacture, 36 (1996), pp. 453464. [3] The American Society of Mechanical Engineers (1996), Surface Texture (Surface roughness, waviness, and lay). ASME B46.1-1995, New York, NY 10017.
197
InteUigent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Selective laser sintering of metal and ceramic compound structures D. Trenke a, N. Mtiller a, W. Rolshofen a a
Institutefor Mechanical Engineering (IMW), Technical University of Clausthal, Robert-Koch-Str.32, 38678 Clausthal-Zellerfeld, Germany
Abstract
In different application scenarios, compound structures of different material like metal and ceramic are needed to increase durability and reliability of industrial goods with reference to surface hardness, wear and temperature stability. Therefore, selective laser sintering has been tested in experimental series, how such structures could be generated. This article describes achieved results of the basic targets (connection mechanism, creation of interfaces, bonding forces, homogeneity and porosity) and concludes with technical application. Keywords: laser sintering, compound structures
1.
Introduction
During extensive series of tests at Institute for Mechanical Engineering (IMW), compound structures of metal and ceramic powder composite were produced by means of alternating fusing with selective Laser sintering at the institute of mechanical engineering. The determination of interactions influenced by this process with reference to melting and laser beam was in foreground. To specify optimal exposure parameters with dependency on different ceramic powder, following aspects were looked on. -
-
Connection mechanisms between ceramic and steel components, Creation of interfaces, Bonding forces between layers and Homogeneity as well as porosity of compound structures
By a metallic and ceramic construction, surface hardness, temperature stability and abrasion stability could be improved by laser sintered components.
198
2.
Direct m e t a l laser sintering p r o c e s s
The process chain can be seen in Figure 1, which starts with a CAD-construction of manufacturing component. This computer model is prepared for the next step with special software for laser sintering. The pre-processed data is then transferred to the Rapid Prototyping plant, where the model is built with the laser in layers. Concluding, material properties of the sintered work piece can be improved by different finishing processes like infiltration or coating [ 1]. The existing Rapid Tooling system "EOSINT M 250 Xtendet" works after the direct metal laser sintering processing (DMLS) of EOS Company [2]. Physical basis for this is a metallic sintering process. First of all, energy of a CO2 laser melts the scanned areas of a planar metallic powder bed according to layer information of the workpiece. Liquefied particles of the powder conglomerate among each other and solidify to a structure. A complete cycle of the DMLS process can be seen in Figure 2, which is passed through corresponding to the number of layers [3]. Inserting of ceramic powder has been done manually.
Figure 1: Process chain for metal sintering at IMW
Mirror
COz laser
a) Exposure of metal powder layer
9 :::::::::::::::::::::: ::~::~i~::~'
b) Lowering of the building- and dosing platform, wiper moves to the right
, ~(5~ ~::~:.::~ :::::::~::.~ :~:. :.:.~:,: ~::~::~i::i~==:~j~,~ . ~::~:.:. :.~ ~ ~:::~~ ~~ :: :.::::: ~:::::~,'~
d) Application of the next powder layer, wiper moves left
c) Raising of the dosing platform
Figure 2: Laser sintering building process
199
Metal layers
Ceramic layers
F i g u r e 3: m e t a l and c e r a m i c b u i l d i n g in layers
3.
Performed Analysis
According to the defined targets from the introductive part, ceramic and metal compounds with different building were produced by selective laser sintering. These experiments can be divided into three parts: -
-
movements of Rapid Tooling system. In order to guarantee a sufficient energy entering, laser power is tuned to 95% and lower velocities are used for scanning ceramic layers than metal, when they are sintered.That way both mono tungsten carbide and chrome carbide can build solid metal and ceramic compound structures (Figure 4), but their surfaces are very harshly.
Building layer by layer of ceramic and metal compounds Laser sintering of metal and ceramic powder composite Coating of sintered steel powder workpieces with ceramics
Energy of C O 2 laser has to be controlled [4, 5], so temperature of melting cannot exceed liquid-gas phase boundary, at the same time each layer is fully fuzed until the underlying layer is reached. For implementation of these targets, amount of energy is varied by different laser velocities with dependency on particular ceramic.
3.1. Layered building of metal and ceramic compound structures
In this experimental series, it was evaluated, if it could be possible to produce solid compound workpieces by alternated sintering of metal and ceramic layers (Figure 3). The metallic powder (DirectSteel 20) is of steel and has a particle size of 20 ~tm. And the ceramic powder involves a mono tungsten carbide (WC-Ni 8317) and chrome carbide (Cr3Cz-NiCr). Layer thickness is 0,1 mm in each experiment. This defined layer level and planar powder beds are achieved by wiper
200
Figure 4: Layer structures from steel/Cr3C2 (upper) and steel/WC (lower)
Micrographs (Figure 5) of different probes show that each new sintered layer disperses into the layer underneath and therefore a definite connection between them is generated. This contact is forwarded, because of depth effect of the laser, where lower layers are melted partly, so that metal and ceramic melted mass trend into one another.
~
C hrome cafoir~..
,:.:............~..., ......~.......,~..:~#~:..:._..::.- :i;..........~: .............
DirectStee1
.~:i,i':!~!;:/:
/ p l a t f o r r a coaling
.~:,:.::~:::.. . . . . . . . .
Buildi~ phtfom
Jii;ii~ii~il ~,~!':f,i!'~':i!}i!i!i~,~:i?:~
structures can be produced with adapted choice of process parameters and mixing ratios (Figure 7). Ceramic quota can vary in a broader range, so that depending on technical applications specific material properties of metal or ceramic come to the fore.
0 |
~ 0 pm |
Figure 5: Micrograph of a built steel/Cr3C2 structure in layers
3.2. Laser sintering of metal and ceramic powder composite Herein these experiments, it is researched, whether it is possible in principle to manufacture workpieces of metal and ceramic powder composite by selective laser sintering (Figure 6). Concerning this, standard steel powder of variable weight is mixed with ceramic powder (A1203, SiO2, WC-Ni 83-17 und Cr3C2-NiCr) and these composites are sintered in the Rapid Tooling system. As well as in this testing, sufficient energy entering is guaranteed by tuning laser power to 95% and use low scanning velocities. Layer thickness is 0,1 mm in each experiment, too. These experiments have shown that solid
Figure 7: Probes of powder compounds steel/SiO2 (upper) and steel/Cr3C2 (lower) In comparison with different test series, carbide ceramics can produce more fine pored and tighter workpieces than oxide carbide. Contrary to samples built in layers, porosity and surface quality is all in all inadequate. Evaluation of samples' micrographs show that pool craters of ceramic and steel interact. Not melted ceramic particles are absorbed and embedded by steel bath. Inside of these structures, areas with pores have been built (Figure 8).
Ceramic particle
Metal particle
Figure 6: Metal and ceramic powder composite
201
~en carbide cle ctSteel
could be coated with any layers. Interpretation of experiments shows, that applied ceramic layers are fix connected with metallic parts. Reasons for this tight contact are on the one hand that melted ceramic is dispersed into porous metallic structure and on the other that ceramic interlock due to metal surface spikes. Those contact mechanisms can be compared with the alternating building of metal and ceramic compounds in layers (Figure 11).
gsten cafo:ide
Figure 8: Powder composite built steel/WC structure
3.3. Coating of sintered workpieces of steel powder with ceramic Subject of these experiments is, if surfaces of sintered workpieces of steel powder can be coated with ceramic layers (Figure 9). Firstly, samples of steel powder are produced with standard parameters. After this, sintered metal parts are coated with ceramic powder, whereas the favoured layer thickness of 0,1mm is adjusted by wiper movements. Also here, sintering is done with increased energy by slow moving the laser. As ceramics made use of mono tungsten carbide, chrome carbide and silicon dioxide (Figure 10). With all ceramics, surfaces of metallic objects
Figure 10: Coating of Cr3C2 (upper) and WC (lower)
Ceramic coating
Laser sintered body material of steel
Figure 9: Sintered body material of steel with sintered ceramic coating
202
5. Tungsten carbide coating
[1] [2] [3]
Transition rune
[4]
Boundarylayer
[5]
DilectSteelBody material
References
Gebhardt, A.: Rapid Prototyping - Werkzeuge Rir die schnelle Produktentstehung, Hanser Verlag, Juli 2000 EOS-Informationsmaterial zum Direkten Metall Laser Sintern und zur EOS1NT M 250, EOS GmbH, Krailing, 2002 Salmon, T.: Selektives Lasersintern von Stahl/Keramik-Verbundstoffen, Diplomarbeit, TU Clausthal, 2004 Meiners, W.: Direktes Selektives Laser Sintern einkomponentiger metallischer Werkstoffe, Dissertation RWTH Aachen, 1999 Song Y.-A.: Selektives Lasersintern metallischer Prototypen, Shaker Verlag, 1998
Figure 11: Micrograph of tungsten carbide coating 4.
Conclusion
The conducted experiments show that tight contacts between steel and ceramic powder can be generated with selective laser sintering. Stability between these two different basic materials is based upon connecting mechanisms (pure mechanical till adhesive bonding). Moreover, measurement of hardness shows for all samples that they contain a better surface tensile strength than objects of only steel powder. Best results have been achieved with non oxide ceramics (Si3N4, A1N, SiC etc.) and carbides (WC-Ni 83-17, Cr3C2NiCr). Obtained hardness (HV 0,5 DirectSteel/WC-Ni 83-17 mixture = 375) is substantially better than for pure metal powder (I-IV 0,5 DirectSteel = 172), although surface quality is not sufficient right now. Based on those results extensive experiments on laser sintering of metal and ceramic compounds will be started to solve difficulties with post processing, as well as get to know more about workpiece properties and contact mechanisms. For this purpose, sintering parameters like amount of energy and gas plus usage of other composites (metal and ceramic) will be researched. Technical application of such compound structures could be mouldings for plastic and metal, tools for sheet forming and moulding ties, coverings for combustion chambers or cylinder heads and valves in engines.
203
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All rights reserved.
The Effect of the Punch Radius in Dieless Incremental Forming L. Carrino, G. Giuliano and M. Strano Universith di Cassino, Dipartimento di Ingegneria Industriale, via di Biasio 43, 03043 Cassino (FR), Italy
Abstract
The dieless incremental forming process is an innovative sheet metal working technology where a considerable amount of knowledge and intelligence is required, in order to obtain accurate and efficient operations. In fact, in conventional forming process the final shape is mostly determined by the die shape. In dieless incremental forming, however, the final shape must be predicted and controlled only by means of a proper process design. For this reason, several issues of this process must still be investigated on a scientific base. This paper is meant as a little step towards a full comprehension of the dieless forming process, and as an aid towards an intelligent process planning. In most sheet metal incremental forming processes (shear spinning, flowforming, dieless forming), the deformation occurs by pure shear. The main process parameters are the feed rate, the part conicity and the punch radius r. Formability in incremental forming has been often investigated and it is well recognized that severe strain may occur before fracture. Thinning and fracture largely depend on the part conicity. Besides, decreasing the feed rate has a positive effect. On the contrary, the effect of the punch radius r has been seldom explored, in quantitative terms. The purpose of the paper is to investigate the effect of r on the formability of thin sheet metals, when plastically deformed by incremental forming. Keywords: incremental forming, sheet metal, pure shear deformation
1. The effect of process variables on formability
In Figure 1, some incremental forming processes of sheets are depicted. In these processes, one or more small punches or rollers plastically deform a metal sheet, by performing concentric or spiral-like trajectories onto its surface. In conventional spinning (Fig. l a) the sheet thickness to is unnoticeably changed by the process. In shear spinning (Fig. 1b) and dieless forming (Fig. 1c), the thickness reduction depends mostly on the part shape. In flowforming, the thickness reduction is determinedbythe clearance between the punch and the die. For all of these processes, the most important parameters are: the sheet material and initial thickness to [mm]; the nominal final part conicity c~ [rad]; the horizontal part curvature radius p [ram]; the punch feed rate f~ [mm/rev], attack angle z[mm/rev] and radius r [mm]. Formability is mainly
204
limited by two risks, described as follows. 9 Wrinkling, which is triggered by an excess of circumferential compressive stresses. In spinning the risk of wrinkling in the undeformed flange area increases for large values ofbothfz and o~[1]. In the dieless forming process, the risk of wrinkling is extremely small [2]. 9 Fracture by tearing, which is triggered by an excess of principal strains. In all mentioned processes, for small values of conicity c~ and large values of feed ratef~, the risk of part tearing increases [2] [3]. It clearly appears that the two most influencing parameters on formability are o~ and fz. As far as the punch radius is concerned, its shape is critical in determining the final sheet surface roughness, in combination with the feed ratefz: larger radii are generally used for obtaining a better surface finish [ 1] [4].
........:-=:::~L
~ :-f---
4-)~ to
Fig. 1. (a) spinning: (b) shear spinning; (c) negative dieless forming. A quite extensive scientific knowledge is available about the effect of the punch radius on the forming fbrce components [4] [5], especially for shear spinning. On the contrary, the potential effect of the punch shape, and particularly of its end radius r on formability has been seldom investigated. Some results available in the literature describe a decrease of formability for small radii [6]. Other sources indicate no significant effect on formability [7]. Previous results published by the authors [8] seem to indicate that formability significantly decreases with decreasing punch radius. The purpose of the present study is to give a deeper look at this issue, and to confirm the results obtained in [8]. The paper is organised as follows: initially, a scientific background is given with a brief literature review about incremental forming processes. Then, a description of the process mechanics is proposed, with details on the effect of the punch radius on the shear deformation mode. Finally, the results of FEM simulations are presented, run with two different commercial packages, validated by means of experiments conducted onto aluminium sheets. The main conclusions are that formability is determined bythe uniformity of the strain distribution and improves if the punch radius increases.
2. The theoretical effect of punch formability in shear forming
radius
We can assume that, unless the deformation is locally perturbed by a small horizontal curvature radius p or by relevant friction forces, the tangential strain is the minor planar strain and is equal to ~r=~in=O. The true strain in the longitudinal direction 1 of Fig. 2, is therefore the major strain and is equal to ~=~ax=-~. A representation of the safe deformation points in the conventional FLD space (~in,~,x) would yield a cloud of points concentrated along the ~,x axis, with some points in the first quadrant. Many papers in the literature [9] show that the forming limits obtained by incremental forming are significantly higher than the conventionally determined F L D o point. However, the FLD representation is misleading. In fact, if the deformation is by simple shear, the planar strains ~ .... ~.~ are not measured along the principal directions of strain ( i and 2 in Fig. 2). These lay at 45 degrees with the polar axes /5 and 2. Indeed, by simple geometrical considerations, the shear strain ,~p is: ~p=cotg(c0/2
(2)
Under the given assumptions, i.e. if the deformation paths always follows uniform shearing, the failure limit is
on
A theoretical basis for explaining the effect of r in shear forming (shear spinning, negative and positive dieless forming) is given hereby. The deformation mechanics of incremental forming processes dominated by simple shear modes is presented. An horizontal and planar sheet metal, with initial thickness t0 is deformed by simple shear to a conical final geometry with vertical conicity c~and final thickness t=t0-sin(c0. An axial cross section is shown in Fig. 2. The true strain, measured in the thickness direction i is therefore: ~=ln(t/to)=ln(sin(c~)) (1)
[ \ .... i+-~-~- . . . .
-blank
Fig. 2. Axial cross section of a shear forming process.
205
sh
ranch
l,,s.
,_t-z
h
Fig. 3. Axial cross section ofa dieless forming withfp >c (left) andfp
2.1. Uniformity of longitudinal strains
(3)
When fp
206
(4)
In Fig. 4, the plot of the maximum elongation vs. the punch radius is given. It shows that as the radius r increases, the risk of non uniform deformation due to tensile stresses is initially constant, then very rapidly decreases until fp=c, i.e. until r reaches a critical value, given by the following Eq. 5:
r= fz *sin(~/cos 2( ~
(5)
After that, the value of l% slowly decreases.
In Fig. 3 an axial cross sectional view of the process is pictured. The circles represent the position of the punch at two subsequent passes with feed ratefz, over the same tangential location. The horizontal component of the feed rate is fp =fz-tg(tz). The chord beneath the contact line between the punch and the sheet is c=r.sin(rd2-~). In the let~ part of Fig. 3, the case when fp>c is printed. The initially horizontal segment (the thick grey line) of the sheet of length b, aider each pass of the punch, will be transformed into a straight inclined segment of length b/sin(s). However, this deformation is unsupportedbythe punch and, as a consequence, there is a risk of thinning and non uniform deformation. The maximum theoretical elongation l%1 of the sheet is:
l%~=l/sin(a)
1O/o2=r~. arcs inOrJr)
2.2. Tangential stra& due to friction From what appears in Section 2.1, in order to have uniform deformation, the punch radius should be as large as possible. The beneficial effect of a larger punch diameter is even greater if friction forces are considered. In the axial direction (see the cross section in Fig. 3), no significant sliding is present and friction forces are nearly irrelevant. On the contrary, in the tangential direction some relative motion between the punch and the tool occurs. The theoretical planar strain cr in the tangential direction should be null. I f er s0 at some points, i.e. if localised thinning at the punch bottom occurs, this can have a detrimental effect on formability. The most significant portion of the tangential strain at the punch bottom is due to friction. In Fig. 5, a tangential cross sectional view of the process is given. The tangential stress due to friction, according to the rope formula [ 10], is proportional to the angle of wrap fl, which can be approximated as:
t
fl = arccos [Ic/2
)
V r > fz Vr
(6)
!% 1.8
Maximum elongation per forming step
] fz=0.773 mm/rev
7--X -o. 97
1.6 1.4
t ..........~
fz=I.5 mm/rev
1.2
'ii
1.0 .
0.8
.
.
.
)
0.6 0.4
i!
0.2
r (mm) i
0.0 0
2
4
6
longitudinal strain (see Fig. 4) and on the amount of tangential strain (see Fig. 6), it might be concluded that formability of sheet metals, in shear incremental forming processes, should decrease as the radius r increases. However, there is a third reason for formability to decrease when r-values are small. In fact, as the punch gets smaller (in respect of the sheet wall thickness), the risk of scratching the sheet surface is greater. Experimentally, it has been verified that, especially with thin sheets, small surface scratches or cracks may cause premature fracture. Besides, when the punch is smaller, there is a local concentration of contact pressure (which might be indicated by the effective stress ~ ) and the risk of tearing for an excess of local shear stress is greater.
Fig. 4. Effect of r on maximum elongation per step
3. FEM simulations and experiments of negative dieless forming tangential direction
Undeformed sheetfz]
"~~_______..........~rhed
Fig. 5. Schematic axial cross section of shear forming
1.6 1.4 1.2
PAM-STAMP
fz~l.5 mm/rev
cz=-0.9 :
/ ~
1.0 0.8
~ k ~ ' ~ fz=0.773 mm/rev
0.6 0.4
....... __._;
0.2 r
0.0 0
10
20
In order to confirm the theoretically predicted effect of the punch radius on the incremental forming process, several FEM simulations with shell elements, with two different commercial software packages (Marc and PareStamp) have been run. In Table 1, the main numerical and Table 1 Main parameters used in FEM simulations
Tangential angle of wrap
fl
3.1. Numerical results
30
(mm)
40
Fig. 6. Effect ofr on the tangential arc length
The angle of wrap significantly decreases with increasing r. In the examples shown in Fig. 6, fl reaches a saturation for r greater than about 40 mm. 2.3. Stress concentration
From a theoretical point of view, combining the effect of the punch radius on the homogeneity of the
time integration explicit type ofshell BWC (see the users' element manual) # of thickness 5 integration points planar integration reduced to 1 point calculated strains G~, ~2, c22, c33
MARC implicit element (see the users' manual) 11
full gaussian all quadrangular, 0.8 initial shape of mm axial length; square, 5 mm elements n/20 hoop arc length refinement level 4 no refinement contact algorithm non linear penalty direct constraint springback no yes 2.5,5, 10,20,40 1.5, 2, 2.5, 3, punch radius r ITI1TI 3.5, 4, 4.5 mm initial to lmm 0.3 mm average f 0.773 mm/rev 1 mm/rev average oc 0.697 rad 1.16 rad number of steps 24 revolutions 6 revolutions shape of square round blankholder initial radius p 50 mm 32 mm friction coeff 0.1 0
207
(a)
Co) Fig. 7. Final mesh for Pam-Stamp (a) and Marc (b) simulations; vertical displacement is plotted.
(a)
(b) Fig. 8. (a) original picture of a deformed part; (b) 25 corresponding FEM profiles, one for each pass of the punch. process parameters used in both simulation sets are shown. It must be noted that, since the element formulation used in Pam-Stamp does not predict normal shearing, adaptive remeshing has been used with a small final element length, in order to better represent the real process mechanics. The refinement of the Pam-Stamp mesh has been activated only in the upper-right quarter of the part (see Fig. 7a), in order to save computational time. The FEM results have been measured along the bisector of that quadrant. The mesh used with Marc is coarser (see Fig. 7b), since 11 integration points have been used and shear strains can be predicted. The accuracy of all FEM simulations has been evaluated by running two experiments, one with the conditions simulated in PAM-STAMP and one with the conditions simulated in MARC. The shapes of the formed specimens has been measured by a Coordinate Measuring Machine (CMM) and has been compared to the FEM profiles (see Fig. 8). The shapes predicted by the FEM codes are surprisingly close to the experimental ones. Starting from the considerations of Section 2, three different indicators have been built and used in order to assess the formability of every simulation run, respectively called 11,12and 13. The first criterion used for evaluating the process formability is the uniformity of the deformation. As stated in Section 2, the maximum
208
tangential true strain is also the minor planar strain s For uniform deformation, er should be equal to 0. Any positive or negative value of tangential strain can be seen as an instability generated by tangemial forces (i.e. bythe angle of wrap 13).A large maximum absolute value of the FEM-calculated minor strain Ii=maX(s FEM)is a clear indicator of reduced formability. The value of the thickness strain is a function of the actual part conicity, (seen as a function of the part depth z). The thickness strain should be equal to ~(a')=ln[sin(a')] for a perfect simple shear deformation. The difference between the FEM-calculated and the theoretical strain I2(a')=max(~FEM(a')-~(c~)) can be taken as a measure of deviation from simple shear, i.e. as another indicator of formability. In Fig. 9, the first two formability indicators are shown as a function of punch radius for the Pam-Stamp simulations: the trend of I2(~ shows a good agreement with the predictions stated in Section 2. However, for small r-values, there seem to be a slight increase of I1. In order to verify the trend of 11 for small r-values, the plot obtained by the Marc simulations (with r<4.5mm) is reported in Fig. 10. It confirms that/1 increases until r reaches about 5 mm. As already stated in Section 2.3, we may take a measure of stress as a third formability indicator. As an example, the maximum equivalent plastic stress:
35%
+
.........................................................................................................................................................
12 (oc=O.760)
30%
~
-m- 12 (a=0.937)
25%
~
-*-
I2 (a=l)
~
12 (o~=1.09)
20% 15%
~
_0_//
\
lO% 5%
, ,1=o ~ - . - - = = : []9
0% -5%
~On~~_
-10%
"
n
9
-o
. . . . . . ,,ir [mm] 25 30 35 40 45 50 55 6u
~
~
.,L
-15% --
Fig. 9. Plot of I1 and I2 vs. r for Pam-Stamp simulations. 0.0045 0.004 0.0035 0.003 0.0025 0.002 0.0015 0.001 0.0005 0
-
................................................................................................................................................................ ~'~~ ~ 1 7 6
,,,O ~
I-.-- I
-
I
2
......................... ~
3
4
5
....................................................................................................................................................
80 o= a. 60 =E 4O 20t
1--13 (t~176 mm) l /
0
In conclusion, all the analyses conducted during this study, either theoretical (see Section 2), numerical (see Section 3.1) and experimental (see Section 3.3) point at the same result: the punch radius should be chosen as large as possible. However, two main factors represent a limitation in using a very large value of r. In fact, as the punch radius increases: 9 manufacturing parts with geometrical complexity (small values of horizontal radius p) becomes very difficult or impossible; 9 the forces required for accomplishing the forming operations become larger.
References r[mm]
1
0
4. C o n c l u s i o n s
-
Fig. 10. Plot of I1 vs. r for Marc simulations.
120
punch with r =2.5 ram.
t
r [mml
i
i
i
i
5
10
15
20
Fig. 11. Plot of I3 vs. r for Pam-Stamp (t0=l ram) and Marc
(t0:0.3 mm).
I3=max(~) can be used. In Fig. 11 the plot of I3 vs. r is printed (for both PAM-STAMP and MARC simulations), showing a clear and monotonous decreasing trend. 3.2. E x p e r i m e n t a l results
Some negative dieless incremental forming experiments have been run using a given tool path with decreasing a-value, and stopped only at fracture, using two different punch radii: 2.5 and 5 mm. Formability has been measured by simply evaluating the maximum part depth obtained with each experiment. The results confirmed that the punch with r = 5mm outperformed the
[ 1] Wong C.C., Dean T.A., Lin J., A review of spinning, shear forming and flow forming processes, International Journal of Machine Tools & Manufacture. 43 (2003) 1419-1435. [2] Strano M., Ruggiero M., Carrino L., Representation of forming limits for negative incremental forming of thin sheet metals, in: Proceedings of the International Deep Drawing Research Group, Germany, 2004. [3] Kawai K~, Yang L.N. et al., A flexible shear spinning of truncated conical shells with a general purpose mandrel, J. ofMater. Proc. Yech. 113 (2001) 28-33. [4] Chen M.D., Forecast of shear spinning force and surface roughness of spun cones by employing regression analysis, International Journal of machine tools and manufacture 41 (2001) 1721-1734. [5] Kobayashi S., Hall I.K., Thomsen E.G., A theory of shear spinning of cones, Transactions of the ASME, Journal of Engineering for Industry 81 (1961) 485-495. [6] Kim Y.H., Park J.J., Effect of process parameters on formability in incremental forming of sheet metal, J. of Mater. Proc. Tech., 130-131 (2003) 42-46. [7] Kegg R.L., A new test method for determination of spinnability ofmetals, Transactions of the ASME, Journal of Engineering for Industry 83 (1961) 119-124. [8] Strano M., Park J.J., Technological Representation of Forming Limits for Negative Incremental Forming of Thin Aluminum Sheets, Journal of Manufacturing Processes, 7,2 (2005) 122-129. [9] Kim Y.H., Park J.J., Fundamental studies on the incremental sheet metal forming technique, Journal of Materials Processing Technology 140 (2003) 7-453. [10] Wagoner R.H., Chenot J.L., Fundamentals of Metal Forming. John Wiley & Sons, 1996.
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Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Air bearings based on porous ceramic composites E. Uhlmann, C. Neumann Technische Universit~it Berlin, IWF, Pascalstr. 8-9, 10587 Berlin, Germany
Abstract Air bearings made of porous materials allow an equal air distribution on the bearing surface. In comparison with conventional orifice bearings, air bearings have a higher load capacity and stiffness. Their dynamic behaviour is improved due to many micro pores, which make the bearings less sensitive to internal and external disturbances. In this study, the characteristics of advances SiC-based fibre-reinforced composites and their utilization for the development of air bearings are discussed. The characteristics of these bearings were examined and the question for usability was addressed in principle. This paper presents the results of investigations and it demonstrated that fibrereinforced ceramic have excellent mechanical and thermal properties. This material could replace current porous bearing materials in view of several difficult static and thermal conditions.
Keywords: Aerostatic Bearing, Ceramic, Machine Tool
1. Introduction Air bearings utilise a thin film of pressurised air between the bearing surfaces to achieve a non-contact operation. In contrast to rolling contact bearings, air bearings avoid problems such as friction and wear since only air is used as lubrication medium. Characterised by a high damping and a silence operation, air bearings are ideal for high speed applications. Currently, air bearings are used for precision applications like linear guidelines in measurement systems. For these applications, it is essential that the motions of air bearings are homogeneous and smoother than rolling bearings, so that the error correction is more effective. Moreover, air bearings are also used for high speed applications like bearings in motor spindles of precision tool machines. The bearings show no wear and have no heat problems even at high relativ velocities due to the air gap between the bearing surfaces.
Pressure injected air bearings can be divided into two classes. Traditional air bearings are designed with one or more orifices and often combined with grooves to improve the bearing properties. Today, innovative air bearings used porous materials, so that a large number of micro cannels control the airflow across the entire bearing surface. In contrast of orifice bearings, porous air bearings are characterised by an excellent air pressure distribution across the surface and a high tolerance to bearing surface damage. Therefore, porous bearings have an improved dynamic and static behaviour. Temperature gradients on machines with guidances based on air bearings can lead to displacements of the bearing surfaces and the reduction of the machine accuracy due to change of air gap or preload force. Therefore, apart from the improvement of dynamic and static properties of air bearings, it is also essential to optimise the thermal behaviour. The aim is to develop new materials that combine the excellent properties of traditionally porous bearing
211
materials and the thermal properties of ceramics. In this paper, the authors presented the advanced ceramic composite CVI-SiC/SiC with an open porous material structure. The usability of this material as air bearing material was discussed. Moreover, the results of investigations were demonstrated for planar thrust bearings and an air bearing design with this material was presented.
2. Applications Thrust bearings are used as precision guidances in precision measuring machines with micro resolution. Additionally, thrust bearings can be preloaded with vacuum that is an elegant solution to increase the bearing stiffness. Several thrust bearing pads with porous fibre-reinforce ceramic were developed for linear guidances at IWF. The bearing pads can be mounted directly by flange (see Fig. 2) or have an integrated ball joint with self adjustment.
Fig. 3: Motor spindle with porous air bearings for precision turning machines The motor spindle consists of modular spindle housing with two integrated planar annular axial bearings and a journal bearing. The drive is flanged on the spindle housing and moved the spindle directly with up to 3000 rpm. The spindle diameters are 70 mm (radial bearing) and 200mm (axial bearing), respectively.
3. Comparison of orifice and porous bearings In orifice bearings the air is supplied to the bearing surface through a small number of precisely sized holes [1]. Since bearings with single orifice have a high pressure gradient between the orifice centre and the bearing boundary, a proper number of orifices are strategically placed on the bearing surface (see Fig. 4).
Fig. 2: Air bearing pad with porous ceramic for linear guidances (flange coverage) The development of porous ceramic composite materials with their excellent thermal and mechanical properties allow the design of air bearings for the optimisation of high precision and high speed machines. Motor spindles for precision tool machines required a constant air gap for steady properties even at highest rotation speeds. Energy dissipation in drives and the air friction in bearings at high relative velocities lead to thermal displacements. The result is a negative influence of static and dynamic spindle behaviour. Ceramic bearings reduce the thermal deformations to a minimum. Therefore, a motor spindle with porous ceramic air bearings was developed at IWF for the investigation and optimisation of spindle behaviour (Fig. 3).
212
ii
ii 'ttl
a)
b)
c)
Fig. 4: Pressure distribution on the bearing surface for orifices (a, b) and porous bearings (c) Porous air bearings enable the supply of air equally across the whole surface of bearing, so that the air flow can be restricted and damped at the same time. This can be achieved by diffusing the air through a porous bearing material, so that a uniform pressure in the air gap is generated (also see Fig. 4). Compared
with orifice bearings, porous bearings have the highest load capacity and stiffness including high vibration stability. One of the first porous air bearing materials was carbon graphite [2, 3]. Subsequently, bearings produced from sinter materials like A1203 and sinter bronze [4] have been described. Here, we focus on a new ceramic material, called CVI/SiC/SiC, for the realisation of a new air bearing design. 4. Properties of ceramic composite CV|-SiC/SiC Designing with ceramics is more difficult compared with steel, because steel is much more tolerant to local stress peaks and material flaws. These disadvantages of monolithic ceramic materials could be overcome by the development of ceramic composites. Such materials are synthesised from the assembly of two or more components in order to obtain specific material properties. One of these ceramic composites is CVI-SiC/SiC, which is composed of a silicon carbide (SIC) fibre reinforcement imbedded in a SiC matrix during the chemical vapour infiltration (CVI). The threedimensional SiC fibre architecture and the SiC matrix leads to a structure with an open porosity of 10 % to 15%, which makes it fluid-permeable (see Fig. 5). The porosity can be modified by variation of structure geometry and the controlled filling of this structure with SiC. The geometrical form of the pores is dependent on fibre direction, and lies between 100 gm and 300 gm for the test pieces. Semi-finished products like tubes and plates of different thicknesses were manufactured in a pilot plant.
substantially. The SiC fibres catch the break in case of sub-critical crack growth, so that the main cause of brittle failure would be eliminated. In contrast to monolith ceramics, pre-stress is not necessary for components made of CVI-SiC/SiC. In case of a structure with a fibre direction of 0 ~ and 90 ~ the elastic modulus of CVI-SiC/SiC has been indicated to be 180 GPa to 220 GPa, the tensile strength lies between 300 MPa and 400 MPa [5 - 8]. For the realisation of a high accuracy in tool machines, it is essential to minimise the thermal deformation of machine components. Compared to steel, which has a thermal expansion coefficient of 11.810 .6 l/K, the thermal expansion coefficient of CVI-SiC/SiC amounts 410 -6 l/K, between 293 K and 573 K, and is much lower. In this background, CVISiC/SiC is an innovative material for the designing of spindles and bearings of precision machines. Moreover, a favourable sliding behaviour of CVISiC/SiC leads to excellent dry-running properties. Tribology investigations showed an improved noisereduced running under boundary and mixed friction conditions compared to monolith ceramics. The dryrunning properties can be further improved by substitution of SiC matrix with carbon. Due to the mechanic properties, the ceramic composite material CVI-SiC/SiC represents an ideal basis for the development of components for high precision applications. The adjustable porosity should enable the use of this material for aerostatic bearings in high precision spindles.
5. Planar thrust bearing Fig. 6 shows the configuration of planar thrust bearing. In the upper part, the porous bearing material, the air inlet, and three distance sensors which measure the air gap are shown. In the lower part, a force sensor was mounted in the basic body.
. . . . . . .
Fig. 5: Fibre structure of porous composite SiC/SiC Contrary to conventional monolith ceramics, the reinforcement with continuous fibres from SiC guarantees an increased tensile strength, fracture toughness and the elastic modulus of ceramic
iiiiiiiiii :ii
...
!: ii~iii!!~iJiiii)!i!~l ?iii!ii!il
Fig. 6: Configuration of planar test bearing: basic body (a), universal adapter with ceramic (b), force sensor (c), distance sensor (d), air inlet (e)
213
The ceramic plates with a diameter of 40 mm were stuck in a universal adapter. The universal adapter served as a quickly disassemble of bearing material, since ceramic plates with different thickness were investigated. The thicknesses of the ceramics were changed by grinding and the bearing surfaces were finished by lapping. After machining, the bearing materials were cleaned to re-open the pores due to the presence of cooling fluid and grinding particles. For the adjustment of the air gaps a reference mass was precisely positioned. The air gap could be adjusted in steps of 1 ~tm. The reference mass should substitute the guide surface. This equivalent surface was precision machined with a roughness of less than 0.1 ~tm.
5.2. Load capacity of planar bearing In Fig. 8, the results of load capacity at several supply pressures are presented. All curves show a tendentious similar behaviour. With decreasing air gap, an expotenial increasing load capacity is demonstated. The maximum value lies at 380 N for a supplypressure of 0.6 MPa and an air gap of 5 ~tm. With increasing bearing gap, the load capacity trended to zero. planar beadng CVI-SiC/SiC
\
diameter
40 mm
thickness 2 mm
.•300 \
supply pressure 0.6 MPa 0.4 MPa --o.- 0.2 MPa
2(]0
5.1. Pressure profile in bearing gap
100
Fig. 7 shows the pressure profile of planar thrust bearing. The measurement was executed with an air gap of 10 ~tm and a supply pressure of 0.6 MPa. There is a difference between the expected profile of pressure and the measured one. Compared to the curve for an ideal porous bearing material, real materials have no constant pressure field over the bearing surface. However, the experimental data showed a curve with a similar trend. An approximate value for the gap pressure can be specified with 0.55MPa for parameters stated above. The following criteria were necessary to achieve this. There must be a high flatness of bearing and guide surface, a high mounting accuracy of the ceramic plate and the bearing housing, and an optimal adjusting method for the measurement.
j
MPa
planar bearing CVI-SIC/SiC diameter
40 mm
thickness 2 mm supply pressure 0.6 MPa air gap
10 um
~
0,2
-30~=I
30
,-G.
Directionof measurement
i 0-30
-20
-10
0
1~0
mm
3O
distance a
Fig. 7: Pressure profile in gap of a planar thrust bearing Additionally, the air flow characteristic through the porous material was determined by measurement of air flow in relation to the back pressure and supply pressure. Flow characteristic lies in crossing area between laminar and turbulent flow.
214
i
10
20
30
40
IJm
60
air gap hs
Fig. 8" Measured load capacity for several air gaps The bearing gap pressure and the load capacity are proportional to each other with the surface as porportionality factor. Therefore, the load capacity curves have the same trend as the pressure curves. In addition, the point of the maximum load capacity change was not reached for small air gaps and it was not determinable by data extrapolation. Due to different throttle effect with variation of air gap, the pressure distribution in gap is also changed. During the measurement, it was identified that it is not possible to realise a constant pressure over the bearing surface (see also Fig. 7) even at decreasing of air gap. For this reason, the mathematically maximum value of load capacity was not achieved. For the further determination of the optimal operating points, the knowledge of the maximum load capacities alone is not sufficient. The investigation of the static stiffness of aerostatic bearing is of decisive importance, because frequent load changes arises in real operation. Since the measured gap pressures do not consider the real pressure distribution in the bearing gap, the stiffness was determined directly over the change of the load capacity curves. The stiffness values give an essential statement about the static and dynamic behaviour. It is also possible to determine the absorption behaviour ofaerostatic bearing for different operating conditions. Fig. 9 shows the stiffness of bearing for the supply pressure of 0.2 MPa to 0.6 MPa for an air gap up to
60 pm. With a supply pressure of 0.6 MPa and an air gap of 5 ~tm, the maximum stiffness lies approximately at 14 N/lam. With increasing supply pressure, the maximum value displaced in direction of smaller air gaps [9]. 16
I
I
planar hearing CVI-SiC/SiC
N/pm I
12
# N
10
.
.
.
.
.
.
.
.
F F = ~ LDPs
2 mm
supply pressure
- - - -
With the help of the segment journals, different bearing sizes were integrated for their load capacity and stiffness in relation to the diameter/length ratio (L/D). The results were presented in the following dimensionless form:
diameter40 mm
_ _ ~ 6
6.1. Optimisation of bearing size
for the dimensionless load capacity and
0.6 MPa - --O-- 0.4 MPa --4>- 0.2 MPa
~ - - - - -
SC
S - ~
4
-
2 - - - 0 0
10
20
30
40
pm
60
air gap hs
Fig. 9: Stiffness in relation of bearing gap and several supply pressures
6. Journal bearing The assembled and investigated journal bearing is shown in Fig. 10. The porous bearing material was press-fitted into the housing and was externally pressurised. Laser distance sensors were used for the measurement of the eccentricity of journals as the result of external forces. Nonporous journals in different diameters and segment lengths were investigated.
LDPs
for the dimensionless stiffness. The variations of load capacity for different values of L/D are presented in Fig. 11. The relation between load capacity and eccentricity could be described as approximate linear. The values of load capacity increase for bearings with decreasing L/D ratios. For L/D ratios less than 0.5 the load capacity becomes smaller. Here, the load capacity for a bearing with L/D of 0.5 is also higher than that for bearings with L/D of 0.3 and 1.2. 0.5 journal bearing CVI-SiC/SiC
,,~ o.4 ~
bearing diameter D = 7 1 mm
0.3
~
-~ ._ ~
~
oi
0.1
S "
0.2
~
0.3
supply pressure p. = 0.6 MPa
f
0.4
L/D 2.4 ---o--1.2 ~0.5 --<1--0.3
~
0.5
0.6
eccentricity ratio e.
Fig. 11" Dimensionless load capacity for pressure of 0.6 MPa at different L/D ratios
Fig. l 0: Test journal bearing with distance sensors and segmented spindle
The results of determination for stiffness showed the same behaviour (see Fig. 12). A maximum value of stiffness is achieved for an L/D ratio of 0.5. Furthermore, the curves show a degressive behaviour. This means, that the stiffness will decrease rapidly with increasing eccentricity. In Fig. 11 and 12, it is clearly shown that there is an optimal bearing size, where the properties of aerostatic bearings have maximum values. This optimum depends on the porous bearing material, which has an characteristic air flow behaviour.
215
1.0
journal bearing CVI-SiC/SiC
~'""q7.,.~.~., cOl
0.8
0.6
~
bearing diameter D=71 mm ,.,..--,------"~--'--'--'---4-.-~...~ ~
p, = 0.6 MPa
D,,-~,,,,,~.,.~O-,,,,.,,.~
._~ 0.4 E ._ "o
supply pressure
~"-'---~..~..
LID .-c>- 2.4 -,<>-1.2 - ~9 - 0.5
--4--0.3
0.2
0 0.1
0.2
0.3
eccentricity
0,4
0.5
0.6
ratio s
Fig. 12" Dimensionless stiffness for pressure of 0.6 MPa at different L/D ratios
Nomenclature a
C D e
F F h L L/D Ps Psp S S S
planar bearing radius radial clearance journal diameter eccentricity load capacity dimensionless load capacity air film thickness (air gap) length of the journal bearing length/diameter ratio supply pressure pressure in bearing gap static stiffness of the bearing dimensionless static stiffness of the bearing eccentricity ratio e/C
7. Conclusion and outlook
Aerostatic planar bearings and journal bearings were manufactured from fibre enforcement ceramic CVI-SiC/SiC. The bearings were tested under several conditions to evaluate the static behaviour. In the first part, the work was focused on the measurement of air pressure profile, load capacity and the determination of stiffness. In the second part, journal bearings with segmented journals for the optimisation of bearing size were investigated for its load capacity and static bearing stiffness. Using ceramic planar bearings with a thickness of 2 mm and a diameter of 40 mm, an air pressure profile with an approximately constant pressure of 0.55 MPa and a load capacity of 320 N was achieved at a supply pressure of 0.6 MPa and 10 lain air gap. At a supply pressure of 0.6 MPa and 5 ~tm air gap, a maximum load capacity of 380 N and stiffness of 14 N/~tm was determined. Bearings with a porous material like CVISiC/SiC demonstrated a good vibration stability under different air supply pressures. For journal bearings, the measurements showed maximum values of load capacity and static stiffness at a length/diameter ratio of 0.5. Structured ceramics with different permeability and air flow characteristic can lead to variance of optimised bearing size. The bearing properties are also dependent on the material design like geometry or number of micro channels. A static and dynamic optimisation for air bearings should be provided by simulation.
216
References
[1] Schulz, B.: Herstellung von aerostatischen Lagern mit Laserentbearbeitung. Mtinchen, TU, Dissertation, 1999 [2] Robinson, C. H.; Sterry, F.: The static strength of pressured gas journal bearings; porous bearings. ED/R 1672 AERE, Harwell 1958 [3 ] Schmitt, J.: Berechnung und Untersuchung aerostatischer Radiallager aus porOsem Werkstoff. Dissertation TU Miinchen 1972 [4] Hopfner, J.: Fertigung von aerostatischen Lagern aus portser Sinterbronze mit oberfl~ichenverdichteter Drosselschicht. Dissertation TU Mtinchen 1990 [5] Sygulla, D.; Mtihlratzer, A.; Agat0novic, P.: Introduction of Ceramics into Aerospace Structural Composites. AGARD Report 795, 1993 [6] Christin, F.: Design, Fabrication, and Application of Thermostructural Composites (TSC) like C/C, C/SiC, and SiC/SiC Composites. Advants Engineering Materials 4(2002)12, pp. 903-912 [7] Zechmeister, H.; Leuch, M.; Mtihlratzer, A.: Properties and Possibilities for Application of CMC in Mechanical Engineering. Ceramic Transactions, The American Ceramic Society, 19(1991), pp. 1095-1105 [8] Singh, J. P.; Bansal, N. P.; Singh, M.: Advances in Ceramic Matrix Composites VIII, Proceedings of the symposium held at the 104th Annual Meeting of The American Ceramic Society, Wiley, UK, 2006 [9] Uhlmann, E.; Neumann, C.: Porous Ceramics used for Aerostatic Axial Bearings. Annals of WGP Production Engineering, 12(2005)1, pp. 105-I08
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
CBN Grinding Wheel Inventory Sizing through Non-Shortsighted Flexible Tool Management Strategies D. D'Addona, R. Teti Department of Materials and Production Engineering, University of Naples Federico II, Piazzale Tecchio 80, 83125 Naples, Italy
Abstract
The development and implementation of a Multi-Agent Tool Management System (MATMS) is the focus of a wide-range research project on agent-based automatic tool procurement in supply networks. The MATMS operates in a context where an aircraft engine producer (customer) requires from external tool manufacturers (suppliers) the performance of dressing operations on worn-out CBN grinding wheels for Ni base alloy turbine blade fabrication. In this paper, a novel non-shortsighted Flexible Tool Management Strategy (NS-FTMS), integrated in the MATMS as domain specific problem solving function of the intelligent agent responsible for optimal tool inventory sizing and control, is presented as an alternative to both traditional tool management and previous FTMS approaches. Keywords: Tool Management, CBN Grinding Wheel, Multi-Agent Systems
1. Introduction
The development and implementation of a MultiAgent Tool Management System (MATMS) is the focus of a wide-range research project on agent-based automatic tool procurement [1-4] in supply networks. The adoption of agent-based or multi-agent technology is founded on the three main system domain characteristics [5]: data, control, expertise or resources are inherently distributed; the system is naturally regarded as a society of autonomous cooperating components; the system contains legacy components that must interact with other, possibly new software components. Multi-agent technology therefore appears to be particularly suitable to support collaboration in supply network management. The MATMS operates in a context where an
aircraft engine producer (customer) requires from external tool manufacturers (suppliers) the performance of dressing operations on worn-out CBN grinding wheels for Ni base alloy turbine blade fabrication [6-9]. In [ 10], the concept of an original non-shortsighted Flexible Tool Management Strategy (NS-FTMS), to be integrated in the MATMS as domain specific problem solving function of the intelligent agent responsible for optimal tool inventory sizing and control, was first introduced. In the present paper, a novel NS-FTMS paradigm is presented as an alternative to both traditional tool management and the previous NS-FTMS approach.
1.1. Traditional tool management The production of an aircraft engine model
217
involves the machining of turbine blades through a predetermined set of CBN grinding wheel part-numbers (P/N). Each P/N can machine a maximum number of turbine blades (pieces) during its planned life cycle. Then, it is shipped to an external tool supplier to be dressed and remains out of stock for a time period defined as dressing cycle time. For each P/N, a sufficient number of CBN grinding wheel serialnumbers (S/N) must be in stock at all times (on-hand inventory) to prevent tool run-out and production interruptions. The P/N on-hand inventory size, I, is controlled by: # of pieces/month, P; # of pieces/wheel, G; # of months required without new or dressed wheel supply, C (coverage period), heuristically selected. The wheel demand, D, for each P/N is given by: D = (P/G) * C - I0 where: P/G = tool demand rate (# of wheels/month); I0 = initial P/N inventory size. In traditional tool management, the CBN grinding wheel P/N inventory size is strategically planned by selecting, on a heuristic basis, an appropriate coverage on-hand inventory (# of S/N for production needs in the coverage period C). This procedure does not always prove adequate: in some cases, the expected inventory level trend matches the required one; in other cases, it is underestimated, with risk of stock-out, or overestimated, with excessive capital investment [5]. Accordingly, the P/N inventory level is increased or reduced on the basis of skilled logistics staff knowledge, resulting in historical inventory size trends: these are fit solutions that prevent tool run-out and useless investment and can be used as a reference for assessing alternative tool management strategies.
1.2. Non-shortsighted flexible tool management strategy In [10], the design and functioning of a nonshortsighted version of the Flexible Tool Management Strategy (NS-FTMS) integrated in the MATMS was first introduced. The NS-FTMS took into account for tool management decision making the value of a future on-hand inventory level, Ifut, instead of the current onhand inventory level, I. In this paper, a novel NS-FTMS paradigm, characterised by a variable reaction time, Trt, function of dressing cycle time predictions, is presented and its performance is compared with both traditional tool management and the previous NS-FTMS approach. The NS-FTMS paradigm is configured as the domain specific problem solving function of the
218
MATMS intelligent agent, the Resource Agent (RA), that looks after CBN grinding wheels optimum tool inventory sizing and control.
1.3. Multi-agent tool management system The MATMS structure has 3 functional levels [4]: - the Supplier Network Level, including the external tool manufacturers in the supply network; - the Enterprise Level, including the logistics of the aircraft engine producer industry; - the Plant Level, including the production lines of the aircraft engine producer industry. The Resource Agent (RA), in the Enterprise Level, merges the functions of tool inventory management, tool resource demand estimation and determination of tool order quantities; its domain specific problem solving function is the FTMS. The RA receives information on CBN grinding wheel end-of-life events from the Production Agents (PAj) in the Plant Level. For each CBN grinding wheel P/N, the RA requests a supplier-independent dressing cycle time prediction to the Dressing Time Prediction Agent (DTPA) in the Enterprise Level [5]. Using this input and the information on production plans running at the Plant Level, it estimates the demand for tool dressings. If it judges necessary to issue a dressing job order for a given P/N, it requests the ODA to select an external tool manufacturer in the Supply Network Level for dressing job order allocation.
2. Tool
inventory
control
The main responsibility of the RA in the MATMS is the optimum tool inventory sizing and control of CBN grinding wheels for turbine blade fabrication, with particular reference to the minimisation of tool management cost and stock-out risk. This task is carried out by the RA through its domain specific problem solving function, the FTMS. Each time a worn-out CBN grinding wheel leaves a production line, a new P/N demand is set up as a function of dressing cycle time. If an accurate estimate of the dressing cycle time of each worn-out CBN grinding wheel is provided by the DTPA, the P/N demand can be compared with the P/N on-hand inventory. Moreover, as a CBN grinding wheel purchase order issued at a given time requires a lead time (Tpur) before the actual delivery takes place, to avoid stock-out it is necessary to have available at any time a minimum stock at least equal to the P/N demand during the purchase order lead time Tpur.
A FTMS was initially presented in [5] whose rationale was to make sure that the part-number onhand inventory size, I, remains within an interval defined by two real-time control limits: the partnumber demand during Tpur (lower limit, Imin) and the part-number demand calculated using the dressing cycle time predicted by the DTPA (upper limit, Imax). The inventory level, I, is left free to fluctuate within the limits [Imin , Imax], provided neither of them is crossed, and whenever I decreases due to a tool wearout event, the CBN grinding wheels are sent out for dressing. Otherwise, the turbine blade producer logistics must either provide additional serial-numbers when the lower control limit is crossed (I < |rain) or reduce the part-number on-hand inventory by suspending the dressing job order allocation when the upper control limit is crossed (I > Imax) to bring back the stock level within the control range. In [11], an improved FTMS procedure was presented whereby the part-number demand rate was not given by the constant ratio P/G but, in order to take into account possible deviations from currently established production plans and/or current estimates of mean tool life, it was expressed by an adaptive function, dR(w), of wear-out events, w. A wear-outevent was defined as an event in the tool management where a number of worn-out CBN grinding wheels with the same part-number are simultaneously proposed for dressing. The adaptive tool demand rate was defined as follows:
P/G, dR(W)
j/t(w),
if t(w) <_ 1 otherwise
(1)
where: w = counter of wear-out events and t(w) - time (months from the start of the FTMS procedure) at which the dressing of the worn-out serial-numbers is proposed. From eq. 1, it can be noted that the part-number tool demand rate P/G is taken as initial value for the adaptive tool demand rate dR(w); this value is kept constant during the first month of flexible tool management and then updated at each wear-out event. In the Adaptive Demand Rate (ADR) approach, the control limits are given by: Imin = dR(w) * Tpur Imax= dR(w) * [TO+l ) + Tint]
(2)
where: j -- counter of worn-out serial-numbers, T (j+ 1) - DTPA dressing cycle time prediction, and Tint =
internal time, i.e. time for a new or dressed CBN grinding wheel to be transferred to the production line (constant for a given manufacturing plant). The on-hand inventory control of a generic CBN grinding wheel part-number is described to illustrate the FTMS functioning for the ADR approach [12]. According to eq. 2, as Tint is a constant typical of the manufacturing plant, Tpur is considered constant on a historical basis, and P/G is updated yearly by the turbine blade producer for each one year period, the upper control limit Imax varies or stays constant as a function of the current adaptive demand rate dR(w) and the predicted dressing cycle time T(j+ 1), whereas the lower control limit Imin varies or stays constant only as a function of the current value of dR(w). At time t = 0, the part-number on-hand inventory size is I0 - I(0). For t > 0, each time a wear-out event occurs, the part-number on-hand inventory level, I(t), decreases, the adaptive demand rate, dR(w), is updated, and supplier-independent dressing cycle time predictions, T (j+ 1), are issued by the DTPA. Each time new or dressed CBN grinding wheels are delivered by the external suppliers, the part-number on-hand inventory size, I(t), grows but the control limits Imin and Imax are not affected. However, if I(t) crosses the upper control limit, I(t) > Imax, at the next wear-out event I(t) - Imax worn-out serial-numbers are kept on-hold in the enterprise warehouse. After control limits updating, if I is within the control range the worn-out CBN grinding wheels must be sent out for dressing but no tool purchase is required. If I(t) crosses the lower control limit, I(t) < Imin, the worn-out tools must be sent out for dressing and further Imin- I(t) serial-numbers must be provided. If worn-out on-hold serial-numbers are available, they are sent out for dressing in partial or total substitution for new tool purchases. At any rate, the number of required CBN grinding wheels exceeding the available on-hold worn-out serial-numbers must be newly purchased.
2.1. Novel FTMS approach The above version of the FTMS has the drawback of a "shortsighted" approach because decisions are only taken on the basis of the current inventory level, I(t), with no consideration for what will happen in the future. To overcome this problem, a "non shortsighted" version of the FTMS (NS-FTMS) procedure is proposed whereby the FTMS takes into account for tool management decision making the value of a future on-hand inventory level, Ifut(t + Trt), instead of the
219
current on-hand inventory level, I(t). Ifut is the on-hand inventory level calculated with reference to a future time (t + Trt) and is given by:
Ifut(t + Trt)= I(t) + X + Y + Z - dR(w) * Trt
(3)
where: - Trt = reaction time of the FTMS; X = number of worn-out tools sent out for dressing before the start of the FTMS procedure (t < 0) and expected to be available at time (t + Trt); Y = number of worn-out tools sent out for dressing during the FTMS procedure (t > 0) and predicted be available at time (t + T~); - Z = number of newly purchased worn-out tools not yet delivered: these will be positively available at time (t + Trt) because in this approach the purchase time Ypur _< Trt. - dR(W) * Trt = number of worn-out tools that will wear-out during the reaction time T~ under constant demand rate conditions. In the first "non shortsighted" approach (NSFTMS/1), if I(t) > Imin, Trt is given by the sum of the mean historical dressing cycle times dot (6 weeks) and the internal time Tint (5 weeks): -
-
approach based on expert knowledge presents the disadvantage of being dependent on skilled staffwhose experience is acquired during years, it is robust and reliable and can be utilised as a reference for assessing intelligent computation procedures of tool management, operating without the support of human experts. From Figure 1, it can be observed that both NSFTMS approaches yield a notable economy over the tool management historical cost for part-number M3941142-1. In Table 1, the yearly tool supply cost for bothNSFTMS paradigms are reported for comparison with the traditional tool management: a 39% saving is obtained for NS-FTMS/1 and a 33% economy is achieved for NS-FTMS/2. The significant cost reduction obtained with the NS-FTMS approaches in comparison with the traditional tool management is due to the fact that the simulated on-hand inventory level is higher than the Imax limit for long time intervals during the tool management period, therefore avoiding useless dressing operations and tool purchases. For both NS-FTMS procedures, the initial tool demand rate value P/G = 8.1 units/month is higher than m
Trt = dct + Tint = 6 weeks + 5 weeks = 11 weeks
(4)
If I(t) < Imin, Trt is considered equal to the purchase time Tpur (9 weeks)" Trt = Tpur = 9 weeks
(5)
In the second "non shortsighted" approach (NSFTMS/2), the FTMS reaction time Tr~ is not considered equal to a constant value (9 or 11 weeks) but is given by the dressing cycle time prediction T (j+ 1) provided by the DTPA plus the value of the internal time Tint: Y~= T (j+l) + Tint
(6)
2.2. Test case applications
The flexible tool management of part-number M3941142-1 was simulated as test case application of the two NS-FTMS versions using one year historical tool management data. In Figure 1, the historical and the simulated inventory level trends are reported versus time, for the reference period of one year, with the indication of the historical tool supply cost and the cost variation of the simulated trend, for the NS-FTMS/1 (Figure l a) and the NS-FTMS/2 (Figure 1b) paradigms. It is worth recalling that the inventory level historical trend is the result of the tool management activity of experienced staff. Though this management
220
the mean adaptive demand rate d Rafter the first month of tool management (see Table 2). This generates an initial drop of the Imax value, the more significant for the NS-FTMS/1 approach where d R = 0.78 P/G = 6.3 units/month, accounting for the higher economy achieved in comparison with the NS-FTMS/2 approach where d R = 0.80 P/G = 6.5 units/month. The historical trends do not take into consideration the demand rate variation during tool management and CBN grinding wheels keep being sent out for dressing or newly purchased, thus maintaining a high on-hand inventory level that generates superfluous capital investment by seeking excessive stock-out risk protection. The more careful behaviour of the NS-FTMS/2, based on a FTMS reaction time determined by dressing cycle time predictions, in comparison with the NSFTMS/1, where the FTMS reaction time is a constant, is responsible in this case for a lower economy in tool management. In fact, for this part-number there are two occasions of stock-out risk insurgence where the inventory level goes under Imin. In both instances, the N S-FTMS/2 reacts more prontly that the NS-FTMS/1 in bringing up again the inventory level, as can be seen from Figure 1. The reduction of stock-out risk necessarily but virtuously implies a lower cost saving.
varies freely within a real time variable control range to achieve optimal tool inventory sizing and control. A test case application of two FTMS paradigms based on tool management decision making with reference to the value of a future inventory level Ifut(t + T~t) were presented to illustrate and assess the new FTMS performance versus traditional tool management based on human expert knowledge. By comparing the historical and the FTMS simulated inventory level trends for a test case partnumber using the two "non shortsighted" FTMS (NSFTMS) approaches, a notable cost reduction was obtained with both FTMS methods that take into account the tool demand rate variations during the tool management execution, preventing unnecessary dressing operations or tool purchases. The slightly lower economy obtained with the NS-FTMS/2 in comparison with the NS-FTMS/1 approach is justified by a reduction of stock-out risk.
3. Conclusions
The design, functioning and performance of a new Flexible Tool Management Strategy (FTMS) paradigm integrated in a Multi-Agent Tool Management System (MATMS) for automatic tool procurement was presented. The MATMS operates in the framework of a negotiation based multiple-supplier network where a turbine blade producer (customer) requires dressing jobs on worn-out CBN grinding wheels for nickel alloy turbine blade manufacturing from different tool manufacturers (suppliers). The FTMS is designed as a domain specific problem solving function of the MATMS intelligent agent whose task is the optimum tool inventory sizing and control of CBN grinding wheels: the Resource Agent. The latter performs its activities on the basis of running production plans and dressing cycle time predictions, utilising the FTMS paradigm whereby the part-number tool inventory size ...............
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......~...........l
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oe ~. o
............
~
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t ....
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~
............",.'::: ............................
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~
i I
-
~
~'
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9/00
"....
33 % i '
11/00
"
i
']
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(b)
Fig. 1. Historical (black) and "non shortsighted" FTMS (NS-FTMS) simulated (green) on-hand inventory level, l, vs. time (one year), for part-number M3941142-1 9(a) first approach (NS-FTMS/1), (b) second approach (NS-FTMS/2).
221
Table 1 Yearly tool supply cost for test case part-number M3941142-1 Part-Number
M3941142-1
Scenario
Supply Cost
Historical
C 54.996
Simulation NS-FTMS/1 Simulation NS-FTMS/2
Cost Variation
Purchased Tools
Dressed Tools
18
73
t~ 33.324
-39%
55
C 36.660
-33%
65
Table 2 Initial tool demand rate, P/G, and average demand rate, d R, during the tool management period for the test case part-number M3941142-1
Part-Number
M3941142-1
Initial demand rate P/G (units/month) 8.1
Acknowledgements This research work was carried out with support from the EC FP6 NoE on Innovative Productions Machines and Systems - I* PRO MS
References [1] [2] [3]
[4]
[5]
222
Wiendahl, H.-P., Lutz, S. Production in Networks. Annals of the CIRP (2002) 51/2, pp 573-586. Fox, M.S., Barbuceanu, M., Teigen, R. Agent-Oriented Supply-Chain Management. Int. J. of Flexible Manufacturing Systems (2000) 12, pp 165-188. Scholz-Reiter, B., Hoehns, H., Hamann, T. Adaptive Control of Supply Chains: Building Blocks and Tools of an Agent-Based Simulation Framework. Annals of the CIRP, 2004, 53/1, pp 353-356. Teti, R., D'Addona, D. Agent-Based Mulptiple Supplier Tool Management System. 36th CIRP Int. Sem. on Manufacturing Systems - ISMS 2003, Saarbrgcken, 3-5 June, 2003, pp 39-45. Yuan, Y., Liang, T.P., Zhang, J.J. Using Agent Technology to Support Supply Chain Management: Potentials and Challenges, M. G. De Groote School of Business (2000) Working Paper N. 453.
Average adaptive demand rate (units/month) 6.3 6.5
[6] Teti, R., D'Addona, D. Grinding Wheel Management through Neuro-Fuzzy Forecasting of Dressing Cycle Time. Annals of CIRP, 2003, 52/1, pp 407-410. [7] Teti, R., D'Addona, D. Multiple Supplier Neuro-Fuzzy Reliable Delivery Forecasting for Tool Management in a Supply Network, 6th AITEM Conf., Gaeta, 8-10 Sept., 2003, pp. 127-128. [8] Fox, M.S., Barbuceanu, M., Teigen, R. Agent-Oriented Supply-Chain Management, Int. J. of Flexible Manufacturing Systems (2000) 12, pp. 165-188. [9] Sycara, K.P. Multi-Agent Systems, AI Magazine, Summer 1998, pp. 79-92. [10] D'Addona, D. Teti, R. Flexible Tool Management Strategy for Optimum Tool Inventory Control, Special Session on ICME, 1st Int. Virtual Conf. on Intelligent Production Machines and Systems- IPROMS 2005, 415 July, 2005, pp. 639-644. [11] Teti, R., D'Addona, D., Segreto, T. Flexible Tool Management in a Multi-Agent Modelled Supply Network, 37th CIRP Int. Sem. on Manufacturing Systems - ISMS 2004, Budapest, 19-21 May, 2004, pp. 351-356. [12] Teti, R., D'Addona, D. Emergent Synthesis Approach to Tool Management in a Multiple Supplier Network, 5th Int. Workshop on Emergent Synthesis- IWES '04, Budapest, 24-25 May, 2004, pp. 41-50.
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Flow Front Analysis In Resin Infusion Process I. Crivelli Visconti, M. Durante, A. Langella, U. Morano Department of Materials and Production Engineering, University of Naples "Federico II" P. Tecchio 80, 80125 Naples- Italy
Abstract The Resin Infusion under Flexible Tool (RIFT) is a closed mould technology to manufacture composite material and particularly large component. This work concerns the analysis of impregnating flow of a thermosetting resin in a RIFT process and the simulation of the process for the manufacturing of the laminate of simple geometry. Starting from the analysis of the impregnating flow, an analytical model based on the electrical analogy has been developed to calculate the value of an equivalent permeability of the fibrous reinforcement. The value of this parameter is requested to realize the process in simulation by FEM. The results of the simulations have been compared with the results obtained by experimental tests carried out using a preform of fiber glass reinforcement and epoxy resin to manifacture plane laminate in composite materials. Keywords" Permeability, RIFT, FEM Simulation
1. Introduction
The first type of infusion process was introduced by the Marco Method [1]. Successively the process was developed by employing of various bagging materials, better compaction and high permeability distribution medium, in response to the need for lower styrene emissions [2] and for low cost of fabrication of large structural components [3] [4]. Some authors propose analytical formulations of governing equations the impregnating flow in order to quantify the effect of process parameters [5] and to calculate the filling time [6]. Also FE program are used to simulate the infusion process in a porous preform [7].
In RIFT process the impregnation of a preform constituted by reinforcement layers with a thermosetting resin is obtained by use of vacuum and a close mould constituted by a rigid part and a flexible tool; this tool, generally a polymeric bag, permits the production of complex and large component reducing the manufacturing cost. In figure 1 the scheme of the process is shown, from this it is possible to note the presence of a resin distributor placed on the preform to facilitate the flow of the resin aspirated by the vacuum generated between the mould and the bag. Flexible Tool Resin Distributor Outlet Point
II
Inlet Point
Preform
!i!i!i!i!i!i!jPiiiiiiiiiiiiiiii .
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
Plane Mould Pump
Vessel
Safety Valve
Fig. 1. RIFT process.
223
Usually the studies on the impregnating process are based on the Darcy's law [8] [9] and generally consider two hypothesis: 9 the resin is considered incompressible (Newtonian fluid and low Reynold's number); 9 the induced deformation in the preforms during the injection is negligible (static fibers). In this paper the possibility of applying a FEM program, RTM-Worx, in order to calculate the impregnation time of a preform, has been evaluated. This program considers only bidimensional flow, in the plane of the laminate, neglecting the transversal flow in the thickness direction. But in infusion process, when resin distributors are used, a transversal impregnating flow from distributor to perform is present. So it is possible to employ RTM-Worx, in this case, setting a value of an equivalent permeability letting to transform the different flows impregnating the preform in different direction as a longitudinal one. An analytical model, based on the electrical analogy, has been developed to calculate the equivalent permeability of the distributor-preform system starting from the permeability values of the distributor and of the reinforcement layers constituting the preform. The values of the filling times for a glass fibre perform, measured by experimental tests and by the FE model setting the value of the equivalent permeability, have been compared.
[~
i ~~iiiiiiiiiiiiiiiiiii~iiiiii~ ~ l"~ resindzstr~butor reinforcement Fig. 2. Complete impregnation scheme.
Now it is necessary to say that if Qd is volumetric flow in the distributor, only Q j 2 will impregnate a part of reinforcement, while the other half volumetric flow will advance in the distributor. This hypothesis is based on some observations about the Darcy's Law. If Qd is the total volumetric flow in the distributor and said Q'~ and Q"d the volumetric flows impregnating respectively the reinforcement and the distributor, it is possible to write the Eq. 1.
Qd = Q'd+Q"d
In the Darcy's law the volumetric flow is directly proportional to the section that the flow crosses. So the flow Q'd is proportional to the area AT generated from the difference between the two flow front in the distributor and the reinforcement (figure 3). If Q"d is greater than Q'd , the distance between the two longitudinal flow fronts increases, with consequent increase of the transversal surface and Q'd. Likewise if Q'd is greater than Q"d the surface of passage decreases so Q'd decreases too. So it is impossible that one is greater than other. This, obviously, is true only to equilibrium condition, after an initial transitory time that we hypothesize negligible. On the basis of this demonstration it is possible to adopt the scheme with two parallel flows impregnating the reinforcement: the first generated by longitudinal flow in the reinforcement, the second coming from the polymeric net and equal to Qd/2 (figure 3).
2. Analytical Model In a RIFT process a resin distributor is placed on the dry preform to reduce the filling time so the flow of resin inside the system is given by three contributions (shown in the figure 2): 9 a longitudinal flow in the distributor; 9 a longitudinal flow in the reinforcement; 9 a transversal flow in the reinforcement (due to different speed of the resin in the distributor and in the reinforcament).
Q j2
QL
Fig. 3. Reinforcement's filling.
224
(1)
The analytical model presented in this work founds itself on Darcy's law and an electric analogy, in which the porous media's opposition to the resin flow is simulated with an electric resistance. It is possible to write the Darcy's law as shown in Eq. 2.
(2) If Ap is compared to a potential difference and Q to a current intensity, the terms in parenthesis can be considered an electric resistance from relationship of Ohm (V=RI), so in this analogy the points to the same pressure will be assumed to the same potential. The two flows impregnating the reinforcement are Eq. 3 and 4. ka Ap a U
Qd / 2 : Q ' d = -
(3)
2ilL
Q, : k, kp a, /.tL
(5)
x
(6)
The total electrical resistence and the volumetric flow obtained applying the Darcy's formula are Eq. 7 and 8. 2 _
1
k d . cr d
-
k~ . ~
2 -
-
"
1 J
r
kdcrd
-
--
Ap /~.x
-
-/a.x
(7)
9 9 9 9
k~ . a~ 2
2k~cr~
-t
1
k, . cr,
(8)
1
+
The preform constituted by 8 layers of reinforcement is placed on the mould, on the preform, separated from it by a peel-ply layer, the resin distributor, constituted by a polymeric net, is placed. At the end a polymeric bag sealed to the rigid mould is employed as the flexible tool in order to cover the preform. The pump aspirates the air contained between the mould and the bag, so generating the vacuum in the preform. The difference between the atmospheric in the vessel and the pressure in the preform pushes the resin into the preform. When the filling process is finished the injection and successively the outlet point are closed.
4. E x p e r i m e n t a l Tests
In which kd and k~ are the longitudinal permeability of the resin distributor and of the preform, (Yd and ~ are the longitudinal cross section of the resin distributor and of the preform. Finally it is possible to calculate the equivalent permeability of the system (Eq. 9).
k~q =
9
a plexiglass plane mould; a system of injection made by a vessel with a graduated scale in which is contained the resin at atmospheric pressure and a duct to bring resin from vessel to mould; a pump to create the void in the mould and a duct with a safety valve to preserve the pump; a polymeric bag to close mould; a net distributor in polymeric material; seal to fix the bag and the ducts to mould; a spiral distributor placed immediately after the point of injection to allow the uniform advancement of the flow front; peel-ply, to allow the separation of the distributor from the laminate after polymerization.
-
kl'o-/ 2
Q
9 9
9
Rt = /.t-x
Rr
Four laminates have been produced by RIFT. A glass fibre woven fabric of 300 gm -2 and SX10 epoxy resin system, of low viscosity and low toxicity, by MATES srl have been used. The equipment used for the experimental tests is the following:
(4)
It is possible to consider the following resistance in parallel position (Eq. 5 and 6). Rd _ 2.1a"
3. Materials and R I F T Technology
kdCr d
2(o_ d + o', )
Initially tests were conducted to measure the permeability of the reinforcement layers and of the resin distributor using the unidirectional flow method, wherein the permeability value can be determined with formula (10).
k_Q/ L zXpa
(lo)
In these tests only layers of distributor and only layers of reinforcement were placed between the mould and the polymeric bag (figure 4).
(9)
225
During the experimental tests the flow front position in the reinforcement layers has been measured and the average values are reported in table 2.
5. FEM analysis
Fig. 4. The permeability tests of the resin distributor (up) and of reinforcement (down). The medium values of permeability of these elements are reported in table 1. Table 1 Permeability values Permeability (m 2) Reinforcement 2.84 * 10"11 Resin distributor 1.23 * 10-9 buosequenuy some laminates were manutactureo oy RIFT using 8 layers of reinforcement and only one layer of resin distributor. m flow front in the fib~ reinforcement
flow front m the resin dis~buitor .
.
.
.
.
.
Fig. 5. Flows front in a RIFT process. Table 2 Flow front position Distance (m) 0.05 0.10 0.15 0.20 0.25 0.30
226
Time (s) 58 147 277 491 817 1290
A FEM programm, RTM Worx, has been used in order to carry out a comparison between the values of the filling time measured by experimental tests and FEM results. The FE model has been generated using a mesh constitued by triangular shell elements. It is possible to set the thickness value but in thickness direction values of the parameter are costant, so the model is bi-directional. Generally for some manufacturing processes the impregnating flow in the preform are bidimensional, but for the infusion process also transversal flow must be considerd. So the permeability value to assign to the elements must consider both the reinforcement and the distributor permeability. The equivalent permeability of the system distributorreinforcement could be calculate with the formula before reported (8). Another simple formula to determine this equivalent permeability could be the rule of mixture (9). Keq = K1Vf+KdVd
(9)
This simple formula doesn't consider the different impregnating flows in the preform. In table 3 the value of the equivalent permeability are reported calculated by role of mixture and by the analytical model presented in this work (paragraph 2). Table 3 Permeability values calculated by formulae Equivalent permeability (m 2) Rule of mixture Electical analogy 1.76 * 10-1~ 9.88 * 1 0 "11 The RTM-Worx program can give the filling time, the pressure and the velocity vector when the physic and geometric properties of the laminates and the viscosity of the resin are known. In figure 6 some phases of FEM analysis are shown, where it is possible to see the triangular shell and the progressive impregnation process. In the figure 7 the comparison among the values of filling time obtained in experimental tests and by the program of simulation setting the equivalent permeability calculated by formula (8) and rule of mixture are reported. From this diagram it is possible to note that adopting the formula (8) to calculate the equivalent permeability the FEM results are very close to experimental tests results.
.....
.. ~:~
......
..
Fig. 6. Simulation of impregnating process.
1600
...........................................................................................................................................................................................................................
1400 1200 '-'
1000
E ~:
800
o") c
"= , -=
.
600 -
S
40O
~I
K
200 I
i
I
I
E
1
0,05
0,1
0,15
0,2
0,25
0,3
0,35
d i s t a n c e [m] 9
= --At-
Experimental tests
RTM-Worxwith electrical analogy RTM-Worx with rule of mixture
Fig. 7. Comparison between experimental tests and RTM-Worx analy.sis 227
6. Conclusions
In this work RIFT process is simulated by a FEM program (RTM-Worx) and the results of FEM analysis are compared with experimental tests. The FE program generates a bidimensional model, considering the properties in thickness direction constant. In order to apply this FEM to simulate the infusion process an equivalent longitudinal permeability of the system resin distributor-preform must be considered. For this an analytical model, based on electrical analogy has been developed to calculate the equivalent permeability. From the comparison with the experimental data it is possible to confirm that setting the equivalent permeability value the FEM simulation is able to determine the filling time of the perform in a RIFT process.
7. References
[ 1] Marc Method, US Patent 2495640, 24-01-1950. [2] Gotch TM. Improved production process for
228
manufacture of GRP on British rail. Procedings of the l lth Reinforced Plastics Conference, paper n 4 november 1998 : Brighton 33-9. [3] Le Comte A. US Patent 4359437, 16-11-82. [4] Lazarus P. Infusion. Patent Boat Builder 1994 ;30:42-50. [5] N.C. Correira, F. Robitaille, A.C. Long, C.D. Rudd, P. Simaceki, S.G. Advani. Analysis of the vacuum infusion moulding process: I. Analytical formulation. Composites: Part A 36 (2005) 1645-1656. [6] Tucker CL, III. Governing equations for resin transfer molding. In: Advani SG, editor. Flow and rheology in polymer composites manufacturing. New York: Elsevier Science, 1994. [7] Kerang Han, Shunliang Jiang, Chuck Zhang and Ben Wang. Flow Modelling and Simulation of SCRIMP for Composites Manufacturing. Composites: Part A 31 (2000) 7 9 - 86. [8] Tucker III CL. Fundamentals of computer modelling for polymer processing, Munich: Hanser Publishers, 1989. [9] William JG, Morris CEM, Ennis BC. Liquid Flow through aligned fiber beds. Polym Eng. Science 1974;14(6):413.
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All rights reserved.
Forces analysis in sheet incremental forming and comparison of experimental and simulation results F. Capece Minutolo, M. Durante, A. Formisano, A. Langella Dept. of Materials and Production Engineering, University of Naples Federico II, P.le Tecchio 80, 80125 Naples, Italy
Abstract Innovative techniques, such as hydroforming and incremental forming, are characterized bythe possibility to be easily adapted to realize a small production lot with low tools cost. In this work, grooves in sheets have been realized by means of the incremental forming technology. The aim ofthis study is the forces analysis in relation to the tool path and its diameter. FEM simulations have been achieved with the purpose to carry out a comparison between the forces values experimentally pointed out and the ones by FEM. FE analysis has besides allowed the individualization ofthe points where the failure conditions take place by the simple evaluation ofthe reached stress values. So, the FE method could be used to determine the tool path in the design phase of the process cycle.
Keywords: Incremental forming, tool path, FE analysis
1. Introduction Conventionally, a metal sheet is formed using punches and dies depending on the component geometry. Because of the recent diversification of the requirement of the client in this field, new flexible manufacturing methods for productions of small lots have been developed. In the incremental forming, a simple shaped tool produces a local plastic deformation in the sheet in a progressive way. The process can easily be schematized with a tool with hemispherical head working on a CNC machine, that causes a plastic deformation on the sheet. The tool shifts both horizontally and vertically, controlled by a program that sets up the path tool, so giving a shape to the sheet. In many papers the high formability using the incremental forming, compared with conventional process, has been pointed out. Kim and Park [ 1] have
analyzed the effects of the process parameters and have set the straight groove test as a method to acquire the formability curves, that, for the incremental forming, can be couched as a scalar equal to emax+ emin, giving, alternately, a horizontal and vertical motion to a punch. This way, a straight and deep groove in the central area of a square specimen, whose periphery is fixed by means of a blocking system, is formed. Besides, in these papers, the different strain level in the edges of the tool paths that determines the failure of the sheet, has been underlined [2,3]. In other papers, the comparison between experimental and FEM results, for different geometries, also complex, has been examined [4-8]. In the present paper, experimental tests of aluminium sheets deformation by incremental forming have been carried out, using punches with hemispherical head. The straight groove test has been adopted and the force acting on the punch has been
229
valued, varying the path geometry and the punch diameter. Besides, a comparison in terms of forces values, between the results of the experimental tests and the results of the FEM simulations, has been performed. The FEM validation is necessary in order to utilize this method for setting up the process, determining the tool path in relation to the carrying out geometries.
2. The experimental campaign Drawing tests have been carried out on a CNC machine. In Fig. 1 a photograph of the implementation of an experimental test is reported; it is possible to notice the supporting framework of the sheet that is rigidly fixed to the superior surface of a 3 channels Kistler load cell for the acquisition of the forces. Steel punches, with hemispherical head, 10 mm and 5 mm in diameter, have been used. The method of the straight groove test has been employed for the evaluation of the forces as a function of the drawing depth. For the realization of the groove, two different tool paths have been used. In Fig. 2 the two typologies of path, respectively denominated A and B, are shown. The feed along z-direction (normal to the surface of the sheet) has been set equal to 0.5 mm while, along xdirection (parallel to the axis of the groove), a value of the feed rate of 300 mm/min has been used, for a length of 50 mm. The material employed, for the sheets, is a 0.5 mm thick aluminium alloy 2024-T3; the mechanical characterization has been realized carrying out tensile tests with a MTS Alliance RT/50 material testing machine. The tests, with controlled feed, have been performed on specimens with two different orientations, that is with an angle of 0 and 90 degrees as regards the rolling direction; for every direction three tests have been executed. Meaningful differences between the stress-strain curves for the two different orientations haven't been found. From the tensile tests, a Young modulus of 72000 MPa, a yield strenght of 325 MPa, an UTS of 520 MPa and an ultimate elongation of 14% have been obtained. A plastic strain ratio [9] equal to 0.62 has been valued. The characteristic parameters of the stress-strain curve that allow to couch the plastic behaviour by the following power law: ~ = 788~ ~ , have been appraised. The drawing tests have been performed using three sheets for each typology. For every test the rpm of the tool was equal to 200. In order to get low values of the friction coefficient, before the tests, a lubricant has
230
been laid on the surface of the sheet. In particular, the value of the friction coefficient has been measured by means of a pin on disc apparatus, using the tool as pin and the sheet as disc. The lubricant has been laid on the sheet as well as for the tests of drawing. The value of the friction forces has been measured by a load cell assembled at the extremity of the supporting arm, opposed to the one in which the pin is placed. The sliding speed has been set equal to the punch feed rate in the motion of achievement of the groove. A value of the friction coefficient of 0.1 has been measured.
Fig. 1. Photograph of the sheet forming test. L=50 mrn
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.
.
.
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Fig. 2. The two paths employed for incremental forming.
For the two typologies of path the failure has been registered at a drawing depth of 7 mm with the punch of 10 mm in diameter and in correspondence of the extremity of the path, where the punch generates a change from a monoaxial stress state to a biaxial one. In Figs. 3 and 4 the graphs of the horizontal and vertical forces measured during the tests are shown. It is possible to note that the greatest vertical forces are reached for the type B path, because it presents the sharper increase of depth, while, in the central zone for the type A path, the values of the force rise because of the continuous increase of the drawing depth. In terms of horizontal force, meaningful differences aren't noticed.
occurred at a depth of 5.5 mm; besides, as already reported in technical literature [1 ], as well as the tool size decreases, the deformation zone reduces and the strain level increases. That produces higher values of stresses in both the directions (parallel and normal to the punch motion), causing consequently a different typology of failure. In Figs. 6 and 7, the photographs of the different types of failure, monodirectional and bidirectional, respectively for the punch with a diameter of 10 e 5 mm, are reported. 1200 1000 800
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-300 time (s)
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Fig. 3. Horizontal forces for type A and B paths. 12oo ~ A
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Fig. 6. Monodirectional failure.
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100
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Fig. 4. Vertical forces for type A and B paths. Subsequently, drawing tests have been realized, employing a punch of 5 mm in diameter, and leaving the other process parameters unchanged, only for the type A path, for which lower maximum values have been obtained, as above mentioned. In Fig. 5 the trends of the measured forces are shown. It can be noticed that the values of the vertical forces are lower than the ones measured in the test with a punch of 10 mm in diameter, depth of drawing being equal. The failure has
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231
3. FE analysis and comparison of numerical analysis with experimental results The drawing process of the sheets, with the two typologies of path and for the different punches, has been simulated with the explicit finite element code LS-DYNA. In the simulations, run time of calculation has notably decreased, using the mass scaling and a high working speed. A punch and a sheet have been used to simulate the process; besides, the geometrical and loading symmetry of the case under examination,
has been exploited; a scheme of the geometries employed in the simulation is shown in Fig. 8. For the punch, a rigid material determined by the value of the density, of the Young and the Poisson moduli, has been employed. For the square elements forming the sheet, a material able to be deformed has been assigned, fixing the density, the Young and the Poisson moduli and the parameters of the power law, K and n, in order to assess the material behaviour. The values of the friction coefficient experimentally appraised have been set.
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Fig. 8. Incremental forming in LS DYNA. In Figs. 9 and 10 the comparisons between the values of vertical and horizontal force respectively for the type A path and for the type B path, measured in the experimental tests and calculated with the FEM simulation, as a function of the time, are shown. In Fig. 11 the same trends of the forces for the test with a punch of 5 mm in diameter and a type A path are reported. In all the examined cases, a considerable agreement between numerical and experimental values can be noticed, with a difference among the force peaks not higher than 10%. For the type A path, in Fig. 12, the stress values according to Von Mises yield condition, produced by the FEM for the elements pointed out in Fig. 13, are
232
shown. It is important to note that these elements are relevant to the extreme point of the drawing path, i. e. where the failure of the sheet occurs. It is possible to note that the values of the stress reach, at the drawing depth where the failure has occurred, the value of the ultimate tensile stress, measured in the tensile tests. The same results can be obtained examining the graph of Fig. 14, relevant to the stresses, calculated by the FEM, in the points where the failure has occurred for the sheet formed with the punch of 5mm in diameter.
55O
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experimental (vertical) - - LS 9 DYNA (vertical) experimental (orizontal) " " "LS DYNA (~176
1000 8OO
,~ ~
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t i m e (s)
-600
Fig. 9. Comparison between experimental and FEM results for the type A path.
1400 1200 1000 800
Fig. 12. Stress values for the type A path with a punch of 10 mm in diameter.
Fringe Levels
experimental (vertical) - - -LS DYNA (vertical) experimental (orizontal) - -'LSDYNA(orizontal),
~
,~
~
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:~
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'2::~176
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_
-200 -400 -600
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Fig. ! 3. Elements of the sheet concerned with stress analysis.
Fig. 10. Comparison between experimental and FEM results tbr the type B path.
55O
600 500
400 A 300 z ,..,.,
5OO
experimental (vertical) - - -LS DYNA (vertical) experimental (orizontal) - - -LS DYNA (orizontal)
450 400
g. 350 3oo
200 o w-
250 200
100
150 tO0
-100
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-200 I -300
]~ time (s)
Fig. l 1. Comparison between experimental and FEM results for the type A path with a punch of 5 mm in diameter.
0
0.0
0.5
1.0
1.5
2.0
2.5 depth
3.0 (mm)
3.5
4.0
4.5
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5.5
Fig. 14. Stress values for the type A path with a punch of 5 mm in diameter.
233
4. Conclusions
References
It has been possible to realize some grooves on the surface of aluminium sheets using the straight groove test with two different tool paths. For both the paths the same value of the maximum depth of drawing has been obtained, but the vertical forces values, instead, result to depend on the typology of the path. Particularly, for one of the two paths, values of the vertical forces lower by 20% than for the other one have been measured. With the test carried out using a punch with a smaller diameter, a smaller drawing depth, smaller drawing forces and a different typology of failure have been obtained. Besides, the drawing forces can be appraised by FEM. It has been noticed, regarding the simulations results, that it is possible to forecast the failure point of the sheet by the simple comparison between the reached stress value and the material ultimate tensile stress. From the above, it results that by FE analysis it's possible to determine the limits of formability on the basis of the scheduled tool path. So, this method could be used in order to define the process parameters to obtain a good quality of the product and the optimization of the manufacturing.
l] 2] 3] 4]
5]
6] 7] 8]
9]
234
Kim YH and Park JJ. Effect of process parameters on formability in incremental forming of sheet metal. J. Mater. Process. Technol. 130-131 (2002) 42-46. Shim MS and Park JJ. The formability of aluminum sheet in incremental forming. J. Mater. Process. Technol. 113 (2001) 654-658. Kopac J and Kampus Z. Incremental sheet metal forming on CNC milling machine-tool. J. Mater. Process. Technol. 162-163 (2005) 622-628. Ambrogio G, Costantino I, De Napoli L, Filice L, Fratini L and Muzzupappa M. Influence of some relevant process parameters on the dimensional accuracy in incremental forming - a numerical and experimental investigation. J. Mater. Process. Technol. 153-154 (2004) 501-507. Ceretti E, Giardini C and Attanasio A. Experimental and simulative results in sheet incremental forming on CNC machines. J. Mater. Process. Technol. 152 (2004) 176184. Cai ZY and Li MZ. Finite element simulation of multipoint sheet forming process based on implicit scheme. J. Mater. Process. Technol. 161 (2005) 449-455. Park JJ and Kim YH. Fundamental studies on the incremental sheet metal forming technique. J. Mater. Process. Technol. 140 (2003) 447-453. Iseky H. An approximate deformation analysis and FEM analysis for the incremental bulging of sheet metal using a spherical roller. J. Mater. Process. Technol. 111 (2001) 150-154. DanckertJ andNielsen KB. Determination ofthe plastic anisotropy r in sheet metal using automatic tensile test equipment. J. Mater. Process. Technol. 73 (1998) 276280.
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Neural network based system for decision making support in orthodontic extractions R. Martina a, R. Teti b, D. D'Addona b, G. Iodice a aDepartments of Odontostomatology, University of Naples Federico II, Naples, Italy b Department of Materials and Production Engineering, University of Naples Federico II, Naples, Ital
Abstract The application of an intelligent computation approach to the support of the clinical decision making on orthodontic extractions is illustrated in this work. Artificial neural networks trained using cephalometric and orthodontic cast measurements can provide a valuable support in the extraction therapeutical option in orthodontics. Keywords: Artificial intelligence, Neural networks, Orthodontic extraction
1. Introduction Dental malocclusions are a highly prevalent pathology in the general population and the greater attention to aesthetic and functional problems have driven to a larger demand of orthodontic treatment in the last years. To date, a critical step in the orthodontic therapy is represented by a correct diagnosis and treatment planning. Orthodontic diagnosis, however, often results very difficult and influenced by subjective interpretation of the measured parameters. For this reason, Artificial Neural Network (NN) based approaches have been proposed as a valid support for diagnosis in orthodontics [ 1-3]. Treatment planning is a decisive and critical moment for the clinician, especially in case of extraction, which is a non reversible procedure. Moreover, the diagnostic parameters needed to define the treatment plan are very numerous and the relative weight of each parameter for a specific patient is not easy to determine. This subjective aspect of orthodontic diagnosis
determines the lack of universality and unanimity in the interpretation of orthodontic data and, consequently, in the treatment selection. A referencing tool for orthodontic data evaluation would be desirable, particularly in controversial cases, where subjective data interpretation could generate incorrect decisions. An innovative and promising approach to this kind of problems is represented by the development and application of intelligent computation procedures [4-5]. The main objective of this work is the realization of an intelligent diagnostic system based on NN for the extractive therapeutical option on the basis of measured orthodontic parameters.
2. Artificial neural network (NN) An artificial Neural Network (NN) is a computational model of the human brain that assumes that computation is distributed over several simple interconnected processing elements, called neurons or nodes, which operate in parallel [6].
235
Age 0 Sex~ C) "'" ~ .-.
...
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Treatment C) ,~Output Layer dden Layer
Fig. 1. Feed-forward backpropagation NN. The outputs of nodes in one layer are transmitted to nodes in another layer through connections that amplify or attenuate the outputs through weight factors. Except for input layer nodes, the net input to each node is the sum of the weighted outputs ofthe nodes in the prior layer. Each node is activated in accordance with the input to the node, the activation function of the node, and the threshold of the node; accordingly, the node fires an output (Fig. 1). In this way, a NN provides a mapping through which points in the input space are associated with corresponding points in an output space on the basis of designated attribute values, of which class membership might be one. NNs can capture domain knowledge from examples, do not archive knowledge in an explicit form such as rules or databases, can readily handle both continuous and discrete data, and have a good generalisation capability. Knowledge is built into a NN by training. Some NNs can be trained by feeding them with typical input patterns and expected output patterns. The error between actual and expected outputs is used to modify the weight of the connections between neurons. This method is called supervised training.
3. NN data processing A supervised NN paradigm [7-9] based on a feedforward back-propagation (BP) NN is employed to provide for the clinical decision on orthodontic extractions. The training set utilised for NN learning was built on the basis of orthodontic casts and radiographic measurements as well as clinical examinations carried out on 48 patients treated at the Department of Orthodontics of the University of Naples Federico II.
236
Fig. 2. Example of orthodontic casts and radiographic measurements. Cephalometric analysis was performed on lateral standardized cephalograms, taken by a single technician using the same x-ray device and a standardized procedure. The cephalograms were made with the mandible in the intercuspal position. The cephalometric analysis was performed by means of RealCeph| software and was addressed to define the subjects' facial typology and sagittal relationship (Fig. 2), using the features reported in Table 1. Patient exclusion criteria were: number or shape anomalies, previous extractions, necessity of extractions for parodontal or endodontic problems, orofacial surgical treatment. All radiographic examinations utilized in the present work were necessary for the orthodontic treatment. For each patient or case, the 32 features reported in Table 1 were available. The 32 features made up a 32-component input vector and the extraction therapeutical option represented the corresponding 1-component output vector classified as belonging to one of two categories: extraction (Od = 1) or not extraction (Od = 0). The input and output features made up a 33component pattern vector for each patient or case. Several NN configurations were tested. Results are presented for the 32 - (4/8/12/16) - 1 NN configurations: - input layer with 32 nodes for the 32-component input vector - hidden layer with 4, 8, 12, 16 nodes - output layer with 1 node for extraction option ("0" for not extraction output vector and "1" for extraction output vector ).
The 32 - (4/8/12/16) - 1 N N m a i n p a r a m e t e r s were: - weights and thresholds r a n d o m l y initialized b e t w e e n - 1 and + 1 - learning coefficient: q = 0.3 or 0.15 - m o m e n t u m : ~ = 0.4 - learning rules: N o r m a l C u m u l a t i v e Delta Rule and C u m u l a t i v e Delta Rule - transfer functions: sigmoid function and h y p e r b o l i c tangent function - learning steps for a c o m p l e t e training: 10,000 100,000 - epoch size was 6. N N training and testing was p e r f o r m e d b y the
" l e a v e - k - o u t " method: one case was kept aside in turn while the r e m a i n i n g were used for training. The kept aside case was then used for testing. This p r o c e d u r e was repeated for all cases in the training set. In the training phase the 3 2 - c o m p o n e n t input vectors were p r e s e n t e d at the input layer o f the 32 (4/8/12/16) - 1 N N and the extraction option was fed into the output layer. In the testing phase the 3 2 - c o m p o n e n t input vectors were p r e s e n t e d at the input layer o f the 32 (4/8/12/16) - 1 N N and the extraction option was expected at the output layer.
Table 1 Orthodontic casts and radiographic measurements" 32 features to be used as NN inputs
.
2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32.
Input features age sex dentobasal discrepancy (DDB) 37/47 impact (37-47) intercanine diameter difference (3 diameter) intermolar diameter difference (6 diameter) distance from canine Angle' s I class dx (3 dx Class) distance from canine Angle's I class sx (3 sx Class) distance from molar Angle's I class dx (6 dx Class) distance from molar Angle's I class sx (6 sx Class) Lee Way Space (LWS) Overjet (OVJ) Overbite (OVB) median line agreement (Med.line) Ricketts' line (Rck line) superior lip thickness (Sup lip) maxillar position (SNLA.) mandibular position (SN/Pg) sagittal intermaxillar relationship (AN/Pg) maxillar inclination (SN\ASN.PNS) mandibular inclination (SN\Go.Gn) vertical intermaxillar relationship (ANS.PNS\Go.Gn) upper incisive inclination (+I/ANS.PNS) lower incisive inclination (-1/Go.Gn) lower incive compensation (-1/A.Pg) interincisive's angle (Ang Inter) inferior sulcus position (Inf S pos) gingival tipology (Geng Tip) gingival recessions' presence (Rec) labial incompetence (Lab incomp) labiomental (Labm Contr) orbicular (Orb Contr) muscles' contraction
Input codes Years Binary (Male = 1, Female = 0) Degree Degree Degree Degree Degree Degree Degree Degree Millimetres , Millimetres Millimetres Degree Millimetres Millimetres Millimetres , Millimetres Millimetres Millimetres Millimetres Millimetres Millimetres Millimetres Binary Ord Scale Ord Scale , Ord Scale Nom Scale Ord Scale Ord Scale Ord Scale
Range 9 -26 0, 1 72.9 - 86.2 71.9-86.4 0 . 0 6 - 7.6 0.1 - 16.2 19.4 - 42.8 9 . 4 - 34.0 89.0 - 125.6 73.2 - 107.4 0.02 - 8.3 , -3.5 - 11 0- 8 42.5 - 166.9 -9,1 - 4 . 7 -5 - 4 - l l - 14 , -4 - 8 -5 - 7 -2 - 7 -6 - 7 4 - 8 0 - 4.4 -2.5 - 2.5 0-1 0-1 0- 1 0- 1 0- 1 0-1 0-1 0-1
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238
Table 2 NN success rate for different number of hidden nodes # of hidden nodes
NN Success Rate (%)
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4. Results
4.1 BP NN paradigm The BP NN output was considered correct if its output (extraction or not extraction) coincided with the decision made for the patient at the moment of treatment. Thus, the NN output was correct if the actual output was lower than 0.5 for a not extraction case or higher than 0.5 for an extraction case. The NN error was calculated as Er = (Oa - Od)/1, where Oa = actual output, Od = desired output, 1 = difference between two adjacent numerical code values. In case of a not extraction case, if the output was lower that -0.5 it was considered equal to -0.5 and, on the contrary, in the case of an extraction case, if the output was over 1.5 it was considered equal to 1.5. The expected extraction diagnosis was correct if Er was between -0.5 and 0.5; otherwise, a misclassification case occurred. The NN performance (success rate) was calculated as the ratio of correct identifications over the total pattern vectors presented at the NN input for all NN configurations considered 32 - (4/8/12/16) - 1.
The results of the NN computation under these conditions are shown in graphical form in Fig. 3. As reported in Fig. 3a, c, e, g, the desired NN output is 0 for 'not extraction' and 1 for 'extraction' sequences. The black symbols represent the actual NN output for 'not extraction' sequences and the white symbols the actual NN output for 'extraction' sequences. In Fig. 3b, d, f, h, the NN error Er is reported for 32 - (4/8/12/16) - 1 NN configurations, respectively. The black symbols "A" represent the correct classification cases and the white symbols "A" the misclassification cases. In Table 2 and Fig. 4, the NN success rate is reported versus the number of nodes in the hidden layer for the considered NN configuration. From Fig. 4, it can be seen that the NN performance depends on the number of nodes in the hidden layer, but is, in all cases, appreciably higher than 75 %.
5. Conclusions This work shows that neural network based decision making support systems may be trained on the basis of clinical data and can be used where "rule based" decision making is unreliable or impossible. This is the case in many clinical situations, especially in orthodontic treatment planning. Neural network systems may, therefore, become an important decision making support tool in orthodontics and find applications both in improving the clinical treatment and in maximizing the cost benefit of the treatment.
Acknowledgements This research work was carried out with support from the EC FP6 NoE on Innovative Productions Machines and S y s t e m s - I ' P R O M S .
References [1] Bricjley, M.R., Shepherd, J.P. Comparison of the abilities of a neural network and three consultant oral surgeons to make decisions about third molar removal. British Dental J., 1997, vol. 182, n. 2.
239
[2] Martina, R., Teti, R., Musilli, M. Neural Network based identification of typological diagnosis through cephalometric techniques, Eur. J. Ortohod., Oct. 2001. [3] Vassura, G., Vassura, M. Evidence Based Orthodontics: Proposta di un originale modello di intelligenza artificiale per la soluzione del problema diagnostico in ortodonzia, WEOC Communication, Firenze, 2003. [4] Bricjley, M.R., Shepherd, J.P., Armstrong, R.A. Neural Networks: a new technique for development of decision support systems in dentistry. J. of Dentistry, 1998, vol. 26, n. 4. [5] Speight, P.M., Elliot, J., Downer, M., Zakrewska, J. The use of artificial intelligence to identify people at risk of oral cancer and pre-cancer. British Dental J., 1995, vol. 179, pp. 383-387.
240
[6] Herz, J., Krogh, A. and Palmer, R.G. (1991). Introduction to the Theory of Neural Computation, Addison Wesley, NY. [7] Rumelhart, D.E., Hinton, G.E. and Williams, R.J. (1986). Learning Internal Representations by Error in Propagation, McClelland, J.L., Rumelhart, D.E. and the PDP Research Group, eds., Parallel Distributed Processing: Exploration in the microstructures of cognition, Foundations: 1. [8] Haykin, S. (1994). Neural Networks: A Comprehensive Foundation, Macmillan, NY. [9] Fahlman, S.E., Lebiere, C. An Empirical Study of Learning Speed in Back Propagation Networks, Carnegie Mellon University Technical Report, CMUCS-88-162, 1990.
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All rights reserved.
Optimization of a hydroforming process to realize asymmetrical aeronautical components by FE analysis F. Capece Minutolo, M. Durante, A. Formisano, A. Langella Dept. of Materials and Production Engineering, University of Naples Federico II, P.le Tecchio 80, 80125 Naples, Italy
Abstract
Hydroforming process is an effective method for manufacturing complicated parts. This process is quite sensitive to the characteristics of the pressure growth laws and the friction between the sheet and the elements ofthe tooling. In this work a numerical simulation has been carried out using the explicit finite element code LS-DYNA with the purpose to optimize a hydroforming process related to the achievement of an asymmetrical aeronautical component. In particular, the number of the steps ofthe manufacturing cycle actually used to produce the component has been reduced considering the FEM results. Keywords: Hydroforming, process parameters, FE analysis
1. Introduction
Hydroforming allows to overcome some of the limitations of conventional deep drawing, increasing the draw ratio and minimizing the thickness reduction of the formed parts. Among the advantages introduced by hydroforming there are a great flexibility and a remarkable reduction of tooling costs. Hydroforming is a process that makes use of a hydraulic pressure to improve the basic deep drawing process. The fundamental parts of the tool for a hydroforming process include a punch, a blank holder and a pressure chamber with a rubber diaphragm that seals the liquid in the chamber. The draw ratio achievable in hydroforming is high (values of about 3.2 are reported in literature) [ 1,2], very little thinning occurs and asymmetrical shapes can be drawn. Many failure types can be found in hydroforming, generally divided into fracture and wrinkling [3]. The process is quite sensitive to pressure variation and
friction between the sheet and the tool [4,5]. Figure 1 shows the various outcomes of the process, varying the pressure vs. the punch stroke. A simple theoretical analysis, related to symmetrical cups drawn in hydroforming with constant fluid pressure [6], has furnished the minimum and maximum pressure values for a successful hydroforming. Furthermore, hydroforming process is influenced by the friction interesting the contacts. The studies have pointed out that the punch surface, especially the punch head surface, plays a fundamental role in the process. Owing to an increase in the punch head roughness, the maximum draw ratio raises almost linearly. The surface roughness of the straight side of the punch has no significant influence on the forming process. Therefore, the employment of different local constraints for the sheet, by using different roughnesses in different areas of the punch and an appropriate pressure cycle, will facilitate a successful
241
forming process. In this work, the evaluation of FEM simulation for the hydroforming process of an aeronautical
Step
oj g
Rupture / failure ar ea / "
.~%~
failure ar ~a
Punch stroke
Fig. 1. Qualitative diagram pressure-punch stroke showing different outcomes of a hydroforming process. component generated in different steps has been considered. The optimization of the production cycle has been carried out considering the output data of the FEM program. The parameters examined to optimize the process have been the rolling direction of the sheet and the maximum deformability in every forming step.
2. Production cycle of the aeronautical component The object of this study is an aeronautical component, currently produced at the Avio S.p.A. plant of Pomigliano d'Arco (Naples-Italy); the production cycle is divided into four steps: the first step carries out the symmetrical part of the component; the following three steps, instead, the asymmetrical one. Between the second and the third step and between the third and the fourth step, an annealing process for the elimination of the residual stresses is foreseen. The hydrodynamic pressure laws for every step are shown in Table 1. The sheets, before the forming process, are cut in a particular form, in order to realize the requested complex geometry, but, in this particular manufacturing process, the sheet orientation with respect to rolling direction, hasn't been considered as a process parameter. The used material is a 0.8 mm thick Inconel 718, a Ni-Cr alloy, with the following chemical composition: Ni=50 + 55%; Cr=17 + 21%; Nb=4.75 + 5.5%; Mo=2.8 + 3.3%; Ti=0.65 + 1.15%; Al=0.2 +
242
Table 1 Hydrodynamic pressure laws imposed to the four-step production cycle. Pressure (mPa) 40 45 48 55 35 39 49 20 30 36 40 28 30 35 39 40
Punch displacement (mm) 0 11.6 16.6 23.6 23.6 28.6 38.6 45.6 45.6 52.3 67.3 69.3 69.3 74.3 79.3 84.3
0.8%; the rest is Fe. The mechanical properties are as follows: elastic modulus E=208 GPa, yield stress ~v=425 MPa, anisotropy factors R0=0.33, R45= 1.13, R90=1.21 and strain hardening exponent n=0.38. In Fig.2 the photograph shows the aeronautical component to be realized; in this one some points are indicated. The quality of the process, in fact, has been determined measuring the value of the thickness in these points. In particular, the thickness reduction could not be more than 20%.
Fig. 2. The aeronautical component.
3. Reliability of the simulation model A numerical simulation has been carried out using the explicit finite element code LS-DYNA. Default input parameters are generally chosen to give efficient, accurate crash simulation results. These defaults are not necessarily optimal for metal forming simulations, therefore the program has been properly set to simulate the hydroforming process [7]. According to the physical model, all tools have been modelled using a rigid element with a four-node shell. In particular, the blank has been modelled using the four-node quadrilateral, fully integrated shell elements and an elasto-plastic material has been chosen. Run time is decreased using mass scaling and, artificially, a high tool velocity. Both these methods introduce artificial dynamic effects, which must be minimized in an engineering sense. For the steps different from the initial one, the input deck in terms of node, element, initial stress and strain information are imported by the output file of the simulation of the previous step. In fig. 3, the results of
the FE analysis are reported for the different simulated steps. The coincidence between FEM and experimental results in terms of external geometry has been verified. In particular, FEM and experimental results, in terms of thickness, for the characteristic points at the end of the fourth step, are shown in Table 2. These results confirm the reliability of the simulation model. Table 2 FEM and experimental results in terms of thickness for the characteristic points. Point
Experimental(ram)
FEM (mm)
A
0.73
0.75
B
0.72
0.70
C
0.73
0.70
D
0.74
0.73
o.~oooo,:|
Fringe
Levels
8.560e-001
Fringe L e v e l s 1.079e+OOO
8.295e-001 . . . .
9.795e,001
8.164e-001
9.300e-OOl
8.032e-001
8.804e,001
7.900e-001
8.309e-001
::
7.768e-001
7.814c-001
7.636e-001
7.318e-001
7.504e-001
5.823e-001 5.328e-001
7.372e-001 7.240e-001
_
_
Fringe L e v e l s
1.079e+000_
1.029e+000 - I 9.798e-001
5.832e-001
_
Fringe Levels
1.503e+000 1.374e+0001 1.246e+000JR 1.117e+000
8.311e-001
_i~i!i,~ t 8.596e-001I
7.615e-001
7,310e-001
7.3198-001
6.024e-001
5.824e-001
4.738e-001
9.303e-001 8.807e-gO1
5.328e-001 5.832e-001
9.883e-001
i
I
-i _
Fig. 3. FE analysis for the four-steps cycle.
243
4. Process optimization
The rolling direction and the material anisotropy haven't been considered in manufacturing process. So, the first phase for the optimization has been the determination of the best placement of the sheet with respect to the rolling direction as a function of the material anisotropy [8]. A material model has been used to account for the material anisotropy, setting the anisotropy coefficient for the different directions. So, different simulations have been conducted using the process in four steps, varying the angle between the "nose" of the sheet and the rolling direction. The results point out that basing on FLD curves analysis, the optimal value of the angle is 90 ~ (Fig.4).
CRLCS {t=0.8 n=0.38)
140
J
I
-
-40
I
-20
0
20
Minor Engineering Strain (%)
CRLCS (t=0.8 n=0.38)
140 -
i
120~' 100-
.[ ~
80r
~
40--
~
20--
60--
0---20-
L -40
-20
0
20
Minor Engineering Strain (%)
200
CRLCS (t=0.8 n=0.38}
150-
Fig. 4. Optimization of sheet cutting. E
After the FEM simulation of the four-step process, the simulation of the process in three steps has been carried out. In order to reduce the number of steps from four to three, the depth has been increased for the second and the third step, until the values for which the deformation of the elements coincided with the FLD curves. In particular, a security zone has been created, generating a curve parallel to the FLD limit one, with a lower deformation at least of 20%. So, for every simulation, the parameters have been chosen basing on a comparison between FLD curves [9] (see Figs. 5-7) obtained, respectively, by simulation and by selecting the values that ensure the largest margin with respect to failure.
244
~, ~
50-
-50
-
-40
'
-20
'
0
20
40
Minor Engineering Strain (%)
Figs. 5-7. FLD curves related to three-step simulation cycle.
The pressure cycle values that have been chosen through the FLD analysis are reported in Table 3.
gable 3 Hydrodynamic pressure laws imposed to the three-step production cycle. Step
Pressure (MPa) 35 42 45 48 35 37 40 20 34 38 35 30
The values of the pressure for the different depth have been calculated according to the control curves present in the handbook of the press. Between the first and the second step and between the second and the third step, an annealing process for the elimination of the residual stresses has been carried out. Finally, the production of the part with the set of optimum parameters has been executed. In fig. 8, the FE analysis and the experimental results for the different steps are reported. FEM and experimental results in terms of thickness for the characteristic points at the end of the three steps, are shown in Table 4. The good agreement between the real process and the simulation can be noticed, suggesting the suitability of the numerical model to simulate the hydroforming process.
Punch displacement (mm) 0 9.44 14.16 23.6 23.6 48.8 53.1 57.6 57.6 77.5 80.9 84.3
FringeLevels 8.547e-001
.......... -|
8.286e~001 ......
8.155e-001 7,8.025e-001 894e-001i 7,TG3e-O01 7.G33e~001 7.502e-001 7.372e-001 7.241 e-g01 _
Fringe Levels 1.216e+000 1.152e§
__ 1
1,087e+000 _ g .....
8.935e-00|I 8.290e-001 -I 7.644e-001 -I 6.999e-001i 6.353e-001
I
~I
5.708e-001
Frinqe Levels
,:! I
I I
Fig. 8. FE analysis and experimental results for the three-steps cycle.
245
Table 4 FEM and experimental results in terms of thickness for the characteristic points. Point
Experimental (mm)
FEM (mm)
A
0.73
0.76
B
0.79
0.80
C
0.70
0.70
D
0.70
0.70
By comparing the thickness values of experimental tests, it is possible to note that for the three steps process the thickness is less uniform than for the four steps process. This is probably due to higher values of friction coefficient in different contact areas between sheet and punch. However, the variation in thickness is very low and, particularly, it is less than 15%.
cycle. References
1]
2] 3]
4]
5]
5. Conclusions
6] By the use of FE analysis it has been possible to realize a hydroforming process optimization, reducing the production cycle from four to three steps. The obtained results have shown the possibility to determine a preliminary procedure, based on FE analysis, for the definition of the best production cycles to sensitively reduce the throughput times and the material waste, connected with the starting of a new product manufacturing. For the new optimized manufacturing cycle, the thinning turns out higher than the one obtained in four steps cycle. This is probably due to the heavier drawing condition in the single step of the optimized
246
7]
8] 9]
Chabert G. Hydroforming techniques in sheet metal industries. Fifth International Congress on Sheet Metal Work, International Council for Sheet Metal Development, (1976), 18-34. Thiruvarudchelvan S and Wang H. Investigations into the hydraulic-pressure augmented deep drawing process. J. Mater. Process. Technol. 110 (2001), 112-126. Lang L, Danckert J and Nielsen KB. Investigation into hydrodynamic deep drawing assisted by radial pressure. Part I. Experimental observations of the forming process of aluminium alloy. J. Mater. Process. Technol. 148 (2004), 119-131. Tolazzi M, Vahl M and Geiger M. Determination of friction coefficients for the finite element analysis of double sheet hydroforming with a modified cup test, Sixth International ESAFORM conference on Material Forming, Salerno, Italy, (2003), 479-482. Lang LH, Danckert J and Nielsen KB. Analysis of key parameters in sheet hydroforming combined with stretching forming and deep drawing. Proc. Instn. Mech. Engrs. Vol. 218 Part B, J. Engineering Manufacture (2004), 845-856. Thiruvarudchelvan S and Lewis W. A note on hydroforming with constant fluid pressure. J. Mater. Process. Technol. 88 (1999), 51-56. Nielsen KB, Jensen MR and Danckert J. Optimization of sheet metal forming processes by a systematic application of finite element simulations. Second European LS-DYNA User Conference, Gothenburg, Sweden (1999), 3-16. Mielnik EM. Fundamentals of elasticity and plasticity. Metalworking science and engineering (1991 ), 89-109. McCaandless J and Bahrani AS. Strain paths, limit strains and forming limit diagram. Seventh NAMRC, Berkeley, California (1979), 184-190.
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Optimization of friction stir welds of aluminium alloys A. Squillace a, T. Segreto a, U. Prisco a, R. Teti a, G. Campanile b a Dept. of Materials & Production Engineering, University of Naples Federico II, P.le Tecchio 80, Naples, Italy b Alenia Aeronautica, Metallic Materials Technology, V.le dell'Aeronautica, Pomigliano D 'Arco, Naples, Italy
Abstract
The effect of rotating and welding speed on the mechanical properties of AA6056 joints made by Friction Stir Welding are investigated. Different welds with rotating speed of 1000 and 1600 RPM and travel speeds of 230, 325 and 460 mm/min were produced. An ultrasonic (UT) non-destructive testing (NDT) procedure was applied to characterize the presence of possible weld defects prior to mechanical destructive testing. The joint mechanical properties were evaluated by means of static tensile tests and fatigue tests. Both static and fatigue properties show an appreciable improvement as both travel and rotating speed increase. However, in the case of maximum travel speed and maximum rotating speed, the welded specimens may display weldment defects that reduce the evaluated fatigue limit for such processing conditions. The correlation between process parameters and weldment static properties was carried out through an analysis of variance (ANOVA) of the data. Keywords: Friction stir welding, Mechanical properties, Ultrasonic NDT, ANOVA
1. Introduction
Fusion welding of A1 alloys has always been a challenge for designers and technicians due to the characteristics of this family of materials, such as high thermal and electric conductivity, high reflectivity, high coefficient of thermal expansion and, above all, presence of a tenacious oxide layer with a melting temperature higher than the material' s one [1 ]. When attention is focused on heat treatable A1 alloys, problems arise due to the need to control the material thermal history that can radically affect its properties. Some 15 years ago, at The Welding Institute of Cambridge, UK, a new welding process was developed, Friction Stir Welding (FSW), which soon appeared to be very attractive for the joining of AI alloys, allowing for the solution of most ofthe above problems [2]. In FSW technology, the heat is
provided by friction generated by the rotation of a tool immersed in the part to be joined. This heat allows for material softening, dramatically reducing its strength, but is not enough to bring the material to fusion; thus, FSW is a solid state welding process. Research programs have been conducted worldwide allowing to emphasize the benefits achievable by FSW, not exclusively with A1 alloys, and industrial applications are growing yearly in almost all industrial sectors. Mechanical properties of FSW and the influence of process parameters such as rotational speed, travel speed and tool shape have been extensively investigated. Due to the impulse provided by the aerospace industry, in most research activities attention was focused on heat treatable alloys, the most common for their superior general properties. These research activities have often demonstrated the achievement of a mechanical joint efficiency close to 100% by properly selecting
247
process parameters, tool shape and post-weld treatments [3 - 6]. Despite the evident advantages, FSW is still a critical process as defects can easily occur in FSWjoints when process parameters are not optimally selected, resulting in a critical drop of the weld mechanical properties. In this work, an experimental investigation, by means of destructive and non-destructive weld characterisation, is presented for a heat treatable A1 alloy for aeronautical applications. Several joints obtained by varying rotational and travel speeds were characterized by static and fatigue tensile tests and by ultrasonic (UT) non-destructive testing (NDT). Experimental results were statistically processed by Analysis of Variance (ANOVA) methods to asses the influence of process parameters on weld quality.
2. Materials and Experimental Procedures
An A1 alloy AA 6056 T4 is considered, with good mechanical and corrosion resistance properties and, above all, easy to weld. Its yield and ultimate tensile strengths are 285 and 320 MPa. Rolled plates with thickness 4 mm, length 200 mm and width of 100 mm were FSW in butt joint geometry with weld bead parallel to the rolling direction. The welding tool has a threaded truncated cone shape probe and a smooth shoulder. The 3.9 mm high probe is removable from the rest of the tool. Three welding travel speeds (230, 325 and 460 mm/min) and two rotational speeds (1000 and 1600 RPM) were used, while tool tilt angle was kept constant at 3 ~. Three joints were made for each set of process parameters. For all joints, welding forces along the X and Z directions were measured by a Kistler dynamometer. The X direction coincides with welding travel speed and plate rolling direction. The Z direction coincides with tool initial penetration and is perpendicular to the plate surface. All joints were subjected to a post welding heat treatment, leading the material to the T78 condition to improve the mechanical and electrochemical characteristics and, in particular, the intergranular corrosion resistance. An ultrasonic (UT) non-destructive testing (NDT) procedure was applied to characterize the presence and geometry of possible weld defects prior to mechanical destructive testing. A Panametrics Model 9100 digital UT instrumentation and a 2 MHz angle beam probe (refraction angle ~ = 70 ~ was used for UT contact testing of welded samples (transverse UT velocity 3100 m/s and signal amplification 69
248
dB). The UT beam scanning path, d, for inspecting the whole weld joint section [7] was calculated on the basis of the welded specimen thickness, s = 4 mm, and the UT probe refraction angle, ~ = 70~ d = s- tan o~ = 4ram x tan 70 ~ = 1 lrnm
(1)
In general, the scanning path extremes are set at distances d and 2d from the weld axis to minimize UT signal attenuation (see Fig. 1). In this case, however, because of the welded specimen"dog bone" geometry and transducer size, the scanning path was moved back to an optimal position defined by distances 7d and 8d from the weld axis (7d = 77 mm and 8d = 88 ram). UT waveforms were detected and visually analyzed for each 1 mm step of the scanning path. The mechanical properties of the joints were evaluated by means of static tensile and fatigue tests at room temperature. Specimens preparation and testing procedures were conducted according to the ASTM-E8 and ASTM-466 standards for static tensile and fatigue tests, respectively. Fatigue properties of the welds were evaluated by using a mechanical testing machine under constant loading control at 20 Hz sine wave loading. Fatigue tests were conducted in axial total stress-amplitude control mode with R = G m i n / ~ m a x = 0 . 1 , for all the travel and rotating speeds utilised. Static tensile test data were statistically analyzed by means of Analysis of Variance (ANOVA) with the aim to evaluate the influence of process parameters on the joints static properties.
3. Results and discussions
3.1. Weldingforces For all welds, the highest force values (see Fig. 2) are in the Z direction during tool penetration: here, the material is still cold and its strength is high. 2d ,_
d
-]
weldment
Fig. 1. Scheme of the UT testing procedure. d = scanning path, s = specimen thickness, a = refraction angle.
400
9 8
.
.
.
.
.
.
.
[] 1000 RPM [] 1600 RPM
.
300 6
238
5
251
244
257
w 200 3
>.
2
100
1
0
0
I,
30 40 50 Time Is] Fig. 2. X and Z direction welding forces vs. time.
7.5
l0
20
230
325 460 Travel speed [mm/min] Fig. 5. Weld YS versus travel speed. 400
F-11000 RPM m 1600 RPM
[] 1000 RPM [] 1600 RPM
300
271
271
271
275
~' 5 x
200
r 9 ,..., ~D
3.22
3.2
2.5
F-
100
9
I
i
460 325 Travel speed [mm/min] Fig. 3. Average X direction force vs. travel speed. 230
7.5 [D 1000RPM
z
7.3
7.34
"7 1 A
5
N o (D r
230
325 460 Travel speed [mm/min] Fig. 6. Weld UTS versus travel speed. Once the tool is penetrated, it moves at travel speed and the X and Z forces tend to constant values; their mean values, averaged over 3 replicates under steady conditions with same welding parameters, are shown in Figures 3 and 4. For any travel speed, the forces increase with rotational and travel speed, except for the Z force with highest rotational (1600 RPM) and travel speed (460 mm/min). 3.2. Static tensile tests
2.5
9
230
325 460 Travel speed [mm/min]
Fig. 4. Average Z direction force vs. travel speed.
Figures 5 and 6 report the static tensile test results. Yield strength (YS) increases with both travel and rotational speeds, with an efficiency as high as 90%. Ultimate tensile strength (UTS) has a similar behaviour, except for rotational speed 1000 RPM and travel speed 325 mm/min, with an efficiency of 86%. Visual examinations showed that fracture always occurred in the weld bead: at the nugget centre or at
249
the boundary of the thermo-mechanical affected zone, on either the retreating or the advancing tool side. Some specimens showed a through-the-width tubular tunnel defect parallel to the travel direction. It is worth noting that weld defects did not significantly influence the joint static mechanical properties.
3.3. Fatigue testing In Figure 7, a fatigue strength improvement with increasing travel speed is noted for 1000 RPM. Similar results were expected for 1600 RPM together with a fatigue strength improvement based on static results. Figure 7 shows that the fatigue strength for 1600 RPM is higher than for 1000 RPM. However, when travel speed is 460 mm/min, the fatigue strength is notably lower than for 230 and 325 mm/min and, for a number of cycles > 7 * 104, even lower than for the 460 m m / m i n - 1000 RPM process condition. This may be due to the onset of defects in some welds with the highest travel and rotational speeds.
a 99.46 % significance level at which the null hypothesis can be rejected, compared with the 96.10% of a. On the contrary, the null hypothesis cannot be rejected for UTS: at a significance level of = 10 %, both co and a have no statistical effect on the UTS of the welded joints. From the A N O V A table, it can be inferred that there is no interaction effects of the two process parameters on the YS and the UTS of the joints. The physical meaning of these conclusions suggests that YS is strongly influenced by both process parameters, co and a, especially by the former. On the contrary, UTS is only slightly affected by process parameters variations. Finally, no effect of process parameters interaction on the final strength of the tested joints was verified. 180
,
!
I
9" 145 -
\
~
~ ,6 ,r
ta.,
4% 5
3.4. ANO VA of static tensile test results To gain a better insight into the static tensile test results, an Analysis of Variance (ANOVA) of the experimental data was performed. ANOVA [8] is a useful tool to identify sources of variability from one or more potential sources, sometimes referred to as "factors." This method is widely used in industry to help identify if variations in measured outputs are due to variability between diverse fabrication processes, or within them. By varying factors in a predetermined pattern and analyzing the output, statistical techniques can be used for an accurate assessment as to the cause of process variation. In this study, the measured outputs, i.e. dependent variables, are YS and UTS. Two factors were taken into account: rotating speed, co, at 2 levels (1000, 1600 RPM) and travel speed, a, at 3 levels (230, 325,460 mm/min). A complete 2factor factorial experiment, with 3 replicates, was performed. In this case, possible interactions between co and a can be accounted for. The static test results are reported in Table 1. The main A N O V A parameters for YS and UTS are in Tables 2 and 3. From the analysis of the YS data variance, it can be concluded that, at a significance level of ~ = 10 %, the null hypothesis (stating that both co and a have no effect on YS) must be rejected and that there is a difference in the treatment levels. Furthermore, co seems to have a larger influence on YS than a, giving
250
I
iI " , -
75 ! i
I
40
1 .E+03
1 .E+04
1 .E+05
1 .E+06
n ~ of cycles Fig. 7. Weld fatigue resistance: 1 m (230 mm/min 1000 RPM); 2 - - (325 mm/min - 1000 RPM); 3 - - (460 mm/min - 1000 RPM); 4 - - - (230 mm/min - 1600 RPM); 5 - - - (325 mm/min - 1600 RPM); 6 - - - (460 mm/min - 1600 RPM). Table 1 YS and UTS data
230 Mm/min 325 Mm/min 460 Mm/min
YS [MPa] 1000 1600 RPM RPM 237 216 227 235 243 220 214 263 237 235 256 211 250 269 232 246 271 237
UTS [MPa] 1000 1600 RPM RPM 269 252 263 261 279 257 293 231 236 251 285 213 293 279 235 269 297 264
Table 2 ANOVA table of YS data
DF SumOfSq MeanSq co 1 1881.50 1881.50 a 2 1417.70 708.85 a - co 2 255.20 127.60 error 12 1 9 6 8 . 6 6 164.05 total 17 5523.07
<
80 I
FRatio PValue 1 1 . 4 6 0.0054 4.32 0.039 0.77 0.48
Table 3 ANOVA table of UTS data
DF SumOfSq MeanS0 FRatio PValue co 1 3.40 107 3.41 113' 0.987 0.340 a 2 6.92 107 3.46 107 1 . 0 0 1 0.396 a - co 2 6.93 107 3.46 107 1 . 0 0 2 0.396 error 12 4.15 108 3.45 10 7 total 17 5.87 108
3.5. Ultrasonic testing Static and fatigue weld specimens were UT NDT inspected through the angle beam technique (see Sect. 2). In Figures 8 and 9, complete UT waveforms for a sound and a defective weld are reported for the 7d and 8d UT probe positions (UT scanning path limits). The dark grey UT waveform for the sound weld shows a high back echo, corresponding to the specimen limit, but no intermediate echo at weld bead location is evidenced (no defect in the weld). The light grey UT waveform for the defective weld shows a lower back echo and a significant intermediate echo, corresponding to the tunnel defect in the weld. This UT response was verified for all 1 mm step UT probe positions in the scanning path. For brevity reasons, only the initial (7d) and final (8d) probe position UT waveforms are shown (see Figs. 8 and 9). The UT NDT procedure can identify the tunnel defects in the weld bead in a very evident way. This is due to the large size of the through-the-width tubular tunnel defect, presenting to the UT beam a defect area of 1 mm (sample thickness) by 10 mm (sample width) orthogonal to the UT beam, as verified by micro-graphic examinations. In Tables 4 and 5, the results of the UT NDT for static and fatigue test specimens are reported in terms of weld defectivity classification. From Tables 1 and 4, it can be noted that the presence of weld defects, in the form of a tunnel within the weld nugget parallel to the travel speed direction, does not influence in any significant way the weld YS and UTS verified through static tensile tests.
back echo
weld
40
,,defect echo
-40 -80 O
)b
110
i74
t'rl Z52
I
I
290
Fig. 8. Complete UT waveforms for a sound weld (dark grey) and a defective weld (light grey). Scanning UT probe position: 7d. The defective weld UT waveform is offset by-50 dB to facilitate the visual examination. weld
80
bead ,i~ il
40
back ech~
i~ ecn~ ,~~|Z'~eh~
-40 -80 I U
'
I 35
,~
I 11 b
1 /4
23Z
i
I
I
Z~4O
Fig. 9. Complete UT waveforms for a sound weld (dark grey) and a defective weld (light grey). Scanning UT probe position: 8d. The defective weld UT waveform is offset by-50 dB to facilitate the visual examination. Table 4 UT NDT results for static tensile test specimens. Static Number Travel Rotational Weld Tensile of Speed Speed Defectivity Specimen samples (mm/min) (RPM) S1 3 230 1000 Low $2 3 325 1000 Medium $3 3 460 1000 High $4 3 230 1600 Low $5 3 325 1600 Medium $6 3 460 1600 High This indicates that static tensile tests are not able to characterise the weld integrity and may lead to the acceptance of weldments carrying tunnel defects.
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Table 5 UT NDT results for fatigue tensile test specimens Fatigue Number Travel Rotational Weld Tensile of Speed Speed Defectivity Specimen samples (mm/min) (RPM) F1 10 230 1000 Low F2 5 325 1000 Medium F3 12 460 1000 Low F4 11 230 1600 Medium F5 8 325 1600 Medium F6 10 460 1600 High From Table 5, it can be seen that fatigue test specimens display different defectivity levels according to process parameters, classified as generating low ( 0 - 30%), medium (30% - 60%), and high (> 60%) defectivity weldments. Figure 7 shows that, by increasing the rotational speed from 1000 to 1600 RPM, fatigue life increases for travel speeds 230 and 325 mm/min where joint defectivity is Low or Medium (see Table 5). For travel speed 460 mm/min, the same behaviour is not verified as the fatigue life of 460 m m / m i n - 1600 RPM welds is lower than the one of 460 m m / m i n 1000 RPM welds when the number of cycles is > 7 * 104. This can be due to the 460 m m / m i n - 1000 RPM weld low defectivity versus the 460 mm/min - 1600 RPM weld high defectivity, notably affected by weld tunnel defects (see Table 5). This not only reduces the 460 m m / m i n - 1600 RPM weld fatigue life versus the 460 m m / m i n - 1000 RPM welds, but also, and in a significant way, versus the lower travel speeds welds (230 and 320 mm/min, 1600 RPM)(see Fig. 7). Fatigue testing demonstrated that tunnel defects are not acceptable as they critically reduce the weld fatigue life. Fatigue testing, however, is expensive and time consuming and, in fact, is used for material characterization at laboratory level but, in general, not for industrial routine quality control. UT NDT, on the other hand, can be extensively employed for 100% quality control, due to its non-destructive nature and ease of application, to obtain a direct correlation between weld UT response and fatigue behaviour.
process parameters on joint yield strength. The effect of process parameters on joint ultimate strength is small or hardly noticeable. Weld defects do not influence the joint static mechanical properties. This indicates that static tensile tests are not able to characterize weld integrity and may lead to acceptance of the welds carrying defects. Fatigue testing demonstrated that weld tunnel defects are not acceptable because they dramatically reduce the weld fatigue life. The UT NDT procedure can identify weld tunnel defects in a very efficient way. By considering that fatigue testing is expensive and time consuming and, in general, not used for industrial routine quality control, UT NDT can be extensively employed for 100% quality control, due to its nondestructive nature and ease of application, to obtain a correlation between weld UT response and fatigue behaviour.
Acknowledgements This research work was carried out with support from the EC FP6 NoE on Innovative Productions Machines and S y s t e m s I'PROMS. Alenia Aeronautica is thanked for providing A1 alloy plates and partly contributing in the experimental procedure.
References [1] [2] [3] [4] [5]
[6]
4. Conclusions Welding forces measured under steady conditions increase with both travel and rotating speeds. X direction forces are significantly lower than Z direction forces, for all set of process parameters. The ANOVA approach suggests a strong influence of
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[7] [8]
ASM Metals Handbook, (1993). Welding, Brazing and Soldering, Vol. 6, 10th edition Dawes, C. J., (1995), An Introduction to FSW and its Development, Welding & Metal Fabricat., 63:13-16 Lockwood, W.D.,Tomaz, B.,Reynolds, A.P.(2002). Mechanical response of FSW AA 2024: experiment and modeling, Mat. Sci. & Eng., A 323:348-353 Biallas, G., Dalle Donne, C., Juric, C. (2000). Monotonic and cyclic strength of FSW A1 joints, Euromat 2000, Elsevier: 115-120 Heinz, B., Skrotzki, B., Eggeler, G. (2000). Microstructural and Mechanical Characterization of a FSW A1 Alloy, Mat. Sci. Forum, 331/337:1757-1762 Juricic, C., Dalle Donne, C., DreBler, U. (2001), Effect of Heat Treatments on Mechanical Properties of FSW 6013, 3rd Int. Symp. on Friction Stir Welding, Kobe, 27-28 Sept. Metals Handbook, (1989), Non Destructive Evaluation and Quality Control, Vol. 17, 9th edition. Montgomery, D.C., Wiley, J., (1996). Introduction to statistical quality control, New York.
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All rights reserved.
Personalised Ankle-Foot Orthoses design based on Reverse Engineering S . M . M i l u s h e v a ~ E . Y T o s h e v a ~ L.C H i e u 2, L . V K o u z m a n o v ~ N Z l a t o v 2 Y . E . T o s h e v ~
/Institute of Mechanics and Biomechanics, Bulgarian Academy of Sciences, Sofia, Bulgaria : Manufacturing Engineering Centre, Cardiff University, Wales, UK
Abstract
Drop Foot (DF) and Foot Drop is an interchangeable term that describes an abnormal neuromuscular disorder that affects the patient's ability to raise their foot at the ankle. Ankle-Foot Orthoses (AFOs) are devices intended to assist orto restore the motions of the ankle-foot complex. In this paper, personalised AFO development which is based on 3D models of the patient' s ankle-foot complex is introduced. Methods of reconstructing 3D models ofthe ankle-foot based on Reverse Engineering were fully investigated from which the new personalised AFOs were proposed. These AFOs were designed to assist the ankle flexion-extension for DF patients. Keywords: Reverse engineering, CAD/CAM, Orthoses, Rapid Prototyping
1. INTRODUCTION Drop Foot (DF) and Foot Drop is an interchangeable term that describes an abnormal neuromuscular disorder that affects the patient's abilityto raise their foot at the ankle There are two common complications from Drop Foot as follows: (i) The patient cannot control the falling of their foot after heel strike, so that it slaps the ground on every step; and (ii) The inability to clear their toe during swing, this causes the patient to drag their toe on the ground throughout swing. The DF treatment type is dependent on the underlying cause, and possible DF treatments include medicinal, orthotic, and surgical. Orthotic treatment is the most commonly used for DF, in which patients may be fitted with AFO, brace, or splint that fits into the shoe to stabilize the ankle (or foot) to restore normal motion
or to constrain and inhibit abnormal motion of the anklefoot (AF) complex. In general, foot orthoses fall into three broad categories: foot function adjustment, foot protection, and those that combine functional control and protection. The functional criteria of the orthotics design 2 are (i) Maintain ankle/foot in optimal alignment (stabilization); (ii) Increase medio-lateral stability of ankle/foot complex; (iii) Provide a functional ankle range of motion, whether it be free motion or limited motion; (iv) keep the weight and amount of material of AFO to a minimum. Since there are highly technical and clinical requirements have to be fulfilled for treatment of a wide range of AF problems, especially for DF patients. Commercially available conventional AFO based on the standard components and sizes show the limitation of functions, as well as flexibility and adjustment to be optimally fitted to specific patient anatomy. Personalised (custom-made) design of AFOs based on 3D models of
253
patient's AF complex and state-of-the-art design and manufacturing technologies would be a good approach to solve these drawbacks and problems. Reverse engineering (RE) 3 is generally defined as a process of analysing an object or existing system (hardware and software) to identify its components and their interrelationships, and investigate how it works in order to redesign or produce a copy without access to the design from which it was originally produced. In areas related to 3D graphics and modelling, RE technology is used for reconstructing 3D models of an object in different geometrical formats. In this paper, different approaches for 3D modelling of the AF complex based on RE data are investigated and emphasised, from which new Personalised AFOs for DF patients are introduced.
2.1 AF complex modeling from laser scanning data The output of the RE data acquisition step using Laser Scanner is point clouds (Fig.l). There are two methods for capturing the external surface of the AF complex: direct and indirect scanning. In indirect scanning, first of all, a wax print model of the AF complex is prototyped. Fig. 2 (a, c) shows the process of making a silicon mold (a) which is then used for fabricating the wax print model of an AF complex (c). Finally, this wax print model is used as a prototype for laser scanning to collect point clouds (Fig.2 (d)) that define geometry of the AF complex.
Patients
2. METHODS RE hardware is used for RE data acquisition, which in the case of 3D modelling is the collection of geometrical data that represent a physical object. There are three main technologies for RE data acquisition: Contact, Non-Contact, and Destructive 3. Outputs of the RE data acquisition process are 2D cross-sectional images and point clouds that define the geometry of an object. RE systems that use transitive techniques such as CT and MRI provide a large series of 2D cross-sectional images of an object. Systems, which use the remaining RE techniques such as Laser Triangulation, Time-OfFlight (TOF), and Structured Light, provide point cloud data. In this study, CT and laser scanner were used as RE data acquisition hardware for 3D AF modeling. MIMICS and Magics RP (Materialise NV) were used for processing CT images and triangle mesh data manipulations. CopyCAD & PowerShape (Delcam Inc.), Pro Engineer (PTC) were used for points and triangle mesh data manipulations as well as geometrical modelling processes. Selective Laser Sintering (SLS) 10 was used as the Rapid Prototyping (RP) technique for fabrication of the AFO prototypes. There are four main steps to design personalised AFOs as follows: (i) Data acquisition; (ii) Data registration, processing and region growing; (iii) Construction of3D NURB AF complex model; and (iv) AFOs development. Figure 1 presents a flowchart from RE data acquisition to 3D Computer Aided Design (CAD) of AFOs.
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RE Data acquisition
RE Point Clouds
CT/MRI images
Data Registration, Processing and RegionGrowing
Triangle Mesh Models
NURBSAF complex models
3D CAD of AFOs
Fig. 1: A flowchart from RE data acquisition to 3D CAD of AFOs. In direct scanning, RE data acquisition is done by directly scanning the AF complex of a patient without using a wax print model. Figure 2(b) shows a direct scanning process using Hymac laser scanner. Although the scanner allows scanning of an object from different angles with certain provided degrees of
freedom, to capture the entire geometry of the AF complex, multiple scans are required. Since using different scan setups, the point cloud from one series of scans is not accurately oriented with respect to the point cloud from another series. Data registration is then used to align and merge these point clouds so that all point clouds in the series are arranged in their proper orientation relative to one another in a common coordinate system. Finally, the complete point cloud that defines geometry of the AF complex is obtained (Fig.2 (d)). After optimising the point cloud data, the triangle mesh model of the AF complex is created .This triangle mesh model is then used as a guide for constructing NURBS models of the AF complex (Fig.2(e)), which is imported into CAD packages for personalised AFO design.
segmented. The segmentation process is based on the grey-level value of image pixels. The object can be defined using one or two thresholds. In the former case, the segmented object will contain all pixels in the images with a grey level value higher (or lower) than or equal to the threshold. In the later case, the pixel grey-level value must be between both thresholds to be part of the segmented object. In order to split the segmentation into separate objects, a region growing technique is used. The information from segmentation and region growing processes is used to reconstruct 3D models of ROI from CT images. Figure 3 presents 3D models of external shapes and bone structures of the AF complex for the left and right leg of a patient; they were constructed from CT images based region growing and thresholding techniques.
(d)
i=i i Fig. 2: (a): Prototyping a silicon mold for making a wax print model of the AF complex. (b): Direct laser scanning of the AF complex using Hymac laser scanner. (c): A wax print model of the AF complex. (d): Point cloud data produced by laser scanning. (e) 3 D NURBS model of the AF complex.
2.2 AF complex modeling from CT scanning data CT images ofthe AF complex in the DICOM format 4 are used as the input for image processing and 3D reconstruction. Segmentation by thresholding techniques is used to define a region of interest (ROI) that presents the object for 3D reconstruction, and in this case, bone structure and external shape of the AF complex are
Fig.3: 3D models of the bone structure (c) and external shape (d) of the AF complex for a right (a) and left (b) leg. Finally, 3 D triangle mesh models of the AF complex generated from a medical image processing (MIP) package are imported into RE soitware for constructing
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NURBS models. 3. RESULTS AND DISCUSSION 3D NURBS models of the AF complex were successfully reconstructed using non-contact RE techniques (laser and CT scanners) (Fig.2 (e) and Fig.3 (d)).
to keep the foot not moving during the scanning process. Some times it is very difficult to assure the accuracy of the acquisition data.
............~ 9
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Fig.4" (a) 3D NURBS models ofthe hand constructed by RE. (b): Personalised AFO prototype fabricated by SLS technique. Based on 3D NURBS models of the AF complex, different models ofpersonalised AFOs were developed. Figure 4(b) shows the new personalised AFO which was fabricated by Selective Laser Sintering (SLS) technique. Another new Personalised AFO is presented in Fig.5; it was designed into two components: lower (foot) and upper (calf). Different types of elastic elements that connect these two AFO components were also investigated. A new proposed AFO uses two waveshaped elastic elements (lateral and medial) (Fig.5 (c)) and one flat-shaped spring support element with one degree of freedom (Fig.5 (a)). The lock mechanism for connecting elastic wave-shaped elements to lower and upper AFO components are shown in Fig.5 (b). Of all three data acquisition methods, CT scanning gives the most accurate data and more information can be obtained, especially for preoperative planning and implant design 5, 7, 9. In addition, the scanning time is quickest and it is very convenient for the patient. The scanning data can be sent to the AFO design team via Internet. However, it is also the most expensive solution. Since different scanning angles are used to complete the scanning of an entire geometry, when applying a direct scanning method, special fixtures need to be used
256
(c) Fig. 5: (a): Flat-shaped spring support element with one degree of freedom (shown on right). (b): A lock mechanism for the proposed elastic wave-shaped elements. (c): The personalised AFO with lower and upper components, which are connected by two elastic wave-shaped elements (lateral and medial). Although more steps are needed to prepare a wax print model, an indirect scanning method is cheap and easy to implement. The accuracy is less than the one of using CT scanning due to errors introduced in rapid tooling processes. However, the accuracy of being around 0.2 to 0.5 mm is acceptable for orthotic applications; and it is much more stable compared to direct scanning. A commercially prefabricated (off-the-shelf) AFO is normally manufactured in quantity without a specific patient in mind. This prefabricated orthosis may be trimmed, bent, molded (with or without heat), or otherwise modified for use by a specific patient. It does not always meet technical and clinical requirements in
DF treatments. In addition, although personalised fabricated orthotics can be manually made for a specific patient, it not only requires high skills in tooling techniques but also lack the accuracy and flexibility in design as well as AFO shapes and types. By applying advanced design and manufacturing technologies (RE, CAD/CAM, RP) personalised orthotics can be made to fit exactly the patient anatomy. The design is virtually simulated, optimised, and prototyped by RP techniques before it is transferred to the production; therefore clinical and technical requirements for the AFOs are completely met and controlled; and finally treatment quality fbr the patient is improved.
4. C O N C L U S I O N We presented the methods of constructing 3D models of the AF for personalised AFO development in which RE techniques using direct and indirect as well as CT scanning were investigated. The presented methods were not only applied for AFO development, but also successfully used for modeling 3 D internal and external anatomical structures (Fig.4 (b)) for design and manufacturing of orthoses, medical devices, implants, and surgical aid tools 5-9 Personalised design is an optimal solution to meet technical, clinical, and cosmetic requirements in AFO development, especially it allows obtaining complete fitting to patient anatomy and optimization in the design and material selection. Finally, based on 3D models ofthe AF complex that is constructed by RE techniques, new personalised AFO was developed. It was designed into two components that are connected by elastic elements to help lift the foot. Virtual simulation in dynamics and kinematics as well as Finite Element Analysis method were applied to optimise the design; and it was fabricated by SLS technique.
[2] Steven H. Carbon Fiber Articulated AFO- An Alternative Design. Journal of Prothetics and Orthotics (1989) 1 (4): 191-198. [3] Pham D.T and Hieu L.C. Reverse Engineering Applications and Methods. In: Reverse Engineering: An Industrial Perspective by Vinesh Raja, Springer-Verlag London LtdPublishers, in press. [4] DICOM standard: http://medical.nema.org [5] Hieu L.C, Vander Sloten J, Bohez E., Khanh L, Birth P.H, Oris P,Toshev Y and Zlatov N. Medical Rapid Prototyping: Applications and Methods. Journal of Assembly Automation (2005) 25 (4): 284-292. [6] Y.E. Toshev, L.C.Hieu, L.P. Stefanova, E.Y. Tosheva, N.B. Zlatov, St. Dimov, Reverse Engineering and Rapid Prototyping for New Orthotic Devices. In: Pham, D.T., Eldukhri, E.E., Soroka, A.J. (Eds.) "Intelligent Production Machines and Systems", Elsevier (2005): 567-571. [7] Stefanova L, Milusheva S, Zlatov N, Yaiar R. and Toshev Y. Computer Modeling of Ankle-Foot Orthoses using CAD Models of the Human Body. Proceedings of the International Conference on Bionics and Biomechanics,Vama (2004): 285-289. [8] Hieu LC, Bohez E, Vander-Sloten J, Oris P, Phien HN, Vatcharapom E and Binh PH. Design and manufacturing of Cranioplasty Implants by 3-axis CNC Milling. Technology and Heakh Care (2000) 8: 85-93. [9] Hieu L.C, Bohez E., Vander Sloten J., Phien H.N., Vatcharapom E., Binh P.H, and Oris P. Design for Medical Rapid Prototyping of Cranioplasty Implants. Rapid Prototyping Journal (2003) 9 (3): 175-186 [10] Pham D.T and Dimov S.S. Rapid Manufacturing, Springer-Varlag London, New York and Heidelberg Publishers (2001).
Acknowledgements The study was supported by Royal Society, UK and the Bulgarian National Research Fund (Grant 14 0 7/04).
References [ 1] Spine Universe: www.spineuniverse.com
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Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Quality evaluation of thermoplastic composite material single-lap joints I.L. Baciu a, I. Crivelli Visconti a, A. Langella a, V. Luprano b, R. Teti a a Dept. of Materials & Production Engineering, University of Naples Federico II, P.le Tecchio 80, Naples, Italy bENEA Research Centre Brindisi, S.S. 7 krn. 713.7, 72100 Brindisi, Italy
Abstract
The joining of glass fibre reinforced polymer (GFRP) matrix composite materials by structural bonding still presents difficulties due to the adopted bonding process, the interface properties and the control of the adhesive thickness. In this paper, the first results of a study on the characterisation of single lap bonded joints made of glass fibre reinforced propylene thermoplastic matrix composite laminates are presented. Two different adhesive types commercially available for thermoplastic polymers have been utilized. The considered factors have been the joint static strength and the joint quality evaluated through advanced ultrasonic nondestructive evaluation. Keywords:
Composite Materials, Ultrasonic Nondestructive Evaluation, Structural bonding
1. Introduction
Polymer matrix composite materials present a large number of applications in various industrial fields where high mechanical properties and low specific weight are requested. The most common composite materials for structural and non-structural applications utilize a thermoset matrix such as epoxy, polyester, etc. Composite materials with a thermoplastic matrix are less utilised because of not yet solved problems regarding their fabrication technology and specific behaviour aspects (e.g. high temperature resistance) [ 1] The collaborative research activities between the Department of Materials and Production Engineering of the University of Naples Federico I! and the ENEA Research Centre of Brindisi focus on the application of thermoplastic matrix composite materials in the fabrication of parts for the rail transportation industry. In particular, the quality evaluation of bonded joints in terms of mechanical strength and structural integrity was carried out to verify the possibility to employ commercial adhesive types to realise bonded
258
joints between panels made of thermoplastic composite materials for internal furnishing elements of rail carriages. The mechanical resistance of a bonded joint depends on various factors [2]: - bonding surface; - thickness of the adhesive; - properties of the adhesive; - adhesion of the bond on the two joining surfaces; - elastic properties of the joint. In this work, glass fibre reinforced propylene (GFRP) matrix composite panels were utilised. Like other thermoplastic resins, this matrix material presents an insufficient chemical affinity with most of the available commercial adhesives [ 1]. Accordingly, the joint geometrical parameters have been purposely set up and two adhesive types were selected as those presenting the best requirements for these composite materials. The joint quality was assessed by destructive tensile testing and ultrasonic (UT) nondestructive evaluation (NDE) [3] on single-lap bonded joints.
2. Materials and experimental work
I00
2.1 Materials and mechanical testing
Glass fibre reinforced thermoplastic polypropylene resin (commingled TWINTEX cloth) panels with weft-warp configuration 00/90 ~ for an average thickness of 2.5 mm were used to obtain the experimental samples. Panels were fabricated by hot press tbrming at the CETMA Laboratories, Brindisi, Italy. For the mechanical characterisation of the adhesives, the ASTM D 5868 standard was considered. This standard provides for the single-lap sample scheme reported in Fig. 1. The single-lap samples were cut from the panels according to the scheme shown in Fig. 2. Two series of samples were fabricated: l Stseries- obtained by sample cutting with a diamond coated blade; 2 n~ series- obtained by sample cutting through an abrasive water-jet process. The 2 "d series samples were prepared because the traditional diamond saw cutting can easily damage the thermoplastic matrix due to local temperature increase, even when using a refrigerating fluid. As a matter of fact, the abrasive water-jet cutting process does not produce a temperature increase in the cutting area. Two commercial structural acrylic bi-component adhesives were considered: 3M Scotch-Weld type 8005 (lower work life: 3 min) 3M Scotch-Weld type 8010 (higher work life: 12 min) A suitable manual applicator EPX type was used to obtain the correct components mixture (ten parts by volume of base material and one part of catalytic material). The theoretical bonding area (see Fig. 1) is 25.4 mm x 25.4 mm, with adhesive thickness varying between 0.25 mm and 0.30 m m . The adhesive layer thickness variation is the consequence of the non uniform thickness of the composite panels and the bonding procedure carried out by sandwiching a distancing insert amid the two glass plates between which the samples to be joined are positioned. During bonding, some differences due to adhesive properties were tbund. Both adhesives require a time period after mixing the two components. Technical specifications indicate that for the 8005 adhesive the time of application must be lower than 2.5 - 3 minutes while for the 8010 adhesive type the application must occur before 1 0 - 12 minutes. Beyond application time, the adhesive assumes a gelatinous consistency with a high viscosity that makes the adhesive unusable.
100
Fig. 1. Geometry and dimensions of single-lap joint 100
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270 340 Fig. 2. GFRP panels and cutting scheme Bonding tests allowed to verify the application time suggested in the technical documentation for each adhesive type. The nominal application times (adhesive work life) resulted overrated and their actual values are ~ 50% of the suggested times. In the 1 st series samples, the bonding was obtained with the adhesive at room temperature (~ 22~ To increase the adhesive application time for the 2 nd series samples and have available enough time to realise a perfect alignment between panels, adhesive cartridge and panels were kept in a low temperature environment (a common refrigerator was used). Moreover, for both adhesive types, a 24 h curing time at 22~ was applied before mechanical testing. Reinforcement tabs were bonded on the single-lap sample extremities (see Fig. 1) for sample alignment during tensile testing on a MTS Alliance RT/50 electro mechanical machine with a 50 kN load cell. The tensile tests were carried out under displacement control and at a constant transverse velocity of 5 mm/min.
259
2.2 Ultrasonic testing Before mechanical testing, all single-lap joint samples were subjected to a volumetric UT scanning procedure (FV-Scan) using the custom made software package Robotest 9 [4]. Pulse-echo volumetric UT scans were carried out with focused, high frequency (5 or 10 MHz) immersion transducers with focal distance 50 or 100 mm. In this work, the 100 mm (50 mm) nominal focal length UT probe was set at 95.7 mm (45.7 mm) from the single-lap joint sample front surface to focus the UT beam at mid-joint. Water and GFRP material UT speeds were 1483 m/s and 2562 m/s, respectively. The hardware configuration of the UT NDE system (see Fig. 3) is made of a number of functional elements: (a) oscillator/detector, generating the voltage pulses; (b) transmitter/receiver UT probe; (c) digital oscilloscope for acquisition, visualization and digitalization of UT pulses; (d) PC for UT data processing and mechanical displacement control; (e) mechanical displacement system, consisting of a 6-axis Staubli RX 60 L robotic arm for UT probe motioning and control. The software for UT NDE is a custom made software package, Robotest 9 developed in the LabView environment, with the purpose to control the UT NDE system displacement with 6 degrees of freedom and provide for the complete UT signal detection, storage and analysis (3D UT NDE) [5]. In this work, UT testing was carried out using the Full Volume Scan (FV-Scan) procedure [6-8]. This type of scan consists in detection and digitization of the whole UT waveform for each material interrogation point during scanning. At the end of scan, UT data are saved in volumetric files containing the whole set of complete UT waveforms. From the UT volumetric file, UT images for any segment of the UT signal, i.e. for any portion of the material thickness, can be obtained and analyzed. The software also allows to retrieve single UT waveforms corresponding to any given in-plane location by mouse clicking on the UT image. The digital oscilloscope was set at 0.5 Volts/div, 0.5 gs/div and sampling frequency 100 MHz, resulting in 1000 samplings for each complete UT waveform. For each single-lap joint sample, a 40 mm x 40 mm area over the bonding was scanned according to the boustrophedon 1 scheme with a 0.5 mm scan step.
1 Boustrophedon (from the Greek [3oagcrcpo(D~8ov = turning like an ox while plowing)" an ancient method of writing in which the lines are inscribed alternatively from right to left and from left to right.
260
LeCroy 9400 Krautkamer ------7/ Digital Oscilloscope Usip 12 Oscillator/Detector
GPIB Plug in Board Ultrasonic Probe
PC ETHERNET Plug in Board
Robot STAUBLI RX60 L
Robot Controller CS7B
Fig. 3. Ultrasonic NDE System. 3. Results and discussion
3.1 Mechanical testing results In Figures 4 and 5, the results of the tensile tests on the 1st and 2 nd series single-lap samples are reported as stress vs. displacement curves. In Tables 1 and 2, maximum stress and elongation at failure are listed. The 2 nd series samples, prepared with an improved bonding procedure in comparison with the 1st series samples and cut by an abrasive water-jet process, present a higher repeatability in the tensile test results (Table 2). In particular, the elongation at failure presents similar values for all tested samples, ranging between 1.15 and 3.40 mm. Moreover, the elongation at failure is notably reduced in comparison with the 1st series samples (Table 2). This behaviour can be due to the fact that the adhesive and the bonding procedure used for the 1st series samples did not allow for a thorough polymerisation. The maximum stress value is lower than 8 MPa whereas the value declared by the adhesive producer is 13 MPa for both adhesive types. By macroscopic analysis of the fractured samples, it can be seen that the fracture surfaces (see Figs. 6 and 7) develop exclusively at the interface between the adhesive layer and the sample surface, without damaging the composite material fibres.
3.2 Ultrasonic testing results In Figures 8 - 11, UT images from FV scans of 1st and 2 nd series single-lap joint samples, bonded with the 8010 and 8005 adhesives, are reported.
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All UT images are obtained by time gating (vertical red lines) the UT signal portion corresponding to the joint interface, including adhesive thickness. Images display the in-plane UT portrayal of the entire bonded area. FV scans were carried out with 5 and 10 MHz probes. The lower frequency was used to account for the high UT attenuation in thermoplastic composites and the higher frequency to obtain improved resolution UT images. As a matter of fact, the 5 MHz probe allowed for the complete UT signal penetration of the single-lap joints and bond area UT images could be reliably generated (see Fig. 8). The 10 MHz UT signal could not traverse the entire single-lap joint; in pulse-echo mode, this condition is generally not advised for UT scanning and the higher resolution due to higher frequency is not utilizable for image analysis.
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10
Displacement (mm)
(b) Fig. 5.2 nd series single-lap joint samples tensile tests: (a) 8005 adhesive, (b) 8010 adhesive However, though the UT signal could not reach the single-lap joint back surface, it reached and included the bonding area at mid-lap joint, as verified through time-of-flight thickness measurements: the joint interface is encountered by the signal at 1.7 gs and completely traversed at 2.4 gs from the front echo (Arnax- 0.7 Its). If the UT time gate is set over this time interval, the reliable generation of improved resolution UT images, illustrative of the bonding area, is achieved (Figs. 9, 11). Accordingly, the 10 MHz UT scans were considered for single-lap joint quality assessment through UT image analysis. Figures 8 and 9 report UT images from 2 "d series 8010 adhesive single-lap samples FV scanned with 5 MHz and 10 MHz UT probes, respectively. It can be easily seen that the resolution provided by the 10 MHz UT probe is decidedly superior.
261
Table 1 1st series single-lap joint samples tensile test results
P0a
8005
Maximu m stress (Mpa) 4.50
P0b
8005
4.98
3.28
P0c
8005
4.56
3.08
0.27
Pla
8005
4.54
5.94
0.29
Plb
8005
3.64
2.49
0.28
Plc
8005
7.61
7.22
0.26
POd
8010
5.8
2.23
0.28
POe
8010
7.08
3.50
0.28
P0f
8010
5.54
4.05
0.29
Pld
8010
5.96
4.82
0.29
Pie
8010
4.44
5.29
0.25
Plf
8010
5.48
2.62
0.28
P3d
8010
6.57
6.61
0.28
P3e
8010
3.90
3.04
0.27
P3f
8010
6.69
6.65
0.29
Sample ID
Adhesive
Elongation Adhesive at failure thickness (mm) (mm) 7.36 0.26 0.29
Table 2 2 nd series single-lap joint sample tensile test results
Fig. 6.2 n' series- Fracture surfaces of the single-lap joint samples bonded with the 8005 adhesive.
Fig. 7.2 "d series - Fracture surfaces of the single-lap joint samples bonded with the 8010 adhesive. 150 %-, O
Sample ID
Adhesive
AI.1
8005
Maximum stress (Mpa) 3.69
A1.2
8005
6.06
3.40
0.30
A1.3
8005
6.44
2.03
0.27
A2.1
8005
5.06
1.55
0.28
A2.2
8005
6.62
2.71
0.28
A1.4
8010
5.51
1.96
0.26 0.26
Elongation at failure (mm) 1.15
Adhesive thickness (mm) 0.28
A1.5
8010
5.67
2.62
A1.6
8010
5.05
2.60
0.28
A3.1
8010
5.78
2.33
0.25
A3.2
8010
5.20
2.27
0.25
Figs. 9 and 10 show UT images from 10 MHz FV scans of 2 nd series samples bonded with the 8010 and 8005 adhesive. No significant bond defect (non homogeneity, delamination, inclusions) is seen, suggesting the two adhesives yield the same bond quality. Fig. 11 shows the UT image from a 1st series sample bonded with 8005 adhesive and scanned with 10 MHz. Here, the joint area shows evident horizontal dark lines, revealing a prominent non homogeneity in the bond.
262
<
0
-150 0 0.5 1 1.5 2
t [~sec]
Fig. 8.2 "d series adhesive 8010, step 0,5 mm, frequency 5 MHz, scan area 40 x 40 mm 2 This low bond quality can be due to the procedure for 1st series samples preparation, where bonding was performed at room temperature and application time was the one suggested by the adhesive producer. As the actual adhesive work life was found to be 50% shorter than the declared one, the 1st series samples may not have achieved a thorough adhesive polymerization in the joint, yielding the poor bond quality evidenced by the UT image analysis.
150 >
<
V
!!!..)
o
: -60
-150 0
0.5
1
1.5
2
t [~tsec]
0
0.5
1
1.5
..............................
i ...........
2 ;
t [~tsec]
. . . . . .
Fig. 9.2 nd series adhesive 8010, step 0.5 mm, frequency 10 MHz, scan area 40 x 40 mm2 ~::,
~,~::
*7~Z~: ~
Fig. 11. 1st series adhesive 8005, step 0.5 ram, frequency 10 MHZ, scan area 40 x 50 mm2
150
References -150 0
0.5
1
1 . 5 ~
t[~tsec]
Fig. 10.2 nd series adhesive 8005, step 0.5 ram, frequency 10 MHZ, scan area 40 x 40 mm2
4. Conclusions A planned development of this work provides for the utilization of neural network based UT image analysis procedures [9] to automatically recognize the possible poor quality of the bond in industrial bonding procedures of thermoplastic composite material assemblies.
Acknowledgements This work has been partially supported by the MIUR MAVET (Moduli Avanzati per VEttori di Trasporto collettivo) and the FP6 EC NoE I'PROMS projects.
[1] ASM Handbook, "Composites", vol 21, ASM International 2001 [2] Shiuh-Chuan H., "Stress analysis of adhesivelybonded lap joints", Composite Structures 47, 1999: 673- 678 [3] Teti, R., Ultrasonic Identification and Measurement of Defects in Composite Material Laminates, Annals of CIRP, 39/1, 1990:527-530 [4] Teti, R., "Digital Ultrasonic Nondestructive Evaluation of CFRP Composites by Complete Waveforms", 7 th Int. Conf. on Comp. Mat., Guangzhou, 22-24 Nov. 1989:649-656 [5] www.panametrics.com, Ultrasonic Transducer Technical Notes, 2003, 32-40. [6] Teti R. Ultrasonic Identification and Measurement of Defects in Composite Material Laminates, Annals ofthe CIRP, 1990, vol. 39/1 : pp. 527-530. [7] Teti R. and Buonadona P "3D Surface Profiling through UT Reverse Engineering", 3 rd CIRP Int. Sere. on ICME, Ischia, 28-30 June 2002:387-394 [8] Teti, R. and Buonadonna P. Full Volume UTNDE of CFRP Laminates, 8th Eur. Conf. on Comp. Mat., Naples, 1-5 June 1998:pp.317-324 [9] Teshima, T. et al., Estimation of Cutting Tool Life by Processing Tool Image Data with Neural Networks, Annals of CIRP, 42/1, 1993:59-62
263
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Springback prediction with FEM analysis of advanced high strength steel stamping process S. A1 Azraq, R. Teti, J. Costa Dept. of Materials and Production Engineering, University of Naples Federico II, Naples, Italy
Abstract In many manufacturing processes involving sheet metal fabrication, springback is a major concern that makes tool design a very complex task. New demands have led to an increase in the use of Advanced High Strength Steel (AHSS) as work material. An increase in strength decreases the formability of the material and increases the springback behaviour. The aim of the numerical simulation carried out in this paper is to verify the stamping process and the shape of the final component for studying springback of AHSS. Finally, a test case of a simple profile stamping process was analysed using the incremental approach of the AutoForm 4.04 Incremental software code for two different AHSS materials: Dual-Phase (DP 600) and Transformation-Induced Plasticity (TRIP 800).
Keywords: Springback, Advanced High Strength Steel, Stamping, Finite Element Methods
1. Introduction As a forming expert in one of the leading car manufacturing companies, Schacher [1] summarized the current pressure on this industry as: "In the past we introduced 3 new models every 10 years, now we introduce 10 new models every 3 years". This drastic reduction of development periods as well as the trend to reduce weight of the cars in order to reduce the fuel consumption leads especially in the car manufacturing industry to a rebuilding ofthe conventional design and manufacturing procedures. Sheet metal forming as an important production process (see for instance [2]) is heavily experience based and involves trial-and-error loops. These loops are repeated the more, the less the experience on the part geometry and the material is. In innovative process design procedures, however, trialand-error loops are reduced by means of modem numerical approximation analysis, which is known ironically also as virtual production. Obtaining
264
consistent and accurate part dimensions is crucial in today' s competitive manufacturing industry. Inconsistencies in part dimensions slow new product launches, increase changeover times, create difficulties in downstream processes, require extra quality assurance efforts, and decrease customer satisfaction and loyalty for the final product. In the sheet metal forming process, a major factor preventing accurate final part dimensions is springback in the work material [3]. Springback is the geometric difference between the part in its fully loaded condition, i.e. conforming to the tooling geometry, and when the part is in its unloaded condition, i.e. free state. For a complicated 3-D part, undesirable twist is another form of springback. The uneven distribution of stress through the sheet thickness direction and across the stamping in the loaded condition relaxes during unloading, thus producing springback. Factors that affect the amount of springback include variations in both process and material parameters, such as friction
conditions, tooling geometry, material properties, sheet thickness, and die temperature [4]. Because controlling all these variables in the manufacturing process is nearly impossible, springback, in turn, cannot be readily controlled. Moreover, as springback is a highly nonlinear phenomenon, numerical simulations and correcting methods become highly complex. 1.1 Forming analysis with AutoForm-Incremental
Finite element methods (FEM) simulations based on incremental approaches [5] offer a full process model that simulates the metal sheet forming stages as accurately as possible in the logical order from blankholder to final flanging. Consequently, incremental simulations are computationally very intensive and time consuming in comparison with the corresponding one step analysis, and require tooling information to be manually inputted [6]. There are two types of incremental codes based on either 'implicit' or 'explicit' mathematical formulations. Implicit codes, such as the AutoForm-Incremental software code [7], typically complete the forming simulation in 1-4 hours, depending on part complexity, whereas the explicit counterparts tend to be 2-4 times slower. In the automotive industry, FEM methods for metal forming analysis are often used, particularly explicit codes like LS-Dyna (LSTC) and Pamstamp (Easy) and implicit codes like AutoForm-Incremental. Explicit solvers can be used to simulate dynamic analysis and allow to evaluate large deformations (like in drawing processes), whereas implicit solvers are suitable to simulate static analysis (like the springback phenomenon). Thus, in metal forming process simulation of car body components, a final implicit static step may be used to obtain a static springback solution after the tool is removed from the die. In this way, the springback solution starts from the stressstrain state of the forming simulation without numerical dynamic oscillations [7]. In this paper, the springback phenomenon is analysed through the use of the AutoForm 4.04 Incremental software code by simulating a test case stamping process applied to advanced sheet metal materials of great interest for the automotive industry. 1.2 Materials
In this study two Advanced High Strength Steels (AHSS) with different hardening curves (see Fig. 1) are considered: Dual-Phase (DP 600) and Transformation-Induced Plasticity (TRIP 800).
DP steels consist of a ferritic matrix containing a hard martensitic second phase in the form of islands. These islands create a higher initial work hardening rate plus excellent elongation. This gives DP steels much higher ultimate tensile strengths than conventional steels of similar yield strength. The microstructure of TRIP steels is retained austenite embedded in a primary matrix of ferrite. In addition to a minimum of 5% by volume of retained austenite, hard phases such as martensite and bainite are present in varying amounts. The retained austenite is progressively converted to martensite with increasing strain, thereby increasing the work hardening rate at higher strain levels [8]. TS (MPa)
TRIPIO00
1200-
~0
1000800
TRIP600
600 400 200 0
0
10
20
EL {%1
30
40
Fig. 1. Differences between Dual-Phase and Transformation-Induced Plasticity AHSS in a tensile strength (TS) vs. elongation (El) diagram. 2. Test case with A u t o F o r m 4.04 i n c r e m e n t a l code
The test case realized in AutoForm 4.04 Incremental code is a simple profile stamping simulation with angle variations in the component vertical side walls using two different Advanced High Strength Steel (AHSS) materials. An incremental simulation was performed for a stamping process consisting of three stages: forming, trimming and springback. The most important criteria to evaluate the stamping process success are the following result variables: formability, thinning and springback (material displacement and angular displacement). 2.1 Input parameters The stamping simulation was performed based on single action press. This procedure is based in a single slide (ram) movement: the punch is stationary, the die moves down in its direction pressing the metal sheet against
265
the blankholder (binder), and then deforms the blank with the punch (Figure 4.6). Cycle time is reduced by approximately 25% in comparison with the double action process. In this case were applied two drawbeads (Db) to uniform the formability of the final part (see Fig. 2). The most influent parameters on final results are described on table 1, Lubrication (Lube), Drawbeads ( force factor [-] (FF), line force [N/mm](LF)) and Force apllied between binder and die (Force).
2.2.1. Formability Formability is the ease with which a metal can be shaped through plastic deformation. Evaluation of the formability of a metal involves measurement of strength, ductility, and the amount of deformation required to cause fracture. The term workability is used interchangeably with formability; however, formability refers to the shaping of sheet metal, whereas workability refers to shaping materials by bulk forming.
inder
Fig. 2. Single action press tools
Material
Blank Thickness
Lube
DP600
0,8mm
0,15
TRIP800
0,8mm
0,15
DP600
0,8mm
0,15
TRIP800
0,8mm
0,15
DP600
0,8mm
0,15
TRIP800
0,8mm
0,15
10~
11~
12 ~
Drawbeads
FF -0,450 LF- 388,2 FF -0,530 LF -457,2 FF -0,450 LF - 388,2 FF- 0,530 LF -457,2 FF - 0,350 LF - 301,9 FF- 0,620 LF- 519,3
Force
1,80E+06 1,90E+06 1,60E+06 1,90E+06 1,80E+06 1,80E+06
Table 1 - Input parameters
2.2 Analysis of results (colour display of result variables) In the following, the analysis of the most important result variables will be discussed. These results can be displayed as coloured and shade images (see Fig. 3 for the results of the failure variable).
266
Fig. 4. Formability. Figure 4 reports the results of the formability variable. 9 Splits: Areas of cracks. These areas are above the FLC of the specified material (see Fig. 5). 9 Excessive Thinning: in this area, thinning is greater than the acceptable value (default value for steel is 30%). 9 Risk of splits: these areas may crack or split. By default, this area is between the FLC and 20% below the FLC. 9 Safe: all areas that have no formability problems. 9 Insufficient Stretch: Areas that have not enough strain (default 2%). 9 Compression. 9 Areas where wrinkles might appear: in these areas, the material has compressive stresses but no compressive strains. 9 Thickening: Areas where wrinkles can be expected, depending on geometry curvature, thickness and tool contact; the material in these areas has compressive strains which means the material becomes thicker during the forming process (see Fig. 6).
2.2.3. Wrinkling criterion 2.2.2. Forming Limit Diagram (FLD) The Forming Limit Diagram (FLD) provides a method for determining process limitations in sheet metal forming and is used to assess the stamping characteristics of sheet metal materials (see Fig. 5). Usually, the Forming Limit Diagram is used in method planning, tool manufacturing and in tool shops to optimize stamping tools and their geometries. The comparison of deformations on stamped metal sheets with the FLD leads to a security estimation of the stamping process.
Wrinkling is one of the major defects in stamping, especially for those parts on the outer skin panels where the final part appearance is critical. In addition, it can damage the dies and adversely affect part assembly and function. The prediction and prevention of wrinkling are, therefore, extremely important. Naturally, wrinkling is a phenomenon of compressive instability under excessive in-plane compression. Plastic bifurcation analysis is one of the most widely used approaches to predict the onset of wrinkling. Figure 7 reports the results for the wrinkling variable.
...............................................................................................................................................................
o
l
Lin. limit: 0.1 ] cq_ ~-|
Lq CI
9~ ~
,--4-
c:5
o
0
c-d
....
0.03
Fig. 7. Wrinkling criterion. "--2.
2.2.4 Plastic-strain ratio (revalue) c:?.
-0.4
-0.3
-0,2
-0,]
Minor
-0.0
0.]
O.Z
0,3
0,4
strain
Fig. 5. Forming Limit Diagram (FLD) graphic.
0.6
1.2
Fig. 6. Thickness.
The plastic-strain is the ratio of the true width strain to the true thickness strain in a sheet tensile test. It is a formability parameter that relates to drawing; it is also known as the anisotropy factor. A high revalue indicates a material with good drawing properties. Figure 8 reports the results of the plasticstrain variable.
o
-~ ~ o . 5
Fig. 8. Plastic-strain.
267
2.2.5. Springback graphic results Figures 9-11 reports the springback variable results in graphical form.
2.2.5.1. Comparation between DP 600 and TRIP 800 of normal displacement in 10~
DP 600
TRIP 800
............../ ........./" .........~i................................ .
i/ ........-~'~.: ..........
............................ '"~""~~
~(L1.620139
~ii.i1.069439 : ~:~.~..... :;:.: -1.49
-~~1.5
Fig. 9. Material displacement in X.
........ Refgeom
Min
~ ~
Fig. 12. Normal displacement for different AHSS. Figure 12 reports the results of the normal displacement in 10 ~ profile. For the same material, the springbak increases with the increasing of angular variation. For the same angle profile, it can be concluded that material TRIP 800 has more displacement than material DP 600 (see Table 2).
Displacement
-0.922
~1.07
10~
Fig. 10. Normal displacement.
11~
12~ 0.00803
~i~4.a9
Fig. 11. Angular displacement.
268
DP 600
TRIP 800 Difference (%)
X (mm)
0,96
1,50
56,3
Y(mm)
0,20
0,23
15,0
Z(mm)
0,95
0,97
2,1
Normal(mm)
1,07
1,49
39,3
Angular[ ~]
4,89
5,06
3,5
X(mm)
1,04
1,63
56,7
Y(mm)
0,22
0,26
18,2
Z(mm)
1,20
1,06
-11,7
Normal(mm)
1,20
1,69
40,8
Angular[ ~]
5,02
5,31
5,8
X(mm)
1,02
1,79
75,5
Y(mm)
0,26
0,26
0,0
Z(mm)
1,29
1,18
-8,5
Normal(mm)
1,30
1,93
48,5
Angular[ ~ 5,20 5,73 Table 2 Displacements for DP 600 and TRIP 800.
10,2
Max
3. Conclusion
A FEM study was carried out on springback, a great obstacle in the application of Advanced High Strength Steel sheets tbr the stamping of automobile parts, with focus on the mechanism of its occurrence and the techniques to counter it. With the introduction of the examples of analyzing springback using FEM, it was shown that the analysis enables to predict the influence of material strength. It is considered possible to decrease springback problems in actual parts in the future by the application of techniques for improving shape fixability and by FEM analysis. |t has been shown that the AutoForm software code is a powerful tool for the stamping process; it can considerably increase the product quality (production of more complicated parts, know-how accumulation for new materials, optimization by variants), reducing the time and cost of production (early checking of producibility ofworkpieces, reduction of development times, cheaper products, reduction of die costs). In [9], the recent developments of modern Advanced High Strength Steel sheets were reviewed, paying special attention to their physical metallurgy. A series of highly formable new high strength steels have been developed using sophisticated physical metallurgy and have contributed to expanding the application of high strength steel sheets, especially for production of automobile bodies and parts. Besides further developments of new types of high strength steels with better mechanical properties, the development of proper forming and welding technology is required to expand the use of high strength steel sheets. Therefore, a key to extending the use of high strength steel sheets for automobile parts is the cooperation of the automotive industry and steelmakers in developing materials and forming and welding technologies simultaneously. A new concept 'early involvement and concurrent engineering' being carried
out through the joint efforts of the automotive industry and steel industry leads to expect a promising future.
References
[1] H.-D. Schacher, Entwicklungstendenzen in der Massivumformung fuer die Automobilindustrie, in: Seminarband Neuere Entwicklungen in der Massivumformung, Stuttgart, June 3-4, 1997 [2] K. Lange (Ed.), Lehrbuch der Umformtechnik. Band 3: Blechumformung, Springer, Berlin, 1975 [3] Cao, J., Liu, Z., Liu, W.K., On the structure aspect of springback in straight flanging, Symp. on Advances in Sheet Metal Forming, 1999 ASME Winter Conf. [4] Cao, J., Kinsey, B., Solla, S., Consistent and Minimal Springback Using a Stepped Binder Force Trajectory and Neural Network Control, J. of Engineering Materials and Technology, 122 / 113, 2000 [5] H Aretz, R Luce, M Wolske, R Kop, M Goerdeler, V Marx, G Pomana and G Gottstein Integration of physically based models into FEM and application in simulation of metal forming processes Modelling Simul. Mater. Sci. Eng. 8 (2000) 881891 [6] Arwidson, C., BernspSng, L., Kaplan, A., Verification of numerical forming simulation of high strength steels, Int. Conf. on Innovations in Metal Forming, Brescia, Sept. 23-24, 2004 [7] AutoForm Engineering Inc. AutoForm-Incremental Reference Manual (http://www.autoform.com) [8] International Iron Steel Institute, Advanced High Strength Steel Application Guidelines, March 2005. http ://www.worldautosteel.org [9] A1-Azraq, S., Teti, R., Ardolino, S., Monacelli, G., FEM analysis of advanced high strength steel car body drawing process, 1st IPROMS Virtual Int. Conf. on Intelligent Production Machines and Systems, 4-15 July 2005:633-638
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Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhd and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
A d i s t r i b u t e d s t a n d - i n agent based a l g o r i t h m for o p p o r t u n i s t i c resource a l l o c a t i o n Petr Benda, Pavel Jisl {bendap 1, j i s l }@labe. f e l k . c v u t . cz
Department of Cybernetics, Czech Technical University in Prague, Prague, Technicka 2, Czech Republic
Abstract Mobile ad-hoc networks (MANET) are expected to form the basis of future mission critical applications such as combat and rescue operations. In this context, communication and computational tasks required by overlying applications will rely on the combined capabilities and resources provided by the underlying network nodes. This paper introduces an integrated F l e x F e e d / A - g l o b e technology and distributed algorithm for opportunistic resource allocation in resource- and policy-constrained mobile ad-hoc networks. The algorithm is based on agent negotiation for the bidding, contract and reservation of resources, relying primarily on the concept of remote presence. In the proposed algorithm, standin Agents technology is used to create a virtual, distributed coordination component for opportunistic resource allocation in mobile ad-hoc networks.
Keywords: agents, multi-agent systems, ad-hoc networks, inaccessibility, defence applications
1. Introduction Communications in military battlefield operations are currently one of the most critical technical capabilities for mission success. From a network perspective, tactical military operations are often characterized by highly dynamic ad-hoc wireless environments and include heterogeneous nodes under resource and security constraints. Furthermore, the communications infrastructure is expected to change its behavior and optimization criteria to adapt to changes in goals and priorities. In recent years, a number of research efforts have focused their attention on this problem, looking for better routing or transport algorithms that would correct the deficiencies observed in the use of traditional wired network protocols. In this paper, we introduce a novel agent-based communications framework designed to help address this issue in these types of environments.
The goal is to provide a framework that will overlay the physical network and transparently provide both services, with minimal changes in current software applications and systems. In our framework, intelligent software agents are used to enable on demand, data-aware capabilities in the network. The agents are mobile, so code and computation can be moved as necessary to opportunistically create capabilities and to react to changes in topology, resource availability, and policies. The framework proposed in this paper leverages from a set of core technologies that have been designed and developed by our research teams for similar types of scenarios. Extensively tested in numerous proof-of-concept applications and demonstrations, these technologies have matured to a point where they complement each other to enable the framework.
271
g-
Physical Node
(Hw Platform, Comm
devices)
Fig. 1. F l e x F e e d / A - g l o b e architecture design 2. I n t e g r a t e d A r c h i t e c t u r e The F l e x F e e d / A - g l o b e integrated infrastructure provides a powerful environment supporting point-to-point messaging, publish-subscribe data streaming, on-demand, opportunistic resource allocation in the dynamics of the environment that is given by (i) mobility of the communication infrastructure (ad-hoc networking) and (ii) changing operational priorities and optimization criteria. For simulation purposes we use a simulator, based on A - g l o b e platform (Si~l~ket al., 2004) (see section 3) that simulates the behavior of real hardware and is extended with geographical and environment simulators. We can simulate the physics of units, their movements and dynamically add and remove the units. The architecture has been designed to operate with the real-life hardware (robots, unmanned vehicles or communication devices) and it consists of several mutually linked components (see figure 1): 9 A - g l o b e s i m u l a t o r - Due to the fact that the real-life experiments can be costly and in some cases it can be impossible to iraplement them, an inseparable component of this architecture is the agent-based simulator (presented in section 3) that models the communication and interaction environment. 9 a d - h o c R o u t e r - The simulator and/or real hardware is connected to the highest levels of architecture over ad-hoc Router. Ad-hoc router supports multiple routing algorithms and is implemented at application level of ISO/OSI specification (ISO, 2005). For connected applications the ad-hoc router acts as normal UDP sockets. This extension provides restrictions in connection between units and components depending on visibility or policies. 9 M o c k e t s - Mobile Sockets are an application level transport layer, designed to transparently provide resource redirection and 272
cross-layer interaction in mobile ad-hoc network environments. 9 F l e x F e e d - Technology used for streamoriented communication between agents. While F l e x F e e d is designed for stream planning and controlling, Mockets provide an infrastructure used for stream transmission. 9 G u a r d - This component is used for controlling the resource allocation and policy checking. Extended with KAoS (Bradshaw, 1997; Bradshaw et al., 1999), the policy framework for F l e x F e e d and NOMADS, it can provide the mechanism for definition, verification, distribution and enforcement of policies restricting agent access to sensor data, bounding agent resources and governing the mode of notification to users. 9 A - g l o b e c o o r d i n a t i o n - The above listed components are closely linked to A - g l o b e coordination mechanism. This is a critical comportent which provides both central and distributed high-level planning and resource allocation algorithms for coordinating the communication flows. The coordination mechanisms represent the main contribution of this paper and presented in sections 3.2 and 4.1. The F l e x F e e d / A - g l o b e integrated architecture was designed on background knowledge of complex frameworks, designed in IHMC (eg. F l e x F e e d , KAoS, NOMADS) and the A - g l o b e project, designed in Gerstner Laboratory at Czech Technical University. Both technologies have its advantages and disadvantages and their connection can provide easy implementable architecture for mobile agents in environments with constrained accessibility.
N |
on
.~
Physical Node (Hardware Platform, Comm devices)
O
~ ~ a r e
PhysicalNode Platform, Cornm devices)
Fig. 2. IHMC provided (left) and Gerstner Laboratory provided (right) components The provided technologies are complementary and in part overlapping (see figure 2). The GL provided A - g l o b e multi-agent platform that implements its own messaging and routing services and supports full agent migration. This is why it can replace the infrastructure provided by F l e x F e e d , Mockets and the ad-hoc router. The policy enforcement is provided only by Guard and is not implemented in A-globe. As the A - g l o b e component is in charge of the coordination mechanisms, in F l e x F e e d only a centralized coordination is
Platform 1
Topic
l~i!iiii!i!ii!i!ii!ii
Messaging
,
:
,
y
Platform
Nii!iiii!iiliiiiiNINiii
2
i!iiiiiN
Fig. 3. A-globe simulation architecture implemented, using the ULM algorithm (Carvalho et al., 2005). This centralized coordination is implemented in A - g l o b e as well, however A-globe also provides partially distributed and fully distributed coordination algorithms and motion oriented coordination planning. The physical nodes can be used in both architectures, but A - g l o b e provides the support for simulation of these reallife physical nodes (discussed in section 3).
3.
A-globe
Simulation
A - g l o b e is Java-based, fast, scalable and lightweight agent development platform with environmental simulation and mobility support. Beside the functions common to most agent platforms (such as JADE, COUGAAR, FIPA-OS or JACK agent platform) it provides a position-based messaging service, so it can be used for experiments witlh extensive e n v i r o n m e n t s i m u l a t i o n and c o m m u n i c a t i o n inaccessibility. Communication in A - g l o b e is very fast and the platform is relatively lightweight (Siglg~ket al., 2004). A-globe is an agent platform designed for testing experimental scenarios featuring agents position and communication inaccessibility, but it can be also used without these extended functions. The platform provides functions for the residing agents, such as communication infrastructure, store, directory services, migration function, deploy service, etc. A - g l o b e platform is not fully compliant with the FIPA specifications, e.g. it does not support interplatform communication. This interoperability is not necessary when developing closed systems, where no communication outside these systems is required (e.g. agent-based simulations). A-globe is suitable for real-world simulations including both static (e.g. towns, ports, etc.) and mobile units (e.g. vehicles). In such case the platform can be started in an extended version with Geographical Information System (GIS) services and Environment Simulator (ES) agent. The ES agent simulates dynamics (physical location, movement in time and others parameters) of each unit.
The platform ensures the functionality of the rest of the system using these main components (see fig. 3)" 9 C o n t a i n e r M a n a g e r . One or more agent containers can run within a single agent platform. Container Manager takes care of starting, execution and finishing of these containers. Containers are mutually independent except for the shared part of the message transport layer. Usage of single agent platform for several containers running on one computer machine is beneficial because it rapidly decreases system resources requirements (use of single JVM), e.g. memory, processor time, etc. 9 M e s s a g e T r a n s p o r t . The platform-level message transport component ensures an elficient exchange of messages between two agent containers running in a single agent platform (single JVM). 9 A g e n t C o n t a i n e r . The agent container hosts two types of entities that are able to send and receive messages: agents and services. Agents do not run as stand-alone applications. Instead, they are executed inside the agent containers, each agent in its own separate thread. The schema of general agent container structure is shown in figure 3. A container provides the agents and services with several low level functions (message transport, agent management, service management).
3.1 A-globe Simulation Support A - g l o b e is suitable for real-world simulations ineluding both static and mobile units (e.g. logistics, ad-hoc networking simulation), where the core platform is extended by a set of services provided by a Master GIS (Geographical Information Systern) container hosting various environment sireulator (ES) agents. For integration with F l e x F e e d infrastructure, the Geographical Information (GIS) Server and Environment Simulator were adopted from the NAIMT project (Rollo et al., 2005) and extended for F l e x F e e d framework. Simulation Server integrates the GIS Server and Environment Simulator into one server, which provides simulation of the environments. Simulation server handles logins and logouts of environments, computes visibility among these environments and handles movements of environments. Environmental Simulator connects the A - g l o b e platform with F l e x F e e d framework. It provides the F l e x F e e d with environment simulation information like positions of environment, informs 273
about other visible environments and can be used for sending movement commands to Simulation Server.
3.2 A - g l o b e / F l e x F e e d
Coordination
The stand-in agents represent their owners and are disseminated across the network in order to represent their owners' accessibility.
4. F u l l y D i s t r i b u t e d C o o r d i n a t i o n
~.1 Stand-in Agents The proposed integrated architecture proposes four different approaches to coordination: 9 c e n t r a l i z e d - A single process (coordinator) searches an F-Graph - a centralized knowledge structure that represents communication accessibility in the network. By means of a Dijkstra-like algorithm, the coordinator finds the optimal feed. This centralized coordination is implemented in F l e x F e e d and uses the ULM algorithm (Carvalho et al., 2005). 9 p a r t i a l l y d e c e n t r a l i z e d - The centralized approach can be partially distributed among regions, where the coordinators maintain their own F-Graphs representing communication accessibility within a particular region. For planning feed and coordination, the region coordinators need to negotiate the path for routing to another region. 9 fully d e c e n t r a l i z e d - With a fully decentralized coordination approach, each agent maintains a local F-Graphs representing communication accessibility in its direct neighborhood. This information is used for planning and coordination of the feed in a peerto-peer manner. 9 m o t i o n e n f o r c e m e n t c o o r d i n a t i o n - Yet another approach to coordination is used in the situations with higher level of communication inaccessibility among the agents. This approach plans and controls not only the communication traffic but also the movements and changes of location of the particular nodes (robots). At this moment the F l e x F e e d framework implements only the centralized coordination. In Ag l o b e simulation scenarios the partially and fully decentralized coordination as well as the motion enforcement coordination were implemented in the NAIMT (Rollo et al., 2005) project. In the remaining part of this document we will be discussing the fully decentralized approach to coordination. In principle, there are two fundamental approaches how such coordination can be implemented - by means of the remote awareness or remote presence agent concepts. While in the former case, each node is expected to autonomously maintain the information about the accessible nodes and perhaps their accessibility in the acquaintance model, in the latter case each individual node creates so called stand-in agents. 274
In standard situations, agents in a multi-agent community need to communicate with each other to accomplish cooperation and coordination of joint activities. Normally it is expected that each agent can contact any other using a reliable communication infrastructure. Often one has to think about situations when a communication subsystem becomes disintegrated and some agents become isolated from the rest of the community. Stand-in agents solve inaccessibility in two ways: the first is the routing communication protocol based on swarming and micropayments within agent community and the second is distribution of social knowledge. In this paper we talk about stand-in agents without social knowledge functionality (Siglg~ket al., 2005), because we want to build only a message passing system there. These stand-in agents provide top-level communication API in mobile network. An important attribute of the stand-in agents is their passive role in the network. These agents are meant to be carried on a physical device and they aren't able to affect position of the device in mobile network anyway. We will address the integration of the algorithm with the generic stand-in agent architecture. Unlike classical middle agent architectures (Sycara et al., 1999) where the prime functionality is devoted towards matchmaking and negotiation, we would like to extend the concept of the middle agent by its capability to autonomously migrate in the network, clone and destruct its copies. We present an abstract architecture of the stand-in agent, that consists of the following components -Swarming c o n t r o l l e r - consists of two modules: population manager ensures cloning, migration and destruction of stand-in agents in the system while the information propagator manages information flows through the agent, more specifically the messages or knowledge to transfer or actions to take. - K n o w l e d g e base, a domain specific knowledge structure of the stand-in agent consists of three parts: activity knowledge, information evaluator and timeout checker. While the activity knowledge contains the domain specific knowledge and the meta-data provided by the propagator, the information evaluator and timeout checker are the algorithms working on this knowledge. The
information evaluator classifies and indexes the knowledge, so that the index values can be used by information propagator to manage its activity and further propagation. It also evaluates the knowledge usefulness. The timeout checker module implements forgetting of the activity knowledge. Stand-in agent functionality universal interface between modules and agent platform. It provides fundamental agent functions (clone, migrate and die), message interface and monitoring listeners, as well as original stand-in agent code. This code depends on the actual type of the stand-in agent. Only this part of relay agent needs to be changed to work properly with another agent platform.
~.2 Stand-in Based Coordination One of the key issues in stand-in operation is their proper location in the network. The distributed stand-in agent allocation mechanism uses only locally accessible information. It does so not only to minimize the network maintenance communication, but it also enables operation in the disruptive or partially inaccessible environment. Locally accessible information is obtained by monitoring stand-in agent's n e i g h b o r h o o d - identifying currently visible targets and other stand-in agents. In principle there are two key approaches to controlling the efficiency of the stand-in agents allocation: where the (1) f o r w a r d s w a r m i n g c o n t r o l stand-in agent migrates its clone only to the locations with higher possibility of future inaccessibility and higher interaction expectancy and (2) b a c k w a r d s w a r m i n g c o n t r o l where the stand-in agents dispatch their clones to every reachable destination and the useless ones are eliminated in the future, reflecting the actual state of inaccessibility. Each of the approaches has its advantages and disadvantages. The forward swarming control is computationally efficient, as it tries to minimize the number of stand-in agents in the system and prevent the possible swarming explosion. This is why this approach seems to be particularly suitable for domains with high scalability and operational efficiency requirements. On the other hand, the backward swarming control has an important advantage. This approach is substantially more domain independent, demands less knowledge about the environment nature and is more robust, as it doesn't explicitly use any prediction about the future of the community.
We have opted for the use of the backward swarming, as this approach is more robust and domain independent. Abstract criteria of the system quality defined in the introduction were also formulated in a precise manner, with descending priority: (i) to provide connection between any two system elements through the minimum number of stand-in agents, (ii) to minimize the number of stand-in agents in the system and (iii) to minimize the number of messages for system operation and/or knowledge maintenance. Population manager is driven by a biology inspired algorithm. Social dominance and altruism models (Thomas et al., 2004) were successfully used to partition the group of agents into those who work for the good of the community and the others, who profit from the altruism of the first group. To ensure the target coverage, stand-in agents can be reproduced in the system using two main propagation strategies: -
full flood f i l l - any stand-in agent initiates full flood filling reproduction strategy when it identifies a new unserved knowledge target in its reach. To decide whether the target is really new, all agents keep a set of served targets, that includes both the other stand-in agents and the knowledge about final users. A target is removed from the set when it is not used for a specified period forget time. - b o u n d e d f l o o d f i l l - this is a depth-limited version of the previous reproduction strategy. After the initiation, the stand-in agents are successively cloned only up to the depth specified by FloodFillDepth constant. This reproduction strategy is triggered by a local accessibility change when the source agent holds relevant, non-expired knowledge.
Both mentioned flooding strategies are time limited. There is a specified constant flood duration during which the stand-in agent retains its reproduction intention. When this period expires, the agent no longer reproduces until the new reproduction is started by the agent itself or the others. To keep the number of stand-in agents close to the optimum, the population manager contains methods that decrease the number of stand-in agents in the system: In random duels the attacking stand-in agent randomly selects an adversary between accessible agents and launches an attack with force proportional to its profit during specific period, as determined by information propagator (see bellow). Besides the attack force, the attack also includes the information about its active target set at the time of the attack. The attacked agent evaluates the attack and decides whether it won 275
or lost. If the attacked agent loses, it removes itself from the system; losing attacker is not penalized for the attack. Attack evaluation compares the active targets first and when one is a subset of the other, its owner loses the fight. When the sets are identical, force of the attack decides the fight - the stronger agent wins. Active target set size is evaluated differently for new and old agents. For new agents that are not yet completely adapted to the environment, the set contains all directly accessible targets, while for old agents it contains only the really used targets. Besides this advantage, the newest agents benefit from the immunity period, during which they can not lose a fight while attacked. Information propagator manages knowledge propagation and use in the system. This component uses virtual payments to reward the other agents for the knowledge, receives payments from the others for the information provided and generates the profit also from acting on behalf of the represented agent. Each agent optimizes its profit, ensuring the overall information flow efficiency. Historical data (represented as probabilities assigned to knowledge characteristics, origins and targets) that are used to identify the targets to which we send the information are periodically updated and the old data is discarded.
5.
Conclusions
The main contribution of this paper is in reporting on the integration of the stand-in technology into the F l e x F e e d framework. This integration provides all stand-in behavior and many changes in NOMADS and F l e x F e e d core were applied. The stand-in agent's implementation was divided into stand-in agent's Core and platformdependent implementation of underlying infrastructure. The stand-in Core is platform independent and provides all the above mentioned behavior. For easy implementation, the interfaces for stand-in required infrastructure were deployed. Platform-dependent part of stand-in agents provides infrastructure for stand-in creation and cloning, messages propagation and solving visibility of containers and environments. A set of experiments was carried out in order to study the stand-in behavior in environment with limited communication ranges. We measured the evolution of the number of stand-in agents at three levels of inaccessibility dynamics. To determine the optimal number of stand-in agents in each moment, we have implemented an efficient centralized algorithm (Sigls al., 2005). In our domain, where the accessibility is distance-based, this algorithm behaves optimally. 276
Future work will involve the integration of coordination components with the rest of the framework to build a prototype for tests and demonstrations. We also plan an extensive set of tests and experimerits to validate the concept and characterize the prototype.
References
Bradshaw, J. M. (1997). Kaos: Toward an industrial-strength generic agent architecture. In: Software Agents, A A A I PressfMIT Press. pp. 375-418. Bradshaw, Jeffrey M., Mark Greaves, Heather Holmback, Tom Karygiannis, Wayne Jansen, Barry G. Silverman, Niranjan Suri and Alex Wong (1999). Agents for the masses?. IEEE Intelligent Systems (March/April) 14(2), 5363. Carvalho, Marco, Florian Bertele and Niranjan Suri (2005). The ulm algorithm for centralized coordination in flexfeed. In: Proceedings of the 9th World Multi-Conference on Systemivcs, Cybernetics and Inforrnatics - Orlando, USA. ISO (2005). Open Systems Interconnection Reference Model.
http ://en. wikipedia, org/wiki/OSl_model.
Rollo, M., P. Nov~kand P. Jisl (2005). Simulation of underwater surveillance by a team of autonomous robots. In: Holonic and Multi-Agent Systems for Manufacturing (Ma~fk, Brennan and P~chou~ek, Eds.). number 3593 In: LNAL Springer-Verlag, Heidelberg. pp. 25-34. Si~ls D., M. Rollo and M. P~chou~ek(2004). A-globe: Agent platform with inaccessibility and mobility support. In: Cooperative Information Agents VIII (M. Klusch, S. Ossowski, V. Kashyap and R. Unland, Eds.). number 3191 In: LNAL Springer-Verlag, Heidelberg. Sigls David, Martin Rehs Michal P~chou~ekand Petr Benda (2005). Optimizing agents operation in partially inaccessible and disruptive environment. In: Intelligent Agent Technology, 2005 I E E E / W I C / A CM International Conference. number P2416 In: IEEE. Sycara, K., J. Lu, M. Klusch and S. Widoff (1999). Dynamic service matchmaking among agents in open information environments.. ACM SIGMOID Record 28(1), 211-246. Thomas, Vincent, Christine Bourjot, Vincent Chevrier and Didier Desor (2004). Hamelin: A model for collective adaptation based on internal stimuli. In: From animal to animats 8 - Eighth International Conference on the Simulation of Adaptive Behaviour 200~ - SAB'O~, Los Angeles, USA (Stefan Schaal, Auke Ijspeert, Aude Billard, Sethu Vijayakumar, John Hallam and Jean-Arcady Meyer, Eds.). pp. 425-434.
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
A Low-Cost 3-D Laser Imaging System B.P. H o r a n a and S. N a h a v a n d i a a
Intelligent Systems Research Lab, Deakin University, Victoria, Australia
Abstract
This paper presents the design and development of a low cost three-dimensional laser imaging system for scanning suitable surfaces. A generic, low cost, off-the-shelf laser rangefinder is used to obtain the primary one-dimensional distance measurement. The rangefinder' s laser beam is reflected by a twin-axis mirror assembly driven by stepper motors providing the system with two angular degrees of freedom, allowing 3-D measurements to be determined. A camera and image processing techniques are used to determine the measured 1-D range value from the generic range-finding device. A computer program then uses the obtained data to create a 3-D point cloud. An algorithm is then used to construct a 3D wire frame mesh representing the scanned surface. The system has an angular resolution of 1.8 ~ and the results obtained demonstrate the system to have an accuracy of approximately ~ 2cm at a scanning distance of 1.0m. Keywords: 3-D scanning, laser scanning, laser imaging
1. Introduction
As today's technology is rapidly advancing, the world's engineering industry is diving deeper into automated processes and highly advanced technology. Laser scanners and 3-D imaging systems are currently used within many industries for various purposes. The use of these systems is limited by their cost, whereas the design of a less complex and far more cost effective 3-D laser imaging system is likely to provide a wide range of applications and processes with a new technological advantage, as well as contributing to the further advancement of automated technology. In order to achieve 3-D scanning capabilities within this system, a 1-D Laser Rangefinder is integrated with other components, providing the ability to obtain surface information in three dimensions [1,2]. One approach to achieving the initial 1-D range value is to use lasers in conjunction with video camera/s [3,4]. This method, however, requires
the use of cameras and in determination of the range value, relatively complex computations are needed. Given the aims of a simple, low-cost system, this method of laser rangefinding was deemed unsuitable. Three popular methods of laser rangefinding not requiring cameras are the Triangulation [5,6], TimeOf-Flight [7] and phase-shift-measurement methods. These methods are all capable of achieving the 1-D range measurement required by this system. Each method provides distinct advantages. The phase-shiftmeasurement or Continuous Wave (CW) method offers a compromise between the Triangulation and Time-OfFlight methods, providing a relatively large measurement range whilst also offering a degree of accuracy sufficient for many applications. This rangefinding method also provides the added advantage of being available as a generic, low-cost, off-the-shelf device and is therefore used in this system. In order to achieve 3-D capabilities from a 1-D rangefinding device, the system needs to be given two
277
degrees of freedom. While this freedom can be achieved by maneuvering the laser device itself[6,7], a simpler, more cost-efficient method is to use a mirror [ 1] to reflect the laser beam about appropriate axes. Utilisation of low-cost, small load stepper motors provides a sufficient method for accurate positioning of the mirror [ 1]. Such a configuration is then capable of obtaining the 1-D range measurement and, combined with the orientation of the mirror, enables the calculation of the 3-D co-ordinates. This proposed system design aims to ultimately present a radically low development cost, thus necessitating a compromise between performance and the development cost of the system.
2.2. Platform hardware design
2.3-D Imaging System
2.1. System Architecture The presented laser imaging system comprises four main components; the laser rangefinder, the twinaxis tilting mirror, microprocessor-based control system and PC used to process the data and construct the 3-D point cloud and corresponding 3-D wire frame mesh, Figure 1. The implemented system components were chosen on the basis that each performs its task effectively, but also that its selection contributes to achieving the low development cost. Achieving the integration of the primary 1-D range measurement within this system involved the modification and adaptation of the generic, low cost, off-the-shelf laser 'tape measure' (AU $500). The rangefinder's laser beam is reflected about two axes,
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providing the system with two angular degrees of freedom. The orientation of the mirror is controlled locally by the microprocessor-based control system. This control system also signals the rangefinder when a measurement is required. The angles of the mirror' s orientation about each axis of rotation are then serially transmitted to the PC-based acquisition software. The acquisition sot~vare then uses a web camera to obtain live video of the measured range distance as shown on the rangefinder's screen. The shown range value is determined within the acquisition software using a specially designed frame-scanning algorithm.
The system's hardware platform is based upon the modification and adaptation of the low-cost, commercial, off-the-shelf laser rangefinding device. The implementation of this off-the-shelf device enables the low development cost to be achieved. This device is intended for use as a hand-held device and, as such, provides no interface for control and data extraction. The rangefinder was modified as little as possible, thus demonstrating the realistic use of such a generic, low-cost rangefinder. The device was interfaced with in a way similar to its intended operation, in that the buttons are pressed and the measured distance is visually observed. Rather than use actuators to actually press the device's buttons, the open loop control of the device is achieved using relays to close the contacts of the appropriate buttons. This constitutes the only modification to the device. In order to extract the determined range distance, a web camera and image processing techniques were used. As opposed to interfacing with the rangefinder' s LCD screen or micro-controller, this method required no further modification to the rangefinder. The camera was mounted above the rangefinder and encapsulated to eliminate the unpredictable effects of ambient light, this configuration shown in Figure 2.
This rangefinder provided a scanning range of 0.30m to 100.0m, offering an accuracy of _+ 3mm. A twin-axis mirror provides a pan-tilt motion and was designed to reflect the rangefinder's laser beam in the elevation and azimuth directions. Low-cost stepper motors directly drive each axis of the mirror assembly, providing a resolution of 200 steps per revolution. This direct-drive configuration thus enables the mirror assembly to mimic the mechanical design ofthe stepper motors, and therefore provides each axis with distinct positions of movement equating to 1.8 ~ about either axis. Given this elimination of backlash or slack in the drive mechanism, the positions of both axes of rotation are assumed to be consistent, provided that the motors have stepped effectively to the expected position. This enables the laser beam to be accurately deflected bythe mirror assembly, given that it strikes at the point of coalignment of both axes. In order to achieve the axial co-alignment, AxisB, including motor (tilt direction), is supported totally by the Axis-A (pan direction), Figure 3. Such a configuration is essential for achieving axial coalignment, however as a consequence places substantial inertial loading on Axis-A, limiting the speed of rotation to considerably less than that of the axis of elevation. Optical sensors provide the system with reference points at the limits of rotation ofboth axes. Apart from these tbur points of reference (upper and lower limits of each axis), the motors are controlled in open loop. The motors were found to exhibit sufficient starting and holding torques to step effectively, assuming the
Fig. 3. Twin-Axis tilting mirror correct voltage sequence is applied. This design therefore eliminates the need of encoders to achieve successful system operation, as well as providing a relatively simple and consistently accurate method of axial positioning. The rangefinder was mounted above the mirror assembly, as shown in Figure 2. Whilst this configuration provides the system with a large scanning range it became necessary to achieve rigid mounting between the rangefinder and mirror to ensure a consistent distance was maintained. This was achieved through use of an aluminum mounting stand designed to be strong enough to overcome disturbances such as moderate vibration. The system is able to scan 330 ~ in the azimuth direction. The scanning range in the elevation direction is, at + 50 ~ far more restricted than the azimuth direction, limited by the physical orientation of the system's components, as shown in Figure 2. The developed hardware therefore provides the required platform allowing the control system to effectively control the system' s operation.
2.3. Control Program The control system' s program code is written in the
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controller. This code controls the operation of all the system' s components locally, operating independently of the acquisition software. The angles of each distinct position of the mirror assembly's stepper motors are predetermined and stored within a LookUp Table. As previously mentioned, this system, given the correct motor voltage sequence, assumes to have moved accurately in the correct direction. This underlying assumption enables the system to operate in open loop, aided with the reference feedback at the limits of rotation of both axes. When the mirror is at a reference point, the control system knows the current angles of reflection. As either axis rotates in a known
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2.4. Acquisition Software The acquisition sottware was developed in Visual Basic, given its ability to provide support to all of the system's components. A vital function of this software is to read the range value from the rangefinder's display. The software achieves this by receiving a live video feed displaying the rangefinder's screen. A simple frame-scanning algorithm was developed in order to perform image processing of the given image frames. Enabling fast, accurate determination of the shown range value, pixel groups representing important areas within the image frame were determined, as shown in Figure 4. The seven-segment type LCD display aids in straightforward determination of the digits displayed. The software algorithm then scans the pixel groups representing each segment of the numeric value within the image frame, and associates these groups with the determined RGB values. Based with comparison to the ON/OFF reference values, the displayed range value is then determined. Given the locations of the pixel groups are static, care was taken to ensure the camera remains in a fixed position relative to the rangefinder at all times, as shown in Figure 2. Ambient light was also found to interfere with this process, and was therefore eliminated by encapsulation and the introduction of a fixed intensity light source. Another essential function of the acquisition software is to perform the calculation and output of the measured 3-D co-ordinates. Given the need to use the
280
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Fig. 5. Calculation of 3-D co-ordinates acquisition software to obtain the range value, the acquisition software also performs calculation of 3-D co-ordinates. This acquisition software seriallyreceives the two required angles from the control system and, combined with the determined range value, enables the relative 3D surface co-ordinates to be calculated. Simplifying the required communication between the acquisition software and the control system, the serial communication is performed only in one direction, from the control system to the computer. Receiving the serial data triggers the acquisition software to visually determine the range value and calculate the 3-D co-ordinate for the particular scanned point. The software also outputs the data to the appropriate text file. Figure 5 represents the variables used to calculate the 3-D co-ordinates for a scanned point. The calculations are performed in a two-stage process. The first two calculations, (Figure 5, LHS) yield the Y distance and the intermediate 'Horizontal Component of Range' value. The second two calculations (Figure 5, RHS) yield the X and Z distances. Y = sin(0v) * (Range-DistTM)
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3.1 System Performance
Provided with the ability to calculate the relative 3D co-ordinates, the system is now fully equipped to perform 3-D scanning.
Providing a basis to determine system accuracy, a sample set of3-D measurements was obtained. These values were obtained by scanning a vertical wall at a scanning distance of 0.570 metres. The point at which the laser coincided with the wall was marked and measured manually, thus enabling comparison between the two sets of values. It is acknowledged that manually measured values may also have some associated inaccuracy, however provide a simple, fast method to approximate the accuracy achieved by the system. The comparison between the measured and acquired values, with respect to each axis, are shown in Figures 9,10 & 11. Comparison of the values obtained above enabled the calculation of the error value with respect to each axis. The mean error values for the X, Y and Z coordinates were observed to be 2.89cm, 2.15cm and 1.91 cm respectively. These values compared with the documented accuracy of the rangefinder, being ~ 3mm, lead to the conclusion that there are substantial sources of error within the system's components. Further to the fact, given this system' s reliance on angular reflection, it is likely that the system accuracy will decrease proportionally as the scanning distance is increased. The resolution of this system is limited by the stepper motors driving the tilting mirror assembly. The motors only perform full stepping of either axis, and
3. System Output The main goal of this system is to allow effective three-dimensional scanning. In order to perform the 3D scanning process, this system follows a scanning algorithm. The algorithm utilises the system' s abilityto rotate the twin-axis mirror more quickly in the elevation direction than in the azimuth direction. The scanning algorithm takes one measurement per step for the full range of the elevation axis. The algorithm then rotates one step in the azimuth direction, and again scans tbr the full range of elevation. This process is repeated until full successful scans through the elevation range have reached the end limit of the azimuth scanning range. The spherical ball (Figure 6) was scanned using the scanning algorithms, obtaining the 3-D coordinates of the scanned surfaces. The given scanning distance was approximately l m. Figure 7 illustrates the ordered data representing the scanned surfaces. The reconstruction algorithm then uses this data, and given the order of the scanning process (scanning algorithm), this algorithm interpolates between appropriate points, constructing a 3-D wire frame mesh representing the scanned surface, as shown in Figure 8. 9
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-0.2
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The acquisition time of the system is limited by the chosen rangefinder, as well as the time taken to position the mirror assembly. The time taken for the system to scan one successive 3-D point was observed at between 2 and 3 seconds. The total scanning time remains dependent on the number of points scanned. This relatively long required scanning time highlights the appeal of this system for scanning only a few points of an object's surface, or 3-D scans where acquisition time is not critical, for example multiple-point fault detection for manufactured parts. The tilting mirror assembly did prove successful in providing the mirror with the required two angular degrees of freedom. The chosen low-cost stepper motors also proved successful in accurately positioning the mirror assembly. The use of these motors also reduced the need for constant position feedback in this system, thereby contributing to the low development cost. The mirror assembly has been identified as a major source of inaccuracy in 3-D measurements performed by the system. Considering the primary focus was on achieving a low-cost development, the observed inaccuracy represents the challenges still to be overcome with the development of precision components, such as the mirror assembly, at such a low cost. 4. C o n c l u s i o n s
The design and development of this 3-D Laser Imaging System has been described throughout this paper. The emphasis of this design was on overcoming cost restraints to enable the development of a system capable of 3-D scanning for less than AU $1000 (excluding the required PC platform). This was achieved and the prototype represents the appeal of such technology for applications where high performance is not critical and where cost restraints currently prohibit the use of this type of technology.
282
0
5
10
15
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Fig. 11. Accuracy in the Z direction
This system does not attempt to rival existing systems, but rather to appeal to a new market for more cost-effective systems offering a compromise between development cost and system performance. References
[1] Blais, F. Review of 20 year of range sensor development. Journal of Electronic Imaging, Vol 3, 2004, pp. 231-243. [2] Curis P and Payeur P. An integrated robotic laser range sensing system for automatic mapping of wide workspaces. Proc. Of Canadian Conference on Electrical and Computer Engineering, 2004, pp. 1135 1138. [3] Lizcano C and Marquez M. Three-dimensional surface reconstruction based on laser triangulation. Proc. Of SPIE The Int. Society for Optical Engineering, Vol. 5622, 2004, pp. 1322 - 1327. [4] Grandori F, Parazzini M, Ravazzani P, Svelto C & Tognola G. Simple 3D laser scanner for anatomical parts and image reconstruction from unorganized range data. Proc. oflEEE 19 th Conf. On Instrumentation and Measurement Technology, Vol 1,2002, pp. 171 - 174. [5] Aitken D, Blais F & MacKinnon D. Modeling an Auto-Synchronizing Laser Range Scanner. American Control Conference, 2003, pp. 1 - 6. [6] Ciami FM, Das PS, Karson JA. & Kocak DM. A 3D laser line scanner for outcrop scale studies of seafloor features. MTS/IEEE Riding the crest into the 21 st Century, Vol 3, 1999, pp. 1105 - 1114. [7] Wagner B and Wulf O. Fast 3D Scanning Methods for Laser Measurement Systems. International Conference on Control Systems and Computer Science, Vol 1, 2003, pp 312-317.
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Filter selection for multi-spectral image acquisition using the feature vector analysis methods Ioannis S. Chatzis, Vassilios A. Kappatos, Evangelos S. Dermatas Department of Electrical & Computer Engineering, University of Patras, Rio Patra 265 00, Hellas.
Abstract
The selection of filters plays a fundamental role for the setup and the performance of a multispectral image acquisition system. When the number of available filters is large, the full search method becomes computationally expensive. In this paper the filter selection problem is defined as a dimensionality reduction process by selecting the filters that contains most of the essential information, using the same criteria as the Principal Component Analysis. The proposed method is also expanded by processing multiple filter estimations to improve the stability and the quality of the results. The filter selection methods were evaluated under the CIE standard illuminants A and D65 using 160 filters and a spectral reflectance database of 1269 curves. K e y w o r d s : Filter selection, k-means, multi-spectral image acquisition
1. I n t r o d u c t i o n
The accurate measurement of the color has proven to be a difficult task for researchers, the primary issues being the highly nonlinear conversion between color spaces, accurate spectral characterization of the illumination and sensor and the reflectance reconstruction. Typically these measurements require a segment or a sample of the material to be removed and taken to a lab containing specialized colorimetric instruments. Added difficulties arise when the color measurements are made during the high-speed production of materials and goods. The time required for this analysis typically is too great to actually affect the production process. In addition, the standard color measurement equipment averages all the color in a predefined region. In multispectral machine vision the objective is to map the response of the system to the reflectance characteristics of the inspected object, offering high spatial and spectral accuracy to the inspection process.
A multispectral imaging system allows the reconstruction of the reflectance of the objects in a scene. Such a system is built around specialized hardware (the acquisition system) that can vary its response to light with respect to different wavelengths: This is actually accomplished by incorporating into the system a set of traditional optical filters with variable transmittance, and taking several shots os the scene with a different filter used for each shot. The selection of the filters incorporated to the system affects the spectral response which in turn affects substantially the accuracy of the reflectance reconstruction task. The design of optimal filters given an optimization criterion has been proposed by several authors [1-7]. A drawback with such methods is the production cost of the optimised filters. Another approach encountered in most existing multi-spectral scanner systems is to use a set of heuristically chosen colour filters, which are typically equi-spaced over the visible spectrum [812]. Although promising results are reported using
283
such systems, the choice of filters seems to remain rather heuristic and likely sub-optimal. An alternative solution is the selection of the camera filters as a subset of a set of readily available filters [2, 13, 14]. The filters are selected taking into account the statistical spectral properties of the objects, as well as the spectral transmittances of the filters, the spectral characteristics of the camera and the spectral radiance of the illuminant [ 15]. The main idea of the approach is to choose the filters so that, when multiplied with the illuminant and camera characteristics, they span the same vector space as the reflectances' that are to be acquired in this particular application [ 12, 16]. The filter selection problem can also be considered as a feature selection problem. While this approach doesn't preclude a straightforward optimization criterion, it follows an information conservation approach through dimensionality reduction. In [ 18] the set of filters were selected using the Feature Vector Analysis (FVA) method. In this paper, the proposed filter selection method is based on the Principal Feature Analysis (PFA), originally proposed in [18] for feature selection applications. The filter selection problem is defined as a dimensionality reduction process of the complete set of filters and is obtained by selecting a subset that contains most of the essential information, using the same criteria as the Principal Component Analysis (PCA). The filters are selected to maximise their degree oforthogonality after projection into the vector space spanned by the most significant eigenvectors [13]. Although this approach remains suboptimal, it avoids the heavy computation cost required for an exhaustive search [14, 2]. Moreover, an alternative 'voting' selection method is proposed based on the estimation of the most frequently selected filters, after multiple iterations of the PFA method. The aforementioned feature selection solutions were evaluated using a great number of filters, and irradiances for various setups of the multispectral image acquisition system. The paper is organized as follows: In section 2, the multispectral system is presented. Section 3 describes the proposed filter selection method. The experimental setup and the results of the evaluation process are described in section 4 and 5 respectively. A summary of conclusions is given in Section 5.
2. Multispectral imaging The behaviour of a generic multispectral acquisition
284
system can be modelled by expressing its output ai(x) as a function of the relevant parameters of the acquisition process. Formally this can be writtern as
a i ( x ) -- j ' E ( x , 2 ) R ( x , 2 ) 3 i ( 2 ) d 2 ,
(1)
2t where i is an index that varies with the filter being used, x is a two-dimensional coordinate vector identifying a point in the scene, 2 is the wavelength, E is the energy that reaches point x in the scene, R is the reflectance, and S~ is the 'sensitivity' of the system when filter i is used. The integration (which is replaced by a summation in actual computations) is performed between )~ and )g2 a s the system is assumed to be blind outside this interval. As the above equation shows, t h e output of the acquisition system is not the actual reflectance data. The derivation of these data requires the establishment of a characterization method and a training set on which the method relies. If the system output for point x is denoted as
a(x) =[ai(x)] i
(2)
and K filters are used, then a(x) is an K-dimensional vector. The corresponding reflectance R(x,)~) is a function of the wavelength 2, but since in practice it is difficult to obtain an analytical form of R, a sampling of its value is customerily considered instead. The light spectrum is then sampled at a discrete number of values of 2, and the reflectance is expressed as
(xt- [R(x,;tj)]j,
(3)
where j is an index that varies with the sample wavelengths. If N sample values of 2 are considered, then r(x) is an N-dimensional vector. To establish a relationship between the system output and the corresponding reflectance, the system characterization function
a(x) which links the output a(x) at a point x in the scene to the corresponding reflectance r(x), must be described or estimated in some way; this is usually done by means of an empirical model based on a chosen training set and characterization method.
The problem of the estimation of a spectral reflectance r(x) from the camera responses a(x) is often formulated as finding a matrix Q that reconstructs the spectrum from the measurement vector
r(x) = Qa(x)
(4)
The minimixation of the mean square error between the actual r(x) and Qa(x) data is inefficient due to the low dimensionality of reflectance spectra. The Moore-Penrose pseudo-inverse of r(x) becomes rank deficient and the estimation errors become large under the presence of noise. Let R E F be the matrix of the reflectance samples REF=[r(xt)r(xe)...r(x,)] with dimensions nxN. As other authors we take advantage of apriori knowledge on the spectral reflectances, by assuming that the reflectance r(x) in each pixel is a linear combination of a known set o P smooth reflectance functions: r'(x)=r(x)b where b is the matrix of the basis functions. As the basis functions in our formulation the first 8 eigenvectors of the matrix ofreflectances REF is used. So we have an initial supervised learning stage for the determination of the matrix b, followed by a supervised learning stage for the detemination of the matrix Q'. The matrix O' maps the multispectral system responses to the training set of reflectance samples, with the selected filter subset, to the coefficients of the linear model which describes the reflectances of the training set. After that for each output of the system:
r'(x) = Q ' a ( x )
(5)
r ( x ) - r'(x)b-'
(6)
We suppose that the multispectral system is builted upon a specialized task in which the space which the irradiances span is known apriori. Given a set of filters with known spectral transmitance, a monochromatic camera with known spectral response and a set of irradiances under given and stable illumination conditions, our method selects the subset of filters, in order to optimally reconstruct the initial set of reflectances given the multispectral system's output. For each selected subset the reflectance reconstruction was executed and the reconstruction error was evaluated using colorimetric and spectral measures.
3. Filter selection m e t h o d s
The selection of the filters plays a fundamental role in the performance of the multispectral image acquisition device. In a multispectral setup, incorporating Q filters out of the available set of K filters, the full search approach becomes computationally expensive as K, Q increase. In the following filter selection methods problem is formulated as a feature selection problem using the PFA method [18] and compared with the FVA method, as proposed in [ 17].
3.1 The PFA method The acquisition system produces an output vector for each object reflectance. The dimension of this vector is equal to the number of available filters. In the case of N available object irradiances and K available filters, a data-matrix P of dimensions (NxK) is composed. The PCA derive the orthogonal matrix of eigenvectors T with dimensions (KxK), with the eigenvectors in its columns. There are many methods of determining how many principal components are required to obtain an adequate representation of the data [19, 20], but a theory to estimate the "optimum" number of PCs is missing. Let k be the number of retained eigenvectors of the matrix P. The resulting matrix T' of (Nxk) elements represents a limited set of basis function, which describe the data in a variance preserving manner. |n the PFA method, the vectors Kt, K2.... Kk, corresponding to the columns of T', are used as features and are grouped into Q clusters using the kmeans algorithm and the Euclidean distance. The clusters' centres represent directions in the k th dimensional space of high concentration of features. For each cluster, the filter with the smallest distance from the cluster center is selected as a principal feature. This step will yield the choice of Q features. The reason for choosing the vector nearest to the mean is twofold. This filter can be thought of as the central feature of that cluster- the one most dominant in it, and which holds the least redundant information of features in other clusters.
3.2 The 'voting' PFA method As the PFA method uses the k-means clustering algorithm, the random initialization of the cluster centers drives to variations ofthe results in succeeding
285
trials of the method. For this reason an additional 'voting' step is added. More specific, the filter selection method is repeated for a specific number of times and the selection frequency for each filter is used to derive the final set: The m filters with the highest frequency are selected. 3.3 The FVA method
The Filter vector analysis method [ 17] treats the system's responses for all the filters and for each reflectance as a feature vector. These vectors are called filter vectors. By performing a PCA on these vectors, the directions in the vector space which show the greatest variance are determined. These directions can be considered as the responses of the system using a set of'artificial' filters which are capable to provide greater variance of responses over the target colours. Finally, the filter vectors which are 'nearest' in a Euclidean manner to the principal directions are chosen.
4. Experiments and Image databases In the experiments, the response of the Sony ICX282 CCD spectral sensitivities, the Rosco ecolour+ set of 160 filters and the Munsell book of colour spectral reflectance database of 1269 curves given in [21], is used. The filter selection methods were evaluated under the CIE standard illuminants A and D65. From the above data, a matrix P (1269xl 60) was composed giving the response of the system for each object reflectance and absorption filter. The PFA and FVA methods were used to select 3-10 filters. The corresponding responses were used to obtain a linear mapping to the eigenvalues of the first 8 reflectance eigenvectors using the pseudo-inverse matrix. The reconstruction error was evaluated using the Root Mean Square error (RMS) and the CIE AE2000 measure [22, 23]. In all experiments the PFA filter selection method was repeated 100 times using different initial estimation for the k-means algorithm.
5. Experimental results Each of the aforementioned methods provides a subset of the initial set of filters for the set of target irradiances. Each subset of filters provided by each method gives colorimetric and spectral error
286
distributions over the set of target irradiances. The mean, the standard deviation and the maximum error are selected as the metrics for comparison of the methods. However, the multiple iterations of the FVA method provide multiple subsets of filters (one for each iteration of the feature selection method, because ofthe random initialization of the k-means algorithm.). One set of filters is selected, according to the minimum mean and minimum max error for the spectral RMS [24], and the minimum mean for the AE2000 errors. Each error rate is defined by three abbreviations such as MinMaxRMS, MinMeanRMS and MinMean AE2000. The last abbreviation denotes the colour distance, the middle denotes the distance estimation method for the total set of 1269 reflectances, and the first corresponds to the estimation error using the multiple iterations of the FVA method. In Fig. 1, the mean AE2000 error between the actual and the reconstructed spectral distribution is shown using the 'voting' k-means algorithm for filter selection. These results are compared with the results from the selection of the set of filters with the MinMeanAE2000 error out of the 100 sets and the results of the FVA method. In the same figure the corresponding standard deviation is plotted for all experiments, carried out for three to ten filters using the illuminant A. The FVA method was run once due to the uniqueness of the results. In Fig. 2 the mean and the maximum spectral RMS error between the actual and the reconstructed spectral distribution is shown using the 'voting' kmeans algorithm for filter selection and compared to the minimum spectral mean RMS error. In the same figure, the corresponding curves for the FVA method, under the illuminant A, is given. The same measures are plotted in Fig. 3, 4 for the illuminant D65. For the illuminant A, the PFA method gives superior results comparing with the FVA method, while the 'voting' selection criterion gives also lower error rates comparing with the FVA. In the RMS criterion, the MinMean PFA criterion gives the best results followed by the 'voting' PFA criterion and the worst performance was measured for the FVA method. For the Illuminant D65, the PFA method also give superior results comparing with the FVA method. The 'voting' selection criterion gives also inferior results comparing to the other two approaches, showing lower stability. For the RMS criterion, all methods give comparable results, except from the case of MinMean RMS error criterion for number of filters greater than seven.
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6. Conclusions
The filter selection is a crucial process for the performance of multispectral imaging system. Because of the non-linear relation between spectral reflectances and colorimetric values, the selection of filters that provide the best possible spectral curve reconstruction doesn't impose that the same filter set provides the best colorimetric values estimation. Moreover, the proposed feature selection methods is not optimal by the means of selecting filter set with the target of obtaining the best reflectance reconstruction or the best colorimetric values
-- 4- -- Voting Max RMS Min Mean RMS ~ = - - -*- -- MinMax RMS = Mean RMS FVA . . . . ,0)01 = Max RMS erros FVA Number of filters Fig. 4. Mean and maximum RMS Error between the actual and the estimated multi-spectral distribution for the three filter selection methods using the illuminant D65
estimation. The proposed method adopts an information preserving manner additionally affected by the randomly initialized k-means algorithm. The experimental evaluation gives high-performance results in the case of iteratively choosing several number of filter sets and picking the best out of them according to the selected criteria. If the number of available filters is sufficiently high, the PFA method and its variant the 'voting' PFA provide solutions in feasible time compared with the full search approach. In all experiments, the PFA method gives superior results comparing with the FVA method.
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Acknowledgements This work was supported by the General secretariat of Research and Technology project PENED2001 No. 3049: " X R O M A "
References [ 1] Vora P. and Trussell H., Mathematical methods for the design of color scanning filters, IEEE Transactions on Image Processing, vol. 6, Feb. (1997), pp. 312-320. [2] Vrhel M., Trussell H. and Bosch J., Design and realization of optimal color filters for multi-illuminant color correction, Journal of Electronic Imaging, vol. 4, Jan. (1995), pp. 6-14. [3] Sharma G. and Trussell H., Optimal filter design for multi-illuminant color correction, in Proc. of the Optical Society of America Annual Meeting, (Rochester, NY), Oct. (1996). [4] Lenz R., Osterberg M., Hiltunen J., Jaaskelainen T. and Parkkinen J., Unsupervised filtering of color spectra, in Proceedings of the 9th Scandinavian Conference on Image Analysis, (Uppsala, Sweden), 1995, pp. 803-810. [5] Lenz R., Osterberg M., Hiltunen J., Jaaskelainen T. and Parkkinen J., Unsupervised filtering of color spectra, Journal of the Optical Society of America A, vol. 13, July (1996), pp. 1315-1324. [6] Wang W., Hauta-Kasari M. and Toyooka S., Optimal filters design for measuring colors using unsupervised neural network, in Proceedings of the 8th Congress of the International Colour Association, AIC Color 97, vol. I, (Kyoto, Japan), (1997), pp. 419-422. [7] Burns P., Analysis of image noise in multispectral color acquisition. PhD thesis, Center for Imaging Science, Rochester Institute of Technology, (1997). [8] Keusen T. and Praefcke W., Multi spectral color system with an encoding format compatible to conventional tristimulus model, in Proceedings of IS&T and SID' s 3rd Color Imaging Conference: Color Science, Systems and Applications, (Scottsdale, Arizona), Nov. (1995), pp. 112-114. [9] Keusen T., Multi spectral color system with an encoding format compatible with the conventional tristimulus model, Journal of Imaging Science and Technology, vol. 40, no. 6, (1996), pp. 510-515. [ 10] Martinez K., Cupitt J. and Saunders D., High resolution colorimetric imaging of paintings, in Cameras, Scanners and Image Acquisition Systems, vol. 1901 of SPIE Proceedings, (1993), pp. 25-36. [ 11 ] Abrardo A., Cappellini V., Cappellini M. and Mecocci A., Art-works color calibration using the VASARI scanner, in Proceedings of IS&T and SID's 4th Color Imaging Conference: Color Science, Systems and Applications, (Scottsdale, Arizona), Nov. (1996), pp. 9497. [12] Maitre H., Schmitt F., Crettez J., Wu Y. and Hardeberg J., Spectro photometric image analysis of fine art paintings, in Proceedings of IS&T and SID's 4th Color Imaging Conference: Color Science, Systems and
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Applications, (Scottsdale, Arizona), Nov. (1996), pp. 5053. [13] Vora P., Trussell H. and Iwan L., A mathematical method for designing a set of colour scanning filters, in SPIE symp. on Electr. Imaging, Science & Techn., Color Hard Copy and Graphic Arts II, vol. 1912, (San Jose, California), (1993), pp. 322-332. [14] Hardeberg J., Brettel H. and Schmitt F., Spectral characterisation of electronic cameras, in Electronic Imaging: Processing, Printing, and Publishing in Color, vol. 3409 of SPIE Proceedings, (Zurich, Switzerland), May (1998), pp. 100-109. [ 15] Vora P. and Trussell H., Measure of goodness of a set of colour scanning filters, Journal of the Optical Society of America A, vol. 10, July (1993), pp. 1499-1508. [16] Hubel P., Sherman D. and Farrell J., A comparison of methods of sensor spectral sensitivity estimation, in Proceedings of IS&T and SID's 2nd Color Imaging Conference: Color Science, Systems and Applications, (Scottsdale, Arizona), Nov. (1994), pp. 45-48. [17] Novati G., Pellegri P. and Schettini R., Selection of filters for multispectral acquisition using the Filter Vectors Analysis Method, Proc. Color Imaging IX: Processing, Hardcopy, and Applications, Proceedings of SPIE Vol. 5293 (R. Eschbach, G.G. Marcu eds.), (2004), pp. 20-26. [18] Cohen I., Tian Q., Zhou X., and Huang T., Feature selection using principal feature analysis, Univ. of Illinois at Urbana-Champaign, (2002). [19] Jolliffe, T., Principal Component Analysis, SpringerVerlag, New-York, (1986). [20] Berthold M. and Hand D., Intelligent data analysis Second Edition, Springer, (2002). [21 ]http ://www.c s.joensuu.fi/--spectral/datab ases/download/ munsell_spec_matt.htm [22] Luo M., Cui G. and Rigg B., The development of the CIE 2000 colour-difference formula: CIEDE 2000, Color Research and application, vol.26, Is.5, (2000), pp. 340350. [23] Luo M., Minchew C., Kenyon P. and Cui G., Verification of CIEDE 2000 using industrial data, AIC 2004, COLOR AND PAINTS Interim Meeting of the International Color Association Porto Alegre, Brazil, November 2-5, (2004). [24] Viggiano J., Metrics for evaluating spectral matches: A quantitative comparison Proceedings of CGIV-2004: the Second European Conference on Colour Graphics, Imaging, and Vision. Springfield,VA: IS&T - The society for Imaging Science & Technology, 2004, in press.
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Global sensor feedback for automatic nanohandling inside a scanning electron microscope T. S i e v e r s a a
Department of Computing Science, University of Oldenburg, 26111 Oldenburg, Germany
Abstract
Within the last years a trend towards the automation ofnanohandling processes emerged. One key problem is the implementation of a global sensor to control the position of handling tools and nanoobjects during the whole manipulation process. A global sensor is required to compensate drifts due to thermal and electric effects, which become important at the nano scale. In consideration of resolution, image acquisition time and depth of focus the scanning electron microscope (SEM) is the most suitable sensor. In combination with image processing algorithms high resolution pose estimation is possible. On the other side, the use of an SEM makes high demands on the image processing, because the images are corrupted with strong additive noise due to the required high frame rates. This paper describes how an SEM can be integrated as global sensor as part of an image processing system. Furthermore, three image processing approaches (correlation, edge based and region based active contours) are evaluated, which enable continuous pose estimation of nanoobjects in noisy SEM image streams.
Keywords: Visual sensors, image processing, scanning electron microscopy
1. Introduction
Micro- and nanohandling covers the field of handling objects with sizes in the range of lam, sub-lam and even a few nm. The most important applications today are microassembly, semiconductor technology, nanotechnology, material research, medicine and biology. Famous examples for nanoobjects that are attempted to be handled are carbon nanotubes (CNT) (Fig. 1). CNTs exhibit unique electrical and physical properties due to their specific atomic structure [ 1]. For this reason, they have a huge application potential in nanotechnology. To learn more about CNT properties and to make use of them in nanotechnology products,
the nanomanipulation and characterization of individual CNTs is required [2]. Significant progress in the nanohandling of CNTs has been achieved by the use of an atomic force microscope (AFM) [3]. Here, nano-objects are manipulated by the tip of the AFM probe in only 2 degrees of freedom (DoF). During the manipulation the cantilever-shaped probe is not available for imaging the surface topography, so that AFM images can only be acquired before and/or after the manipulation. Thus, the handling ofnanoobjects by an AFM probe has to be carried out in a "blind" way. The integration of a nanohandling cell into the vacuum chamber of an SEM can solve this problem by real-time monitoring of SEM images.
289
demands on the image processing. High update rates of the pose data for the robot control require a short image acquisition time of the SEM images. As a result, the image noise increases as frame averaging or averaging of the detector signal is time consuming. This paper focuses on the development of a global sensor, consisting of an SEM and image processing software, to enable automatic nanohandling (Fig. 2). Therefore, the paper is divided into two parts. The SEM as a component of an image processing system is introduced in Section 2, followed by the description of the proposed image processing algorithms for the continuous pose estimation in Section 3. The paper is concluded in Section 4. Fig. 1. SEM image of CNT and gripper. The length of the CNT is approximately 6 ~tm. In order to increase the efficiency ofnanohandling, process automation is an important task. Within the last years a trend towards the automation of nanohandling processes emerged [4,5,6], but one unsolved key problem is the implementation of a global sensor to control the position of gripper and nanoobject during the whole manipulation process. Several robotic systems are available for micro and nanohandling. Some of them can be used for automatic positioning of tools or nanoobjects inside an SEM but all of them can only be used in a teleoperated mode, if nanoobjects have to be handled. For automatic nanohandling continuous pose estimation of tool and object is necessary for their closed-loop positioning. Therefore, a global sensor is required to detect drifts due to thermal and electric effects, which become important at the nano scale. The SEM is the most suitable sensor due to resolution, image acquisition time and depth of focus. On the other side, the use of an SEM makes high Scanning electron microscope
High- / Lowlevel Control
2. Integration of the SEM The first problem to be solved is the integration of the SEM into the image processing system. An easy but not elegant way is connecting the video out, that comes with some SEMs, to a standard frame grabber. But the video standard (e.g. PAL) does not cover the real frame rate and scan size of the SEM. In addition, the frame rate is limited to 25 fps, whereas the SEM is able to scan small regions much faster (Tab. 1). Thus, realtime processing of SEM image is not possible by using analogue video out. i, I
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290
The requirement for real-time processing of SEM images is a fast access to the digitized images to process each frame within the image acquisition time. Therefore, the DISS5 hardware from point electronic is used, which can be used with nearly every available SEM [7]. DISS5 provides external beam control and digital image acquisition with direct access to the digitized images. External beam control means that scan area and scan size of the electron beam, a so called region of interest (ROI), can be chosen TM
arbitrarily. This enables a software connection between beam control and image processing algorithm (Fig. 3). Size and position of the ROI are set automatically, depending on the position of the target to be tracked (Fig. 4). Table 1 Acquisition time / frame rate of arbitrarily chosen ROls ROI (pixels) Acquisition time (ms) Framerate (fps) 640x480 256 x 256 128x128
76.8 16.4 4.1
13 61 244
The advantages of using ROIs are high robustness against mismatches, increased processing speed and
fast image acquisition due to smaller input images. At the beginning of the tracking sequence, a start ROI has to be estimated in the full image. After the target is found in the image, a new ROI is defined automatically around the centre of gravity of the target. Movements of the target object are detected by the pose estimation algorithm, and the ROI shifts with the centre of gravity of the pattern match. With this technique, processing speed and robustness of the algorithm are improved. The developed software setup is depicted in Fig 3. The vision software includes a graphical user interface (GUI) that allows the configuration of the image processing algorithms and image display. Only the magnification is changed by the SEM software of the manufacturer. However, an automatic switching is possible via a remote interface.
3. Image Processing 2.1 Noise
Fig. 4. Variable scan area.
The main noise in SEM images is caused by the secondary electron detector and is distributed homogenously over the image [8]. Noise reduction is usually carried out in scanning electron microscopy by averaging frames or by averaging the detector signal of each pixel. The disadvantage of averaging is time consumption, which makes tracking of moving robots difficult. Therefore, averaging is not considered in the following in order to enable the positioning of microrobots as fast as possible. A further source of noise is gray level fluctuation, which is a significant issue in scanning electron microscopy. Gray level fluctuations occur due to electrostatic charge and due to variations of the alignment of the target, electron beam and secondary electron detector. The fluctuations make pattern matching algorithms difficult to succeed, if the gray levels of the filter mask (pattern) are fixed, which is mostly the case. Another important source of noise is background clutter, which is caused by objects in the image background and/or in the image foreground. Objects in the image foreground can hide the target object while background clutter often masks edges. In consideration of the problems mentioned before, the image processing algorithms have to fulfil the following requirements: high robustness against noise (additive, gray level fluctuations, clutter), real-time capability and ability to calculate the desired poses (x,
291
y, ~0 ...) of the targets.
interval [0...L] by two dimensional spline curves r with (N~ -1) basis polynomials B of degree 2. Each basis polynomial is multiplied with a weight q. A one dimensional B-spline is defined as:
3.1 Correlation Correlation techniques are powerful methods for object detection and position estimation in extremely noisy images. Detailed description of correlation based techniques for processing noisy images and many applications are presented in [9]. The application of correlation to SEM images is described in [ 10,11 ]. The result of a cross correlation between two images is a matrix which shows possible displacements of similar input images. Cross correlation is defined as:
C : F-' IF(1). F(p)* ]
(1)
Where I is the input image and p is the target pattern. F denotes the Fourier transformation, C the correlation coefficient matrix and * the conjugate complex. Correlation is an effective approach to find objects in images with additive noise. The disadvantage is high computational time, which increases with improving the image resolution. If orientation and scale of the target object are needed for control purposes, the computational time increases further. Hence, there are limits when applying correlation to real-time object tracking. By using ROIs as described in the previous chapter, the performance drawback can be overcome. It has been shown in [ 12], that real-time tracking in the the x/y plane and orientation estimation is possible for ROIs with up to 256x256 pixels. The disadvantage of the cross correlation approach is that changes of the targets' shape are difficult to recognize. If the accuracy of the coarse positioning is insufficient, additional SEM magnifications are needed for an iterative positioning. Thus, additional pattern vectors have to be used as the scale of the target changes with the magnification. A further problem is shape variation by object deformation e.g. during gripping.
N B -1
x(s)-~qnB,(s)
for 0 < s < L
(2)
n=0
The shape of the B-Spline can be manipulated by a variation of the weight vector of the basis polynomials, therefore the weights are control parameters for tracking. A two-dimensional spline curve r(s) = (x(s), y(s)) is built up from two B-splines with the same basis polynomials. Usually, not all possible shapes of the contour are of interest. Just the shapes which can be adapted by the target object are relevant. Therefore, possible variations of the B-Spline are confined to a shape space of dimension n representing degrees of freedom of the target object. For tracking an object in the plane, a three-dimensional shape space is required, with the DoF x, y and qg. To work with different magnifications, a shape space with one more DoF for scaling is necessary. For this reason, the shape space of Euclidean similarities is the best choice. To fit the contour of the target object with the BSpline, an appropriate control vector has to be determined. A coarse control vector is initially defined by hand, which is adapted iteratively to the target shape. The deviation between spline curve and object contour is estimated by measurement lines, which are arranged orthogonally (with normal vector fi(s) ) to the B-Spline with equidistant space. Each measurement line is searched for intersections with the target object. The intersection points r~s;) can be found by an edge detector applied to the lines. After that, a distance between intersection point and B-Spline is calculated. Finally, the control vector adapts the target, taking into account all the distances calculated. 2
3.2 Edge based active contours The main difference between object tracking with cross correlation and with active contours is the representation of the target object. The parameterization of the target by its contour enables multidimensional tracking by using a simple mathematical framework [13]. The contour of the target object is mathematically defined over the
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Tracking is carried out by recursive fitting, which means recursive calculation of Eq. 3. The fitting is carried out with respect to the underlying shape space, which means that the contour is fitted to the target using Euclidean transformations (x, y, ~0and scale). The challenge of applying this approach to SEM images is the choice of a robust edge detector as the
feature detector is crucial for a successful tracking. The best performance, with respect to additive noise and gray level fluctuation, has been achieved with the matched filter.The error of the detected edge position grows with increasing noise but remains within an acceptable range. Sensitivity against additive noise, which leads to low pose accuracy, is the main disadvantage of this approach. The calculation speed depends linearly on the number of control points and on the length of the measurement lines but not linearly on the image size. Normally, micro and nano manipulators like grippers and AFM probes have a simple geometric shape. As a result, their shape can be modeled using just a few control points. Often 15 - 30 control points are enough to model the shapes. An Active Contour with 15 control points, 61 measurement lines (21 pixels long) can be minimized within 23 ms. A more detailed description of this approach is presented in [12].
3.3 Region based active contours Besides edge based minimization of an active contour, as described in the previous section, region based minimization is an alternative. This approach is also called image segmentation. The aim of this approach is the segmentation of image regions in consideration of specific statistical parameters. It is assumed that the gray levels x of the whole image s can be described with one probability distribution. A further assumption is that specific statistical parameters (e.g./~, or) are different for single image regions [14]. The gray levels in SEM images are Poisson distributed:
By using Eq. 4 and Eq. 6 an energy function E can be defined that becomes minimal, if the regions a and b are completely separated by 0.
.
/N~
with f ( z ) = - z In z , x: gray levels, N: number of pixels. The segmentation process is initialized with a polygon (active contour), which is iteratively minimized using Eq. 7. A segmentation process is depicted in Fig. 5. The minimization is carried out by stochastic movement of each polygon point. After each movement, the energy E is calculated and the movement is canceled if E is higher than before. Otherwise, the polygon is updated. Tracking is carried out by minimizing the polygon in each frame, starting with the final polygon of the last frame. The disadvantage of this approach is a high sensitivity against clutter, because the polygon tends to expand over objects (clutter) with similar mean gray level. Hence, stable tracking becomes impossible in cluttered environments. The solution of this problem is the restriction of the minimization to the possible transformations of the target. Instead of randomly moving each polygon point, the whole contour is
n
P(x) - Z (Y(x- n)e -P p ne N n!
(4)
From this, a maximum likelihood estimate /)(s) for the gray levels can be defined:
~(s)-argmaxL(s /J) - a r g m a x n P ( x It
I/1 )
(5)
xE s
If an image is segmented into two regions a (object) and b (background), separated by 0, the maximum likelihood estimate of O(s) can be calculated by: tg(s) - arg max (L(a I~,,O)L(blf~h,O))
(6)
Fig. 5. The segmentation is initialized with a rectangle (top left). After each iteration (left to right, right to left), the contour is extended with additional polygon segments (top down). minimized in the transformation space (e.g. Euclidean
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similarities: x, y, q~and scale) of the target object. The advantage of this approach is a very high robustness against additive noise as result of taking into account the probability distribution of the gray levels (Eq. 4). The calculation speed depends mainly on the size of the ROI, but is far below the image acquisition time, even for large ROIs (Tab. 2). Table 2 Performance of region based Active Contours ROI (pixels)
Polygon Euclidean SEM image segments Segmentation (ms) Acquisition (ms)
400x300 400 x 300 640 x 480 640 x 480
33 69 33 69
15 16 30 31
30 30 77 77
4. Conclusion One key challenge for a successful automation of nanohandling processes is the continuous pose estimation of the nanohandling tools and nanoobjects. To solve this problem, the application of a global sensor, consisting of an SEM and image processing algorithms, is proposed. The SEM has been integrated into an image processing system and it has been shown how a ROI can be set automatically, depending on the target to be tracked. Thus, efficient tracking has been achieved. Three algorithms for real-time processing of noisy SEM images have been implemented and tested in the following. The approaches have benefits and drawbacks. Cross correlation can be used if only an estimation ofx, y, ~0is needed and if small ROIs can be applied. Active contours offer more possibilities for the automation of nanohandling processes. The advantage of these approaches is the possibility to enable realtime tracking within the shape space of Euclidean similarities. The region based active contour approach outperforms the edge based due to a higher robustness against additive noise.
Acknowledgements Parts of this work are based on the cooperation between the University of Oldenburg and the University of Cardiff. Financial support in the framework of the ARC-initiative of the German Academic Exchange Service (DAAD) and the British
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Council is gratefully acknowledged (Grant-N~ 313ARC-XVIII-D/0/40828).
References [1] S. Iijima. Helical microtubules of graphitic carbon, Nature, vol. 354, pp. 56-58, 1991 [2] T. Belin, F. Epron. Characterization methods of carbon nanotubes: a review, Material Science & Engineering B, vol. 119, pp. 105-118, 2005 [3] F.J. Rubio-Sierra, W.M. Heckl and R.W. Stark (2005). Nanomanipulation by Atomic Force Microscopy, Adv. Eng. Mat., vol. 7, pp. 193-196 [4] M. Nakajima, F. Arai, L. Dong, M. Nagai, T. Fukuda, "Hybrid Nanorobotic Manipulation System inside Scanning Electron Microscope and Transmission Electron Microscope", Proc. of IEEE/RSJ Int. Conference of Intelligent Robots and Systems, 2004, pp. 589-594 [5] D. Misaki, S. Kayano, Y. Wakikaido, O. Fuchiwaki, H. Aoyama, "Precise Automatic Guiding and Positioning of Micro Robots with a Fine Tool for Microscopic Operations", Proc. of the 2004 IEEE/RSJ International Conference on Intelligent Robot & Systems (IROS'04), Sendai, Japan, pp. 218-223 [6] S.Fatikow, Th.Wich, H.Ht~lsen, T.Sievers, M.Jghnisch: "Microrobot System for Automatic Nanohandling inside a Scanning Electron Microscope", IEEE Int. Conference on Robotics and Automation (ICRA), Orlando, Florida, U.S.A., May 15-19, 2006 [7] www.pointelectronic.de [8] L. Reimer: Scanning Electron Microscopy- Physics of Image Formation and Microanalysis, Springer, 1998 [9] F. Goudail, P. R6fr6gier: Statistical Image Processing Techniques for Noisy Images - An Application-Oriented Approach, Kluver Academic / Plenum Publisher, 2003. [10]T. Sievers, S. Fatikow: "Pose estimation of mobile microrobots in a Scanning electron microscope" Proc. of the 2nd International Conference on Informatics in Control, Automation and Robotics (IC1NCO), Barcelona, Spain, September 2005 [ 11]HW Tan, JCH Phang, JTL Thong. Automatic integrated circuit die positioning in the scanning electron microscope. Scanning 24 (2), 86-91, 2002 [ 12]T. Sievers, S. Fatikow: "Real-Time Object Tracking for the Robot-Based Nanohandling in a Scanning Electron Microscope", Journal of Micromechatronics- Special Issu on Micro/Nanohandling, to appear 2006 [ 13]A. Blake, M. Isard: Active Contours, Springer, 2000 [14]P. R6fr6gier, F. Goudail, C. Chesnaud. Statistically Independent Region models applied to correlation and sementation techniques, Euro-American Workshop on Optoelectronic Information Processing, pp. 193-224, 1999
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Print-through prediction using ANNs Kesheng Wang* and Berit Lorentzen Knowledge Discovery Laboratory Department of Production and Quality Engineering, Norwegian University of Science and Technology, N-7491 Trondheim, Norway
Abstract A newsprint mill is using modeling techniques to control print-through on the paper machine. The model analyses the key variables in process and furnish which affect print-through, and thus enables quality teams to predict print-through and make the necessary adjustments to the furnish or the process. In this paper, we use Artificial Neural Networks (ANNs) modeling approach to control print-through level instead of regression models. The intelligent model is flexible, noise-resistant, fast and reliable. The model is developed by a commercial Artificial Neural Network development tool - Neuframe. Relative merits of ANNs for the automatic visual inspection task have been discussed. The system developed can be used for quality control and production management of paper products. Keywords: Print-through, Artificial Neural Networks, visual assessment, quality control.
1.
Introduction
In a newsprint mill, there is always a need to assess the quality of the different paper products. One of the important issuers is the level of print-through, which measures how much of the printed text and graphics is seen on the reverse side of the paper [1 ]. This is especially important when working with newsprint. The qualitative definition of print-through is the visibility of a print on the reverse side. This is affected by many factors such as ink composition, paper furnish, opacity, ink type and paper structure. [1 ] The requirements for low print-through restrict the possibility to produce printing paper with very low basis weight, and a method for objectively estimating the print through values will be a useful tool in production and product development. Traditional approaches for predicating print through are using linear regression and some other mathematical ways. [6] However, the relationship
between print through and scanning factors are complex and highly non-linear. The current method for analysing print-through is visual judgement by humans. It is not very accurate and dependent very much on judgements of individuals. Scanners and different pre-processing techniques may give input variable values quickly and demand little effort, but they will result in a certain amount of noise. All of these make it difficult to establish correct mathematical model. [7] Computational intelligence (CI) is an advanced information process technique. [2] [5] It can easily to tackle non-linear, uncompleted and ambiguous systems. In this paper, we propose an Artificial Neural Networks (ANN) approach to predicate print through based on the scanning parameters of the paper. ANNs are based on the biological structure of the brain and can be learned from experienced datasets. Creating a ANN model could be done in a few minutes using a development tool. However, before doing this, the problem will have to be analysed, and the data must be properly pre-processed. Without a good
* Corresponding author: E-mail: [email protected]; Tel.: +4773597119; Fax:+4773597117
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understanding of the task and dataset, the modeller will probably not be able to produce a good result. In this study, the Neuframe a development tool of ANNs, has been used. [3] It is a robust and easily learned tool, which is also able to be linked to Excel and export networks as standard programming code. This paper is organized into six sections. Estimation of print through due to the parameters obtained by scanning process is described in section 2. An ANN model, data acquisition and data pre-process are discussed in section 3. The solution processes are presented in section 4. The main results and discussions are shown in section 5. Conclusions of the study are drawn in section 6. 2.
Problem descriptions
2.1.
Print-through
Print-through, the visibility of a print on the reverse side, is one of the most important quality parameters of printing paper today. It is influenced by factors such as ink composition, paper furnish, opacity, ink type and paper structure [1 ]. The requirements for low print-through restrict the possibility to produce printing paper with very low basis weight, and a method for objectively estimating the print-through values will be a useful tool in production and product development. There are many approaches to measure the value of print-through. One popular method of evaluating the value of print-through is called visual judgement, where several experienced judgers rank the different paper samples from 0 to 100 and the average is calculated as the "objective" value of print-through shown in Table 1. There are large variations in the judgers' rankings. Table 1. Visual assessment and average values. Sample
1 2 3 4 5 6 7 8 9 10 11
296
J1
0 5 15 30 25 55 65 70 70 70 100 ,.
.........
,
J2
0 0 19 10 19 44 70 88 100 100 82
J3
30 30 30 40 40 40 70 70 70 80 100
J4
0 40 25 50 60 55 85 65 75 90 100
AVG
8 19 22 33 36 49 73 73 79 85 96
STDEV
15,0 19,3 6,6 17,1 18,3 7,7 8,7 10,1 14,4 12,9 9,0
2.2.
Analysis data
The data to be modelled are results from the paper analyses done at the Paper and Fibre Institute (PFI). They include 18 different print-through scores, ranging from 0 to 100. Unfortunately, these are not evenly spaced, and the accuracy is not necessarily high. There was a lack of accurate data between 50 and 75, which could cause some problems when training a model. The maximum and minimum values are calculated with a confidence interval of 95% using: Xmax_mi
n
=
X ~__
1,96 cr
This measure indicates the precision of the visual method. Not all samples are necessarily suited for training a model. The input values come from scans of the reverse side of printed paper samples, using an Epson Perfection 3200 scanner in reflective mode, with a resolution of 600 dpi. From these pictures, a number of parameters can be found through different methods of post-processing. The parameters available in this analysis are: 1. Greylevel analysis parameters. (Average greylevel, Variance of greylevel, Skewness, Standard deviation, Quantile, Variance in the given wavelengths in x-direction, and Variance in the given wavelengths in y-direction) 2. Fast Fourier Transform (FFT) analysis parameters. (Total energy, Energy wire (w), Wire index (w), and Highest peak (w)) 3. Other analysis parameters. (GSM, Visual Assessment, and Stdev Visual). 3.
3.1.
Artificial Neural Networks
Consideration
In this project, SBP (Standard Back-Propagation) algorithm in the Neuframe [3] was selected as the ANN model. For some pre and post-processing and model analysis, MS Excel was used. The different model settings show some kind of relevance to the problem, but many yield a significant error in calculating the quality assessment. The confidence level of the ultimately chosen model has to be high if it is to replace manual inspection. The strength of ANNs is the way they mimic the human brain, which leads to solve the high non-linear problems. ANNs methods are usually easy to use, but the quality of the
pre-processing and the completeness of the data sets will determine the quality of the model. Preprocessing and a thorough understanding of the problem have to be done before the actual network building, and this task might account for more than 75% of the time spent on a ANN project.
3.2.
SBP Artificial Neural Network
Standard back-propagation is the most commonly used algorithm in ANN practical applications. It uses a supervised learning method, which is a good approach when the training data also contains classification information. With the solution known, the network is trained to fit these target outputs. This algorithm includes a forward phase where an input vector is run through the system, and a backward phase, where each node's weight is updated based on the error of this vector's output [5]. One will usually set a maximal difference between the known training targets and the calculated outputs. The training algorithm will then adjust the node weights until this error level is reached. When the network is trained, another set of data might be used as query data or evaluation data. This set is only run through the forward phase, and the results may be analysed - or used in the target application. SBPs work well on problems where an algorithmic solution is hard to define, or would be too complex to work out mathematically. They are best suited for problems with large numbers of examples of the different states, including input and output data. These data should cover the entire range of values possible; if not, the model will be limited to some parts of the problem [3]. Deciding the network structure is usually a trial-and-error-process, though, and a badly structured network will lead to an unsatisfactory model.
4.
4.1.
Solution procedure
Pre-processing
An important issue in this problem is the robustness of the model. Ideally, one will be able to scan paper samples, and plug the images directly into a program including both pre-processing and analysing modules. This should give an unbiased estimate of the print-through value, like a black box. Thus, the ANN module has to recognise the inputs from any possible print-through value between 0 and 100. These may be handled by the encoders included in Neuframe, scaling the input data to values between 0 and 1. The challenge with using encoders, however,
is the way they limit the input data ranges. If a certain value is lower than the lowest known value in the encoder, the model will not run. The final model might have to use another; more flexible way of normalising data, but for the initial investigations, scale encoders was used. For the analysis, 11 different samples of paper were available, each with a different visual assessment. These were scanned 7 times each, giving a total of 77 datasets with inputs and targets. For query data, randomly chosen numbers from this set were used. Later, data for 9 more samples were prepared, each with 10 scans. The models created from the old data did not work too well for all values in the new set. This was probably caused by the uncertainty of the training data, and a better method could be using only training targets with low uncertainty. Testing the samples with higher standard deviations on such a model will probably give high error values, but these might not be correct. For the output formats, there were two alternatives: continuous numbers or discrete categories. When classifying the training targets and outputs as numbers, the network would give numbers between 0 and 100 as results. This looks very accurate, but it is not necessarily the case. The relatively low number of learning inputs limits the network's accuracy, and the program does not include any confidence measures in the query results. Another option is dividing the training targets into categories, like [0,10), [ 10,20) etc. These were labelled by capital letters - A, B, C etc. These need to be decoded by Neuframe (Figure 1) to be processed by the neural network. One advantage of this approach is the way query outputs are displayed. The outputs are categorised, and Neuframe is able to calculate a confidence measure. What could be a problem, though, is the lack of data from every category. There are no training data for the interval [50,60), and thus the model may not be able to recognise accurately any new data sets with such a visual assessment.
4.2.
Choosinginputs
The original data contained many different measures, many of which were correlated. The mathematical model uses "Average", "variance y-dir 0.1-0.3" and "energy wire w", which are good inputs for this kind of model. The advantage of an ANN model in this case, is its ability to use correlated data as well as non-correlated. Actually, more data might often result in a better model. The training data for
297
this kind of model should be representative of as many characteristics of the problem as possible. If the data set is somehow incomplete, some query inputs might not be correctly recognised.
4.3.
Modelling
The different model types used in this study were all based on the templates supplied with Neuframe. The training and query data were all saved and sorted in Excel spreadsheets, and copied to the Neuframe models. In the initial stages of creating a model, a number of different approaches were tried, with settings resulting in a quick training process. These trials gave an indication of how well the data worked with the different models, and were a useful support in further modelling. In some cases, adding noise to the training patterns may reduce the effect of local minima, but this is not necessarily the case. Noise was not used in any models. Setting a low training error might make the model more accurate, but also more specifically trained for the given training set. Low training errors are not always desirable, however; the network's ability to generalise might be lost. This is called overtraining, and is usually a result of an extensive training time and too low training error [3]. Sometimes the network is able to train to such an exceedingly low error level, but usually, the error will only converge on a higher value. Any further training is useless, as the error will not be reduced. As quality measures for the models, average and standard deviation of the absolute error were used, based on query results and visual estimates. The standard deviations for the visual assessments ranged from 7 to 19; the final model would have to be more accurate than this. For the final model, a good method for deciding the final output for a sample would be calculating the average of the query output for the different scans. Another method is averaging the input data, but this might not be as accurate. Finding the optimal number of nodes and layers of a standard back-propagation ANN can be a timeconsuming task. Neuframe is, however, able to generate an automatic structure, which works well in some problems. In this problem, different selections of data worked best with different numbers of nodes. When using 25 inputs, nine nodes seemed to work best as shown in Figure 2. Reducing the number of inputs to three changed the optimal number of nodes to ten, but these numbers are purely trial-and-errorbased, and are only valid for models with one hidden layer. The information about the effect of node count
298
on the result was found by changing the number of hidden layer nodes, holding all other parameters constant. To isolate the effect of node count on average error, both sets were normalised to values from 0 to 1. The errors are quite different, but they still show the same: five, seven, nine and ten nodes give low error values. The node count's effect on the model's accuracy has to be evaluated before choosing the final structure; the automatically generated structure is not always the best.
Figure 1. SBP structurefrom Neuframe template.
X1 ~,Hidden Layer: X2 ~ d e S o 25 scanningvalues~ /~: ~ Y ~ Classification X24 --C/~/value X25 Figure 2. The ANN usedfor the prediction. 4. 4.
Connection to other programs Neuframe is able to connect to various kinds of
databases, both for inputs and outputs. This streamlines the process of analysing data, and a good Neuframe model can, when trained, be used as a black box with little or no user interaction. Another option is to extract the code from the network. The network can be saved as Java or C code, which can be run without using Neuframe. There are several advantages to using standard programming languages, the most important being the wide compatibility and the possibilities of further programming. 5.
5.1.
Results and discussions
ANN, non-categorised output
The non-categorised ANN model is able to give any values between 0 and 100 as outputs. The 25
training inputs were normalised with a scale encoder. A training error of 0,05 has been set up. Three different configurations of training/query data were tested. As Table 2 shows, the results differ somewhat from the real values. The average errors and standard deviations are not too large, but this does not mean that the results show the objectively real values. The standard deviations were lower than with the manual method, and ANNs like this clearly have potential for modelling perceptual print-through. Table 2. Query errors, 3 different combinations of training and query data, 25 input variables Real values
Query res.1
0 0,2 8 24,0 13,4 14,5 18,4 16,3 19 16,3 22,25 26,6 33 25,4 36 23,4 49 46,4 64,6 64,6 72,5 65,6 73 70,8 74,4 72,2 75,8 77,2 79 70,8 85 93,3 90 83,5 95,5 92,4 Average error Stdve error
Abs. error 1 16,0 1,1 2,1 2,7 4,3 7,6 12,6 2,6 0,0 6,9 2,2 2,2 1,4 8,2 8,3 6,5 3,1 0,0 4,9 4,4
Query res. 2 3,5 9,2 18,2 26,2 45,8 67,7 76,3 82,2 79,7 74,8 79,4 67,3 24,4 67,9 23,2 44,9 23,0 85,3
Abs. error 2 4,2 0,2 4,0 3,2 4,8 1,9 13,3 6,7 10,2 10,6 11,7 16,4 7,9 9,8 8,9 4,0 20,7 0,0 7,7 5,6
Query res. 3 75,1 28,5 6,3 19,7 49,0 66,8 65,8 89,1 72,6 77,1 66,6 78,5 20,7 56,7 23,1 31,9 5,5 61,0
Abs. error 3 6,3 7,1 1,3 0,0 5,7 8,6 6,4 0,4 7,9 23,4 0,5 12,7 19,1 9,9 4,1 13,5 3,6 0,0 7,2 6,6
To test the versatility of this continuous outputapproach, some of the assessment values from the training data set were excluded. If a model, trained with only low and high values, is able to recognise samples with medium values, the Artificial Neural Network approach might be useful for this type of problem. The inputs for visual assessments between 50 and 75 were not included in the training data for this model. The query results for each sample was averaged and compared with the real values, as shown in Table 3. The model was able to classify some values accurately, while others were clearly wrong. Again, this seems to be caused by the uncertainty of the visual values. The input values from the samples classified as 72,5 and 73 are clearly different, with one query value being 55 and the other being 74.
Table 3. Query errors with values 50-75 excluded from training data inputs, training error 0,04. I Real values 64,6 72,5 73 74,4 75,8
5.2.
Averaged query 82,1 55,4 73,5 83,3 72,4
Abs error 17,5 17,1 0,5 8,9 3,4
ANN, categorized output
Categorised query outputs might be easier to interpret than numerical outputs - and definitely harder to misinterpret. Using discrete categories will only tell if a sample is within a certain range or not, and the possibility of this being correct is substantially larger than with the other approach. A problem, though, is that such a model requires example data from every category to work well. A categorised output will have to be decoded before reaching the query result matrix. This decoder can add an additional column with a percentage value indicating how well the input maps to the query output value. This confidence measure is a good indication of how well the model is working. There was no data set for the "F" category (50-60), though, so this particular data set cannot be used for a final model. The complete set of data gave a model yielding the outputs in Table 4. Table 4. All judges, visual results query result confidence measure. Visual results A
Query result A B B B
D
UNDEFINED E UNDEFINED
H
H H H I H J
Confidence measure 99,07% 75,84% 99,36% 99,84% 66,68% 85,57% 12,16% 98,73% 99,95% 2,39% 99,91% 94,42% 83,51% 85,55% 94,34% 97,35% 23,80% 99,63%
As the confidence measures show, this is a reasonably good model. Most of the paper samples are
299
correctly categorised, and the ones that are wrong, are at most one category from the real one. These assessments might even be more correct than the given visual ones, which are based on educated, though subjective guesses. Some samples might be wrongly categorised using the visual method, and this will cause bugs in the ANN model. The initial set of data, for which the real judging values were known, was also tested. Table 5 shows the results from a model of assessment values from judge 1. These values are not affected by the standard deviation caused by the different judgements, but there are still a few errors. Several different tests of this judge showed that the categories B and C were usually switched in the ANN model. This could be interpreted as human error, or at least as an indication of the difficulties in visually judging print-through. Table 5. Judge 1 categorised. Visual results A B C D F G H H H
6.
Query result A A C B D F G H H H
Confidence measure 79,28% 94,49% 69,03% 98,16% 96,44% 99,61% 95,31% 99,27% 99,40% 94,03% 95,89%
Conclusions
Artificial Neural Networks are able to handle illdefined problems with large number of experimental data sets. Using a continuous output, ranging from 0 to 100, ANN produced a model capable of handling data unknown from the training. A categorised target data set gave more easily interpreted results than the continuous output method, with both categorisations and confidence measures. One of the main challenges with the print-through modelling was indicating training targets that were correct. Inaccurate training data will obviously give an inaccurate model. Some data sets resulted in models that could reach a low training error without starting overtraining, but these would have to cover the entire training target range. A number of challenges and problems were encountered. The variability of print-through assessments done by the human judges seemed to be the major obstacle for making a good ANN model. The most important
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success factor is that the entire input data set should, if possible, span the entire input and output range. Also, a model made with a larger training input volume would probably be more accurate. The intention of the final model is to integrate the existing paper scan analysis tool with the classification strength of computational intelligence methods. Neuframe's ability to export trained networks to standard programming code makes it a useful tool for this kind of modelling. More work is clearly needed before a functioning and powerful ANN model can replace the human eye, but the method has clearly shown the potential.
Acknowledgement The authors are members of the EU-fund FP6 Network of Excellence for Innovative Production Machines and Systems (I'PROMS) and thank Gary Chinga for supporting the collection of data. Thanks also to the National Natural Science Foundation of China for the financial support under grant No.50375090.
References Antoine, C. and Eriksen, O, Print-through defect in newsprint - A literature survey, NRP report 18, 2003, p. 21. [21 Kalogirou, S. A., Prediction of flat-plate collector performance parameters using artificial neural networks, Solar Energy, vol. 80, no. 3, 2006, pp. 248-259. [3] Neuframe User's Guide, Neural Computer Science- Manufacturing Intelligence, UK. [4] Hecht-Nielsen, R., Neurocomputing, AddisonWesly Publishing Co, 1990. [5] Wang, K., Applied Computational Intelligence in Intelligent Manufacturing systems, Advanced Knowledge International, Pty Ltd, Australia, 2005. [6] Camo ASA, Newsprint mill uses computer model to control print-through, Pap. Technol., vol. 39(5), 1998, pp.47-49. [7] O'Neil, M., Jordan, B. Aspler, J., Predicting subjective print-through in uncoated woodfree papers printed by Heatser Offset, llth [1]
International Printing and Graphic Arts Conference, Bordeaux, France, 1-3 Oct. 2002. vol. 1 session 1.
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Visual servoing controller for robot handling fabrics of curved edges P.Th. Zacharia ~, I.G.Mariolis b, N.A. Aspragathos ~, E.S. Dermatas b Department of Mechanical & Aeronautics Engineering, Rion, Patras, Greece b Department of Electrical & Computer Engineering, Rion, Patras, Greece
Abstract
This paper proposes a visual servoing manipulator controller to guide fabrics during the sewing process. The task ofthe end-effector is to handle a randomly located fabric on a table and feed it to the sewing needle along the desired seam. The proposed system is capable of handling fabrics having straight line edges and curved edges with arbitrary curvatures. The vision system identifies the type of the fabric edges and the system decides the procedure that should be followed, so that the fabric is successfully handled. The experimental results demonstrate the efficiency and the robustness of the proposed approach.
1. Introduction
Fabric assembly involves complex operations in which human vision and manual dexterity are essential. Therefore, current industrial manipulators should be capable of manipulating limp materials, since the application of flexible automation to the textile industry is extremely beneficial. An automated machine system requires flexibility in order to handle various types and sizes of fabric and perform 'sensitive' operations such as sewing, where the fabric must be held taut and unwrinkled in order to obtain high seam quality. This is a really complicated procedure where vision and force sensors should be used in order to provide successful stitches, since the sewing process requires precision and quality. In addition, using Artificial Intelligence techniques, it is possible to successfully model problems that are afflicted with uncertainty, subjectivity, ambiguity and vagueness. Only a few researches [1,2,3] have worked on the automatic feeding of fabrics with curved edges into the sewing machine. E. Torgerson et al. [1] introduced a
method for the manipulation of various types of fabric shapes. The determination of robot motion paths is based on visual feedback defining the location of the fabric edges in world coordinates. The system detects the workpiece edge, determines the orientation of the edge points, filters out superfluous edge points for nonpolygonal shapes and computes some geometric parameters. Using these parameters, the coordinates of the robot path point is computed. The developed algorithm was proved to be effective for both polygonal and non-polygonal shapes, whereas the accuracy of the approximation to the desired seam line depends on the camera resolution. M.Kudo et.al [2] developed an automated sewing system comprised by two robots handling the fabric on the table. Visual information was used to control seam path and its deviation from the desired trajectory. Sewing along a curved line is achieved using the internal robot commands for straight-line motion and the visual servoing. The fabric is translated in the sewing direction and rotated about the needle according to the visual feedback information. These two actions were carried out simultaneously. Thus, the
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commanded trajectory was a straight line, but the visual servoing system followed a paper pattern and the trajectory error was within +0,5 mm. S.Amin-Nejad et al. [3] developed a positionbased visual servoing system for edge trimming of fabric embroideries by laser. Two methods for seam trajectory generation were introduced. In the first method, the tracking trajectory is determined only by using the vision data, whereas in the second method, the predetermined path data is modified by the vision data. In their work, a tracking controller uses a feedforward controller in the tangential direction ofthe seam and a feedback controller in the normal direction. The method was implemented for three types of seam patterns: straight line, sinusoidal and circular. However, there is little work done in the field of robot sewing as far as arbitrary curved edges are concerned, but there is some work done in curve approaching in other fields. S.Omirou [4] presents an algorithm for cutter offsetting in CNC machining. The algorithm is based on the direction and proximity criteria. In each iteration, there are eight candidate fixed steps. The best step is the one that satisfies the direction criterion while, at the same time, it satisfies a criterion of proximity that expresses a measure of closeness to the offset. The experimental results presented approved the effectiveness and simplicity of the algorithm. Visual servoing is essential during the sewing process. The visual servoing systems are based on two basic approaches: position-based and image-based visual servo control [5]. In addition, there are the 2 1/2 D visual servoing systems, where a combination of the two aforementioned approaches is used and the error to be minimised is specified both in the image and the pose space. In the sewing process, the robotic handling target requires sophisticated control for the path determination of the cloth. In the present paper, the designed robot control system is based on artificial intelligence and visual servoing in order to handle uncertainty of the system. In this paper, a visual servoing controller is presented for handling during the sewing process. The system identifies the shape of the fabric and then a fuzzy controller guides the fabric to the sewing machine. The focus of this paper is the sewing of fabrics with edges of arbitrary curvature by approximating the curve with small straight-line segments. It is of great importance that the maximum deviation between the real and the desired seam line should be less than an acceptable limit, since seams
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that do not meet this constraint are defective and the joined pieces can not be used for the final product.
2. The robot sewing process The proposed system is a combination of imagebased and position-based control system. The imagebased analysis is used for the identification of the fabric's shape. After the image acquisition of the fabric, the features (vertices for straight edges and dominant points for curved edges), the needle-point and the seam line's orientation are derived from the image analysis. However, the position of the needle is also known in the robot coordinate system. The position of the end-effector on the fabric is random and unknown in the image coordinate system, but the robot system gives feedback of the current position of the robot in the robot coordinate system. Moreover, the relation between the robot- and the image- coordinate system is known from the calibration of the camera. For the movement of the fabric towards the needle, the image based-approximation is used, since both the distance and the orientation of the movement are known in the image coordinate system. For the rotation of the fabric around the needle, the rotation angle is computed in the image coordinate system, but for the rotation of the robot around the needle, the needlepoint is known in the robot coordinate system.
2.1. Transfer, sewing and rotation about the needle Initially, the camera captures the image of the fabric without the gripper on it. The straight-line edges are approximated as described in [6]. For the curved edges, a number of points on the curve are determined as it is described in Section 2.2. Joining processes, such as sewing, are usually performed on a seam 'line' situated inside the fabric edge. The distance between the outer edge and the seam 'line' depends on the piece of cloth that is going to be sewed and is proposed by the manufacturer. In our experiments, this distance is arbitrarily set to 5 mm. Since the outer edges have been approximated by lines, the seam' line' has to be defined. For the straight lines, the seam line is found by transferring the lines 5 mm inside the outer edge and the intersection of these lines constitute the seam vertices. For the curved lines, the control points are transferred 5 mm inside the outer curved edge and the seam curve is defined using these new control points. After the seam edges of the fabric (dashed-line in
Fig. l) have been found, the sewing process is ready to start. The sewing process followed for the case of curved edges is similar to the one described in [6] for straight line edges. Assume that the first edge that is going to be sewed is the curved one, which has been approximated by a number of straight-line segments. needle
1
\x r \
orientation error (0) of the next side in relation to the sewing line and its time derivative are computed. These are the inputs of the fuzzy system that controls the rotation of the fabric around the needle, whereas the output is the angular velocity of the end-effector. When this side of the fabric approaches the sewing line, it is ready for sewing.
2.2. Image features extraction !
seam line
Fig. 1 The fabric lying on the table The process that is followed in order to sew a piece of fabric with a robotic manipulator can be decomposed in three separate processes-phases described in the following paragraphs. Phase 1." The starting point of the first straight-line section (i.e. the point the needle touches at first) is known in advance. The position-error (r) from the needle and the orientation error (0) in relation to the sewing line are computed (Fig. 1). Next, the designed fuzzy decision system outputs the linear and angular velocity of the fabric. In particular, the position error (r) and the orientation error (0) and their change with the time are the input data, whereas the linear and angular velocity of the end-effector are the output data. Given the time step At and the orientation angle q~, the new position of the end-effector is computed. The fabric is transferred to a new position as a result of the movement of the end-effector, which is stuck on the fabric so as no slipping between the gripper and the fabric occurs. This process stops when the edge of the fabric reaches the needle with the desired orientation within an acceptable tolerance. Phase 2: The curve is ready to be sewed. At each step of the algorithm, the fabric is sewed along the current straight-line segment. During sewing, the fabric is guided along the sewing line with a constant velocity, which should be the same with the velocity of the sewing machine, so that good seam quality is ensured. Phase 3: The curved side of the fabric has been sewed and the fabric is rotated around the needle until the next side coincides with the sewing line. The
The vision feedback consists oftwo images bytwo separate cameras. The first image contains the total area of the workspace, while the second is an image of a greater resolution in the area near the needle. The first image is used for tracking the fabric in the working area, while the second is used for the calculation of the deviation from the desired seam path. This is used as the error feedback to the designed visual servoing system for the path planning by the robot. The novel part in this work in relation to [6] as far as vision is concerned is the determination of a set of points approximating the curved seam path. At first the acquired intensity image of the second camera is automatically thresholded and a binary image is produced. In the next step the boundaries of the image's main object are extracted in the form of pixel coordinates in clockwise order. The Teh-Chin algorithm [7] has been implemented in order to extract the dominant points of the curve. The algorithm has been slightly modified so as to be applied to open curves and to smooth the noisy contour. The main contribution of [7] is the determination of the Region of Support of the curve without the a priori use of a scaling factor. In fact the scale is determined at every point allowing both fine features and coarse features to be detected. However, the authors have not addressed the case of noisy curves. In this case undesired dominant points may occur detected by the algorithm as fine features. In order to overcome this problem a smoothing factor has been added by simply increasing the step of the algorithm searching for the Region of Support. Normally it is set to 1 pixel, however in our case it is increased to Stp pixels.
The Stp is defined by Eq. 1,where Nis the length ofthe curve in pixels and a is a real number a E (0,1] declaring the percentage of the integral square error (ISE) [8] of the approximation that is included in each
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step. The ISE depends on the amount of noise in the curve and is set experimentally, taking into consideration that as noise increases so does a. This way the noise is filtered, but the algorithm can still discriminate multiple higher scales. Moreover another modification of the algorithm has been necessary since only closed curves were considered. According to the algorithm every point lies at the centre of its Region of Support, however the criteria set by Teh-Chin in [7] hold in the case of an asymmetric region, which is the case near the edges of an open curve. Thus the area examined in every step of the algorithm in our case is bounded by the two edges of the curve. 2.3.Robot sewing Robot sewing is a complicated task that demands high accuracy, so that good seam quality is produced. The fact that current robots present limitations in their movement makes the whole process even harder. In the special case where the curve is an arc section, the sewing process is relatively easy, since the robot is programmed to follow part of a circle. However, the up-to-date robots can only be programmed to make straight or circular motions. Therefore, sewing a fabric with arbitrary curvatures is a much more complicated task and can only be performed by approximating the curve with small straight-line movements. The procedure that is followed is described in Section 2.3.2. 2.3.1. Robot sewing arc sections In the special case where the curve is a section of a circle, the sewing process along the arc section is rather easy. Since the points that constitute the arc section are known from the image, the centre of the circle can be easily found by the equation that describes the circle. The centre of the circle can also be found in the robot coordinate system, since the relation between millimetres and pixels is known. It is obvious that sewing arc sections is a position-based procedure. For sake of simplicity, a piece of fabric with one edge that is constituted by a semicircle edge is going to be sewed (Fig.2). Suppose that the needle is at point A, point O is the centre of the circle and point B is the position of the robot end-effector on the fabric. The end-effector B should make a semicircle with centre O and radius OB in order that all points of the semicircle edge pass through point A. This can be done by programming the robot to make a circular movement
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of 180 ~ through the internal robot command (MVR) and defining three points in the robot coordinate system: the centre, the starting point B, and the ending point of the end effector.
i
,,7
B m'/
Fig. 2 Fabric with a semicircle edge It should be mentioned that the pieces of fabrics that constitute real clothing are mainly composed of arbitrary curvatures. Circular or semi-circular seam paths are mainly used for aesthetical or decorative reasons. 2.3.2. Robot sewing arbitrary curves The need for approximating the curved edges through straight lines arises from the fact that current robots can only be programmed to make straight or circular motions. However, sewing a curve with arbitrary curvatures is a complicated task that requires special treatment. Our major goal is to ensure that the deviation from the desired path is lower than a predefined acceptable limit, so that the seam can be considered successful and the seam quality is satisfactory. In our method, the curved edge is approximated by small straight-line sections defined by sequential dominant points. The dominant points are detected by the image analysis, so that the maximum deviation between each line segment and the corresponding curve section is lower than 12 pixels (--1 mm), as described in Section 2.2. Fig.3 shows the piece of fabric that is going to be sewed and the dominant points detected on the curved edge. Firstly, the dominant points (A, B, C, D,...) are extracted from the image. Then, the 'seam line', which is inside the outer edge, is found. The fabric is moved from its initial random position so that the first dominant point coincides with the needle-point A" (Fig.4). After that, the fabric should be rotated about the needle by the angle ~, so that the straight-line section A'B" coincides with the sewing line. Since the
respectively. Consequently, the next line-segment to be sewed is defined by the needle-point and the next dominant point.
3. Experimental results
Fig. 3 Image of the fabric at the area near the needle coordinates of the needle point A" and the dominant point B" are known in the image-coordinate system, the orientation angle of line section A'B" can easily be computed. The angle ~ is computed as the difference between the sewing line's orientation angle minus the orientation angle of the line section A'B'. sewing line
I I I
a(xA,YA
r ' /'/~
A; ................
B"
,, ~ D(XD YD) ~ ~ end-effector
The experiments were carried out using a robotic manipulator with 6 rotational degrees of freedom (RV4A) and controlled by a PC AMD Athlon (tm) 64Processor 3000 + 1,81 GHz running under Windows XP. The robot has been programmed in Melfa-Basic language in Cosirop environment, while the analysis of the visual information is performed with Matlab 7.1. The vision system consists of a Pulnix analog video camera at 768x576 pixels resolution RGB
Fig. 5 Image of the fabric at the area near the needle
~_
~
c,- - ~ = " ~ ~ " ~ - - . i ~
~
]
Fig. 6 Binary image of the fabric's boundaries Stp-1, 19 dominant points (black circles)
~S
Fig. 4 Robot motion for sewing the fabric To achieve the rotation of point B" around point A" by ~ counterclockwise, the end effector should rotate by ~ around point A" on the xy-plane. Since point A" and the current end-effector's position are known in the robot coordinate system, the final point ofthe end-effector is computed. After the robot moves to its new position rotating around the needle, line section AB has obtained the same orientation with the sewing line. The end-effector moves straight with a constant speed (equal to the sewing machines speed) in the direction of the sewing line (expressed by the vector S ), until point B" reaches point A'. Point B' has been definedin the robot coordinate system using the relationship between pixels and millimeters. After that, the whole procedure is iterated, until the last straightline segment is sewed. At this point, it should be mentioned that point B reaches point A with a small deviation resulting from the vision errors and the robot position error, which is within + 1 pixel (=0.08 m m ) a n d + 0.03 mm
Fig. 7 Binary image of the fabric' s boundaries Stp=10, 4 dominant points (black circles) with focal capabilities ranging from l m-oc and an analog video camera of the same resolution using Sony CCD and Samsung zoom lenses. Both cameras are fixed above the working table in a vertical position, so that the fabric is kept in the field of view of the first camera during the servoing and the area near the needle is in the field of view of the second camera. The cameras are connected to Data Translation Mach 3153 frame grabber through coaxial cables. The shape of the fabric consists of two straightline segments and an arbitrary curve, its colour is red and it has large resistance in bending so that its shape remains almost unchangeable and flat without folding or puckering during handling on the table. A pointer fixed at the contact point-position was used instead of an actual sewing machine, because the intent of the demonstrations was the approximation of an arbitrary curve and not the effects associated with the sewing process. At this point certain parameters of the image
305
feature extraction stage have been set. The image near the needle (Fig.5) has a resolution of 305 dpi, which means that 1 mm in space corresponds to 12 pixels in the image, Nhas been set to 120 pixels (corresponding to 1cm) and a to 0.0833, so the step has been 10 pixels. In Fig.7 the fabric's boundaries (white curve) and the detected dominant points by the modified algorithm are illustrated, while the results of the original Teh- Chin algorithm are shown in Fig.6. It is clear by these figures that in our case a smoother, but still accurate, version of the noisy contour is derived.
without requirements in special geometrical computations. Fuzzy logic in robot motion control increases the intelligence of robots and enhances the capability of dealing with uncertainty. Considering the future research work, the proposed algorithm can be extended so that it can take into account the distortions presented during handling of fabric by the robot.
Acknowledgements
This work is financed by the General Secretariat for Research and Technology of Greek Government as part of the project "XROMA-Handling of non-rigid materials with robots: application in robotic sewing" PENED 01. University of Patras is partner of the EUfunded FP6 Innovative Production Machines and Systems (I'PROMS) Network of Excellence.
5,0 _~ 4,0 o,~
= 3,0
@ ~
"~ 2,0
References
1,0 0
5
10
15
20
25
curve perimeter
30
35
40
Fig. 8 Deviation between real and desired path
[1] Torgerson E. and Paul F.W. Vision-Guided Robotic Fabric Manipulation for Apparel Manufacturing. IEEE Control Systems Magazine (1988), 15-20.
In order to validate the feasibility of the algorithm, the sewing process is repeated many times. For all tested cases, the algorithm is proved to be quite robust and efficient. The deviation (in pixels) between the real and the desired curve is shown in Fig.8. The maximum deviation is 4,7210 pixels (=0.39 mm) and is lesser than the maximum acceptable limit of 12 pixels. The average value for the deviation is 2,9172 pixels (--0.24 mm), which is acceptable.
[2] Kudo M., Nasu Y., Mitobe K. and Borovac B. Multi-arm robot control system for manipulation of flexible materials in sewing operation. Mechatronics 10, (2000), 371-402.
4. Conclusions
[5] Hutchinson S., Hager G.D. and Corke P.I. A tutorial on visual servo control. IEEE Transactions on Robotics and Automation 12(5) (1996), 651-670.
In this paper, a method for sewing fabrics with curved edges is introduced. The curve is approached through small straight motions defined by the dominant points detected by the image analysis and then correcting the path rotating the fabric around the needle. The secondary goal of approaching the curved line within an acceptable limit is satisfied. The experimental results show that the proposed approach is an effective and efficient method for guiding the fabric towards the sewing machine, sewing each one (straight- or curved) edge and rotating it around the needle. The system demonstrates flexibility, since fabric of any shape, size or colour can be sewed
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[3] Am in -N ejad, S., Smith J.S. and Lucas J. A visual servoing system for edge trimming of fabric embroideries by laser. Mechatronics 13, (2003), 533-551. [4] Omirou S. A locus tracing algorithm for cutter offsetting in CNC machining, Robotics and Computer-Integrated Manufacturing 20, (2004), 49-55.
[6] Zacharia P., Mariolis I., Aspragathos N. and Dermatas E. Visual servoing of a robotic manipulator based on fuzzy logic control for handling fabric lying on a table. IPROMS Virtual Conference, 4-15 July 2005, 411-416. [7] Teh C.H.and Chin R.T. On the detection of Dominant Points in Digital Curves. Trans. Pattern Analysis and Machine Intelligence 11, (1989), 859-872. [8] Rosin P.L. Techniques for assessing polygonal approximation of curves. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, (1997), 659-666.
Intelligent Production Machines and Systems D.T. Pham, E.E. Elduldari and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
A Novel Self-Organised Learning Model with Temporal Coding for Spiking Neural Networks D.T. Pham, M.S. Packianather and E.Y.A. Charles MEC, Cardiff University, Cardiff CF24 3AA, UK.
Abstract
In this paper, a novel self-organised learning model with temporal coding is proposed for a network of spiking neurons which encode information through the timing of action potentials. The development of this learning model is based on recent findings in biological neural systems. The Hebbian-type learning equation for the proposed model utilises the time difference between the input and output spikes. The proposed spiking neural network learning model was tested on two sets of benchmark data. Clusters were formed in the output space based on the position of the output neurons and their firing time. The results show that networks trained using action potential timings are capable of learning complex tasks. Keywords: Spiking neural networks, Temporal coding, Hebbian learning, Self-organisation.
1. Introduction
Artificial Neural Networks (ANNs) are one of the most powerful and flexible computational tools available for solving a wide range of problems in many domains. Although the creation and development of ANNs were inspired by biological neural systems, ANNs are considered to be quite limited compared to their biological counterpart due to their much simplified structure and behaviour [1,2]. These considerations led to increased interest in temporal coding spiking neurons which are more biologically realistic artificial neurons and in Spiking Neural Networks (SNNs) based on such neurons. Generally, in biological neural systems, information is conveyed to neurons through neural connections by stereotype electric pulses called spikes. It was widely believed that the information is coded in the frequency of the spikes [3]. The analogue variable in classical neural networks corresponds to the firing rate of a neuron [2].
However, experimental evidence collected over the past few years indicates that many biological neural systems use the timing of single spikes to encode and process information [2,4]. This method known as temporal coding is now accepted as the coding mechanism in biological neural systems. This method of passing information with single spikes could be utilised efficiently for implementing SNNs with VLSI circuits [5]. In the past few years networks of spiking neurons with temporal coding have become an important research area. Hopfield [6] introduced the idea of using the timing of action potentials to represent the values for computation within a network. Maass [2] realised sigmoidal networks (ANNs with sigmoidal activation) and networks of spiking neurons with temporal coding and proved that SNNs are more computationally powerful than networks with sigmoidal activation. Several learning algorithms for SNNs with temporal coding were
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proposed in [7,8,9,10,11]. The results show that the efficiency of SNNs is better than that of sigmoidal neural networks. The aim of this study is to achieve a learning algorithm for networks of spiking neurons with temporal coding, particularly a self-organising learning model. For this purpose, an unsupervised learning model and a mapping technique both based on the firing times of the neurons are proposed. A simple network with spiking neurons is used where the training encodes the input patterns in the connection weights. The proposed Hebbian model is similar to Kohonen's self-organising network in structure and organising behaviour. The network model is simulated through software using a digital computer and applied successfully to cluster two benchmark data sets. The results demonstrate that the proposed model performs better than sigmoidal neural networks and other SNN leaming models. The proposed model achieved higher accuracy with fewer neurons and fewer learning cycles. The rest of the paper is organised as follows. Section 2 briefly explains the spiking neuron and computing with temporal coding. A summary of previous work on SNNs is given in Section 3. In Section 4, a new model for self-organisation is proposed. The results of applying the two sets of bench marking data sets to the proposed SNN model are presented and discussed in Section 5. Section 6 concludes the paper.
2. The Spiking Neural Network A SNN is a network of spiking neurons similar to an ANN where the neurons can be placed in layers and connected through weighted connections. In addition to a weight, a connection in the spiking neuron also has a delay value which will delay the effect of an input spike at the receiving end.
2.1 The spiking neuron The spiking neuron is an artificial neuron which is a mathematical model of a biological neuron. There are several artificial neuron models in the literature which vary in their level of abstraction as well as their descriptive details. The models considered in SNNs are simple phenomenological models which describe the biophysical mechanisms responsible for generating neuronal activity of the neuron by means of its membrane potential. A better example is the Spike Response Model (SRM) [12], which was the base model for the much popular
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SNN model proposed by Maass [13]. The spiking neuron proposed in [ 13] is a simplified SRM neuron aided with temporal coding and assumptions regarding the inputs to the network and the effect of previous spike generations. Equations (1) and (2) describe this simplified spiking neuron. Here a neuron is considered to fire only once during a cycle. This is in accordance with the finding that a single spike is adequate to convey information when the firing time of the spike is considered.
uj(t)= ~Wj"i at-ti)
(1)
e( t)=( t/z) exp(1-t/v)
(2)
where the variable uj(t) specifies the potential of neuron j at time t, e(t) is the spike response function and z'is the time constant ofe(t). When uj(t) reaches a threshold value an output spike will be generated. The output of the neuron is the time when it generates an output. The spike response function e(t), which describes the effect of an input spike at time t, is shown in Figure 1.
2.2 Computing with spiking neurons The model described above is continuous and in order to simulate the model through digital hardware a discrete approach is necessary. For a particular activity, computation is performed over a continuous time window which is divided up into intervals of constant duration. By varying the number of intervals, the precision of computation can be increased or decreased. Within each time slice, the new state of a neuron will be calculated [ 14]. During the simulation the inputs are temporally encoded and presented to the network for training. Temporal encoding represents the input based on the spike timing. A relatively high input value can be specified with an early input spike while a low value could be represented with a late spike relative to the simulation time window [9]. The input spikes will increase the potential of a receiving neuron and an output spike will be generated when this potential exceeds the threshold. A neuron will be allowed to fire once during a cycle and the firing time will specify the output of the neuron. A highly activated neuron will tend to fire earlier [3]. Hence, the neuron which is the first to fire, among a group of neurons, for a particular input vector is more likely to represent that input vector.
inputs. These RBF like models use multiple connections with different delays instead of a single connection. During the learning process, the appropriate delayed connection is selected. In addition to the multiple connections in these models, the proposed population coding further expanded the size of the network thus increasing the computational complexity.
1.0
0.8 c"
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4 Proposed unsupervised learning model
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Fig. 1. Spike response function.
3. Previous work on unsupervised learning in SNNs A number of researchers have proposed several learning algorithms for SNNs. Hopfield [6] introduced an RBF like learning method for a network of spiking neurons based on the timing of firing events. Ruf and Schmitt [7] proposed a Hebbian based learning model which modifies the connection weights according to the time difference between the pre- and post- synaptic firing events. It was showed that the learning rule based on this model was able to modify the connection weights to some value which represents the time difference between the input and output spikes. Later, Ruf and Schmitt [8] proposed a scheme for self organisation in a network of spiking neurons based on single firing events. This work showed that the SNN model along with temporal coding is capable of preserving the topology of the input space very similar to Kohonen's self-organising map (SOM). Selforganisation in SNNs was also achieved by Natschl~iger and Ruf [9] and Bohte et al. [ 10]. These authors have based their work on that of Hopfield [6]. Here the learning model stores the input patterns in the delays which enable the input spikes to arrive at the output neuron simultaneously. The work by Ruf and Schmitt [7,8] marked one of the first steps in spike time based self-organised learning. These models were found to be with limited precision and capability for clustering when applied to real world data. The same limitations were found in the model described in [9]. To alleviate this Bohte et al. [10] introduced a population coding scheme to code the
The network architecture chosen is a simple two-layered fully connected feedforward network. The first layer is the input layer with the number of neurons equal to the number of input parameters. The output layer is constructed with spiking neurons described in section 2.1. Neurons can be aligned in a single or two-dimensional grid. The two layers are fully connected with feedforward connections. Each connection is assigned a random weight value and a delay value. An improved learning model is proposed in section 4.4 based on new findings in computational neurobiology combined with the previous research on self-organised learning in SNNs. Improvements are proposed on spike time based learning (section 4.1) and for stabilising the learning (section 4.2). Along with these improvements a novel spike-time-based cluster mapping procedure is also proposed.
4.1 Spike time based learning Hebbian model is the underlying technique for most of the unsupervised learning in neural networks. According to the Hebb hypothesis, the efficiency of synapses will be increased (excitation) if they are repeatedly active shortly before a post synaptic spike. The strength of the synapses which are active shortly after the post synaptic spike will be decreased (inhibition) [6]. The learning model for this study is based on the learning rule suggested in the biological synaptic plasticity modelling studies by Song et al. [15] and van Rossum et al. [16]. The amount of change due to the spike activity is defined by equations (3a) and (3b) as shown in Figure 2. g(St) = e -a / r,,~p _ b
if &>O
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if6t
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where 6t is the time difference between the presynaptic and post-synaptic firing events; C,~pis the
309
synaptic time constant for depression and potentiation, and is selected to be relative to the spike response function as shown in the Figure 2; b is the bias term. 1.0
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4.2 Stabilising the learning An important improvement proposed in this study is the stabilisation of weights during the training process, which is a major problem encountered in previous models. This problem arises due to the true nature of Hebbian based learning in that it does not have a constraint to restrict the growth or reduction of connection strength. Repeated application of the rule will result in values over or below the allowed range [15]. Two current methods of dealing with this problem are imposing a hard constraint and performing a computationally expensive normalisation procedure [17]. However both methods are considered to be biologically unrealistic. In recent modelling and experimental studies on biological nervous systems, it was found that the synaptic connection strength change is affected by the current connection strength. The change was found to be inversely proportional to the current connection strength during excitation but independent during inhibition [16,18]. Based on these findings a simple rule given in equation (4) is proposed for stabilising weight updating. This rule specifies the allowed maximum increment during excitation and a constant value during inhibition. By choosing appropriate values for time constants this rule would stabilise the learning procedure effectively.
310
f(w~.)= ( w ~ - w~.) / w=~
(4)
where v and u are two competing neurons and w~, is their connection strength. Wmax is the maximum connection strength.
4.3 Neighbourhood realisation Cooperation among the competing neurons is essential in order to realise self-organisation [8]. In the Kohonen SOM, the neighbourhood is realised through physical location of a neuron relative to the winning neuron. The winner neuron in sigmoidal networks is the one which produces the highest output [17]. A similar method is utilised here to order the neurons where the winner neuron is the one which fires first among all the competing neurons. During the training process connections of the neurons which are laterally closer to the winner neuron are updated proportionally to its distance from the winner neuron. The effect on neuron u relative to a neuron v is given by equation (5):
d(u, v) = exp(-(distance(u, v)//2 cr2)
(5)
where distance(u,v) is the lateral distance between neurons u and v in the output lattice; a is the maximum neighbourhood width.
4. 4 Learning rule The proposed learning rule in equation (6) is based on the three suggestions made in sections 4.1, 4.2 and 4.3. The rule is applied to every connection of all active neurons v during the learning process to update the connection strength w~,.
6Wvu= q g( St) f (wv,) d (v, winner)
(6)
where r/ is the learning rate; g, f and d are defined in equations (3a, 3b), (4) and (5); winner is the winning neuron at a particular instant.
4.5 Interpreting the output The output of the network is the firing time of each neuron. The exact firing times of neurons are considered to incorporate a very large amount of information [3]. A neuronal coding scheme based on the firing times known as spike timing code has been analysed in various studies [19].
Table 1 Clustering accuracy of the proposed model Data set Iris Cancer
Output neurons 5x5 5x5
SNN Training cycles 10x 150
10x650
In previous learning models, the time of neuronal firing is utilised only for finding the winning neuron. However, this work incorporates the exact firing time of the winner neuron to map the clusters. In this study, it was found that similar input patterns tend to excite a particular output neuron to fire within some well defined time interval. The firing time of a winning neuron along with its position in the output layer grid formed clusters which reflected the topographical mapping of the input space. Hence a cluster can be specified with a particular set of neurons and a particular firing time interval. The clusters formed by the proposed network model for the applications considered in this study are shown in Figures 3 and 4.
5. Results and Discussion
5.1 Clustering accuracy of the proposed model The clustering accuracy of the proposed model was estimated by applying the learning model to cluster two bench mark data sets. The Iris data set and the Wisconsin breast cancer data set [20] were chosen as both are frequently used for this purpose. The results of applying the proposed selforganising SNN learning model to these data sets are given in Table (1). Figures 3 and 4 show the clusters formed for the data sets. In figure 3, class 1 (Iris Setosa), which is linearly separable from the other two is represented by a separate neuron. The two linearly inseparable classes (Iris Versicolour and Iris Virginica) are represented by a group of neurons and are separated by their firing times. So far, there are no results for clustering real world data sets with self-organising SNNs available in the literature. For comparison purposes, Table (1) includes the results obtained with a Kohonen SOM for the same data sets. The results show that the learning accuracy obtained is comparable to that for Kohonen networks. In addition, it can be seen that
Accuracy % 94 97.5
Output neurons 10xl0 10xl 0
Kohonen's SOM Training cycles 25x150 25x650
Accuracy % 92.5 97
the proposed network model, with reduced dimensions, required fewer learning cycles.
5.2 Weight adaptation It was found that after training the weights of the connections were in a unimodal distribution whereas previous models in [7] and [15] had a bimodal distribution. This shows that the effect of each input through the connection to the winner neuron was balanced after training with respect to the spike strength and the connection strength.
6. Conclusion
In this paper, a novel self-organising learning model has been proposed for SNNs with temporal coding. By utilising the information available in the timing of single spikes, the model demonstrates its capability to learn complex non-linear tasks. The efficiency of the model has been demonstrated by applying it to cluster two benchmark data sets. The accuracy obtained is comparable to that of traditional networks and the SNN model used is smaller in size and needed fewer training cycles. The clustering results presented in this paper demonstrate that the network should be applicable to real problems in the manufacturing domain.
A c kn owl edge me n ts
This research was carried out with support from Cardiff University, the MEC and the EC FP6 I* PROMS NoE.
References
[ 1] Zador AM. The basic unit of computation. Nature Neuroscience supplement, 3, (2000), 1167.
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Fig. 3. Clusters obtained for Iris data set.
Fig. 4. Clusters obtained for Cancer data set.
[2] Maass W. Fast sigmoidal networks via spiking neurons. Neural Computation, 9(2), (1999), 279-304. [3] Thorpe SJ, Delorme A and van Rullen R. Spike-based strategies for rapid processing. Neural Networks, 14(6-7), (2001), 715-726. [4] Gerstner W, Kempter R, van Hemmen JL and Wagner H. Neuronal learning rule for sub-millisecond temporal coding. Nature, 3, (1996), 76-78. [5] Hellmich HH, Geike M, Griep P, Mahr P, Rafanelli M. and Klar H. Emulation engine for spiking neurons and adaptive synaptic weights. IEEE-INNS, (2005), 32613266. [6] Hopfield JJ. Pattern recognition computation using action potential timing for stimulus representation. Nature, 376, (1995), 33-36. [7] Ruf B and Schmitt M. Learning temporally encoded patterns in networks of spiking neurons. Neural Processing Letters, 5(1), (1997), 9-18. [8] RufB and Schmitt M. Self-organisation of spiking neurons using action potential timing. IEEE Transactions on Neural Networks, 9(3), (1998), 575-578. [9] Natschl~iger T and Ruf B. Spatial and temporal pattern analysis via spiking neurons. Network: Computational Neural Systems, 9(3), (1998), 319-332. [10] Bohte SM, La Poutre H, and Kok JN. Unsupervised clustering with spiking neurons by sparse temporal coding and multilayer RBF networks. IEEE Transactions on Neural Networks, 13(2), (2002), 426-435. [ 11 ] Tao X and Michel HE. Data clustering via spiking neural networks through spike timing dependent plasticity. IC-AI, (2004), 168-173. [12] Gerstner W and van Hemmen JL. How to describe neuronal activity: spikes, rates or assemblies? Advances in Neural Information Processing Systems, 6, (1994), 463-470.
[ 13] Maass W. Networks of spiking neurons: the third generation of neural network models. Neural Networks, 10(9), (1997), 1659-1671. [14] Jahnke A, Schnauer T, Roth U, Mohraz K and Klar H. Simulation of spiking neural networks on different hardware platforms. Proceedings of the 7th International Conference on Artificial Neural Networks, (1997), 11871192. [15] Song S, Miller KD and Abbott LF. Competitive Hebbian learning through spike-timing dependent synaptic plasticity. Nature Neuroscience, 3(9), (2000), 919-926. [16] van Rossum MCW, Bi GQ and Turrigiano GG. Stable Hebbian learning from spike timing-dependent plasticity. Journal of Neuroscience, 20(23), (2000), 8812-8821. [17] Haykin S. Neural Networks - A comprehensive foundation (2 nd edn). Prentice Hall, New Jersey, 1999. [18] Bi GQ and Poo MM. Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. Journal ofNeuroscience, 18(24), (1998), 10464-10472. [19] Panchev C and Wermter S. Hebbian spike-timing dependent self-organisation in pulsed neural networks. Proceedings of the world congress on Neuroinformatics, 2001. [20] Newman DJ, Hettich S, Blake CL and Merz CJ. UCI repository of machine learning databases. http ://www.ics.uci. edu/-~mlearn/MLRepository.htm, University of California, Irvine, Dept. of Information and Computer Sciences, 1998.
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Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Publishedby Elsevier Ltd. All fights reserved.
An algorithm based on the immune system for extracting fault classes from instance histories D.T. Pham, A.J. Soroka Manufacturing Engineering Centre, Cardiff University, Cardiff CF24 3AA , UK
Abstract This paper presents a novel means of extracting a fault class from instance histories for variants of a core product that may contain different attributes and values in different orders. This extracted fault class is then used to generate new rule sets for use in a fault diagnosis expert system. A solution based on the biological Immune System is proposed and developed, and is then tested upon several datasets. The algorithm outperforms other algorithms based upon the Immune System when querying such instance histories. Keywords: Immune system, machine learning, fault diagnosis, intelligent manuals
1. Introduction With mass customisation a manufacturer will produce variants of a product, which, although slightly different from one another, are based upon a basic 'core' product. Within an Intelligent Product Manual (IPM) system [1], it is envisaged that these product variants will have their own knowledge bases for fault diagnosis. In the agent-based system proposed in [1],[2], these variant-related knowledge bases will be augmented with new data as service technicians provide reports of new faults. As these reports are submitted, they are added to an instance history which contains all of the fault instances used by a learning algorithm to generate the current knowledge base. With time these histories will become a rich source of product fault information which was not known when the knowledge bases were originally compiled. It is probable that a service technician will at some point encounter a fault that both he and the fault diagnosis system are unable to identify. As mentioned previously, there are potentially a number of other instance histories distributed throughout the system, which theoretically could contain useful information
pertaining to this fault. Therefore it would be logical to query these other instance histories to see ifa similar or identical fault has been encountered previously and is recorded within one of them. 2. Format of Instance Histories and Training Data
2.1 Consistent formatting All variants of a product can be assumed to be built upon a basic core product. This means that a proportion of the knowledge within the instance history will be similar or consistent between all variants. To obtain information from an instance history that is being queried there is a need for the format used for representing the data within instance histories to be consistent throughout all variants. When producing data to generate a rule set, there are essentially two forms of value, namely fault class and attribute. Fault class categorises what is being represented by the attributes within an instance. An attribute can for example be a component, sub-system, system, or indeed any object or characteristic that can have a value or range of values assigned to it. Attributes can take two forms:
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Common: An attribute that is common to all product variants. The attribute can however have values that occur for all variants or values that are unique to a particular variant. Unique: Attributes and attribute values that are unique to a particular variant. The proportion of attributes that are unique is variable and is very much dependent upon the product. The first column of an instance history table will contain the fault class and the subsequent columns all refer to attributes. All of the common attributes are grouped together, in an order that is maintained throughout all instance histories. In addition to this, all common attribute names and values are represented in the same manner, i.e. the spelling and the case are identical. The unique attributes are also grouped together after the common attributes. Unique attributes require to be handled specially.
2.2 Non-conformity of Instance Histories Because of the dynamic nature of the instance histories and the differences caused by variants having unique attributes, problems can arise when querying. For example, the attributes within the case histories may be in different orders, instance histories may contain non-identical attributes and are likely to have differing numbers of attributes. They can be a result of both differences between product variants and also the dynamic nature of the instance histories. When a new fault is encountered, there is usually an area within the new scenario where there are no differences between it and the instance history. This area is labelled the "pre-specified region" or "cyregion". The region that has not been highlighted consists of attributes unique to variants and also of attributes specified by service technicians. As such, it is assigned the name "user-specified region" or "vregion", because the attributes within that particular region are specified by the users of the IPM as new fault reports are submitted.
3. Immune-System Based Retrieval The immune system possesses a powerful pattern recognition feature that can be utilised where a 100% match cannot be achieved, such as the current problem in which the only regions that may be identical are the cy-regions. Despite this, the bond (quality of match between antibody and antigen) should be over a sufficiently large region for the match to be stable [3]. This ensures that weak bonds are filtered out so that no
314
binding will take place between an antigen and antibody that match weakly. Another important feature is the ability to match in more than one dimension [4], due to the 3D nature of antibodies and antigens. The process enables elements contained within an antibody to bind with elements in an antigen that are not explicitly in the same position. In models of the immune system, this is achieved by using a process ofbit-shifting binary elements [4], [5]. Most computational models of the immune-system adopt a binary representation for the antibody and antigen elements. However, it is not practical to use a binary based representation. This is not an issue as a method was presented in [6] for representing attributes.
3.1
Immune-System-based Retrieval Algorithm
To solve the problems outlined earlier a new algorithm is proposed. This algorithm is based upon several existing immune matching functions adapted into a form better suited to the problem domain within which they are being applied. The main retrieval algorithm comprises two separate matching algorithms, one for the cy-region and the other for the v-region. These two algorithms in turn depend on a generic bitmatching, or element-matching, algorithm.
3.1.1
Bit-matching Algorithm
There is a requirement for a separate algorithm to handle the low-level matching between the various elements of the antibody and antigen. This enables match scores to be calculated using different scoring techniques for each separate region. The bit-matching algorithm proposed here calculates the quality of match between the antibody and antigen elements. It is a variation on those described in [4],[7]. These algorithms themselves are based upon mathematical models of the immune system that attempt to replicate the matching processes occurring within the real immune system. One of the most significant differences between the bit-matching algorithm developed here and the immune system is that complement matching is not used, but rather a logical AND matching function is employed. This is because the antibody and antigen elements are not represented in binary form as in [3], [4], [5], [7], [8], but in several different forms, such as numerical (integer and real) and text strings, and therefore the complement of an element cannot always be obtained. AND matching has proved successful [6] and is ideally suited to this particular application. The algorithm for bit-matching is shown in Fig. 1.
As well as matching integers and text strings the algorithm also introduces a means for determining the quality of match between those antibody and antigen elements that are both real numbers. The quality of match is proportional to the degree of similarity between the two elements. So that inexact matches might be performed, it is important to know the match quality, as it is highly probable a range of numbers will be applicable to a particular class of fault. The range within which an antibody and antigen will be able to match is determined using the threshold of the system. The latter parameter is normally used to prevent antibodies binding with antigens when the match is too weak [4],[5],[7],[8]. This allows the antigen and antibody elements to bind with a strength proportional to the degree of similarity between them. In the algorithm presented in Fig. 1, Steps 1 to 4 determine the match if the elements are either text strings or discrete values being represented by integers. The algorithm is not case-sensitive so as to ensure that matches are not missed purely because an attribute value is represented partially or wholly in a differing case. If the two elements are found to be identical, then a match of 1 (step 3) is returned. Otherwise, a match of 0 is returned (step 4). Steps 5 to 10 calculate the match between two real numbers. In Steps 6 and 7, the upper and lower limits for binding between numerical antibody and antigen elements are calculated based upon the threshold of the system. Step 8, checks whether the value of the antibody is between the two limits. If so, the match is calculated in Step 9. The match is based upon the difference between the antibody and antigen elements. This is then divided by the difference between the antigen element and the lower limit of the matching range thus giving a match between 0 and 1. In Step 10, a match of 0 is given if the antibody element does not fall within the calculated range. Step 11 returns 0 if the elements are not of the same type so cannot match. Step 12 returns the value of the match to the relevant matching algorithm.
3.1.2
RegionMatching Algorithms
Due to there being effectively two regions within the instance history, different problems need to be addressed to obtain the match scores for these regions and hence the overall match score. Therefore, two algorithms are proposed that meet the requirements of their respective regions. Because the o-region is common between all instances and their histories, the algorithm developed for matching the o-region is relatively straightforward. This algorithm is required to match element against
corresponding element, as opposed to using more complex bit-shifting strategies such as those applied in [4],[7]. This is primarily because there would be minimal benefit derived from using bit-shifting, as the attributes are in the same relative positions, and also because of the added benefit of reducing the amount of computation by avoiding unnecessary bit-shifting. The algorithm used for the matching of the oregion is based upon the algorithm described in [7]. It is biased towards matches that have more contiguous bits. The algorithm is shown in Eq. 1 where abin denotes the nth element of antibody i, agjn represents the nth element of antigen j, match is a function which has a value of 1 or 0 depending on whether its arguments match or not or in the case of real numbers a value of between 0 and 1, r is the number of regions in which there is more than 1 element matching contiguously and Cr is the number of contiguous matches in the rth region.
(1) The size of the c~-region is specified with respect to the antigen, as the o-region for different antigens in relation to the antibodies may be different. In the work detailed in [7] contiguous regions must have at least 2 matching elements; that is Cr must be at least equal to 2. By using lower values for Cr the overemphasis on contiguous regions is reduced, so that it is still possible for an antibody to have higher scores than other antibodies with more matching elements but smaller contiguous regions. match(antibodyElement, antigenElement):Step1: IF (antibodyElement IS text OR integer) & (antigenElement IS text OR integer) Step 2: IF irrespective of case (antibodyElement IS SAME AS antigenElement) Step 3: match = 1 ELSE Step 4: match =0 ELSE Step 5: IF (antibodyElement IS real number) & (antigenElement IS real number) Step 6: upperLimit = antigenElement * [(1-threshold%*0.01 )+ 1] Step 7: IowerLimit = antigenElement * threshold% * 0.01 Step 8: IF antibodyElement _>IowerLimit & antibodyElement < upperLimit -I antigenEl. . . . ,-antibodyElemen, 1 Step 9: match=l L ~ ; . t ~ [ ~ ELSE Step t0: match = 0 ELSE Step 11: match = 0 Step 12: RETURN match
Fig. 1. Bit-Matching Algorithm
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In the v-region the algorithm must calculate the match score where an element representing an attribute of an antigen may not be in the same position as the corresponding element in the antibody. A straightforward element-to-element match would not necessarily be effective, particularly when the size of the v-region is large compared to the size of the antigen or antibody. The antigen may also have a different number of elements or different elements altogether due to its dynamic nature. These factors necessitate that the abilities of the immune system to match in more than one dimension be implemented. This will enable the algorithm to calculate the quality of the match using a method other than a nth element to nth element direct mapping match. This should facilitate the discovery of potentially matching elements or attribute values in the instance history or in a new fault scenario. The algorithm for the matching of the v-region is based upon the commonly used bit-shifting technique [4],[7]. Bit-shifting is required because, as mentioned above, the elements within this region of the antigen are likely not to be in the same order or of identical type to those in the antibody. The algorithm computes a match score Mij as shown in Eq. 2 and Eqs. 2a-e. In Eq. 2 Gk is a control value that ensures that any negative component scores ~ are eliminated and thus a final match score cannot be negative. The variable k refers to the bit displacement that will take place. Its range is determined by the number of elements, Ev, in the v-region and a threshold s. Parameter s is in turn based upon a percentage of the length of the v-region. Having a threshold ensures that no attempts will be made to obtain a match when the number of elements actually being matched is less than the threshold. This avoids removing needless computation as no positive match could be achieved. In equation eq. 2, the lower limit for Cr is 2, as opposed to 1 which was used for the (y-region matching algorithm. However, instead of summing factors 2 crk for all contiguous regions only the factor for the largest region (amongst all k shift positions) is used. Also, that factor r is now multiplied rather than added to the first part of the match score formula. The net effect of all this is again to reduce the overemphasis on contiguous regions.
/
where
316
-diff
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< k < diff
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(2b)
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3.1.3
/
(2e)
7
Dynamic Threshold
The threshold within the matching algorithm determines the minimum quality of match that is required for an antibody and antigen to bind with each other. The threshold could be constant but this would necessitate very careful consideration as to what value it should be set at to achieve optimum performance with regards to whether antigens are bound (identified) and if the binding is correct. If the threshold is set too high, it is possible that no binding would take place. Conversely, if the threshold is set too low, it is possible that "false positives" might be returned. A dynamic threshold is therefore required that can adjust itself to the current circumstances of the system, and consequently be more appropriate for each different query. Such a dynamic threshold should provide a balance between the numbers of antigens bound and the accuracy of the binding. The dynamic thresholding algorithm is a variation on the one presented in [9] - modified to take into account the size of the v-region as a proportion of the total number of elements (taken from either the antibody or the antigen depending on which has the more elements). Eq. 3 shows the thresholding algorithm, where Thresholdl is the new threshold in terms of number of elements. Threshold0 is the current threshold (no. of elements), averageMatch is the average of the matches, Ev is the number of elements in the user-specified region, E(Y is the number of elements in the prespecified region and i is the number of antibodies. This equation reflects the history of matches that have occurred, in that the new threshold is calculated based upon the average of the matches for all antibodies with a given antigen. The average number of matches is computed according to a very simple matching algorithm that does not use thresholds, bitshifting or contiguous regions, as can be seen in Eq. 3a. Therefore, the algorithm itself is not affected by changes in the threshold. This also allows the threshold to be decreased if the threshold in the previous iteration was found to be greater than the average match. This ensures that the threshold cannot reach excessively high values. The threshold as a percentage
ofthe total number of elements is calculated using [Eq. 3b]. Thresh~
= Thresh~176 +
-2
+ 1 + Ecr + E l )
(3)
averageMatch= i
i
Threshold%=I Thresh~ I xl00 numberElements 3.1.4
(3a) (3b)
Concentration Function
When the system has identified several antibodies that bind with the antigen with an equal match score, a method is required to select the most appropriate antibody. This is achieved by comparing which out ofi antibodies is the best match for the antigen. This "concentration" algorithm, [Eq. 4], uses pure bit-to-bit matching with no thresholds or contiguous regions. The removal of the threshold ensures that the comparison is fair, as it is feasible that when the original matches were made the thresholds were different due to the dynamic nature of the thresholding. Ignoring contiguous areas ensures that any antibodies that have fewer actual matches are filtered out.
bestMatch- max i I Z (match(abin' agn )1
(4)
n
3.1.5
Overall Algorithm
The overall algorithm IFCLEAR (Immune-system inspired Fault-Class Retrieval Algorithm) combines the components presented previously to yield a fault class retrieval procedure that makes essentially an 'informed-guess' classification of the new fault scenario. Partially mimicking the process an engineer would adopt, a mixture of logical deduction and intuition, firstly narrowing down the number of alternatives via the (y-region and then making an informed choice based upon the v-region. IFCLEAR, presented in Fig. 2, will in steps 2-4 first check to see how well the o-regions of the antibody and antigen match, and if this match exceeds the calculated threshold (step 5). This is analogous to operations in the immune system where an antibody must match the antigen sufficiently well to form a stable bond. The (y-region is given a high degree of importance as it is the only area where there is no uncertainty relating to the elements and the attributes they represent. In steps 6-14 inclusive, the matching of the v-regions takes place. If the overall match
between this antibody and the antigen is better than that in any of the previous tests then, in steps 11-13, the antibody becomes the first in the list of matching antibodies replacing all other antibodies in the list. If the score is equal to the current best score, in step 14, the number of best matching antibodies contained within the list is incremented by one. Step 15 then appends the antibody to the list. Step 16 calculates and updates the threshold. This is done until no more antibodies available. If in step 17 there are several antibodies with equal match score with respect to the antigen, then the concentration function is applied (step 18). If only one antibody matches the antigen then that antibody is returned (step 19). Step1: Step Step Step Step
2: 3: 4: 5:
Step Step Step Step Step Step Step Step
6: 7: 8: 9: 10: 11: 12: 13:
Step 14: Step 15: Step 16: Step 17: Step 18: Step 19:
Get Antigen REPEAT Get next Antibody Match o-regions of Antigen and Antibody Calculate match score for o-region If o-region match score _>2. . . . f el . . . . ts in ~ THEN Compare t~-regions Calculate match score for t~-region Overall score = o-region score + t~-region score IF Overall score >_Best match score IF Overall score > Best match score Best match score = Overall score Best matching antibody = current antibody Number matching antibodies = 1 ELSE Number matching antibodies++ Append antibody to list of best matching antibodies Determine new threshold UNTIL no more antibodies available IF Number matching antibodies >1 Use concentration algorithm to get best matching antibody Fault class = class of best matching antibody
Fig. 2. IFCLEAR Algorithm 4. Results and Discussion
To test the algorithm, several data sets were prepared from the commonly available Yeast, Flag and Soybean data sets. The Yeast data set consists of instances that are defined by attributes having either real number or string values. The 10 classes contained within the data set are representing by different numbers of instances. To generate the test data (antigens), examples were taken from each of the 10 classes within the data set. The Flag data set contains only one instance per class. This characteristic will enable assessing the performance of the algorithm when it encounters faults each defined by one instance only. Two test data sets were created, FlagN based upon the mainly nominal representation of attribute values and FlagT with the
317
numerical and text attribute values. To choose examples for the test data, 10 instances were picked at random from the data set. The Soybean data set is represented by instances that cover nineteen separate classes with numbers of examples per class ranging from 13 to 0.3% of the data set. The test data comprises an example of each different type of class randomly chosen from the data set. The Soybean data set is represented in two forms: the discrete integer form and the mixed text/numerical form, SoyN and SoyT respectively. In addition to IFCLEAR, the algorithms tested were: the Farmer algorithm (FarmerF) [4], FarmerS which is a simplified version of the Farmer algorithm and which does not use bit-shifting and is similar to the algorithm used in [8], Huntl [7] and Hunt2 which is a bit-shifting version of Huntl [5]. If necessary, the algorithms were modified to use AND based matching instead of complement matching enabling them to deal with non binary elements. Table 1 shows the probabilities over all the data sets tested. The first is the empirical probability that the algorithm will find a suitable fault class, P(B). This is directly related to the proportion of antigens bound. The second is the probability that the classes which have been bound are correct, P(C), which is related to the accuracy of the bindings. The third is the probability that a correct answer will be returned, P(B and C). This last criterion is important as it considers both whether an antigen is bound and the accuracy of the binding. This can be considered to be the overall accuracy, that is whether the class returned as a result of a query is correct. As a general algorithm for fault class retrieval, IFCLEAR outperforms the other algorithms examined. As IFCLEAR has classified all antigens presented, it could be considered that this might have had a detrimental effect upon its performance. However, the results show that this is not the case because the accuracy over all the data sets for IFCLEAR is higher than for the other algorithms. Table 1 Probabilities Over All Data Sets Algorithm
P(B)%
P(C)%
P(B and C)
FarmerS
79.4
95.8
76.4
FarmerF
80.2
82.2
65
Hunt1
98.6
92
90.6
Hunt2
99.8
83
82.8
IFCLEAR
100
97.4
97.4
318
5. Conclusions This paper has presented a new algorithm for extracting a fault class from instance histories. Incorporating a matching technique that utilises both conventional non-bit-shifting and bit-shifting matching, the algorithm has shown performance improvements over other immune-system-based matching algorithms.
Acknowledgements The MEC is the co-ordinator of the EU funded FP6 Network of Excellence- I'PROMS.
References [ 1] Pham DT., Dimov SS., Setchi RM., Peat B., Soroka AJ., Brousseau EB., Huneiti AM., Lagos N., Noyvirt AE., Pasantonopoulos C., Tsaneva DK., and Tang Q. Product Lifecycle Management for Performance Support, ASME JCISE, Vol. 4, No. 4, (2004), pp305-315. [2] Pham DT, Dimov SS, Soroka AJ, An Agent System for the Gathering of Product Fault Information, Proceedings of the 2nd IEEE International Conference on Industrial Informatcs (INDIN '04), 24th-26th June 2004, pp 536-539 [3] Hightower R., Forrest S., Perelson A.S., The Baldwin Effect in the Immune System: Learning by Somatic Hypermutation, Belew R.K and Mitchell M. (Eds.), Adaptive Individuals in Evolving Populations, (1996),pp 159-167 [4] Farmer J.D., Packard N.H., Perelson A.S., The Immune System, Adaptation and Machine Learning, Physica, Vol. 22, Part D, (1986), pp 187-204 [5] Hunt J.E. and Cooke D.E., Learning Using an Artificial Immune System, Journal of Network and Computer Applications: Special Issue on Intelligent Systems: Design and Application, Vol. 19, (1996), pp 189-212. [6] Hunt J.E., Cooke D.E., Holstien H., Case Memory and Retrieval Based on the Immune System, IN Welose M. and Aomodt A., (Eds.) Lecture Notes in Artificial Intelligence 1010: Case-based Reasoning Research and Development, (1995), pp 205-216 [7] Hunt J.E. and Cooke D.E., An Adaptive, Distributed Learning System based on the Immune System, Intelligent Systems for the 21st Century, Proceedings International Conference on Systems, Man and Cybernetics, Vol. 3, (1995), pp 2494-2499 [8] Forrest S., Javomik B., Smith R.E., Perelson A.S., Using Genetic Algorithms to Explore Pattern Recognition in the Immune System, Evolutionary Computation, Vol. 1, No. 3, (1993), pp 191-211 [9] Cooke D.E. and Hunt J.E. Recognising Promoter Sequences Using an Artificial Immune System, Proceedings of the Third International Conference on Intelligent Systems for Molecular Biology, (1995), pp 89-97.
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Control Chart Pattern R
nition Using Spiking Neural Networks
D T Pham and Shahnorbanun Sahran M a n u f a c t u r i n g E n g i n e e r i n g centre Cardiff University Cardiff CF24 3AA, UK
Abstract
Statistical process control (SPC) is a method for improving the quality of products. Control charting plays the most important role in SPC. A control chart can be used to indicate whether a manufacturing process is under control. Unnatural patterns in control charts mean that there are some unnatural causes for variations. Control chart pattern recognition is therefore important in SPC. In recent years, neural network techniques have increasingly been applied to pattern recognition. Spiking Neural Networks (SNNs) are the third generation of artificial neural networks, with spiking neurons as processing elements. In SNNs, time is an important feature for information representation and processing. Latest research has shown SNNs to be computationally more powerful than other types of artificial neural networks. This paper proposes the application of SNN techniques to control chart pattern recognition. The paper focuses on the architecture and the learning procedure of the network. Experiments show that the proposed architecture and the learning procedure give high pattern recognition accuracies.
Keywords: Control charts, Pattern recognition, Spiking neural networks.
1. Introduction Control charts, developed by Shewhart in 1931, have been the most widely used charts in manufacturing, providing the capability to detect unnatural process behaviour and to indicate whether a manufacturing process is under control [4]. Control charts probably play the most important role in SPC. Control charts are the means for displaying and monitoring variations in a process [1]. A typical control chart consists of a centre line corresponding to the average statistical level and two control limits normally located at ___30" of this value, where 0" is a measure of the spread, or standard deviation in a distribution [2]. There are six main classes of patterns in control charts, normal, upward trends,
downward trends, upward shifts, downward shifts, and cycles [3]. Figure 1 shows examples of patterns in each class. A literature review has shown that research activity in control charts has greatly increased since the 1980's [5]. However, the Shewhart-type control charts do not provide any pattern-related information because they focus only on the latest plotted data points and seem to discard useful information contained in previous points [2]. Identification and analysis of unnatural patterns require considerable experience and skill on the part of the practitioners. Ideally, shop-floor operators should implement the control charts [8] but usually they lack the experience and skill for control chart pattern recognition and interpretation.
319
..... ^ ^
I . . . . v" v " "~F'--Normal
....
" V V " V
Upward Shift i i
Downward Shift [
. . . . . . . . .
ill iiiiiiiiiiiiill .............
Upward Trend
..................
v
Downward
~
Cycle
Figure 1" Six main classes of control chart patterns In recent years there has been intensive research into developing control chart pattern recognition systems. The most popular technique is artificial intelligence which ranges from expert systems [1, 3] to neural networks [6, 7, 13]. Neural networks which will be the focus of this paper generally consist of a number of interconnected processing elements or neurons. How the inter-neuron connections are arranged and the nature of the connections determine the structure of a network. How the strengths of the connections are adjusted or trained to achieve a desired overall behaviour of the network is governed by its learning algorithm. Neural networks can be classified according to their structures (feed forward or recurrent) and learning algorithms (supervised or unsupervised). Pham and Oztemel [13] described a class of pattern recognisers for control charts based on the learning vector quantisation (LVQ) network. They proposed an extended version of the LVQ learning procedure, called LVQ-X. LVQ is a feed forward network with a supervised learning algorithm. Numerical comparisons showed that LVQ-X has better classification accuracy within a shorter training time than LVQ and two of its variants, LVQ2 and LVQ with a conscience mechanism. Currently many neural network researchers are interested in spiking neural networks. A spiking neuron is a simplified model of the biological neuron. It is, however, more realistic than the threshold gate used in perceptrons or sigmoidal gates (employed in MLPs). A clear justification of this is that, in a network of spiking neurons, the input, output, and internal representation of information, which is the relative timing of individual spikes, are more closely related to those of a biological network. This representation allows time to be used as a computational resource. It has been
320
shown that networks of spiking neurons are computationally more powerful than these other neural network models [16]. However, spiking neural networks still lack good learning algorithms and an architecture suitably simple for a time series application such as control chart pattern recognition. This paper will focus on the learning procedure and architecture of spiking neural networks for classifying control charts. 2. Background of spiking neural network
Spiking neural networks are networks of spiking neurons. The status of a neuron is determined by the integration of its excitatory and inhibitory postsynaptic potential (EPSP, IPSP). If the action potential exceeds a certain threshold, the neuron fires, sending signals to each neuron to which it connects through a synapse. Action potential is an electric potential associated with the concentration of charged ions inside the cell. When the cell receives a signal, the signal may cause it either to increase or decrease the potential. A postsynaptic potential can either be positive and called excitatory (EPSP) or negative and called inhibitory (IPSP). A postsynaptic potential results from the firing of other neurons (presynaptic neurons) that are connected through a synapse to a postsynaptic neuron. A synapse plays an important role in neuronal information processing. It is responsible for transforming the spike into a postsynaptic potential (PSP), which causes a variation in the postsynaptic neuron potential. Immediately aider a neuron fires, its potential is drastically lowered, which prevents it from firing repeatedly in some circumstances. An action potential takes a certain time, called synaptic delay. Investigations of the postsynaptic neuron in Figure 2 show an action potential exceeding the threshold value of 0 .
membrane voltage I
0
....... '
t~f)
-
SAP
time
Figure 2: Action potential in the visual cortex of a monkey.
3. An overview of neural coding schemes In real biological systems, signals are encoded by information using specific coding methods. Basically, there are three different coding methods: rate coding, temporal coding, and population coding. Rate coding is the earliest neural coding method. The essential information is encoded in the firing rates and the rate is counted as a spike in an interval T divided by T (averaged over time). More recently, there has been growing recognition that the traditional view of mean firing encoding is often inadequate. Experiments on the visual system of a fly and studies of the middle temporal (MT) area of the monkey have indicated that the precise timing of spikes can be used to encode information. Such a scheme is called temporal coding [22, 23]. In temporal coding, the timing of single spikes is used to encode information. It is considered that the timing of the first spike contains most of the relevant information needed for processing. Population coding is another coding scheme in which information is encoded in the activity of a given population of neurons firing within a small temporal window. This work adopts temporal coding as the code used by neurons to transmit information.
connected spiking neural network with multiple delayed synaptic terminals. The different layers are labelled H, I, and J for the input, hidden, and output layer respectively as shown in Figure 3. The adopted spiking neurons are based on the Spike Response Model to describe the relationship between input spikes and the internal state variable. Consider a neuron j ,
J '
.
~
+ ~+++++-~i .......... v.+:
..........................,. .................-
........................ .............+++?+
......................... +++.,,.+ ...........
times t i , i ~ O j. It is assumed that any neuron can generate at most one spike during the simulation interval and discharges when the internal state variable reaches a threshold. The dynamics of the internal state variable x j ( t )
This structure consists of a feed forward fully
are described by the
following function:
x (t) =
wo y j (t )
(1)
iE Dj
y j(t) is the un-weighted contribution of a single synaptic terminal to the state variable which described a pre-synaptic spike at a synaptic terminal k as a PSP of standard height with delay d k. - ~(t - t i - d k )
(2)
The time t i is the firing time of pre-synaptic neuron i, and d k the delay associated with the synaptic terminal k . Considering the multiple synapses per connection case, the state variable x# ( t ) o f neuron j receiving input from all neurons i is then described as the weighted sum of the pre-synaptic contributions as follows: m
xj (t) - Z Z w/'Yko , (t) i~Dj
(3)
k=l
The effect of the input spikes is described by the function 8 (t) and so called the spike response function and
w 8 is the weight describing the
synaptic strengths. The spike response function ~" (t), modelled with an C~-function, thus implementing a leaky-integrate-and-fire spiking neuron, is given by: t
t 1-- 8 zE (t) = -1" Figure 3: Feed forward spiking neural network
of immediate pre-
synaptic neurons, receiving a set of spikes with firing
y~
4. A typical spiking neural networks 4.1 Spiking neural networks architecture Spiking neural networks have a similar architecture to traditional neural networks. Elements that differ in the architecture are the numbers of synaptic terminals between each layer of neurons and also the fact that there are synaptic delays. Several mathematical models have been proposed to describe the behaviour of spiking neurons, such as the Hodgkin-Huxley model [17], the Leakey Integrateand-Fire model (LIFN) [14] and the Spike Response Model (SRM) [20]. Figure 3 shows the network structure as proposed by Natschlager and Ruf [ 19].
having a setDj
for t > 0, else 8 (t) = 0
(4)
z" is the time constant which defines the rise time and the decay time of the postsynaptic potential
321
(PSP). The individual connection, which is described in [19], consists of a fixed number of m synaptic terminals. Each terminal serves as a sub-connection that is associated with a different delay and weight (Figure 3). The delay d k of a synaptic terminal k is defined as the difference between the firing time of the presynaptic neuron and the time when the postsynaptic potential starts rising. The threshold 0 is a constant and is equal for all neurons in the network.
4.2 SNN for unsupervised learning procedure Previous research by Bohte et al [24] on unsupervised learning used the Winner-Takes-All learning rule to modify the weights between the source neurons and the neuron first to fire in the target layer, using a time-variant version of Hebbian learning. The firing time of an output neuron reflects the distance of the evaluated pattern to its learned input pattern. The first neuron to fire is chosen as the winner. If the start of the PSP at a synapse slightly precedes a spike in the target neuron, the weight of this synapse is increased, as it exerts significant influence on the spike-time by virtue of a relatively large contribution to the membrane potential. Earlier and later synapses are decreased in weight, reflecting their lower impact on the target neuron's spike time. For a weight with delay d ~ from neuron i to neuron j , Bohte et al used equation (6) to update the weights. Aw~ - r/ L ( A T ) - r/(1 - b)e where the parameter b
P~
+b
(5)
determines the effective
integral over the entire learning window, f l sets the width of the positive learning window, and c determines the position of this peak. The value of At denotes the time difference between the onset of a PSP at a synapse and the time of the spike generated in the target neuron. The weight of a single terminal is limited by a minimum and maximum value
of
0
and
Wmax
respectively.
In
their
experiments, At is set to [0-9] ms and delays d k to 1-15 ms in 1 ms intervals (m =16). The parameter values used by Bohte et al for the learning function L(At)
were
b = -0.2,c = -2.85,fl
322
set
to:
= 1.67,r/ = 0.0025
and
Wma x
--
2.75.
To
model
the
postsynaptic
potentials, they used an a-function with Z" =3.0 ms as in equation (4).
5. Spiking neural network in CCPR 5.1 Networks structure This paper proposes a new architecture for spiking neural networks for control chart pattern recognition. The proposed architecture consists of a feed forward network of spiking neurons which is fully connected between the input and hidden layers with multiple delayed synaptic terminals ( m ) and partially connected between the hidden and output layers, with each output neuron linked to different hidden neurons. An individual connection consists of a fixed number of m synaptic terminals, where each terminal serves as a sub-connection that is associated with a different delay and weight between the input and hidden layers. The weights of the synaptic connections between the hidden and output neurons are fixed at 1. Experiments were carried out with a number of network structures with different parameters and learning procedures. The networks finally adopted had 60 input neurons in the input layer, which means the input patterns consisted of the 60 most recent mean values of the process variable to be controlled. One input neuron was therefore dedicated for each mean value. There were six output neurons, one for each pattern category, and six hidden neurons (the number of hidden neurons here depends on the number of classes). Figure 4 shows the details the networks used. Number of inputs= 60
Number of outputs = 6
Number of hidden neuron for each output category = 1 Scaling range = 0 to 1
Initial. range = 0 to 1
Learning rate = 0.0075
Delay intervals 15 (ms) in 10 (ms) intervals Time constant = 170 (ms)
Coding Interval=0 to 1O0
Synaptic delays = 1 to 16 (ms) Figure 4: Details of the proposed SNN used for control charts At the beginning of training, the synaptic weights were set randomly between 0 and +1. The input vector components were scaled between 0 and 1. Using a temporal coding scheme, the input vector components were then coded by a pattern of firing times within a coding interval and each input neuron
allowed to fire once at most during this interval. ats
-
O
Output layer Hidden layer oe
~-~ Figure 6 .............
Inputs
adjusted since only the connections between the input and hidden neurons had multiple synaptic terminals. The adopted spiking neurons were based on the Spike Response Model [18] with some modification to the spike response function in order for the networks to be applied to control chart pattern recognition. The spike response function used in this architecture has been modified to:
c(t)
--
Figure 5" A structure proposed for the spiking neural network
1
~
1-
( e -('+'t~ ,~i -e
- st-----~) (1+ / '~
(6)
tce tci
In this spike response function, tce and tci represent the maximum and minimum time constants respectively and tce=170 (ms) andtci=20 (ms). Here, st is equal t o ( t - t; - d k) where t is the
Hidden layer y
simulating time (0 to 300), t z is the firing time of Input laver Figure 6: Multi-synapse terminals for the spiking neural network In this work, the coding intervals AT were set to [0100] ms and the delays dk to {1,..., 15 } [ms] in 10 ms intervals. The available synaptic delays were therefore 1-16 ms. The PSP was defined by an O~function with a constant time "Z"=170 ms. Input vectors were presented sequentially to the network together with the corresponding output vectors identifying their categories as shown in Figure 7. Unlike the network structure used in [19] and [23], the proposed structure helps to reduce the complexity of the connections where the multiple synaptic delays only exist between the input and hidden neurons. Pattern Normal Inc. trend Dec. trend Up. shift Dow. Shift Cycle
Outputs 1 2 1 0 0 1 0 0 0 0 0 0 0 0
3 0 0 1 0 0 0
4 0 0 0 1 0 0
5 0 0 0 0 1 0
6 0 0 0 0 0 0
presynaptic neurons and d k represents the delay with k=16. With this proposed spike response function, the spiking neural network technique worked well for control chart data. Bohte et al [23] have stated that "Depending on the choice o f suitable spike response functions, one can adapt this model to reflect the dynamics o f a large variety o f different spiking neurons." 5.2 S p i k i n g n e u r a l n e t w o r k l e a r n i n g p r o c e d u r e in control charts
In this work, the unsupervised learning equations in (5) were employed to create a supervised learning equation using the following update equations. If the winner is in the correct category, then W
dw
new
~
-
W
old
"Jff
rl
dw -
dw
where
e
1
- q
> 0
,rA
e
(8)
If the winner is in the incorrect category, then 142 n e w
~
W
old
--
dw
where
Figure 7" Representation of the output categories Only single connections between the hidden and output neurons and the weights were fixed to 1. This reduced the number of weights that had to be
In the simulation, the parameter values for the
323
learning function L(AT) were set to: r/ = 0.0075, fl = 35, ,~, =x/(2 * (22 / 7)) , AT = [0-100], t~ - 0.8, w,e w is the new value for the weight and Wold is the old weight value. The parameter r/is the constant learning rate. Parameter fl sets the width of the positive part of the learning window and AT denotes the time difference between the onset of a PSP at a synaptic terminal and the time of the spike generated in the winning output neuron. Parameter 6~ was used because in supervised learning there is a priori information about the training sets. For this supervised learning procedure, the Winner-Takes-All learning rule modifies the weights between the input neurons and the neuron first to fire (winning neuron) in the hidden layer. The winner will be activated to 1 and the others to 0. The output vectors indicate the identified pattern (see Figure 7). In this learning procedure, only if the winning neuron is in the correct category and the start of the PSP at a synapse slightly precedes a spike in the target neuron, is the weight of this synapse increased, as it exerts a significant influence on the spike-time by virtue of a relatively large contribution to the membrane potential. 5.3 Training Set Experiments have shown that the ability of the networks to generalise was strongly affected by the quality of data available and the effectiveness of the techniques used for analysing the data. The process simulator designed to create the required training data set has been described in [1]. There were 498 training patterns (83 patterns in each category) generated using this simulator. The patterns were sequentially applied to the network. 5.4 Results The generalisation ability of the network, or the classification accuracy level it achieved when classifying patterns it had not previously been taught, was tested using a data set comprising 540 previously unseen patterns (90 patterns in each category). These were also generated using the aforementioned process simulator. The number of test patterns used in this paper is fewer than in Pham and Oztemel [ 1]. The accuracy level was calculated using the following equation:
324
Accuracy (%)
=
No.of patterns correctlyclassified xl00 Total number of patterns tested
The results obtained with the proposed architecture and the supervised learning procedure for control chart pattern recognition are presented in Figure 8 together with the results obtained with an LVQ network [13] and a back propagation neural network. Pattern recogniser
Num.of Learning Test training Performanc performanc epochs e e LVQ-X 20 100.00% 97.70% Backpropagation 200 100.00% 95.00% Spiking NN 15 99.93% 97.85% Figure 8. Results of three different pattern recognisers applied to control chart data set. 5.5 Discussion In Bohte et al's work, a large set of weights have to be adjusted since a connection between two neurons corresponds to sixteen sub-connections, so the size of the network increases drastically with the number of neurons. Modifications were made to suit the typical spiking neural networks used for control chart pattern recognition. The resulting neural network has a simple structure of spiking neurons for control chart data, reducing the complexity of implementation. As in LVQ-X, there is no dependency on the initial values of the weights in spiking neural networks for control chart pattern recognition. The training and adaptation time of the spiking network clearly was shorter than that for LVQ and backpropagation networks[I,13]. At the end of 10 training epochs, the network was able to classify correctly 98.73% of the training data set and 95.89% of the test set. Aider 15 training epochs, the overall recognition accuracy level was increased to 99.93% for the training set and 97.85% for the test set. This shows clearly the superior performance of the spiking neural networks technique in an application to control chart data over the other procedures using traditional neural networks. 6. Conclusion Previously, neural networks have proved capable of data smoothing and generalisation. This paper has shown that spiking neural networks also have a good capability in data smoothing and generalisation. This permits them to recognise noisy control chart patterns not identical to those they have been taught,
as indicated by the good results presented in the paper. Future work will be directed at applying spiking neurons on learning vector quantisation instead of ordinary neurons. The robustness of the proposed spiking neural networks technique on control chart pattern recognition will also be evaluated. Acknowledgement The authors are members of the EU-funded FP6 I'PROMS Network of Excellence. They wish to thank Mr E Charles for his help with the Spiking Neural Network part of this work.
References 1. Pham D T, Oztemel E. Control chart pattern recognition using neural networks. Journal of Systems Engineering, Vol.2 (4), pp. 256-262,1992. 2. Yang M S, Yang J H. A fuzzy-soft learning vector quantisation for control chart pattern recognition. International Journal of Production Research. Vol.40 (12), pp. 2721-2731, 2002. 3. Grant E E, Leavenworth R S. Statistical quality control, 6th edn. McGraw Hill, New York, 1988. 4. Montgomery, D C. Introduction to statistical quality control. Wiley, New York, 1997. 5. Woodall W H, Montgomery D C. Research issues and ideas in statistical process control. Journal of Quality Technology. Vol.31 (4), pp.376-386, 1999. 6. Cheng C S. A neural network approach for the analysis of control chart patterns. International Journal of Production Research. Vol.35 (3), pp. 667697, 1997. 7. Guh R S, Hsieh Y C. A neural network based model for abnormal pattern recognition of control charts. Computers and Industrial Engineering. Vol.36 (1), pp. 97-108, 1999. 8. Juran J M, Gryna, F J JR. Quality Planning and Analysis. Tata McGraw Hill, New Delhi, 1982. 9. Nelson L S. The Shewhart control chart-test for special causes. Journal of Quality Technology. Vol.6 (4), pp. 237-239, 1984.
Production Research. Vol.35 (7), pp. 1875-1890, 1997. 13. Pham D T, Oztemel E. Control chart pattern recognition using learning vector quantisation networks. International Journal of Production Research. Vol.32 (3), pp. 721-729, 1994. 14. Mass W. Networks of spiking neurons: the third generation of neural network models. Neural Networks,. Vol. 10 (9), pp. 1659-1671, 1997. 15. Baldi P, Heiligengerg W. How sensory maps could enhance resolution through ordered arrangements of broadly tuned receivers. Biological Cybernetics. Vol. 59, 1988. 16. Gerstner W, Mass W, Bishop C. Pulsed Neural Networks. MIT Press, Cambridge, 1999. 17. Hodgkin A L, Huxley A F. A quantitative description of membrane current and its application to conduction and excitation in nerve. Journal of Physiology. Vol. 117 (4), pp. 500-544, 1952. 18. Gerstner W, Kistler W. Spiking Neuron Models: Single Neurons, Populations, Plasticity. Cambridge University Press, Cambridge, England, 2002. 19. Natschlager T, Ruf B. Spatial and temporal pattern analysis via spiking neurons. Network: Comp. In Neural Systems. Vol.9 (3), pp. 319-332, 1998. 20. Bialek W, Rieke F, de Ruyter van Steveninck R, Warland D. Reading a neural code. Science. Vol.252 (5014), pp. 1854-1857, 1991. 21. Bialek W, Rieke F, de Ruyter van Steveninck R, Warland D. Spikes:exploring the neural code. MIT Press, Cambridge, 1997. 22. Bohte M. Unsupervised clustering with spiking neurons by sparse temporal coding and multilayer RBF networks. IEEE Transaction on Neural Networks. Vol.13 (2), pp.426-435, 2002. 23. Bohte M, la Poutre H, Kok J N. SpikeProp: Error-Backpropagation for networks of spiking neurons. ESANN'2000, Bruges (Belgium). Pp. 419425, 2000.
10. Nelson L S. Interpreting Shewhart X control chart. Journal of Quality Technology. Vol.17 (2), pp.114-116, 1985. 11. Perry M B, Spoerre J K, Velasco T. Control chart pattern recognition using back propagation artificial neural networks. International Journal of Production Research. Vol.39 (15), pp. 3399-3418, 2001. 12. Pham D T, Wani M A. Feature-based control chart pattern recognition. International Journal of
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Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Engineering applications of clustering techniques D.T. Pham a, A.A. A f i f y a a
Manufacturing Engineering Centre, Cardiff University, CardiffCF24 3AA, UK
Abstract The amount of data being collected in engineering is increasing exponentially and it is no longer practical to rely on traditional manual methods to analyse these data. Clustering, which automatically finds natural groups in the data, is an important data exploration technique. It has many applications in different areas of engineering, including engineering design, manufacturing system design, quality assurance, production planning and process control. Many clustering algorithms have been proposed from different research disciplines. However, efforts to perform effective and efficient clustering on large data sets only started in recent years with the emergence of data mining. This paper provides a review of various clustering algorithms in data mining and describes a number of important engineering applications of these algorithms.
Keywords: Data mining, clustering, engineering applications
1. Introduction Clustering is the unsupervised classification of data points into meaningful groups. It could be defined as the process of assigning a large number of data points to a smaller number of groups so that data points in the same group share the same properties while, in different groups, they are dissimilar. Clustering has many applications, including image segmentation, information retrieval, web pages grouping, market segmentation, and scientific and engineering analysis [1, 2]. In addition, it can be used in data cleaning and exploratory analysis [3]. Clustering is often the first step in data mining analysis. It identifies groups of related instances that can be further explored. For example, rather than focusing on each instance in the database, instances can be clustered first, and each cluster can be summarised and represented by its statistics, such as its mean, deviation etc. Subsequent analysis can be based on this compressed representation. Classification
326
learning algorithms can also be applied to each cluster to discover the patterns it possesses. The paper is organised as follows: Section 2 provides a review of different clustering techniques in data mining. Successful engineering applications of clustering techniques are reviewed in section 3. Section 4 concludes the paper.
2. Clustering techniques Many clustering techniques have been proposed over the years from different research disciplines [4-6]. These techniques can be broadly classified into four categories: partitioning methods, hierarchical methods, density-based methods and grid-based methods. This section introduces each of these categories and presents some representative algorithms from them. A number of other clustering techniques that do not fit in these categories are also discussed.
2.1. Partitioning methods Partitioning algorithms had long been popular clustering algorithms before the emergence of data mining. These algorithms separate data points into k specified disjoint clusters such that data points in the same cluster are as similar as possible while data points in different clusters are as dissimilar as possible. They can be categorised into those related to the kmeans method [7-9], the k-medoids method [10, 11] and the EM (Expectation Maximisation) method [12, 13]. The three algorithms have different ways of representing their clusters. On the other hand, they share the same general approach when computing their solutions. They employ an iterative relocation technique which seeks a local rather than global optimal solution. The general weaknesses of partitioning-based algorithms include a requirement to specify the parameter k and their inability to find arbitrarily shaped clusters.
2.2. Hierarchical methods The hierarchical clustering method produces a nested sequence of clusters ranging from one to n clusters for a data set of size n. It can be modelled as a tree structure, called a dendrogram, which shows how the clusters are related. The root of the tree represents one cluster, containing all data points, while at the leaves of the tree, there are n clusters, each containing one data point. By cutting the tree at a desired level, a clustering of the data points into disjoint groups is obtained. In principle, there are two distinct hierarchical approaches. The hierarchical agglomerative approach operates in a bottom-up manner, by performing a series of agglomerations in which small clusters, initially containing an individual data point, are merged together to form larger clusters. At each step of the agglomeration process, the two closest clusters are fused together. There are many variants to define the distance between two clusters [14]. Centroid-based distance calculation uses the dissimilarity between the centres of two clusters. Single-link computation uses the shortest distance between two data points, each coming from one of two clusters. The group-average method employs the average distance between all pairs of data points in two clusters. Through adopting different distance metrics, these clustering algorithms add their own biases to the characteristics of the data set to be clustered. For example, the single-link method tends to discover long-chained clusters while the centroid and average approaches have a tendency to find globular clusters.
The hierarchical divisive approach operates in a top-down manner, starting with one cluster covering the entire data set and progressively dividing single clusters into sub-clusters until each one contains only one data point. This approach is impractical due to its high computational cost. Advantages of hierarchical clustering include embedded flexibility regarding the level of granularity and the ability to deal with different types of attributes. Disadvantages of hierarchical clustering are the difficulty of scaling up to large data sets, the vagueness of termination criteria and the fact that most clustering algorithms cannot recover from poor choices when merging or splitting data points. In the data mining context, several algorithms have been developed to scale up hierarchical clustering algorithms. Examples include BIRCH [15] and CURE [16].
2.3. Density-based methods Density-based methods regard clusters as dense regions of data points separated by regions of low density. These methods are less sensitive to outliers and can discover clusters of arbitrary shapes. Three major algorithms under this category are DBSCAN [17], OPTICS [18] and DENCLUE [19].
2.4. Grid-based methods Grid-based methods work with data indirectly by constructing summaries of data over the attribute space. They divide the attribute space into a finite number of cells which form a grid structure on which all of the operations for clustering are performed. Gridbased methods are fast and handle outliers well. On the other hand, the use of summarised information causes them increasingly to lose effectiveness as the number of dimensions increases. Representative algorithms are STING [20], WaveCluster [21] and CLIQUE [22].
2.5. Other clustering methods A number of other clustering algorithms have been developed: fuzzy clustering, artificial neural networks and genetic algorithms. This section briefly discusses these algorithms.
2.5.1. Fuzzy clustering Most of the clustering algorithms described above produce non-overlapping crisp clusters, meaning that a data point either belongs to a cluster or not. The issue of uncertainty support in clustering task leads to the
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introduction of algorithms that use fuzzy logic concepts in their procedure. Fuzzy clustering algorithms are partitioning methods that can be used to assign data points to their clusters. These algorithms can handle uncertainty in the data as they provide a degree of membership when associating a data point to a cluster. There are many fuzzy clustering methods being introduced [23]. The fuzzy c-means is the best known and most widely used algorithm [24].
2.5.2. Artificial neural networks Artificial neural networks have emerged as viable methods for numerous applications in engineering [25]. They have been successfully used in both classification and clustering. Well-known examples of artificial neural networks used for clustering include self-organizing map (SOM) [26] and adaptive resonance theory (ART) models [27]. One of the chief advantages of neural networks is their wide applicability; however, they also have two particular drawbacks. The first is the difficulty of obtaining suitable learning parameters. The second is the often time-consuming training required. 2.5.3. Genetic Algorithms Genetic algorithms are stochastic search techniques that use principles inspired by the process of biological evolution [28]. Various genetic algorithms have been proposed for solving the clustering problem [29, 30]. In these systems, solutions (typically, valid data partitions, or cluster centroids) are represented by bit strings whose particular interpretation depends on the application. The search for an appropriate solution begins with a population, or collection, of initial solutions. Members of the current population give rise to the next-generation population by means of operations such as random mutation and crossover. At each step, the solutions in the current population are evaluated relative to a given measure of fitness (typically, fitness is inversely proportional to the squared error value), with the fittest solutions selected probabilistically as seeds for producing the next generation. The process performs generate-andtest beam search of the solutions, in which variants of the best current solutions are most likely to be considered next. Genetic algorithms have a potentially greater ability to avoid local minima than is possible with the localized search employed by most clustering techniques. On the other hand, they have a high computational cost.
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3. Applications engineering
of clustering
techniques
in
Clustering techniques can be useful tools for exploring the underlying structure of a given data set and are being applied in a wide variety of engineering disciplines. This section discusses some applications of clustering techniques in engineering. Different clustering algorithms implicitly have different biases and therefore often have their own strengths when handling data sets with particular properties. It can be argued that there will not be a universal clustering algorithm that could be applied to arbitrary data. It is therefore important to have a clear understanding of the requirements of a particular application and to choose the technique that best fits those requirements. In general, to be useful in an engineering application, a clustering algorithm should have the following abilities: 9 dealing with different types of data (numerical, categorical, text and images), 9 handling noise, outliers and fuzzy data, 9 discovering clusters of irregular shapes, 9 dealing with large data sets and data of high dimensions, 9 producing results that are easy to understand, 9 being insensitive to the order of the input data, 9 being simple to implement. There are several engineering areas where clustering has been successfully employed as an essential step. Group technology (GT) seeks to identify and utilize similarities of product design and manufacturing processes in order to achieve economies in both the design and the manufacturing life cycle. The principle of GT has been applied in many fields such as part design, process planning, scheduling, tooling, facility layout, manufacturing cell formation and costestimation. The application of GT can achieve many benefits such as reduced set-up times, throughput times, material handling cost and work-in-process inventory. An engineering problem associated with implementing GT is part family formation. Similar parts, based on a certain similarity of characteristics, are grouped into a family. A number of activities can benefit from successful implementation of this concept. For example, a design engineer faced with the task of designing a new part can use GT to identify similar designs that may exist. The information from existing designs can then be used to refine the design
of the new part as well as in subsequent planning and production activities. Also, a process planner can create standard plans for the groups of similar parts, which can then be modified to suit new parts. Various automated part family forming systems applying clustering techniques have been developed [31, 32]. In order to deal with the noise or uncertainties in the data, several researchers have utilized the fuzzy set theoryto improve the performance of the classical clustering techniques [33, 34]. Another GT problem is the machine-part cell formation problem. The assignment of a group of similar parts to a cell of machines having common processing characteristics greatly improves the efficiency of batch manufacturing. However, conventional approaches to the problem of machinepart cell formation have been computationally inefficient for large machine-part matrices. Clustering approaches have been used to reduce the computational complexity [35, 36]. A number of studies have addressed part-machine grouping with the consideration of operation sequences [37, 38]. Feature recognition techniques play a key role in achieving the goal of integrating computer-aided design (CAD) and computer-aided manufacturing (CAM). Various feature recognition approaches using clustering techniques have been proposed [39, 40]. In manufacturing systems such as FMCs (flexible manufacturing cells), one of the most important issues is to detect tool wear under given cutting conditions as accurately as possible. Xiaoli and Zhejun [41] developed a method for monitoring tool wear in boring operations using acoustic emission (AE) information. The experimental results indicated that the proposed method has a high monitoring success rate in a wide range of cutting conditions. The growing importance of providing service to customers, e.g. post-sale assistance, supplying of spare parts, upgrading and integration of new elements in installed systems, enhances the importance of planning and management of upgrading parts in most manufacturing industries. These parts are generally too many and heterogeneous and it is very difficult to forecast their demand. Cesarotti et al. [42] studied the application of clustering techniques for the partitioning of these parts into families in order to simplify the control, management and planning of their production and supply. The results confirmed the validity of the models, encouraging possible future developments in this direction. The chemical manufacturing industry produces a wide variety of products serving many other sectors as well as the consumer market. To meet the continual
change in market demand, it is necessary to devise an operational strategy which can move the plant rapidly to new operating conditions while minimising the loss of product during product changeover. Traditionally, operators have used their knowledge and experience to find a new operating condition producing a desired product through trial-and-error. During the process, off-specification products may be produced, which causes economic loss. Sebzalli and Wang [43] presented an industrial case study of applying principal component analysis [44] and fuzzy c-means to discover effective operational strategies for rapid product changeover through analysis of 303 data points collected from a refinery fluid catalytic cracking process. In semiconductor manufacturing, cracks, scratches, contaminants, process variations, and errors by operators or equipment are all production problems that cause defects on wafers. It is highly desirable to find and classify defect signatures recorded in the data sets for final electrical tests. Kundu et al. [45] investigated the clustering of defects on semiconductor wafers. Understanding the dynamics of the transport of momentum, heat and mass in turbulent flows is important in many engineering applications involving fluid flow. Vernet and Kopp [46] developed a pattern recognition technique for the identification of critical points in coarsely spaced experimental turbulent flow data based on fuzzy c-means clustering. Statistical Process Control (SPC) is a methodology based on several techniques that is aimed at monitoring manufacturing process output measurements. Control charts are the most widely applied SPC tools used to identify unnatural variations of monitored measurements, as well as to locate their assignable causes. With the widespread exploitation of automated production and inspection in several industrial applications, the SPC tasks traditionally performed by quality practitioners have to be automated. Several researchers have investigated applications of unsupervised learning techniques for manufacturing quality control [47, 48]. From the above-mentioned problems, features of successful applications of clustering in engineering can be summarised as follows: 9 the problem is of a sufficient degree of complexity, 9 the problem can be formulated to match existing learning algorithms, 9 ample training data are available in an appropriate format,
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9 the training data are representative, 9 the data are free from noise or can be cleansed cost-effectively, 9 the methods used for clustering are suitable for the application under consideration.
4. Conclusions Clustering is a very complex problem. This paper has provided a clear categorisation of existing clustering algorithms. It is noted that different clustering algorithms implicitly have different biases and therefore often have their own strengths when handling data sets with particular properties. For instance, the k-means clustering algorithm has a tendency to discover well separated spherical clusters. It can be argued that there is no universal clustering algorithm that could be applied to arbitrary data. A more practical approach is to develop a tool kit that contains different kinds of clustering algorithms. It is the responsibility of domain experts to select an appropriate clustering algorithm based on their understanding of the data and available data visualisation tools. The paper has also discussed several applications of clustering algorithms in engineering. Although these algorithms can overcome a broad range ofproblems in engineering, they are still not widely applied.
Acknowledgements This work was carried out within the ERDF (Objective One) projects "Virtual Enterprise Network for Manufacturing Industry in Wales" (Venturing Wales) and "Supporting Innovative Product Engineering and Responsive Manufacturing" (SUPERMAN) and within the EC-funded Network of Excellence "Innovative Production Machines and Systems" (I'PROMS) and the EPSRC-funded Innovative Manufacturing Research Centre at Cardiff University.
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[21] Sheikholeslami, G., Chatterjee, S. and Zhang, A. WaveCluster: A multi-resolution clustering approach for very large spatial databases. Proc. of the 24th Int. Conf. on Very Large Data Bases, New York, 1998,428-439. [22] Agrawal, R., Gehrke, J., Gunopulos, D. and Raghavan, P. Automatic subspace clustering of high dimensional data. Data Mining and Knowledge Discovery, 2005, 11, 5-33. [23] H6ppner, F., Klawonn, F., Kruse, R. and Runkler, T. Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition. John Wiley & Sons, New York, 1999. [24] Pal, N.R., Pal, K., Keller, J.M. and Bezdek, J.C. A possibilistic fuzzy c-means clustering algorithm. IEEE Trans. on fuzzy systems, 2005, 13 (4), 517-530. [25] Pham, D.T. and Liu, X. Neural Networks for Identification, Prediction and Control. Springer-Verlag, London, 1999. [26] Kohonen, T. Self-Organizing Maps. 3rd edn, SpringerVerlag, Berlin, 2001. [27] Carpenter, G.A., Grossberg, S. and Rosen, D.B. Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system. Neural Networks, 1991,4, 759-771. [28] Pham, D.T. and Karaboga, D. Intelligent Optimisation Techniques: Genetic Algorithms, Tabu Search, Simulated Annealing and Neural Networks. SpringerVerlag, London Heidelberg, 2000. [29] Frgnti, P., Kivij~irvi, J., Kaukoranta, T. and Nevalainen, O. Genetic algorithms for large scale clustering problems. Computer, 1997, 40 (9), 547-554. [30] Garai, G. and Chaudhuri, B.B. A novel genetic algorithm for automatic clustering. Pattern Recognition Letters, 2004, 25 (2), 173-187. [31] Caudell, T.P., Smith, S.D.G. and Johnson, G.C. An application of neural networks to group technology. Applications of Artificial Neural Networks, 1994, 1490, 612-621. [32] Smith, S.D.G., Escobedo, R., Anderson, M. and Caudell, T.P. A deployed engineering design retrieval system using neural networks. IEEE Transactions on Neural Networks, 1997, 8 (4), 847-851. [33] Tsai, C.-Y. and Chang, C.A. A two-stage fuzzy approach to feature-based design retrieval. Computers in Industry, 2005, 56 (5), 493-505. [34] Wang, C.-B., Chen, Y.-J., Chen, Y.-M. and Chu, H.-C. Application of ART neural network to development of technology for functional feature-based reference design. Computers in Industry, 2005, 56 (5), 428-441. [35] Dimopoulos, C. and Mort, N. Evolving knowledge for the solution of clustering problems in cellular manufacturing. Int. J. Prod. Res., 2004, 42, 4119-4133. [36] Durmusoglu, M.B. and Nomak, A. GT cells design and implementation in a glass mould production system. Computers & Industrial Engineering, 2005, 48,525-536. [37] Nair, G.J and Narendran, T.T. CASE: A clustering algorithm for cell formation with sequence data. Int. J. Prod. Res., 1998, 36 (1), 157-179.
[38] Park, S. and Suresh, N.C. Performance of Fuzzy ART neural network and hierarchical clustering of partmachine grouping based on operation sequences. Int. J. Prod. Res., 2003, 41 (14), 3185-3216. [39] Zulkifli, A.H. and Meeran, S. Decomposition of interacting features using a Kohonon self-organizing feature map neural n etw ork. Engin eerin g Application s of Artificial Intelligence, 1999, 12, 59-78. [40] Li, W.S., Ong, S.K. and Nee, A.Y.C. A hybrid method for recognizing interacting machining features. Int. J. Prod. Res., 2003, 41 (9), 1887-1908. [41] Xiaoli, L. and Zhejun, Y. Tool wear monitoring with wavelet packet transform- fuzzy clustering method. Wear, 1998,219, 145-154. [42] Cesarotti, V., Rossi, L. and Santoro, R. A neural network clustering model for miscellaneous components production planning. Production Planning & Control, 1999, 10 (4), 305-316. [43] Sebzalli, Y.M. and Wang, X.Z. Knowledge discovery from process operational data using PAC and fuzzy clustering. Engineering Applications of Artificial intelligence, 2001, 14, 607-616. [44] Jolliffe, I.T. Principle Component Analysis. Springer, New York, 1986. [45] Kundu, B., White Jr., K.P. and Mastrangelo, C. Defect clustering and classification for semiconductor devices. Proc. of the 45th Midwest Symposium on Circuits and Systems (MWSCAS-2002), 2002, 2, II-561-II-564. [46] Vetoer, A. and Kopp, G.A. Classification of turbulent flow patterns with fuzzy clustering. Engineering Applications of Artificial Intelligence, 2002, 15, 315326. [47] Pacella, M., Semeraro, Q. and Anglani, A. Adaptive resonance theory-based neural algorithms for manufacturing process quality control. Int. J. Prod. Res., 2004, 42, 4581-4607. [48] Pacella, M. and Semeraro, Q. Understanding ARTbased neural algorithms as statistical tools for manufacturing process quality control. Engineering Applications of Artificial Intelligence, 2005, 18 (6), 645662.
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Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Fusing neural networks, genetic algorithms and fuzzy logic for diagnosis of cracks in shafts K.M. Saridakis, A.C. Chasalevris, A.J. Dentsoras, C.A. Papadopoulos Dept. of Mechanical Engineering & Aeronautics, University of Patras, 26500 Rio Patras, Greece
Abstract
During the last decades, the engineering community has extensively studied crack identification in rotating machine elements. Although the proposed analytical models may be capable of identifying cracks on the basis of modal analysis, response measurements or other techniques, the required time for performing the underlying computations is restrictive in real-time diagnosis applications. This paper introduces a framework for implementing soft-computing techniques, namely artificial neural networks (ANN), fuzzy logic (FL) and genetic algorithms (GA), for identifying cracks in rotating shafts while diminishing the required computational time. In the context of the current approach the cracks are considered to lie on arbitrary angular positions around the longitudinal axis of the shaft at any distance from the clamped end and characterized by three measures: position, depth and relative angle. The reduction in computational time is achieved by approximating the analytical model with a neural network and by replacing the exhaustive search of the solution space with a genetic algorithm whose objective function relies on a fuzzy logic representation. Results concerning the efficiency of the proposed framework in terms of accuracy and computational time are also presented.
1. I n t r o d u c t i o n
The existence of cracks in machine elements produces a coupling phenomenon between the longitudinal and lateral vibration that has been used for crack identification by many researchers [1-6]. Based on the consideration that a response due to a crack exists in a direction different from the direction of excitation, researchers managed to calculate the complete matrix of the local compliance that is induced by the crack. The non-diagonal terms of this matrix show the directions in which coupling exists. Therefore, the first three modes of free vibration have been determined by using a local flexibility matrix for the crack. According to this analysis and the accompanying experimental procedure, the beam is given a longitudinal, harmonic displacement at its fixed end and the response is plotted as a function of the excitation frequency. At small crack depths, peaks occur at the natural frequencies of longitudinal vibration. As the crack depth increases, other peaks appear due to the coupling with bending vibration. The local compliance in each direction is calculated using the crack stress intensity factor [7]. The need for identifying cracks and their characteristics is reported in multiple application domains. The crack identification must always be
332
performed within a specific time, with sufficient accuracy and without interrupting the operation ofthe cracked member. Significant research activity has been reported towards the development of the methodology that may address the problem of identifying cracks and their characteristics in real field applications by using the previously described coupled response. Nikolakopoulos et al. [8] proposed crack identification methods through measurements of the dynamic response in eigenfrequency measurements. Papadopoulos et al. [ 10] investigated the dynamic behavior of a shaft in torsion with a transverse crack respectively and developed nomograms for finding the depth and the location of this crack. Gounaris et al. [ 11 ] presented a method for the determination of depth and location of a transverse surface crack in a beam. The method is based on two response measurements at a certain point. It is considered that the crack always remains open and that the method may be applied in both underwater and air structures. Dimarogonas and Papadopoulos [12,13] investigated also the crack diagnosis in turbine rotors. The detection of the location, the angle and the depth of the shaft cracks may be considered as an inverse problem. Although inverse problems have received increasing attention from the engineering
community during the past few decades, solution methods are still in the initial stage of development and application. Conceptually, computational techniques for the solution of inverse problems usually consist of two parts: numerical discretization methods for ill-posed objects (e.g. the shaft with two cracks) and iterative procedures that are used to search for the actual geometrical configuration (e.g. the crack location and size). The discretization methods include finite difference methods, finite element and boundary element methods. However, the difficult part in formulating and solving numerically realistic inverse problems lies in utilizing robust iterative procedures. These iterative procedures may be performed in the form of conventional optimization techniques or computationally intelligent heuristics. Most of the conventional numerical optimization methods are based on the computation of the gradient of the function to be optimized. One of the advantages of the gradient-based solution methods is that for a welldefined problem, good initial starting points usually lead to fast convergence, especially for quite simple inverse problems. However, it is not always easy to construct a well-defined mathematical model for practical inverse problems that are usually severely illposed due to the inaccessibility of crucial data. Even for a simple inverse problem, cumbersome processes are required in order to ensure good convergence. Furthermore, the computational cost is significant since such techniques require the iterative computation of the Jacobean matrix of the system. Latest advances in artificial intelligence (AI) tend to enhance the available efficient analytical evaluation tools and procedures in a variety of scientific domains. An artificial intelligence field of major importance is comprised by the so-called soft-computing techniques that include genetic algorithms (GA) [14,15], fuzzy logic (FL) [16] and artificial neural networks (ANN) [16,17]. The combination of soft-computing techniques for resolving scientific problems has led in results that could not have been extracted with traditional methods. In [ 18] a genetic algorithm-based method for shaft crack detection is proposed and described. The method treats shaft crack detection problem as a finite element problem whose solution is achieved by means of genetic algorithm. The utilization of genetic algorithms avoids some of the weaknesses of conventional gradient-based analytical search methods e.g. the difficulty in constructing welldefined mathematical models directly from practical inverse problems. A technique for multiple crack identification obtained from cantilever steel beams is presented in
[19]. The modal parameters of the lower modes are used for the non-destructive detection and sizing of cracks in beams. Other researchers elaborated a continuous evolutionary algorithm (CEA), which is suitable for solving inverse problems and has been implemented on PC clusters to shorten calculation time [20]. This technique overcomes the difficulty of finding the intersection point of the superposed contours that correspond to the eigenfrequency caused by the crack. However, it is hard to select the initial trial solution for optimization because the defined objective function has is characterized by local minima. The problem of local minima calculation becomes more complex as the number of independent variables of the objective function increases. An objective function of two variables (e.g. crack depth and position) is much more sensitive in the change of each variable than this of six variables (depth, position and rotational angle of each crack) that will be used in this paper. In the present paper, it is shown that by fusing neural networks, genetic algorithms and fuzzy logic, the minimization of a function of six variables is feasible with an accuracy that makes the proposed diagnosis model both reliable and computationally inexpensive. 2. The analytical framework
model
and
the
proposed
The local compliance method is used here for modeling two cracks in a homogeneous and isotropic rotating shaft, with young modulus ofelasticityE and Poisson ratio v [21 ]. The radius of the cross section is R and the shaft is subjected to loads Pi. The crack depth is a. The crack can be bounded in the x direction by b and-b, and in the y direction by 0 and ax (see figure 2). The dimensionless boundary b can be calculated using the Pythagorean Theorem (see figure 2) and is given as b - x / l - ( l - h - ) 2
The
cracked strip of finite width dx and height hx has a crack d e p t h a . If Y - x / R
a n d h - h x / R , then
h - - 2,,/1-22 . The crack depth is given by the following formula: 15/
~ - -~R- (1- ~ - ) -
- (1- h-)
(1)
For the model of figure 1 the following functionsbased on the strain energy density factor - may be used:
333
F~ h-x :F~ ~
0"752+2"02Y+0"37 1-Sin hx 2hx)
Where Fo ( Y ) I = tan x Y / X Y / c o s xy -~x 2hx / 2hx / 2hx and:
F2
Y
=F o
Y#'~='reC~
Y
0.923+0.199
1-Sin
l~
kt
(3)
(4)
xy
2hx)
performed then a genetic algorithm is deployed for solving the inverse problem of finding the cracks characteristics whose outputs fit optimally with the measured responses. The proposed approach is depicted schematically in figure 3.It must be stated that neural network, fuzzy and genetic algorithm Matlab toolboxes [22] have been utilized in the presented approach.
Y
~J"" ,,,..~
iUiiiiNiiiiN:'',
._o
i
q
?s5(8' ('~ =--32 r i~'~dE~-'I/?2-7 ( / ~_~,~,~, . . . . (1-y2)(~-I+oc)F2 (~-l+oc) ~x ~
(5)
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(6)
~+~
fix
dy
?2P-x~ b-'5(a'qg'=32r I.....ate1-4IE(~-l+a,) 1-~F1 C(~_l+~) )F2/(y_,+,~))dy (7) ~4(~,{0)___~__.32I a~ I E(~-I+o01-X/im~Fl(Y-I+o0; (~-1+~); dy o
k+y:
The bounds for the integration of the above equations are - ( x b~ to ( x b2 for the variable x as in [7], and from Y2 + k to x/1- x 2 for the variable y are defined as follows" b 1 - b c o s O - s , b2 - bcosqg+s and s = ( R - a ) s i n O
b1 = b c o s 0 - ( R - a)sin r ,b 2 - b c o s O + ( R - a ) s i n 0 y, - b sin 0 + (R - a) cos ~, Y2 - - b sin 0 + (R - a) cos = Yl - Y2 ::> k - (b 2 - x) tan (,o The proposed framework uses the described analytical model for producing values for the compliance factors (from which the responses may be computed). These values are then used for training a neural network that approximates the analytical model. If a simulation is
334
,2
'~.- h
......:::::::::::::::::::::::::::::: ........ ............
--L
Fig. 1. Model of cracks in the cracked shaft. The local compliance is expressed in (5), (6), (7) and (8) with respect to coordinate system X ' O ' Y (see figure 2) which is defined with point O' to be 1 - a units far from O in positive Y direction as in [9]. The dimensionless local compliances contain only the variables ~ and (p so they are defined as:
x _~,~,~)
,,; x, x
Fig. 2. The geometry of crack section when q0>0.
Rotatingshaft j
I Solutions'
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[Fit....... luation
[
Fig. 3. Direct and inverse crack identification problem with deployment of ANN and GA. 3. Approximation of the analytical model In the present work, the utilized ANNs are not subjected to the sequence of input vectors occurring. They are static networks that propagate the values to the layers in afeed-forward way. The training of the neural networks is performed through a backpropagation algorithm which is a gradient descent algorithm where the network weights are moved along the negative of the gradient of the performance function. This algorithm presents many variations [16,17]. A set of compliance factors values is calculated by using the computational model described in the
previous section. The inputs for the calculation of each of these compliance factors are provided with a variation of crack depth a in the interval [0, 0.4], of the rotation angle ~o in the interval [0,360], and of the crack position b in the interval [0,1]. About 2500 sets of values in the form {~i,qoi,bi,Cxxi} are recorded for each compliance factor using the above mentioned input intervals. The objective at this point is to extract two approximated functions that describe these compliance factors in accordance with the existing sets of values. The function for the compliance factor c--s5is approximated with a neural network with 4 hidden layers with 5, 20, 5, 1 neurons respectively. These layers use the transfer functions 'logsig', 'purelin', 'tansig', 'purelin' [ 16,17]. The training of the neural network for Css (as well as for the other compliance factors) is performed with Levenberg-Marquardt backpropagation algorithm [16,17]. For the training of the Css neural network, 2500 recorded sets were used for 1500 cycles of training (epochs). The training resulted in a mean square error (MSE) for the training examples equal to 5.58573"10 -1~within a training time of 31 minutes with an Intel| Pentium| M 1.6 GHz processor. Figure 4 depicts the C55 values from the computational model approximated by the neural network for crack depth ~=0.2. It is evident that the approximation is extremely accurate for this crack depth and for this compliance factor, since the two curves coincide. If deviation is diagnosed, better results may be obtained if neural networks with more layers and more neurons are used. This however will lead to an increase of training time.
inserted as an input to the fuzzy inference system, a degree of membership is extracted (figure 5). 0,030
C55 for crack depth a=0.1
I
0,025 0,020 --
0,010
--
del
....... 9..... Neural network model
\
0,015
k
Smalldeviation of
0,005 0,000
2'o4;
0
. 60 . . . 80. . 100 . . .120. . 140
'
160
'
180
angle
Fig. 4. Values ofC55 using the analytical model and the approximated function for crack depths ~=0.2. Membership function 1
Computed values
Measuredvalue
Fig. 5. Triangular fuzzy sets for the inputs of the fuzzy
4. Determination of fuzzy objective function The existing approaches related with the identification of cracks through solving an inverse problem use algebraic expressions for the objective function, which is used for the optimization. For the domain of crack identification, the efficiency of an objective function depends on the number of scoped parameters (crack depth, position, etc.) and the number of points at which the responses are measured. In the context of the current paper, four points were used for the measurement of the responses on both vertical and horizontal planes and two fuzzylogic-based objective functions were introduced [16]. These objective functions are represented through a fuzzy inference system with six inputs and one output. The inputs correspond to the six cracks characteristics. For each input, a triangular fuzzy set is considered. For these triangular sets the top point corresponds to the computed value. Each time a measured value is
objective functions. The degrees of membership may be aggregated according to two different strategies: a. compensating and b. non-compensating. The compensating strategy results to a weighted mean membership value, whereas the non-compensating strategy outputs a value by utilizing a 'min' operator. The two fuzzylogic-based objective functions have a target value equal to one (fuzzy memberships' maximum value is unity). All the proposed functions are evaluated for their efficiency with respect to the required computational time.
5. Deployment of the Genetic Algorithm
Genetic Algorithms are algorithms that imitate the natural selection for performing tasks like search, optimization, classification etc. The GAs outperform the efficiency of traditional optimization techniques in searching non-linear and non-continuous spaces
335
that are characterized by abstract and/or poorly understood expert knowledge. In contrast with the standard algorithms, GAs generate a population of points for each iteration that approach the optimal solution by using stochastic operators. Despite their robustness and the possibility of balancing efficiency and computational cost, the extraction of 'almost' optimal solutions may be considered as one of their weaknesses. This deficiency is restricted in the current approach, by enhancing the GA with another technique that uses the results of the search as starting points for searching absolute global extremes. Scoped Parameters (Goal Values) ~a
al 0.2
qh 10
as 0.3
q~ 20
bl 0.05
b~ 0.1
0.26
296
0.30
325
0.0175
0.0804
688
0.22
306
0.30
4
0.0344
0.1456
370
0.20
346
0.30
328
0.0872
0.0976
321
0.20
15
0.29
2
0.0522
0.1011
299
0.22
42
0.29
9
0.0121
0.0939
172
0.19
8
0.29
15
0.0131
0.1203
1244
0.21
63
0.30
4
0.0758
0.0792
1235
0.2
11
0.30
317
0.0025
0.1277
1298
0.19
358
0.35
1
0.0174
0.2675
1469
0.20
4
0.30
32
0.0699
0.1238
897
93%
94%
Table 1. Results for the two fuzzy objective functions. The way with which variable parameters of the problem are codified into individuals and the population size for each generation are deliberately defined depending on the problem under consideration. Further choices and adjustments may be made in the context of GA search (selection strategy, crossover points, mutation rate and function etc.), that could influence the efficiency of the results as well as the computationa!~ time. These choices are usually problem-oriented and several runs must be deployed before an efficient adjustment is achieved. The following section describes the settings that have been made for the deployment of the genetic algorithm in the considered problem. The GA takes the characteristics (bl, b2, al, (12, q)l, q~2) of the two cracks as input arguments. The population type is set to double vector and the population size is set to 100. The GA is terminated after 50 generations or- if no better solution has been found- after 20 generations or 180 seconds. The variance of the input arguments is permitted for the
336
intervals [0, 0.4] with a resolution of 4 decimal points for the crack depths and the integer interval [0, 360] for the rotation angles. Rank and stochastic uniform have been selected as types for fitness scaling and selection functions respectively. The reproduction is performed with elite count set to 8 and crossover fraction set to 0.8. The mutation is performed according to a Gaussian function with scale and shrink values set to 2 and 1 respectively. The crossover is performed through a heuristic function with ratio set to 2. This function creates children that lie on the line containing the two parents, a small distance away from the parent with the better fitness value in the direction away from the parent with the worse fitness value. The migration operator performs with direction set to both and fraction set to 0.5. Finally, another optimization method (gradient-based or pattern search) may be optionally deployed having as starting points the points given by the solution extracted by the GA. This may lead to the avoidance of locally optimal solutions while producing more precise results. From the results obtained it becomes evident that the proposed fuzzy objective functions are characterized by high accuracy concerning the resulting values (see table 1). These values are obtained significantly faster (at least 5 times) when compared with those obtained via conventional algebraic objective functions. The present method makes possible the cracks identification in real-time applications in considerably short computational times (3-25 minutes) comparing to the time needed by the analytical models (2-4 days). 6. Conclusions
In this paper, the problem of the dynamic behavior of a beam with circular cross section and two cracks was studied. The motivation of the current research work has been the deployment of computational intelligence techniques for supporting the effectiveness of the existing analytical model and for enhancing its efficiency. The analytical model, which relies on the method of integration of the strain energy density function along two cracks, is approximated with an artificial neural network that is used in order to solve the inverse problem of the crack identification. A genetic algorithm produces values for the crack attributes (position, depth and angle) as input arguments to the neural network, and searches for a solution comparing the inverse solution with the actual/experimental responses. In the context of the current paper, the actual/experimental responses are replaced with the responses extracted through the precise analytical model, since it is not
possible to generate experimentally a required variety of cracks with different characteristics. For the genetic optimization, two objective functions on the basis of fuzzy logic are utilized. The accuracy of the described model, in combination with the significantly low computational time proves that the current approach can be a good solution for real-time crack identification systems. The proposed framework may be applied for approximating other analytical models of shafts presenting multiple cracks. The latter ascertainment may be considered as future work.
Acknowledgements University of Patras is a member of the EU-funded I ' P R O M S Network of Excellence.
References
cracked Timoshenko Shaft. Ingenier-Archiv, 57, pp. 257-266. [11] Gounaris G.D., Papadopoulos C.A., Dimarogonas A.D. (1996). Crack identification in beams by coupled response measurements. Computers & Structures Vol. 58, No. 2, pp. 299-305. [ 12] Dimarogonas A.D. Papadopoulos C.A. (1988). Crack detection in turbine rotors. Proc. 2nd Int. Symp. on transport phenomena, Dynamics and design of rotating machinery. Vol. 2, Honolulu, pp. 286-298. [13] Dimarogonas A.D. Papadopoulos C.A. (1990). Identification of cracks in rotors. 3rd EPRI Incip. Fail Conf., Philadelphia, PA. [14] Goldberg D. (1989). Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading, Massachusetts. [15] Koza R.J. (2000). Genetic programming, on the programming of computers by means of natural selection. The MIT Press.
[1]
Dimarogonas A.D., Paipetis S. A (1983). Analytical methods in rotor dynamics. In Applied science. London, pp. 144-193.
[ 16] Kasabov K.N. (1996). Foundation ofneuralnetworks, fuzzy systems and knowledge engineering. MIT Press.
[2]
Dimarogonas A.D. (1982). Crack identification in aircraft structures. 1st National Aircraft Conf., Athens.
[3]
Dimarogonas A.D. (1988). EXPERTS, A Fuzzy Logic, Neural Network Structured Expert System Shell for Diagnosis and Prognosis: Users Manual. Clayton Laboratories, St Louis, Missouri, U.S.A..
[17] Bose N.K., Liang P. (1996). Neural network fundamentals with graphs, algorithms and applications. McGraw-Hill International Editions.
[4]
Anifantis N., Dimarogonas, A.D. (1983). Identification of peripheral cracks in cylindrical shells. A. S.M.E. Wint. Ann. Meeting, Boston, U.S.A.
[5]
Papadopoulos C.A., Dimarogonas A.D. (1987). Coupled longitudinal and bending vibrations of a rotating shaft with an open crack. J. Sound Vibration, 117, 81-93.
[6]
[7]
Papadopoulos C.A. (1987). Coupled vibrations of cracked shafts. Ph.D. Dissertation, University of Patras, Patras, Greece. C. A. Papadopoulos, Some comments on the calculation of the local flexibility of cracked shafts, Journal of Sound and Vibration 278 (2004) 12051211.
[8]
Nikolakopoulos P.G., Katsareas D.E., Papadopoulos C.A. (1997). Crack identification in frame structures, Computers and structures Vol. 64, No 14, pp. 389406.
[9]
Winfried Theis, Lgngs- und Torsions- schwingungen bei quer angerissenen Rotoren, Untersuchungenaufder Grundlage eines Rissmodells mit 6 Balkenfreiheitsgraden, Reihe 11: Schwingungstechnik Nr. 131, VDI Verlag, Dfisseldorf 1990.
[18] He Y., Guo D., Chu F. (2001). Using genetic algorithms to detect and configure shaft crack for rotor-bearing system. Computer methods in applied mechanics and engineering, Vol.190: 5895-5906. [ 19] Ruotolo R., Surace C. (1997). Damage assessment of multiple cracked beams: Numerical results and experimental validation. Journal of sound and vibration, Vol. 206 (4): 567-588. [20] Mun-Bo Shim, Myung-Won Suh. (2003). Crack identification using evolutionary algorithms in parallel computing environment. Journal of Sound and Vibration, Vol.262: 141-160. [21 ] Liu D., Gurgenci H., Veidt M. (2004). In situ damage detection in frame structures through coupled response measurements", Mechanical systems and signal processing, Vol.18: 573-585. [22] MATLAB 9 Mathworks Inc, version 7.0.1.24704.
[10] Papadopoulos C.A., Dimarogonas A.D. (1987). Coupling of bending and torsional vibrations of a
337
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufactu~g Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Optimization of Assembly Lines with Transportation Delay Using IPA Iyad Mourani a, Sophie Hennequina, Xiaolan Xie a'b a
INRIA/MACSI team and LGIPM, ISGMP-Bat. A. Ile du Saulcy, 57045 Metz Cedex 1 France {mourani, [email protected]} b Ecole des Mines de Saint-Etienne 158 cours Fauriel, 42023 Saint-Etienne cedex 2 France [email protected]
Abstract.
This paper addresses the optimization of assembly lines with important transportation delays and with constant demand. Machines are subject to time-dependent failures and times to failure and times to repair are random variable with general distribution. In the continuous flow model proposed in this paper, material flowing out a machine waits a period of time called delay for material transfer before arriving at its downstream buffer. A simulation-based optimization method is used for determining optimal buffer levels in order to minimize the long run average cost. The optimization algorithm is based on the Infinitesimal Perturbation Analysis (IPA) technique for estimation of gradients along the simulation Keywords" continuous-flow model, assembly lines, transfer delays, simulation-based optimization, perturbation analysis.
1. Introduction
Generally, continuous flow models have been used for optimal control and design of manufacturing system. Optimization techniques of system parameters are simpler than those for discrete parameter optimization. In addition, the continuous flow models offer an interesting way to reduce the complexity inherent in traditional discrete parts modeling. Although continuous flow models offer an interesting way to reduce the complexity inherent in traditional discrete parts modeling, existing continuous flow models neglect some important characteristics of manufacturing systems such as production lead-times and transportation delays. In existing continuous flow models, material flows continuously and instantaneously from machines to machines. However, many manufacturing processes
338
have significant delays in the material flow, such delays occur in oven processes (e.g. semiconductor diffusion), drying processes and testing. These delays usually have great impact on performance measures such as customer response time and workin-process. Unfortunately, most existing continuous flow models do not take into account these delays. To our knowledge, only four exceptions. (see [1, 2, 3,4]) The IPA techniques have been widely considered for control and optimization of discrete event systems. In the pioneer work, motivated by buffer storage optimization problem in a production line, Ho et al. [5] developed an efficient technique called perturbation analysis (PA). It enables one to compute the sensitivity of a performance measure with respect to the system parameters by a single simulation run.
Ho and Cao [6] developed an IPA technique for the efficient computation of n-dimensional gradient vector of performance measure, J(O), of a Discrete Event Dynamic System with respect to its parameter 0 (such as buffer size, inflow rate, service rate) using only one statistical experiment of the system. (see also Glasserman, [7]). Fu and Xie [8], estimated the derivatives of the throughput rate with respect to buffer capacity for continuous flow models of a transfer line comprising two machines separated by a buffer of finite capacity and subject to operation-dependant failures. Xie [9] addressed the performance evaluation and optimization of failure-prone discrete-event system by using a fluid-stochastic-event graph model, which is a decision-free Petri net. Haouba and Xie [10], addressed the flow control problem of multi-level assembly production systems in which a finished product is obtained from the initial components by successive assembly operations. Their goal is to adjusting the production of the machines in order to minimize the total cost. In this paper a single-part-type assembly production system with continuous material, unreliable machines, and finite buffers is considered. Material transfer between a machine and its downstream buffer takes a finite amount of time called delay. Machines are subject to time-dependent failures. Time to failure and times to repair are generally distributed. The goal of this paper is to find, by using IPA technique, the vector of optimal buffer level, which minimizes the long run average cost. The rest of the paper is organized as follows. Section 2 presents the assembly line with delays. Section 3 presents the IPA technique and the cost function. Numerical results are given in Section 4. Section 5 concludes the paper.
2. Assembly line with delays This paper considers a continuous flow model of a single-part-type assembly production system. The system is composed of N-1 input machines (Mr, -/1/12.... MN4), one assembly machine Mx, and N buffers (Bz, B2, ..., Bx_~, BN) where BN represents the downstream buffer of the assembly machine MN and (B~, B2, ..., B>~) represent the buffers which separate the input machines M, from the assembly machine MN. All the buffers have a finite capacity. Material flows continuously from outside the system to the input machines M,, where i = 1, 2, .., N-1, then waits a period of time called delay r, for
material transfer, before arriving to its downstream buffer B/, then the assembly machine MN, then waits a period of time called delay Z'Nbefore arriving to the last buffer BN, where it leaves the system (see Fig. 1). It is assumed that ~ > 0, V 0 < i < N. Hence a delay is considered between a machine M, and its downstream buffer B;. That means that the parts produced on Mi would not arrive immediately in the buffer B/. So, a delay will be occurred in the delivering of the parts to the downstream machine. These delays are introduced to explicitly account for processing times and transportation times.
Fig. 1. Assembly line with a delay between Mi and Bi More precisely, each machine Mi (with i = 1, 2 ..... N) is characterized by: * ai(t): the failure state of machine M/. with ai(t) = 1 if Mi is up and a~(t) = 0 otherwise. Let a ( t ) = [al(t), a2(t),...., aN(t)]. 9 r~(t): the remaining life time (until failure of machine Mi if a~(t) = 1 or repair of machine M/ if ai(t) = 0). 9 u~(O ~ [0, Ui]: the production rate of the machine Mi with U/as the maximal production rate of Mi. 9 udM): the rate of the flow entering buffer Bi taking into account the delay ri for material transfer, with udM) = u~(t- zT) (see Fig 2). 9 TBF~,k: k th time to failure of machine M/. For each i, TBFi, k are independent and identically distributed (i.i.d.) random variables of general distribution. 9 TTR~,k:If h time to repair of machine Mi. For each i, TTR~,~ are i.i.d, random variables of general distribution. The control policy is defined as follows: o For the assembly machine m x (see Fig. 1):
I
0, g~
~f ~,.(0 = 0, /f all x~(t) > O, y~, < h,,, ~ ( t ) = 1, u~,(t)= min(ud~(t),U~,), if onex~(t)=O, yv O,y~ = h~,,a~,(t) =1, {min(ud~(t),D, Ux) , if onex,(t)=O,y, =h~,,av(t)=l.
339
where i = 1, 2, .., N-1, and udo(t) = 0% UN+l(t) = D (D represents the demand rate which supposed to be constant). 9 For the input machines M/ where i = 1,2, .., N-l: /f
f~
bli (t) -" g i
0~"i (t)
= 0,
if Yi < h i , ofi ( t ) - l,
min(Vi,UN(t)),
i f Yi = hi, ~
1.
Machines are subject to time-dependant failures. This means that a machine can fail even it is not working on a part. Each machine can be either operational (ai(t) = 1) or down (ai(t) = 0). When it is operational, it can be either working or idle (if it is starved or blocked). It is assumed that the input machines Mi where i = 1, 2, .., N-1, are never starved and the assembly machine M N is never blocked. Each buffer Bi (with i = 1, 2 ..... N) is characterized by: 9 h;: the buffer capacity of the buffer B;. It is extended to include both materials in buffer Bi and materials that have been produced by machine A4~_1 and are in transit to buffer B;. Physical meaning of this buffer capacity is either the number of kanbans in a JIT system or the physical buffer capacity plus the capacity of convey connecting machine Mi_I and buffer B;. Then, the inventory level x~(t) and the inventory position are defined as follows: 9 xi(t) ~ [0, H]" the quantity of material in the buffer B; with xi(t ) ~ [0, h i] V i < K, xK(t ) ~ (_ oo, hK]. Let x(t) = [x,(t), x2(t) ..... Xu(t)]. 9 y;(t): the inventory position of the buffer Bi, i.e. the sum of the inventory level xi(t) and the parts in-transit to Bi (see Fig 2). hi is the upper limit ofyi(t). Let y(t) = lye(t), y2(t), ..., yu(t)]. r ....... --__-...................
~.IM ~ ~ d ,
(t ~ ) ~
~-'
VX~(t)l '
M,.+
1 [[
Fig. 2. Delay between Mi and Bi Let Ak be the inter-arrival times of events (time between e~ and e~+l i.e., A~ = t~+l - t~), where e~, tk represents, respectively, the kth event and the epoch of event ek with to = O. Consider that an event ek causes a change in the production rate ui of the machine Mi at time tk. The effect of this change on the downstream buffer Bi will arrive after ~ time units (see Fig. 2).
340
Different events are possible: the failure of a machine Mi, the repair of M i, buffer full of B i (i.e. yi(t) reaches H), buffer empty of B i (i.e. xi(t ) becomes 0) denoted respectively, Fi, Ri, BFi, BE r Furthermore, any change in the production rate ui (with i = 1, 2, ...., N) of a machine M/is coupled with an event called (DLi). Thus if there is a change in a production rate ui at time t~, so udi will change to the same rate at time (t~ + zT). So e~ e { Fi, Ri, BFi,
BEg, DLi }. For the implementation purpose of the simulation, a FIFO queue is used to record all active events (DLi). Whenever the production rate of a machine change at time t, one adds into the queue a couple containing the new value of u/and t + zT, i.e. the corresponding time of the change of udi. The dynamics of the system are given by the following equations: 9 The state S(t) of the system at time t, V t ~ [t~, tk+l) y~(t) = yi(t~) + [(ui(tk) - ui+~(tk)) • (t - t~)], xi(t) = xi(t~) + [(ud/(t~) - ui+~(t~)) • ( t - tk)], ri(t) = ri(tk) - ( t - tk).
9
Next event epoch Ak = tk+~ - t~ can be determined as follows: Iri(tk ),
if ek+1 --R i or F i
I
AIC - tic+l -tic
lui+,(tk)_udi(tic),
if eic+, = BEi
hi - Yi (tic )____ [ui(tic)-ui+l(tic) ,
if
eic+l=
ltNi(tk_~i) + "l"i --tlc
if
eic+l = D L i
BE i
= ~[
where N~(t) is the index k of the event ek corresponding to the last change of ui prior to time t. 9
Next state S(tk+l):
ITBF, or TTRi, ri(tic+~) = ~ri(tk ) - Aic
xi (tic+l)
if eic+l = R i or F, otherwise
= ~0,
Lxi(tk)+((udi(tic)-ui+l(tic))• h i,
yi(tic+l)= yi(tic)+((ui(tk)-u~+l(tic))•
tk+~ = t~+ Ak
if ek+, = B E i otherwise if ek+1 = B F i otherwise
3. I P A a n d c o s t f u n c t i o n
JN(O) = ix
For the purpose of IPA technique, one derives firstly the next event epoch Ak, and then the next state S(tk+l) with respect to the vector of optimal buffer level which is given by: 0 = h. The initial conditions proposed in our case are" x~(O) = y~(O) = h / 2 and c~(0) = 1. Note that for the assembly machine M x , ui+l(C) = D, and for each input machine M, where i = 1, 2, .., N - l , ui+~(tk)= ux(tk). 9 The derivatives of the next event epoch Ak with ____>
Or+(t~ )
where N is the constant number of events, gi(xi(s)) corresponds to the inventory cost which is given by: g(x,(s))=
{
c+x+ if x+(t) >_0 - c - x , if x,(t)
+
where c , c denote the unit inventory holding cost and backlogging cost with c + > 0, c-> 0. It can be rewritten as follows: + 1 N-1 "JN (O) -- --~N k~:o = E i aik
with
if ek+, = R~ or
aa'
1
Ox,(tk )
u'+'(tk )--ud'(tk ) 1
if ek+I = B E
at? '
u,(t~ )-u,+ (C ) ( "
+ )' if
O0
e~+,=
O0
lk+l aik ( 0 ) -- ~ g i ( X i ( S ) ) tk
+ - - --+- O0 O0
30
if
ek+~ = DL~
~o
-
if e~+~= R, or F,
Ori(tk ___) )
OAk _+
30
O0
-+
30
-
otherwise if ek+1 = BE t
Ox~(tk) -+ +(ud~(C)_u~+,(t~))OA~
O0
otherwise
O0
"[Oh, _+ ~
C+
___)
a6
Lo,
if i= j
r ~~ j
2
OXi,k+ 1
+
+
00
)xAk•
OXi,k
OA k
00
00
+
+ )zx~ +---:-(x,,~+, +x,,~))
-+
Gi~(O ) = - ( xi'k+' + xi'k )xA k Xcif i = j
' at~ --0
a~
The average cost function is the long run average inventory holding and backlogging cost
2
-+
OGik (0) +
Oyi(to) _ ~0.5,
= ( xi'k+l -'~ xi'k
Case 2" xi, k ~ O, Xi, k+ l ~ 0
respect to 0 = h is given as follows: , Ox~(to)_~0.5,
((
2
0o
_+
ar,(t0)=0
O0
__)
G,k (0) OGik(O)
oo 3o The derivatives of the state S(to) at time to with -+
'
events Gi~(O ) and its derivative OGi~(O ) / 0 0 . Thus all the possible cases needed for exact computing of the cost function and its derivative will be discussed. In the following and for the sake of simplicity Xi, k, Xi, k+1 are used to denote respectively xi(tk) and xi(t~+l) used before.
otherwise
Otk+1 --.=Ot~ OA~ . __>-ntO0
tx
C a s e 1" xi, k ~ O, Xi, k+1 ~ 0
if e x+1 = B C
Oyi(t~+') - t aO O0 Oy+(t~ ) +(ui(t~ )--Ui+,(t~ )) OA~
tx
_--->
30
O,
ax,(t~+,)
_+
OtN/O0 j(O)+L~zOGik(O)
For calculating this derivative, we need to evaluate the cost function between each two successive
respect to 0 = h is given as follows: o,
_-~
OJN(O)-+
The derivatives of the next state S(tk+~) with
Ori(t~+,)
ds.
The derivative of the cost function with respect to the vector of optimal buffer level can be written as follows" -+
9
ds
__~
respect to 0 = h is given as follows:
OAk
+ _l "i~, gi(x,(s))
30
-
c- ((OXi,k+ , +
2
+
30
OXi,k
OA k .
+ )A~ +----z-(x,.~+,
30
+ x,,~))
30
C a s e 3" xik -> O, xi~+~ -< 0 _
-+
aik ( 0 ) "- 1 (C+Xi2,k -~- C X~,k+1 )
2
u,+~.k - udi. k
__+
J(O) defined as the limiting function of:
341
i)Gik(O____ )) 00
1 (c +Xxi, k x "3OXi,k ''~-Jt-c-XXi,k+ U/+l,k-udi,k O0
1X
30Xi,k+l --) ) O0
Case 4" xk < 0, xk+l > 0 -
Gik (0) = ___>
2
+
2
l ( - c xi, e + c xi,k+l)
2
Ui+l,k -udi, k
-
=
2
+
2
l (c xi, k + c xi,~+1) 2
udi,k -/'/i+l,k
c +X Oxi,~+l ) OGik(O) 1 ( c - X x . ~,k X Oxi,~ --> = __._> + Xi,k+l X 30 udi'~ - ui+'.~ 30 30
4. N u m e r i c a l results
In this section, the following algorithm for minimizing the average cost j(~) is used. The basic idea is to approximate the optimization of long run average cost by the optimization of a sample path function. In this paper, the following property is exploited: If (i) the total number of events N is defined as a function of failure/repair events such as K-th repair of machine M N and if (ii) common times to failure (TBF) and times to repair _...)
(TTR) are used for all O, then the sample function JN(0) is a continuous function. Then a gradient-based optimization algorithm to minimize jN(~) is used. It is expected that this
F o r numerical experiments, we consider an assembly line with 3 identical machines (2 input machines M1, M2, and one assembly machine M3) except the delays with 2-~ = 2.0 time units, 2-2= 2.2 time units, 2"3= 2.5 time units. Times between failure and times to repair are exponentially distributed with rate 5~- and/4- respectively, i.e., mean time between failure MTBFi = 1/A,. and mean time to repair MTTRi ll, u4. Initial conditions are x;(O) = yi(O) = hJ2 and a~(O) = 1. The parameters are summarized in Table 1. :
Table 1 Simulation data c+ 5
c250
MTBFi 100
MTTRi 20
Ui
4
D 0.5
The simulation-based optimization is performed for N = 1000 repairs of machine MN. First the simulation is run for evaluating exhaustively the cost function j ( ~ ) b y varying hi, h2 and h3 along integer points. The minimal cost is obtained with hi = 12. The resulting cost function for hi = 12 is plotted in Fig. 3.
optimal solution converges to true optimal solution of j(~) as N increases. The gradient-based optimization procedure is as follows"
(
hf +'= h~ + s. ~ i J x ( ~ )
1+
where s, is the step-size at iteration n. Two step-sizes are used. At the beginning, the An~ijo step-size is used (see [11, 12]). Consequently, when the cost improvement __..)
_._)
Ju(O"+~)--Ju(O" ) is smaller than a given percentage, one switches to standard gradient optimization procedure (see [ 13]): Sn=T ]
JN(O n) / llVJN(On)ll 2
At the beginning, r/is chosen such that sn is equal to the last Armijo step-size. It reduces it if no improvement of JN(o) is observed atter a certain number of iterations.
342
Fig. 3. Cost function versus the buffer levels for an assembly line with 3-machine The cost function is convex and there exist a minimum cost value equal to (330.72 monetary unit) which corresponds to the following vector of buffer level: (hi = 12 parts, h2 = 12 parts, h3 = 35 parts). The simulation-based optimization algorithm is then run to optimize the assembly line model. The cost function converges rapidly towards the optimal value of Fig. 4 (330.43 monetary unit), and the vector of optimal buffer level corresponding to that
optimal cost value is: (hi = 11.49 parts, h2 = 11.45 parts, h3 - 35.3 parts) as given in Table 2. Table 2 Results of simulation-based optimization Cost value (monetary unit) 419.03
Vector of buffer level (hi(parts), h2(parts), h3(parts)) (20.00,20.00,20.00)
(with Armijo step-size) 2 (with Armij o step-size) 3
372.14
(17.88, 18.59, 28.01)
335.63
(12.63, 13.46, 32.22)
331.94
(11.53, 11.45, 35.29)
4
330.43
(11.49, 11.45, 35.30)
N ~ of iteration 0 1
References
[1] [2]
[3]
[4] [5]
5. C o n c l u s i o n s
In this paper, a continuous flow model of a single-part-type assembly production system is considered. The system is composed of N-1 input machines, one assembly machine mx, and N buffers of a finite capacity with transportation delay and constant demand. Material waits a period of time called delay for material transfer before arriving from machine to its output buffer. Machines are subject to time-dependent failures. Times to failure and times to repair are random variable with general distribution. A simulation-based optimization algorithm has been used to determine the vector of optimal buffer level, which minimizes the long run average cost. As a future research it is important to study the convexity of the cost function obtained by simulation. In addition, it would be interesting to consider the case of an assembly line where the machines are subject to operation-dependant failures.
[6] [7] [8]
[9]
[10] [11] [12] [13]
A c k n owl e d g e m e n ts
Van Ryzin, G.J., S.X.C. Lou and Gershwin S.B. Scheduling job shops with delay. Int. J. Prod. Res., vol. 29, no. 7, pp. 1407-1422, 1991. Mourani, I., Hennequin S. and Xie X., Continuous Petri nets with delays for performance evaluation of transfer lines. Proceedings of IEEE International Conference on Robotics and Automation (ICRA2005), Barcelona, Spain, pp. 3732-3737, 2005. Mourani, I., Hennequin S. and Xie X. Simulationbased Optimization of a Single-Stage Failure-Prone Manufacturing System with Transportation Delay. Proceedings of International Conference on Industrial Engineering and Systems Management (IESM), Marrakech, Morocco, 2005. Mourani, I., Hennequin S. and Xie X. Optimization of continuous-flow transfer lines with delay using IPA. submitted to INCOM'2006. Ho, Y.C., A. Eyler and Chien T.T. A gradient technique for general buffer storage design in a serial production line. International Journal of Production Research, vol. 17, pp. 557-580, 1979. Ho, Y. and Cao X.R. Perturbation analysis of discrete event dynamic systems. Boston, MA: Kluwer Academic Publishers, 1991. Glasserman, P. Gradient Estimation via Perturbation Analysis, Kluwer Academic Publisher, 1990. Fu, M. and Xie X. Derivative estimation for buffer capacity of continuous transfer lines subject to operation-dependent failures. Discrete Event Dynamic Systems: Theory and Applications, vol. 12, pp. 447-469, 2002. Xie, X. Fluid-stochastic-event graphs for evaluation and optimization of discrete-event system with failures. IEEE Transactions on Robotics and Automation, vol. 18, no. 3, pp. 360-367, 2002. Haouba, A. and Xie, X. Flow control of multi-level assembly systems. Int. J. Computer Integrated Manufacturing, vol. 12, no. 1, pp. 84-95, 1999. Armijo, L. Minimization of functions having Lipschitz continuous first-partial derivatives. Pacific Journal of Mathematics, vol. 16, pp. 1-3, 1966. Polak, E. Optimization algorithms and consistent approximations, Springer-Verlag, New York, 1997. Nedid, A. and Bertsekas D.P. Incremental subgradient methods for nondifferentiable optimization. SIAM on Optimization, vol. 12, No. 1, pp. 109-138, 2002.
INRIA is partner of the EU-funded FP6 Innovative Production Machines and Systems (I'PROMS) Network of Excellence. http://www.iproms.org
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Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All rights reserved.
Prediction of workpiece surface roughness using soft computing B. Samantaa, W. Erevelles b and Y. Omurtag b a
Department of Mechanical and Industrial Engineering, Sultan Qaboos University, Muscat, Oman b School of Engineering, Mathematics and Science, Robert Morris University, Moon Township, Pennsylvania, USA
Abstract
A study is presented to model surface roughness in end milling using adaptive neuro-fuzzy inference system (ANFIS).The machining parameters, namely, the spindle speed, feed rate and depth of cut have been used as inputs to model the workpiece surface roughness. The parameters of membership functions (MFs) have been tuned using the training data in ANFIS maximizing the modeling accuracy. The trained ANFIS are tested using the set of validation data. The effects of different machining parameters and number of MFs on the prediction accuracy have been studied. The procedure is illustrated using the experimental data of end-milling 6061 aluminum alloy. Results are compared with artificial neural network (ANN) and previously published results. The results show the effectiveness of the proposed approach in modeling the surface roughness. Keywords: Manufacturing systems, Surface roughness, Soft computing
1. Introduction
Surface roughness is widely used as an index of product quality in finish machining processes. The quality of surface is of great importance in the functional behavior of the machined components. The factors affecting the surface roughness are the machining conditions, workpiece material and tool geometry. There have been several attempts to model and predict surface roughness in machining processes. Some of the recent references are listed here along with the corresponding machining processes: milling [1-3], turning [4, 5] and grinding [6, 7]. Reference [8] presented a recent review of various approaches for predicting surface roughness in machining. The approaches are based on machining theory, experimental investigation, designed experiment and artificial intelligence (AI) including artificial neural network (ANNs) and neuro-fuzzy systems.
344
Fuzzy logic (FL) has been used in many practical engineering situations because of its capability in dealing with imprecise and inexact information [9, 10]. The powerful aspect of fuzzy logic is that most of human reasoning and concept formation is translated into fuzzy rules. The combination of incomplete, imprecise information and the imprecise nature of the decision-making process make fuzzy logic very effective in modeling complex engineering, business, finance and management systems which are otherwise difficult to model. This approach incorporates imprecision and subjectivity in both model formulation and solution processes. The major issues involved in the application of FL or fuzzy inference system (FIS) are the selection of fuzzy membership functions (MFs), in terms of number and type, designing the rule base simulating the decision process as well as the scaling factors used in fuzzification and defuzzification stages.
These parameters and the structures are, in general, decided based on trial and error and expert knowledge. In adaptive neuro-fuzzy systems (ANFIS) proposed in [ 11 ], the advantages of FL and ANNs are combined for adjusting the MFs, the rule base and related parameters to fit the training data set. In a recent paper [3], the prediction of surface roughness in end-milling has been presented using ANFIS. The effects of two types of MFs, namely, triangular and trapezoidal, on the prediction accuracy have been studied. In this paper, the approach of [3] is extended to use ANFIS with normalized data and different number and type of MFs for predicting surface roughness in end-milling. Comparisons are made between the performance of ANFIS and ANN for different combination of inputs. Results are also compared with [3]. The results show the effectiveness of the inputs in prediction of the surface roughness. The procedure is illustrated using the experimental data of[1 ].
node '0" is expressed as follows:
0~. -/ao(x i),i - 1,m, j - 1,n
where/,t,j represents the jth membership function for the input x;. Several types of MFs are used, for example, triangular, trapezoidal and generalized bell function. The parameters of these MFs are termed as
premise parameters. Layer 2 (Product layer): For each node 'k' in this layer, the output represents weighting factor (firing strength) of the rule 'k'. The output (wk) is the product of all its inputs as follows:
02
( x i ) , i - 1, m,k - 1,R
l ~ik
3. Adaptive neuro-fuzzy inference system (ANFIS) In this section, the main features of ANFIS are briefly discussed. Readers are referred to [1 l] for details. Figure 1 shows the ANFIS structure for a system with m inputs (Xl...Xm), each with n MFs, a fuzzy rule base of R rules and one output (y). The network consisting of five layers is used for training Sugeno-type FIS through learning and adaptation. Number of nodes (N) in layer 1 is the product of numbers of inputs (m) and MFs (n) for each input, i.e., N=m n. Number of nodes in layers 2-4 is equal to the number of rules (R) in the fuzzy rule base. Layer 1 (Fuzzy'cation layer): It transforms the crisp inputs x; to linguistic labels (A!j, like small, medium, large etc.) with a degree of membership. The output of
(2)
Layer 3 (Normalized layer)." The output of each node 'k' in this layer represents the normalized weighting factor ( wk ) of the kth rule as follows:
0 3_
,k-l,R
2. Data used Lou [1] presented experimental data of surface roughness in end milling 6061 aluminum alloy with different machining conditions of spindle speed, feed rate and depth of cut. In [3], two sets of data were used- the first set of 48 runs (training set) for a prediction model and the second set of 24 runs (testing set) for testing the prediction model. In the present work, the training and testing data sets, have been normalized by dividing each data vector by its maximum to limit the values within 1.0 for better training speed. The normalized data sets have been used in ANFIS for training and testing the prediction model.
(1)
(3)
k
Layer 4 (De-fuzzification layer): Each node of this layer gives a weighted output of the first order Sugenotype fuzzy if-then rule as follows:
0 4 - w- k f k , f k
- Z PkiXi + r k , i - l , m , k - l , R
(4)
J
where theJ~ represents the output of the k the rule and the parameters Pki and r~ are called consequent
parameters. Layer 5 (Output layer)." This single-node layer represents the overall output (y) of the network as the sum of all weighted outputs of the rules :
Z wkfk,k-l,R ~
0 5-
(5)
k
ANFIS requires a training data set of desired input/output pair (xl, Xe...Xm, y) depicting the target system to be modeled. ANFIS adaptively maps the inputs (Xl, X2...Xm) to the outputs (y) through MFs, the rule base and the related parameters emulating the given training data set. It starts with initial MFs, in terms of type and number, and the rule base that can be designed intuitively. ANFIS applies a hybrid learning method for updating the FIS parameters. It utilizes the gradient descent approach to fine-tune the premise parameters that define MFs. It applies the least-squares method to identify the consequent parameters that define the coefficients of each output equation in the Sugeno-type fuzzy rule base. The training process continues till the desired number of training steps
345
(epochs) or the desired root mean square error (RMSE) between the desired and the generated output is achieved. In addition to the training data, the validation data are also optionally used for checking the generalization capability of FIS. layer 1
layer 2
layer 3
tayer 4
9
layer 5
Z
:Y
m Hj
=
z___.a W i , j x i +
bj )
(6)
i=1
bj 1 and wij 1 represent respectively the bias and the weight of the connection between the jth node in the hidden layer and the ith input node. The superscript 1 represents the connection (first) between the input and the hidden layers. The output vector y = (yl y2 .. yM)r of the network is obtained from the vector of intermediate variables u through a similar transformation using activation function @ at the output layer. For example, the output of the neuron k can be expressed as follows: where
R
'
~
,
.
~
~
"'R
~
--R
"1 xm
Yk - (,~ ( Z
W~,kUl + b2
(7)
l=l
Fig. 1. Basic structure of ANFIS 4.
Artificial neural network (ANN)
Artificial neural networks (ANNs) have been developed in form of parallel distributed network models based on biological learning process of the human brain. There are numerous applications of ANNs in data analysis, pattern recognition and control [12, 13]. Among different types of ANNs, multi-layer perceptron (MLP) neural networks are quite popular and used for the present work. Here a brief introduction to MLPs is given for completeness. Readers are referred to texts [ 12, 13] for details. MLPs consist of an input layer of source nodes, one or more hidden layers of computation nodes or 'neurons' and an output layer. The number of nodes in the input and the output layers depend on the number of input and output variables respectively. The number of hidden layers and the number of nodes in each hidden layer affect the generalization capability of the network. For smaller number of hidden layers and neurons, the performance may not be adequate. Whereas with too many hidden nodes, the MLP may have the risk of over-fitting the training data and poor generalization on the new data. There are various methods, both heuristic and systematic, to select the number of hidden layers and the nodes [13]. Figure 2 shows a typical MLP architecture consisting of three layers with m, Q and M nodes for input, hidden and output layers respectively. The input vector x = (xl x2 .. Xm) r is transformed to an intermediate vector of 'hidden' variables u using the activation function (pl. The output uj of the jth node in the hidden layer is obtained as follows:
346
where the superscript 2 denotes the connection (second) between the neurons of the hidden and the output layers. There are several forms of activation functions % and q)2, such as logistic function, hyperbolic tangent and piece-wise linear functions. The training of an MLP network involves finding values of the connection weights which minimize an error function between the actual network output and the corresponding target values in the training set. One of the widely used error functions is mean square error (MSE) and the most commonly used training algorithms are based on back-propagation. In the present work an MLP with one hidden layer has been used. The input layer has nodes representing the normalized input features. The number of input nodes has been varied from 1 to 3 and the number of output node is 1. The number of hidden nodes has been taken as 15, based on several trial results. The target values of the output node for training the network have been the normalized values of surface roughness. The sigmoidal activation functions have been used in the hidden and the output layers to maintain the outputs within 1. The training algorithm of LevenbergMarquardt has been used along with back-propagation. The ANN has been trained iteratively using the training data set to minimize the performance function of mean square error (MSE) between the network outputs and the corresponding target values. No validation data have been used in the present work. The classification performance of the MLPs has been assessed using the test data set which has no part in training. The gradient of the performance function (MSE) has been used to adjust the network weights and biases. In this work, a mean square error of 10-6, a minimum gradient of 10-1~ and maximum iteration
number (epoch) of 500 have been used. The training process would stop if any of these conditions are met. The initial weights and biases of the network have been generated automatically by the program. Input layer xI
--/~
Hiddeil layei
/ ,4
,.f-',~
u t
Output laye[ Yl ,./~
~-
2
x
............................................... "~
in
YM
predicting the surface roughness. Table 1 ANFIS prediction results with bell type MF
1-3 1-3 1-3 1,2 1,2 2,3 2,3 2 2
2 3 4 2 3 2 3 2 3
Fig. 2. Basic structure of ANN 5. Results and discussion
In this study, the number of input features has been varied from 1 to 3 (speed, feed rate and depth of cut) and the number of MFs has been in the range of 2 to 4. The types of MFs include generalized bell curve, triangular and trapezoidal. The initial input MFs have been generated spanning uniformly over the range of each input, Fig. 3. The final input MFs, tuned in ANFIS to suit the training data, are shown in Figs. 4(a)-(c). Results of training and test success of ANFIS with different inputs and number of MFs are presented. Training time is also shown for a PC with Pentium processor of 1.7GHz and 1 GB of RAM. It has been observed that considerable experimentation is needed to find the suitable mix of inputs for optimum training and test accuracy in modelling the surface roughness, especially if the number of inputs to choose from is considerably large. Table 1 shows the results of training and test success of ANFIS for different number of inputs (1-3) with generalized bell curve type MFs. For first five cases (inputs 1-3 and 1-2), RMSE varies in the range 0.0000-0.0795 for training and 0.0531-0.0835 for test. The average percentage test error varies in the range of 1.40-4.99%. The best test performance is with 3 inputs and 3 MFs. Average percentage error in training (computed but not presented) for the same cases varies from 0.36 to 2.13%. The training time increases with number of inputs and number of MFs. For the remaining cases, both raining and test accuracy deteriorate indicating the inadequacy of these inputs in
Training Time (see) 0.831 2.263 8.052 0.601 0.781 0.511 0.821 0.391 0.321
Inputs MF
RMSETraining
RMSE- Average Test test error (%) 0 . 0 5 3 2 2.68 0.0531 1.40 0 . 0 6 6 2 4.99 0 . 0 7 1 6 3.44 0 . 0 8 3 5 2.77 0 . 1 0 9 6 4.61 0 . 1 1 6 8 3.88 0 . 1 1 3 9 5.41 0 . 1 1 6 5 4.01
0.0538 0.0315 0.0000 0.0795 0.0769 0.1053 0.1018 0.1183 0.1165
1[r ~ MF1
MF2
o_0,8/ ~
MF3
i/
~0.6
/i
E ~0.4
,/,'
,,,, ',,,,,, ,,,,'
,//
;>
,''' ''",,
D
~
~u 7;i.;i
.... /
.........
_
0.5
0.6 0.7 0.8 0.9 Inputs (speed, feed rate, depth of cut) Fig. 3. Initial membership functions of inputs MF1
1-
MF2
MF3
/ .....................,
o. 0.8
;/i//'/
//, ................................... '"'\,,. /;//"'"
/
;,\
E <~ 0.4
cl 0.2
/ ....---../'~ ...
0 ........
0.5
0.6
"\
.......-.....-.....
0.7 0.8 Input 1 (speed)
"'\.....~._~..
0.9
(a)
347
1~
MF1
.
9
" U F2
/.t . . . . . .
,-, 0.8
/
E 0.6 E -o 0.4
1.2 \
i
"\
\
,
/ \,
o~ (D
--
/
,~\
/
.///
\\"\
'\/
g
c3 0.2
~x.
///
~ 0.4
/ ......
0:3
0:4
0:5
0:6 0:7 Input2 (feed rate)
_
g o.2
0 Z
\...
0.8
......... ." " \
-...- .'\ / ,,/
-o.2 0
0:9
10
f . . ..... . ... ... . . .
20
index i
~"
MF2 /
0.8
//
/
30
40
50
(a)
(b) MF 1
.
8 0.6
.
/ /
9
0.8
"
/
/i
Actual Predicted
--
/.-
M F 3
1.2
MF3 \,
',,
/
'\
\\
/i
/
/
--
Actual P redicted rediction P error
--
//
0.8
= 0
-Q E 0.6 E 0 0.4
i
8 o.6
",,, /
'1::
g 0.4
~ C3 0.2
//i/
._U
\"",.\...\,,,
0.2
0 Z
,......... .. / ~
/ ,
/
\
....
0 0.2
0:3
0:4
015 0:6 0:7 Input3 (depth of cut)
0:8
019
(c) Fig. 4. Final membership functions of inputs: (a) speed, (b) feed rate, (c) depth of cut. Table 2 shows the results with other two types of MFs-triangular (triag) and trapezoidal (trapz). The average percentage test errors are found to be 2.75 and 1.21%, better than 4% and 6.70% reported in [3]. Table 3 shows the results of training and test success of ANN for different number of inputs with 15 (N) neurons in the hidden layer. Results are comparable with ANFIS. Table 2 ANFIS prediction results with other MF type Inputs MF Training RMSEtype Time Training (see) 1-3 triag 2.073 0.0315 1-3 trapz2.103 0.0315
348
RMSETest 0.0503 0.0534
Average test error (%) 2.75 1.21
-0.%
1
&
1'0
index i
l's
2'o
25
(b) Fig 5. Results of surface roughness (a) training, (b) test
t
~
,
Z Z
<0.8 ~9
/, /
s
0.6
§
t: if)
g
//
.~ 0.4
/
0"~.2
/
/.
//
/
//
/
.,
/4~/
*
//
/,Z"
/.~"/ /
//-
/
.t/~,__
~k
~.~.
//+.
e 1.,// //
0'.4 0'.6 0'.8 Predicted surface roughness (AN FIS)
Fig. 6. Scatter plots of predicted surface roughness using ANFIS and ANN.
References
Table 3 ANN prediction results Inputs
N
1-3 1, 2 2, 3 2
15 15 15 15
Training Time (sec) 1.242 0.240 0.791 0.171
RMSETraining
RMSETest
0.0030 0.0757 0.1015 0.1187
0.0587 0.0811 0.1112 0.2277
Average test error (%) 2.36 4.37 5.84 6.09
Figures 5(a) and 5(b) show the comparison of actual surface roughness and the predicted values for training and test data sets for the case of first 3 inputs, each with 3 MFs. The variation of prediction error is shown also for each data set. Figure 6 shows the scatter plot of predicted surface roughness using ANFIS and ANN respectively for 3 inputs. The predicted values are quite close for most of the data points. The present results based on ANFIS and ANN are better than those of [3] for the same data sets.
6. Conclusions Results for various design parameters for ANFIS model to predict surface roughness in end-milling are presented. The average percentage error in prediction has been in the range of 1.40-2.68% for ANFIS and 2.36% for ANN with 3 inputs. The use of particular MFs and the normalization of data sets gives better prediction performance compared to [3] for the same data sets. However, ANFIS shows better performance in terms of higher accuracy even with only 2 selected inputs. The selection of inputs will be particularly advantageous in presence of large number of sensors in the context of sensor fusion. In addition, the transparency of the solution process in case of ANFIS is expected to give the user better confidence. The inherent capability of ANFIS in working with imprecise (noisy) information would also be an added advantage over traditional approaches based on multiple regression.
[1] Lou SJ and Chen JC. In-process surface roughness recognition (ISRR) system in end-milling operation. International Journal of Advanced Manufacturing Technology 58 (1999) 100-108. [2] Yang J. and Chen J. A systematic approach for identifying optimum surface roughness performance in end-milling operations. J. Industrial Technology, 17 (2001) 1-8. [3] Lo S-P. An adaptive-network based fuzzy inference system for prediction ofworkpiece surface roughness in end milling. Journal of Materials Processing Technology 142 (2003) 665-675. [4] Huang L and Chen J. A multiple regression model to predict in-process surface roughness in turning operation via accelerometer. Journal of Industrial Technology 17 (2001) 1-8. [5] Kirby ED, Zhang Z and Chen JC. Development of an accelerometer-based surface roughness prediction system in turning operations using multiple regression techniques. Journal of Industrial Technology 20 (2004) 1-8. [6] Samhouri M, and Surgenor B. Surface roughness in grinding: off-line identification with an Adaptive Neurofuzzy Inference System. SME Technical paper TPO5PUB159, 2005, p 1-8. [7] Zhou, X and Xi F. Modeling and predicting surface roughness of the grinding process. International Journal of Machine Tools' and Manufacture 42 (2002) 969-977. [8] Benardos, PG and Vosniakos G-C. Predicting surface roughness in machining : a review. International Journal of Machine Tools & Manufacture 43 (2003) 833844. [9] Zadeh, LA. Fuzzy sets. Information and Control 8 (1965)338-353. [10]Yen, J and Langari R. Fuzzy logic: intelligence, control and information, Prentice Hall, Upper Saddle River, N J, 1999. [11]Jang, JSR. ANFIS: Adaptive-Network- Based Fuzzy Inference System. IEEE Transactions on Systems, Man and Cybernetics 23 (1993) 665-685. [12]Wasserman PD. Advanced Methods in Neural Computing, Van Nostrand Reinhold, New York, USA, 1995. [13]Haykin S. Neural Networks. A Comprehensive Foundation, 2 nd Edition, Prentice Hall, New Jersey, USA, 1999.
Acknowledgements The work was done while the first author was at Robert Morris University (RMU) as Rooney International Visiting Professor. The support of RMU to carry out the research is gratefully acknowledged.
349
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhd and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Service Orientation in Production Control W. Beinhauer, T. Schlegel Fraunhofer Institute for Industrial Engineering, Nobelstrasse 12, D-70569 Stuttgart, Germany
Abstract
Service orientation is one of the most discussed topics in the world of application integration. It seems to be the new paradigm for application integration infrastructures and directly bridges between the process-oriented business world and the application-oriented world of IT. However, the benefits of service orientation are not limited to large-scaled service enterprises such as insurance companies. It has the potential of enabling other major developments in production control as the rise of intelligent decision support systems that allow for advanced maintenance, failure prediction and machinery protection. In this paper, we present the basic principles of service orientation, discuss its benefits and shortcomings, and show how this is being adapted to the INT-MANUS common infrastructure. It is shown, how service-orientation relates to knowledge management and business intelligence and what we can expect from service-oriented production control systems of the future. Keywords: Service orientation, production control, ad-hoc networks
1. Introduction
In the recent years, production control has focused on an increased level of atomisation and the ongoing application of flexibly programmable robots for various purposes throughout the manufacturing process. This development is a consequence of the increasing market pressure towards shorter product innovation cycles, more specialisation of manufactures goods and mass customisation. The actual driving force behind the atomisation and maintenance is the need for shorter development cycles and for a broadening range of products. Another major development in production control is the rise of intelligent decision support systems that allow for advanced maintenance, failure prediction and machinery protection. Such decision support systems
350
aggregate information from different sensors within a machine complex indicating critical data for the operation, abrasion and durability of tools and equipment. Based upon descriptive models, boundary conditions and threshold values, appropriate pre-emptive action or recovery mechanisms can be invoked. In conventional business software, the need for increased flexibility, re-configurability and protocol adaptation has been faced by the introduction of the principle of service orientation. The conventional orientation of business software according to business application is being replaced by an alignment ofsottware modules alongside business processes. Likewise, manufacturing processes are guided through production lines consisting of various machines. Production control systems that focus on a singular machine cannot keep an eye on the whole picture yet
yielding a consistent state of the overall state of a production plant. By shilling the focus from singular machines within the production process towards the integrated manufacturing process, hence shilling from machine control towards manufacturing process control, various advances in both production flexibility and machine maintenance can be achieved. In this paper, we give an introduction into the principles of service orientation in production control and point out the expectances to integrated production control that are made in the INT-MANUS project which deals with intelligent manufacturing systems and service-oriented ad-hoc networks in production control.
2. Design Principles of Service Orientation The major design principle of service orientation is the separation of the description of the interfaces of a software module from its implementation. Through this decoupling, direct dependence between the software components involved is eliminated. By this way, it becomes possible to break up stiff structures and to generate flexible architectures with the desired properties in view of flexibility and reconfigurability. Nevertheless, present drafts and implementations of service oriented infrastructures raise a further set of challenges, whose solution would permit a higher level of flexibility than today. First of all, this concerns the dissolution of structural dependences between different sot~ware modules. The invocation of services requires the calling entity to be fully informed about the interface of the service provider. The request must exactly match the requirements given by the service provider, and the service consumer has to be aware of in which format the resulting values will be given back. This implies a structural dependency between service provider and service consumer which is contradictory to the idea of flexibility. Another important issue in dynamic process composition is the determination of service end points at runtime. Currently, the choice of an appropriate service provider lies with the consumer - who in turn would need its own heuristics for decision making. Therefore, a centralized component that mediates between service consumers and service providers would be useful in order to achieve full dynamic behaviour without overloading the service requestors with additional reasoning capability. We will show how this can be achieved by means of a centralised routing component that acts as a semantic router and dynamic protocol
bridge. Finally, current process and workflow engines are based on static process descriptions such as in BPEL. However, processes in manufacturing are subject to change, and the knowledge about processes is an enterprise asset worth to be kept in a dedicated component. Such a process repository, linked to a domain ontology, can serve as a case base for later experience based process composition. 2.1. The basic system architecture As in every conventional SOA, an enterprise service bus (ESB) is used as a communication backbone in the proposed 1NT-MANUS production control system. The service bus interconnects all relevant machines and their sensors and actuators, respectively. The ESB is responsible for asynchronous messaging, decoupling of message contents and message infrastructure data, and service location. All services i.e. all machine sensors are attached to the service bus. Additionally, infrastructure components are linked to the ESB, being responsible for message routing, transaction control or machine state freezing. Consumer 1
....
i:::
84
Fig. 1. Schematic view of a service-oriented architecture Attached to the ESB, there is a series of services. In conventional SOAs, one usually differentiates between services that provide actual contents or that carry business logic (often referred to as application services), and those that are responsible for infrastructure tasks, such as message routing, service location, protocol adaptors, security and so on. Abstractly speaking, these services communicate to each other as consumers (the invoking application) and providers (the responding application). Content providing services are invoked by service consumers by the mediation of the service bus. Hence, the consumer does not have to know about the
351
exact location and the implementation details of a providing service. There can be even several providers or different versions thereof. Service consumer and service provider are completely decoupled. It lies in the responsibility of the interconnecting ESB to locate an appropriate server and to bridge between application protocols or the convert parameters so that they fit into the interface specification of the respective service. This enables the maximum flexibility of service oriented architectures. Services in the INT-MANUS architecture are actuators and sensors that are wrapped by a web service binding. Thus, sensors can be reached by SOAP messages and reply by SOAP messages. There are autonomously acting sensors that send their values proactively, which is mandatory for alarming messages, and sensors that have to be triggered in order to reply their respective values. This will be the case for reporting internal machine status like abrasion, number of treated goods and so on. The same way, actuators are wrapped as web services, allowing other applications to trigger appropriate action on them. Finally, a robot dispatching agent will be included in the INT-MANUS service platform. Manufacturing goods have to be transported from one machine to another, which can be either achieved by plant workers or by selfsteering robots. In INT-MANUS, a robot dispatching service attached to the service bus will manage the deployment of a fleet of robots that invoke corrective actions, part removal and similar tasks.
2.2. Externalisation of process descriptions The basic principle behind service orientation is integration by decomposition. Hence, when decomposing a monolithic software system into smaller software modules, they have to be recomposed in a flexible manner so that the same application logic is implemented. However, due to its modularity, this application logic is easily adaptable to new business needs. The partition of services into application services and infrastructure services offers the possibilityto externalise the description of the run-time behaviour of an executed process to a dedicated component, what would be a process description. In INT-MANUS, a process repository has been introduced, that contains all information and run-time behaviour of processes to be executed. The processes are represented and stored in an abstract manner, since the binding to concrete service implementations is subject to the ESB. Hence, the 1NTMANUS architecture allows the externalisation of
352
process descriptions and the modification thereofwithout touching the services wrapping sensors and actuators.
Component A
Component B
Component C !
iiiiiiiiiii!iil iiiiiiiiil iiii!iiil _
~
,
Fig. 2: A process repository and its run-time environment Such chains of service invocations are performed by a run-time engine that processes the commands according to the description held in the process repository. That allows for new features in production control, since the process descriptions themselves are part of a company's assets and represent a value on their own.
2.3. Knowledge management and production control As stated above, process descriptions are part of a company's assets and are therefore worth of being stored and archived. Such descriptions represent certain know how. Moreover, the storage and semantic classification of process descriptions offers the possibility of their later retrieval by case based reasoning (CBR). Hence, once found solutions can be retrieved as soon as a similar problem arises. This requires, of course, a sufficient semantic description of both of the applicable solutions as well as an abstract modelling of the problems so that a similarity measure for CBR can be introduced.
2.4. Extensibility and ad-hoc networking So far, only standard components have been mentioned. However, in order to make the INT-MANUS infrastructure a production oriented framework, semantics have to be included into the messages. Therefore, a new application protocol above SOAP as a transport protocol is being introduced. The XML-based
application protocol is used for message routing in SOAP headers and allows for ad-hoc networking. Hence, machines become autonomous and possibly mobile nodes that communicate over the ESB. The principle used in the 1NT-MANUS architecture is that of an unified messaging system- hence, messages ofwhatever device and for whatever purpose are routed according to rules specified in the ESB routing component. Moreover, the ESB offers a publish and subscribe mechanism, where new services, i.e. machine sensors, can register themselves at run-time. The publish subscribe mechanism is fully compliant to the semantic message protocol and stores machine identifiers and the structure of a machine (i.e. its components and sub-components). The provision of a publish subscribe mechanism is essential to the openness and extensibility of the overall network in the sense of ad-hoc networking, which has been one of the requirements. The semantic application protocol is currently under revision and its final release will be published in the near future. It is set to be capable of complying with wide spread control systems like Siemens Sinumeric and others. The XML-based protocol contains different sets of machine data for real-time failure analysis as well as for long-term machine performance and durability. Thus, data for failure analysis such as the machine type and serial number, identifiers for machine components and sub-components, information on the production line and failure codes for the description of appearing misfunctions are contained. Likewise, operating data such as raising times and setting times for the machine control, damping factors and interpolation data are collected and spread over the service infrastructure.
are supported by dedicated software modules such as CRM systems, ERP, planning sol, ware, service monitoring, billing and so on. Because of their inability to offer open interfaces towards other applications, these modules are otien referred to as silos. However, service companies in particular have profited greatly from the introduction of business intelligence solutions, which collect data from different modules, representing different business numbers. These are aggregated on scorecards yielding meaningful business reports that allow for a deeper insight into the market and help steering the enterprise. Analogously, service orientation in production control can lead us to a more in-depth knowledge about the efficiency and sustainability of machine deployment. Instead of business indicators that are filled into scorecards, machine operation parameters are collected by sensors and are filled into semantic models that represent the state and the working conditions of a machine. Additionally, production-oriented machine data can be correlated to business-oriented data, yielding more data on the efficiency of the production processes and organisational aspects. Machine data can be collected and evaluated alongside a production process for one good, spanning over a certain number of machines performing the respective manufacturing steps. However, machine data can also be aggregated for a single type of machine that is deployed in many production processes among different companies. The situation is depicted in Fig. 3 and Fig. 4.
3. Application to production control 3.1. Use cases
Today's manufacturing plants typically consist of a certain number of production machines, each of them controlled by an isolated control system and software. Otien, failures of one machine lead to a standstill of the whole production line. Moreover, diminishments in quality of the manufactured goods due to abrasion of machines can only be detected at one machine. An integrated view of the production process that reflects interdependencies between the machines is lacking. This corresponds to the case of software silos as otten seen in old-fashioned service enterprises such as banks and insurances. Here, different parts of business processes
M1
M2
M3
Fig. 3: Monitoring of the manufacturing process.
In the first case, a decision support system allows for advanced maintenance, failure prediction and machinery protection, since information from different sensors from different machines collect data for the operation, abrasion and durability of tools and working goods. This is helpful primarily for the company that runs the machines.
353
return on invest, benefits for the machine producer and so on. Currently, no data could have been collected on that.
Row o r e . , ~
5. Future Work M1
M1
. . . . . . . . . . . . . . . . . . . . . . . . . .
M1 ESB
Fig. 4: Monitoring of machine reliability. Moreover, the decision support system is set to provide corrective action where possible. Depending on the parameters collected by the sensors and the actuators installed, these actions could encompass the slow-down of machines, improved cooling or greasing, or calling of maintenance staff. In the second case, information about the functionality, abrasion and run time properties of one single type of machine is collected and evaluated. This way, correlations between external parameters and the machine deployment can be revealed, which is a basis for the machine improvement by the machine tool constructor. Hence, there are different beneficiaries of the service-oriented production control.
3.2. Drawbacks and shortcomings Despite the obvious advantages of a SOAP and semantics based protocol for production control, there is a major drawback that concerns the reliability ofmessage routing and the inability of real-time applications as they are needed or even prescribed for safety and security functions. The lack of real-time functionality can be solved by a work-around by attaching critical messages to other transport protocols than SOAP such as reliable JMS or MQ series message pipes. The required infrastructure has been proposed by IBM and other with the WS-I interoperability framework, that tends to evolve as a generally accepted standard. On the other hand, a decentralized network infrastructure raises the robustness of the overall plant. Where necessary, such as for the robot dispatching, local features are centralised. Another major advantage of message conversation of services lies in the flexible extensibility and process control. Besides the technical attributes, 1NT-MANUS has to proof its economical advantageousness in terms of
354
As stated above, the INT-MANUS framework of a service-oriented middleware for production control heavily relies on the appropriateness of the semantic application protocol, which is currently under development. First evaluations have been made in logistics, where the above architecture has been applied in order to combine production logistics and transport logistics. The results obtained were promising, and the system is currently under implementation with a pilot customer.
Acknowledgements The authors would like to thank the INT-MANUS consortium and the KOMPASS consortium. Work has been carried out by Fraunhofer Institute for Industrial Engineering (IAO), Stuttgart, Germany. Fraunhofer IAO is member of the I'PROMS network of excellence. The work is related to the INT-MANUS project, funded by the European Commission under the 6 th framework programme, and KOMPASS, a project financed by the German Federal Ministry for Education and Research.
References [ 1] Kossmann, D. und Leymann, F.: Web Services. Informatik Spektrum 27 (2), 2004. [2] Brandner, M., Craes, M., Oellermann, M. und Zimmermann, F.: Web services-orientedarchitecture in production in the finance industry. Informatik Spektrum 27 (2), 2004. [3] Bussler, C.: The Role of the Semantic Web Technology in Enterprise Application Integration. In the Bulletin of the IEEE Computer Society Technical Committee on Data Engineering. Volume 26, No. 4 pp. 62-69. December 2003. [4] Berners-Lee, T.: The Semantic Web. http://www.w3c.org, last access: Jan 14th, 2006 [5] Narayanan S. und Mcllraith, S.: Simulation, Verification and Automated Composition of Web Services. Proceedings of the eleventh international conference on World Wide Web. pp. 77-88, 2002. [6] Yanchun, Z. und Benchaphon, L.: Web Service Composition with Case-Based Reasoning. Proceedings of the Fourteenth Australian Database Conference. Conferences in Research and Practice in Information Technology. Volume 17, pp. 201-208, 2003. [7] Beinhauer, W., Kuhn, W., Reisner, S.: Implementierung
prozeBorientierter Ans~itze zur Systemintegration mit service-orientierten Architekturen. In: Aktuelle Trends in der Softwareforschung, Stuttgart / Karlsruhe 2005, pp. 69-86.
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Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All rights reserved.
S t a t i s t i c a l a p p r o a c h to n u m e r i c a l d a t a b a s e s : clustering using normalised Minkowski metrics D.T. Pham a, Y.I. Prostov v M M Suarez-Alvarez a a Manufacturing Engineering Centre, Cardiff University, Cardiff CF2~ OAA, UK b Department of Higher Mathematics, Moscow Institute of Radio Engineering, Electronics and Automation- Technical University, 78 Vernadsko9o pr., Moscow 117~5~, Russia
Abstract Pre-processing or normalisation of data sets is widely used in a number of fields of machine intelligence. Contrary to the overwhelming majority of other normalisation procedures, when data is scaled to a unit range, it is argued in the paper that after normalisation of a data set, the average contributions of all features to the measure employed to assess the similarity of the data have to be equal to one another. Using the Minkowski distance as an example of a similarity metric, new normalised metrics are introduced such that the means of all attributes are the same and, hence, contributions of the features to similarity measures are approximately equalised. Such a normalisation is achieved by scaling of the numerical attributes, i.e. by dividing the database values by the means of the appropriate components of the metric. K e y w o r d s : Clustering, Distance Measures, Normalisation
1
Introduction
The understanding of a complex problem is naturally approached by partitioning, i.e. by breaking the problem into smaller homogeneous parts each of which can be analysed and explained separately. It is for this reason that clustering algorithms aimed at finding smaller, more homogeneous groups from a large collection of objects, have been studied extensively [1,2]. In particular, efficient clustering is a fundamental task in data science, where the goal is to discover similarities within a large data set (with many thousands or even millions of records).
356
Clustering is an inductive process [3]. Given a data set, any clustering (produced by an algorithm or a human) is a hypothesis to suggest (or explain) groupings in the data. This hypothesis is selected from a large set of possibilities and is represented (structured) in some way. It becomes a model for the data, and can potentially constitute a mechanism to classify unseen instances of the data. The mathematical formulation of the inductive principle is called the clustering criterion [4-6]. It discriminates one grouping hypothesis over any another for the same data set. Models are structures that could be used to represent clusters, while the induction process selects
a "best fit" model for a given d a t a set. Parametric statistics (sometimes referred to as statistical inference) a t t e m p t s to fit a probabilistic Inodel to the data. In a number of machine intelligence problems, an object is represented by its features [7]. In database applications, the features are called attributes or fields. The features can be written as a vector variable, namely the feature vector A, where A - {A1, A 2 , . . . Ak}. Each a t t r i b u t e can have a finite or infinite (continuous) number of possible values. In this paper, only numerical or quantitative features are considered, i.e. the case when the feature domain Dom(Aj) is ordered and can be represented on the axis of real numbers. A database is a set of records and hence it can be represented as a matrix aij of size IN x k] where N is the number of records, and k is the total number of attributes, 1 < i < N and 1 < j _< k. During clustering, it is necessary to understand how far any two different feature vectors Ai and A,~ are from each other or how alike they are. A measure of the degree of closeness (likeness) is called a similarity measure [8]. In clustering analysis, it is common to calculate the similarity or dissimilarity between two feature vectors Ai and A,~ using a distance measure. Indeed, it is natural to employ the Euclidean metric (distance) PE (or L2 metric) 1//2
(1) as a measure for continuous numerical features because it is used in everyday situations and is well understood. However, other similarity measures are also adopted. These include the Tchebysheff (Chebyshev) or m a x i m u m norm
IAi-A~
-max 3
the city block distance (or L1 metric) k
I]Ai-A.~
-E
a~j-a~j,
(3)
j--1
This paper is concerned with the normalisation of data sets. Normalisation of features is necessary to equalise approximately their contributions to the similarity measures employed. In general, when there is no prior information about the relative importance of the attributes, one has to assume t h a t all attributes are equally relevant. However, a direct application of the geometric measures for attributes with large ranges will iraplicitly assign larger contributions to t h e m than to attributes with small ranges. In addition, the numerical values of the ranges depend on the units of measurements. Thus, if all attributes are equally i m p o r t a n t in measuring similarity between feature vectors then one should not employ distance measures like the Euclidean distance (1) without normalisation of d a t a (see, e.g. [91). If it is known in advance t h a t some attributes are irrelevant to the problem under consideration then they can be removed from the feature vector. In spite of the importance of d a t a normalisation, there are few papers devoted to normalisation methods for d a t a sets (see, e.g. [9]). Contrary to earlier normalisation procedures t h a t were targeted mainly at normalisation of the feature components to a [0, 1] interval, here, a statistical approach to humerical features of data sets is adopted and new normalised metrics are employed such t h a t the average contributions of all attributes to the measures are equal to one another from a statistical point of view. As an example of a metric for the distance between feature vectors, the Minkowski metric is considered. This is a very general case because the Euclidean distance (1) and the city block distance (3) are particular cases of the Minkowski distance (2) when p = 2 and p = 1 respectively.
aij-a,~jl;
2
Statistical malisation
The Minkowski distance PM (or L~ metric)
approach
to nor-
of feature
vec-
tors
(2) where p is a positive number, 1 _< p < +oc; and
Consider data sets with numerical attributes. W i t h geometric similarity measures, usually no assumption is made about the probability distribution of the attributes, and similarity is based
357
on the distances between feature vectors in the feature space [9]. However, each record (row) of a database may be regarded as a random sample of the population under consideration, i.e. one has a database of N observations (samples) and each sample (record) is a realisation of possible values of the feature vector A. Essentially, there are only two types of attributes, namely numerical and categorical. Other types of attributes can be mapped to these two types. Real-life data sets are often mixed, i.e. they consist of both numerical and categorical types. For mixed data, the vector of features A can be split into A = (X, Y), namely the vector of numerical features X = (X1,... ,Xk), and the vector of categorical features Y. However, it will be assumed here that all the features are of the same type, namely the attribute vector is the vector of numerical features A - X. From a probabilistic point of view, a population is regarded as known when the probability density function fxj (x) for each random variable (numerical attribute) Xj is known. Evidently, the distribution fx5 (x) is independent of the record number i. It is assumed usually that each numerical feature has a normal distribution with mean #j and variance crj. However, other distribution functions may be also used to describe the database values. The mean #j and the variance cry may be estimated using standard statistical methods [10]. The normalisation procedure can be implemented in different ways. For example, Aksoy and Haralick [9] reviewed five normalisation methods for numerical data, namely linear scaling to a unit range, linear scaling to a unit variance, transformation to a uniform [0, 1] random variable, rank normalisation, and normalisation by fitting distributions. All of these approaches are intended to normalise each feature component to the [0, 1] range. The first of these approaches normalises the data by dividing the attribute value Xij by its range using scaling with a shift -
(x
j -
X
,n)/(X
ax
-- X.,n).
(4)
Here Xi*j is the normalised attribute value in the database, and X~ax and X~in are the maximum and the minimum values of attribute X d respectively. This is the most cited method of normalising data sets. For example, the use of the
358
Minkowski norm for clustering real world data has been studied recently [11] and equation (4) has been used for normalisation. Evidently, the results scaled by (4) do not depend on the original units of data measurements, and this linear scaling will transform the data to the range [0, 1]. However, this normalisation procedure does not achieve equalisation of the attribute means. Indeed, if numerical attributes X] and X2 were normalised by (4) then the probability density functions f x 2 (x) and f x ; (x) can be such that the mean for X] is, say, 0.01 while the mean for X* is, say, 0.9 and therefore these attributes will give non-equal contributions to the similarity measures used. A common normalisation procedure is to transform the attribute Xi into a random variable with zero mean and unit variance by 5 -
(x j -
(5)
where >O = E ( X j ) is the sample mean and cry its standard deviation for attribute Xj respectively. Also, it has often been suggested that out-ofrange records be removed on the assumption that this would just eliminate outliers [9]. However, truncating of out-of-range records could lead to loss of information from the database. It was noted that providing all attributes are normally distributed, there is a 68% probability that an attribute value normalised by (5) is in the [-1, 1] range. If one applies an additional shift and rescaling as
Xi~ - 0.5[(Xij - pi)/(3crj) + 1]
(6)
then this guarantees 99% of the values to be in the [0, 1] range [9]. However, any shifting of the whole attribute column does not affect the distance metrics (1)-(3). Hence, such additional shifting has no practical applications to clustering of data sets. It is reasonable to expect that the mean contributions of individual features to the overall similarity measure should be equal. Therefore the goal of a normalisation procedure is equalisation of attribute contributions. The goal of the normalisation procedures reviewed in [9] was the normalisation of each feature component to the [0, 1] range and not the equalisation of feature means. As already discussed, if the means of the different normalised attributes
are not equal to one another then these variables give non-equal contributions to the similarity measures.
and its mean is #j
pj --
xfxj(x)dx.
(9)
oo
3
Normalisation vectors
for
of
The Euclidean distance has the following very convenient property t h a t is valid for any arbitrary distribution function, namely, the mean contribution of the a t t r i b u t e to the Euclidean measure is
feature
Minkowski
met-
rics To obtain a new normalised metric in the general case of the Minkowski metric, one should calculate the mean contribution of each j - t h attribute to the metric EIX~ 5 - X , ~ 5 I p and divide the a t t r i b u t e in all records by this mean (if the mean is equal to zero then this a t t r i b u t e should be removed from the feature vector). The procedure ensures t h a t the means of the different normalised attributes are equal to one another. Therefore, these variables should give equal contributions to the similarity measure used. The expectation of an arbitrary function r of a system of two r a n d o m variables X and X I with the density function f ( x , x ~) of the system (X, X ' ) is defined as
E[O(X, X')] -
r
x') f (x, x ' ) d x d x 1.
-
X ~ j ) 2 _ 2cr~
(10)
where crj2 is the variance of the distribution given by:
(x - #j)2 fx~ (x)dx - oj.2
EE(Xij - pj)2] _ oo
Proof: E(x~5 - x~)
~ - EE(x~j - ,~)-
(x~
- ,j)l ~
or
E ( X i j -- X m j ) 2 __ 1 1 § where I1
--
F-J[(Xij
--
~j)2],
and
cx~
z~ - E [ ( x ~ j
If the r a n d o m variables X and X ~ are independent of the density functions f l ( x ) and f2(x') respectively then
f(x,x')-
E(Xij
fl(x)f2(x').
- ,j)].
Since the distribution f x j (x) is the same for both 2 Also X i j and X ~ j , I1 - I2 - Gj.
I~ - f / ( ~
Hence, the expectation of the contribution of the j - t h attribute to the Minkowski metric is
- ,j)(x~5
- ~5)(~' - .J)f~3 (~)f~ (~')dxdx'
-- (:X~
and taking into account (9), gives
(x)
E x~j-x.~jI ~ - H
t~-~' ~fx~ (~)f~ (x')dxdx'.
-- r
(7)
Consider first a particular case, p - 2, of the Minkowski distance pM, namely the Euclidean distance (1) /92 [ X i , X m ] __
(Xil
_
X m I )2 @ . . . ~_
(Xik
_ Xm
k)2
.
Hence, equation (10) is correct. If one introduces the following normalised variables Xil
Xi*l - -
-
-
~1 ,...Xik
* -- -
Xik
-- ~k
(s)
Let the j - t h feature component be distributed randomly with a density function f x j
(N3
then the mean contributions of all normalised components X~ and X * to the square Euclidean distance (8) are the same ~(X~*l - X;~l) ~ .....
E(X~*k - X ~ )
~ -
1.
359
Clearly, the condition of equivalence of the normalised attribute means is satisfied for the Euclidean metric by the transformations represented by equations (5) and (6). In the general case the mean pj and the expectation of the contribution of the j - t h attribute to the Minkowski metric may be estimated using standard statistical methods. Hence, the sample analog of the probability is 1 I N and the mean is estimated by the sample mean X j --~j __ X l j
= x.j) = (1/N).(1/N)
- Xrnj p -
~-.x
~ r,s=l
1 X r j -- X sj p N2
Thus, although the Minkowski distance PM is just a generalisation of the Euclidean distance from p = 2 to an arbitrary p, it does not have such a general property as the above property (10) that is valid for an arbitrary distribution fx5 (x). As mentioned above, it is usually assumed that each numerical feature has a normal distribution with mean #j and variance cry. Consider now the case when one knows in advance that the values of the j - t h attribute are distributed normally. In this case, PM has quite an attractive property. Let f x ~ ( x ) be a normal distribution with mean #j and the variance a 2. The expectation of the contribution of the j - t h attribute to the Minkowski metric can now be calculated. Substituting -
1
into (7) gives E l X i j -- X m j p --
360
(
[
-
1
~/~7~j
9I
x~ - x ~ P r 1 6 2
and r
( -(xi2cr~J)2 -
-- exp
).
Introducing new variables si and sm 8i
= 1 / N 2. (11) Using (11), it can be determined that the contribution of the j - t h attribute to the Minkowski metric is estimated by the sample mean of IXij X m j Ip, i.e. E Xij
I --//~
q- . .. -~- X N j
N The random variables X i j and X ~ j are independent. For the sake of simplicity, assume that the attribute X j takes unequal discrete values X l j , X 2 j , . . . , X N j . Then for specific numbers r and s, P(Xij = x.j,X~j
where
Xi -- p j :
and
crj
Sm :
X m -- p j
oj
yields p
ElXi j - Xmj
p -- o-__~j . Ip
27c
where 8i ISi -- Sm p exp(----~-)exp( - s --~i n )dsidsm.
Iv -
Introducing a constant Cp for each normal distribution O(2
1 cp -- - ~
Si
Isi-s,~] p e x p ( - - ~ - ) e x p ( - s-~' ~ )dsids,~. -- CX~
gives EIx
j
-
=
Thus, a modified Minkowski metric p* for a database is 1/p (ph)[Xi,Xm]__(~oLjlXij_Xrnjp)j=l
where ctj = 1/(aPcp).
4
Conclusion
In this paper, a numerical database is regarded as a random sample of objects for the domain under consideration. In most existing approaches to normalisation, scaling is used for the Euclidean metric and/or the normal distribution of the variables in order to ensure that values are in the [0, 1] range. In general, this does not enable equal contributions of the features to the metrics. In this paper a statistical approach is applied to normalisation of all attributes of the feature vectors of
data sets. New normalised metrics are introduced such that the means of contributions of all attributes are the same. Hence, contributions of the features to similarity measures are approximately equalised. Such a normalisation is achieved by scaling of the numerical attributes. If one knows in advance that some attributes have larger contributions to similarity measures than the rest of the attributes then this can be taken into account by appropriate weighting of attributes. It is straightforward to apply the weighting procedure to metrics that have already been normalised by the above described procedure.
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Estivill-Castro, V. and Murray, A.. Discovering associations in spatial data-an efficient medoid based approach. In: X. Wu, R. Kotagiri, and K. Korb, editors, Proceedings of the 2nd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD98), pp. 110-121, Melbourne, Australia, 1998. Springer- Verlag Lecture Notes in Artificial Intelligence 1394.
t!]ialkidi, M., Batistakis, Y. and Vazirg[annis M. (2001) On clustering validation t,~chniques. KDnuggets: News, p. 19 r 1,3, September 2001. www.db- net.aueb.gr /mhalk/papers/validity -survey. pdf.
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Halkidi, M., Vazirgianis, M. and Batistakis "~. (2000) Quality scheme assessment in the c ustering process. In: H. Zighed, D.A. Kon~orowski and J. Zytkow, editors, Principles o[ Data Mining and Knowledge Discovery, 4r European Conference, PKDD, pp. 265276, Lyon, France, September, 13-16 2000. Springer Verlag Lecture Notes in Computer Science 1920.
[7] J~in A.K., Murty M.N., and Flynn P.J. ([999) Data Clustering: A Review. A CM (:ornputing Surveys, 31,264-323. [8] Looney, C.G. (1997) Pattern Recognition ~_rsing Neural Networks. Oxford University F ress, New York. [9] 3ksoy S. and Haralick R.M. (2001) Feature normalization and likelihood-based similarity n~easures for image retrieval. Pattern Recogr~ition Letters, 22, 563-582. [10] Spiegel M.R. (1975) Schaum's Outline of ~'heory and Problems of Probability and Statistics. McGraw-Hill, New York. [11] Doherty K., Adams R., Davey N. (2004) Non-Euclidean Norms and Data Normalisation. In: M. Verleysen (Ed.), Proc. 12th l~iuro. Symposiumon Artificial Neural Netu orks, Brugges, Belgium, d-side publications, Brugges, pp. 181 186.
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Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All rights reserved.
Technology Readiness Model for Enterprises Ercan Oztemel and Tulay Korkusuz Polat Sakarya University, Engineering Faculty, Dept. of Industrial Engineering, 54187, Sakarya, TURKEY
eo~temel(~akatTa,edu.tr, korkusu~(~akatTa.edu.tr
Abstract
This paper presents an innovative technology management model for the enterprises. It provides an overview of existing technology assessment models and introduces TRM (Technology Readiness Model). The proposed model assesses the technology from operational, tactical, and strategic aspects.
1. I n t r o d u c t i o n
The speed of technological developments are increasing rapidly everyday. The enterprises need to follow new innovations and new technologies to preserve the competitive advantages. There are various aspects of technology to be taken into account. At each level of the enterprise there are technology related decisions as well as the use of technologies with different characteristics in nature. Technology management and technology transfer are the subject of various studies [See for example, 1-3]. There have been some studies on defining the level of technological readiness in enterprises. Technology and Process maturity model called CE which is developed by Karandikar et.al [4]. This study was later extended to include collaborative Technologies and called CERC [5]. In these models, technology maturity and process maturity is considered as the key elements of technology management. Technological maturity is evaluated in terms of information sharing coordination and communication. Process maturity on the other hand is evaluated with respect to leadership, team formation, management systems, products and agility. 5 process maturity levels, 3 technology maturity levels are defined and enterprises evaluated using these levels. Most of the later studies are based on this study.
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Similar to CERC, Khalfan [6] introduced a model called CERAM which was developed for assessing the engineering readiness for construction type of business. This model evaluates the concurrent engineering activities of a construction company with regard to process and technologic readiness. It brings the idea of taking strategy dissemination as well as customer focus and project standards into account. Seeking for technology readiness in construction industry continued with BEACON (Benchmarking and Readiness Assessment for Concurrent Engineering) which was introduced by Centre for Innovative Construction Engineering (CICE) and SERVQ of Loughborough University in 2002. This model also checks out the concurrent engineering activities and measures the performance of mainly supply chain of particular company. The novelty of this approach was to introduce performance measurement in addition to process readiness (maturity). This obviously extended the previous models taking the human being and project issues into account. Technology assessment is enriched in this model with task and integrity support [7]. Capability maturity model (CMM) was also introduced for assessing the maturity of the software systems developed for Ministry of Defense in USA [8]. It defines various level of
technological readiness starting from ad-hoc use of technology and follows the stages given below. 9 Applied technology, 9 Standard and consistent technology, 9 Reliable technology 9 Continuously improving technology. Although there is not much implementation this model could be extended to other areas.
on information focused analysis. Some basic issues such as technology portfolio, technology forecasting, technology requirements analysis, technology change rates, technology innovation etc. seem not to be explicitly evaluated. Technology assessment module which is proposed here is developed to fill this gap. This model can be called as TRM.
Business Process Maturity Model (BPMM) which was introduced by Fisher [9] evaluates technology as part of the complete assessment of the business. Although the focus of their model is not the technology readiness assessment, it evaluates information Technologies and infrastructure. However, the complete technological background of enterprises seems to be limited only to information Technologies which is only a small set of the total technological issues.
Note that the proposed technology readiness model is a part of Strategic Enterprise Resource Management (SERM) methodology [10]. The TRM checks the technology in 3 different levels mainly, strategic, tactical and operational. At each level, the technological elements have different values weighted in an overall scale. Figure 1 shows the technological readiness area with respect to these levels. The shaded area shows the technology readiness area of a particular enterprise. Sectoral possibilities of technological readiness are also shown in the figure where the outer triangle shows the ideal case.
One of the main deficiencies of these models was that, technology assessment was mainly based
Strategic II
A
Tactical
gical
-1
100
100
Operational
Figure 1: Technology readiness area
To be able to measure each level some technology elements is introduced. These are; 9 Knowledge and information baseline where the following capabilities of the enterprise is evaluated. They can be considered as sub components. o Management Information System And Data Processing o Agent Based Applications o Return of Investment o Enterprise Resource Planning 9 Databases 9 Software o Technology Knowledge Management
o
9
9
Technology Identification and Selection Technologic Infrastructure where the following is evaluated. o Technologic Suitability and Position Map o Automation o Communication Networks o Information Networks o Services o Machine park Strategic Baseline where the following is evaluated. o Strategy Development o Technology Portfolio
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o o
Technology Prospect/Forecasting Technology Transfer And Adoption o Technology Change Rates o Technology Innovation o Technology Scenarios And Roadmaps o Technology Creation o Disruptive Technologies Management Baseline where the following is assessed. o Technology Categorization and Planning o Technology Risk Management o Human Resource Planning o Technology Requirement Handling o Technological Investment and Capital Management o Cost Management o Quality Management
o
Technologic Competitors Analysis
The relationship weights of these components and organizational technology levels as described above is given in Table 1. Note that the weights are defined through several session of discussion with manufacturing people both operational and managerial. Besides an extensive questionnaire is designed and sent out to all scientific and manufacturing community to establish a more concrete baseline for weighting. Table 1 will be revised after some new knowledge arises. Note that these elements and weigh factors were defined through analysis of the technology management process and a series of consultation together with technology experts. However, the technology elements and weights values can be aligned in accordance with the related sector when it needs to be set up for a specific sector. This also depends upon the implementation of the model for SMEs or big companies.
Table 1 Weighted values of each technology assessment element
Strategic
Tactic
Operational
Technology Infrastructure
10
15
40
Technology Knowledge and Information Management Technology Strategy Baseline Technology Management Baseline Total
30
30
30
50
30
10
10
25
20
100
100
100
assessment criteria. The weights of Table 2 are defined through the same procedure of applied for Table 1.
Each sub component of technology assessment element is evaluated using several criteria. Table 2 shows an example of a subcomponent and its Table 2 Technology infrastructure Assessment matrix
364
Weight s
Emerging (strong need)
Machine life cycle
10
0
Technologic suitability (position map) Automation level Intelligent machining Humanless Factories Application Tools
12
In need
Sufficient enough
Good
Very Good
10 2.4
4.8
7.2
9.6
12 10
10 11
Excellent
2.2
4.4
6.6
8.8
1.6
3.2
4.8
6.4
1.6
3.2
4.8
6.4
Integration services Coordination services Communication services AGVs Prototype development Pilot production TOTAL
10
0
2
7
0
1.4
2.8
4.2
5.6
9
0
1.8
3.6
5.4
7.2
5
0
1
5
0
1
5
0
1
10
100
100
Technology readiness assessment matrix for each component of TRM is designed as similar to the one described in Table 2. During the technology assessment process, the enterprise is evaluated taking all technology related issues into account based on the components and criteria as described above. 2. A Case study
The proposed Technology Readiness Model is applied to a hypothetical company for the proof of concept. The implementation in a real company is
yet to be organized. The work along this line continues. The company in question is evaluated with respect to each technology, elements, related components and criteria. Table 3 indicates the results of evaluation on technology infrastructure. Similar tables were defined for the other components as well. Underlined numbers in each row of the matrix in Table 3 indicates the technological level of particular components possess by the company. For example, the company has a machine park which is sufficient enough regarding the machine life cycle.
Table 3 Assessment of technology infrastructure for a specific company Weight Emerging Sufficient s (strong In need enough need) Machine life [ 10 cycle Technologic suitability (position map) Automation level Intelligent machining Humanless Factories Application Tools Integration services Coordination services Communication services AGVs Prototype development Pilot production TOTAL
Good
Very Good
Excellent
7.2
9.6
12
12
0
10
0
11
0
2.2
4.4
6.6
8.8
8
0
1.6
3.2
4.8
6.4
3.2
4.8
6.4
2.4
4.8
lO
8
1.6
10
2
7
1.4
2.8
4.2
5.6
9
1.8
3.6
5.4
7.2
5.4
15.2
12
6.4
5
0
5
0
5
0
TOTAL
lO
46
2
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Similar to the technological infrastructure, other components were also assessed for the
company specified. Table 4 shows the over all results of the assessment.
Table 4 The overall technology assessment
Weight
Strategic
Tactic
Operational
Technology Infrastructure Technology Knowledge and Information Management Technology Strategy Baseline
46
4.60
6.90
18.40
55
16.50
16.50
16.50
25
12.50
7.50
2.50
Technology Management Baseline
35
3.50
8.75
7.00
37.10
39.65
44.40
TOTAL Based on the results presented in Table 4, the technology readiness area of this particular company can be given as in Figure 2.
Strategic
gical
Tactical
Operational 100
100
Figure 2: Technology Readiness Area of the particular company
3. Conclusion Measuring the technological assessment is one of the main concerns in technology management activities of the enterprises. The technology readiness model proposed in this study is intended to provide a systematic approach to measure the level of technological readiness of the enterprises. The proof of concept study produced encouraging results. The study continues in seeking the right weight values and applying the model in a real company. Note that the model can be implemented in both Small and Medium Enterprises (SMEs) and big companies provided that business specific modifications can be made. These modifications
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can be done using artificial intelligence techniques such as expert systems. However neural networks can be used to define the weight factors. The technological readiness levels can be calculated and some remedies can be provided sing expert systems as well. Agent technology can be utilized along this line. The study will be extended for creating intelligent technology readiness and technology managements.
Acknowledgements Sakarya University is a partner of the Innovative Production Machines and Systems (I'PROMS) Network of Excellence funded by the European Commission under the Sixth Framework
Programme (Contract www.iproms.org
No.
500273).
References
[1] Levin D.Z., Barnard H., "Technology Management Routines That Matter to Technology Managers", International Journal of Technology Management, Special issue on Flexibility, Learning and Innovation, June 2005. [2] Jassawalla, A.R., Sashittal, H.C., "Accelerating Technology Transfer: Thinking about Organizational pronoia", J. Eng. Technol. Manage. 15, page 153-177, 1998. [3] Phaal R., Farrukh C.J.P., Probert D.R., "Technology management tools: concept, development and application", Technovation 26, page 336-344, 2006 [4] Karandikar, H.M., Wood, R.T., Byrd, J. Jr., "Process and Technology Readiness Assessment for Implementing Concurrent Engineering", Proceedings of the Second Annual International Symposium of the National Council on Systems Engineering (NCOSE), Seattle, WA, July 2022, 1992. [5] Karandikar, H.M., Fotta, M.E., Lawson, M., Wood, R.T., "Assessing Organizational Readiness for Implementing Concurrent Engineering Practices and Collaborative Technologies", Proceedings of the Second Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises, April 20-22, 1993, Morgantown, WV, pp. 83-93. Los Alamitos, CA: 1EEE Computer Society Press, 1993 [6] Khalfan, M.M.A., Anumba, C.J., Siemieniuch, C.E., Sinclair, M.A., "Readiness Assessment of the Construction Supply Chain for Concurrent Engineering", European Journal of Purchasing & Supply Management, page 141-153, 2001. [7] ServQ 2003 http ://www.servq.infb/Servqweb/ServQ%20Brochure%2 OOctO3.pdf (available on February 2006) [8] Aouad, G., Cooper, R., Kagioglou, M., Hinks, J., Sexton, M., "A Synchronised Process/IT Model to Support the Co-Maturation of Process and IT in the Construction Sector", Construction Informatics Digital Library, paper w78-9.content, 1998, http://itc.scix.net/ (available on February 2006) [9] Fisher, D., "The Business Process Maturity Model a Practical Approach for Identifying Opportunities for Optimization", September, 2004, http ://www.bptrends.com/publicationfi les/10%2D04%20 ART%20 BP%20 Maturi t~!/o20 M odel%20% 2 D% 20F isher %2Epdf (available on February 2006) [10] Oztemel E., Korkusuz Polat T., "A General Framework for SERM (Strategic Enterprise Resource Management)", 4 th International Symposium on Intelligent Manufacturing Systems, IMS'2004, 6-8 September 2004
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Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
A critical analysis of current engineering design methodologies from a decision making perspective K.W. Ng National CAD~CAM Programme, Advanced Manufacturing Technology Centre, Sirim Berhad, 1, Persiaran Dato' Menteri, 40000 Shah Alam, Selangor, Malaysia
Abstract
The paper aims to critically analyse and evaluate current established engineering design methodologies from a decision making perspective particularly those of a prescriptive type. The analysis is based on findings from investigation on research literature related to current engineering design methodologies. Possible areas for overcoming their deficiencies while utilising their existing strengths are also proposed. Any improvements suggested would be focused on introducing practical and intelligent features to assist designer to make the right design decision at the right time. Keywords: engineering design, design methodology, functional requirements, decision making
1. I n t r o d u c t i o n
Literature studies have classified the design methodology research work related to decision making into three types1; namely normative, descriptive and prescriptive decision making models. Each of these models individually has advantages and disadvantages [1,2]. This paper will generally review these three models and critically analyse the current established design methodologies as well as suggest future direction of research on design methodology from a decision making perspective.
1.1. Normative Design Models
Normative design models are rational mathematical models which utilise probabilities [4], statistical, and multi-attributes utility-based approaches. These models quantify design attributes to enable designers to make decisions based on utility function. Thus, they are able to assist designers to produce consistent, repeatable, unique, and optimum design solution. They are also compensatory type of design methodologies (allowing trade off). The main issues with a normative model are that it is difficult to quantify attributes/criteria related to engineering design accurately and consistently in a realistic manner. Recent research developments in normative design models are the decision-based design model [5, 6] and Suh's axiomatic method [7].
1.2. Descriptive Design Models 1According to Buchanan [3], normative models describe how decisions should be made while descriptive models describe how decisions are made and prescriptive models describe how decisions should and can be made.
Descriptive design models are models that attempt to assist or support designers from the perspective of the
369
actual or natural 2 design process (from a cognitive point of view). These descriptive models work on the mental or cognitive processes of a designer by describing, simulating and emulating their cognition during design [ 1]. Some of the descriptive models are protocol studies [8], question-based approach [9], McDonnell's model [ 10], and Reymen' s domain-independent model [ 11 ]. Most of these models emphasise on determining the nature of design problems before attempting to solve them via cognitive techniques. Reymen's descriptive model was developed as a basis for a domainindependent prescriptive model. There are inherent issues related to these models that may lead to inconsistent or irrational outcomes. These descriptive models are usually non-compensatory and may be resource intensive, not generic, and may be difficult to implement particularly for protocol studies. These characteristics are apparent as there are basically psychological models which reflect the nature ofhumans (or designers).
1.3. Prescriptive Design Models
Most established engineering design methodologies are step-oriented methodologies with the exception of Suh's axiomatic approach [7]. Step-oriented design methodologies aim to improve the design process by allowing designers to design in a systematic framework which is based on phases of pre-categorised design activities. In general, step-oriented engineering design methodologies can be simplified as shown in Figure 1.
I~g, )
.@quirement~[.i Conceptual~ 2pec~ations) l 1~s;gn
E.gi.,~..e0,,ig. .
decision
gmbodimeet~__~ Detmi D~gzgn Design
1
decision
Figure 1: Models of Step-Oriented Engineering Design Methodology A design phase 3 (represented by a rectangle in Figure 1) contains a list of recommended generic design activities, rules and guidelines for a designer to carry out his or her design activities following a systematic design process. Hence, these methodologies assist designers generate design via a framework that manage their design activities and does not interfere with the cognition or the mental process of creating a design solution.
Prescriptive design models are models that advise or prescribe techniques, and methods to assist designers in designing. Most of these models focus on providing guidelines, rules, and procedures to assist designers. Established design methodologies by Pahl and Beitz [ 12], Pugh [ 13], Roozenburg [ 14], Ullman [ 15], Cross [ 16], Hubka and Eder [ 17], French [ 18] and Ulrich and Eppinger [19] are among the well known prescriptive design models. These models possess a combination of normative and descriptive model characteristics. This is because one must know the nature of the design problems before trying to correct them and the way to correct them is usually based on rational mathematical techniques. Thus, a good prescriptive model needs a good descriptive model [20]. Prescriptive design models inherit both positive aspects of normative and descriptive design models by providing a more systematic way to design. It is this generic systematic approach of these types of prescriptive design models that has created immense interest among the research communities.
At the end of each design phase, the designer is required to decide (refer Figure 1; the curvy arrow with the word decision) on which design alternative that will be selected to proceed to the next design phase. Most step-oriented design methodologies recommends decision analysis tools such as decision matrix [12], multi-attribute/criteria decision analysis tools such as SMART [21], SMARTER [22,23], and AHP [24]. These tools can assist designers to make decisions in selecting the "best" solution concept to proceed to embodiment design phase. The solution concept is usually a design with pre-determined configuration of required functional components. In the embodiment design phase, each pre-determined configuration component of the "best" solution concept will be developed further with regards to shape, orientation, and location 4 (including interface between configured
2. An Overview of Current Established Engineering Design Methodologies
3Conceptual design, embodiment design, and detail design are individually a design phase. Some design methodologies do not have an embodiment design phase [13].
2
The term natural means a designer is allowed to design according to his or her preference without rules, procedures, etc.
370
4In embodiment design, the shape, orientation and location are determined without determining the exact value i.e. the dimensions (for shape), the beatings (for orientation) and the co-ordinates (for location).
components). Unlike step-oriented design methodologies, Suh's axiomatic design methodology [7] is a function-oriented based methodology. This methodology is initiated by a list of identified functional requirements 5 and these functional requirements are decomposed and mapped directly to a hierarchy of design parameters from top down in a zig zagging manner as shown in Figure 2. Though the functional requirements and the design parameters are decomposed into hierarchies, the lower level functional requirements cannot be determined without first determining the design parameters at the level above. Although Suh's axiomatic design methodology [7] is a normative design methodology, it has a framing effect and this forces designers to develop a design solution that comply with 2 main axioms i.e. the independent axiom and the information axiom. FlulclicrnaIRequiren~l|.s
DesignParea~et~'s
Figure 2: Suh' s axiomatic design methodology
3. The Deficiencies and Strengths of Current Established Engineering Design Methodologies Step-oriented engineering design methodologies provide a useful systematic framework for structuring of the design process, generation of design concepts, and tools for evaluation and decision in design [25]. However, step-oriented design methodologies are rarely followed by practical designers [26] and do not even work under ideal laboratory conditions [27]. Weth [28] also found that experienced practical designers (without utilising step-oriented methodology) are actually practicing some form of function-oriented methodology that is more time-saving and is still able to produce successful design solutions. Stempfle [26] work showed that one of the design team that used step-oriented design methodology failed to solve the design problem posed in his experiment because the designers did not 5 Suh's design methodology is also a normative design methodology that defines desired outputs as functional requirements.
refer back to the design requirements consistently throughout the design process. This failure may be attributed to the nature of these step-oriented design methodologies which advocate searching for a design solution that meet design requirements rather than deriving a design solution from design requirements. Further empirical studies [29, 30] showed that designers have a tendency to forget, ignore, misinterpret or lose track of the design requirement specifications during the design. Empirical studies conducted by Cooke [31] showed that designers have difficulty in identifying and detecting the source of a design error when an error happens. Cooke' s studies concluded that the source of a design error is not due to a particular source but to a sequence of minor design decisions which individually may seems correct but collectively lead to a design error. Thus, identifying and determining the source of error is not as easy as it seems. Akin [32] also demonstrated that minor design decisions6 are made throughout the design process within a design phase before leading to the design solution. Current step-oriented engineering design methodologies did not consider these minor design decisions sufficiently which can inevitably cause the design problems described by Cooke. The deficiencies highlighted so far are prescriptive design methodologies that are of a step-oriented type. The framing structure of Suh's axiomatic design methodology [7] provides some level of traceability and it is also a function-oriented methodology. At a glance, it seemed that Suh' s methodology has addressed some of the issues raised by empirical studies. Detailed literature studies showed that there are inherent flaws in Suh's axiomatic methodology. As showed by Thurston [33], Suh's method is most likely to be impractical if the design problems are complex. Suh's method is difficult to put into practice if the design project has a severe time constraint because a design solution that complies with the two axioms may not be found during that duration. Lewis [34] also proved that Suh's methodology is fundamentally flawed as his method forces designers to conform to a particular preference structure. Issues, deficiencies, and differences highlighted by empirical studies suggest that there is a need to look at how designers design in reality to improve the current 6 Akin's study considers a design decision to be any and all intentional declarations of action/information for the design problem at hand and represents it as a "novel design decision" which is known as a minor design decision in this report.
371
design methodology or to propose a new design methodology. Practical and intelligent features should be incorporated into current engineering design methodology to provide better support to designers. The new design methodology should allow designers to use their preference and enable them to review their design preference while producing a design output that meets all mandatory functional requirements.
4. Desired Features Methodology
of Engineering
2) 3) 4)
Allow designers to design in accordance to his or her preference or natural way, Enable traceability of minor design decisions, Be able to attract/encourage designer to use it, Facilitate the meeting of the design requirements while trying to generate the design solution.
Horvfith [35] showed there is a link between characteristic 1 and characteristic 3. Hence, a design methodology that assists designers to design in their natural way will be likely used. In order to assist designers to design in accordance to their preference, a flexible and dynamic design methodology is needed. Quinn [36] carried out an extensive study on how designers design in reality. Quinn's study showed that designers design with uncertainty (due to incomplete information or knowledge) and enrich themselves with information and knowledge as their design progresses. Quinn concluded that designers design to "satisfice ''7 in reality. Quinn also found that designers act on urgent design requirements first and can recognise the available time to explore the remaining design requirements. Thus, a design methodology should provide time checking with dynamic updates on new information input and for changes of information from stakeholders throughout the design process. Finally, a design methodology should help designer to make better decisions when there are changes
7 "Satisfice" is an action to find a design solution that is not an optimum or the "best" design but the one that satisfy all mandatory design requirements.
372
Further investigations have also found out that designers prefer methodologies that link with computer-aided design (CAD) [35] as a tool to model the physical conceptualisation of a design output. The need to link to CAD is further strengthened by Kroes [37], who found that there is a gap between functional conceptualisation and physical conceptualisation in design methodology.
Design
Summarising the deficiencies of current established design methodologies highlighted earlier, the new design methodology model should have features to address the following issues: 1)
to earlier design decisions by providing an indicator or indicators to guide them.
To enable the capture of minor design decisions, a traceable framework is important. A design methodology should have a traceable framework that is able to capture a certain level of a designer's cognition. Although, protocol studies have been developed and used to perform this, it is not a practical approach. However, a protocol studies approach is a good starting approach to analyse design more deeply, particularly as an opportunity to measure designing [38]. The ultimate design methodology is one that assists a designer in generating a design solution that meets design requirements. This may seems difficult to achieve but a design methodology should at least provide an indication on how well a design process is progressing with regards to meeting design requirements. This may not help a designer in generating a "satisficing" design output in the first design iteration but it will significantly assist in reducing design iterations. An indicator and a traceable framework will allow designers to review and improve their design decisions in a systematic and welldirected way. The ability of a design methodology to provide an indicator to a designer on how well his or her design process is progressing towards meeting its design requirements will be an important basis for intelligent design. This indicator will also able to assist designer to review his or her knowledge and information that lead to particular design decisions. In addition to that, the indicator will also allow the designer to recognise what information is needed to enable him to make better decision. This will allow him or her to decide when to postpone a decision and how long he can delay it. Thus, a design methodology with such an indicator will provide an intelligent design support to a designer that assists him or her to make better design decisions. In order to achieve this intelligent support, the indicator will be developed on a dynamic model which encourages
flexibility and agility. Finally, such an indicator will also offer the designer an intelligent feature to predict the design output if design changes are made.
5. Conclusions The findings suggest current established engineering design methodology is not widely used in practice, is lacking in traceability and that most established design methodologies are of a step-oriented type. Step-oriented design methodology does not focus on deriving design solutions from design requirements but rather searches for a design solution which may or may not meets the design requirements at the end. Functionoriented design methodology such as Suh's axiomatic
design has better focus in meeting design requirements but enforces biases. It is important that a design methodology allows designers to design based on their preference with a traceable framework, with some level of cognition capturing in a structured manner and linked to CAD. Finally, the design methodology should also provide an indication to designers on how well their design is meeting design requirements throughout the design process. This indication will provide a basis for flexible intelligent design support development and provide intelligent assistance to designers where appropriate. Table 1 summarises the results of analysis on current established design methodologies, Suh's axiomatic methodology and desired design methodology.
Comparison Attribute
Current Established* Design Methodology
Suh 's Methodology**
Basis of Technique
Guidelines, mathematical tools & rules
Matrices/Mathematical Tools
Basis of design decision
Both cognitive and utility analysis
Axioms
Quality Design Output
Plausible Solution
Unique Optimum Solution
Trade off'* Character
Prefer trade off Difficult to quantifying attributes accurately and consistently; Little traceability
No trade off May be difficult to find solution; Biases designer; Plausible traceability
Strength
Systematic management of design
Solution found will meet design requirement
Type of decision making model
Prescriptive design methodology
Normative design methodology with framing
Limitation
Desired Design Methodology Graphical support link to CAD Both cognitive and utility analysis with indicator on meeting design requirements Plausible solution and unique optimum solution (depending on time constraint) Prefer trade off None Systematic design approach; Solution found will meet design requirement; Good traceability Descriptive design methodology with decision analysis tools
* The term "established" in this table refers to step-oriented design methodologies such as Pahl & Beitz, Pugh, Roozenburg, Ullman, Cross, Hubka, French and Ulrich (exclude Suh' s axiomatic method). ** No trade off is also known as non-compensatory which means all design requirements must be met while allow trade off (also known as compensatory) means any design requirements can be replaced by another one. Table l: Results of analysis on current design methodologies and desired design methodology
References [ 1] Finger, S. and J. R. Dixon (1989). "A review of research in mechanical engineering design. Part 1 :Descriptive, Prescriptive, and Computer-based Models of Design Processes." Research in engineering design 1:51-67.
[2] Evbuomwan, N. F. O., S. Sivaloganathan, A. Jebb (1996). "A survey of design philosophies, models, methods and systems." Journal of Engineering Manufacture 210(4): 301-320. [3] Buchanan, J., S. Dillon, J. Comer (1999). A comparison of descriptive and prescriptive decision making theories.
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Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
A Novel Method of Measuring the Similarity of Designs D.T. Pham, Y. Wu, S. Dimov Manufacturing Engineering Centre, Cardiff University, Cardiff CF24 3AA, UK
Abstract This paper introduces an approach for assessing the similarity of design models based on graph representation. The aim of this work is to develop a method to query a case base of design models, which are in the form of attributed graphs containing information about function, feature and structure, and to identify existing designs with graphs similar to those for the target problem. Similarity of design models is measured by concurrently applying a feature-based similarity measure and a structure-based similarity measure. The paper presents an example to illustrate the proposed approach.
Keywords: Conceptual design; Intelligent CAD; Similarity measures
1. Introduction The design process can be divided into four main phases: design task planning and clarification, conceptual design, embodiment design and detail design [1]. Conceptual design is the most important phase because it is during conceptual design that key decisions are made. At the conceptual design stage, information is usually fuzzy and incomplete. If past experience of similar design tasks can be utilised, this would help overcome difficulties with the lack of precise information. However, till now most common CAD systems offer few possibilities for the reuse of existing designs [2]. Case-Based Reasoning (CBR) promises to provide a way to support design by reminding designers of previous solutions that can help in new situations. Indexing, case retrieval and case modification are key issues in case-based design. In many CBR systems, case retrieval relies on the similarity between the new problem context and cases in the case base. Methods of grading similarity have been developed. However, they have the
following drawbacks. Some methods use simple indexing and classification schemes that do not incorporate sufficient product information to allow detailed comparisons of similarity among complex designs; some involve complicated geometric comparisons that do not measure similarity according to criteria for domain specific technical knowledge [ 3 ] . This paper proposes a novel approach to measuring similarity based on employing a model that integrates design knowledge in a graph-based form. The rest of the paper is organised as follows. Section 2 presents a review of design representation techniques and approaches to measuring similarity. Section 3 introduces a new method to represent designs. Section 4 describes the proposed approach to measure the similarity of designs. Section 5 gives an example to illustrate that approach.
2. Design Assessment
Representation
and
Similarity
2.1. Representation of a design case
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One of the main difficulties in supporting conceptual design is the complexity of modelling the different aspects of a product. Most design cases are feature-based. A feature is an information unit describing a region of interest or some characteristics of a product [4]. It can be either an individual attribute, a set of attributes or one or more derived attributes. Graph-based representation [5] focuses on the relationships among the elements of a design. Usually the nodes represent distinct features and the edges represent associations among features. Geometric representation [6] enables a design to be described as a geometric model which includes 2D or 3D geometric shapes. Every representation technique has its own focus. So as to build a comprehensive design model, it is necessary to integrate various aspects of design information.
2.2 Approach to measuring similarity Bridge [7] classified approaches to measuring similarity of object representation as geometric, structural and feature-based. With a geometric approach, a set of features is extracted from the structural representation, which is used as an ndimensional vector to which distance measures can be applied. Relatively simple distance measures include the Euclidean distance, Manhattan distance, and Hausdorff distance [8]. With a structural approach, similarity is measured by graph matching. Cost-based distance measurement is frequently adopted for this purpose. This method involves modifying the graph for an object to transform it into the graph for the object with which it is to be compared. The number of modifications required is then taken as the similarity measure. A feature-based approach makes use of feature commonality and differences. Tversky's theory of similarity, as described in [9], characterises the similarity of two entities as being a weighted sum of a function of the identical features of the two entities and a function of the distinctive features in each of the entities. The reviewed methods compare either features or structures separately. In order to have a more comprehensive similarity measure, it is helpful to consider these two factors together. 3. Representing a design case
The content of a design case provides the basis for indexing, retrieving, and adaptation of the design. It is believed that design needs a multiplicity of representations [ 10]. Thus, according to the proposed
376
method, different aspects of design information are represented by three models: a "Function" model, a "Feature" model and a "Structure" model. Each model consists of primitive units comprising elements (E), attributes (A) and relationships (R), which make up a graph. This is denoted as G = [E, A, R]. Element: An element is a basic unit. It is represented by the nodes of a graph. An element can be either an abstract entity or a physical entity, and it is identified by its label (name). Attribute: Most elements have attributes, e.g., colour, shape and material. Each attribute takes on a value. The value may be numerical (e.g., the width of a chair), or nominal (e.g., the material from which the chair is made can be wood or plastic). Relationship: Relationships between elements are represented by edges in a graph and are described with relation variables included in the data structure. If the elements are abstract entities, the relationships will be abstract. If the elements are real objects, the relationships can be abstract or they can be positional relationships involving numerical or nominal data. Using these three primitive units, the models of a design case can now be explained. Function model: The function model refers to the purpose of the design and explicitly describes the way in which the design is to be used. In a function model, an element is a function and a relationship is the abstract interrelation between two functions. A function model can be denoted as Gfunction- [E, R]. Taking a car body design, for example, two elements of a function model are "to provide space for passengers" and "to provide space for the engine", and the relationship between those elements is "and". Feature model: The feature model represents the design in terms of features. It describes the characteristics of the design. Each element corresponds to a feature, which has an individual attribute, a set of attributes or derived attributes taken from elements of other models. A feature model can be denoted as afeature = [E, A]. For a car body, an element of a feature model is "dimensional feature", and its attributes are the "length", "width" and "height" of the car body. Structure model: The structure model concerns the physical organisation of the design. It is described at three levels: assembly model, part model and geometric model. An assembly model consists of elements representing assemblies and relationships signifying the interrelations between the assemblies. A part model is made up of parts, which are the basic units of a design model. In a geometric model, each
node represents a geometric entity, e.g. line, curve and box. There are positional relationships between the nodes. Among these three levels, there are relationships between assembly model and part model, as well as between part model and geometric model. These relationships are subordinate relationships and are abstract. Thus, there are two types of relationships in a structure model: positional relationships (numerical and nominal) and subordinate relationships (abstract). A structure model can be denoted as Gstructure = [E, A, R]. For example, in an assembly model of a car body, two elements are "engine bay" and "passenger cabin"; their attributes are "length", "width" and "height"" the relationship between the elements is a positional relationship. "Engine bay" has "bonnet", "side cover" and "front" as parts. The relationships between "engine bay" and its parts are all subordinate relationships of the type "has". The use of graph-oriented methods to represent a design is intended to enable the integration of different kinds of knowledge expressed in different forms. Thus, the notion of integration is more in terms of types of knowledge and concepts than just of representation.
4. Similarity Measure It is proposed that design similarity is evaluated using a combination of two methods: Structurebased similarity evaluation and Feature-based similarity evaluation. Both methods employ graphbased representation.
4.1 Structure-based similarity measure To assess similarity of structure, the equivalence of two graphs needs to be defined first. Assume there are two graphs G1 and G2. Ei(G) = let(G), e:(G) .... en(G)] is used to denote the elements at the ith level, in which e,(G) is the nth element of graph G in level i. Ri(G) = [rl(G), r2(G) . . . . r,(G)] denotes the relationships between the elements in level i and Pi(G) = [pI(G), p:(G) . . . . p,(G)] denotes the relationships between level i and level i-1.. There is equivalence between graphs G1 and G2 at the ith level, i.e. Qi(G1, G2), as a Boolean variable, is true if and only if: a. the numbers of elements at the ith level in G1 and G2 are equal; b. the numbers of relationships at the ith level in G1 and G2 are equal; c. for each ej(G1), there is a corresponding e~(G2) such that eg(G1) = ek(G2);
d. for each rj(G1), there is a corresponding r~(G2) such that ry(G1)= rk(G2); e. for each pj(G1), there is a corresponding pk(G2) such that py(G1) = p~(G2). e/(G1) = e~(G2) if the labels of the elements are the same. Attributes are not considered in assessing structural equivalence, because only structure is of interest here; rj(G1) = rk(G2), pj(G1) = pk(G2) if the types of the relationships are the same. Let Ti(GI, G2) be a Boolean variable and Ti(G1, G2) - Q1/~ ... /~ Qi. Ti(G1, G2) is true if Q I(G1, G2), Q2(G1, G2) ..... Qi(G1, G2) are all true. Then two graphs G1 and G2 are equal if and only if for all i, Ti(G1, G2) is true. For ease of adaptation, a cost-based distance calculation method is applied to grade similarity. The degree of similarity between G1 and G2 can be determined by assessing the minimum number of primitive operations (structural modifications) that need to be applied to G1, in order to make G1 and G2 equivalent. Primitive operations that can be performed on a graph are adding, removing and replacing an element or a relationship. Adding/removing an element or a relationship is to append/delete an element or a relationship to/from a graph. Replacing an element or a relationship is the operation of removing an existing element or a relationship and adding a new one. The dissimilarity D~ is given by the sum of the number of primitive operations as shown in Eq. (1).
D~, - Z n l
+ 2~Y'n2
(1)
where n l - number of added/removed elements and relationships; n: - number of replaced elements and relationships. In Eq. (1), n: is multiplied by 2 because the replacement operation involves two steps, removal and addition. This method of assessing similarity is applied to the function model and the structure model. When working on the structure model, the method is executed to different levels according to the requirement of the user. Operating on the geometric level is different from operating on the other levels. The first difference is that entity types are compared instead of labels. As geometric entities in different designs may have different names, elements cannot be identified by their labels. The second difference is that only subordinate relationships are compared. There are many positional relationships in a geometric model, and they can be easily modified
377
and generated. It is difficult and unnecessary to compare all the positional relationships in a complex design. This method calculates the similarity of the graph structure, which reflects the difficulty of modifying the structure of a given design model into a target structure. Following similarity assessment, all the compared designs are arranged in an ascending order of similarity. Two lists are obtained, which respectively represent function similarity and structure similarity between a particular design and a given set of design cases.
4.2 Feature-based similarity measure Features used for comparison are of two types. One is features from the feature model, which are usually for describing function, structure and behaviour. The other type is optional features, which are attributes or elements extracted from other models by the user. In order to find design cases that the designer really needs, features are subjectively graded by the user. Let Ne denote the number of pairs of features that are equal to each other within a specified tolerance band, and Nm the number of features in the presented case that do not exist in the given design. Feature similarity SU can be calculated using the following equation:
Sf = ~ k i N e - ~-WkiNm (i = 1 .... n, 0 ~ki E l ) (2) where k/-grades of features, Ne- number of equal features, Nm- number of missing features.
organisation of a design; on the other hand, a geometric model explicitly expresses a design. As previously mentioned, geometric models are easily modified so that they are less important during design reuse. Due to the complex nature of a design, users may encounter different situations and may require different similarity measures. A numerical weighting method is adopted to address this problem. The user may define a parameter w(0~<w~
S =w I Nfeature+W2 Nfunction+W3 Nstructure (0 ~Wl, w2, w3 ~1) (3) where Nfeature, Nfunction and Nstructur e denote the positions of a design on the lists ranking its feature, function and structure similarity with a given design. The design with the largest S is the most similar to the specified design. By adopting this weighting method, the user can freely choose the measures that can be employed and decide their importance in a particular situation. Figure 1 illustrates the proposed similarity assessment approach. 5. An Illustrative Example
4.3 Similarity assessment (S) The previous sections have described two methods of measuring similarity. Compared to feature similarity, structure similarity is usually recognised as the first factor to be considered, because the aim of similarity evaluation is to use an existing design model as the basis for further design. The more similar a structure is to a desired specification, the easier the modification will be. On the other hand, feature similarity is also important due to the ease with which features can be compared. As for the difference between function and structure, function is the basis of a design and determines its purpose, while structure reflects the organisation of the design. With regard to structure, an assembly model and a part model represent the general
378
J DesignModel J
.5
Feature-based
similaritymeasure 1 ~rua. . . . b~s~d s~rn~arity . . . . . . .
g
Fu on I I S'trUOtpa~~re mc hssembly Geometric ModelI ,,, model
Feature
Optional feature
model
model
W1J~.
model
w2~
Similaritassessment '
w?~
Fig. 1. Similarity assessment method
model
Design models of car bodies are used to exemplify the method. Assume there are three models dO, dl and d2, where dO is the input model while dl and d2 are the models in the case base. dl and d2 are to be compared to dO in order to determine which of them is more similar to dO.
5.1 Feature-based similarity measure (Sf) Table 1 shows comparisons between the feature models of dO and d / a n d between dO and d2 using the feature-based similarity measure (Sf). Table 2 shows a comparison between dO and dl on the optional features using Sf. The listed attributes are extracted from the structure model and used as optional features for comparison. The result is listed in Table 3. The feature-based similarity measure SU can be calculated according to Eq. (2): S/dO, dl) = ~ k i Ne-ZkiNm = 0.5 X5+0.3 X5+0.2 X 4 = 4.8 S/dO, d2) = Z k i Are- Zk,N,, : 0.5 X2+0.3 X2-0.3 X l = 1.3 S/dO, dl) > Sy(dO, d2), which means that from the perspective of features, dl is more similar to dO.
Table 1 Comparison of the feature models of dO and d/and of dO and d2 using Sf kj
dO
Tolerance
d i~
band Type
0.5
Comparison
-
L(str){dO, dl}> L(str){dO, d2}.
A set of weights is provided by the user to determine the importance of each result. Weights for this example are given as (0.4, 0.3, 0.3) and similarity assessment is executed according to Eq. (3): S(dO, d l ) = 0.4 X 2 + 0.3 X 2 + 0.3 X 2 = 2
S(dO, d2) = 0.4 X1 + 0.3 X 2 + 0.3 X1 = 1.3 S(dO, dl)> S(dO, d2). Thus, the conclusion can be drawn that dl is more
Comparison with
Saloon
Saloon
E
Compact
NE
Length
0.5
4-0.1
5029
4775
E
4262
NE
Width
0.5
4- 0.08
1902
1800
E
1751
E
Height
0.2
4-0.05
1 492
1435
E
1408
NE
Wheelbase
0.2
4-0.08
2990
2830
E
2725
NE
Front track
0.2
4-0.05
1578
1512
E
1484
NE
Reartrack
0.2
4-0.05
1582
1526
E
1493
NE
Weight
0.3
4-0.2
1865
1570
E
1375
E
Fuel
0.3
-1
15.5
12.2
E
9.7
E
Top speed
0.3
4-0.1
237
226
E
201
NE
Acceleration
0.3
(-1.0.1)
8.1
9.1
NE
11.1
NE
d2
consumption
0-1 001r E: equal; NE: not equal
Table 2 Comparison of the optional features of dO and d/and of dO and d2 using Sf Tolerance
dO
d'/
band
5.2 Structure-based similarity measure (SO Since the function models of dO, dl and d2 are all the same, D~,(fun~{dO, dl}= O, D~(fun){dO, d2} = O, and &(fun){dO, dl} = &OCun){dO, d2}. In this example, the structure-based similarity measure &, is applied at all the levels of the structure model. The structure models of dO, dl and d2 are listed in Figure 2. From Eq. (1), D~(~tr){dO, dl} = 8, D~(str) {dO, d2}=25. Therefore, &(str){dO, dl}> &(str){dO, d2}, which means that from the perspective of structures, dl is more similar to dO. So far, the following results have been obtained: Sf(dO, dl) > St(dO, d2); &,(fun){dO, dl} : &(fun){dO, d2};
d2
with d f
Passenger
Comparison
0'2
with d'~
Comparison with
0.5
4-0.1
2090
1967
E
2725
NE
0.5
+0.1
1902
1800
E
1751
E
0.3
4-0.06
1120
1100
E
1000
NE
0.3
___0.1
1139
1080
E
d2
cabin~.length Passenger cabin\~dth Passenger cabin\height Boo,length
M
E: equal; M: missing feature; NE: not equal
Fable 3 5ummary of comparison results
Number of equal features Numb er missing features
dO and dl k=-0.5" 2+3=5, k=-0.3 93+2=5, k=0.2 94+0=4.
dO and d2 k=-0.5 91+ 1=2, k=0.3 92+0=2, k=-0.2:1 +0 = 1.
of k=0.3 91.
similar to dO than d2.
6. Conclusion This paper has proposed a new approach to measuring the similarity of designs using graphbased representations. Compared to previous
379
methods, the proposed approach has the following advantages: Incorporating design knowledge to assess design model similarity. Design knowledge such as functions, behaviours and structures is extracted and used for comparison of designs. - Involving more product information by considering both the feature and the structure of the product. - Considering user preferences. At different stages of the similarity assessment operation, users are involved by giving their preferences to guide the operation, for example, such as assigning weights, specifying optional features and selecting the levels of structure model to be compared. - Producing a case representation model that is comprehensive enough to represent conceptual design problems in different fields. Acknowledgements
The authors are members of the EU-funded FP6 Network of Excellence for Innovative Production Machines and Systems (I'PROMS). References
[1] Pahl, G. and Beitz, W., Engineering Design: A Systematic Approach. The Design Council and Springer Verlag, London, 1996. [2] Wang, C., Horvath, I., and Vergeest, J. S. M., "Towards the Reuse of Shape Information in CAD," Proceedings of the Tools and Methods of Competitive dl,-'
Engineering 2002, Wuhan, China, pp. 103-116, 2002. [3] Elinson, A. and Nau, D. S., "Feature-based Similarity Assessment of Solid Models," Proceedings of the 4th ACM Symposium on Solid Modeling and Applications, Atlanta, Georgia, pp. 297-310, 1997. [4] Brunetti, G. and Golob, B., "A feature-based approach towards an integrated product model including conceptual design information," Computer-Aided Design, vol. 32, pp. 877-887, 2000. [5] Gero, J. S. and Tsai, J.-H., "Application of bond graph models to the representation of buildings and their use," Proceedings of the 9th International Conference of the Association for Computer Aided Architectural Design Research in Asia 2004, Seoul, Korea, pp. 373385, 2004. [6] Dimov, S. S., Brousseau, E. B., and Setchi, R. M., "Automatic formation of rules for feature recognition in solid models," Proceedings of the 1st Intelligent Production Machines and Systems (I'PROMS) Virtual Conference, Elsevier, Oxford, pp. 49-54, 2005. [7] Bridge, D. G., "Defining and combining symmetric and asymmetric similarity measures," Proceedings of the 4rth European Workshop on Case-based Reasoning (EWCBR98), Berlin, pp. 52-63, 1998. [8] Ohbuchi, R., Otagiri, T., Ibato, M., and Takei, T., "Shape-Similarity Search of Three-Dimensional Models Using Parameterized Statistics," Proceedings of the Pacific Graphics 2002, Beijing, China, pp. 265275, 2002. [9] Keane, M. T., Smyth, B., and O'Sullivan, J., "Dynamic similarity: A processing perspective on similarity," in Similarity & Categorisation, M. U. Hahn, Ed. Oxford: Oxford University Press, 2001, pp. 179-192. [10] Dym, C. L., Engineering Design A synthesis of Views. Cambridge University Press, Cambridge, UK, 1994.
:!::
Fig. 2. Structure model of dO, dl and d2
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Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, IYK. Published by Elsevier Ltd. All rights reserved.
An I-Ching-TRIZ inspired tool for retrieving conceptual design solutions D.T. Pham, H. Liu and S. Dimov The Manufacturing Engineering Centre, Cardiff University, Cardiff CF24 3AA, UK
Abstract
This paper discusses the representation of the inventive principles of the TRIZ theory of Innovative Problem Solving as symbolic expressions. Each expression comprises two sets of relations between predicates and objects respectively. The expressions are arranged in a two-dimensional matrix, the Inventive Principles Matrix (IPM), according to the predicates and objects that they contain. The structure of the IPM is inspired by the I-Ching system. A conceptual design problem is translated into a set of queries stating the objectives of the design and the constraints on the solution. The queries, also represented in a symbolic form, are then used to search the IPM for suitable solutions. The paper describes an application of the proposed IPM and query technique to the problem of designing a bushing. Keywords: TRIZ, I-Ching, Innovative Design, Conceptual Design.
1. Introduction
2. Previous work on creative design and TRIZ
Design is a process involving the application of human intelligence. The intuition and experience of the designer play a significant role which cannot be replaced by any current computer-aided tools or artificial intelligence technology. Conceptual design is the first phase of the design process. Most basic functions of a new product and the majority of design solutions are generated in this critical stage, which will affect the attributes in the later detailed design phase. Conceptual design is a very important task in computer-aided-design (CAD) [ 1], particularly when new and innovative products are to be created. There is a need for a methodology to help designers to solve inventive problems and generate solutions during conceptual design.
Concept generation involves using creativity and imagination to develop approaches to achieve the design objectives while satisfying the constraints [2]. The set of 40 Inventive Principles of classical TRIZ [3] discovered by Altshuller is a useful creativity tool for a variety of problem solving situations [4]. On the other hand, they are often criticised for their often illogical sequencing, their level of overlap, the gaps that they contain, and most of all, the difficulty people experience in remembering them all. Researchers have made efforts to evolve TRIZ principles. Osborn [5] simplified the 40 classical principles and developed the SCAMMPERR model which comprises the behaviours of "Substitution", "Combination", "Adaptation", "Magnification", "Modify", "Puttingto-another-use", "Elimination", "Re-arrangement" and "Reversal". Buzan [6] discussed the connections
381
between TRIZ and mind-mapping and the need to think in Time and Space. Mann [7] combined NeuroLinguistic Programming (NLP) thinking and the SCAMMPERR model. Nakagawa [8] simplified TRIZ into five general elements in Unified Structured Innovative Thinking (USIT) and constructed a new problem solving process. Besides assisting concept generation, this model also applies the TRIZ philosophy to problem specification. In order to make the principles easy to remember, each of the above-mentioned methods tried to simplify or restructure them. Matrices are regarded as a possible way to reorganise and represent theses principles systematically. Althuller's classical 39x38 TRIZ contradiction matrix is too large and can confuse the user [3]. At the same time, the 3x5 matrix developed by Mann is too simple to represent the meaning of the principles comprehensively. Thus, there is a need for a new matrix of appropriate dimensions to express and evolve inventive principles.
3. Newly structured symbolic expression of inventive principles 3.1 Formation of I-Ching-based Framework I-Ching [9] has been selected to improve TRIZ theory. This is because of the common points between I-Ching and TRIZ. First, the two theories both aim to improve creativity and inventiveness. Second, both methods are generated by induction. Finally, they both have the same philosophy of dialectics, which has been shown to be useful for creative design thinking [10]. In general, I-Ching is the first theory that conforms to the three laws of dialectics underpinning TRIZ [ 11 ]. The I-Ching 8x8 hexagram matrix is suitable for representing TRIZ principles. The I-Ching hexagram matrix has a systematic structure. Trigrams located on the first row and first column are arranged along two coordinate directions by following simple rules. A Cartesian coordinate system can be added to the matrix to illustrate the sequence of placement of parameters on the first row and the first column of the 8 x 8 matrix.
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Physical world
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382
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3.2 Generation of x-axis and y-axis parameters
3.3 Representation of inventive principles
As shown in Figure 1, I-Ching philosophy proposes the Limitless ("Wuji") which represents the source of creativity and produces two forms, namely "yin" (negative) and "yang" (positive). The two forms produce four phenomena, known as "small yang", "great yang", "small yin", and "great yin". Then, the four phenomena act on the eight trigrams. I-Ching trigrams comprise three lines: a line may be broken in which case it represents "yin" or unbroken, in which case it represents "yang". Representing eight fundamental problem-solving behaviours, the eight trigrams are derived from Wuji in three stages (see figure 1). The details of the derivation will be described in future papers. The first four behaviours are "automation", "alteration of degrees", "transformation" and "contradiction". These involve uncertainty and human decisions. The last four elements of "combination", "segmentation", "replacement" and "move" are relatively explicit behaviours. The three lines in an I-Ching trigram correspond to three aspects of each basic solution. These can be time, space and other human factors such as personal belief, value and perception. The eight behaviours can thus be divided further into twenty-four sub-parameters. Normally, a solution to a problem is composed of two parts from a grammar point of view: the predicate and the object. The x-axis parameters mentioned above relate to behaviours and play the role of the "predicate" in a sentence. On the other hand, the 8 y-axis parameters play the role of the "object" in a sentence. The 8 parameters on the y-axis can also be divided into two groups. The first four parameters are "action", "function", "environment", and "system". These are general terms with a high abstraction level. They are arranged along the y-axis according to whether they represent external or internal aspects of attributes. The last four parameters are re-organised from 12 types of fields in TRIZ [7]. A "field" is defined as any source of energy within a system. These four parameters are arranged from macro to micro aspects into: "measurement", "physics", "energy" and "micro level". As with the x-axis, each one of the main parameters on the y-axis is divided into 3 subparameters. However, the y-axis parameters are not a two-level hierarchical structure. Each one of the 24 sub-parameters can be divided again to express a more specific attribute when needed. The details of the sub-parameters are shown in Appendix A.
As mentioned before, each inventive principle is a method for solving problems. This normally consists of a behaviour and a system or an attribute, expressed separately by parameters on the x-axis and y-axis. In some cases, the solution is complex and has to be represented by combining two or more parameters and their relationship. Therefore, each single concept Cm can be described as an expression of two sets Rx and Ry of predicates and objects, as shown in Eq (1). Cm=Rx[alx,a2x...aix]Ry[bly,b2y...biy](i,j=l, 2...)
(1)
In Eq (1), Rx and Ry separately show the relationships between parameters on the x-axis and the y-axis. Rx and Ry can be one of the relationships RI, R2, or R3 in Table 1. Table 1 Five types of relationship Symbol R Meaning R1 Or V Part of/in R2 > Comprise R3 () Sequence R4 Rs ~ For
Example ax V ay ax < ay ay > ax ax (Cm) Cm---~Cn
As shown in Table l, the first type is the logic relationship "OR" represented by "V". R2 and R 3 are "inclusion" relationships, which can have three meanings under different conditions: 1) ax is part of ay; 2) ax is an attribute of ay; 3) ax is the value of an attribute of ay. Ul, R2, and R3 are used to represent a concept. The other two relationships in Table 1 are employed in expressions of inventive principles. R4 represents the sequence relationship: concept Cm is implemented first, then ax acts on Cm. R5 is an objective relationship: concept Cm is performed to satisfy Goal Cn. Eqs (2), (3) and (4) show the three possible expressions of an inventive principle. P,=Cm P2=[ax](Cm) P3=Us[Cm,Cn]
(m=l, 2...) (m=l, 2...) (m, n=l, 2...)
(2) (3) (4)
Expressions (1), (2), (3) and (4) make up the proposed symbolic expression system for inventive principles. All the classical 40 inventive principles, including sub-principles with details can be represented using those expressions. These principles are mapped into corresponding cells in a matrix as shown in Table 2.
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4. Analysis of the Inventive Principles Matrix 4.1. Elimination of redundant information The Inventive Principles Matrix (IPM) expresses innovative principles in a format which can reduce the amount of repeated information found in the classical collection of inventive principles. It can be seen from Table 2 that only ninety-three solutions are contained in the matrix. Five solutions with redundant information have been eliminated, including Principle 35F (change other parameters), which is too vague.
derived from TRIZ. There are 35 trend lines in TRIZ and 32 detailed USIT sub-methods. The trend lines indicate the direction in which a system evolves [7]. Conceptual solutions are extracted from them and expressed in symbolic form. Only five of the expressions are different from the symbolic expressions of classical inventive principles. They are identified in grey in Table 2. These new principles with examples and their symbolic expressions are shown in Table 3.
4.3. Retrieval of working solutions 4.2. Generation of new principles Although the matrix looks like a closed system, due to the last four parameters on the y-axis being structured in a multi-level hierarchy (see the Appendix B), systems relating to inventive solutions can be added to this matrix in the future. Moreover, the matrix can suggest solutions to general problems. In order to solve more specific problems, the knowledge/effects database offered by TRIZ theory could be integrated by constructing an interface between it and the y-axis parameters. Besides TRIZ inventive principles, other TRIZ and TRIZ-derived tools also contain novel solutions to problems. However, those tools are not directly expressed as solutions. The main meaning of the tools has been extracted and mapped to the matrix. The creative thinking tools adopted include the trends of evolution in TRIZ theory, and Unified Structured Inventive Thinking (USIT) which is
Compared with text, the IPM offers a more precise and systematic way for information retrieval because it stores keywords of a solution without any redundant information. Boolean search is selected as the search strategy; this technique retrieves those expressions which are true for a query expressed in terms of keywords and logical connections. The standard format of a query is Q = (alx OR azx . . . O R . . . a i x ) AND (bly OR b2y...OR...bjy). This query will retrieve any principles of which the symbolic expression comprises (alx OR azx ...OR...aix) in the predicate expressions and (bly O R b z y . . . O R . . . b j y ) in the object expressions. Any sub-level parameters should be considered if aix or bjy appears in the query expressions. If no expression matches the query, then b2y is modified to its super-level parameters until the result is found. One exception is that "system" y4 is
Table 3 New principles References Trend line No. 24 in TRIZ Trend line No. 36 in TRIZ
Expression [6c][6aabc<4] [2b][6aabc] [lc][3a] [2b]([4][7])
Method 2(g) in USIT Method 3(g) in USIT
[lc][8] [2a]([6a][5]) [2c]([6a][5]) [7b][4>5]
Method 4(f) in USIT
[lc][4c]
Description Use light damping or un-damped control system Reduce damping Make use of free and readily available sources Reduce number of energy conversions in energy flows within a system Make use of property of micro level Conduct detection or measurement function as quickly as possible Make the detection or measurement function unnecessary to skip Introduce a measurement or detection on a copy of the object Solve problem in current system by combining with neighbours system and improving super-system
Examples 9 Aircraft flight control architecture 9 Hydraulic systems 9 Car with internal combustion engine converts chemical to heat to mechanical 9 Locomotives
9 Nanometre technology 9 Micro-robots 9 User-adjustable lenses eliminate need for measurements by optician 9 X-ray inspection of welds
9 Introduce a chisel between a hammer and rock to improve the rock-breaking capability
385
defined as the super-level of Ys, Y6, Y7, and Y8 because they represent specific aspects of the "system" (y4).
5. Case study A bushing is usually designed as an assembly containing coaxial inner and outer sleeves made from metal or another rigid material. A coaxial rubber layer can be assembled between the outer and inner sleeves. A typical design of a rubber bushing is shown in Fig. 2(a).
Q 1= (la) AND (6b OR 3ca) Q2= (lb) AND (6b OR 3ca) Query Q 1 means that the objective is a solution enabling the system to change under different conditions ((x-axis)la. dynamics) and the system involved in this solution is rubber in a bushing ((yaxis)3ca, intermediary; (y-axis)6b. material). A similar explanation applies to Q2; the solution will satisfy the requirements of a single adjustable ((xaxis)lb) bushing. The search and retrieval procedure will be described in future papers. The retrieved results are shown in Table 4. Table 4 Alternative solutions: ---
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5.1 Background and Problem Definition The rubber insert generally provides vibration damping and various degrees of mobility between the connected mechanical components in one or more translational and/or rotational directions. The performance characteristics of the device are usually determined by the stiffness of the rubber. The values of the stiffness constants in different directions are optimised by trial and error. Obviously, this procedure is expensive and timeconsuming. There is a demand for a bushing whose stiffness can be varied in the different coordinate directions, without the need for changing the bushing. However, in some cases, a constant but adjustable stiffness in one or more directions is desirable. Patent US2002113349 defined the above problems and was granted for providing solutions to them.
5.2 Retrieval of working solutions The query expressions for this design are shown below:
386
4
[4b][6b] ~ [la][4] is an expression of Principle 40. It can be interpreted as "to change from uniform to composite (multiple) materials", which clearly suggests changing the existing bushing to a bushing made of multiple materials (rubber). In this case, a bushing in a variety of rubbers with different stiffnesses might be useful. Due to the different stiffnesses required in different coordinate directions, a variety of rubber cylinders with different stiffnesses can be arranged in the different coordinate directions. Fig.2(b) shows an axial cross section of the proposed bushing. Outer sleeve 301 and inner sleeve 302 are separated by rubber inserts 303 comprising a plurality of rubber cylinders 304 in the x direction and 305 in the y direction whose cross section is seen as Fig.3(a). There is no expression in the IPM that can satisfy Q2 and the search criteria need to be modified. Higher levels of y-axis parameters are selected, and the result is found when the modified query becomes Q2=(lb) AND (4). The second problem in this case is how to adjust the stiffness of a bushing. [lb][4] is an expression in IPM. Its corresponding textual meaning is "to make the system adjustable". This principle is the same as the description of requirements, but as a solution, it is too general to use. Solution [8b>lb][4] suggests making the system adjustable as a pre-emptive measure. Thus,
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Fig. 3(a). Proposed bushing 1, (b). Proposed bushing 2 it might be possible to adjust the stiffness by changing the pre-compression on the rubber cylinders before any external radial forces are applied to the bushing. In Fig.3(b), this objective is achieved by adjusting the preload on the rubber in advance with bolts (907) and holding plates (905, 906). These holding plates can be pushed forward or retracted in the radial direction thus changing the pre-compression of the respective group of rubber elements and the stiffness in this direction. [lb>6b][4] suggests making a subsystem easy to remove. This also matches the design of this patented bushing: if the rubber cylinder no longer requires to be compressed, the holding plates can be easily removed by undoing the bolts.
products. New York, NY, Chapman & Hall. [2] Hyman, B., 1998, Fundamentals of engineering design. New Jersey, Prentice-Hall. [3] Altshuller, G., 1988, Translated by Anthony Williams. Creativity as and Exact Science. New York, Gordon and Breach. [4] Fey, V.R., E.I. Rivin, 2005, Innovation on Demand : New Product Development Using TRIZ.. Cambridge, Cambridge University Press. [5] Osborn, A.F., 1993, Applied Imagination." Principles and Procedures
of Creative Problem Solving.
Hadley, MA, CEF Press. [6] Buzan, A., 1993. The Mind Map Book. London, BBC Books. [7] Mann, D. L., 2002. Hands-on Systematic Innovation. Belgium, CREAX. [8] Nakagawa, T., "Learningand Applying the Essence of TRIZ with Easier USIT Procedure", ETRIA World Conference: TRIZ Future 2001, Nov. 7-9, 2001, Bath, UK, pp. 151-164. [9] Wilhelm, R., 1983. I-Ching or Book of Changes. London, Routledge & Kegan Paul. [10]Quinsan C., "Generative Design: Rule-Based Reasoning in Design Process", International Conference on Generative Art, 2002, Milan. (Available at http ://www.generativeart.com/papersGA2002/41.htm , last accessed: 03 April, 2006) [11] Wei, T., 1977. An exposition of the I-Ching, or Book of changes. Hong Kong, Dai Nippon Printing.
Appendix A, X-axis parameters 6. Conclusion This paper has proposed a new matrix of inventive principles that is based on I-Ching and TRIZ. An example of how to search the matrix and retrieve working solutions for the conceptual design of a new type of bushing has been provided. Future work will focus on integrating this method with design representation and concept adaptation.
Key wor~
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Meaning
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c. Make the system optimised
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c. IncreaseJDecrease fi~lly
a. O p p osilJon
a. M a k e m opposite action
b. Inversion
b. Invelg the system to an opposite state
c. Re cui~el-lce
Acknowledgements
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a. M a l ~ the system homogeneous
b. Non-unifon~dty
b. Make the system nc~a-homogeneous
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The authors are members of the EU-funded FP6 Network of Excellence for Innovative Production Machines and Systems (I'PROMS).
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c. Re-introduce the elemer~ atler it has bean out of use (in time) a. M o v e (in space)
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conceptual
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b. Precede
b. Precede (in time)
c. Delay
c. Delay (intime)
of mechanical
387
Appendix B, Y-axis parameters
388
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Design for Rapid Manufacturing functional SLS parts Walter Kruf M. Sc., Bart van de Vorst B. Sc., Hessel Maalderink B. Sc., Nico Kamperman M. Sc. TNO Science and Industry, BU Design and Manufacturing, PO Box 6235, 5600 HE, Eindhoven, The Netherlands, [email protected]
Abstract
Mass customisation requires flexible and fully automated production technologies. Layered Manufacturing Technologies (LMT), in the application of Rapid Manufacturing (RM), demonstrates high potential in a production chain where product design is generated automatically based on physical and/or personal requirements. The business unit Design and Manufacturing of TNO Science and Industry, performs research on Rapid Manufacturing (RM) technologies like multi material inkjet printing, micro SLA and Selective Laser Sintering (SLS) in order to elevate these processes to a level where they can be used as true Rapid Manufacturing. This paper presents the ongoing studies done on the SLS process, focussing on materials properties and reproducibility, RM texturing software, coatings for SLS parts and design rules for SLS. This paper concludes with a simple yet illustrating example of the application of Rapid manufacturing using intelligent design approach. Keywords: Rapid Manufacturing, SLS, design rules
1.
Introduction
The mass production of customised consumer goods or automatically generated structural components requires flexible and fully automated production processes able to materialise complex intelligent and knowledge based designed components. [1,2] Applications for this approach can be found in the realisation of optimised construction parts or implants based on load and boundary conditions using morphing sotiware and automatically generation of (functional) consumer goods based on used input like physical characteristics but also user characteristics like age, sex and social and economical back ground. An example of the latter approach are hearing aids [3] and Vogt en Weizenegger's Sinter chair [4]. Rapid Manufacturing based on Layered Manufacturing Technology enables the
flexible mass production of this kind of products. Selective Laser Sintering (SLS) is the main processes currently being used and further investigated, being the most feasible process for this approach on the short term. The research performed at TNO aims to broaden the applicability of SLS for Rapid Manufacturing.
2.
The SLS process
Selective Laser Sintering is a Rapid Manufacturing technique in which parts are created layer by layer out of, for example, polyamide (PA) powder directly from 3D CAD data. A layer of powder is spread on a preheated powder bed and a laser selectively melts the desired areas. A new layer of powder is applied and the process is repeated
389
Expander .aser
Powder/ ~ n d Hopper ~rt
Design and Manufacturing (D&M) intends to broaden the boundaries of the SLS process and to position it as a true Rapid Manufacturing process. The main four research topics on SLS are: 1. Materials 2. RM Texturing 3. Coatings 4. Design rules for SLS.
3.1. Materials
Fig. 1. Schematic SLS process (see Fig. 1) until a complete batch of parts is finished. Since even the most complex 3D shape can be represented by relatively simple cross-sections, there are virtually no limitations to the complexity of the geometry.
Because of the increasing control of the SLS process and the improved mechanical properties, SLS parts find their way in end-products more and more. Some examples of RM products by SLS are hearing aids [ret] and machineparts (see Fig. 2).
One of the limiting factors in SLS is uncertainty concerning the mechanical properties atter processing and the overall mechanical properties. TNO has studied the variations in mechanical properties of SLS parts and their predictability with the aim to improve the applicability of SLS as a production technology. This variation is for example caused by differences in the powder bed temperature, the laser power, the composition of the used powder and the thermal influence of the parts in the batch. These factors influence the melt, flow and crystallizing of the material. Only if these parameters are adjusted in a proper manner, the desired microstructure, which consists of hard crystalline particles embedded in a sorer matrix, can be achieved (see Fig. 3).
Fig. 2. RM machine part Especially when the series are relatively small, the absence of the need for a large investment, for example an injection mould, counts as a great economical advantage. In case of machine-parts, the main advantages are the integration of several functions in one part, the redundancy of 2D drawings and the time savings in production.
3. Research at Design & Manufacturing One of the research programs of the Business Unit
390
Fig. 3. Microstructure of SLS product, seen in polarized light. The available materials like PA 12 and Glass filled PA are sufficient for rapid prototyping purposes, but are not strong enough or too brittle for numerous other applications like the machine industry. A broader variety of materials available for SLS will broaden it's application for RM. TNO is currently developing a high strength nano-composite SLS material which can be processed on an ordinary SLS machine, the desired
mechanical properties resemble those ofpolycarbonate. The challenge is to combine a high tensile strength with good impact strength, properties that are needed for dynamic loaded parts.
3.2 RM texturing The look and feel of a consumer product is essential for its success. Many products, whether they are household appliances or technical products, have a textured surface for this reason or simplyto cover up the sinkmarks ofthe injection moulding process. In order to position the RM process as a true production technology, it is essential to be able to produce products with high quality surface finish using e.g. textures. Until now it wasn't possible to simply apply a texture to a CAD model, in such a manner that the product could be produced with this texture. Normally, a texture is applied to the CAD model for visualization purposes only, for example an orange ball with a bumpy skin to imitate an orange. If one would send this file to the RM machine, a smooth ball would be produced, instead of the desired textured orange with the desired look and feel. The sotiware developed in the RM texturing project enables the user to simply apply any texture to the whole product or a to part of it. There are many applications areas in which RM textured products can be of use. The areas we are concentrating on are: 9 Medical, improving the surface structure for implants for e.g. improved bone ingrowth, 9 Arts, applying textures to objects 9 Cosmetic, to enhance the exterior of the part (see Fig. 4.) 9 Sports; improving grip ort he airflow over a surface
Fig. 4. textured RM product
3.3 Coatings The combination of the geometrical freedom of SLS parts and the application of metal coatings enables unique possibilities in stiff and lightweight design. On one hand, the geometry of the construction can be designed for an optimal load distribution and therefore result in a high material exploitation ratio. On the other hand, the increase in stiffness is high in relation to the added mass of the coating, because the mechanical properties of the thin metal layer (see Fig 5). Other application areas of metallic coatings are high wearresistance, vacuum chambers and EMC shielding. A method has been developed to apply a metallic layer on SLS parts. After a first treatment of the SLS substrate, a thin layer of copper can be applied electroless. In a second step, more layers of different metals can be grown onto the part by a galvanic process. Organic coatings have been developed to enhance the visual and tactile appearance, the skin-friendliness and the durability of SLS parts. These coatings are on basis of polyurethane, epoxy or acryl. The identified application areas are personalized products like medical orthesis and complex shaped parts like duct channels. It has proven to be possible to apply a subsequent metal closing layer, for example titanium, on both organic as metal coatings by Physical Vapor Deposition, opening the way in high vacuum applications.
391
is of great importance to know what the best method is to introduce and transfer loads. Within the project, nine common load situations have been analyzed by the energy method. Every possible load situation can be described with a combination of these specific load situations. For each of these situations, design rules for SLS have been set up (see Fig. 6).
Fig. 5. Ni coated lightweight structure
3.4 Design rulesfor functional SLS parts Rapid Manufacturing by Selective Laser Sintering can be very useful in the machine industry as this market is characterized by capital extensive goods, small series, customized parts, and highly complex parts. Present production techniques for creating these units (milling, sandwich plates) can be costly or not flexible enough for realizing complex geometries. With a Rapid Manufacturing technique like Selective Laser Sintering (SLS) it is possible to meet the requirements. That is, when knowledge with respect to light and stiff constructions, warpage and tolerances is available. In previous research projects (Rapid manufacturing, [ 1], Rapid manufacturing of complex parts [2]), it was stated that material properties of sintered nylon (like tensile strength and impact strength) differ from those of injection moulded nylon. Furthermore, it was concluded that the mechanical properties and accuracy of SLS parts are influenced by the geometry. The general design and construction rules for non-loaded parts in SLS that resulted from these projects where mainly based on the possibilities and restrictions of the process to realize specific geometries. The first stage of the research program "Functional SLS parts" has concentrated on three main questions in order to setup design rules for constructing part for the machine industry; 9 What is the best method to introduce and carry through loads in SLS parts; 9 how to construct SLS parts in order to get good shape accuracy of the end-use product; 9 how to deal with tolerances in the possibilities of SLS? To be able realize stiffand lightweight design in SLS, it
392
Fig. 6. An example of a solution for an open profile with a torque load. The elements realize stiffness that equals a closed profile. Finite Element Analysis show the near homogeneous stresses. Stress concentrations are often the cause of failure of a construction and although the influences off or example holes are common knowledge for homogenate materials, this was until now unknown for SLS parts. A large series of stress situations have been investigated in this project.. The achievable shape accuracy is depends highly on shrinkage during processing, which is inherent to the SLS process. Also, the shrinkage is not uniform because of the thermal variations in the powder bed, influences of other parts in the powder bed and the layer by layer production approach. This shrinkage leads to internal stresses in the SLS parts and to warpage. The stresses occurring between the individual layers results in curling of the bottom layers of the product. The warpage of produced parts is investigated by means of the material properties (like thermal properties), the shape of the product (like wall thicknesses and dimensions) en process conditions (like powder bed temperature) and orientation/position in the build. The aim was to understand the phenomena and to establish design rules for diminishing or compensating the warpage.
4. Case: Rapid Manufacturing Machine parts This case is about the redesign of an uncomplicated machine part in such a way that it is suitable for rapid manufacturing (see Fig. 8). The function of the custom made bracket is to position several parts within a production machine. At the moment the brackets are made ofaluminium using milling as production method. Afterwards they are anodized to give the parts a wear resistance and aesthetic coating.
Fro=en
elements Soft Kill Option
Design Review
4.2 Focus points To obtain an optimal design proposal all requirements must be well determined. Only then a reliable and therefore useful output file can be achieved. The requirements can be categorized in a number of groups: 9 Determine the correct frozen elements. 9 Set bounding box. 9 Specify the forces on the features 9 Determine the correct material properties
4.1 Working method
Bounding box
(CAO) software [..]. Within the CAO sot~ware the Soft Kill Option (removing non-efficient material) and FEM analysis of the initial design were performed. The output was used to create a new design optimised for Selective Laser Sintering. Using FEM software a number ofdesign iterations were made to achieve optimal design for Rapid Manufacturing (see Fig. 7).
FEM Analysis -design
During the entire design chain the engineer must constantly verify if Rapid Manufacturing will satisfy all demands stated within the specifications. Using CAO sot~ware with the correct material settings the design can be examined frequently.
4.3 RM design features
Fig. 7. From conventional design to Rapid Manufacturing
Besides shape optimisation and weight reduction several other features are incorporated within the design. With Selective Laser Sintering it is not possible to create functional (metric) threads. Therefore inserts, originating from injection moulding, are integrated into the design. By adding ventilation holes in the design air is able to escape during the placement of the insert and redundant material can flow away. The highest strength and most optimal stress distribution is assured when build in the right direction.
4.4 Comparison
Fig. 8. The RM and the original bracket. Using 3 D CAD software a initial 3 D model was created. According to the specifications, the bounding box, frozen elements and the applied forces were defined and used as input for the Computer Aided Optimization
Comparing the original design made using conventional production techniques and Rapid Manufacturing design a clear distinction can be noticed. A weight reduction of 57 % is reached and the production costs are decreased with 86%, stated that the SLS production machine has a high degree of nesting. Another major advantage is the option to customise the parts and the possibility to "order on demand". Costs for FEA and CAD haven't been taken into account.
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4.5 Conclusion
On this moment, SLS can well be suitable as a production method for small functional parts. The design of these products is not more complicated than engineering for other materials when familiar with RM. CAO software can be a supportive and powerful tool. To increase strength, to optimise the surface quality or to enhance chemical resistance when Nylon is not requested, SLS parts can be applied with a metallic coating. Using for example wet chemical plating techniques a CuNi based coating can be applied to the RM bracket (see Fig. 9). For a successful large scale implementation of this relatively young production technology in intelligent manufacturing and supply chains, more research has to been done. Together with companies from, amongst others, the machine-industry, TNO is e.g. investigating the long-term performance of materials and SLS constructions under load and stress. As soon as the quality of the parts can be guaranteed over a certain period of time and within certain specifications, market acceptance will be reality opening the route towards mass customisation.
Fig. 9. Metalised SLS part- CuNi plated References
[ 1] Hopkinson,N., Hague, R. and Dickens, P., 2006, Introduction to Rapid Manufacturing, Rapid Manufacturing, an industrial revolution for the digital age, Edited. Hopkinson, N., Hague, R.J.H. & Dickens, P.M., John Wiley & Sons, Ltd, ISBN-13 978-0-47001613-8 [2] Sears,J.W, 2001, Solid freeform fabrication technologies: Rapid prototyping- rapid manufacturing, International Journal of Powder Metallurgy, Vol. 37 No. 2, pp 29-30 [3] Caloud,H., Pietrafitta, M. and Masters, M., 2002, Use of SLS technology in direct manufacture of Hearing
394
aids, 2002 SLS users group conference, San Francisco, California, USA, September29 - October, 2002 [4] Schilpenoord:"Techniekin Vorm, Selective Laser Sintering", Items 6, 2002. [5] W.Kruf,L.T.G. van de Vorst, E.J. Moeskops, H.H. Maalderink, R.C.J. Deckers. Design for Rapid Manufacturing Functional SLS parts TNO-report 43/05.013938. Eindhoven, 2005. [6] N. Kamperman, B. vd Vorst, E. Moeskops, J. d Vlieger. SLS Materials. TNO-report. Eindhoven, 2005. [7] H.H. Maalderink, SLS functional coatings. YNO-report. Eindhoven, 2006.
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhd and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Life Cycle and Unit Cost Analysis for Modular Re-Configurable Flexible Light Assembly Systems J.Heilala a, J. Montonen a, K. Helin b, T. Salonen a, O. V~i~it~iinen a a VTT Technical Research Centre of Finland, P.O. Box 1000, FI-02044 VTT, FINLAND b VTT Technical Research Centre of Finland,P.O. Box 1300, FI-33101 Tampere, FINLAND
Abstract
This article presents a methodology for the design of a modular semi-automated reconfigurable assembly system using component-based simulation and life cycle cost analysis. To ensure that an assembly system is appropriately designed, system measurement schemes should be established for determining and understanding design effectiveness. Measurements can be classed into two categories: cost and performance. Understanding manufacturing costs is the first step towards increasing profits. The authors are developing an analysis tool that integrates Overall Equipment Efficiency (OEE), Cost of Ownership (COO), and other analysis methods to improve the design of flexible, modular reconfigurable assembly systems. The development is based on selected industrial standards and the authors' own experience in modular assembly system design and simulation. The developed TCO (Total Cost of Ownership) methodology is useful in system supplier and end-user communication, helps in trade-off analysis of the system concepts and improves the system specification. Keywords: Assembly systems design, life-cycle and unit cost analysis.
1. Introduction
The objective of modern assembly processes is to produce high quality products with low cost. Throughput, utilization, and cycle time continue to be emphasized as key performance parameters for the planning of new assembly systems and they do have an effect on the cost efficiency of the system. Understanding life cycle related costs as early as in the assembly system design phase is the first step towards increasing profits. This article presents a methodology for the assembly system design evaluation, using system life cycle modelling and a Total Cost of Ownership (TCO) analysis. The authors are developing a TCO analysis tool that integrates Overall Equipment Efficiency (OEE), Cost of Ownership (COO) and
other simulation-based analysis methods to improve designs of flexible, modular re-configurable assembly systems. The analysis tool development is based on selected industrial standards and the authors' own experience from assembly system design and simulation. The TCO method is useful in system supplier and end-user communication and helps in trade-off analysis of the system concepts (see Fig. 1.). Product miniaturization, increasing precision and yield requirement increases automation even in low-cost labour countries. The key issue is to find and optimize a suitable automation degree in a fastchanging market situation. This is a task for both the end-user and assembly system supplier. Currently there are demands for common processes worldwide, and thus the engineers also
395
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2. Assembly system trade-off analysis method To assure that an assembly system is appropriately designed, system measurement schemes should be established for determining and understanding design effectiveness. Measurements can be classed into two categories: cost and performance. Throughput, utilization, and cycle time continue to be emphasized as key performance indicators (KPI) for existing operations and for the planning of new assembly systems, but the cost issues also need to be analyzed, and as early as possible in the system design phase. The purchase cost of the system is just one parameter to consider when performing a cost of ownership analysis. Operating cost, other variable costs, throughput of the system, yield and scrap cost, and the useful life of the system are other factors affecting the cost efficiency of the system. Different cost estimation methods have been devised; a few of them measure intangible costs such as flexibility, product yield, parts quality, process time variation, system modularity, re-use value, and so on. Although not all of these intangibles are easily understood, their costs may be measured by indirect methods. In many cases, a cost estimation method can be derived from performance measurements. For example, flexibility affects the capital investment plan. Yield and quality are related to capacity and material handling costs. Process time variation may cause problems with workstation utilization or inprocess inventories [ 1]. The idea is to cover the whole system life cycle
396
scenario with modelling and especially the costs issues, fixed, recurring and yield costs. Engineers also need to estimate the number of good products produced during the life cycle of the system. The planned system utilization, availability, efficiency and rate of quality and yield have an effect on the number of good products produced and thus on the unit production cost.
2.1 Modular re-configurable assembly systems Modular structure and reconfiguration are needed in the current market climate, where system changes occur at shorter and shorter intervals. Typically, the fastest changes are in computer manufacturing and consumer goods. The typical life in production varies from 6 months to 3 years and typically there are 2-8 variants. The life-cycle is longer in the automobile industry, and especially in military or medical applications. Assembly systems need to outlive the product they were originally designed for and modularity is one solution. The use of a modular structure in the architecture of the assembly system has many advantages. Design of a modular system is just like selecting suitable modules from an e-catalogue and placing them in the right order to achieve the correct process flow and system layout. The end user and system integrator can more easily configure the system and later reconfigure it to meet the customer's future needs. Modularity is also a costefficient solution; it supports step-by-step investment, and later upgrades or modifications to the system are also easier. Most of the modules should be standard, with known catalogue prices, this helps in calculating the cost of investment. The aims are also to minimize product-related special customization. Typically, some equipment vendors for the electromechanical industry estimate that 85% of the
final assembly system equipment is re-usable. [2] Simulation and virtual factory technology is used for manufacturing system design. The reconfigurable and modular solutions for final assembly systems need equally modular design tools. Each modular building block of the real system needs to have a digital component to be used in simulation model building, reconfiguration and analysis. Component-based simulation software with 3D capabilities is ideal for the design and configuration of modular reconfigurable systems. The simulation platform should support discrete events analysis, like material flow, machine utilization and also robotics simulation. At least one item of commercial software has these features in a single platform [3]. 2.2 Life-cycle consideration
As mentioned earlier, engineers need to calculate all the costs arising during the lifetime of the equipment. The life-cycle of the system in the design phase is based on scenarios. Usually, endusers have product roadmaps and estimations for new variant or product family introduction. Thus engineers can estimate the different products and variant life in production and also estimate the change needed for the assembly system. The change could occur at six month intervals. If the basic assembly process is the same, only the product specific system parts need to be changed; gripper fingers, part feeding, etc. When modelling the future scenarios, it is possible to estimate the needed changes to the system and thus the cost effects. 3. Theories for the TCO analysis toolkit
The standardized method basic equation for calculating the CO0 was originally developed for wafer fabrication tools [5] and has become a common reference between equipment suppliers and equipment users in the semiconductor industry. There is a dedicated commercial tool on the market. In the arena of electromechanical assembly, it is not yet well known; instead a similar calculation is used. The basics of Cost of Ownership (COO) are simple: all the cost during the system life-cycle divided by the number of good units produced [4, 5]. Thus COO depends on the production throughput rate, equipment acquisition cost, equipment reliability, throughput yield, and equipment utilization. The basic COO is given by the following
equation, CO0 per unit = all the cost/number of good products. COO = (FC + VC + YC)/ (L * THP * Y * U) (1)
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The use of CO0 is an implementation of Activity-Based Costing (ABC), which helps in understanding all the costs associated with a decision. |t improves decisions by relating costs to the products, processes, and services that drive cost. Without such a linkage, it is difficult for organizations to understand the full impact of their decisions on their operating cost structure. With this linkage, COO provides a consistent data-driven method for arriving at important strategic and operational decisions. The DEE (Overall Equipment Efficiency) is increasingly being used in industries such as the automotive industry to assess the manufacturing efficiency of the running systems. DEE is a key performance indicator of how machines, production lines or processes are performing in terms of equipment availability, performance or speed and quality produced. It identifies losses due to equipment failure, set-ups and adjustments, idling and minor stops, reduced speed, process defects and start up. DEE is based on reliability (MTBF), maintainability (MTTR), utilization (availability), throughput, and yield. (See Fig. 2. and 3.)
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Fig. 3. From total time to good units All the factors are grouped under the following three sub metrics of equipment efficiency [6, 7]: 1. Availability 2. Performance efficiency 3. Rate of quality The three sub-metrics and OEE are mathematically related as follows: OEE, % = availability x performance efficiency x rate of quality x 100. The Overall Equipment Efficiency analysis used by the authors is based on a standard [6, 7], and there is a systematic way to classify and study equipment efficiency and time losses. OEE methodology is also one way to specify allowed MTBF, MTTB and availability data for a system in the design phase. OEE analysis can show the time losses and helps in identifying the actual time the system is producing good units (see Fig. 2 and 3). This can be used for evaluating different production work time and shill arrangements. Life-cycle aspects are analysed with annual data as shown in Fig. 4. The engineer doing the analysis can vary cost factors on annual bases as well as production volume, i.e. OEE analysis for all the changes. For example, a system upgrade with new hardware adds cost to the fixed and also to the Year 1 Fixed costs: - Acquisition, - Facilities, - Decommission Recurring cost: - Factory interface - Equipment Management - Maintenance - Control - Inputs Operation labour Quality/Performance, Yield - Scrap/Rework Number of good units, OEE analysis COO
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In the prototype tool, the authors have integrated a commercial component-based simulation [3] with the Excel analysis workbook [8,9]. The VTT TCO Excel workbook also works as an independent analysis tool, if the user enters all data for the calculations manually. An overview of the current integration is shown in Figure 5. Developers are using a COM interface, Python scripts, and Excelinternal links. In the simulation model configuration, layout building, each component added to the model increases the purchase price function, and similarly every human operator added to the model increases the labour cost function. Selecting a country sets country-specific salary data; other data is either default values or the user needs to enter data manually into the specific place in the Excel workbook. In the most advanced existing prototype, the model builder interactively sees the effects of his or her selection in the simulation sol/ware user interface. Most of the data is stored in the Excel sheets and the simulation model can read and write in selected cell. Thus the same Excel workbook can even be used without the simulation. Integrating different analysis methods creates data for decision-making. The aim is to make analysis as easy as possible, using default values, which are user-editable. There are a lot of other factors and parameters which could entered into the calculation, but which are currently not used. Naturally there are limitations since the TCO tool presented here is a functional prototype, proof of concept and the development continues. Visual Components 3D Create|
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398
recurring cost and could have an effect on production volume or yield.
Fig .5. Overview of the TCO analysis toolkit.
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6.1 Semi-automated assembly line analysis 5. Analysis workflow The following case illustrates the use of the developed methodology, (see Fig. 6). This methodology can be applied to an individual workstation, piece of equipment or process comparison. It can also be used for the production line level if the user enters some of the key parameters based on the bottleneck machine. In the deeper analysis, both hierarchical levels should be studied, the most important workstations and also the production line level. The idea is to create an individual Excel workbook from all the assembly system concepts, baseline solutions. The analysis results provide information for comparison and decision-making. An engineer can create different scenarios for one baseline solution as well; see Table 1 (work time, bad quality, automation, modularity, etc.). The readers should remember that, as with all simulation analysis, results are sensitive to input data quality.
The case presented is from electromechanical final assembly. The initial data is, briefly, the following: a study of the final assembly line. Line layout is shown in Fig 7. There are reserve places for future, planned upgrades. In the scenarios the production country is Finland and worker cost per year is 51 750 tF. The cycle time of the bottleneck machine is in all cases 7 s. Equipment investment is 185 000 6 and the needed floor space is 200 m2, and the yearly cost for the floor space is 200 t~/m2. Estimated system life-cycle is 5 years. Other data is in Table 1. For example, how can we justify automation? The unit cost is not very sensitive to the initial purchase price of the equipment. The variable or recurring cost and yield cost have bigger impact. Thus if we lower the personnel cost and the same time increase quality, we are able to justify investments in automation. Wrong or false input does not produce the right results. Knowing this, the authors are not aiming at
Table 1. Scenario key data for comparison CO0 t~ / product Total Cost • Fixed Cost C Recurring Cost tF Yield cost tF Overall Equipment Efficiency (OEE) % Performance Efficiency from OEE % Quality Efficiency from OEE % Availability Efficiency from OEE % Calculated volume (product/year) Number of workers and support workers Cost of product C (components) Cost of rework tF workdays/week shifts/day shift length [hr]
Automation 11.04 81 272800 723 788 80 177 879 371 132 32.68 78.71 95.02 43.70 1 472 315 8+ 2 10.31 20.00 5 2 8
Quality 11.33 80716677 553 454 79 575 930 587 293 31.63 78.72 91.93 43.71 1 424 822 12 + 2 10.44 20.00 5 2 8
Two Shifts 10.85 82857318 553 454 82 190 430 113 434 33.90 78.72 98.52 43.71 1 527 117 12 + 2 10.08 20.00 5 2 8
One Shift 11.17 40801 833 540 915 40 204 230 56 689 16.22 77.73 98.45 21.19 730 678 6+ 1 10.09 20.00 5 1 8
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absolute results in the design phase but, rather, at obtaining data for the comparison of design alternatives. Later on, real factory data and accounting data can be used to verify the models and thus improve the results in the next evaluation round and new system designs. The challenges are in the evaluation of system modularity, flexibility and reuse value. 7. C o n c l u s i o n
Selecting the most appropriate assembly system can offer enormous benefits in terms of product quality, cost reduction and manufacturing productivity. However, selecting the right system for a product depends on a large number of factors, and is not a simple problem. This paper proposes a systematic approach to support decision-making in this area and presents a methodology for selecting assembly systems for a product based on technological efficiency and economic considerations. The theory behind the analysis is also briefly explained. The authors believe that COO, OEE, modelling and simulation are becoming increasingly important in high-tech decision-making processes. COO provides an objective analysis method for evaluating decisions. First, it provides an estimate of the lifecycle costs. The analysis highlights details that might be overlooked, thus reducing decision risk. Finally, COO allows communication between suppliers and users (Figure 1). They are able to speak the same language, comparing similar data and costs using the same analysis methods. Both suppliers and manufacturers can work from verifiable data to support a purchase or implementation plan. The lifetime cost of ownership per manufactured unit is generally sensitive to production throughput rates, overall reliability, and yield. In many cases it is relatively insensitive to initial purchase price; this can be pinpointed with the proper use of analysis. With correct parameters, an engineer can justify investments in flexibility and automated equipment, or at least determine threshold values. OEE is usually a measurement of single machine performance. In the example presented, the calculations are used for a bottleneck machine and, in practice, the Overall Throughput Efficiency of the assembly line is calculated. With a serial line and one product, as used in the example, this can be quite simple. The analysis is more complex with mixed production and a layout with parallel operations. Simulation studies can pinpoint bottleneck
400
equipment and line balance issues. OEE analysis is process or equipment-centric and the material flow or work in progress (WIP) is not analyzed - another reason for using factory simulation. Integrating the Total Cost of Ownership analysis into the simulation provides an effective method to evaluate system alternatives from the cost standpoint; it improves the quality of decisions. An overview of the development is given in Figure 5. The challenge is to bring system reconfiguration, modularity and high mix, low volume production environment to the analysis with minimum interaction from the user. Now it requires a lot of interaction with the engineer doing the analysis. A c kn owl edge me n ts
The authors wish to acknowledge the financial support received from the National Research Agency of Finland (TEKES), VTT and Finnish industry. The development is part of the MS2Value project (www.pe.tut.fi/MS2Value/index.html). The first draft of the methodology development was made in the Eureka Factory E!-2851 E-Race project. References
[1] We-Min Chow. Assembly Line Design, Methodology and Applications. Marcel Dekker, Inc., New York and Basel, 1990. [2] iNEMI Technology Roadmaps 2004 Edition. Final Assembly. International Electronics Manufacturing Initiative. December 2004 [3] Visual Components 3D Framework. www.visualcomponents.com. [4] Ragona, Sid. Cost of Ownership (COO) for Optoelectronic Manufacturing Equipment. 2002 Microsystems Conference. Rochester, New York. [5] SEMI E35-0701, 2001, Cost of Ownership for Semiconductor Manufacturing Equipment Metrics, SEMI International Standard, http://www.semi.org. [6] SEMI E 10-0304, 2004, Specification for Definition and Measurement of Equipment Reliability, Availability, and Maintainability (RAM), SEMI International Standard, http://www.semi.org. [7] SEMI E79-0304, 2004, Specification for Definition and Measurement of Equipment Productivity, SEMI International Standard, http://www.semi.org. [8] Heilala, Juhani; Helin, Kaj; Montonen, Jari. Total cost of ownership analysis for modular final assembly systems. ICPR 18. Salerno, 31 July - 4 August 2005. [9] Heilala, Juhani; Helin, Kaj; Montonen, Jari; Voho, Paavo; Anttila, Mika. Integrating cost of ownership analysis into component-based simulation. CARV2005. Munich, 22 - 23 Sept. 2005.
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Material-driven solution finding- functional materials in the design process P. Dietz a, A. Guthmann a, T. Korte a a
Institutfiir Maschinenwesen, TU Clausthal, Robert-Koch-Str. 32, 38678 Clausthal-Zellerfeld, Germany
Abstract It is often necessary to combine materials and make use of them in a functional way. This can be achieved by using a methodology corresponding to the requirements, integrating the material properties into the early phase of the design process. New possibilities can be found by interconnecting the constructional components material, design and technology. Procedures to integrated materials are presented in this article and the necessity to motivate the faculties material, design and technology to work together interdisciplinarily to create innovative products is shown.
1.
Introduction Because of competing or conflicting demands of modern and market-driven products, materialselection puts a restriction on the designer. An interdisciplinary collaboration of the areas material, design and technology, based on product requirements, can be useful to find new possibilities, properties and principles which can be used in the phase "search for solutions" of the design process and can lead to new innovative products. In the context of the national-funded research project "Requirements-driven conceptual design (methodology) of constructions with incompatible materials" (DFG DI 289/31-1) [ 1] a procedure for the design process has been developed. By using this it is possible to incorporate the material selection, corresponding to the product requirements, in the concept-phase of the design process. Approaches to analyse and structure materials have been designed. A model with material examples is shown which can be used to map the basic functions known from design methodology (separating, dissipating etc.) to materials. In this way materials can be used to fulfil a certain function based on their functional properties. After defining and clarifying the problem and
the development of product requirements and goals, functions should be developed by incorporating materials and their properties. With this procedure the material becomes a solution-immanent functionmedium useful for designing innovative products. This leads to requirements on the defined materials which can lead to more material developments. Using the achievements of this research project an interdisciplinary collaboration of all of the faculties concerned with the product formation process should be enabled to effect the development of innovative products. 2.
Material-driven design process A procedure has been developed to include the materials in an early phase of the design process. It departs from previous approaches of the material in the design process where the material is integrated as a potential functional medium or as a resource to fulfil requirements or goals of a product. Functions and goals of the product are drawn together with properties and functions of the material. Fig. 1 shows the procedure of the material-driven design process.
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The clarification of the problem occurs at the beginning of the design process. The requirements given to the product have to be formulated, analysed and correlated. Because of the desired interdisciplinary collaboration of the faculties material, design and technology the spectrum of requirements becomes very complex. On this account a precise and exact problem formulation is necessary. The requirements have to be checked and reworked until all quality criteria are fulfilled,
402
meaning a constant adaptation of the demands. A list of requirements containing demands and conditions describing the goals and conditions of the set job is the result. In the next step a functional analyses and the creation of a function structure are undertaken. The overall function is divided into functions with lower complexity. Parallel to this a formulation of ambitions can be arranged to increase the level of abstraction and decrease the level of detail. Objectives of qualitative and quantitative fixed product requirements will be defined [2]. The progressive abstraction is a method to formulate intentions and goals. It belongs to the group of systematic problem specifications. The progressive abstraction has two aims: Work out the connection between a problem and the goal system of the person solving the problem Show the level of measures at which solutions can be most effectively achieved to contribute to reaching the
ambition Approach: The simplest form of the progressive abstraction is to repeatedly adopt the question "What is of substantial importance?" and to always aim for fundamentally right answers. Based on these answers the problem is formulated in the next higher abstraction level. The process is carried on until a method of resolution, according in the best way possible to the conditions of the problem, is found. If this way of formulating goals will be used, it is
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conceivable that the product to be developed and its elements/components respectively will be described by its properties, its behaviour and its functions. The requirements can be fulfilled by properties, functions and the conforming behaviour (Fig. 2). It is possible to map the developed description of a product onto materials, by explicitly searching for properties, functions and behaviour-attributes of materials which can be used to fulfil requirements alone or in combination. A function analysis also allows an integration of materials in an early phase of the design process. The break-down of the overall function of the product to be developed leads to a number of basic functions which have to be fulfilled to achieve a solution of the problem. The level of abstraction is lower in contrast to the method of formulating goals. By analysing materials and their properties it is possible to find active materials which can be a function-medium because of their properties. 3.
Materials
To identify and adopt functional materials or combinations of functional materials it is necessary to analyse the material and its behaviour to clarify the connection between the favoured/required functions and the properties/possibilities of the material.
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Solution immanent materials For an innovation of the design process it is of
interest to consider active materials which lead to a jl design because of their J . properties and which are not connection only a substantial medium of joined with a constructive function. Examples for solutioninternal inter high immanent (functional-) pressure materials fulfilling a function directly due to their properties are:
Piezo-materials: allow the convertion of mechanical to electrical energy and vice versa. An elongation is the reaction to an applied voltage or an electrical signal behavior of a product is created because of pressure. Example of use: actuators for shock absorption. Magnetostrictive-materials: Change their mechanical properties because of an outer magnetic field. Example of use: Engines, hydraulic actuators Shape-memory-materials: Memorise their original form. When heated above a certain temperature, deformed parts return to their origin shape. Example of use: Stents in medical technology, pumps. Electrical-Theological-materials: Fluids that change their viscosity because of an electrical field. Example of use: regulating dampers. The examples of solution-immanent materials show the large influence a material can have on a design and that a design can indeed only be made feasible due to the choice of material. A high innovation potential appears by looking at the material as an active function-medium. Not only in the range of products to be developed, but also in the area of manufacturing technology and materials.
3.2. Material-analysis The usage, or rather the identification of functional-materials, assumes an analysis of the material and its properties. So far knowledge about strength, weight and costs are enough to detail or optimize a design or a product. For material-driven solutions and innovations a deeper going materialanalysis is necessary. This is useful to indentify not only apparent properties but also hidden properties
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choosen in a methodical way to fulfil the basic functions and the formulated goals and requirements of the product (Fig. 4).
Material I
I
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and attributes not recognizable at a glance, but that are able to fulfil functions, too. Fig. 3 shows a functional classification used to describe materials. A material analysis assumes a large amount of interdisciplinary collaboration between the faculties design, technology and materials. The problem for the designer is the lack of knowledge about the complex material properties and their availability. To provide knowledge and to generate products in a planned way and not by coincidence a conceptual design of catalogues is advisable, helping with the appointment of requirements and solution-finding. In these catalogues material properties will be sorted by basic functions for example. Beginning with a technically feasible function, the solution search starts at the corresponding basic function and leads to a relevant structure. Therefore materials will firstly be categorised by static properties, then variable properties etc. A classification of these categories using aspects of a lower level occurs. If possible these aspects are divided into areas according to the basic function like transforming, separating, transferring etc. By using the basic functions the look-up field can be extended. The advantage of the high level of abstraction is that the designer is not bound to a special function. Finally the look-up field has to be restricted to find an adequate method of resolution for the technical problem. A system results that enables materials to be
404
I
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amplifying technology
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manufacturing technology into the design process is necessary to intensify and enlarge the interdisciplinary collaboration. This integration can lead to an amplification of existing principles. It is further possible that the application of an adequate technology causes material-properties to fulfil functions. The technology of Rapid Prototyping shows how the integration of manufacturing-technology into the solution-finding process can cause a change of a materials' use to precisely become a functionmedium. Molds for creating fibre-composite parts manufactured by Rapid Prototyping show porosity as a result of the manufacturing process (because of the size of the used powder). Because of the porosity, air can pass through the molds while forming the parts. A venting or suction system is not necessary. r =
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Material-driven basic functions
A potential approach to find new solutions using a combination of materials and manufacturing technologies is to divide the known basic functions at first into methods for fulfilling these functions (Seperating materials: filter, centrifuges, sieve, extractor etc. ). The methods will be overlaid with a "table" filled with different materials and manufacturing technologies. With methodical use of these tables, material combinations and manufacturing technology can be searched and found which aid the realisation and fulfilment of the respective basic function. Rapid Prototyping for example makes it possible to build up parts which can be used for filtering, made possible by the availability of metal or plastic powder with various grades of porosity. Further treatment is not necessary. The metal powder, used as the basis for the parts, on its own is no advance while searching for a solution. Only because of an adequate technology an innovative solution can be developed. It is necessary to provide the designers with knowledge of materials and manufacturing technologies in an early phase of the design process to make the product development effective. Information about materials, technologies e.g. should be stored in databases and be made manageable by easy methods of requesting information and making choices. More examples for changing the properties of a material by using a well suited manufacturing technology and making the application possible are: Shot blasting: residual compressive stress in the part, increase of the fatigue limit Heat treatment: increase of the strength parameters Internal high pressure forming/joining: Change of the properties because of plasticizing Surface coating: surface properties will be improved, e.g. wear protection 5.
Solution-integrating design
To provide a complete integration, the design must be integrated in the solution process, too, because it is often necessary to design the parts in a special way before applying a technology. Innovative solution-immanent materials lead to an increase of the requirements when designing the product, because conventional design-rules do not use the potential of the material properties effectively. Often the material is iteratively adapted
to the construction until it fulfils the requirements. Innovative materials set requirements on the design which bring about their function. Because of that a close interaction of material-analysis and design is necessary. The designer has to identify and to realise the conflicting requirements. The previously mentioned process of Rapid Prototyping is a good example to clarify the necessary collaboration of the three faculties (design, material and technology). To develop an innovative and successful Rapid Prototyping product the Rapid Prototyping-driven geometry has to be checked closely during the design phase. Otherwise problems can occur during the manufacturing process. Details of a Rapid Prototyping-driven geometry can be seen in [4] et al.
6.
Innovation process
By considering all faculties and by the use of solution-immanent materials it is possible to create an innovation-cycle whereby new and innovative products, according to the market requirements, arise. Innovative products do not inevitably arise out of new technologies, materials or insights. Only the combination and integration of all faculties leads to achievable improvements. It is not sufficient to provide only technical and specific information to the faculties. Rather it is necessary to change and acquire requirements and to look at the design, material-science and manufacturing as a complete unit. Searching for solution-immanent materials ideally leads to a self-energizing cycle of interacting innovations and inventions (Fig. 5).
7.
Conclusion
A procedure is introduced that allows it for designers to involve materials in the conceptionphase of the design process. Procedures to analyse and to structure materials were developed and an archetype made to map materials to basic functions was introduced. The faculties technology and design were surveyed because the development of new and innovative products cannot be achieved only by analysing and involving materials in the design process. The interdisciplinary collaboration leads to an increase of the innovation-potential of products, to an innovation-cycle.
405
Manufacturing / technology
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/
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~
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Acknowledgement The Institute of Mechanical Engineering, Clausthal University of Technology, is partner of the EU-funded FP6 Innovative Production Machines and Systems (I'PROMS) Network of Excellence.
406
References
Materialtechnic/ research
Design
Innovative materials
/
A
\
[1] DFG 289/31-1: Anforderungsgetriebene Konzeption (Methodik) von Baukonstruktionen aus inkompatiblen Werkstoffen, technical report, publication foreseen, 2006. [2] Kruse, P.: Anforderungen in der interdisziplinfiren Systementwicklung: Erfassung, Aufbereitung und Bereitstellung. Dissertation, TU Clausthal, 1995. [3] Korte, T.: Funktionale Werkstoffe im Konstruktionsprozess. Unpublished assignment, TU Clausthal, 2003. [4] Klemp, E.: Unterstfitzung des Konstrukteurs bei der Gestaltung von Spritzgussbauteilen hergestellt im Rapid Prototyping und Rapid Tooling Verfahren. Dissertation, TU Clausthal, 2002.
IntelligentProductionMachinesand Systems D.T. Pham, E.E. Eldukhd and A.J. Soroka(eds) 9 2006 CardiffUniversity,ManufactttringEngineeringCentre, Cardiff, UK. Publishedby ElsevierLtd. All fights reserved.
Neuro-fuzzy case-based design" An application in structural design K.M. Saridakis ~, A.J. Dentsoras ~ P.E. Radel b, V.G. Saridakis b, N.V. Exintari b a
Dept. of Mechanical Engineering & Aeronautics, University of Patras, 26500 Rio Patras, Greece b Peri Hellas, 19400, Koropi Athens, Greece
Abstract
A design approach and a design tool are presented that are based on human analogical reasoning for providing solutions and are used for the design of formworks for the construction of slabs.. Through a case-based design process, past solutions are compared with the current design case, and a subset of them is retrieved according to a defined similarity measure. In the present work, the retrieval process is performed on the basis of a competitive neural network, which is submitted to unsupervised training by using the existing design solutions. The design case is represented in terms of sets of design parameters and associated fuzzy preferences. In engineering design problems whose solutions could not be adapted in order to meet new requirements, the adaptation process is substituted by another approach that evaluates the retrieved design solutions according to the aggregation of the fuzzy preferences assigned to the current design problem. Therefore, the highly evaluated solutions may be manually adapted and modified by the designer based on both his/her creativity and experience. In engineering domains like structural engineering design, which can not be modelled computationally due to many different underlying disciplines, the designer' s personal capabilities may be augmented by a design tool such as the one presented here that substantially assists decision-making.
1. Introduction
Design has always been a basic human activity. The technological explosion during the last century along with the increasing need for optimal design products have emerged the need for establishing formal design methodologies and design models [1]. During the last decades, the research activity focused on both surveying and understanding the design rationality and studied difficult issues such as design knowledge representation, retrieval and optimality of solutions [2]. The traditional design models and methodologies [1] are not capable of addressing efficiently all the abovementioned issues and they cannot ensure the computational applicability (which is a constant demand for domains like engineering). As a consequent the need for meta-modelling in order to address engineering design problems becomes obvious [3]. This meta-modelling has been seriously augmented by the development and the increasing utilization of artificial intelligence techniques [4] and especially by the artificial intelligence domain known as soft computing, which is comprised by Fuzzy Logic (FL) [5], Artificial Neural Networks [5,6] (ANN) and Genetic Algorithms (GA). Design approaches that
combine soft-computing techniques [7,8] can outperform the conventional design frameworks and they can be deployed as integrated design tools. Many approaches based on soft computing have been applied in multi-disciplinary and demanding engineering domains [9] such as structural design [10] and civil engineering [11] and provided sufficient results. There are, however, engineering problems that cannot b e - from a computational point of view - fully modelled in order to provide detailed solutions and this fact limits the applicability of the developed soft-computing approaches. Moreover, the emerging artificial design intelligence cannot overwhelm the natural intelligence of human designers, who tend to rely on their own creativity and experience. Additionally, it is unanimously accepted that the designers provide solutions on the basis of analogical reasoning. According to this internal process, the individual designer recalls past/existing design solutions in order to solve the current problem. The latter ascertainment has generated a scientific domain called cased-based reasoning (CBR) [12] or - in the case of designcase-based design (CBD). During the last two decades, the penetration of soft computing techniques into the case-based design has been thoroughly
407
researched [13], resulting to various hybrid design techniques [ 14-17]. Nevertheless, in both conventional and soft-computing-enhanced case-based design approaches, the retrieval process is followed by a phase, during which the retrieved solution(s) are adapted to meet the new design objectives. This adaptation process cannot be performed automatically by any existing case-based design system if multidisciplinary design problems that require detailed solutions are addressed. The current research work has been deployed towards the direction of assisting the designer in the domain of formwork design by retrieving successful past design solutions, which are then evaluated with a fuzzy inference module. On the basis of the latter evaluation, the designer may select a highly ranked solution and adapt it manually for converging with the current design problem specifications. In this way, existing design experience is reused while the human creativity and judgement are not neglected.
the slab for a building (see figure 2). More details, analytical representation and solution for this problem may be found in [ 18].
Fig. 2. Peri Multiflex formwork for slabs: main and secondary girders and supportive props [ 18]. 2.1 The generic case representation
2. The framework The framework is based on two cooperating modules. The first module, named DeSC (Design with Soft Computing), is responsible for the fuzzy representation of the design case. The second module, namely CBD (Case-Based Design), retrieves past design cases by deploying a neural network. The outline of the framework is shown in figure 1. The system supporting this framework was developed by using the corresponding soft computing toolboxes of Matlab software [ 19]. A more detailed description of these modules is given in the following paragraphs.
aggregation of fuzzy preferences
Fig. 1. The outline of the framework. For addressing the problem of formwork design for slabs the developed system was used with the support of Peri Hellas Co. The formwork is designed and constructed before pouring the concrete forming
408
A parametric design problem can be expressed in terms of design entities called design parameters (DP). A design parameter is a characteristic of the design case and its variance affects the final design outcome. A design parameter may refer to a physical attribute of the design object, to a function that the designed system should perform, or to a metric that characterizes the performance. Firstly, both quantitative and qualitative design parameters of the design problem and their associative relations must be stated. The relationships among the design parameters may be expressed in terms of computational formulas, empirical rules, selection matrices, experiment values etc. The expressed dependencies are registered in a DSM matrix [20] that performs partitioning and ordering of the design parameters. From this partitioned DSM a tree structure with the design parameters may be extracted. This tree then represents the design problem and ensures the existence of bottom-to-top solutions. The design parameters are classified in two basic categories, the dependent and primary (non-dependent) design parameters. The solution search is deployed in the design space formed by the variation of variable primary DPs and the variable dependent DPs. For the present case the associative relations became available or were extracted from the design knowledge (selection tables, empirical rules etc.) provided by Peri Hellas (table 1) [ 18]. A hierarchical structure of the DPs for the design problem was also
constructed (figure 3). This tree is not necessary for the case retrieval process that follows, but it helps when the designer performs manually the adaptation of the retrieved solution. This tree reflects how the interrelated DPs take their values providing a deep understanding for the design problem.
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performance variables (PVs) for both optimization and case-based retrieval tasks. These design parameters may be either quantitative or qualitative. For the qualitative ones, numerical mapping is performed for making the case-based retrieval possible, and by the end of the retrieval process, linguistic values are reassigned. For example, if the design parameter 'girder type' is considered that acquires its value from the domain {'GT 24', 'VT 20'}, then these values are mapped to the discrete numerical values {1,2} respectively in order to deploy the retrieval process. After finishing with the case retrieval, the numerical values may be mapped again back to the initial linguistic values.
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Fig. 3.A hierarchical tree for the slabs formwork design problem. Although the formwork design for slabs is a welldefined problem, its final solution cannot be provided only through parametric computations. There are other disciplines, for example spatial constraints, material availability, cost assessment, etc., which must be considered before a final solution is deployed. Furthermore, the detailed solving of the problem under consideration requires the visualization of the solution in an appropriate CAD solution (figure 4). Under these circumstances, it becomes evident that the elaboration of a tool capable of providing or modifying detailed final solutions in an automated way is extremely difficult.
2.2 The neural case-based module
The case representation in the CBD module is performed through pairs of DP labels and values. It is a good strategy to use the DPs that are considered as
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As mentioned before, the design parameters of a design problem may be classified in different categories (fixed, variable, primary, dependent). Furthermore, a sub-set of these DPs, which are considered as important for the final design performance, may be further characterized as performance variables (PVs). The designer defines his/her preferences about specific values of the identified PVs before the retrieval process begins. Some of these PVs are characterized as a priori fixed (for example, the slab thickness is considered constant). For these PVs, the assignment of a single scalar preferred value is permitted. Another set of PVs are identified as variable and the designer may assign fuzzy preferences for intervals of their values. Additionally to the preferences on specific PV values,
409
the designer must also define the PVs' relative importance. The latter is modelled through a set of weighting factors. Figure 5 shows a snapshot of the system's interface for defining fuzzy preferences, weighting factors and aggregation strategies. A critical issue in design case retrieval is the determination of the similarity measures among cases [21 ], especially when the engaged design entities are expressed with fuzzy representations [22]. In the current approach, the scalar preferences assigned to the fixed PVs are utilized directly during the retrieval process. On the contrary, the fuzzy preferences assigned to the variable PVs must be compiled to single scalar values before they can be used. The determination of these scalar values must be done under the consideration that they must capture adequately the information modelled through a fuzzy preference. It is reasonable for this compiled scalar value to represent a value of the PV with high levels of preference in the initial fuzzy set representation.
Fig. 5. Definition of fuzzy preferences, weighting factors and aggregation strategy for the PVs. Moreover, in case of continuous spaces of values of PVs, these high levels of preferences may occur for multiple values. The system provides the 'medium-ofmaximums' (MOM) as default defuzzification method for compiling the fuzzy preferences into scalar values. This method searches for the value for which the preference is maximum and if there are more than one values, then the medium of them is considered. Optionally the designer may select the 'centroid' defuzzification method [4], if it is estimated that it models better the specific fuzzy preference. The possibility of selecting one of the available defuzzification methods for each variable PV independently is also provided. The determination of both the similarity measures and the deffuzification method for each fuzzy
410
preference makes possible the retrieval of similar past solutions. The general idea of this retrieval process is summarized on the statement 'Search for past cases (solutions) characterized by PV values that are similar to the PV values defined bythe designer. Competing neural networks [2,5] and k-means clustering algorithm [2,5,15] are utilized for accomplishing the retrieval tasks. Competing neural networks are capable of performing classification tasks rapidly and efficiently, even with design problems with a large number of design parameters. The k-means algorithm presents many variations, it is generally based on a defined number of clusters (k) and its main operation is to move a training example (solution) to the cluster, whose centroid is closest to that example. Then the algorithm revises the centroids of the clusters by incorporating this movement. Initially, it is necessary to define the PVs that should be considered for the retrieval process. These PVs could be the entire set of PVs of the design problem or just a subset of it. Supposing that a set R of PVs is formed, then a competing neural network with R input nodes may be considered. This neural network is submitted to unsupervised learning [4,5] using as training examples all the cases stored in the case base. For each example-case only the fields corresponding to the PVs previously selected for retrieval are restored and 'feed' the neural network's inputs. The neural network is designated to classify the training example cases in k clusters that are formed by using the k-means algorithm [4,5]. This algorithm operates by moving an example case to the cluster whose centroid is closest to this example and continues by updating the centroids for the revised clusters. The number k of clusters to be used must be defined in advance. The value of k may be determined by estimating the square root of the number n of training examples (cases) in the casebase ( k = , f n ) [5,13] . Thus the neural network is structured with k neurons in each competitive layer. The system utilizes the 'nearest neighbor' method [5,13] for computing a weighted global distance. Several functions for defining the global weighted distance measures have been proposed [5,13] and could be utilized alternatively. There is no optimal distance measure and the selection depends on the characteristics of the considered design problem and on the designer's preferences. The architecture ofthe competitive neural network is described in detail in [24]. By the completion of training, the neural network is able to classify the input vector of scalar designer's preferences to a specific cluster.
Considering a sufficient number of neurons, every cluster of similar input vectors will have a neuron that outputs 1 only when a vector in the cluster is presented, outputting 0 in all other cases. Then all design cases contained in this cluster will be retrieved and proposed as similar solutions to the designer.
2.3 Fuzzy evaluation of the retrieved design solutions The designer' s preferences ~t(PV) are deployed on sets of values of the PVs. The aggregation of the preferences of the PVs requires the selection of an aggregation function P that reflects a strategy about how competing attributes should be traded-off. The function P attempts to formalize the balance of conflicting design goals and constraints by preserving the design rationality. An aggregation function must comply with some specifications in order to be suitable for implicating fuzzy preferences in engineering design. The evaluation of the design solutions is performed after the various individual preferences have been combined or aggregated. The overall preference ~to (P-V) is a function that aggregates all the preferences on the performance variables for a particular design case. The selection of the trade-off strategy requires adequate knowledge of the design problem under consideration and the specific knowledge on the relation between the aggregated parameters [23]. Two contradictory tradeoff strategies bound the family of the available aggregation functions appropriate for design, the fully compensating and the non-compensating strategy. According to the non-compensating strategy (Pmin), a high preference corresponding to a demand for a specific value/attribute for one design parameter cannot compensate against a low preference corresponding to a specific value/attribute of another design parameter because each of these values may independently determine the performance of the design output. Therefore the latter trade-off strategy that takes into consideration the lowest preference during the stage of aggregation: emin((IL[1,Wl),...(~l,j,Wj) ) - - min(g,,...,p~) (1) The fully compensating strategy (P~) reflects how a more preferable value/attribute partially compensates for a less acceptable one and is expressed through the geometric weighted mean: PII((Pl, W, ).... (pn, Wn))=
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strategy between the limiting cases of the functions Pmin and P~I. Intermediate aggregation strategies may be used in terms of generic parametric aggregation functions. These functions must be appropriate for the design procedure [23] and should preserve the parametric character that allows the aggregation of preferences with the same function and for all tradeoff strategies. Figure 6 shows the evaluation for the design solutions for the slab formwork problem, which are retrieved by the CBD module using the described methodology. As shown in figure 6, the aggregated preference is given in the first column taking values in the interval [0,1 ]. The most preferred design solutions are characterized by overall aggregated preference equal to 1. From this point forward the designer may select the highest according to evaluation solution and submit it to a detailed adaptation process. Before that, he/she is capable of having a rough estimate of the final solution in both technical and economical terms.
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The present paper refers to engineering design domains characterized by design knowledge that cannot be fully computationally modelled.Under these circumstances the design engineers prefer to rely on their own personal creativity and experience, occasionally augmented by a kind of analogical reasoning that takes into advantage similar past design solutions. In the present work and in order to exploit the existing design knowledge in the field of formwork design for the construction of slabs, a system has been developed that retrieves existing design solutions, by using a fuzzy case representations and neural-network-based retrieval mechanism. The retrieved solutions are then evaluated through a fuzzy inference system and the optimal solutions according to the criterion of
411
maximum overall fuzzy preference may then be manually adapted or modified bythe designer in order to meet the current problem's specifications. In the example presented the proposed system performed adequately for the prescribed objective and proved to be a valuable tool for making preliminary decisions and pre-cost-assessments before the detailed design phase begins. Future work should be extended towards enabling the existing design case representation to incorporate additional knowledge (e.g. design features drawings), while hybrid combinations of additional soft computing and AI-techniques could be further examined
Acknowledgements The present research work has been supported by Peri Hellas Co. University of Patras is a member of the EU-funded I'PROMS Network of Excellence.
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Vico F.J., Veredas F.J.,. Bravo J.M, Almaraz J. (1999). Automatic design synthesis with artificial intelligence techniques, Artificial Intelligence in Engineering, Vol.13: 251-256. Loia V., Sessa S., Staiano U A., Tagliaferri R. (2000). Merging fuzzy logic, neural networks, and genetic computation in the design of a decision-support system, International Journal of Intelligent Systems (15): 575-594. Dote Y., Ovaska S.J. (2001). Industrial applications of soft computing: a review, Proc. ofthe IEEE, pp. 1243-
1265. [10] Murawski K., Arciszewski T., De Jong K. (2000). Evolutionary Computation in Structural Design, Engineering with Computers (16): 275-286. [ 11] Rajasekaran S., Febin M.F., Ramasamy J.V. (1996). Artificial fuzzy neural networks in civil engineering, Computers & Structures, Vol.61 (2): 291-302. [ 12] Maher L.M., Balachandran B.M., ZhangM.D. (1995). Case-based reasoning in design, Lawrence Erlbaum Associates, Publishers, Mahwah, New Jersey. [13] Pal K.S., Dillon S.T., Yeung S.D. (2001). Soft Computing in Case Based Reasoning, Springer Verlag, London. [14] Dubois D., Prade H. (1998). Fuzzy set modelling in case-based reasoning, International Journal of Intelligent Systems (13): 345-373. [15] Rosenman, M. (2000). Case-based evolutionary design, Artificial Intelligence for Engineering Design, Analysis and Manufacturing 14:17-29. [ 16] Tsai C.Y., Chang C.A. (2003). Fuzzy neural networks for intelligent design retrieval using associative manufacturing features, Journal of Intelligent Manufacturing (14): 183-195. [17] Malek M., Amy B. (1997). A pre-processing model for integrating case-based reasoning and prototypebased neural network, Connectionist Symbolic Integration, Chapter 8, Lawrence Erlbaum, Hillsdale. [ 18] PERI~ International MULTIFLEX 9girder formwork design handbook. [19] MATLAB 9
Mathworks Inc, version 7.0.1.24704.
[20] Browning Tyson R. (2001). Applying the design structure matrix to system decomposition and integration problems: A review and new directions, IEEE Transactions on Engineering Management, Vol. 48, No. 3, pp. 292-306. [21] Richter M.M. (1995). On the notion of similarity in case-based reasoning, Mathematical and Statistical Methods in Artificial Intelligence, Springer Verlag, pp. 171-184. [22] Saridakis K.M., Dentsoras A.J. (2002). Incorporation of fuzziness to the organization and representation of case knowledge in case-based design systems, Proc. of 6th ESDA Conference, Istanbul, Turkey. [23] Law S. (1996). Evaluating imprecision in engineering design, PhD thesis, California Institute of Technology, Pasadena, CA. [24] Saridakis K.M., Dentsoras A.J. (2006). Case-DeSC: Case-based design with soft computing techniques, accepted for publication to Expert Systems and Applications
Intelligent Production Machines and Systems D.T. Pham, E.E. Elduldariand A.J. Soroka (eds) 9 2006 CardiffUniversity, ManufacturingEngineering Centre,Cardiff,UK. Published by Elsevier Ltd. All rights reserved.
Process Planning Support for Intermediate Steps Design of Axisymmetric Hot Close-Die Forging Parts R.H. Radev a a
Department of Material Science and Technology, University ofRousse, 8 Studentska Street, Rousse 7017, Bulgaria
Abstract Hot close-die forging processes are broadly used in manufacturing at the present time. The researchers still not strictly answered on the significant questions about necessity and shape of intermediate steps. General algorithm for process planning support for intermediate steps designs ofaxisymmetric hot close-die forging parts are presented in this study. Example of using this algorithm also is included in the paper.
Keywords: Hot close-die forging, Intermediate steps, Process design
1. Introduction Hot close-die forging processes continue to be one of widely used in manufacturing at the present time. Theoretical backgrounds of these manufacturing processes have been developed at the beginning of XX century. Important meaning for this was the development of the theory of plasticity. Unfortunately, the researchers still not strictly answered on the one of the significant question in the area of forging- about necessity of appliance of intermediate steps while hot close-die forging is performed. There are different approaches used in engineering practice. Most of them are based on the experience or trial-error method. As a result of generalization of above methods expert systems have been created and are into use. Recently, criterions for necessity of intermediate steps have been proposed in several studies [ 1,2,3, 4]. The necessity of intermediate steps is in strong connection with shape of these steps. The shape design is important task for engineers too. Design methods are proposed in the works of many investigators, for instance [5,6,7,8].
Despite of too many proposed methods and approaches for the different subtasks of a real planning process of hot close-die forging, process design is embarrassed because of very hard to formalizing ofthe process planning rules. In the present work, general algorithm for process planning support for intermediate steps designs of axisymmetric hot close-die forging parts are presented.
2. General algorithm for process planning support for intermediate steps designs of axisymmetric hot close-die forging parts The presented on the figure 1 algorithm consider only a part from the whole forging process - design of intermediate steps of forging. Especially, the method discusses the necessity of intermediate steps and their geometrical shape. The input data include forging part drawing and initial billet dimensions. Like interstitial result, the processed data consists a set of alternative forging sequences. These alternatives can be different as regards of the numbers of intermediate steps and their shape. Later, from the different cases the designers can choose appropriate variant for specific
413
Start
)
Input data: / Forging part drawing and billet dimensions /
@
:r' tena necessityI I ! of i n t e r m e ~
Choice of criterion for the J" necessity of intermediate step
Necessity of intermediate step procedure
NO
_
Forging without intermediate step
~YES Designing of the intermediate | 9 step's shape by different l" alternative methods
J
!i2torU2t2Onnda~odns !1[I ~for d e s i ~ i n g / ~
~p'
YES y
I Shape 1 I I Shape2 I ...
@
Q
4, No IShapeN I
@
Design of intermediate shapes procedure
Fig. 1. Algorithmforprocessplanningsupportfor intermediatestepsdesignsofaxisymmetrichotclose-dieforging.
414
~ Compose of forging sequence 1
Compose of forging sequence 2
Forgingsequence procedure
Compose of forging sequence N
Simulation of forging process ] (FEM, model material)J /
l
I
Choice of forging sequence fromle the different alternatives j
l
I
Rules
Send data for design of forging dies
Q-End-") Fig. 1. Algorithm for process planning support for intermediate steps designs of axisymmetric hot close-die forging (continuation). forging part according to the technological requirement. Like an integral part of the process planning of forging sequence, verification by simulation of hot close-die deformation process is included. Shown algorithm allows the intervention from process designer. This is possible in three cases: 9 Determining criteria for necessity of intermediate step; 9 Choosing instructions and recommendations for designing of intermediate steps' shapes; 9 Choosing the rules of forging sequence from the different alternatives. Thus, the engineers have opportunity to influence upon
the final results according the specific conditions ofthe forging process. These three points are the same, which there are not strong generally accepted rules and recommendations. This peculiarity ensures flexibility of the proposed algorithm.
3. Exampleprocessdesignanddiscussion In this paper as an example, illustrating proposed method for process planning support, forging part with shape shown on figure 2 is used. As a criterion for necessity of intermediate step proposed in [4] shape complexity factor is used. Applying the algorithm shown above necessity of only one intermediate step
415
between the initial billet with dimensions ~45X84 and final shape of forging part is needed. Different intermediate shapes are designed according with the research works [2, 5, 7, 8, 9, 10].
1
also in fewer grades for preform 2. This is a symptom for necessity of billet with larger volume than used.
I
I I
if3
J
1
2
3
~6S
Fig. 2. Investigated axisymmetric forging part with "H" type cross section They are shown on figure 3. Die filling together with the total values of the works done for different intermediate dies are used for the choosing of the most suitable decision of the process planning task. Software package for finite element analysis of metal forming processes was used to verification derived decisions after applying proposed method (fig 1). The simulation was carried with the low-carbon steel, forged on hydraulic press. The initial forging temperature was TF =1200~ The temperature of the die was TT=300~ The lubricant used for simulation was emulsion of graphite and water. In order to investigate the effect of the preform shape on the forces and works done of the final impression data for their values have been received. Results are shown in Table 1. Comparing total works done for different preform dies, it is clearly, preform 1 ensure the smaller work done. Values of total works done for cases 2, 5 and 6 are higher, but still close to preform 1. Moreover, distribution of works done among preform and final impressions for preforms 1 and 6 are about equal. This corresponds to already well-known forging practice. Filling of final dies was studied in this work too. Defect free forging in finishing impression was obtained for preforms 1, 5 and 6. Unfilling of die was the result of computer simulation for preforms 3 and 4,
416
2j l 4
-Z 5
6
Fig. 3. Different intermediate shapes designed according [9,5,7,8,10,2].
Obviously, this will bring bigger flash amount, which will provoke more intensive die wear and major consumption. Especially, this will be stronger asserted for cases 3 and 4. For example, the difference of total works done for forging with used in this study billet for preform 1 and preforms 3 and 4 come at 30+35 %. This difference will increase if billet volume enlarges. The values of effective strains for preforms providing filling of final dies are analyzed also in this study. Most equable is distribution of effective strains in final impression for forging with preform 1.
Table 1 Values of the forces and the works done for different cases of die forging Preform Forging in preform die Forging in final die F
1 2 3 4 5 6
[MN]
W
0,0888 0,2688 1,8139 1,3818 0,1081 0,1421
[kJ]
F
3,7323 3,3155 9,0691 8,3503 1,9023 4,2915
3,5282 1,5676 1,1523 1,2689 1,8151 3,4948
A part of the above pointed out algorithm is also used for the determination of necessity of preform shapes for the eleven different forgings shown on figure 4 [11].
-j ~164
_
0
0
[MN]
303
175
W
[kJ]
4,8716 5,5880 2,5262 2,6723 7,1363 4,8147
Total values w [kJ] 8,6039 3,6170 8,9035 1,8364 2,9662 11,5953 11,0226 2,6507 9,0386 1,9232 9,1062 3,6369
F
[MN]
3. C o n c l u s i o n s
The results obtained applying the proposed algorithm for process planning support for intermediate steps designs of axisymmetric hot close-die forging allow to conclude: 9 It is suitable method, which enables to get several alternatives for shape and number of intermediate stages and to select the most appropriate case for specific part. 9 The algorithm is flexible and addition can be done, especially in connection with the criterions for necessity of intermediate steps, the instructions and recommendations for preform design and the rules for choice of eligible variant for close-die forging. 9 Investigations are needed to confirm the opportunity to using this algorithm for forgings with more complex shapes. References
16
6
.[
I
Q408
j
[
r
0406
Fig. 4. Different investigated forgings for determination for necessity of preform shapes [ 11].
[1] Zhao, G., E. Wright, R. V. Grandhi, Forging preform design with shape complexity control in simulating backward deformation, International Journal of Machine Tools & Manufacture 35 (1995) pp 1225-1239. [2] Zhao, G., E. Wright, R. V. Grandhi, Computer aided preform design in forging using the inverse die contact tracking method, International Journal of Machine Tools & Manufacture 36 (1996) pp 755-769. [3] Biglari, F. R., N. P. O'Dowd, R.T. Fenner, Optimum design of forging dies using fuzzy logic in conjunction with the backward deformation method, International Journal of Machine Tools & Manufacture 38 (1998) pp 981-1000. [4] Tomov, B., A new shape complexity factor, Proceedings of Conference on Advances in Materials and Processing Technologies (AMPT) (1997) Guimgraes, Portugal, pp 861-866. [5] BptoxaHOB,A. H., A.B. Pe6eJicm4~, Fopaqa~ IUTaMnOBKa - KOHCTpyI4pOBaHI4e I4 pac~IeT mTaMnOB, FHTIIMJI, MOCKBa, 1962. [6] Lyman, T. (Ed.) et al., Metals Handbook, vo|.5 Forging and Casting, American Society for Metals, Metals Park
417
OH, 1970. [7] Biswas, S. K., W. A. Knight, Preform design for closed die forgings: experimental basis for computer aided design, International Journal of Machine Tools & Manufacture 15 (1975)pp 179-193. [8] Tomov, B., T. Wanheim, Preform design based on model material experiments, Proceedings of the International Conference on Advanced Mechanical Engineering & Technology (AMTECH), section 2, Rousse (1993) pp 147-156. [9] Radev R., B. Tomov, Preform Design in Hot Die Forging, Proceedings of 1 l th International Scientific Conference on Achievements in Mechanical and
418
Materials Engineering, Gliwice-Zakopane, Poland, 2002, pp455 -458 [10] Biglari, F. R., N. P. O'Dowd, R.T. Fenner, Optimum design of forging dies using fuzzy logic in conjunction with the backward deformation method, International Journal of Machine Tools & Manufacture 38 (1998) pp 981-1000. [ 11 ] Pa)IeB P., qncaeHo ri3cae)~Bane Ha Heo6xoaI4MOCTTa OT npe;iaapnxesiHH npexo~In npH ropemo maMnoBaHe Ha OCOCI4UeTpI4qUI4I43KOBI(I4,Mem~IyHapo)Iua nayqsa Kos~epesIII4~l Amtech 2005, TOM44, cepI4~ 2, Pyce, Bf,arapi4s, 2005, c. 199-204.
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Smart Design for Assembly using the Simulated Annealing Approach Hao Shen, Aleksandar Subic School of Aerospace, Mechanical & Manufacturing Engineering, RMIT University, East Campus, Bundoora 3083, Victoria, Australia Abstract: This paper is concerned with combinatorial optimization problem applied to spatial layout of components in mechatronic devices. There is a growing need in industry to create a flexible and intelligent design tool, which allows the engineer to handle the complexity and non-linearity involved with developing of 3D mechatronic device more effectively. The main objective of this research is to develop a feasible optimization algorithm based on Simulated Annealing (SA) method. SA represents a family of randomized algorithms that are used to solve a wide range of combinatorial optimization problems. This paper presents a conceptual model of an integrated design environment based on the SA algorithm embedded within a CAD software Solid Works. The focus is on the structural optimization of housing and layout design, with the objective to minimize the required assembly space. Also we present an attempt to automate the design process by introducing the integrative modeling approach for a set of design modules.
Key Words: Design for Assembly, Simulated Annealing, Combinational Optimization.
1. Introduction Space optimization for an assembly of components in complex hybrid systems is a challenging structural design task. Experience and common sense have been the usual engineering tools relied upon in the design process. In recent years the application of modeling and simulation techniques have gained increased importance as they allow for flexible and optimal handling of a complex 3-dimensional design space. In order to fully utilize the potential of such approaches it is essential that new more advanced computational techniques are developed capable of producing an intelligent design environment. Simulated Annealing (SA) [1, 2, 3, 4] mimics the physical annealing process and is successfully used to obtain an intelligent solution for a variety of combinatorial optimization problems. This process can be formulated as the problem of finding (among a potentially very large number of solutions) a layout solution with lowest energy or cost function. In other words, an example of a combinational optimization problem can be formulated as a pair (R, C), where R is the finite or possibly countable infinite set of configurations and
C is a cost function. It is assumed that C is defined such that the lower the value of C, the better the corresponding configuration, with respect to the optimization criteria. During the past decade, SA technique has been utilized in such diverse areas as, for example, electrical engineering, nesting problem, packing problem, operation research, code design, image processing, and molecular physics. In many of these applications the existing algorithms performed poorly whilst SA has shown such salient features as flexibility, ease of implementation and a modicum of complexity. This paper presents the concept of space optimization methodology applied in design of mechatronic devices within a 3D envelope by embedding the annealing optimization procedure into a commercial CAD system. The embedded optimization procedure aims to intelligently create an optimal solution for a minimized, highly integrated assembly. This optimized solution is then fully designed within Solid Works CAD environment using the identified 3D envelope as the design space.
419
2. Overview of the simulated annealing method
3.1 Overlap detection and evaluation SA is a generally applicable, stochastic technique based on the analogy between simulating the metallurgical annealing process and solving large combinational optimization problems. The algorithm was first derived by Metroplis et al. [ 11 ] in 1953 and was further extended by Kirkaptrick et al. [ 12] in 1983. Within the algorithm, an initial design state is chosen and the value of the cost function for that state is evaluated. A step is taken to a new state by applying a move, or operator, from an available move set. This new state is evaluated; if the step leads to an improvement in the cost function, the new design is accepted and becomes the current design state. If the step leads to an inferior state, the step may still be accepted with Boltzmann probability. This probability is a function of a decreasing parameter called temperature, based on an analogy with the annealing of metals, given by:
The easiest way to detect an overlap between two components is to detect the overlaps of their projections in every 2-D plane (x-y, y-z, or z-x plane). If there is no overlap between the projections in any 2-D plane, then there is no overlap between these two components. A multiresolution detection scheme [14, 15] could be adapted to obtain reasonable running time. Rough analyses are performed at the start of the algorithm (low-resolution) and more refined analyses (highresolution) are performed towards the end of the algorithm where more accurate evaluations are required.
where AC is the change in cost function due to the move and T is the current temperature. The Probability a c c e p t would be a uniform random number between 0 and 1 (0 < Probability accept< 1).
At each iteration in the optimization process, the annealing algorithm perturbs (moves) the position of components and requires overlap detection and evaluation. The amount of overlap is calculated as the sum of the area of overlap projected at each 2-D plane at a specified level of resolution, giving an accurate evaluation at that level of resolution.
3. Problem formulation and solution strategy
3.2 Spatial constraint and annealing violation
In this work, the 3D layout problem can be described as packing a batch of components of different sizes into an 'envelope'. The packing tasks are characterized by the following four objectives:
There are special relationships between particular components or housing constraints, such as the placement constraints of a component, the assembly constraints, the electronic interference avoidances, the electro-mechanical-interface match-ups, the wiring restrictions, etc. It is necessary to constrain components with respect to the envelope and each other.
Probability
accept --
e -AC/T
(1)
9 Fitting the components into the specified envelope; 9 Observing topological connections or assembly relationships between components; 9 Avoiding any overlap between components and the envelope protrusion; 9 Achieving high packing density, or minimizing the overall space of the envelope. Obviously, this layout problem is non-liner, combinational and discontinuous. That makes the SA algorithm a promising approach in finding the global, or at least very good, local optimum. The strategy is to move components around within a pre-defined space and analyze each move on its efficiency towards minimizing a combined cost function. The function includes design goals such as minimizing the packing density, the relationships between components, and the penalty terms, that evaluate the amount of components overlaps, envelope penetrations and spatial constraint violations.
420
During the annealing process, components are permitted to overlap under the assumption that the future moves will lead the non-feasible layouts to superior feasible layouts.
For those particular components with restrictions in a given direction (a particular absolute position or orientation with respect to a linear combination of global coordinate axes), it is simple to place the component in a feasible initial position and its moves are restricted in such a way that it cannot be moved to a non-feasible point or be rotated along a non-feasible axis. Similarly, for those components with particular restrictions to the global coordinate axes, the constraints may be violated under the assumption that allows the annealing to 'pass through' non-feasible layouts and lead to feasible layouts. The constraints are violated at the beginning of the algorithm and the layouts are pushed into the feasible range as the algorithm is processed. In this work, two types of such constraints are adopted [5] with the employment of the violation
process, which is constrained by centers' or by extents. Constrained by center is to restrict the position of component based upon its origin of coordinate system. Constrained by extent is to restrict the position of all points of the component rather than only its origin. These constraints could be implemented to firstly separate the constraint into x, y, z directions to restrict the parallel transfers of a component and its rotations, then the annealing violation for each component is calculated and a penalty function could be created that penalize the cost function for the constraint violation.
4. Smart design for assembly The optimization algorithm/procedure is programmed in Visual C++ [14, 15, 16] and embedded into commercial CAD software SolidWorks [19, 20, 21] so that the designer can concurrently recall the optimization software while developing the design within a CAD environment. The algorithm parameters can be adjusted through the provided User Graphic Interface. All interfaced components can be filled into a '3D-envelope' in a piecewise fashion producing a minimized 'envelope' that represents the design space. Figure 1 shows the logical flow chart of process implementation. Once the optimization algorithm has been embedded in SolidWorks, a smart CAD design environment is established ready for design of a wide range of portable mechatronic devices and also other compact equipment that require high density packing performance. At any time, a designer can concurrently recall the software to input or change the running parameters through the GUI to simultaneously run more than one optimization process. It allows the designer to develop different configurations of assembly and compare its packing performances before selecting the final design solution. The development time can be significantly reduced in this way thus achieving an efficient design assembly. More over, with this smart system, the designer can link the preprocessed subassemblies or modules into a new envelope to provide integrative tailored modeling solutions for even much more complex assembly space in a systematic manner. Figures 2 and 3 provide an illustration of this potential. Below are some major parameters and definitions used in the developed smart system: 9 Initial temperature- Our initial temperature To is based on the acceptance rate. As we measured overlap in percentage, so the initial temperature To is set at 100. If the new layout is 50% worse (inferior layout, but may still be accepted) than the old one at To, then the
Probability accept--0. 606, this means there is still a relatively large chance that the inferior solutions can be accepted at the beginning of the process. 9 Final temperature-- The final temperature Tf is set at 0.8. If the new solution is 1% worse than the old one at T7, then the Probability . . . . pt =0.287, this means that there is not much chance that the inferior solutions can be accepted at the final stage of the process. 9 Maximum iterations f o r each t e m p e r a t u r e - To achieve an efficient processing time and capability of finding a reasonably good local optimum, we set the maximum number of iterations at 2000. 9 Equilibrium c o n d i t i o n - There are a variety of ways to confirm if the equilibrium has been achieved. We set an upper boundary for the possible number of neighborhood moves. If there is no improvement in the foregoing 10 moves, we decide that the equilibrium has been attained at that temperature. 9 Cooling s c h e d u l e - The performance of this algorithm also depends on the cooling schedule which is essentially the temperature updating function. Two schedules are employed in this work. -- Proportional decrement scheme, Tk+l= 0tTk, where 0
(2)
-- Lundy and Mees scheme [20], Tk+l= Tk / (l+[3Tk), where 13>0, representing a suitably chosen parameter.
(3)
The designer can individually change the Initial temperature, final temperature, Maximum iterations f o r each temperature to produce different solutions with the same spatial constraints or change the represented spatial constraint parameters to re-run the algorithm to create different solutions within the same running conditions.
5. Summary and conclusions The paper has presented a novel smart algorithm for space optimization applied in design of mechatronic assemblies using the Simulated Annealing technique. The developed algorithm can be used as an optimization tool in Design for Assembly to intelligently minimize the housing space for such devices where a large number of different sections and components are interfaced in the assembly. The optimization algorithm/procedure was programmed in Visual C++ and embedded into commercial CAD software environment. All interfaced components can be assembled effectively within a '3D-envelope' in a
421
piecewise fashion thus producing a minimized 'envelope' that represents the design space.
devices and also other compact equipment which require high density packing performance. An interesting aspect for future work would be to improve the detection of overlap in order to minimize the computational time involved.
The developed optimization algorithm can be used for optimal design of a wide range of portable
J
I
----+
Accept current solution
Yes
J
T
R Reduce the
First create an initial layout, decode it using a suitable decode knowledge
~1 temperature te
Calculate the overlap between components, set the initial temperature
y
r
No
Yes
Yes m
No d "1
new layout has
,,,overlap; or has more o v e r l a p / / within a c c e p t a ~
"
No
Suitable neighborhood move from the current strin~
I Figure 1 Logical flow chart of space optimization algorithm / procedure using SA
{,,~)(. 0 0 ~ o
'l
o O0 " o 0~7)
(a) Solution One of conceptual boat engine assembly
(b) Solution Two of the same assembly with changed assembly constraints
Figure 2 Comparison of different pre-processed assembly solutions
422
Take the best solution
(End)
iL._i
Figure 3 Integrative tailored modeling solutions
REFERENCES
1.
2. 3.
4.
Emile Aarts, Jan Korst, "Simulated Annealing and Boltzmann Machines: a stochastic approach to combinational optimization and neural computing", John Wiley & Sons Ltd, 1989. P.J.M. van Laarhoven, E.H.L.Aarts, "Simulated Annealing: Theory and Applications", D.Reidel Publishing Company. Kathryn A. Dowsland, "Some Experiments With Simulated Annealing Techniques for Packing Problems", 0377-2217/93, Elsevier Science Publishers B. V. P.Jain, P.Fenyes, et al., "Optimal Blank Nesting Using Simulated Annealing", Journal
10. Han G.C, Na S. J. "two-stage approach for
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Szykman S, Cagan J. "Constrained three dimensional component layouts using simulated annealing." ASME Journal of Mechanical Design 1997, 119 (1): 28-35. Szykman S, Cagan J. "A simulated annealing approach to three-dimensional component packing." ASME Journal of Mechanical Design 1995, 117 (2A): 308-14. Szykman S, Cagan J. "Synthesis of optimal non-orthogonal routes." ASME Journal of Mechanical Design 1996, 118 (3): 419-24. Szykman S, Cagan J, Weisser P. "An integrated approach to optimal three dimensional layouts and routing." ASME Journal of Mechanical Design 1998, 120 (3): 510-2. Rao RL, Iyengar SS. "Bin-packing by simulated annealing." Compute Mach Appl 1994; 27(5): 71-89.
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nesting in two-dimensional cutting problem using neural network and simulated annealing." Journal of Engineering Manufacturing 1996:509-19. Metropolis, N. Rosenbluth, et al. "Equations of state calculation by fast computing machines." Journal of Chemical Physics, 1953, 21, 1087-1092. Kirkpatrick, S., Gelatt, C.D. Jr. and Vecchi, M.P., "Optimization by simulated annealing", Science, 1983, 220(4598), 671-679. Sorkin GB. "Efficiennt simulated annealing on fractal energy landscapes." Algorithmic a 1991; 6:367-418. Meagher, D., "Geometric modeling using octree encoding." Computer Graphics and Image Processing, 1982.19(2), 129-147. Aref, W.G. and samet, H., "An algorithm for perspective viewing of objects represented by octrees." Computer Graphics Forum, 1985, 14(1), 59-66. Steve Holzner, "Fast Track Visual C++ 6.0 Programming", John Wiley & Sons, Inc. 1998. Pappas Christ, H "Visual C++6: the complete reference" Berkeley, Calif.: Osborne/McGraw-Hill, c 1998. Zaratian, Beck, "Microsoft Visual C++ 6.0 programmer's guide." Redmond, Wash.: Microsoft Press, c 1998. Planchard, David C., "Engineering design with SolidWorks 2004 : a step-by-step project based approach utilizing 3D solid modeling", Schroff Development Corporation, c2004. Lueptow, Richard M. "Graphics concepts with SolidWorks." Edition: 2nd ed. Date:
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21. 22. 23. 24.
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Upper Saddle River, NJ: Pearson/Prentice Hall,c2004. Tickoo, Sham. "SolidWorks for designers Release 2004." Date: Schererville, IN: CADCIM Technologies, 2004. Lundy, M., Mees, A. "Convergence of an annealing algorithm." Mathematical Programming, 34, 111-124. Erik K. Antonsson, Jonathan Cagan, "Formal Engineering Design Synthesis", Cambridge University Press, 2001. T.W. Leung, C.H. Yung, Marvin D. Troutt, Applications of genetic search and simulated annealing to the two-dimensional nonguillotine cutting stock problem. Computers & Industrial Engineering 40 (2001) 201-214.
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 CardiffUniversity, Manufacturing EngineeringCentre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Virtual Environment Auditory Feedback Techniques to aid Manual Material Handling Tasks 9 o v , a F a i e z a A b d u l Az"l Za, Ieuan A. N i c h o l a s b D . T . P h a m ,a Stefan D l m Manufacturing Engineering Centre, Cardiff University, PO Box 925, CardiffCF24 3AA, UK b Welsh e-Science Centre, Cardiff School of Computer Science, Cardiff University, CF24 3AA, UK a
Abstract
This paper compares and contrasts three types of auditory feedback to aid safe manual material handling operations. The work reported aims to reduce lower back injuries by teaching people to gauge the forces acting on their lower back whilst moving an object from one location to another. This is particularly useful if the movement is repetitive in nature. Lifting tasks were performed in a virtual environment. Sensors monitored the operator' s head and hand movements in real-time. An analysis of Task Completion Time, Percentage of Harmful Lifts and Response- -to-Feedback Time was used to identify the appropriateness of each feedback technique. Keywords: Virtual Environments, Auditory Feedback, Lower Back Pain, Manual Materials Handling
1. Introduction
Estimated costs associated with lost days and compensation claims related to musculoskeletal disorders including back pains and repetitive motion injuries range from $13 billion to $20 billion worldwide annually [ 1]. Manual Materials Handling (MMH) tasks such as lifting, lowering, pushing, pulling, holding and carrying continue to be a hazardous activity [2]. Lifting and lowering tasks comprise a large percentage of the total MMH tasks, accounting for 69% ofall MMH tasks [3]. It has long been a major concern that lifting can cause back injuries, notably Lower Back Pain (LBP) affecting the L5/S1 disc in particular. LBP accounts for approximately one-third of all workers' compensation costs [4, 5].
MMH tasks can be designed using the NIOSH Lifting Equation [6], but adopting virtual environments (VEs) to simulate such tasks could have several important benefits including simulating heavy lifts without risk of injury, recording of lifting trajectories and real-time monitoring of lower back stresses. This paper discusses the use of real-time auditory feedback to indicate the lower back stresses experienced by the lifter during a manual lifting task. Real-time feedback allows an operator to make adjustments whilst a task is in progress. Auditory feedback has been used extensively to convey information in computer applications. Sound can be utilised to improve the user' s understanding of visual signals or can stand alone as an independent source of information. Zahariev and MacKenzie [7] conducted research to investigate how performance of a "reach, grasp and place" task was influenced by adding auditory and graphical feedback. They found
425
that auditory feedback clearly facilitated performance. According to Brewster [8], having a combination of visual and auditory information at the humancomputer interface can greatly assist interaction. In everyday life, both senses combine to give complementary information about the world. The visual system gives people detailed information about a small area of focus, whereas the auditory system provides general information from all around, alerting them to things outside their peripheral vision. Massimino and Sheridan [9] examined "peg-ina-hole" tasks in teleoperation using several types of additional feedback provided to the user when a collision between the grasped object and the rest of the environment occurred. A sensory substitution proposed by the researchers consisted of a haptic vibration applied to different parts of the user' s hand. For some tasks, the user failed to decrease the insertion time compared with the case of visual feedback. In this case, the researchers [9] assumed that "visual feedback alone appears to have dominated and was sufficient to allow the subject to perform the task as quickly as possible". This paper focuses on evaluating different auditory feedback techniques which provide lowerback stress indications to the operator. The work was motivated by previous findings on the effectiveness of auditory feedback and the authors' interest in trying this feedback modality as a means to assist operators in performing manual lifting safely. Formulae are available for defining safe working limits for lifting tasks. Those formulae are associated with the NIOSH Lifting Equation [6], which shows the relationship between the recommended weight limit (R WL), the maximum weight that could be lifted for a particular task, and the lifting index (LI). LI expresses the weight that will actually be lifted (the load weight) as a ratio of R WL. LI provides a single number that indicates the level of safety or acceptability for a particular lifting operation. R WL and LI are given by Eqs 1 and 2 below: R WL -
L C • HM • VM • DM • A M • FM • CM
(Eq. 1)
LI -
where:
426
L R WL
(Eq. 2)
RM LI L LC
Recommended Weight Limit Lifting Index Load weight Load Constant (23 Kg; the weight that all workers are assumed to be able to lift under optimal conditions)
HM
Horizontal Multiplier (calculated from distance in front of worker)
VM from of
Vertical Multiplier (calculated height of origin or destination lift)
AM
Asymmetry Multiplier (1.0 for lifting in the sagittal plane)
FM
Frequency Multiplier (calculated from lifting rate)
CM
Coupling Multiplier (e.g., type of handles on object being lifted)
DM
=
Distance Multiplier Vertical distance between the origin and destination of the lift
This work examines whether auditory stimuli can be used to alert operators to their manual lifting performance, with a view to minimising the forces on their lower back when they perform lifting tasks in a virtual environment. Instead the whole task being characterised with just one ex post facto parameter such as LI, the use of a VE and auditory signals enables an operator to receive continuous feedback throughout the task.
2. Experimental set-up The VE software was designed and programmed using CAVELib API. The application recorded the following variables to a data file: time, operator head and hand position/orientation and operator personal details. An Onyx 300 visualisation server was employed to generate images. A Portico Workwall was used as a large-scale display device. Stereoscopic 3D images were created through the wearing of LCD shutter glasses. The glasses refresh rate was 120Hz (60Hz update for each eye). Sixdegrees-of- freedom electromagnetic sensors together with Trackd software were employed to track the position and orientation of the operator' s head and the object to be lifted. A 120dB auditorium amplifier
...................................
was used to produce sound. The system architecture is presented in Figure 1.
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Human Participant
~ .....
..~..... ~.- s,. ~<~.:.2u. . . . ~ .'~ ~
Figure l" System Architecture
The specifications for each of the three auditory feedback methods experimented with are listed in Table 1. Different sound files were used with each method. Figure 2 shows snapshots of the sound files for the White-noise (WN), Pitch (P) or Tempo (T) feedback methods. Table l" Specifications of Auditory Feedback Methods Auditory
White Noise
Pitch
Tempo
Feedback
Detailed Spec.
Same frequency
Same frequency
Different frequencies
(4 k H z )
(360 Hz)
(1, 1.7, 5 Hz)
Different intensities
Same intensity
Same intensity
(20, 50, 70 dB)
(50 dB)
(50 dB)
Same amplitude
Different amplitudes
Same amplitude
(10 dB)
(5, 15, 35 d B )
(30 dB)
{7~i!T:
: 777717777:=
7777777:: y77 77:
Figure 2" Snapshots of Sound Files for White-noise (top frame), Pitch (middle frame) and Tempo Feedback (bottom frame)
3. Experimental procedure Operators were invited to perform the lifting task as if this were their daily task on an eight-hour shift. They were asked to perform lifting experiments without auditory feedback ( N F - No Feedback) and with each of the above-mentioned three auditory feedback methods (WN, P and T). The order of the four experiments was arranged according to the LatinSquare Design so as to minimise learning effects. A schematic representation of the lifting task is shown in Figure 3. To begin each experiment, the user would lift a box of dimensions 300mm wide x 40mm long x 150mm deep, fitted with handles, from a lower shelf(Shelf 1) to an upper shelf(Shelf2).
427
4. Results %~RTICAL
(+)
Data from the electromagnetic position/orientation sensors and time measurements were analysed to yield the following results: Task Completion Time (TCT), Percentage of Harmful Lifts (PHL) and Response-to-Feedback Time (RFT).
Shelf 2
4.1 Task Completion Time (TCT) Shelf I
I
VD
V 9
1,,
..........................................
v
Ho I-ID
Figure 3" position
Schematic representation of shelving
Vo and Ho are the original vertical and horizontal positions of the box, i.e. the positions at the start of each trial. VD and HD are the vertical and horizontal destination positions of the box, i.e. the positions at the end of each trial. The upper shelf(shelf2) would change colour when the destination location was reached and this would indicate that the user could place the box at that location. Operators were asked to become familiar with the lifting procedure in a practice session before carrying out 10 trials per audio feedback method. For each trial involving audio feedback, the operator was required to move the box from the starting location to the final location while receiving audio feedback on their performance. The forces applied on the lower back were calculated using the NIOSH equations (Eqs. 1 and 2) which resulted in a value for LI. The LI working range was partitioned into three zones labelled as "Safe", "Risky" and "Harmful", where: Safe" 0 < LI < 0.32 Risky: 0.32 < LI < 0.37 Harmful: LI > 0.37 Operators were told that the objective of the lifting task was to stay, as far as possible, within the safe zone throughout each trial. The positions and orientations of both an operator's head and hands were recorded.
428
Figure 4 shows the average TCT for all conditions. The TCT for experiments not involving feedback was the lowest since, without feedback, the operators did not have to be concerned with controlling the LI value. A one-factor ANOVA technique was used for Task Completion Time analysis. This showed that there was an important effect of technique, with F(3,76) = 21.99, p < 0.05. Additional analysis is necessary to determine where differences occurred. A widely accepted approach for conducting pair-wise comparisons of treatment effects is the Tukey Honest Significant Difference (HSD) test. Apost-hoc Tukey test (at p < 0.05) was carried out and the results showed significant differences between all pairs except WN and P. Even though all comparisons with NF were significant, these can be ignored as no feedback was present in the NF condition. Participants therefore managed to complete the task faster since they did not need to monitor their lower back condition. This may result in a dangerous lifting technique if no experience in safe ergonomic lifting is acquired. As can be seen, Pitch was found to give the best TCT (mean = 10.78, s.d. = 3.22), followed by White Noise (mean = 11.49, s.d. = 2.27) and Tempo (mean = 15.47, s.d. = 4.17).
4.2 Percentage of Harmful Lifts (PHL) PHL (see Figure 5) was analysed and ANOVA again revealed a significant effect of technique, with F (3,76) = 86.21, p < 0.05. A post- hoc Tukey test was applied to determine which result is significantly different. This revealed that PHL differed significantly between White Noise and Tempo (p < 0.05), and between Pitch and Tempo (p < 0.05). NF was also shown to give significantly greater PHL than the feedback techniques.
0.8 .-. 0.7 0.6
20 "~ 15
8 0.5
O
a) 10 [...
0.4 '0.3 0.2 0.1 0.0
;i~;i!;Zii~:~Ji!8i4 =====================================
5 0 No Feedback
Whitenoise
(NF)
(WN)
Pitch (P) Tempo (T)
White-noise
Pitch (P)
Tempo (T)
(WN) Audio Feedback Techniques
Audio Feedback Techniques
Figure 4" Task Completion Time (TCT) for Auditory Feedback Techniques Pitch was the best auditory feedback technique (mean = 19.25, s.d. = 4.7). White Noise was only slightly worse than Pitch (mean = 22, s.d. = 6.13). The Tempo feedback technique was the worst (mean = 38.25, s.d. = 7.12) significantly larger than Pitch and White Noise. By comparing the result for NF (mean = 53.5, s.d. = 11.13) and those for the auditory feedback techniques, it is clear that auditory feedback can be used effectively to aid users in performing safe manual lifting tasks.
~50
...
4o ~30 20 9
vl0 0
A one-factor ANOVA technique was used for This indicates that there was no important effect of technique, with F(2,57) - 1.76, p < 0.05. The analysis shows that Pitch feedback was the best technique to employ as the user can respond to the feedback quickly (mean = 0.51, s.d. = 0.42), followed by White Noise (mean = 0.64, s.d. = 0.32). Tempo feedback was the least effective audio feedback technique (mean - 0.715, s.d. = 0.29).
RFT analysis.
5.
~60
(D
Figure 6: Response-to-Feedback Time (RFT) for Auditory Feedback Techniques
No
White-
Feedback
noise
(NF)
(WN)
Pitch(P)
Tempo (T)
Audio Feedback Techniques
Figure 5" Percentage of Harmful Lifts (PHL) for Auditory Feedback Techniques
4.3 Response-to-Feedback Time (RFT) Figure 6 shows Response-to-Feedback Times (RFT) for lifting trials using the White Noise, Pitch and Tempo methods. The Figure does not include data for No Feedback (NF) trials as the aim here was to examine differences in the time needed to bring L1 values within the safe working zone.
Discussion
Overall, the Pitch technique was superior to the other techniques examined. It was the best for Task Completion Time (TCT), the best for Response-toFeedback Time (RFT), and also the best at alerting operators to potentially harmful lifting manoeuvres, although, compared to White Noise, it was only marginally better for TCT and Percentage of Harmful Lifts (PHL). The Tempo feedback technique performed most poorly in the trials and should not be considered for real-time virtual ergonomic environments. Operators found that Tempo was difficult to react to as the feedback given was not immediately obvious. Therefore, if audio feedback is required, the Pitch technique should be adopted.
6. Conclusions and future work
This paper has shown that auditory feedback techniques can assist users to perform safe manual lifting by informing them of the forces acting on their lower back. Pitch was found to be the best auditory feedback technique, while White Noise was slightly lower in performance when compared to Pitch.
429
Tempo was poor compared to Pitch and White Noise. Audio feedback is useful in a virtual environment for warning an operator of potentially harmful lifts as this kind of feedback does not require constant concentration on the part of the operator. However, visual techniques, such as causing the box to flash/change colour to warn of potentially harmful movements, could be adopted in cases where the use of sound is not appropriate (for example, when the environment is noisy). In other situations, it might be appropriate to combine audio and visual feedback methods to reap the benefits of both modalities.
Acknowledgements The authors would like to thank the European Commission (I'PROMS FP6 Network of Excellence) and Welsh e-Science Centre for their support of this work.
References
[1] K. Palmer, Work-related musculoskeletal
[21
[3]
[4]
[5]
[6]
[7]
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disorders: report, workshop summary, and workshop papers. Occupational and Environmental Medicine, 57(6), p. 431, 2000. U.S. Department of Health and Human Services, "Musculoskeletal Disorders (MSDs) and Workplace Factors," National Institute for Occupational Safety and Health, Cincinnati, OH 1997. V.M. Ciriello, S. H. Snook, L. Hashemi, and J. Cotnam, "Distribution of manual materials handling task parameters," International Journal of Industrial Ergonomics, vol. 24, pp. 379-388, 1999. B.S. Webster and S. H. Snook, "The cost of compensatable low back pain," Journal of Occupational Medicine, vol. 32, pp. 13-15, 1990. E.F. Friedman and A. Perets, Outwitting Back Pain: Why Your Lower Back Hurts and How to Make It Stop: Lyons Press, Guilford, CT 2004. T. Walters, V. Putz-Anderson, and A. Garg, "Revised NIOSH equation for the design and evaluation of manual lifting tasks," Journal of Ergonomics, vol. 36, pp. 749-776, 1993. M . A . Zahariev and C. L. MacKenzie, "Auditory, Graphical and Haptic Contact Cues for a Reach, Grasp, and Place Task in an
Augmented Environment," Proc 5 th Int Conf on Multimodal Interfaces, ICMI '03, Vancouver, British Columbia, Canada, pp. 273-276, 2003. [8] S.A. Brewster, "Non-speech auditory output," in The Human Computer Interaction Handbook Lawrence Erlbaum Associates, USA, 2002, pp. 220-239. [9] M.J. Massimino and T. B. Sheridan, "Sensory Substitution for Force Feedback in Teleoperation," Presence, vol. 2, pp. 344-352, 1994.
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
An efficient meta-heuristic for the single machine common due date scheduling problem Andreas C. Nearchou Department of Business Administration, University of Patras, 26 500 Patras, Greece email." [email protected]
Abstract A new meta-heuristic for scheduling multiple jobs on a single machine so that they are completed by a common specified date (neither earlier, nor later) is addressed in this paper. Costs are set depending on whether a job finished before (earliness), or atier (tardiness) the specified due date. The objective is to minimize the total weighted earliness and tardiness penalized costs from the specified common due date. Minimizing these costs pushes the completion time of each job as close as possible to the due date. Extensive computational experiments over public benchmark problems show the effectiveness of the developed approach. In particular, the proposed meta-heuristic put new improved upper bounds on the majority of the benchmarks test problems.
Keywords: Differential evolution, common due date scheduling, early/tardy scheduling.
1. Introduction The common due date scheduling problem (CDDSP) can be formally defined as follows [1]: consider n jobs (numbered 1, 2 ..... n) to be processed without interruption on a single machine which can handle only one job at a time. Each job j (1= 1..... n) is available at time zero, requires a positive processing time pj and ideally must be completed exactly on a specific (common for all jobs) due date d. Penalties are incurred whenever a job is completed before or after this due date. Therefore, an ideal schedule is one in which all jobs finish on the specific due date. Assuming that Q is the completion time of job j, then the earliness and tardiness ofjob j are given by the relations, Ej= max(0, d-Cj) and Tj=max(O,Cj-d), respectively, for all j-1 ..... n. The objective is therefore to find a processing order of the n jobs that minimizes
n
2 aj.Ej + flj.rj
j=l
(1)
Where, a,j, flj (j=l .... ,n) are the earliness and tardiness (nonnegative) penalties, respectively, for job j and constitute data input to the scheduling problem. A common due date d is called unrestrictive if d > pj (j'= 1,2 ..... n) holds, otherwise is called restrictive. Even the simplest formulation of CDDSP leads to a combinatorial optimization problem (COP) that is NPhard [1 ]. In this work we consider one of the hardest versions of the basic CDDSP, that with a restrictive common due date and different earliness/tardiness penalties. The study of earliness and tardiness penalties in scheduling is a relatively new area of research that received considerable attention in the last 15 years due to its compliance with the new principles of just-in-time
431
(JIT) inventory management. In J I T approach jobs are to be completed as close as possible to their due date, neither too early, nor too late. Early jobs result in inventory holding costs, while late jobs result to penalties such as loss of customer goodwill and loss of orders; therefore, the emphasis with J I T technology is that earliness as well as tardiness of jobs should be discouraged. This paper investigates the application ofdifferential evolution (DE) on the solution of the restricted CDDSP with general earliness and tardiness penalties. Extended evaluations are performed measuring the performance of DE over public available CDDSP benchmarks with known lower (or upper) bounds. The results obtained are very promising since, new improved upper bounds were generated by the proposed method in the majority of the benchmark test instances. The rest of the paper is organized as in the following: section 2 describes briefly the basic DE model for function optimization. Section 3 introduces DE for the solution of CDDSP, while section 4 analyzes and discusses experimental results over complex benchmarks test instances. Finally, section 5 concludes the contribution of the paper.
The superscriptj (J= 1,2 .... ,D) in Eq.(3) represents the J-th component of the respective vectors. Crossover is performed with probability Pc. Acceptance process follows crossover. During this process the costs of the trial and target vector are compared, and the target vector is updated using the following formula, if Cost(tri, k ) < C o s t ( x i , k ) then Xi, k+l : tri, k
else Xi, k+1 : Xi, k
The processes of mutation, crossover and acceptance continue for each individual until the completion of the population/2. The overall process continues until some user-defined stopping conditions are met. The mechanism described above is only one variant of the basic DE algorithm known as scheme DE 1 [ 1]. There are also some other DE variants, differ in the way they create the mutant vector (Eq. (2)). One such general variant is given by mi, k = ~. XBest, k + (1--~)XrS, k
+ F.(Xrl,~ + Xr2,k -- Xr3,k -- Xr4,~)
2. Differential Evolution
DE is a population heuristic developed by Storn and Price [2] for minimizing functions of real variables. It utilizes a population of N, D-dimensional floating-point vectors to search the feasible region of the optimization problem towards the global minimum. The initial population F={Xl,o,X2,o....XN,0} , is taken to be uniformly distributed in the search region of the given problem. In each generation, all vectors in /-" are targeted for replacement. The population of the next generation is created by applying mutation, crossover and acceptance operators on all the target vectors in/2. In particular, for each target vector xi, k, i = 1,2,...,N, first a m u t a n t vector mi.~ and then a trial tri, k vector are generated using the following relations, mi, k = Xrl,k + F . ( X r 2 , k - Xr3,k ) trJi, k : mJi, k w i t h
probability P c
or
(2) (3)
trJi, k = xJ~,~ with probability 1 - P c
Where, rl, r2, r3 s {1,2,...,N} and rlCr2r162 k denotes the generation number. F is a positive scaling coefficient and constitutes a control parameter for DE.
432
(4)
(5)
Where, ~: is an integer coefficient denoting whether the construction of the mutant vector will be performed by combining five population vectors (~=-0), or by combining four population vectors together with the population best vector (~-=1). Again, r 1, r2, r3, r4, r5 are mutually distinct integers taken within the range [ 1,N]; moreover, all are different from index i.
3. The proposed DE for the CDDSP
Two alternative approaches are commonly used to represent a solution for CDDSP. One way is to use permutation strings representing the physical sequence of the jobs. For the case of the restricted CDDSP many of these solutions are known to be sub-optimal since they do not satisfy the V-shaped optimality [ 1]. An alternative approach is to search for optimal solutions within a smaller solution space containing only the V-shaped solutions [3]. In the proposed DE the V-shaped representation of the solution space was adopted. Hence, for an n-job CDDSP, a candidate solution is a vector containing n floating-point numbers (genes) taken in the range [0,1 ]. Each floating-point number corresponds to a specific job 1, 2,...,n with that order. Meaning that, the
1st gene corresponds to job- 1, the 2 "d gene to job-2, etc. The value of a gene indicates the type of the job, either early, or late in the following way: a gene' s value less or equal to 0.5 in the vector indicates that the corresponding job is early otherwise the job is late. Early jobs are moved in the start of the schedule and sequenced in nonincreasing order of the ratiopj/g. Late jobs are moved in the end of the schedule and sequenced in non-decreasing order of the ratio pj/~. Using the above representation there are cases where V-shaped property is not maintained. In these cases, the total processing time of the jobs in set E (containing the early jobs) exceeds the due date. To maintain feasibility we used a repairing technique inspired by the work of Lee and Kim [3]. The main idea is to perform appropriate movements of some jobs before or atter the due date so that to adjust the selected chromosome to represent a V-shaped schedule. Assuming an n-job CDDSP, and a selected vector representing a schedule with a total number of m early jobs, the repairing technique is as follows: Step 1: Let Cmbe the completion time of the last job in E; if Cm > d then
feasible = FALSE; j = m; while not feasible do
~~Movejob j from set E to set T// E = E \ [j]; T=T u [j];
//randomly alter the value of genej // gene(j) = Rnd (0.51, 1); j=j-1; if Cj < d then feasible = TRUE; endwhile
Reschedule the jobs in T in non-decreasing order of the ratio p / ~ V je 7", endif
Step 2: If Cm < d then
j=m+l; while Cj _
//Move job j from set T to set E// T = T \ [j]; E = E w [j]; gene(j) = Rnd (0, 0.5); j:j+l; endwhile endif
Reschedule the jobs in E in non-increasing order of the ratio pj/~. Vj~ E;
This procedure is stochastically applied on the population of the individuals with a small probability and acts on both the phenotype (the actual generated schedule) and the genotype (the floating-point vector), as well. Function geneO') denotes the value ofthej-th gene in the selected chromosome, while Rnd(x,y) returns a random number uniformly selected over [x,y].
4. Numerical results
The proposed DE algorithm was implemented in Delphi Pascal and run on a Pentium-IV (1.7 GHz) PC. The algorithm was tested over the public benchmarks of the restricted CDDSP, recently proposed by Biskup and Feldmann [4]. These benchmarks include 280 test instances generated with less or more restrictive due dates, ranging from small size instances with 10 jobs to large size instances with 1000 jobs. There are ten instances for each size problem. The degree of the due dates' restriction for these benchmarks is determined by the value of a parameter h (=0.2, 0.4, 0.6, 0.8). Test instances generated with h=0.2 are easier to be solved than those generated with greater values of h. The interested reader can download the full set of these benchmarks from the Web page with URL:
http ://peop le. brunel, ac. uk/-mastjjb/jeb/orlib/schinfo.ht ml The following three performance criteria were used to quantify the effectiveness of the proposed DE; while, to get the average performance of the algorithm, three runs on each benchmark test instance were performed and the solution quality was averaged: i.) The average deviation from optimality in %, calculated by avg.dev%=((COStDu- UByZJB)xlO0. Where, CostDu is the cost (given by Eq.(1 )) of the best schedule achieved by DE for a specific benchmark test instance. UB is the corresponding upper bound determined by Biskup and Feldmann [4]. ii.) The average % solution effort estimated by avg.effort%=((Gop/TG)xlO0. Where, Gop, is the generation number at which DE achieved its best solution for a specific test problem, and TG is the total number of generations the algorithm run. iii.) The actual average processing time consumed in seconds (avg.cpu). As with any EA, much experimentation was performed in preliminary tests in order to determine the suitable
433
values for the control parameters of the DE. Finally the following settings were adopted: population size N - 10. Pc = 0.01. F was determined by F - (2-Pc / N) ~. This relation was showed by Zaharie [5] to be critical for the computation of the appropriate values for the control parameters of DE. Mutant vectors were created using Eq.(5) (with ~: = 1). The algorithm was leti run for a maximum ofnjobsx500 generations, with njobs the total number of the jobs to be scheduled. Furthermore, a slight modification of the standard acceptance rule in DE algorithm has been performed. In particular, each component of the updated target vector (see Eq.(4)) is further randomly mutated (i.e., it is substituted by a random number drawn uniformly within the range (0,1)) with a small probability p = 0.1. The idea with this scheme is to more enforce the exploration ability of the algorithm. Table 1 illustrates the results obtained by DE over the various benchmarks. In particular, Table l(a) contains the results for the less restrictive due dates CDDSP (h -- 0.2), while Table 1(b)-(c) show the results for more complex problems (with h=0.4, h=0.6, respectively). It is worth noting that, optimal solutions as reported in [4] are only exist for the test instances with 10 jobs and have been achieved using an integer programming formulation with L1NDO software. For each benchmark category, Table 1 includes the offset of the generated solution from the upper bound in %, the % effort, and the actual CPU time in seconds spent by the algorithm until the convergence. These three metrics are averaged over the 10 test instances of each benchmark category. Therefore, for instance, in Table 1(a) and for the benchmarks with 10-jobs, the proposed DE managed to achieved the exact optimal solutions quite fast, in about 0.2 sec. In the rest benchmarks ranging from 20 to 1000 jobs, DE was find able to put new improved upper bounds in all the test instances. This fact is illustrated in Table 1(a)-(c) with the negative average deviation from optimum solution. For instance, one can see from Table 1(a), that the existing optimal solutions for 20-jobs benchmarks have been improved by approx. 3%, while the improvements for the rest benchmarks were approx. 6% for the 50- 100-, and 200jobs benchmarks, and approx. 6.5% and 6.7% for the most complex benchmarks with 500- and 1000-jobs, respectively. Furthermore, from Table l(a) one can observe that DE is quite fast for small size instances needing in average less than 2sec for problems with up to 50-jobs. Of course, the computation times are increased with the number of the jobs to be scheduled.
434
Table 1 Experimental results over the Biskup and Feldman [4] benchmarks. h=0.2 njobs
avg.dev avg.effort %
%
avg.cpu (sec)
2.34
0.22
10
0.00
20
-3.06
1.03
0.32
50
-6.08
25.13
3.15
100
-6.17
50.78
24.73
200
-5.77
70.64
256.56
500
-6.43
81.34
4,398.07
1000
-6.72
87.46
8,568.67
(a)
njobs
avg.dev %
h=0.4 avg.effort %
avg.cpu (sec)
l0
0.00
7.39
0.53
20
-1.55
21.46
2.92
50
4.49
49.15
4.57
100
4.85
64.46
24.611
200
-3.72
78.50
230.086
500
-3.57
84.05
3,855.081
1000
-4.38
86.36
1,3128.12
(b)
njobs
avg.dev %
h=0.6 avg.effort % avg.cpu
(sec)
10
6.54
5.94
0.701
20
2.77
8.78
1.579
50
0.53
21.49
3.028
100
1.14
31.02
14.227
200
1.05
69.13
216.39
500
1.72
78.73
3,580.758
1000
1.28
77.49
1,2794.99
(c) Similar high quality performance can be observed from Table 1.(b) for the benchmarks problems generated with h=0.6. Here, again, in the case of the 10-jobs benchmarks, DE managed to achieve the exact optimum
solutions. For the rest benchmarks, the existing upper bounds were improved by approx. 1.5% for 20-jobs, 4.5% for 50-joba, 5% for the 100-jobs CDDSPs, and so on. Finally, in the most difficult benchmarks category examined in this work, the results are still promising. In particular, for the large size problems those with 100 to 1000 jobs the average deviation was nearly 1% from the existing optimum, 0.5% for the case of 50-jobs, and approx. 2.8% for 20-jobs. A rather large deviation (--6.5%) was reported in the case of the 10-jobs benchmarks. Another factor of interest is the percentage effort spent by the algorithms in order to reach the nearoptimum solution. This value implies two things: first, how difficult was for the algorithm to reach a nearoptimum solution, and second, how able is the algorithm to improve this solution in the next iterations. The lower the value of the % effort, the less the ability of the algorithm to improve the best-so-far solution even more. For instance, take the results shown in Table 1.(a) and the case of the 1000-jobs instances. Here, the percentage effort spent by the DE algorithm is a number near to 88 on average, which means that the algorithm spent approximately 88% of the maximum permitted number of iterations to reach a near-optimum solution. Therefore, there is a high possibility for the algorithm to improve more this solution if we let it run for some more iterations. Consequently, one can conclude that the proposed DE has the ability to generate solutions ofeven higher quality for the large size instances with 500 and 1000-jobs, respectively. As it is evidence, the same does not hold for the case of the test instances with smaller number of jobs. Here, the near-optimum solution was achieved rather early by DE and did not be improved for a large number of iterations.
Acknowledgements
This work is integrated in the Innovative Production Machines and Systems (I'PROMS) Network of Excellence.
References
[ 1] Baker K. and Scudder G. Sequencing with earliness and tardiness penalties: A review, Operations Research, 38, (1990), 22-36. [2] Storn R. and Price K. Differential Evolution- A simple and efficient heuristic for global optimization over continues spaces, Journal Global Optimization, 11, (1997), 241-354. [3] Lee C.-Y. and Kim S.J. Parallel genetic algorithms for the earliness-tardiness job scheduling problem with general penalty weights, Computers and Industrial Engineering, 28, (1995), 231-248. [4] iskup D., and Feldmann M. Benchmarks for scheduling on a single machine against restrictive and unrestrictive common due dates, Computers and Operations Research, 28, (2001), 787-801. [5] Zaharie D. Critical values for the control parameters of differential evolution algorithms, In: Matougek, Radek and Ogmera, Pavel (eds.), Proc. of MENDEL 2002, 8th Int. Mendel Conference on Soft Computing, Brno, Czech Republic, (2002), 62-67.
5. Conclusion
This paper investigates the use of a differential evolution (DE) algorithm for the solution of the single machine common due date scheduling problem (CDDSP) with general earliness and tardiness penalties. Extensive experiments were performed over 210 public benchmarks problems for which lower and upper bounds were known. The results obtained show a remarkable high performance for the DE algorithm putting in the majority of the benchmark test-instances new improved upper bounds. Moreover, the proposed method is rather fast and easily implemented.
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Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UIC Published by Elsevier Ltd. All fights reserved.
Evolutionary Approach to Measure Production Performance B. Denkena, C. Liedtke Institute of Production Engineering and Machine Tools, Hannover University, 30823 Garbsen, Germany
Abstract
In order to compete on global markets today's production needs to keep track and continuously optimize its performance. It is crucial for a performance measurement system to enable to a general survey fast, so that trends can be identified timely in advance. Generally a broad set ofperformance indicators forms the basis of such a system. By combining these measures with benchmarking and target-setting a successful system for assessing and improving performance can be established. The BETTI| Benchmark (Benchmark Tool to Improve the Production Performance) is such an exemplary performance measurement system. In order to increase its effectiveness it has recently been extended by continuously including the experience and knowledge attained in previous benchmarks into the analysis. Additionally it allows appraising the consequences of actions targeted at improving production performance. Keywords: Production Management, Performance Measurement, Benchmarking
1. Introduction
In order to stay competitive today' s production has to deal with numerous challenges. Market shares are hard-fought; competitors from Far East push on the market and beat down prizes. Due to unrivalled production costs western companies have to exploit every competitive advantage they can hardly find. This means to exhaust production performance. Therefore a realistic appraisement of the productive efficiency is vitally important in order to identify and utilize potentials to improve performance. Performance measurement has already been applied for a long time. While Frederick Winslow Taylor's [1] approach was predominantly based on time measurement, the managers of the 1960s mainly used short term financial criteria such as quarterly earnings. During the 1980s and 1990s the situation changed in a significant way: self assessment, quality awards, benchmarking, ISO 9000, activity-based costing, capability maturity model, balanced scorecard,
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workflow-based controlling, etc. were the buzzwords which dominated discussions in the field of performance evaluation [2]. Nowadays industry asks for fast and comprehensive solutions. In this paper we introduce a benchmarking method that meets industry's requirements for an effective tool to enhance performance. Mainly characterized by its high comparability and its fast, efficient application this procedure exceeds the potentials of most existing approaches.
2. P e r f o r m a n c e M e a s u r e m e n t
In order to be effective a performance measurement system needs to provide the essential information that will allow a fast general survey on current performance and efficiency. Although a comprehensive approach is certainly desirable it is not feasible in industrial practice. Predominantly a topdown approach finds favour with most executives. A
certain set of measures provides compressed information and can be detailed on demand. The basis for such a performance measurement system nowadays is formed by performance indicators [3]. A performance indicator is a policy relevant statistics, number or qualitative description that provides a measure of whether an organization is performing as it should. Operational facts and coherences are summarized and thus described in a compressed meaningful form that enables to evaluate the organization's current situation and to predict its future development. Apart from the assistance in operational problem solutions, performance indicators also serve as early warning indicators timely signalling evidence of critical developments. Therefore performance indicators are not just an extensively used tool in operational practice, but also an indispensable instrument for controlling [4]. In order to evaluate whether an organization is achieving its goals or not, it is necessary to have an explicit definition of what those goals are and how the performance in achieving these objectives can be measured. Quite often anything that a number can be attached to is called a performance indicator. Such questionable indicators are neither very meaningful nor of much practical help. Poorly selected performance indicators will rather cause more confusion than they will help to increase transparency. When measuring performance, an explicit statement of objectives is indispensable in order to serve as a guide or a reference point [5]. Performance indicators have a comparative dimension that permits a value judgment to be made about the organization or process. This reference point may be a goal, an absolute standard, or a past value. A performance indicator therefore permits an assessment, judgment or evaluation to be made about the performance of an organization. When analyzing a performance index, it is common for the analysis to focus on the proportion without regard to the statistical effect of the sample size (e.g. number of parts, number of production steps). This value appears as the denominator of the index. For example, when comparing yields over a group of manufacturing cells making the same product, a simple comparison oftheir yields is flawed if the number ofparts manufactured in each cell is not taken into account. Due to their nature performance indicators always bear the risk of their misinterpretation when considered individually. In fact it is a common mistake assessing performance developments just by single indicators without taking into account their interdependencies with other indicators. Thus creating a system of interdependent and complementary performance
indicators will lead to a comprehensive system that accomplishes the task of providing compressed information while identifying critical developments by looking at the whole picture [6]. It is by identifying a range of benchmark values that an indicator develops meaning and substance. Establishing average and best practice performance based on real data permits a range to be identified. In doing so, it makes people aware of improvements that are orders of magnitude beyond what they would have thought possible. Benchmarking provides these information. It allows to gain insight into the performance and effectiveness of best practices and thus to reveal unexpected potentials. It has been defined as the search for industry's best practices that will lead to superior performance. Interest in benchmarking has exploded since 1979 when Xerox first introduced it [7]. Today benchmarking as a management-tool is widely used. It has spread geographically to large parts of the world and proliferated in a variety of manufacturing and service businesses, including health care, government and education organizations [8].
3. Benchmarking By nature definitions of benchmarking typically include the comparison with a corresponding partner as well as some elements of a continuous improvement process. When addressed to executives one often finds aspects both of 'best practice' and 'improvement of performance'. Andersen [9] defines benchmarking as a process of continuously measuring and comparing one's business processes to comparable processes in leading organizations to obtain information that will help the organization to identify and implement improvements. Many regard benchmarking as a method to compare key figures, often financial ones, for the purpose of ranking the organization in relation to competitors or the industry average. This might have been the main application ofbenchmarking earlier, but today it is a far more multifunctional tool that is widely applicable. During the past decade benchmarking has received significant attention, especially after its inclusion among Malcolm Baldrige Award criteria. Many companies that have embraced benchmarking practices attained numerous benefits and succeeded in their businesses [ 10]. Benchmarking using pre-established indicators as a framework can result in a continually updated range of performance values. This provides the foundation for a much more dynamic assessment methodology
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than does a static set of criteria or rating protocol. As such, benchmarking contributes to the continual improvement in performance values, and at a broader scale, to the on-going progress towards a more sustainable future.
4. Setting Performance Targets Targets involve the identification of a preferred future performance. One of the most difficult parts of setting performance targets, however, is justifying that the target achieves the right blend of feasibility and challenge. By basing targets in part on a range of existing performance values, it is possible to identify what might be achievable for any given organization. It is through this process that desired future performance is identified and can then be translated into policies or actions. To establish an effective performance measurement system a comprehensive framework of interdependent and complementary performance indicators is needed to serve as a solid basis. As a result of the indicator's constitution, factors such as corporate image, employee educational background, technical know-how, or quality of management cannot be regarded in such a performance indicator system. Nevertheless they should also be taken into consideration as explanatory factors [ 11 ]. Since operations are in general managed from a functional perspective, modelling processes is a fundamental step to understand the flow of information and resources through the processes of the internal value chain. Modelling can also bring the benefit of helping in assessing performance of operational and supporting processes [ 12]. Performance indicators are not absolute facts carved in stone. They are best interpreted as indicative, suggestive or diagnostic. They are essential to the benchmarking process; however they themselves do not provide complete answers. Performance indicators should be used to identify differences between benchmarking partners, and changes in organizations over time. They act as pointers to identify areas or subjects for further analysis. It is through this additional focused work that the real value from benchmarking can be achieved.
5. Betti| Benchmark The BETTI| Benchmark (Benchmark Tool to Improve the Production Performance) is an exemplary
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tool that fulfils the needs for a progressive performance measurement system. It is a standardized method that has been specifically developed by IFW (Institute of Production Engineering and Machine Tools, University of Hannover, Germany) and European partners in order to help small and medium-sized enterprises (SMEs) to improve the performance of their manufacturing and assembly processes [13] (fig. 1).
PP "I-S
Production Planning Techical Sales
Fig. 1" Focus of BETTI| Benchmark SMEs are defined as companies that employ between 50 and 500 people. Target companies produce in batch sizes that typically vary between one and several thousands. Main industrial sectors are mechanical engineering, electrical engineering, and machine building industry. The BETTI| Benchmark method evaluates SME performance against best performers from other sectors. It focuses on functional and generic benchmarking, although- to some extend - internal as well as competitive benchmarking analysis are possible [ 14]. Since the BETTI| Benchmark is addressed to certain companies operating in a specific field, a uniform questionnaire is used. Thus the object of the benchmarking analysis is mostly determined. Due to the fact that employees with access to production and personnel data are able to fill out the questionnaire, it is easier to form a benchmarking team. The questionnaire collects the data required for the analysis in a consistent structured way, providing definitions and details on how to present the data. Based on the data collected, a set of performance indicators and explanatory factors is calculated. These characteristic numbers are used to select comparable companies from the database (fig. 2). Thereby the time consuming search for adequate benchmarking partners becomes needless since the BETTI| Benchmark database contains production data and valuations of more than 120 European enterprises located in 11 countries.
For each performance indicator three to seven selections are made to evaluate the performance against a group of companies similar in a certain relevant aspect. Besides the standardization of the procedure the selection of comparable companies can be regarded as the major advantage of the BETTI| Benchmark. It is the selective choice based on explanatory factors of best fitting companies from the database for each individual comparative value that the BETTI| Benchmark attains its high comparability from.
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Fig. 2: The BETTI| Benchmark method The BETTI| Benchmark does not just comprise of a simple comparison of individual figures. By including a graphical representation of the production structure and the process flow into the analysis it goes beyond. Although, due to its nature, this production model obviously cannot be directly included into the performance evaluation it is vitally important to identify the cause of a lack of performance. Once the data indicates a possible problem a close look at the production structure significantly helps to identify the corresponding critical process and thereon to generate solutions to remedy the deficiency.
They are frequently unable to appraise the whole extend of their actions to improve the production performance, even though they are well-meant. The proximate consequences can have catastrophic dimensions. Therefore a methodological integration of quantitative performance indicators and qualitative appraisals as well as the comprehensive use of substantial expertise is desirable in order to support the evaluation ofbenchmarking results. On this account it is the objective of our recently implemented enhancement of the BETTI| Benchmark to integrate expertise into the evaluation of production performance and to enable to appraise the consequences of actions targeted on improving performance by simulating certain scenarios. The existing data and characteristic numbers of a certain number of companies are analyzed for general trends and predictions in order to define the codomain of the performance indicators. Taking these into account, a statistical analysis of the database is performed to search for interdependencies among performance indicators and explanatory factors (fig. 3). This analysis is done by taking advantage of analytical methods such as correlation, regression, or variance [ 15]. The existing database ofthe BETTI| Benchmark provides enough company data to ensure a statistically firmed predication. The detected correlations between performance indicators and explanatory factors are used to develop an individual formula for each single performance indicator representing both hidden and obvious coherences among the included factors. These mathematical models of the performance indicators allow anticipating their response on modification of their significant input variables.
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6. Potential for Perfectioning
Even though the BETTI| Benchmark features all the rudiments required for an effective performance measurement tool it still does not fulfil all desires. Most existing approaches provide simple profiles of strengths and weaknesses in the form of quantitative data for further interpretation. Expertise acquired in previous investigations is neglected or insufficiently used. In order to achieve respectable results it needs a skilled and experienced expert to interpret the data acquired. Inexperienced consultants tend to give their recommendations based on schoolbook knowledge without regard to the specific situation of the company.
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analysis as described above and additionally allows manipulating individual performance indicators (fig. 4). Thus one receives a direct feedback on the consequences of measures to improve the company's performance. For example reducing batch size positively influences throughput times but negatively affects production costs, which might worsen the company's entire performance. Hence the software visually supports the user to consider all relevant performance indicators simultaneously in order to determine a well-balanced optimum for the company's overall performance.
Fig. 4: Screenshot of prototype software tool for evaluation Of course the gap to best practice is also considered, whereas our approach additionally offers the possibility to estimate the effort necessary to catch up with best practice and to assess possible disadvantageous secondary effects. Thereby a company's performance can be optimized by taking into account its individual constraints without proposing patent remedies based on few benchmarks. The skilled expert is enabled to easily share his knowledge whereas the inexperienced consultant learns from decisions made in former projects while working on a current evaluation. The software improves itself with each evaluated data set. In order to include knowledge gained in previous evaluations, commented settings chosen in earlier projects, which show characteristics similar to the actual case can be loaded to serve as a reference point and as an indication for possible actions. For testing of the mathematical models designed data sets were applied, which confirmed the behaviour that was predicted in advance. The default values corresponded with the calculated results. Acceptance trials of the software prototype also showed good
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results. Data sets were initially excluded from the data base and used to validate the efficiency of the software system. The resulting suggestions for improvement that were determined in the trials resembled those that were actually recommended and implemented in the corresponding projects.
7. Conclusion This paper introduces a new approach to measure and optimize production performance of SMEs. The fundamentals and limitations of existing methods are described in order to identify the demands on an effective modern performance measurement system. The presented system is based on benchmarking using predefined performance indicators. It uses a new model, based on an existing database. In contrast to most existing approaches that provide simple profiles of strengths and weaknesses in the form of quantitative data for further interpretation it features a methodological integration of quantitative performance indicators and qualitative appraisals as well as the comprehensive use of substantial expertise. Experiences acquired in previous projects are integrated into the evaluation. Consequences of actions targeted on improving performance can be appraised by simulating corresponding scenarios based on mathematical models. The proposed model does not aim at substituting competent benchmarking consultants. It is intended to support the versed expert in his decision making process.
Acknowledgements The content of this paper is an outcome of the project "Knowledge-based Benchmarking of Production Performance for Batch-size Production", which is funded by Deutsche Forschungsgemeinschaft (DFG) within the project To 56/169-1.IFW is partner of the EU-funded FP6 Innovative Production Machines and Systems Network of Excellence (www.iproms.org).
References [ 1] Taylor, F.W.: The Principles of Scientific Management (1911). Dover Publications, New York 1998 [2] Kueng, P.: Supporting BPR Through A Process Performance Measurement System. BITWorld'98 Conference, New Delhi 1998, pp. 422-434
[3]
Hvolby, H.H.; Thorstenson, A.: Performance Measurement in Small and Medium-sized Enterprises. Third Conference on Stimulating Manufacturing Excellence in Small and Medium Enterprises, Coventry University 2000, pp. 324-333 [4] Bititci, U.; McCallum, N.; Bourne, M.; MacBryde, J.; Turner, T.: Performance indicators for sustainable competitive advantage: the next frontier. 2nd Int. IFIP Workshop, Hannover 2002, pp. 2-11 [5] Liedtke, C.: Indicators for Benchmarking of Production Performance. 14th International DAAAM Symposium, Sarajevo 2003, pp. 269-270 [6] Maleyeff, J.: Benchmarking performance indices: pitfalls and solutions. Benchmarking: An International Journal 10 (2003) 1, pp. 9-28 [7] Camp, R. C.: Benchmarking: The Search for the Industry Best Practice that Leads to Superior Performance. ASQC Quality Press, Milwaukee 1989 [8] Camp, R. C.: Business Process Benchmarking: Finding and Implementing Best Practices. ASQC Quality Press, Milwaukee 1995 [9] Andersen, B.: Industrial Benchmarking for Competitive Advantage. Human Systems Management 18 (1999) 3/4, pp.287-296 [ 10] Underdown, R.; Talluri, S.: Cycle of success: a strategy for becoming agile through benchmarking. Benchmarking: An International Journal 9 (2002) 3, pp. 278-292 [11] Andersen, B.; JORDAN, P.: Setting up a performance benchmarking network. Production Planning and Control 9 (1998), pp. 13-19 [12] Carpinetti, L. C. R.; De Melo, A. M.: What to benchmark? A systematic approach and cases. Benchmarking: An International Journal 9 (2002) 3, pp. 244-255 [ 13 T6nshoff, H. K.; Rietz, W.; Rotzoll, M. A.: Improvement of Production Structures Using Benchmarking. In: Bruns, N. D (Ed.) Managing enterprises- Stakeholders, Engineering, Logistics and Achievement. Mechanical Engineering Publications Limited, London 1997, pp. 265-271 [14] Denkena, B.; Apitz, R.; Liedtke, C.: Knowledge-based Benchmarking of Production Performance. Business Excellence '03 Conference, Guimaraes 2003, pp. 166171 [15] Denkena, B.; Apitz, R.; Liedtke, C.; Rietz, W.: Wissensbasiertes Benchmarking ffir kleine und mittelst~indische Unternehmen. Z W F - Zeitschrift ffir wirtschaftlichen Fabrikbetrieb 98 (2003) 9, pp. 407-410
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Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Feature selection for SPC chart pattern recognition using fractional factorial experimental design Adnan Hassan, Mohd. ShariffNabi Baksh, Awaluddin M. Shaharoun and Hishamuddin Jamaluddin Faculty of Mechanical Engineering, Universiti Teknologi Malaysia 81310 UTM Skudai, Johor Bahru Malaysia
Abstract Feature selection is one of the important steps in designing a pattern recognizer. This paper presents a study to select a minimal set of statistical features for SPC chart pattern recognition using the fractional factorial experimental design. A resolution IV design was used to identify the significant features from a list of ten possible candidates to represent the input data streams. Further judgment was adopted to arrive at the final selection in the light of some ambiguities among confounded two-factor interactions. The final six selected features set comprising, autocorrelation, cusum, mean, standard deviation, mean-square value, and skewness as the input vector resulted in an average correct classification rate of 97.1% and standard deviation of 0.878. The methodology adopted in this study could be applied to other feature selection problems beside for SPC chart pattern recognition. Keywords: Feature selection, Pattern recognition, Statistical process control
1. Introduction Statistical process control (SPC) charts are widely used for 'listening to the voice of the process' [1]. They were introduced by Shewhart in 1924 and remain among the most important SPC tools. The key feature of control charts is the provision of the method to differentiate whether a particular processes is operating within a statistically stable or an unstable state. Unstable processes may produce distinct time series patterns. Identification of these patterns coupled with engineering knowledge of the process would lead to focused diagnosis and troubleshooting. Developments in computing technology have motivated researchers to explore the use of artificial intelligence technologies such as expert systems, artificial neural network (ANN) and fuzzy sets to automatically and intelligently recognise control chart patterns (CCP). Input data representation is an important step in the design and application of a pattem recognizer. Input data in their original format are normally large in dimension, and their key
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properties are often hidden and contaminated with noise. Dimensionality reduction can be achieved through extraction of statistical features from the data streams (feature extraction). However, using all candidate features to represent the input data may not necessarily result in better discrimination capability. There may possibly be interactions between the features. The presence of excessive input features can burden the ANN training process, while using too few features may result in insufficient representation. Thus, there is a need to identify the right mix of the statistical features. This exercise is commonly referred to as feature selection. Theodoridis and Koutroumbas [2] noted that the feature selection step is very crucial in designing a pattern recogniser. An appropriate mix of relevant features is expected to have crucial impact on the learning and generalization performance [3]. A smaller ANN size can lead to faster training and generally more effective and efficient recognition. Most of the existing SPC pattern recognition models in the literature use normalized raw data as
the input vector to the recognizer. These data representations normally produce large ANN structures and are not very effective and efficient for complicated recognition problems [4]. These drawbacks can be overcome with the use of features for representing data as demonstrated in pattern recognition applications for hand written [5], characters [6], and grain grading [7] among others. Very limited works have been reported using features extracted from SPC chart signals for input data representation. Pham and Wani [8] represented input vectors using nine geometric features, namely, slope, number of mean crossings, number of leastsquare line crossings, cyclic membership, average slope of the line segments, slope difference, and three different measures for area. The aspect of feature selection was not included in their studies. The objective of this paper is to propose a minimal set of statistical features for SPC chart pattern recognition selected using the fractional factorial experimental design. The rest of this paper is organized as follows: Section 2 discusses the sample SPC chart patterns and statistical features, Section 3 describes the ANN pattern recognizer, Section 4 discusses the feature selection and Section 5 concludes the paper.
mean-square value (MSV), median (MEDIAN), standard deviation (STD), range (RANGE), maximum cusum (CUS), skewness (SKEW), kurtosis (KUR), slope (SLOPE), and average autocorrelations of lag 1 and 2 (ACOR).
3. The ANN pattern recognizer Various researchers have shown the feasibility of ANN for SPC chart pattern recognition. The present study adopted the ANN as a tool in developing the recognition system, based on Multilayer Perceptions (MLP) architecture. The number of input nodes was set according to the actual number of statistical features used as the input vector. It falls between 2 to 10 nodes corresponding to the number of features included in the investigation. The number of nodes in the hidden layer was set to 6 which was chosen based on trial and error. The number of output nodes was set to 6 which is the number of pattern classes. Back-propagation with adaptive learning rate and momentum was adopted as the training algorithm. It was assumed that all patterns were fully developed when they appeared within the recognition window. The extracted statistical features were then normalized such that their values would fall within [-
1,1].
2. Sample SPC chart patterns and statistical features This study focuses on recognition of six control chart patterns plotted on the Shewhart X-bar chart, namely, random, shift-up, shift down, trend-up, trenddown, and cyclic. Since a large amount of samples were required for the recognizers' training and they were not economically available, simulated data was used. The parameters used for simulating the data streams are given in Table 1. Each data stream consisted of 20 subgroups averages of time sequence data sampled with a rational subgroup size of 5. Table 1. Parameters for simulating SPC chart patterns Pattern Type
Parameters (in terms of 0")
Linear trend-up Linear trend-down Sudden shift-up Sudden shift-down Cyclic Random (stable process)
gradient:O.015 to 0.025 gradient: -0.025 to -0.015 shift magnitude: 0.7 to 2.5 shift magnitude: -2.5 to -0.7 amplitude: 0.5 to 2.5 mean = O; standard deviation -1
The values of the parameters varied randomly in a uniform manner between the limits shown. Random noise of 1/3 O" was added to all unstable patterns. These parameters were chosen to keep the patterns within the control limits. To achieve dimensionality reduction, the following statistical features were extracted from the sample patterns: mean (MEAN),
4. Feature selection using fractional factorial experimental design Keeping the number of features as small as possible and with correct combination is in line with the desire to produce recognizers with good generalization capabilities [3]. Fractional factorial experimental designs (FFED) were used to identify features that had substantial effect on the recognizers' performance. FFED are statistical quality engineering techniques which are widely used in screening experiments for product and process design. Detailed discussion on FFED can be found in Montgomery [9].
4.1 Preliminary feature screening In the preliminary feature screening, a two-level 10-5 fractional factorial design was used. This zv design requires only 32 experimental runs and provides information at resolution IV. Table 2 provides the design matrix for the 2 j~ zv
fractional
factorial design, the feature-column assignment and the experimental results. Since no prior knowledge was available on possible interactions, the features were assigned randomly to the columns. The factor
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Table 2. Design matrix for 210-5 fractional factorial and experimental results IV RUN 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
ACOR (A) -1 +1 -1 +1 -1 +1 -1 +1 -1 +1 -1 +1 -1 +1 -1 +1 -1 +1 -1 +1 -1 +1 -1 +1 -1 +1 -1 +1 -1 +1 -1 +1
SLOPE (B)
CUS (C)
MEAN (D)
MSV (E)
STD (F)
-1 -1 +1 +1 -1 +1 +1 +1 -1 -1 +1 +1 -1 -1 +1 +1 -1 -1 +1 +1 -1 -1 +1 +1 -1 -1 +1 +1 -1 -1 +1 +1
-1 -1 -1 -1 +1 -1 +1 +1 -1 -1 -1 -1 +1 +1 +1 +1 -1 -1 -1 -1 +1 +1 +1 +1 -1 -1 -1 -1 +1 +1 +1 +1
-1 -1 -1 -1 -1 -1 -1 -1 +1 +1 +1 +1 +1 +1 +1 +1 -1 -1 -1 -1 -1 -1 -1 -1 +1 +1 +1 +1 +1 +1 +1 +1
-1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1
+1 -1 -1 +1 -1 +1 +1 -1 -1 +1 +1 -1 +1 -1 -1 +1 +1 -1 -1 +1 -1 +1 +1 -1 -1 +1 +1 -1 +1 -1 -1 +1
(feature) in each column is independent and free from being confounded with other factors. The performance of the recognitions was measured based on percentage of correct recognition. A total of 2160 and 720 sample patterns were used in the training and recall phases of each of the screening run, respectively. Additional 720 sample patterns were used for in-training validation for early stopping to avoid over fitting during training. Each run was replicated using 10 different data sets in order to minimize the random error. The recognizer responses as given in Table 2 indicate that when all the features were included (run 32), the mean performance was not the best (92.3%). Better or somewhat similar performance results were achievable with a reduced number of features as indicated in trial run 6 (93.8%), run 12 (93.6%), run 24 (92.8%) and run 26 (96.4%). This suggests that there were effects of possible interactions between the features. It was necessary to identify the features to be retained and those to be omitted. The experimental results (32 runs x 10 replicates) were analyzed using the MINITAB statistical package for ANOVA and interaction effects. Table 3 summarizes these tentative
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SKEW (G) +1 -1 -1 +1 -1 +1 +1 -1 +1 -1 -1 +1 -1 +1 +1 -1 -1 +1 +1 -1 +1 -1 -1 +1 -1 +1 +1 -1 +1 -1 -1 +1
KUR (H)
MED (J)
+1 -1 -1 +1 +1 -1 -1 +1 -1 +1 +1 -1 -1 +1 +1 -1 -1 +1 +1 -1 -1 +1 +1 -1 +1 -1 -1 +1 +1 -1 -1 +1
+1 -1 +1 -1 -1 +1 -1 +1 -1 +1 -1 +1 +1 -1 +1 -1 -1 +1 -1 +1 +1 -1 +1 -1 +1 -1 +1 -1 -1 +1 -1 +1
RANGE % Correct (K) Recognition +1 +1 -1 -1 -1 -1 +1 +1 -1 -1 +1 +1 +1 +1 -1 -1 -1 -1 +1 +1 +1 +1 -1 -1 +1 +1 -1 -1 -1 -1 +1 +1
58.1 37.3 51.6 84.2 48.9 93.8 84.9 73.7 67.6 80.3 70.6 93.6 83.9 84.6 70.9 82.5 76.8 88.8 75.1 87.2 71.3 76.4 69.7 92.8 63 96.4 74.3 73 78.9 90.1 71.5 92.3
significant effects along with the 2 factor interactions and their aliases. Interaction here means the effect of the first feature depends on the level of the second feature (synergistic effect). The symbol '*' is used to indicate the pair of interacting features. As noted earlier, this study used a resolution IV design for running the experiments. The source of ambiguity in this design was that all the 2-factor interactions were confounded with other 2-factor interactions. One possible approach to resolve the confounding was by using resolution V design that would free the two factor interactions from other two factor interactions. However, the resolution V with 10 features was not attractive from economic considerations since it would require 128 runs compared with only 32 runs in the present study. Jones [10] noted that, often, engineering judgement or some clarification experiments be conducted to clarify such ambiguity economically. This approach was adopted in this study. The list for tentative significant main effects listed in Table 3 does not include features MEDIAN, RANGE and SLOPE. Possibly, features MEDIAN and RANGE were duplicates to features MEAN and STD respectively. However, the role of feature
SLOPE was still unclear at this stage. By assuming these three features also did not contribute significantly in any of the two factor interactions (or their aliases), led to elimination of related "unimportant" interactions. Thus, the tentative significant two factor interactions listed in Table 3 were reduced to the following: MEAN*MSV, STD*SKEW, CUS*KUR, MEAN*SKEW, MSV*STD, ACOR*SKEW, CUS*MSV, and STD*KUR. It should be reiterated here that there is a risk of reaching false conclusions when making such assumptions. However, one can minimize such risk by conducting clarification and confirmation runs
[lo].
The interaction subplots in Figure 1 were scrutinized to evaluate the sensitivity of the remaining main effects and their interactions. As indicated by the dotted response line in the ACOR*SKEW subplot, the recognizer performance was significantly improved when both features Autocorrelation and Skewness were included.
Excluding any of these features severely reduced the recognizer performance. This subplot also indicates that by including feature autocorrelation, the response became more sensitive to changes in skewness. This may not be good if the feature autocorrelation was contaminated with noise. However, it was decided that both autocorrelation and skewness features should be included since the improvement in the recognition performance was very significant. The subplots for CUS*KUR and STD*KUR suggest that significantly higher recognizer performance could be achieved when the second feature (kurtosis) was excluded. The same subplots also indicate that the inclusion of both features significantly reduced the recognizer's performance. Since these interactions (CUS*KUR and STD*KUR) were confounded with other 2-factor interactions, the decision to exclude the feature kurtosis was tentative and subject to clarification runs.
Table 3. Tentative significant main effects and two factor interactions MAIN E F F E C T S
INTERACTIONS AND
ACOR SKEW STD MSV MEAN CUS KUR
ACOR*MED MEAN*MSV SLOPE*MEAN ACOR*SKEW CUS*MSV ACOR* RANGE
+ + + + + +
ALIASES SLOPE*RANGE STD*SKEW SKEW*MED MEAN*RANGE STD*KUR S L O P E * M E+ D CUS*KUR + MEAN*SKEW + MSV*STD
.._._----'-
~ -
_
__
-I o ii7i
L
~-
...--
~
_
___-~
.i
---_.___
_____----
----_
ik ~i r -1 me ~
Figure l" Interaction subplots
445
Table 5 shows that the reduced model fitted satisfactorily with a small lack of fit error (F0 = 1.27 and p = 0.193). Features listed in the reduced model would form a reference setting for the clarification and confirmation experiments. Table 6 summarises the settings and experimental results for these experiments. Setting 1 is the reference setting that includes all the tentatively accepted features. Settings 2 through 6 remove one of these features (as marked '-1' in the respectively column) for each of the subsequent experiments. Finally, setting 7 adds feature slope to the reference setting. This setting attempted to clarify the effect of feature slope that was assumed "insignificant" at the early stage of the analysis. Each of the above setting was evaluated using 720 test patterns (120 patterns for each type). The evaluation for each setting was replicated using 10 different test sets. The reference setting 1 resulted in 97.10% for average correct recognition with standard deviation of 0.88. Its corresponding predicted value based on the reduced model was 96.54%. Table 6 shows that when omitting any of the tentatively accepted features, the performance of the recogniser was significantly reduced. Similarly, the recogniser's performance deteriorated (92.55%) when feature slope was added to the reference setting. Thus, the minimal feature set should comprise of the following features: autocorrelation, cusum, mean, mean-squarevalue, standard deviation, and skewness.
The response lines in the subplots for interactions MEAN*MSV, MEAN*SKEW, STD*SKEW, MSV*STD, CUS*MSV indicate that the inclusion of the first features (row features) only marginally improved the recognizers' performance while the second features (column features) were at "+1" settings. The solid response lines for these subplots also indicate that the exclusion of the respective first feature (row) resulted in the recognizer becoming more sensitive to changes in the second (column) features. This may be undesirable if the change in the level of second features was contributed by noise. At this stage, it was more appropriate to maintain features MEAN, MSV, SKEW, STD and CUS since the evidence available did not warrant their exclusion. Thus, clarification and confirmation runs were necessary to clarify the ambiguity and minimize the risk of false conclusions.
4.2 Clarification and Confirmation Experiments Before proceeding with clarification and confirmation experiments, it would be useful to fit the previous experimental results to a reduced linear regression model. A reduced model was formulated by screening out the "unimportant" features, namely, median, range, slope, kurtosis and their related interactions as identified in the earlier analysis. Table 4 provides the results of this fitting and Table 5 gives its corresponding analysis of variance.
Table 4. A reduced model for the recogniser's performance Term Constant ACOR CUS MEAN MSV STD SKEW ACOR*SKEW CUS*MSV MEAN*MSV MEAN*SKEW
Estimated Effect 13.101 5.483 6.435 6.953 8.546 10.702 5.083 -4.467 -6.279 -5.244
Regression Coefficient 76.378 6.551 2.742 3.218 3.476 4.273 5.351 2.541 -2.233 -3.140 -2.622
t
p-value
63.01 5.40 2.26 2.65 2.87 3.53 4.41 2.10 -1.84 -2.59 -2.16
0.000 0.000 0.024 0.008 0.004 0.000 0.000 0.037 0.066 0.010 0.031
Table 5. ANOVA for the recogniser's performance (reduced model) Source of Variation Main Effects 2-Way Interactions Residual Error Lack of Fit Pure Error Total
446
Deg. of Freedom 6 4 309 21 288 319
Seq.Sum of Squares 38323 9018 145280 12327 132953 192620
Adj. Sum of Squares 38323 9018
Adj. Mean Square 6387.2 2254.4
145280 12327 132953
470.2 587 461.6
p-value
13.59 4.80
0.000 0.001
1.27
0.193
Table 6. Settings and results of confirmation/clarification experiments Settings ACOR +1 +1 +1 +1 +1 +1 +1
CUS +1 +1 +1 +1 -1 +1 +1
MEAN +1 +1 -1 +1 +1 +1 +1
Femums MSV +1 +1 +1 -1 +1 +1 +1
STD +1 -1 +1 +1 +1 +1 +1
SKEW +1 +1 +1 +1 +1 -1 +1
SLOPE Nil Nil Nil Nil Nil Nil +1
Experimental Results* (% Correct) Mean Std. Dev. 97.10 0.88 90.35 7.30 91.04 10.20 93.38 7.02 93.44 6.80 81.26 27.38 92.55 13.39
Predi cted (% Correct) Mean 96.54
* B a s e d on 10 replicates, each c o m p r i s i n g a total o f 720 test s a m p l e s (120 for e a c h type)
5. Conclusions A final feature set comprising autocorrelation, cusum, mean, mean-square-value, standard deviation, and skewness is proposed. Rejection of features median and range is possibly due to their being redundant, given that other features are included. The central tendency and spread characteristics of the patterns may have been captured sufficiently by the statistics mean and standard deviation respectively. The results did not favor the inclusion of the feature kurtosis, possibly due to its role in providing information on the fourth moment. As such, kurtosis was relatively less robust to noise. The results also did not provide sufficient evidence to support the inclusion of the feature slope. This is possibly due to the limitation of resolution IV fractional factorial design, which had some ambiguities among 2-way interaction. However, the approximate method adopted in this study, that is the fractional factorial design coupled with judgment seems to be sufficient for this feature screening. This study has shown that fractional factorial experimental design is a viable approach for feature selection. Its comparison with other feature selection techniques can be an opportunity for further study. Although an ANN-based pattern recognizer for SPC chart pattern is the particular application presented here, the methodology adopted in this study can be applied to other feature selection problems. Currently, we are extending this work to recognize control chart patterns as they are developing [ 11 ].
Acknowledgments The authors thank Research Management Center, Universiti Teknologi Malaysia (UTM) and the Ministry of Science, Technology and Innovation (MOSTI), Malaysia for IRPA research grant Vot. No. 74227.
References [1]
Oakland JS. Statistical Process Control. Butterworth-Heinemann. Oxford (1996). [2] Theodoridis S and Koutroumbas K. Pattern Recognition Systems. Academic Press, San Diego. (1998). [3] Battiti R. Using Mutual Information for Selecting Features in Supervised Neural Net Learning. IEEE Transactions on Neural Networks. Vol. 5, No. 4 (1994) pp. 537-550. [4] Hassan A, Baksh MSN, Shaharoum AM and Jamaluddin H. Improved SPC Chart Pattern Recognition Using Statistical Features. International Journal of Production Research. Vol. 41, No. 7. (2003) pp. 1587- 1603. [5] Zeki AA and Zakaria MS. New Primitive to Reduce the Effect of Noise for Handwritten Features Extraction. IEEE 2000 Tencon Proceedings: Intelligent Systems and Technologies for the New Millennium. (2000) pp. 24-27. [6] Amin A. Recognition of Printed Arabic Text Based on Global Features and Decision Tree Learning Techniques. Pattern Recognition. 33, (2000) pp. 1309-1323. [7] Utku H. Application of the Feature Selection Method to Discriminate Digitized Wheat Varieties. Journal of Food Engineering, 46, (2000) pp. 211-216. [8] Pham DT and Wani MA. Feature-based Control Chart Pattern Recognition. Int. J. Prod. Res. Vol. 35, No.7 (1997) pp. 1875-1890. [9] Montgomery DC. Design and Analysis of Experiments. 5th edn., (2001) John Wiley & Sons, New York. [10] Jones B. Design of Experiments. In Quality Engineering Handbook (Pyzdek, T. and Berger, R.W., eds.) (1991). Marcel Dekker, New York, pp. 329-387. [11] Hassan A and Baksh MSN. Recognition of Developing SPC Chart Patterns Using ANN and Stability Tests. International Symposium on Bio-inspired Computing. 5-7 September 2005, Johor Bahru, Malaysia.
447
Intelligent Production Machines and Systems D.T. Pham, E.E. Elduldad and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Optimization of fixture layout by means of the genetic algorithm T. Aoyama a, Y. Kakinuma a, I. Inasaki a a
Department of System Design Enginnerings, Keio University, Yokohama,223-8522, Japan
Abstract The fixturing process for holding and locating workpieces on machine tools is essential in manufacturing systems. In this study, a new fixturing support system is proposed. The elastic deformation of the workpiece caused by the fixturing forces is analyzed by the finite element method, and the optimum fixturing position which results in the minimum form error of the surface to be machined is determined. The genetic algorithm is applied to the optimization process of the fixturing condition. Keywords: Fixture, FEM, Genetic algorithm
1. Introduction The fixturing process for holding and locating workpieces on machine tools is essential in manufacturing systems. This process is one of the important factors affecting machining accuracy and has so far been carried out manually by skilled workers. In order to realize fully automated manufacturing systems in the near future, particularly in small-batch machining and assembly, it is necessary to automate the fixturing process. One of the key factors in realizing an automatic fixturing process is to develop a fixturing support system[I,2,3]. This system provides several important items of information for fixturing such as positioning the workpiece and fixturing forces. In this study, a new fixturing support system is proposed. The elastic deformation of the workpiece caused by the fixturing forces is analyzed by the finite element method, and the optimum fixturing position which results in the minimum form error of the surface to be machined is determined. The genetic algorithm is applied to the optimization process of the fixturing condition.
448
2. Search of the optimal clamping position by means of the Genetic Algorithm Fig. 1 shows an example of a workpiece model used in this study. Considering the basic clamping method, the workpiece clamp was applied using four clampers which were given a uniform clamping force of 3,000N. Young's modulus of the workpiece, Poisson's ratio and density of the material were set at E = 2.1E11 (N/m2), a3= 0.3 and p= 7860 kg/m 3, respectively. Deformation of the workpiece was evaluated using flatnesses of the machined surface as an evaluation factor on the assumption that the surface of the workpiece model is to be machined by a vertical milling machine. As shown in Fig. 1, it was arranged that parts numbered 1 to 40 on the workpiece are clamping positions, the top is the evaluation surface, and the seating is the datum surface. The method to determine the optimum clamping positions utilizes FEM and all clamping position combination conditions are analyzed by FEM for selection of a number of clamping positions among 40 positions, and flatness
of the machined surface is searched to find the minimum flatness. For example, assuming clamping at 4 positions, there are 91,390 combinations from which to select the 4 clamping positions. This may cause a problem by taking too much time for the calculation. Therefore, it is concluded to resolve the problem by applying the genetic algorithm which is the optimization method for determining clamping position combinations. Genetic algorithm (GA) is the optimum value searching method simulating the process of organism gene evolution in nature [4,5,6]. In this study the GA method was applied to fixturing problems for the model described above as shown in Fig. 2. The features are described as below: (1) Each gene (string) is coded as shown in the clamping positions in Fig.1. Strings showing clamping positions correspond to numbers (positions) in Fig. 1. (2) Fitness given to each string is shown as reciprocal numbers of flatness of the machined surface. Namely, a better string with smaller flatness is given higher fitness. (3) To avoid destruction or elimination of excellent strings by reproduction, crossover, mutation, etc. an elite gene saving strategy is adopted. The strategy unconditionally reproduces some different kinds of excellent strings to the next generation. (4) According to the concept that the optimum solution exists around favorable solutions, end conditions are established so that search compulsorily runs around the solution when one and the same solution has continued for more than 10 generations, and if the solution improves computation continues, or if the solution does not improve computation is ended recognizing the solution as optimum. The end conditions include the vicinity search function which enables hybrid search with highly random GA resulting in highly efficient search expectations. (5) In order to avoid selection of unsteady and inappropriate clamping positions on the workpiece shape a space evaluation method is schemed to compulsorily eliminate strings representing clamp positions with a polygonal space formed by a line connecting the clamp centers less than a certain percentage of the projected space of the workpiece. Adding the above described functions to GA the following parameters are established to perform an efficient search: Population size of a generation: 100 Crossover probability:0.5 Mutation probability:0.1
No. of elite strings of a generation: 10 Space percentage: Eliminate less than 40% As a result, from execution using conditions described above, the optimal clamping positions are (5, 16, 25, 39) in Fig. 1 and flatness is found to be 4.717E-21am. On the other hand, when 4 clamping positions (1, 20, 21, 40) of the workpiece are easily selected flatness is 2.156E-1 lam. Comparison of these flatness figures resulting in 0.235 times showed greater improvement in flatness. In addition,
I j
P" '
/
/
... ~
Fig. 1 Workpiece model.
( start ) (e~te I n l1u a l ~ i
i
ill
"ie'~';~l
e-,o.,,oi~.-ri l Fitnm .............
L?- - - - ~
;
Sa
..... } ....... ,.____
~
New
Popu|aUon)
~ E N D ~
....
NO
Fig.2. GA for fixturing optimization.
449
convergence to the optimal solution in random number sequence is greatly different on search efficiency because GA depends greatly on random numbers. Fig. 3 shows the relationship between number of generations and flatness resulting from GA execution on 30 kinds of random number sequences. The thin dashed line shows the result of the fastest convergence among 30 kinds of random number sequences, the thin solid line shows the result of slow convergence, and the bold solid line shows an average. Even in the result of the slowest convergence the optimal solution is reached at around 30 generations 9 The average number of generations to reach the optimal solution is 16 generations. Population of a generation of GA is 1.4e-1
Population ,size = 1 0 0 Probability of crossover = 0 . 5 Probability of mutation = 0.1 Number of elite strings = 1 0 Space percentage = 4 0 %
1.2e-1
ft 1.Oe-1 ~ 8.0e-1
age
Worst Best
Average
generation to reach optimal solution = 16th generation
~ 6.0e-1 4.0e-1
0
10
20
30
Generations
Fig.3 Optimization process of fixturing.
defined as 100, the sample strings are 1,600, and namely, the number of calculations of flatness is 1,600 times, with the result that a remarkably high efficiency of optimal value search is achieved.
3. Adjustment of clamping force The above describes how to determine the optimal clamping positions all with uniform clamping force. However, clamping force is adjusted on each clamper with consideration of shape and rigidity of workpieces in the actual fixturing process. In this study an effort is made to improve flatness on the evaluation surface by finding the optimum clamping force with an algorithm for adjusting clamping force in this combined system. For example, in the case of uniform clamping force applied to 4 positions as shown in Fig. 4(a), deformation level distribution of the evaluation surface is roughly represented as shown in the figure indicating 4 deformation groups affected by each clamper. G1 to G4 in Fig. 4 represent deformation groups, and A to D show maximum deformation level in each deformation group. Each clamper affects the evaluation surface, differently resulting in values of the maximum deformation level of each group. Further improvement of flatness on the
La'r0 io '~176176 F2 _,eve, G4 Strengthen fixturina force
Turn down fixturing fo~
Small
9 Positionof max. deformation in each deformation group C) Deformation group
(a)
(b) Fig. 4 Adjustment of clamping force.
450
evaluation surface can be expected by adjusting the different deformation and uniformly distributing them separately on the evaluation surface as shown in Fig. 4(b). An algorithm of clamping force adjustment using the method described above is described below. Deformation level of A to D under uniform clamping force can be represented by the following equation. Where, Dxr: Deformation level affected by clamper X to position Y on unit load, Prl - PF4: Clamping force of each clamper, 8A-SD:
P"I /
/
Pr4J LDlo D2o D~o D~oJ ~0
(I)
Deformation level at each position. Here, the following relationship is obtained by calculating the inverse matrix from the matrix at the right hand side. In equation (2), the ratio of clamping force can be obtained by making 8A = 8B = 8C = 8D. In addition, the actual clamping force can be obtained
~.
[D,A D,a Ds, D,AI[Pr,]
,=
/D,, D=,
ao
LDlo D~o Dso D,oJLPvd
D=c
D,cllP, H //
/
by multiplying clamping force ratio with a constant rate. 4. Calculation of the maximum permissible machining force
In the actual fixturing process, it is necessary to firmly hold the workpiece so that a fine displacement will not occur on the workpiece when machining force is applied to the workpiece, and to which it is necessary to determine clamping force. Accordingly, the pattern of the fine displacement occurring on the workpiece by machining force is analyzed, and the maximum machining force (maximum permissible machining force) is obtained within the range not causing fine displacement on the workpiece, which is established as a criterion to determine permissible clamping force. When outer force including machining force is applied on the workpiece clamped by a clamper, the strength and direction of force will be roughly
classified according to sliding motion between the workpiece and pallet and the lifting motion of the workpiece. At this time, it is considered that fine displacement may occur under displacement patterns with minimum force required for sliding or lifting. Therefore, the required force to cause each displacement pattern to occur on a workpiece was calculated, and the minimum force was determined as the maximum machining force sustainable for the clamping conditions. The following describes the method of determining the maximum permissible machining force for two kinds of workpiece displacement patterns. 4.1. Workpiece sliding motion[7]
As shown in Fig. 5, in the case that the workpiece is held by friction force between the workpiece and base plate, the workpiece sliding motion occurs, when greater machining force than friction force functions on the workpiece.Then, sliding motion of the workpiece is roughly classified as rotational sliding motion and linear sliding motion, and the sliding motion with the smaller required force dominates. In this study, the sliding pattern is discriminated by calculation of necessary force for rotational linear sliding motion of the workpiece, and the maximum permissible machining force is determined. The algorithm is described as follows. (1) First, counterforce distribution on the seating surface is calculated by FEM. (2) Second, using equation (3), the necessary force (Cfl) for linear sliding motion of the workpiece is obtained by multiplying the total counterforce by the coefficient of friction. i
CF,=u~R,
(3)
Rotational center I
Cutting position Cutting surface Cutting direction Friction force
Fig.5 Rotational sliding motion
451
where, CFl = necessary machining force for linear sliding motion, Ri = counterforce at each point,la=coefficient of friction (0.2), I - total number of points on datum surface. (3) Thirdly, taking the case that workpiece rotational sliding motion occurs and the rotational center of the workpiece and positions acted on by machining force are assumed. (4)Under item (3) above, the necessary force for rotational sliding motion of the workpiece is calculated by the equation of moment balance. Equation (4) is used for calculation of moment balance. 1
CF," • d:~= u ~ R' • d~
(4)
Where, CFr = machining force necessary for rotational sliding motion, die = distance between rotational center and position acted on by machining force, di = distance between the rotational center and each nodal point. Here, the machining force direction is limited to the right direction against the straight line connecting position acted on by the machining force and the rotational center, where the moment in a constant machining force is maximum as shown in Fig. 5. (5) Equation (4) is used to calculate all rotational centers on the datum surface and all positions acted on by machining force on the machined plane surface, and the minimum value is defined as the maximum permissible machining force (CFrmin) for rotational sliding motion. (6)Comparing CFI with CFrmin, the smaller value is defined as the maximum permissible machining force for the clamping condition. It is understood that if CFrminis smaller, workpiece rotational sliding motion occurs under the clamping condition, and if it is larger, workpiece linear sliding motion occurs.
balance of machining force moment as shown in the following equation with assumption of the rotational axis. 4
CF~.d~.sin ok= ~ F ~ . d f ~.cos O~ ~-1 + m" g" dg'cos Og
(5)
Where, CFk = maximum force not causing lifting motion of the workpiece, de = distance between the machined position and the rotational axis, Fi = each clamping force, dfi - distance between each clamping position and rotational axis, Oi= angle of vector connecting rotational axis and each clamping position and the clamping direction, dg = distance between center of gravity of workpiece and rotational axis, ~= angle of vector connecting rotational axis and machined position and machining direction, 6h'= angle of vector connecting rotational axis and each clamping position and clamping direction, Og= angle of vector connecting rotational axis and center of gravity of workpiece and weight direction, m = weight ofworkpiece All candidates for rotational axis are calculated using the formula, and the minimum value was defined as the maximum permissible machining force for lifting motion.
5.2. Lifting motion o f the workpiece Workpiece lifting motion[8] occurs using the edge on the datum surface as the axis, with smaller force than necessary for sliding motion under some workpiece shapes or clamping conditions. The rotational axis for this case can be considered as a straight line connecting the outer circumference of the datum surface. For example, in the case of the model shown in Fig. 1, (A) to (E) shown in bold line in Fig. 6 are candidates for a rotational axis. The maximum force within the range where lifting motion of the workpiece does not occur is obtainable from clamping force, tare weight, and
452
Fig.6 Lifting motion
6. Calculation example Fig. 7 shows the result calculated on the optimal clamping positions (5, 16, 25, 39) as shown in Fig. 1 where the total clamping force was given as 12000N. Each clamping force was adjusted as Pvl = 3088N, PF2 = 2817N, PF3 = 2984N, and PF4 = 3111N, and F4 farthest from the evaluation surface shows the maximum clamping force. Then, the
o
,I---F~r=r~ ~=r=,---"-~
[ FI I W [ v3 ~,F4
F=
= 5 = ~6 = 2s ~ 39
: : : :
3087.748 (N) ] 28~6.9~7 C~) I 2 9 ~ . 0 s 0 oN) j 3111.285 (N) j /
~ P M o u l t of ~k~lng r n o t l o n ~
( * Ro=o~C~.~, ( o,06, o,14. -0.30) [ I o ~,,,o.o, co.,~ [ /
I ,.l[ll( 0"30 ' O'Oa"'0'04 ) [ [ ~ D i ~ of Curdn8 Force |
/ [
~l oo,2.4o.o.oo)l
Max ~ r m m i t a 9 cuninsForce 1136.330 ( N ) / /
~ ' ~ R e ~ l l t oq INthqQntotlon- - ~
/ F l a t n e s s = 4.111 E-2 ( p m )
~ ?
p.rm,ma=** 9
(o.oo,-t.oo,ooo)/
1
Max~.~=ibleC.~.=Force[
~"
lifting axis
J
fo.=,~
ional sliding = 1136.330 ( N ) I
r sliding
= 2400.000 ( N ) ] = 3945.917 ( N ) )
Fig. 7 An example of the calculated results. Table 1 Comparison of the optimized results. Flatness
M a x permissible cutting force
No
4.111E-2 (um)
1137 (N)
yes
4.717E-2 ~m)
1136 (N)
Adjustment of clamping force
(2) It is found that flatness can be improved by adjusting clamping force to uniform affect of each clamper to the machining surface. (3) Optimization of clamping positions and clamping force is realized by taking both workpiece deformation level and workpiece holding factor into consideration, based on the maximum permissible machining force obtained within the range where fine deformation does not occur on the workpiece.
................
flatness is 4.111E-2Bm which shows further improvement when compared with the result of Fig. 1. Further, concerning the maximum permissible machining force, the necessary machining forces are CFr = 1136N for rotational sliding motion, C F I 2400N for linear sliding motion, and C F k - 3946N for lifting motion. Accordingly, the maximum permissible machining force is 1136N under this clamping conditions, that is, the workpiece has the highest probability for rotational sliding motion. Now, comparing results of uniform clamping force with those of adjusted clamping force, as shown in Table 1, results of adjusted clamping force are better for flatness, however, inferior for the maximum permissible machining force. In this case, as one method to determine good clamping conditions or not, flatness can be compared under similar maximum permissible machining forces. 7. Conclusions
The authors developed a clamping condition optimization system to determine the optimum clamping positions and clamping force by analyzing deformation of the workpiece model using FEM. The results obtained in this study are summarized as follows. (1) It is suggested that the genetic algorithm can be applied to the optimization of clamping positions and the effectiveness is confirmed.
Acknowledgements
A part of this study was cooperatively performed with IMS. Authors appreciate Dr. Kazu Watanabe, Hitachi Via Mechanics Ltd., for his useful advice for pursuing this study. References
[1] Menassa R and Devries W. Optimization methods applied to selecting support position in fixture design. J. Engineering for Industry. 113 (1991)412-418. [2] Mark R Rearick S. Hu J and Wu SM. Optimal fixture design for deformable sheet model metal workpiece. Trans. NAMRI/SME Annals. 111 (1993) 407-412. [3] Pelinescu D.M and Wang M.Y. Multi-objective optimal fixture layout design. Robotics and Computer-Integrated Manufacturing. 18, 5-6 (2002) 365-372. [4] Grefenstette J.J Genetic algorithms for machine learning. Kluewer Academic Publishers, 1993, pp 1-32. [5] Krishnakumar K and Melkote S.N. Machining fixture layout optimization using the genetic algorithm. International Journal of Machine Tool and Manufacture. 40-4 (2000) 579-598. [6] Kaya N. Machining fixture locating and clamping position optimization using genetic algorithms. Computers in Industry. 57-2 (2006) 112-120. [7] Lee S and Cho K.K. Comparison off limit surface approach with other approaches in fixture planning with friction. Annals of the CIRP. 43-1 (1994) 331-335. [8] Noaker P.M. Commonsense clamping. Manufacturing Engineering. 11 (1992) 43-47.
453
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
The Bees Algorithm- A Novel Tool for Complex Optimisation Problems D.T. Pham, A. Ghanbarzadeh, E. Kog, S. Otri, S. Rahim, M. Zaidi Manufacturing Engineering Centre, Cardiff University, Cardiff CF24 3AA, UK Abstract A new population-based search algorithm called the Bees Algorithm (BA) is presented. The algorithm mimics the food foraging behaviour of swarms of honey bees. In its basic version, the algorithm performs a kind of neighbourhood search combined with random search and can be used for both combinatorial optimisation and functional optimisation. This paper focuses on the latter. Following a description of the algorithm, the paper gives the results obtained for a number of benchmark problems demonstrating the efficiency and robustness of the new algorithm.
Keywords: Bees Algorithm, Function Optimisation, Swarm Intelligence
1. Introduction
2. Intelligent swarm-based optimisation
Many complex multi-variable optimisation problems cannot be solved exactly within polynomially bounded computation times. This generates much interest in search algorithms that find near-optimal solutions in reasonable running times. The swarm-based algorithm described in this paper is a search algorithm capable of locating good solutions efficiently. The algorithm is inspired by the food foraging behaviour of honey bees and could be regarded as belonging to the category of "intelligent" optimisation tools [ 1]. Section 2 reviews related work in the area of intelligent optimisation. Section 3 describes the foraging behaviour of natural bees and the core ideas of the proposed Bees Algorithm. Section 4 details the benchmarking problems used to test the efficiency and robustness of the algorithm and presents the results obtained. These show that the algorithm can reliably handle complex multi-model optimisation problems without being trapped at local solutions.
Swarm-based optimisation algorithms (SOAs) mimic nature's methods to drive a search towards the optimal solution. A key difference between SOAs and direct search algorithms such as hill climbing and random walk is that SOAs use a population of solutions for every iteration instead of a single solution. As a population of solutions is processed in an iteration, the outcome of each iteration is also a population of solutions. If an optimisation problem has a single optimum, SOA population members can be expected to converge to that optimum solution. However, if an optimisation problem has multiple optimal solutions, an SOA can be used to capture them in its final population. SOAs include the Ant Colony Optimisation (ACO) algorithm [2], the Genetic Algorithm (GA) [3] and the Particle Swarm Optimisation (PSO) algorithm [4]. Common to all population-based search methods is a strategy that generates variations of the solution being sought. Some search methods use a greedy criterion to decide which generated solution to retain. Such a criterion would mean accepting a new solution if and only if it increases the value of the
454
objective function (assuming the given optimisation problem is one of optimisation). A very successful non-greedy population-based algorithm is the ACO algorithm which emulates the behaviour of real ants. Ants are capable of finding the shortest path from the food source to their nest using a chemical substance called pheromone to guide their search. The pheromone is deposited on the ground as the ants move and the probability that a passing stray ant will follow this trail depends on the quantity of pheromone laid. ACO was first used for functional optimisation by Bilchev [5] and further attempts were reported in [5, 6]. The Genetic Algorithm is based on natural selection and genetic recombination. The algorithm works by choosing solutions from the current population and then applying genetic operators such as mutation and crossover- to create a new population. The algorithm efficiently exploits historical information to speculate on new search areas with improved performance [3]. When applied to optimisation problems, the GA has the advantage of performing global search. The GA may be hybridised with domain-dependent heuristics for improved results. For example, Mathur et al [6] describe a hybrid of the ACO algorithm and the GA for continuous function optimisation. Particle Swarm Optimisation (PSO) is an optimisation procedure based on the social behaviour of groups of organisations, for example the flocking of birds or the schooling of fish [4]. Individual solutions in a population are viewed as "particles" that evolve or change their positions with time. Each particle modifies its position in search space according to its own experience and also that of a neighbouring particle by remembering the best position visited by itself and its neighbours, thus combining local and global search methods [4]. There are other SOAs with names suggestive of possibly bee-inspired operations [7-10]. However, as far as the authors are aware, those algorithms do not closely follow the behaviour of bees. In particular, they do not seem to implement the techniques that bees employ when foraging for food.
3. The bees algorithm 3.1. Bees in nature A colony of honey bees can extend itself over long distances (more than l0 km) and in multiple directions simultaneously to exploit a large number
of food sources [7,8]. A colony prospers by deploying its foragers to good fields. In principle, flower patches with plentiful amounts of nectar or pollen that can be collected with less effort should be visited by more bees, whereas patches with less nectar or pollen should receive fewer bees [9,10]. The foraging process begins in a colony by scout bees being sent to search for promising flower patches. Scout bees move randomly from one patch to another. During the harvesting season, a colony continues its exploration, keeping a percentage of the population as scout bees [8]. When they return to the hive, those scout bees that found a patch which is rated above a certain quality threshold (measured as a combination of some constituents, such as sugar content) deposit their nectar or pollen and go to the "dance floor" to perform a dance known as the "waggle dance" [7]. This mysterious dance is essential for colony communication, and contains three pieces of information regarding a flower patch: the direction in which it will be found, its distance from the hive and its quality rating (or fitness) [7,10]. This information helps the colony to send its bees to flower patches precisely, without using guides or maps. Each individual's knowledge of the outside environment is gleaned solely from the waggle dance. This dance enables the colony to evaluate the relative merit of different patches according to both the quality of the food they provide and the amount of energy needed to harvest it [10]. After waggle dancing on the dance floor, the dancer (i.e. the scout bee) goes back to the flower patch with follower bees that were waiting inside the hive. More follower bees are sent to more promising patches. This allows the colony to gather food quickly and efficiently. While harvesting from a patch, the bees monitor its food level. This is necessary to decide upon the next waggle dance when they return to the hive [ 10]. If the patch is still good enough as a food source, then it will be advertised in the waggle dance and more bees will be recruited to that source. 3.2. Proposed bees algorithm As mentioned, the Bees Algorithm is an optimisation algorithm inspired by the natural foraging behaviour of honey bees to find the optimal solution [4]. Figure 1 shows the pseudo code for the algorithm in its simplest form. The algorithm requires a number of parameters to be set, namely: number of scout bees (n), number of sites selected out of n visited sites (m), number of best sites out of
455
m selected sites (e), number of bees recruited for best e sites (nep), number of bees recruited for the other (m-e) selected sites (nsp), initial size of patches (ngh) which includes site and its neighbourhood and stopping criterion. The algorithm starts with the n scout bees being placed randomly in the search space. The fitnesses of the sites visited by the scout bees are evaluated in step 2. 1. 2. 3. 4. 5. 6. 7. 8.
Initialise population with random solutions. Evaluate fitness of the population. While (stopping criterion not met) //Forming new population. Select sites for neighbourhood search. Recruit bees for selected sites (more bees for best e sites) and evaluate fitnesses. Select the fittest bee from each patch. Assign remaining bees to search randomly and evaluate their fitnesses. End While.
Fig. 1 Pseudo code of the basic bees algorithm In step 4, bees that have the highest fitnesses are chosen as "selected bees" and sites visited by them are chosen for neighbourhood search. Then, in steps 5 and 6, the algorithm conducts searches in the neighbourhood of the selected sites, assigning more bees to search near to the best e sites. The bees can be chosen directly according to the fitnesses associated with the sites they are visiting. Alternatively, the fitness values are used to determine the probability of the bees being selected. Searches in the neighbourhood of the best e sites which represent more promising solutions are made more detailed by recruiting more bees to follow them than the other selected bees. Together with scouting, this differential recruitment is a key operation of the Bees Algorithm. However, in step 6, for each patch only the bee with the highest fitness will be selected to form the next bee population. In nature, there is no such a restriction. This restriction is introduced here to reduce the number of points to be explored. In step 7, the remaining bees in the population are assigned randomly around the search space scouting for new potential solutions. These steps are repeated until a stopping criterion is met. At the end of each iteration, the colony will have two parts to its new population
-representatives from each selected patch and other scout bees assigned to conduct random searches. 4. Experiments Clearly, the Bees Algorithm as described above is applicable to both combinatorial and functional optimisation problems. In this paper, functional optimisation will be demonstrated. The solution of combinatorial optimisation problems differs only in the way neighbourhoods are defined. Two standard functional optimisation problems were used to test the Bees Algorithm and establish the correct values of its parameters and another eight for benchmarking the algorithm. As the Bees Algorithm searches for the maximum, functions to be minimised were inverted before the algorithm was applied. Shekel's Foxholes (Fig. 2), a 2D function from De Jong's test suite, was chosen as the first function for testing the algorithm. 25
1 f(Y) = 119.998 - Z 2 '=' J + Z ( x i - a o ) 6
(1)
i=1
_(-32~ -16
ao"
32 - 3 2
-65.536
0
16
32
-32
-32
-32
...
0
< x i < 65.536
For this function, "~'max f('~max
(-32 ,-32 )
= )-
119 .998
.........
......
iliiii iiiiiii~
iiiiiiiiiiiililii !J~i 3iii i !iiii ...... iii~ i~il ~'.'.'.'!'.:':.':.~.':.~.'.:'~'
.......~,,:~:q~
Fig 2. Inverted Shekel's Foxholes
456
16 3,') 2)
... 32 32 3
The following parameter values were set for this test: population n- 45, number of selected sites m-3, number of elite sites e - l , initial patch size ngh=3, number bees around elite points nep-7, number of bees around other selected points nsp=2. Note that ngh defines the initial size of the neighbourhood in which follower bees are placed. For example, if x is the position of an elite bee in the i th dimension, follower bees will be placed randomly in the interval X~e + ngh in that dimension at the beginning of the optimisation process. As the optimisation advances, the size of the search neighbourhood gradually decreases to facilitate fine tuning of the solution.
.I 9rJlYl 503
50)
Fig 4.2D Schwefel's function Inverted Shekel's Foxholes 115125. ~ 105 95 85 ~ 75 65 55
'
.....................................................................................................................................................................
2550 ~.............................................................................................~ ~ ~ 2450 2350 -~
225o1 2150~p
/ / /
/
45
35 - ~// 25
0
,
,
,
500 1000 1500 2000 Visited Points ( Mean number of function evaluations )
Fig. 3 shows the fitness values obtained as a function of the number of points visited. The results are averages for 100 independent runs. It can be seen that after approximately 1200 visits, the Bees Algorithm was able to find solutions close to the optimum. To test the reliability of the algorithm, the inverted Schwefel's function with six dimensions (Eq. 2) was used. Fig. 4 shows a two-dimensional view of the function to highlight its multi modality. The following parameter values were set for this test: population n=500, number of selected sites m=15, number of elite sites e=5, initial patch size ngh=20, number of bees around elite points nep=50, number of bees around other selected points nsp-30. 6
f ( Y ) - - y ' -x, sin([~,l) i=1
< x i <500
Xma x --
(420.9,420.9,420.9,420.9,420.9,420.9)
f(Xma x
) = 2513
.9
For this function,
= 2050 ~_~ 1 9 5 0 i 1 8 ~ o i 1 7 5 0 { 1650 + 1550 I 0 500,000 1,000,000 1,500,000 2,000,000 2,500,000 3,000,000 Visited Points ( Mean number o f function evaluations )
Fig 3. Evolution of fitness with the number of points visited (Inverted Shekel's Foxholes)
-500
Inverted Schwefel's Function ( 6 Dimensions )
(2)
Fig 5. Evolution of fitness with the number of points visited (Inverted Schewefel's Fuction) Fig. 5 shows how the fitness values evolve with the number of points visited. The results are averages for 100 independent runs. It can be seen that after approximately 3,000,000 visits, the Bees Algorithm was able to find solutions close to the optimum. The Bees Algorithm was applied to eight benchmark functions [6] and the results compared with those obtained using other optimisation algorithms. The test functions and their optima are shown in Table 1. For additional results and a more detailed analysis of the Bees Algorithm, see [ 1]. Table 2 presents the results obtained by the Bees Algorithm and those by the deterministic Simplex method (SIMPSA) [6], the stochastic simulated annealing optimisation procedure (NE SIMPSA) [6], the Genetic Algorithm (GA) [6] and the Ant Colony System (ANTS) [6]. Again, the numbers of points visited shown are averages for 100 independent runs. Table 3 shows the empirically derived Bees Algorithm parameter values used with the different test functions.
457
Table 1. Test Functions No Function Name 1
Function
Global Optimum
De Jong
[-2.048, 2.048]
m a x F :(3905.93)
X(1,1) F=3905.93
Goldstein & Price
[-2, 21
minF :[1 +(X,+X2+ 1)2(19-14Xl -~-3X~-14X2+6 X l X 2 -t-3X22)] X[30+ (2X-3X2)2(18-32X,+ 12X~+48X2-36X~X2+27X22)]
Interval
100(X ~ X2)2
(l-x1)2
2
minF =a(xz-bXl +CXl-d) 2+e(1- f)cos(x)+e Branin
[-5, lo]
5.1(7) 2 5 a : l , b : - - ~ - 2-2 ' c = - ~ X 7 , d = 6 , e : l O ' f : l X 7 822
Martin & Gaddy
[0, 10]
min F = (Xl
Rosenbrock
(a) [-1.2, 1.2]
(b)[-10, 10]
min F =
X2
100
2
(Xl
X2 )2 + (1
X l )2
3
Rosenbrock
[-1.2, 1.2]
minF = ~ { 1 0 0 ( X ~ Xi+I )2 + (1 Xi) 2} i-1
Hyper sphere
[-5.12,5.12]
min F = ~ X i
6
[-512,512]
F=0
max F = 0.1+ ~
Xi
func no
=
mean no. of evaluations
mean no. of evaluations
2 3 4 5a 5b 6
Z 100 100
1
100 100 99
10780 12500 21177
100 100 94
4508 5007 3053
4000
x(0,0,o,o,o,0,0,0,0,0)
_l!2Icos/X?_~./+l
F=10
i=|
ANTS
GA
NE SIMPSA
mean no. of evaluations 10160 5662
100 100
mean no. of evaluations 6000 5330
1
15468
1
1936 1688 6842 7505 8471 22050
1
200000
1
50000
100 100 100
F=0 X(1,1) F=0
x(o,0,0,0,o,0)
2
\ i:l
Table 2. Results SIMPSA
X(-22/7,12.275) X(22/7,2.275) X(66/7,2.475) F=0.3977272
X(1,1,1,1) F=0
i=l
Griewangk
F=3
x(5,5)
+ ((Xl + X2 10) / 3) 2 2
x(o,-1)
7325 2844 10212
100 100 100 100 100
Bees Algorithm mean no. of evalumions 100
868
100
999
100
1657
100
526
100 100
631 2306 28529
100
7113 1847
**** Data not available The optimisation stopped when the difference between the maximum fitness obtained and the global optimum was less than 0.1% of the optimum value or less than .001, whichever was smaller. In case the optimum was 0, the solution was accepted if it differed from the optimum by less than 0.001. The first test function was De Jong's, for which the Bees Algorithm could find the optimum 120 times faster than ANTS and 207 times faster
458
than GA, with a success rate of 100%. The second function was Goldstein and Price's, for which the Bees Algorithm reached the optimum almost 5 times faster than ANTS and GA, again with 100% success. With Branin's function, there was a 15% improvement compared with ANTS and 77% improvement compared with GA, also with 100% success.
Table 3. Bees Algorithm parameters fun no
n
m
e
nl
n2
ngh (initial)
1
10
3
1
2
4
0.1
2 3 4
20
3
1
1
13
0.1
30
5
1
2
3
0.5
20
3
1
1
10
0.5
5a
10
3
1
2
4
0.1
5b
6
3
1
1
4
0.5
6 7
20
6
1
5
8
0.1
8
3
1
1
2
0.3
8
10
3
2
4
7
5
Functions 5 and 6 were Rosenbrock's functions in two and four dimensions respectively. In the two-dimensional function, the Bees Algorithm delivers 100% success and good improvement over the other methods (at least twice fewer evaluations than the other methods). In the four-dimensional case, the Bees Algorithm needed more function evaluations to reach the optimum with 100% success. NE SIMPSA could find the optimum with 10 times fewer function evaluations but the success rate was only 94% and ANTS found the optimum with 100% success and 3.5 times faster than the Bees Algorithm. Test function 7 was a Hyper Sphere model of six dimensions. The Bees Algorithm needed half of the number of function evaluations compared with GA and one third of that required for ANTS. The eighth test function was a ten-dimensional function. The Bees Algorithm could reach the optimum 10 times faster than GA and 25 times faster than ANTS and its success rate was 100%. 5. C o n c l u s i o n
This paper has presented a new optimisation algorithm. Experimental results on multi-modal functions in n-dimensions show that the proposed algorithm has remarkable robustness, producing a 100% success rate in all cases. The algorithm converged to the maximum or minimum without becoming trapped at local optima. The algorithm generally outperformed other techniques that were compared with it in terms of speed of optimisation and accuracy of the results obtained. One of the drawbacks of the algorithm is the number of tunable parameters used. However, it is possible to set the parameter values by conducting a small number of trials.
Other optimisation algorithms usually employ gradient information. However, the proposed algorithm makes little use of this type of information and thus can readily escape from local optima. Further work should address the reduction of parameters and the incorporation of better learning mechanisms. A c kn owl edge me n ts
The research described in this paper was performed as part of the Objective 1 SUPERMAN project, the EPSRC Innovative Manufacturing Research Centre Project and the EC FP6 Innovative Production Machines and Systems (I'PROMS) Network of Excellence. References
[1 ] Pham DT, Ghanbarzadeh A, Koc E, Otri S, Rahim S and Zaidi M. The Bees Algorithm. Technical Note, Manufacturing Engineering Centre, Cardiff University, UK, 2005. [2] Dorigo M and Stiitzle T. Ant Colony Optimization. MIT Press, Cambridge, 2004. [3] Goldberg DE. Genetic Algorithms in Search, Optimization and Machine Learning. Reading: Addison-Wesley Longman, 1989. [4] Eberhart, R., Y. Shi, and J. Kennedy, Swarm Intelligence. Morgan Kaufmann, San Francisco, 2001. [5] Bilchev G and Parmee IC. The Ant Colony Metaphor for Searching Continuous Design Spaces. in Selected Papers from AISB Workshop on Evolutionary Computing. (1995) 25-39. [6] Mathur M, Karale SB, Priye S, Jayaraman VK and Kulkarni BD. Ant Colony Approach to Continuous Function Optimization. Ind. Eng. Chem. Res. 39(10) (2000) 3814-3822. [7] Von Frisch K. Bees: Their Vision, Chemical Senses and Language. (Revised edn) Cornell University Press, N.Y., Ithaca, 1976. [8] Seeley TD. The Wisdom of the Hive: The Social Physiology of Honey Bee Colonies. Massachusetts: Harvard University Press, Cambridge, 1996. [9] Bonabeau E, Dorigo M, and Theraulaz G. Swarm Intelligence: from Natural to Artificial Systems. Oxford University Press, New York, 1999. [10] Camazine S, Deneubourg J, Franks NR, Sneyd J, Theraula G and Bonabeau E. Self-Organization in Biological Systems. Princeton: Princeton University Press, 2003.
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Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eels) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Agents in the supply chain" lessons from the life sciences J. Efstathiou A. Calinescu b aDepartment of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK b Computing Laborato~, University of Oxford, Oxford OX1 3QD, UK
Abstract
Supply chains are complex, dynamic organisations that may have undesirable properties, such as the Bullwhip effect. Attempts to map supply networks have been frustrated by the dynamic nature of these organizations. A multidisciplinary research project at the University of Oxford is integrating researchers from the life and social sciences with engineers, physicists, computer scientists and engineers to address fundamentally the modelling and understanding of complex, agent-based dynamic networks. The goal of the project is to enable the design and management of complex distributed systems, such as supply chains. The case study networks of the project include fungal networks, slime moulds and ant colonies. This paper will introduce the research project and explain the advantages of agent-based modelling of supply networks. Keywords: Supply networks, agent-based systems, networks
1. Introduction
Manufacturing organisations are under pressure to deliver rapidly, flexibly and leanly. These apparently incompatible goals make life particularly difficult in the global supply network. Although the Internet offers enormous potential for distributing information rapidly around the supply base, we have yet to understand and exploit fully the potential of the Internet to distribute material, as opposed to information, efficiently and effectively. Furthermore, we must recall that information is expensive to generate, transmit, process and store, and it might not be feasible for all participants in a supply network to treat information in the same way. Increasing the amount and frequency of information distributed around the Internet might not be the best and only way to improve material flow. Hence, we must explore ways in which the management of information can enhance the performance of manufacturing supply networks.
A multi-disciplinary project at the University of Oxford is bringing together researchers from the life, social, physical and mathematical sciences to address this problem, within the context of a larger project on complex, dynamic, agent-based networks. This cluster of researchers will investigate ways in which lessons from the life sciences may be transferred to the design and management of complex networks of entities in supply chains and other distributed networks. The outline of the paper is as follows. Section 2 will develop the notion of the supply chain as a complex network, and elaborate on some of the characteristics of the supply network and how they are likely to compare with other complex agent-based networks. Section 3 will define complex agent-based dynamic networks in general. Section 4 will introduce the activities of the Cabdyn research cluster and explain its research agenda, including some of the findings from the life sciences, especially on the study of fungal networks. Section 5 lists some open
461
questions for the design and operation of manufacturing supply networks, Section 6 indicates some of the recent findings from our work so far, and Section 7 concludes the paper with a short summary.
2. Supply chains as complex networks Although early models of supply chains modelled supply chains as linear structures, it was quickly realised that supply chains were much more complex systems, with a tree-like structure of suppliers feeding goods, material and information towards a major supplier to the consumer or retail market. The automobile industry in particular is credited with identifying the roles and responsibilities of the separate tiers within the supply tree, and proceeding to rationalise the structure of their supplier base so as to focus on price and quality. The Japanese automobile supply system is arranged to support what is generally supposed to be a much more stable system of long-term, committed relationships between suppliers and the ultimate focal firm. The focal firm is well-informed and can exert a governance capability on its suppliers, who are willing to commit their fortunes to a well-informed and authoritative market leader. The medley of the open, competitive market is substituted by the "visible and informed hand of the buyer" [1], which imposes a discipline on the suppliers, while enabling development, in products and processes, and providing adequate return on investment. These keiretsu are focussed on separate firms, competing in the global marketplace, so that competition is between the supply systems rather than the separate, global automobile companies. The existing models of the supply system may be summarised as: 9 Simple, stable linear systems, that deliver product to focal firms, 9 Stable tree-like structures, committed to a wellinformed focal firm, with competition between supply systems, 9 An open market of uncommitted, dynamic firms that constantly compete with each other to win orders based on price and agility. The truth is probably a mixture of all of these. Attempts to map supply chains have had limited success, despite many efforts. We would assert that part of the reason for this difficulty lies in the dynamic nature of supply systems, in other words that they will not stand still long enough to be captured by conventional academic methods. So, rather than
462
focussing on the stable characteristics of supply networks, and trying to map a static system we should look instead on supply networks as dynamic systems, model those systems and see how that helps us to understand and develop supply systems for the future. An important development in the supply world is, of course, the Internet. We are just beginning to understand how the Internet will enable the rapid flow of goods, expertise, information and material around the world. However, many researchers are identifying virtual organisations with new methods of exchanging information [2,3]. The globalisation of the world economy is introducing new practices and many more new companies of all sizes into the supply network. Given these many models and many technological advances, how may we develop a model of the supply system that will guide us forwards? We will present a model of the supply system as a dynamic network in the next section, and identify some of the characteristics of such a network.
3. Complex agent-based dynamic networks We characterise a general complex agent-based dynamic network as having, inter alia, the following characteristics.
3.1. Network structure The network structure should have: The number of nodes in the network may change over time. 9 The direction of flow between nodes of the network may be uni- or bi-directional. 9 The links may have a range of weights or strengths, equivalent to the volume of flow between nodes in the network. 9 Flows between nodes may be intermittent or continuous. 9 The direction of flow between nodes may reverse. 9 The material flowing between the nodes may change in nature. 9 The number of in-links and/or out-links to/from nodes may not be the same for all nodes.
9
3.2. Network dynamics The dynamic characteristics of the network structure include: 9 The links between nodes may be formed or broken. 9 The number of in-links or out-links to/from each
node may change over time. 3.3. Network agents The characteristics of the agents in the network include: 9 Agents may be homogeneous or heterogeneous. 9 Agents may learn. 9 Agents may forage to seek out new resources. 9 Agents may leave the network and new agents may join. 9 The distribution of types of agents may change over time. The above are characteristics of complex agentbased dynamic networks, and are not confined to supply networks alone, although it is clear that many of the characteristics listed above are relevant to a supply system, whether structured linearly, as a tree or as a dynamic, market-based network. 3.4. Network behaviour A number of measures have been devised to compare networks of this kind and recent research has identified and measured the characteristics and behaviour of many real world networks that exhibit some of the above characteristics. See for example [4] for a comprehensive survey of networks, including random graphs and scale-free networks, and real-world applications such as cells and genes, the spread of diseases, traffic, economic networks and food webs. A crucial feature of such networks is that there is not a central co-ordinating mechanism. Agents act independently, although they may observe the behaviour of other agents and copy their behaviour if the other agents appear to have a better than random chance of succeeding in the network. Each agent is acting to maximise its own benefits, but by participating in the network, it does better than if it were acting outside of the network structure. Whatever overhead is involved in maintaining links and communicating with other agents is outweighed by the benefit that it gains. Given this lack of central co-ordination and management, one of the surprising observations of these networks is that sometimes apparently planned or organised behaviour emerges spontaneously. Indeed, this is sometimes listed as one of the defining characteristics of these networks, i.e. emergent behaviour. Examples of this are convoys forming in traffic networks, toroidal swim patterns in schools of fish and the emergence of lanes in the flow of
pedestrians and ants [5]. In designing and managing complex agent-based networks, one of the challenges is to devise networks that achieve desirable emergent behaviour, while avoiding undesirable emergent behaviour. Thus, in the design of supply networks, we would like to achieve networks that are capable of achieving the delivery of goods reliably, at predictable times, and at reasonable cost, while enabling innovation in the introduction of new products. Before we can achieve these goals on the design of manufacturing supply networks, we need to understand more about the design of complex networks. The Cabdyn research cluster is embarking on a research program to achieve those aims, based on a multidisciplinary understanding of complex networks. Cabdyn's research plan will be outlined in the next section.
4. The Cabdyn research project A multi-disciplinary project at the University of Oxford is bringing together researchers from the life, social, physical and mathematical sciences to address this problem, within the context of a larger project on complex, dynamic, agent-based networks. The goals of the project are: 1. To integrate the tools from the many disciplines for measuring and modelling agent-based networks. 2. To identify the behaviour of the kinds of agents within existing networks and their modes of interaction that give rise to desirable behaviour of the global network. 3. To explore how man-made networks may extend or modify their current behaviour in order to acquire desirable properties from these networks. The research methods of the existing project involve experimentation on biological systems, e.g. fungal networks, slime moulds and ant colonies. These biological models have demonstrated their ability to 9 forage effectively for food sources, 9 adapt their behaviour in times of shortage of resources, 9 distribute nutrients efficiently around the network, 9 withstand assault by predators and accident, and 9 survive for millennia. Previous related research has focused on the structure of networks or on the behaviour of agents. This project will be one of the first pieces of work to integrate both sets of work to achieve a more thorough understanding of the emergent properties of complex
463
dynamic networks. These networks are of more interest, since designed or engineered networks in the real world are likely to have the characteristics of these networks. The first phase of the project is the integration of tools and techniques for the measurement of network characteristics, so that networks from the life sciences may be compared with those from the engineered world, including computer networks, business networks and supply networks. A preliminary study of a voting group has led to new ideas on the measurement of network dynamics, leading to understanding of the persistence of links in a network and the ability of some agents to anticipate the preferences of other voting members [6]. A suite of software integrating these tools will be one of the early outcomes of the project. The second phase is the "reverse problem", i.e. the identification of the network dynamics and agent behaviour that could account for a particular set of observed or desired behaviour. Preliminary computer simulations of dynamic agent-based networks have successfully modelled some aspects of the behaviour of fungal networks, suggesting that the "reverse problem" of identifying the crucial aspects of the agents' and network dynamics may be addressed. However, there will be many possible combinations of agent behaviour and network dynamics that could possibly account for global network behaviour. Some new scientific principles will need to be identified to select among the candidate models. Preliminary thinking suggests that maximum entropy in the micro domain and some property of smoothness and differentiability will be needed in the macro domain. The third phase of the project is the "forward problem", i.e. the application and extension of these ideas to existing or man-made networks in order to transfer, to some extent, the desirable properties to naturally occurring networks to engineered networks. The networks that may be interested in the solution of this problem include supply chains, virtual manufacturers, distributed computer networks and distributed organisations. The first two phases of the project will require a new approach to the modelling and measurement of supply chains, identifying: 9 the structure of supply networks in different manufacturing sectors, 9 the dynamics of information and material flow within these networks, 9 models ofbehaviour of agents at different tiers of the network.
464
This data will be required to construct simulations of existing supply networks, which may then be modified in order to test the effectiveness of the proposed new strategies for network structure, information flow and local decision-making. The performance measures of interest will include system robustness, flexibility, information load and material flow velocity. The consequences of such a project will also be of interest in the development of protocols for the exchange of manufacturing information around a "Manufacturing Grid", but with respect to the dynamics of information flow, rather than its format only. We have already mentioned the difficulties of mapping supply chains, so this project will take a different approach. Rather than focusing on mapping the static structure of the supply network, the approach will focus on capturing the dynamics of the agents in the networks and the characteristics of the flow of information and material that agents may deliver. We will be able to use agent-based simulations to compare differing patterns of network structure and patterns of communication, in order to understand the emerging characteristics of the dynamic network. The agent-based approach to understanding supply chains has several advantages compared to traditional discrete event simulations. Agent-based modelling (ABM) has the potential to aid our understanding of the behaviour of planning and control structures applied to complex networks of resources and flows. One of the major attractions of ABM is the potential to observe, understand and explain the paths for emergent behaviours, both desirable and undesirable. Agentbased models may highlight design and management strategies that might otherwise not be considered. A key insight could be in understanding the level of order that is optimal for particular performance objectives. Insights may be generated on how much prescription or autonomy is desirable or whether such systems can operate as self-organising systems. More generally, can system architectures be found in which desirable global behaviour emerges without the need for centralized control? In the design of business systems, particularly for the larger more complex operations, robustness and low risk attributes are more desirable than limited goals of short-term optimality. Indeed robustness and low risk may be the true hallmarks of optimal business systems design. ABM may offer an approach that throws light on how to attain such system attributes and provide generic results for the design of operations planning and control systems. The potential long-term generic benefits of using
ABM in manufacturing and supply chains, derived from the specific features in Table 1 include: 9 More realistic models of manufacturing systems; 9 Enhanced abilities to investigate a specific problem; 9 Better insight into methods of organization, design, management and control; 9 Generation and validation of general knowledge on manufacturing and complex systems; 9 Understanding the benefits and drawbacks of emergent behaviour. The benefits of ABM come with increased costs of identifying the problem, gathering the information required at the required level of detail, generating and validating the model, and interpreting the results. The practical issues in ABM include [7]: 9 The number of agents can be adjusted; 9 The number and type of characteristics of each agent can be adjusted; 9 The level of complexity of the agents can be adjusted (including adaptability); 9 Agents can aggregate and disaggregate; 9 As for any modelling technique, a problem/purpose needs to be specified prior to the definition of the agent-based model; 9 The data is easily translatable into the model, and the results are easier to understand and interpret; 9 ABM has high computational requirements when applied to large complex systems; 9 Access to data.
5. Open questions on agent-based modelling and complex manufacturing and supply chain systems
9
9
Investigate the validity of the theoretical results obtained for one or two-dimensional systems when applied to multi-dimensional systems. How should the channel capacity (and therefore the information rate) between agents be decided. Should the channel capacity be different at different levels? How complex should agents be?
2.
Practical questions (case study and simulations)
9
Observe and analyse how do organizations manage information. Address how to deal with incomplete, corrupt, difficult, or costly-to-obtain measurements in manufacturing systems. Investigate and assess the impact of localised optimisation in decision-making on the performance of manufacturing and supply chain systems. Identify specific instances of emergence and assess their impact on the organization.
9
9
9
9
3.
Generic knowledge
9
Assess the impact that the assumptions on the environment, the manufacturing system and the information context have on the agent-based model and on the insights it provides. Compare ABM with the results provided by discrete-event modelling techniques. Validate the theory using real case study data of large-scale complex systems such as manufacturing systems, supply chains or the Internet. Advance towards understanding the interdependences between complexity and sustainability, such as what must be done to achieve sustainability or whether sustainability is consistent with ongoing economic growth [ 11,12]. The role of contracts in managing and controlling supply chains.
9 9
9
Major open questions facing researchers in manufacturing and supply chain systems include [8, 9]:
1.
Theoretical questions
9
Assess the impact of the level of information centralisation versus decentralisation on [8, 10]: Collective and localised decisions; Number of levels; Communication costs; Information volume, structure, content, storage and processing requirements; Global system structure, behaviour and performance; Identify and/or define the methods used by the agents and the network to cope with incomplete or poor quality information. Identify the costs of information measurement, transmission and processing.
9
9
9
6. Interim results on supply networks as complex dynamic systems An extensive dataset on the New York Garment Industry has been mad available to the Cabdyn research cluster. The data consists of financial transactions between firms within the New York Garment industry over a period of 11 years. The data records the size of the transactions between firms, aggregated over periods of months of years. Refunds are also recorded. So far, the analysis has concentrated on the investigating the structure and dynamics of the unweighted, undirected network [13]. The number of
465
active firms in the network declines from about 3,000 to over 1,000, with a total of over 7,000 distinct firms appearing in the data. This is a rare example of a shrinking network, something that does not occur very often in nature. Preliminary findings show that, despite the network's changes in size and population, some network statistics remain remarkable consistent over time, particularly average shortest path, betweenness and degree. We have also found that long-term, embedded relationships between firms and short-term, market relationships may be distinguished. We have investigated the effects of these market relationships on the likelihood that a firm will survive in the market. The data covers the period of the introduction of the North American Free Trade Agreement. This had a measurable effect on the network's statistics, and the behaviour of the participating firms. 7. Conclusion This paper introduces the Cabdyn (Complex Agent Based Dynamic Network) cluster at the University of Oxford. The cluster has received funding from the EU and the University of Oxford, and is embarking on an ambitious project to develop understanding of the emergent behaviour of networks. The project is grounded in studies of real-world networks, including fungal networks, as well as social networks such as business or innovation networks, and engineered networks, for example supply chains and somputer networks. Over the next three to four years, we will be integrating measures and models from a number of academic disciplines to develop a software suite to support new models of system behaviour. There is a large number of unanswered research questions, on the generic nature of networks as well as on the specifics of supply networks. We hope to report preliminary work in a series of conferences, conference presentations, journal articles and books over the next few years. Acknowledgements We acknowledge support from the EU under FP6 project MMCOMNET. Oxford University is a member of the EU-funded I'PROMS Network of Excellence. We gratefully acknowledge colleagues in the Cabdyn rese arc h c luster: http ://sbsxnet.sbs.ox.ac.uk/complexity/complexity_splash_2003.asp
466
References [1] Nassimbeni G. Supply Chains" A Network Perspective, in Understanding Supply Chains, pp 43-68, eds. New S and Westbrook R, Oxford University Press, 2004. [2] Evans P and Wurster TS. Getting real about virtual commerce, Harvard Business Review, (1999), 77 85-94. [3] Wise R and Morrison D. Beyond the Exchange" The Future of B2B, Harvard Business Review (2000) 78:86-96. [4] Bornholdt S and Schuster HG. Handbook of Graphs and Networks, Wiley-VCH, Weinheim, 2003. [5] Couzin, ID and Franks, NR. Self-organised lane formation and optimised traffic flow in army ants. Proceedings of the Royal Society of London, Series B. (2003)270, 139-146. [6] Fenn D, Suleman O, Efstathiou J and Johnson NF. How does Europe Make Its Mind Up? Connections, cliques, and compatibility between countries in the Eurovision Song Contest. Physica A: Statistical Mechanics and its Applications (2006) 360 576-598 [7] Bonabeau E. Harnessing Business Complexity through Agent-Based Modelling, Seminar given to LSE, UK, February 25, 2003, http ://www.psych.lse.ac.uk/complexity/PDFiles/S e minars/Bonabeau_seminarFeb03.pdf [8] Calinescu A. Manufacturing Complexity: An Integrative Information-Theoretic Approach, D.Phil Thesis, University of Oxford, UK, 2002. [9] Johnson NF, and Hui PM. Crowd-Anticrowd Theory of Collective Dynamics in Competitive, Multi-Agent Populations and Network. Presented at the Workshop on Collectives and the Design of Complex Systems, Stanford University, USA, August 25-28, 2003. [ 10] Jones AT, Reeker LH, and Deshmukh AV. On Information and Performance of Complex Manufacturing Systems, Tackling industrial complexity: the ideas that make a difference, Proceedings of the 2nd International Conference of the Manufacturing Complexity Network, University of Cambridge, UK, 9-11 April 2002, G. Frizelle and H. Richards (Eds.), 173-182, 2002. [11 ] Kauffman SA. Colloquia Live. Complexity Research and Its Challenge to Other Disciplines. http ://chronicle.com/colloquylive/2001/05/complex ity/2001. [12] McCarthy IP. Manufacturing Fitness and NK Models, Tackling industrial complexity: the ideas that make a difference, Proceedings of the 2nd
International Conference of the Manufacturing Complexity Network, University of Cambridge, UK, 9-11 April 2002, G. Frizelle and H. Richards (Eds.), 27-40. [13] Saavedra S., Reed-Tsochas F., Efstathiou J., Uzzi B., Emergent structures and dynamic processes in a distributed manufacturing network, Submitted to ECCS06, Oxford, September 2006.
467
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All rights reserved.
Coordination model in the supply chain R. Affonso a, F. Marcotte a, B. Grabot a a
Laboratoie de Gestion de Production, Ecole Nationale d'Ing6nieurs de Tarbes, 4 7,Avenue d'Azereix, F-65016 Tarbes Cedex, France
Abstract
In the current economic environment, companies have to be able to adapt themselves to the market changes, while keeping their global efficiency. Therefore, coordination and collaboration between companies become an important point to be considered when considering the overall performance of the supply chain. First of all, the respect of the global objectives of the supply chain requires the companies to change their policies in order to adapt their local behaviour to these global objectives. On the other hand, changes in one of the partner may affect the overall behaviour ofthe supply chain. Thus, the main objective of this paper is to describe how the parameters ofthe collaboration of a company in a supply chain can influence the parameters of collaboration of the others. Key elements to model inter- company coordination are proposed, considering the collaboration aspects.
Keywords: Coordination, collaboration, key parameters
1. Introduction
Companies are facing an instable environment with a short visibility on the market. Consumer constraints are more and more important, including smaller quantities, higher variability, increased customization, higher quality, shorter delivery and product life cycle. It is therefore important for companies to be able to adapt quickly while keeping an overall efficiency. Overall efficiency is the purpose of integration and co-ordination intra- and interenterprise. Thus, the interest in the field of Supply Chain Management (SCM) has long been increasing among researchers, consultants and managers. With the generalisation of such supply chains, a change has been noticeable in the way the companies coordinate, from a supply chain where each company operates independently in his own self-interest, using only locally available information, to a supply chain with a fully coordinate decision-making approach, in
468
which all information and decisions are aligned to accomplish the global system objectives [12]. A problem is that, even in such networks, the partners remain legal and economic autonomous entities, which require to also taking into account their specific objectives and constraints. Thus, the coordination and the collaboration in a supply chain will be in charge satisfying at the same time local requirements of each company, and global requirements imposed by the market. This article suggests an analysis of global and local coordination elements in a supply chain management. These elements are the core of the collaborative process. The paper is organised as follows: the problem of coordinating partners in a supply chain is first addressed. Then, a theoretical model is suggested. In the forth section, a supply chain taxonomy is described, based the companies' added value to the final product, and the company dependence on the
supply chain. Finally, we discuss some preliminary and perspectives of work related to the intelligent control of supply chains...
2. Supply chain management: coordination problem
a
complex
Supply chains consist of networks oforganisations which are involved, through upstream and downstream linkages, in the different processes and activities that produce value in the form of products and services in the hand of ultimate customer [2]. SCM is so a coordination and integration of all those activities associated with moving goods from the raw stage through to the end user, for sustainable competitive advantage. This includes activities like systems management, sourcing and procurement, production scheduling, order processing, inventory management, transportation, warehousing, and customer service [4, 11]. Some authors [2, 7] consider that co-ordination and integration mechanisms are the basic characteristics of SCM. Malone [9] defines co-ordination as a pattern of decision making and communication among a set of actors who perform tasks to achieve goals. In that context, integration aims at breaking boundaries between company functions, then between companies in the SC. The understanding of the co-ordination mechanisms can help managers to select the most appropriate action from a set of alternative solutions. A deep understanding of the interactions between functions and between companies is a pre-condition for bringing the managers to a real collaborative approach. The partners of a collaborative supply chain bring at the same time new resources and constraints to the collaboration. Collaboration can so contribute to the reduction of local weaknesses, if companies can share tasks and responsibilities to reach a given objective. Nevertheless, it is necessary to give up the former configuration of a dominant company linked to its suppliers, for another where the supply chain is composed of a group of allied companies which relations will be based on their mutual objectives, and not on a hierarchical link. In that context, the risk is that choices related to global requirements can be inadequate for one of the companies, preventing from having a constructive relation between companies. According to their local strategies and according to their weight within the supply chain, companies will involve themselves in a more or less strong way.
To avoid this, it is important to identify, at the same time, local and global implication stakes of a supply chain, the resources brought to the collaboration, but also the risks, constraints or local requirements related to these resources. Thus, if the global effectiveness ofthe supply chain depends on the coordination and collaboration results between the different partners, the quality of these collaboration and coordination is directly linked to these stakes, to these resources and to these constraints and local requirements. The choice of joining a supply chain can be motivated by multiple reasons. According to Le Run [8], the decision of externalising, of keeping the internal competences, or of being partner, will depend on the balance between two factors, namely the function criticality for the company, and the competence level of this company in the management of this function (see Fig. 1).
Researcha [ ~~~~ .s. -~
~
competence partner [
Bea partner
a~
~-..=
] f i !!!I
Weak
Strong
Company competences to control this function Fig. 1. Strategic criteria of choice. For Chung [3], the supply chain stake is rather to improve the sales forecasts and to reduce the inventory. To reach this, he proposes the Collaborative Planning, Forecasting and Replenishment (CPFR) to integrate companies, developing and supervising operational plans and the forecast between partners. Other studies suggest simple information sharing as being the most important stake. Teck-Young Eng [13] proposes a "Mobile SCM" to extend the management system intra- and inter- company. It allows the members of the supply chain to make on line transactions, in order to share and exchange updated information. These exchanges should provide a better customer service, a better logistic, transport and inventory level control. ERP, Web Portals and Internet are also useful tools for coordinating companies in the supply chain by controlling information flow, processes and transactions, especially in the case of a centralised management of the supply chain.
469
On the other hand, after having interviewed industrial mangers, we could note that the supply chain members usually have a poor visibility on the internal processes of the other members; the supply chain is so often reduced to a peer to peer relationship. The supply chain members usually do not know their partners' constraints, the consequences of given decisions for them, and finally the limits beyond which collaboration is not anymore a win-win relationship. In order to address this problem, it is necessary to have more knowledge and understanding about key parameters insuring a good collaboration between partners In the literature, there are many studies about the collaboration advantages, but few of them describe precisely the key parameters that must be analysed and taken into account to ensure an efficient coordination of the companies, i.e. guaranteeing the satisfaction of the global and local requirements for each of them. This article suggests an analysis of these parameters using as a base the GRAI model of a decision activity [4].
3. Coordination model
The GRAI conceptual model of a Decision Activity (DA) describes the environment ofa DA, and its links with the other DAs. This DA model also defines the concept of decision frame [4]. A decision frame is composed of the elements required to take decisions, i.e. mainly objectives, degrees of freedom and constraints on the use of these degrees of freedom. In [10], the GRAI model is completed taking into account the decision environment and the precise definition of the coordination elements, which constitute the decision frame concept. The elements of a Decision frame must be considered on a local (local company management), but also on a global point of view (supply chain management). Thus, for each company of the supply chain, a decision activity will represent the necessary elements to the local control. Then, the possible links between elements of decision centres belonging to different companies can be identified. Some necessary parameters of the coordination
470
between members in the supply chain have been added in the decision frame (see Fig. 2): "Supply chain interest" represents for instance the stake for a company to be a member of the supply chain. The whole supply chain will be also taken into account for a decision frame, through its global objective. The decision frame elements for a company are: Operational Objective: result to be reached for the system managed for the decision centre. Once the objectives are defined, a hierarchy is built. The first objective of this hierarchy will be the priority, the others becoming the criteria to be optimized in the solution choice. For instance, for a company, the operational objectives are represented by the production cost reduction, the customer service (respect of the quantity of products to be supplied per period), and the product quality. After defining the objective hierarchy, the customer service becomes the priority (operational objective), and the production cost reduction and the product quality improving become the criteria. Decision Variable: degree of freedom which allows reaching the operational objective. These decision variables modify the control system. For example, to manufacture products, the company can use subcontracting, overtime, interim ... Constraints: information that represents the limits of the decision variable operation. For instance, "subcontracting is only used for the turning activities"; "the overtimes are limited to 120h/month". These constraints ca also be generated by the others supply chain members. Performance Indicator: quantified data that measures the satisfaction of the operational objective or the performance defined by criteria. Supply Chain Interest: objective that the company tries to reach when it joins of a supply chain. In the case of the Supply Chain decision frame, the only difference is that instead of an operational objective and a supply chain objective, only a global operational objective will be defined. This objective will express the result to be reached by the whole supply chain.
" c 0
Dec|s~:n [r~r
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o L
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c
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"
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Fig. 2. a) Local model of decision activity; b) Model of a decision activity of a supply chain. Analysing these parameters in a supply chain context allows companies to evaluate which decision variables a company can share with its partners within the supply chain, and the additional local constraints which proceed from this relation. It also allows an evaluation of the coherence between the global actions (supply chain decision variables) and the local strategies. Of course, according to the case, the interactions between decision frame elements will depend on the context. In order to better understand these interactions, the criteria that can influence the collaboration modes have been investigated, leading to a supply chain taxonomy.
industry, where the assembly lines impose the rhythm of the whole supply chain. On the other hand, in the case of distribution for example, the strong value is added by the first supply chain link (the manufacturer) compared to the others partners (the distributors). Still in this case, the collaboration principles are generally reduced to a direct hierarchical relation. The DRP (Distribution Requirement Planning) principles are often used in this case. Added Value ( A V )
10
4. S u p p l y c h a i n t a x o n o m y Comp. 3
There are two aspects that influence significantly the companies' behaviour in the collaborative approaches. The first one is linked to the added value (Figure 3). In a supply chain, companies add more or less value to the final product. When the downstream company is the one that adds more value to the product, the collaborative relations can easily become hierarchical ones because, in this case, the costs of capacity adjustment (or of inventory, etc. ) for the downstream company are more important than elsewhere in the supply chain. Thus, it is generally less costly that the other companies of the supply chain adapt themselves to the best solution for the downstream company. An example is the aircraft
Comp. 2
Comp. 1
Companies AV in upstream
- ~ AV in downstre~n
Fig. 3. Cumulated added value. The other point is the dependency of the company on the supply chain to which it belongs. This dependency is mainly characterized by the cash flow associated to the supply chain. Companies usually belong to several supply chains at the same time, and the cash flow that each supply chain represents for a given company influences how this company is involved in the supply chain. A company involved in a supply chain representing a small part of its cash flow (CF) will have a weak dependency on this supply
471
chain, and so a certain power, even if its added value (AV) on the end product is also weak, e.g. the manufacturer of flake in the cosmetic industries. On the other hand, the same company, with an important cash flow share realised in this supply chain, will have a strong dependency related to the supply chain leader, so a propensity to the submission. Once again, the coordination model proposed can highlight the importance of the different coordination elements, and the relation between them. It identifies the interactions between coordination elements in the supply chain related to the influences linked to the companies' added value and their dependence related to this factor. Figure 4 illustrates the types of relation that exist between companies in the supply chain.
..~
co
~
ii:ii shared
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risk
~' ~
"~
Dependent
(D
Weak
Strong
Cash flow that the supply chain represents for the company (CF) Fig. 4. SC companies' classification related to the added value and the cash flow. The leader company (with a strong AV and CF) will impose its own decision frame on the global level. It will also submit its exigencies to the dependent company (weak AV and strong CF).
Supply Chain
I
Oper.Obj.:
- Customer service (lead time) to 100%. D.V.:
- Stock in downstream; - Strategic component inventory in upstream.
Cir. : - Inventory capacity. Perf. Ind. :
- Service Rate 9 Constraint
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:
9
~I~
~
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~-. Dependence relation
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- Service Rate. Int. : Maintain its market.
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.
.
.
.
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Cir. : - M ax. Overtime = 5% (not a lot of fiexibilite) ; - Supply lead time.
-Material i n v e n t o r y ; ~- Strat, O v e me gjc m e . c o mp orient mvento~!,: 'Cir. : - Inventory capacity ; -Max. Oveltime = 20%. P e r f . Ind. : - Service Rate; - Total cost/Nominal Cost. Int, : Increase its market.
Perf. Ind. : - Service
Rate; - Total cost / Nominal Cost. Int. : Market diversification.
Fig. 5. Illustration of coordination mechanism of companies in a supply chain.
472
r--
r,Obj,: (1) Customer service (lead time)t~ 100%; (2) C o st re duction.
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-
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~
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Leader
The company with a weak AV and CF (constraint partner) are not directly submitted to the leader influence. On the other hand, if this company is the only supplier of the supply chain, it becomes a constraint for the leader (except if this company wants to increase its position in the supply chain). The relation between this kind of company and the leader can look like a collaboration relation, but it is rather the leader that has to relax its exigencies to maintain a good performance of the supply chain. Finally, the company with a strong AV but a weak CF will be a strategic partner of the leader of this supply chain. This case is the only one where there will be a real relation of collaboration, since this partner is important for the supply chain performance and the company is not dependent on the supply chain. Both parts have interest to collaborate. Figure 5 shows an example of theses types of relations between companies. Companies are represented by their decision frame, and the influences between the parameters of theses decision frames are summarised. We can note the influence of the leader upon the dependent company through its objective allocation, the collaborative relation with the strategic partner, and the constrained relation between the first link and the leader. In a given supply chain, all these types of partnership do not necessarily exist but it is interesting to notice that the various types of collaboration can be simply depicted thanks to the suggested concepts.
5. Conclusion
This article underlines the importance of defining the key parameters that should be taken into account in order to have a good coordination between companies in a supply chain. In that purpose, a model of the environment of the control decision has been suggested. The constitutive parameters allow describing the support elements of the collaborative approach, which allow the coordination of different actors of the supply chain. This conceptual model also allows evaluating how the parameters influence each other related to the different types of relation that can exists between companies in a supply chain. Through this analyse, we can note that, among different interests and relations of power between companies, collaborative relations are not always priority. Developments of this preliminary study are in progress, which should allow the definition of a more
accurate collaborative model for supply chains. Finally, an industrial case, applied in the Sales and Operation Planning scope, will allow us to validate this approach.
References
[1] Carlsson S., and Hedman J., From ERP Systems to Enterprise Portals. In Future of ERP systems: Technological Assessment (Adam ed.), Hersey, PA, Idea Group Publishing, 2004, pp 254-277. [2] Christopher Martin L., Logistics and Supply Chain Management, Pitman Publishing, London, 1992. [3] Chung W.W.C., Leung S.W.F., Collaborative planning, forecasting and replenishment: a case study in copper laminate industry, Production Planning & Control, 2005, 16 (6), pp 563-574. [4] Doumeingts G., M6thode GRAI : m6thode de conception des systbmes en productique, Th6se d'6tat : Automatique 9Universit6 de Bordeaux I, 1984. [5] Garcia-Dastugue, Sebastian J., Lambert Douglas M., Internet-enabled coordination in the supply chain, Industrial Marketing Management, 2003, 32 (3), pp 251263. [6] Kelle P., Akbulut A., The role of ERP tools in the supply chain information sharing, cooperation, and cost optimization, International Journal of Production Economics, 2005, Vol. 93-94, pp 41-52. [7] Lee H.L., Ng S.M., Introduction to the special issue on global supply chain management, Production and Operations Management, 1997, 6 (3), pp 191-192. [8] Le Run P., Mise en place de d6marches collaboratives : gdn6ralit6s, Technique de 1' Ing6nieur, 2003, Vol. AGL 1, article AG 5 230. [9] Malone, T.W., Modelling coordination in organizations and markets, Management Science, 1987, 33 (10), pp 1317-1332. [10] Marcotte F., Contribution /t la moddlisation des syst6mes de production : extension du mod61e GRAI, thesis of University of Bordeaux I, 1995. [11] Romano P., Co-ordination and integration mechanisms to manage logistics processes across supply networks, Journal of Purchasing and Supply Management, 2003, 9 (3), pp 119-134. [12] Sahin F., Powell R.E., Information sharing and coordination in make-to-order supply chains, Journal of Operations Management, 2005, 23 (6), pp 579-598. [13] Teck-Yong Eng, Mobile Supply Chain Management: challenges for implementation, Technovation, 2005.
473
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 92006 Cardiff University, ManufacturingEngineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Incorporating delay and feedback in intelligent manufacturing strategy selection D.T.Pham, Y.Wang and S.Dimov
MEC, Cardiff University, Cardiff CF24 3AA, UK.
Abstract
In a previous paper, the authors have discussed a new manufacturing strategy selection method that examines simultaneous events and attempts to extract dynamic causal relationships between them. The use of events with the same time stamps permits only the mining of direct, simultaneous associations. This paper proposes a new Dynamic Causal Mining (DCM) approach that takes into account events occurring at different times. This enables the discovery of delayed and feedback relationships among events. The paper gives a simple example illustrating the operation of the new DCMmethod. Keywords: Intelligent manufacturing, Dynamic Causal Mining, System modelling, Strategy selection
1. Introduction
The previous paper
[1] proposed a new
manufacturing strategy selection method combining Game Theory and Dynamic Causal Mining. The combination creates a new type of game, which focuses on the dynamic causal relationships between the action patterns of each player rather than the rationality of optimal selection.
Dynamic Causal Mining (DCM) searches for simultaneous dynamic causal relations in a database. However, DCM ignores delays and feedback, which are an important part of any dynamic system. A delay is the time difference between two dynamic causal events occurring in different parts of the same system. A delayed dynamic causal relationship between two attributes implies a change of attribute values as shown in Figure 1. A dynamic attribute A~ at a time point t~ causes a change to attribute A2 at a time point h. A feedback relation means that the change in A2 at t2, due to A~ at h, would in turn cause the value of A~ to alter at t3.
I
tl
r]~
t2
t3
Figure 1. Delayed dynamic causal relation 474
This paper suggests incorporating delay and feedback into DCM to identify dynamic causal relationships between attributes based on separate time stamps. This makes the algorithm more suited to dynamic modelling and enables the discovery of hidden dynamic structures, which can be applied to predict the future behaviour of a dynamic system. The remainder of the paper is organised as follows. Section 2 briefly reviews the area of System Dynamics involving feedback and delay. Section 3 gives definitions of terms used in the paper. Section 4 discusses dynamic causal rules and their behaviour. Section 5 gives an example illustrating the proposed method. Section 6 concludes the paper. 2. System dynamics
The main concept in System Dynamics is to understand how each entity in a system interacts with another and then to create a model based on a dynamic hypothesis formulated for the system [1, 2, 3]. System Dynamics identifies the basic structure of a system, the behaviour of which can be understood through simulation or causal modelling [4, 5, 6, 7]. System Dynamics is based on individual understanding of the system. Each person may have a different opinion, and this can lead to conflict between the modellers. Often, there is some delay in
receiving feedback [8, 9], but the longer the delay, the harder it is to operate the system effectively. The delay can be modelled by an input flow into the system and an outflow at a specified later time [10]. 3. D e f i n i t i o n s
Let D denote a database which contains a set of n records with attributes {A1, A2, A3 . . . . Am}, where each attribute is of a unique type (sale price, production volume, inventory volume, etc). Each attribute is associated with a time stamp t. The records are arranged in a temporal sequence (t~, t2, ... tn). Table 1 is an example of such a database.
A1
1
9
2 3 4 5
17 l0 4 7
of records of a given polarity combination to the total number of records in the database. For database Dnew and any two attributes AA~ and AA2, the different types of support are defined below:
Fully sympathetic support (AA1, ARe AR 0 = freq
('Jl-Z~ii,'Jl-Ati+l,'Jf-Ati+2)
(3.a)
n freq (-~, ,-A,,+, '--Z~'i+ 2 )
(3.b)
/7
Table 1. Original database D Time
dynamic causal rules (or relationships), fully sympathetic support, fully antipathetic support, self-sympathetic support, selfantipathetic support and neutral support. Support is defined as the ratio of the number
Fully antipathetic support (AR1, AR2, AR O = freq (+~,-~,+,,-~,+~)
(4.a)
n
A2 2 3 12 16 24
freq (-~, ,+ ~i+, ,+ A,,+2)
(4.b)
n
Self-sympathetic support (AR1. ARe AR1) = freq (+ ~, '-~i+, '+ ~,+2)
(5.a)
n
The causal relationships between attributes in D can be identified by examining the polarities of the corresponding changes in the attribute values. Let Dnew be a new database constructed from D such that attribute z~4,,,,/vi
freq (-~i '+ ~+, ,-~,+2 )
n Self-antipathetic support (AR 1, ARe, AR 0 =
freq (+ ~, ,+ ~,+~,-~,+~ )
(6.a)
H
freq (-~, ,-~,+, ,+ A,,+2)
in D, ewis given by:
(5.b)
(6.b)
/7
A lt,, ,At t , - A m,it , - A ,, ,i+
(1)
where m identifies the attribute of interest. Consider two time stamps t~ and t~+~. The delay time kti is equal to the difference between two consecutive time stamps. Ati = ti+l - ti
(2)
Since the sequence t~, t2. . . . tn is arranged in ascending numerical order, Ati is always positive. Table 2 illustrates the new database Dnew derived from Table 1. Table 2. Derived database D ...... At Atz
Ate At3
At4
AA~ +8 -7 -6 +3
AA2 +1 +9 +4 +8
Neutral support (AAm) = freq ( 0 )
(7)
where n is the total number of time stamps and m identifies the attribute of interest, freq (+ ~, ,+ A,,+,,+ A,i+2) is a function giving the number of times an increase in AA~ is followed by an increase in AA2 which induces another increase in AA~ with respect to the time stamps Ate, Ati+l and Ati+2. Similarly, freq ( - ~ ,-A,i+, ,-z~ff/+2 ) is a function giving the number of times a decrease in AA1 is followed by a decrease in AA2 which induces another decrease in AAl with respect to the time stamps Ate, At~+~ and Ati+2. The neutral support indicates the frequency of value 0 in a derived attribute. This support is used to prune ineffectual attributes.
Five measures are used to identify
475
Dynamic causal rules are used to predict
future
dynamic
behaviour.
A
dynamic causal rule consists of variables Table 3. Derived database Dnewwith arrows indicating support counting direction At At1
At2 At3 At4
AA1 +8 \
connected by arrows denoting the causal influences among the attributes. Figure 2 shows the notation adopted for a dynamic causal rule. Two variable attributes, A1 and A2, are related by a causal link, denoted by an arrow. Each causal link is assigned a polarity combination with a time stamp. Each polarity is assigned a time stamp. According to equations (3), (4), (5) and (6) and as already seen in Table 4, there are eight polarity combinations of interest,
AA2 +1 -7 ~_"~/ +9 -6 ~ 9 +4 +3 lg +8 t
Table 3 shows the derived database
Dnew with arrows indicating the direction in which supports are counted. In this example, the neutral support is 0 since there is no record of value 0 in AA1 and AA2. The other supports are counted by following the direction of the arrows. A leti-to-right arrow indicates the causal
relation
right-to-let~ l~2,Ati+ 1 ~
AAI,~i ~
AA2,At;+' and
arrow
a
indicates
~l,Ati+ 2 The result is shown in
Table 4.
Supports(AA1,
(+,+,+) 0
(-,-,-) 0
(+,-,-) 0
(-,+,+) 1/4
(+,-,+) 0
(-,+,-) 0
(+,+,-) 1/4
(-,-,+) 0
AA2, AA1) All supports relate to the frequencies of relationships. Given a user specified support, the problem of Dynamic Causal Mining [1] is to find all rules where the support is larger than some user-defined threshold.
4. Dynamic causal rule representation The discovery of dynamic causal rules with delay deals with attributes associated with separate time stamps. This means that if any two variables, Al and A2, are truly dynamically causally related, then a change to A1 at tl causes a change to A2 at t2.
Linkpolarity Variable A1
Attribute
~
Variable A2
Causallink
Figure 2. Dynamic causal rule
476
(+ At; ,+At,+, ,+&+2 ) , )
' )
,
, )
,
(-& ,+&+,,-at,+2) , (+ At, ,+ At,+,,-At,+2 ) and (-a; '-&+, '+&+2 )
"
This
polarity
representation differs from that used in classical causal rules [9] (either + o r - ) , which is too simple to model dynamic behaviours in real world systems.
4.1 Dynamic causal rule classification A dynamic causal rule can be either strong or weak. A weak rule is a set of attributes
Table 4. Counting result
Supports(AAI, AA2,AA1)
namely,
Attribute
with polarity that partially fulfils equation (3), (4), (5) or (6). A strong rule is a set of attributes with polarity that completely fulfils equation (3), (4), (5) or (6). There are four types of strong rule, fully sympathetic, fully antipathetic, self-sympathetic and self-
antipathetic. 4.2 Fully sympathetic rules A fully sympathetic rule causes an increase or decrease in the output of a target system. It reinforces a change with more change in the same direction. This can lead to rapid growth, e.g. in a virus population, which could be difficult to stop. Fully sympathetic behaviours are depicted in Figure 3a and 3b. Examples of fully sympathetic behaviour can be found in manufacturing. For instance, as funds are invested to increase the capacity of a plant, more products will be manufactured which will generate more funds which can be again invested to create more capacity.
Performance
Time
Figure 3a. Positive fully sympathetic behaviour Time
Performance
Fully antipathetic behaviours are shown in Figure 4. When there is a difference between the goal state and the current state, a gap is created. A feedback signal is generated that tends to reduce that difference, the larger the gap the larger the feedback signal. The signal will continue to exist as long as the difference is non zero. Oscillatory behaviour arises when significant time delays exist in a fully antipathetic relationship, as shown in Figure 5. Time delays cause feedback to continue after the goal has been attained, which leads to over correction.
Performance
Figure 3b. Negative fully sympathetic behaviour r
Clearly, this ideal example ignores external competition and supply and demand issues, which can reduce growth.
4.3 Fully antipathetic rule A fully antipathetic rule represents an adjustment to achieve a certain goal or objective. It indicates a system attempting to change from its current state to a goal state. This implies that if the current state is above the goal state, then the system forces it down. If the current state is below a goal state, the system pushes it up. A fully antipathetic rule provides useful stability but resists external changes.
f
Time
Figure 5. Oscillatory behaviour There are many basic modes of dynamic behaviour and combining fully sympathetic and fully antipathetic rules creates even more modes [9]. One of the most commonly observed behaviour patterns in complex and dynamic systems is S-shaped growth. S-shaped growth is the result of interaction between afully antipathetic and a fully sympathetic rule. After a start-up period, the growth is rapid but it gradually slows down, as shown in Figure 6.
Performance
Performance Time Time
Figure 6. An S-shaped behaviour Figure 7 shows an example of a combination of a fully antipathetic rule (a) and two fully sympathetic rules (s).
Performance
Time
Figure 4. Fully antipathetic' behaviour
477
Table 6. Derived database
s
Figure 7. Dynamic rule combination
4. 4 Self-sympathetic and self-antipathetic rules Self-sympathetic behaviour and selfantipathetic behaviour are globally the same as their "full" counterparts. 5. An illustrative example
Table 5 shows a database, where the first column indicates the time instant, which could be hours, weeks, or years. The numbers in the columns have the same units. They could, for example, be purchase prices or sales levels etc. The first row of Table 5 can therefore be interpreted as in week 1, company 1 decides to produce 9 units; company 2 chooses to make 2 units etc. Dynamic Causal Mining is to be applied to this data to derive any dynamic causal relationships between these production volumes in order to assist a company in deciding its future manufacturing strategy. Table 5. Original database Time 1 2 3 4 5 6 7 8 9 10
A1 9 17 10 4 7 6 6 11 20 21
A2 2 3 12 16 24 18 18 21 13 8
A3 10 10 10 10 10 10 10 10 10 10
A4 22 28 27 22 14 11 5 5 1 8
A5 20 20 20 20 20 20 20 20 20 5
A6 100 100 100 100 3 100 100 100 100 100
A7 13 13 6 2 10 5 4 9 2 8
Table 6 shows the database that results after the difference calculation of equation (1).
478
AA1
AA2
AA3
AA4
AA5
AA6
AA7
+8 -7 -6 +3 -1 0 +5
+1 +9 +4 +8 -6 0 +3
0 0 0 0 0 0 0
+6 -1 -5 -8 -3 -6 0
0 0 0 0 0 0 0
0 0 0 -97 97 0 0
0 -7 -4 +8 -5 -1 +5
Table 7 illustrates the "pruned' database. Pruning is carried out to remove columns (attributes) where the level of neutral support is below a set minimum. In this example, columns with seven or more zeros (meaning seven or more occasions when there are no changes to the values of the corresponding attributes) are removed. In general, when the number of zeros in a column is high in relation to the total number of entries, the corresponding attributes can regarded as unaffected by attributes represented in the other columns. Even if the few remaining non-zero entries are large in magnitude, their effect on the sympathetic~antipathetic support counts will be small. Table 7. Pruned database A A1 +8 -7 -6
A A2 +1 +9 +4
A A4 +6 -1 -5
A A7 0 -7 -4
+3
+8
-8
+8
-1
-6
-3
-5
0
0
-6
-1
+5
+3
0
+5
+9
-9
-4
-7
+1
-5
+7
+6
Table 8 shows the supports for the attributes in Table 7 taken in pairs. The supports are calculated according to equations (3), (4), (5) and (6).
Table 8. Counting results
AAl&AA2 AAI&AA4 AAl&AA7 AA2&AA4 AA2&AA7 AA4&AA7 AAI&AA2 AAI&AA4 AAI&AA7 AA2&AA4 AAz&AA7 AA4&AA7
(+,+,+) 0 0 0 0 0 0 (+,-,+) 0.1 0.1 0.1 0.2 0.2 0
(-,-,-) (-,+,+) (+,-,-) 0 0.1 0 0.1 0 0.1 0 0 0.2 0 0 0.1 0 0.1 0.1 0.2 0 0 (-,+,-) (-,-,+) (+,+,-) 0.1 0 0.1 0 0.2 0 0.1 0.2 0 0 0.1 0 0 0.1 0.1 0.2 0 0
Suppose the support threshold is set to 0.1, which means any attribute pair with support larger than or equal to 0.1 is considered dynamically causally related. The following results are obtained. (-,-,-): (AA,&AA4), (AA4&AAT) (-,+,+): (AA,&AA2), (AA2&AAT) (+,-,-): (AAI&AA4), (AAI&AA7), (AA2&AA4), (AA2&AA7) (+,-,+): (AAl&AA2), (AAI&AA4), (AAI&AAT), (AA2&AA4), (AA2&AA7) (-,+,-): (AA~&AA2), (AAI&AAT), (AA4&AA7) (-,-,+): (AAI&AA4), (AAI&AAT), (AA2&AA4), (AA2&AA7) (+,+,-): (AA~&AA2), (AA2&AAT) Thus, for the given database, the strong self-sympathetic rules are (AAl&AA2) and (AAj&AAT). The only strong selfantipathetic rule is (AA2&AAT). The only strong fully antipathetic rule is (AA2&AA7). The derived rules reveal to decision makers that changes in attribute A~ will be reinforced and changes in attribute A2 will tend to be opposed. Such a finding would not have been possible without considering delayed and feedback relationships. 6. Conclusion
This paper has proposed a new method of manufacturing strategy selection by incorporating delay and feedback into DCM. To enable this, a new form of DCM is suggested, which focuses on the dynamic causal relationship between records with different time stamps, rather than records with the same stamp. A simple example has illustrated that interesting causal relationships can be discovered using the proposed DCM method.
Acknowledgement The authors are members of the EU-funded FP6 Network of Excellence for Innovative Production Machines and Systems (I'PROMS). Reference
[1] Pham, D.T., Wang, Y., Dimov, S. Intelligent Manufacturing Strategy Selection. Proc. 1st Virtual International
Conference on Intelligent Production Machines and Systems, Elsevier, Oxford, pp 363-369, 2005. [2] Weinberg, M. G. An Introduction to General Systems Thinking. WileyInterscience, Dorset House, New York, 1975. [3] Hutchins, C. L. Systemic Thinking." Solving Complex Problems. Aurora Professional Development Systems, St. Louis, Missouri, 1996. [4] Diehl, E., Steerman, J. Effects of Feedback Complexity on Dynamic Decision Making. Organizational
Behaviour and Human Decision Processes, volume 2, pp 198-215, 1995. (https://dspace'mit'edu/bitstrealrd 1721.1 /2491/1/SWP-3608-28936061.pdf, last accessed: 16 August, 2006.) [5] Sweeney, L. B., Sterman, J. D. Bathtub Dynamics: initial Results of a Systems Thinking Inventory. System Dynamic Review, Wiley, pp 249-286, 2001. (http ://web.mit.edu/j sterman/www/Batht ub.pdf, last accessed: 16 August, 2006.) [6] Forrester, J. W. Industrial Dynamics. Mass: MIT Press, Cambridge, 1961 [7] Forrester, J. W. System Dynamics and
Learner-Centred-Learning in Kindergarten through 12th Grade Education. (D-4337). Mass: MIT Press, Cambridge, 1992. (http://sysdyn.clexchange.org/sdep/Road maps/RM1/D-4337.pdf, last accessed: 16 August, 2006.) [8] Senge, P. The Fifth Discipline. Doubleday, New York, 1990. [9] Sterman, J. Business Dynamics." Systems
Thinking and Modelling for a complex World. Irwin/McGraw-Hill, Boston, 2000.
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[10] Shaffer, S. C. System Dynamics in Distance Education and a Call to Develop a Standard Model. International Review of Research in Open and Distance Learning, volume 3, 2005. (http://www.irrodl. org/index.php/irrodl/art icle/view/268/458, last accessed: 16 August, 2006.)
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Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Towards a Reconfigurable Supply Network Model T. Kelepouris a, C.Y. Wong a, A.M. Farid a, A.K. Parlikad a, D.C. McFarlane a a
Centre for Distributed Automation and Control, Institute for Manufacturing, Department of Engineering, University of Cambridge, Cambridge CB2 1RX, United Kingdom
Abstract
As organisations increasingly need to cope with planned or unplanned strategic and operational changes, the capability to easily and quickly reconfigure their supply chains is becoming an important criterion. In this paper, we propose a set of characteristics and benefits of a so called Reconfigurable Supply Network that allows rapid adjustment of supply chain entities at all levels of enterprises within the network. We propose the use of a Reconfigurable Manufacturing System design principle as a starting point towards building such a model. We extract the key characteristics of Reconfigurable Manufacturing Systems and extend these characteristics to a supply network. We then discuss design principles to enable reconfigurability in a supply network. Keywords: Reconfigurable, Supply Network, Modularity
1. Introduction
Rapid business changes are becoming a norm [1 ], especially since competitive advantages are not always sustainable, only temporary [2]. In the pace of technological and market changes founded by complexity, information and fluctuations [3], organisations have to develop strategies to enable easy reconfiguration of their supply network, as the ideal supply network for one set of conditions is almost surely not ideal for another [4]. The purpose of this paper is to provide an initial approach for developing a Reconfigurable Supply Network (RSN). An RSN is one that is designed for easy rearrangement or change (addition/removal) of supply network entities in a timely and cost-effective manner. Our vision of RSN is one that allows for rapid reconfiguration, akin to the grid manufacturing concept and intelligent product driven supply chain [5], incorporating intelligence enabled by emerging technologies such as web services, intelligent software
agents and RFID. The motivation for this work came with the realisation that in order to enable an intelligent supply network there is a basic need to examine ways to enable easy reconfiguration of the supply network to meet current and future industrial requirements. We note that there are existing literatures on achieving agility, flexibility and responsiveness, but limited in examining the issue of reconfiguration, a prerequisite towards enabling a highly adaptable intelligent system. An Extended Enterprise focuses on the sharing of information across the supply chain to achieve overall value creation and delivery systems through a confederation oforganisations [6]. However, it focuses on existing supply chain partners and does not allow, in principle, quick reconfiguration or addition/removal of supply chain partners as all supply chain processes are deeply embedded within each confederated partners. On the other hand, the concept of an Agile Enterprise is extended from flexible manufacturing systems [7] where the concept of agility refers to the
481
use of "market knowledge and virtual corporation to exploit profitable opportunities in a volatile marketplace" [8]. This is similar to a Virtual Enterprise, which is described as a network of organisations from which temporary alignments are formed [9] to rapidly obtain the products that they want [ 10, 11 ]. In such a way, both agile and virtual enterprise are driven by market opportunities rather than other strategic factors. General supply chain literature has also examined the issue ofreconfiguration [ 12] but with the focus on enabling information sharing and lacks a methodology towards understanding and developing an RSN at all levels of the enterprise. In this paper, we will first explore the relevant issues of reconfigurable manufacturing systems as a basis for developing an RSN model. We will then look at some of the supply network and enterprise modelling techniques and propose a conceptual model that could enhance our understanding of supply networks. Furthermore, we propose a methodology and a set of characteristics towards achieving an RSN. Finally, we will suggest a way forward to build a reconfigurable supply network model.
2. Reconfigurable Manufacturing Systems The previous section motivated the investigation of RSNs. This section extracts lessons from the Reconfigurable Manufacturing Systems (RMS) and product design literatures in order to draw analogies that may be applied on RSNs. Specifically, this discussion is divided into two parts; the benefits of reconfigurable manufacturing systems, and its characteristics. 2.1 Benefits of Reconfigurable Manufacturing Systems Within the field ofRMS, Mehrabi et al [ 13] define an RMS as: "[A manufacturing system that] is designed for rapid adjustment of production capacity and functionality in response to new circumstances by rearrangement or change of its components". This definition implies that the benefits ofa reconfigurable manufacturing system arise from a constantly changing marketplace. With respect to RMS, these changes include 1.) increasing frequency of new product introductions due to shorter product life cycles, 2.) changes in parts for existing products to improve product customisation 3.) large fluctuations in the quantity and mix of product demand 4.) changes in government safety and environmental regulations and 5.) changes in process technology resulting in higher quality products [ 14]. The primary benefit of an RMS is the ability to
482
react to these changes rapidly and cost-effectively. In order to achieve this benefit, an RMS must be rapidly (re)designed for new product applications, change quickly over to those new products, adjust capacity fast and incrementally, and finally incorporate the introduction of manufacturing processes for increased product variety [ 15]. 2.2 Characteristics of Reconfigurable Manufacturing Systems In order to achieve the benefits illustrated in the previous section, an RMS must have a number of key characteristics. Mehrabi [ 15] identifies them to be: 9Modularity: The degree to which all system components, both software and hardware are modular. 9Integrability: The ability with which systems and components maybe readily integrated and future technology introduced. 9 Convertibility: The ability of the system to quickly changeover between existing products and adapt to future products. 9 Diagnosability: The ability to quickly identify the sources of quality and reliability problems that occur in large systems. 9 Customisation: The degree to which the capability and flexibility of the manufacturing system hardware and controls match the application (product family). The above characteristics enable rapid reconfiguration of manufacturing systems. We will extend these characteristics to suit the concept of an RSN later in section 4.1.
3. Supply network and enterprise modelling Taylor [ 16] suggests that there are three categories of business models: a) conceptual, b) mathematical, and c) simulation models. Each of them offers different capabilities and limitations. Conceptual models are informal and descriptive, while mathematical and simulation models are more formal and used for prediction and business optimization. Shapiro [17] provides an overview of how mathematical programming optimization can be used for modelling and optimization of supply chain networks. He also analyzes conceptual models for strategic planning and supply chain operations optimization. In recent years researchers tend to focus more on supply chain networks rather than traditional linear supply chain models. McDonald and Rogers [18] describe how value transfer in the supply chain evolves from the traditional linear model to a "holistic" supply network model. Similarly, Ayers [19] explores the evolution from the traditional supply chain to a supply network
<
Supply Network Dimension
>
j
f'.,,\ 9
.4f" . . . . . . .
'~",,
/ /
,./ / ./
, /"
/
~
~
\
l-'roeesse.~ "- .
/
" -,,\
\
Enterprise Activities
Resources P r Jc d u c t s
"
"~
Enterprise Dimension
""
I
",\,.
.......................................................................................................................................................
\"
~.~
Fig. 1. S u p p l y N e t w o r k and Enterprise M o del
of partnerships. McCormack and Johnson [20] propose a conceptual supply network model which they use to examine the impact of internal and external situational factors on the performance and the "esprit de corps" of the supply network. Dong et al. [21 ] and Ettl et al. [22] are examples of analytical approaches to supply network modelling. Apart from supply chain modelling, there has been extended work in enterprise modelling as well. Vernadat [23] provides a profound description of the most important manufacturing-focused enterprise modelling reference architectures including ISO, CEN ENV 40 003, CIMOSA, GIM, PERA, ARIS and GERAM. In the next section we will propose a conceptual model by which we can address supply network reconfiguration issues.
3.1 Scope of the model Ross [24] suggests that every modelling technique should be characterized by the definition of the purpose, the range, the viewpoint and the detailing level of the model. In order to address supply network recontigurability, in this section we propose an enterprise model which describes both the intraorganizational structure and the inter-organizational interactions of an enterprise. The range of this model spans from main business processes down to specific organizational resources with regard to intraorganizational structure, and covers both inbound and outbound interactions of a firm with regard to supply network interactions. This model describes the organization from an operational point of view, focusing on the supply-network-related processes of the organization. Moreover, the model provides definitions for the different operational elements which will be studied with respect to reconfigurability; however it does not provide a detailed description of the attributes of these elements or how these may interact with each other.
3.2 The supply network and enterprise model According to Vernadat [23], an enterprise model is a consistent set of special purpose and complementary models describing the various facets of an enterprise to satisfy some purpose of some business users. Having described the purpose of the model in subsection 3.1, we use two complementary models to achieve this purpose. First, we use the definitions of functional components at different organizational levels, provided by Vernadat [23], that compose a generic organizational model. This part of the model describes the intra-organizational facet of our model. Secondly, we adopt the Supply Chain Operations Reference (SCOR) model [25] under which the interorganizational interactions are modelled and a supply network perspective is given to our overall model. The two models are integrated in the processes modelling level and form a two-dimensional overall model, as depicted in Figure 1. On the supply network dimension the business processes that realize the supply network interaction are provided as defined by SCOR [25]. These are Plan, Source, Make, Deliver and Return. On the enterprise dimension a hierarchical structure of functional components is defined. At the higher level lie the business processes which consist of a sequence (or partially ordered set) of enterprise activities, the execution of which is triggered by some event and will result in some observable or quantifiable end result. At the next level, enterprise activities are defined as a set of partially ordered basic operations executed to perform the things to be done within an enterprise. Activities are performed by the functional entities of the enterprise and transform an input state into an output state. Activities are carried out by resources which are human or technical entities and can play a role in the realization of a certain class of tasks, when available [23]. At the lowest level lie products. Although nota part of the organizational structure itself, they provide input and output to the physical system and the resources of
483
the organization, as defined by systems organization theory [23] and by general systems theory [26] as Vernadat remarks. Hence, processes (and the activities that compose them), resources and products are the organizational entities that are subject to reconfiguration. Based on this model, in the next section we suggest the characteristics of an RSN, demonstrate the benefits that stem from this ability and propose the basic principles of designing a reconfigurable supply network. 4. Towards a Reconfigurable Supply Network Model
It is not the aim of this paper to provide a definitive model on an RSN. However, in this section, we will discuss the characteristics and benefits that could be derived from such a supply network. We will also examine ways in which such a network could be built. 4.1 Characteristics o f a Reconfigurable Supply Network
In Section 2.2, we discussed the characteristics of Reconfigurable Manufacturing Systems (RMS). We will now extend these characteristics to a supply network. While the RMS literature largely focuses on resources (and to some extent on product) within a manufacturing facility, an RSN consists of products, resources as well as processes within all levels of enterprises across the supply network. Therefore, in order to adapt the characteristics of RMS to RSN, we append the definitions of these characteristics to include the supply network entities identified in section 3.2. We propose the following characteristics of an RSN: 9Modularity: The degree to which all product, process and resource entities at all levels of enterprises of supply network are modular. 9Integrability: The ability with which all enterprises within the supply network and their processes and resources maybe readily integrated and future process and resources introduced. 9 Convertibility: The ability of the product, process and resource entities within enterprises of supply network to quickly changeover between existing products and adapt to future products. 9 Diagnosability: The ability to quickly identify the sources of problems, which hamper supply network effectiveness and efficiency, which occur across the supply network. 9 Customisation: The degree to which the capability and flexibility of the supporting
484
infrastructure for supply network match the application (supply chain activities). These characteristics will enable the supply network entities to be rapidly rearranged resulting in easy reconfiguration of a supply network. 4.2 Benefits o f a Reconfigurable Supply Network
A Reconfigurable Supply Network with the characteristics described in the previous section allows rapid adjustment of supply chain processes to achieve strategic and operational objectives such as: 9Rapid response to changes in customer requirements 9Rapid outsourcing/in-sourcing activities 9Rapid addition or removal of supply network partners 9Achieving a responsive manufacturing system These benefits are not an exhaustive list but provide an indication of the types of scenarios where it would be most beneficial to have such a supply network. As a general rule, the more a supply network is subjected to planned or unplanned changes, the more it will benefit from having a Reconfigurable Supply Network. In practice, achieving a totally Reconfigurable Supply Network is difficult as it requires time and a collective effort. It is envisioned that once benefits are prioritised, the network could then be designed to meet these benefits in stages. The following section will propose a methodology to design such a supply network. 4.3 Designing the Network
In order to build a reconfigurable network companies must design their supply network using visible design rules and hidden design parameters [27]. Hidden design parameters are decisions that do not affect the design beyond the local module. Reforming the definitions of Baldwin and Clark [27] for the case of supply networks, visible design rules include a) an Architecture which specifies what modules will be part of the network and what their functions will be b) Interfaces that describe in details how these modules will interact and c) Standards for testing a module's conformity to the design rules and comparing modules' performance relative to another. Modules in the case of a supply network could either be entities that belong to any of the levels of the model in Figure 1 or whole enterprises that compose a supply network when interconnected. In Figure 2, we provide an example of the application of the above rules in the case of collaboration practices between supply network partners by using the analogy of this design with the
design of the internet according to the TCP/IP protocol
Application Transport,
Intemet
Net. Interface TCPfIP layers
Coll~or~aion Practice_,-. CPFR, :rMX etc. Electromc Doc. ,~cl~rds EDI etc. C ~ c
~
Interface
W~b Services, F T P etc.
IT infrastructure S C Collabcr
l:~actices
Fig. 2 Intemet and Supply Network Design
[28]. Intbrmation sharing and collaboration will require the design of an infrastructure according to an architecture that will define different levels as shown in Figure 2. Specific interfaces and standards shall be defined for each of the levels. Moreover, the levels should be independent from each other with regard to design and operation, meaning that a change in one level should not affect the operation of another. We note that the levels mentioned at this point are different from the levels of the enterprise model presented in section 3. In order to demonstrate the importance of the characteristics proposed in section 4.1, let us consider a simple example in which a manufacturer decides to change one of his main suppliers. Process modularity will enable efficient process modification at the manuthcturer's side (in case a process must be changed, e.g. order receipt) without affecting other enterprise processes, therefore minimizing changeover costs. Product integrability will ease the changeover procedure minimizing product compatibility issues, while process integrability will enable the two parties to effectively integrate process (e.g. shipment and receipt) as well as to introduce new ones if necessary. Product convertibility from the new supplier's view will give him a competitive advantage compared to other suppliers, enabling him to convert his product to meet the manufacturer's needs. From the manufacturer's point of view, convertibility will enable him to modify his product and processes so that these are compatible with a wider variety of compatible suppliers. Finally, the ability of customisation will enable the supplier to efficiently meet any special requirement that the manufacturer has. Diagnosability will enable the two parties to quickly discover deficiencies in the newly established relationship and solve them.
The next step in this research will be to utilise these design principles and develop a model of an intelligent Reconfigurable Supply Network that exhibits the characteristics identified in section 4.1. Such a network will be able to continuously monitor its performance and automatically adapt to changing requirements, utilizing emerging information and communication technologies for efficient reconfiguration. The model will also include performance measures for these characteristics as well as an overall "reconfigurability measure" that will indicate the reconfiguration capability of a supply network. This paper provides the initial examination of an RSN network that allows for further research in intelligent information systems to support supply network processes. Current on-going research is working towards this direction. We aim to validate our model through a series of case studies of companies that undertook some kind of supply network reconfiguration. Furthermore, we will assess the impact of specific reconfiguration scenarios on the companies. We aim at measuring the performance of each company with regard to each of the critical reconfiguration characteristics and link these measurements to the overall reconfiguration performance of the company. In this way, we shall be able to determine the correlation between these characteristic and the overall reconfiguration capability of the enterprise.
5. Conclusion In this paper we have extracted the key characteristics of reconfigurable manufacturing systems and we propose a way for applying them in the enterprises of a supply network in order to enhance the ability of the network to be efficiently reconfigured. We do this using a model that addresses both the intra enterprise activities and the inter-enterprise interactions. We then suggest the key characteristics that the supply network entities should have and the way an RSN should be designed. Further research shall be headed towards defining a formal model for describing reconfigurable supply networks and employing the model to achieve specific supply chain improvements.
Acknowledgement The Institute for Manufacturing of the University of Cambridge is partner of the EU-funded FP6 Innovative Production Machines and Systems (I'PROMS) Network of Excellence. http://www.iproms.org
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References
[1] Peters, T., Thriving on Chaos: Handbook for A Management Revolution. 1987, New York: Alfred A. Knopf. [2] Fine, C.H., Clockspeed." Winning Industry Control in the Age of Temporary Advantage. 1998, Cambridge,MA: Basic Books. [3] Nohria, N. and J.D. Berkley, An Action Perspective: The Cruz of the New Management. California Management Review, 1994. 36(4): p. 70-92. [4] Metz, P.J., Demystifying Supply Chain Management. SCMR Thought Leadership Series, 1998.1(2). [5] Wong, C.Y., et al. The intelligent product driven supply chain, in IEEE International Conference on Systems, Man and Cybernetics. 2002. Hammamet, Tunisia. [6] Christopher, M., Logistics and Supply Chain Management. 1998: Prentice Hall, 2nd Edition. [7] Nagel, R. and R. Dove, 21st Century Manufacturing Enterprise Strategy. 1991: Incocca Institute, Leigh University. [8] Naylor, J.B., M.M. Naim, and D. Berry, Leagility: Interfacing the Lean and Agile Manufacturing Paradigm in the Total Supply Chain. International Journal of Production Economics, 1999.62: p. 107118. [9] Dowlatshahi, S. and Q. Cao, The Relationships among Virtual Enterprise, Information Technology and Business Performance in Agile Manufacturing: An Industry Perspective. European Journal of Operational Research, 2005. Article In Press. [10] Cho, H., M. Jung, and M. Kim, Enabling technologies of agile manufacturing and its related activities in Korea. Computers Industrial Engineering, 1996.30(3): p. 323-334. [11] Sharp, J.M., Z. Irani, and S. Desai, Working towards agile manufacturing in the UK industry. International Journal of Production Economics, 1999.62(1-2): p. 155-169. [ 12] Liu, E.R. and A. Kumar. Leveraging Information Sharing to Increase Supply Chain Configurability. in Twenty-Fourth International Conference on Information Systems. 2003. [13] Mehrabi, M.G., A.G. Ulsoy, and Y. Koren, Reconfigurable Manufacturing systems and their enabling technologies. International Journal of Manufacturing Technology and Management, 2000.1(1): p. 113-130. [14] Koren, Y., et al., Reconfigurable manufacturing systems. CIRP Annals - Manufacturing Technology, 1999.48(2): p. 527-540. [15] Mehrabi, M.G., A.G. Ulsoy, and Y. Koren,
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Reconfigurable manufacturing systems: key to future manufacturing. Journal of Intelligent Manufacturing, 2000.11(4): p. 403-419. [16] Taylor, D.A., Supply chains: a manager's guide. 2004, Boston, MA: Addison-Wesley. [17] Shapiro, J.F., Modeling the supply chain. 2001, California, CA: Duxbury, Thomson Learning. [18] McDonald, M. and B. Rogers, Key Account Management: Learning from Supplier and Customer Perspectives. 1 9 9 8 , Oxford: Butterworth-Heinemann. [19] Ayers, J.B., Handbook of supply chain management. 2001, Boca Raton, Florida: St. Lucie Press. [20] McCormack, K.P. and W.C. Johnson, Supply Chain Networks and Business Process Orientation." Advanced Strategies and Best Practices. 2003, Boca Raton, Florida: CRC Press LLC. [21] Dong, J., D. Zhang, and A. Nagurney, Supply Chain Supernetworks With Random Demands, University of Massachusetts. [22] Ettl, M., et al.,A supply network model with basestock control and service requirements. Operations Research, 2000.48(2): p. 216. [23 ] Vernadat, F., Enterprise modeling and integration : principles and applications. 1996, London: Chapman & Hall. [24] Ross, D.T., Structured Analysis (SA): A Language for Communicating Ideas. IEEE Transactions on Software Engineering, 1977.3(1): p. 16-34. [25] Council, S.C., Supply Chain Operations Reference model, S.C. Council, Editor. 2005, Supply Chain Council. [26] Le Moigne, J.L., La Theorie du Systeme General. 1977, Paris: Presses Universitaires de France. [27] Baldwin, C.Y. and K.B. Clark, Managing in the age of modularity. Harvard Business Review, 1997. 75(5): p. 84-93. [28] Tanenbaum, A.S., Computer Networks. 2003, New Jersey: Prentice Hall PTR.
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldttidari and A.J. Soroka (eds) 9 2006 CardiffUniversity, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
A novel adaptive process planning framework Berend Denkena, Alessandro Battino Institute of Production Engineering and Machine Tools (IFW), University of Hannover, Schoenebecker Allee 2, 39823 Garbsen, Germany
Abstract
Today's successful production companies are characterized by factors once neglected, like the ability to learn from experience and to flexibly adapt to changes in the environment. In order to significantly improve such qualities, in current research activities a new specie of learning and intelligent components is being developed. The capacities of such "gentelligent| components" to gather information and to act autonomously offer new potentials for increasing efficiency in production. One of the current concerns is that a big part of manufacturing orders cannot be processed as planned because of disturbance arising after completion of planning activities. In this paper, a novel approach for the partial integration of process planning and production control is presented, which allows exploiting the potentials of gentelligent| components in order to flexibly react to disturbances in production. Keywords: Process planning, production control, flexibility
1~ Introduction
The need for a higher level of flexibility in production is a matter of common knowledge. The big efforts made in the last decades significantly improved the flexibility of manufacturing systems, factory structures, production control as well as business models. Nevertheless, the capacity to flexibly adapt to changed situations remains a critical factor in every field that is affected by planning tasks. Every planning activity implies assumptions on future conditions and therefore faces uncertainty. Even excellent plans can later turn out to be not adequate anymore due to meanwhile changed conditions. Considering the production environment, disturbances like breakdowns, missing devices or broken tools, can arise between the planning and the execution of processes, significantly decreasing the business efficiency. Production planning and control (PPC) systems should permit to decrease the effects of such disturbances through an early recognition of deviations and a prompt replanning.
Different studies [1,2,3] have shown that the flexibility of PPC can be significantly improved only when alternative technological solutions can be adopted during the replanning of manufacturing operations. Yet, the current situation is characterized by a complete separation between process planning and production control. Rigid, sequential process plans are prepared directly after product development, without considering logistic issues like e.g. limited resource capacities. The production itself can take place even months later. Meanwhile, the conditions on the shop floor change, and this causes approximately as much as 30 percent of the planned process sequences not being applicable anymore [4]. In this case, PPC systems can suggest to modify the sequence of queued orders or to reschedule an order on another equal resource (in case this is available). These are pure contingent logistic remedies, offering just a limited range of possibilities and not considering technological requirements. If a rescheduling is not possible, the personnel is forced to manually modify the process plan, causing time delays,
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additional costs and providing a solution that is feasible, but usually not optimal in consideration ofthe global result. Within the Collaborative Research Centre "Gentelligent| Components in their Lifecycle" (CRC653), new technologies are being researched, in order to increase the integration of process planning and production control. A flexible process planning is pursued, which is able to adapt to current shop floor conditions through exploitation of the potentials offered by novel, learning, intelligent and collaborative components also depicted as "gentelligent| (GI). In this paper, the principal existing approaches to increase flexibility of process planning are shortly described (section 2); afterwards, the CRC653 project is introduced (section 3) and a novel method for adaptive process planning is presented, also including a brief description of its fundamental aspects (section 4). Finally, a summary and potential future developments are outlined (section 5). 2. Methods for flexible process planning and integration with production control The many approaches available in scientific literature regarding flexible production planning and control can be traced back to few main frameworks. These are shortly described in the following section and schematic represented in fig. 1.
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Traditional Dev.~ Planning .... ,9 Dynamic Planning
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Fig. 1. Methods for flexible process planning
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2.1 Dynamic process planning A radical approach to face disturbances in production consists in completely delegating the decisions to the shop floor. A high number of recent research projects has aimed to achieve what are called decentralized, dynamic or (near) real-time process planning and production control systems. In this field, a wide experience has been collected at the Institute of Production Engineering and Machine Tools (IFW), especially through the projects DEDEMAS [5], HMS [6], MaBE [7] and IntaPS [8]. Hereby, a common goal is the avoidance of centralized control so that activities are carried out without planning: products and resources - or their reflection in a software environment- make autonomously (near) real-time decisions during the execution. Sometimes, process planning can be considered as fully integrated in the production control: only after completion of one operation the plan for the next one is generated, so that current conditions on the shop floor are always taken into account (dynamic, closed-loop or generative process planning [ 11 ]). This fully decentralized method can be seen as the extreme opposite in comparison with the traditional strategy (fig. 1). The main strengths of the method are the dynamic elimination of disturbances, which are dealt with as if they were business as usual. An important aim is also the autonomous, decentralized solution of the scheduling problem (renowned for being NP-hard). Even if this approach is a very promising one for the future, at the moment some critical aspects are limiting a widespread application in industrial environments. With fully decentralized decisionmaking only a local optimum can be reached, while the global result is in most cases unpredictable. The integration in legacy systems like Enterprise Requirements Planning (ERP) reveals difficulties since, for instance, it is not known in advance which resources should be booked or when an order will be ready. This causes problems also in cost calculation and in pursuing of strategic goals. Further, the step-by-step procedure limits the solution space for subsequent operations: after different processing operations can be discovered that the resource required for the last operation is not available. Finally, such systems are mostly suitable for the control of activities not requiring preparative operations. Successful applications deal for instance with scheduling or assembly problems, where no planning is needed. In manufacturing operations, the time required for preparation (e.g. generation of NC
programs) would cause relevant adequately planned in advance.
delays
if not
2.2 Just in time planning
A different solution to avoid disturbances consists in postponing the planning as much as possible, so that its completion takes place just before the start of production (SOP) [9]. As a result, disturbances occurring right after the product development do not compromise the validity of process plans, since they can be considered in the planning phase taking place at a later date. In this "just in time" planning, like in dynamic planning, the absence of a preliminary plan is problematic for production management (resource booking etc.). The reactivity is not as high as in dynamic planning, since the plans are not revised anymore during execution. Moreover, the calculation of the time required for the whole planning is awkward and a wrong planning start time may cause extremely harmful postponements of the SOP. 2.3 Alternative planning
Alternative planning differs from the traditional one only in the fact that instead of one single process chain, various alternative process chains are planned [ 10]. The complete planning phase takes place atter the design. Thus, there is no integration with the control. Just before the SOP, it is possible to consider the current conditions and select one of the available process chains. The drawbacks consist in the limited reactivity and the big effort required for the planning phase (proportional to the number of alternative process chains). 2.4 Nonlinear planning
Nonlinear process planning was introduced in order to represent all feasible and reasonable alternatives in a single (net) plan through AND/OR operators [1,12]. Also in this case, the complete planning takes place after product design, causing high cost and not including logistic considerations. The main advantage is the richness of possibilities available to react to disturbances during execution.
just in time planning - since a preliminary plan is available with long advance - but maintains its advantages. The plan is sequential, so that a relatively long time is required for an eventual modification during (or after) the detailing phase. 2.6 Management method." planning on a rolling horizon basis
In management theory, a widespread method to deal with uncertainty consists in planning on a rolling horizon basis (also called gliding planning) [14]. The method is based on a hierarchical structure and plancontrol-revision interaction. A long term rough planning is carried out for the whole planning horizon, then the latter is divided in periods, and a detailed (short-term) planning takes place for the first period. During the first period, the deviations are controlled, the long-term plan for the next periods is revised and the detailed planning for the second period is made considering the actual developments as well as updated forecasts for the future periods. This way, the planning activity glides on the time axis. This intuitive method and its possible application to process planning will be further discussed in sect. 4.
3. Gentelligent| Components in their Lifecycle The summarized analysis of existing theories for improving planning flexibility has been carried out in the frame of the Collaborative Research Centre 653.
~,~!!~i!~i!~!~i~!~d~!~~i~!!!!~!i!i~!i!~i~!!~!!i~!!?!!!~!!!~!~!~!~ ~ ~i~Ii~!'(84"i'!!"gi!i~i!i!'!ii!!i~!i!i= 'i'i'si!'~i!~!!'!ii~i!!i!ii!~:i!~!'~!i'!"!i~i'!~i!ii'i~i!ii!~!!~!~ "!"!i'i~ii!si~i! Generation
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2.5 Hierarchical planning
Hierarchical planning [13] is constituted by two main phases: a rough planning made just after product design, and a detailed planning made as late as possible. This method eliminates some problems of
Fig. 2. Gentelligent| Components in their Lifecycle The long-term goal of the project is the elimination of the physical separation between components and
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their corresponding information. The future components will be "gentelligent| because they will be able to store basic data on forces, acceleration and temperatures exerted on them during lifecycle. Furthermore this data can be "inherited" by the next generations (fig. 2). Thus, learning mechanisms that are typical of biology will be available for intelligent industrial products, which could be used for instance for unique product identification, or for copy protection, selective production and assembly control or for determining the causes of machinery malfunctions. CRC653 creates the scientific prerequisites for the manufacture of gentelligent| components and develops methods for the exploitation of hereditary, component-inherent information in production technology [ 15]. In this context, one aim is the realization of partly automated planning and monitoring of cutting manufacturing processes on the basis of workpieceintrinsic information. The process planning occurs within a novel simulation-based module called "virtual planer", which interacts with the gentelligent| components in order to constantly use up-to-date information. Thus, a flexible and decentralised reaction to changing conditions on the shop floor is realized. The use of simulation permits to validate the autonomous selection ofprocess parameters and to set thresholds for monitoring. In order to implement the described virtual planer, a framework for process planning activities has to be elaborated, permitting a partial delegation of the planning tasks to the execution phase. In the following a possible approach is presented.
information can be passed to the PPC system in order to book the resources and to provide information to the business management. Directly before the start of production, the detailing phase takes place. Current conditions on the shop floor are taken into account in order to reevaluate the process chains, so that a modification of the original rough plan can be made and a new process chain be selected. Afterwards, the first operation step is detailed up to the determination of the NC programs and to its simulation. During detailing and execution of the first step, the remaining possible process chains are re-evaluated, a new selection takes place and the detailed planning of the second step is carried out. The detailed planning carries on "gliding" in this way simultaneously to execution and adapting itself to the current conditions until the achievement of the finite product. In a production with GI-components, it would be as if a workpiece has an active route plan available in which, at the end of each operation, information about the best route to follow is updated.
,,~ Dynamic Planning
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4. Adaptive process planning Innovation often arises exporting known methods to new areas. In this case, the developed novel adaptive planning framework originates from the application of the previously introduced p l a n n i n g on a r o l l i n g h o r i z o n b a s i s - already employed in management t h e o r y - to manufacturing process planning and control. As a consequence, process planning is divided in two phases (hierarchic): a rough planning is carried out directly after product development, while a detailed planning takes places simultaneously to production control. In rough planning, for each processing step all reasonable technological solutions are determined, so that a nonlinear process plan arises. Every viable route contained in the net structure of the nonlinear plan is roughly evaluated considering cost, quality and time factors. The process chain with the best evaluation value is preliminarily selected and the corresponding
490
OptimumA
Applicability Fig. 3. Comparison of flexible process planning methods
The adaptive planning is a combination of the methods presented in sect. 2, which allows taking advantage of some features of hierarchical, nonlinear, dynamic and gliding planning. A comparison of the described methods on the basis of estimated flexibility and applicability is represented in fig. 3. Here flexibility is defined as gained capacity to react to changes in comparison with the traditional method (very low flexibility) and with the dynamic method (maximum flexibility). The applicability provides an estimation of feasibility and of acceptance in industrial
production on the basis of implementation costs, interoperability with legacy systems, complexity, etc. The optimum would be a method with the same industrial acceptance of the traditional method, but as flexible as the dynamic planning (here intended as realtime planning and control). The already good position of adaptive planning is expected to be improved in future especially in terms of applicability through the introduction of gentelligent| components. Some fundamental aspects of adaptive planning are described in the following sections.
4.1 Integrated process and product model Today, the most promising approach for the partial automation of process planning consists in adopting an integrated model. The product information (product or order model) should be matched with the available processing technologies (process model) to find out which operations can be adopted for the processing of the product features. The product model includes its geometric and functional characteristics. One widespread standard to describe product (lifecycle) information is ISO 10303-4x 'STEP'. The process model describes the technologies available, their possible combination (topology) and an analytical description of the relations among characteristic parameters. Through a mapping of the product model with the process model, the determination of nonlinear process chains is supported. The further integration with logistic information (already suggested by different authors [16,17]) allows a comprehensive evaluation of different solutions (sect. 4.5).
4.2 Scheduling of detailed planning tasks While the duration of rough planning is of little importance, the detailed planning phases must be scheduled as exactly as possible, in order to reduce time buffers. This guarantees that planning is close to execution and that plans are available in time at the beginning of an operation. However, due to the number of steps to be carried out and to their high variance, the determination of the time needed for planning is an awkward task. A promising method, until now mostly used for the estimation of product design duration, is based on time indicators [13]: in a first observation phase, the actual activity durations are registered and then used to define indicators providing an estimate on the duration of future activities. The first step for the application ofthis method to process planning consists in determining parameters that influence the total duration. Afterwards, an "observation phase" is carried
out, in which values for the parameters are registered and evaluated. On the basis of such values, the indicators are finally calculated. Since both qualitative and quantitative parameters influence the planning duration, a fuzzy based approach for the calculation of time indicators is currently under development.
4.3 Simulation and monitoring Together with the proposed adaptive planning, simulation is the other main component of the virtual planer. During detailed planning, simulation is used to verify the validity of generated plans and to identify thresholds for the process parameters. Such values are integrated in the process plans and passed on to the monitoring systems, so that learning and early warning of risk situation is achieved. In modern companies, diagnostic data about the production is already made available by Manufacturing Execution Systems (MES). The information collected includes logistic information, as well as basic technological information, such as the energy consumption of the resources. Further diagnostic possibilities will be offered by gentelligent| components: these will collect for instance cutting forces, accelerations and temperatures. All available information will be analyzed and anomalies will be directly communicated to the adaptive planning module, where they will be considered in the evaluation of the process chains. Besides, information will be further elaborated in order to achieve new thresholds for the simulation as well as suggestions for the process planning. GI-components will moreover collect information during the lifecycle, providing further possibilities to optimize process parameters in order to meet the required quality level.
4.5 Evaluation and selection of process chains In the proposed method, the analysis of the possibilities included in the nonlinear process planning is carried out during every detailed planning step. Anyway, in contrast with dynamic approach, there is not a comparison and a choice among single process steps, but each time whole process chains are considered. Hence, for instance during the execution of operation n, all possible process chains including the first n operations are analyzed. A process chain performance function is calculated for each route and the one with the highest value is selected. The operation n+ 1 will be selected accordingly. During the subsequent execution, all possible process chains
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including the first n + l operations are analyzed, and then the iteration starts again. With such dynamic evaluation of process chains the risk is minimized to select a process that seems to be optimal, but leads to a route including a disturbance (e.g. an out-of-work machine). The consideration of technological, logistic and economical aspects implies conflicting objectives between alternatives. Moreover, the objectives are of different nature and in some cases non quantifiable. In order to deal with this multi-objective decision problem, scale values are defined for every objective and aggregated into a weighted sum, resulting in the mentioned process chain performance. The evaluation permits to consider also strategic goals through an adequate adjustment of the mentioned objectives' weights. Moreover, when some values are found to be outside the thresholds, the process chains including the corresponding resources can be penalized by changing the values of the weights. Thus, the products will in this case tend to choose another route, while, at the same time, the virtual planer will search for improved process parameters.
5. Conclusions After a review of the available methods for the integration of process planning and production control, a novel approach conceived in the frame of the CRC653 "Gentelligent| Components in their Lifecycle" has been introduced. The method combines the management theory of gliding planning with nonlinear process planning, allowing at the same time a partial decentralization. The main advantages are the high flexibility in reacting to disturbances combined with the possibility to control the global result in the consideration of strategic goals. Moreover, the method is characterized by a relative high scalability, i.e. it could be gradually introduced in industrial environments, thanks to the presence of a preliminary rough planning. This allows integration in legacy systems for preliminary calculation of costs and booking of resources. The adaptive process planning described here is an approach, which suits the characteristics of gentelligent| components. In the next project developments, a method for planning manufacturing processes will be selected, structured in detail and implemented. The final aim is to make the most of the extensive possibilities offered by GI-components in order to achieve a learning and intelligent production as well.
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Acknowledgements The CRC653 is funded by the Deutsche Forschungsgemeinschafl (DFG). IFW is partner of the EU-funded FP6 Innovative Production Machines and Systems Network of Excellence (www.iproms.org).
References [1] Beckendorff U. Reaktive Belegungsplanung mr die Werkstattfertigung. Fortschritt-Berichte VDI Nr. 232, VDI-Verlag, Dfisseldorf, 1991. [2] E1MaraghyWH and E1Maraghy W. Integrated Assembly Planning and Scheduling. CIRP Vol. 41/1 (1992). [3] Shin J et al. An investigation of the influence of alternative process plans in a dynamic shop floor environment. SMC-IEEE Conf. Tucson, USA, Oct. 2001. [4] Detand J et al. A computer aided process planning system that increases the flexibility of manufacturing. IPDES (Esprit project 2590) Workshop, 1992. [5] T6nshoff HK, et al. A mediator-based approach for decentralized production planning, scheduling, and monitoring. 2na CIRP ICME Sem., Capri, Italy, 2000. [6] Zwick M and Brandes A. Scheduling Methods for Holonic Control Systems. Proc. 12th Int. DAAAM Symposium, 24-27th October 2001, Jena, Germany. [7] Denkena B, Woelk PO and Battino A. A Multi-agent Approach for Production and Logistic Control of a Virtual Enterprise, 3rd Int. APE Conf., Warsaw, 2004. [8] Denkena B, Battino A and Woelk PO Intelligent software agents as a basis for collaborative manufacturing systems. Proc. I'PROMS Virtual Conference 2005. [9] Chryssolouris G and Chan S. An integrated approach to process planning and scheduling. CIRP Annals, Band 34 (1985)Nr. 1 pp. 413-417. [10] Krause FL and Altmann C. Integration of CAPP and scheduling for FMS. Proc. IFIP-CAPE, Bordeaux, 1991. [11 ] Iwata K and Fukuda Y. A new proposal of dynamic process planning in machine shop. C1RP int. workshop on CAPP. Hannover, 1989. [12] Kim S e t al. Integrated development of nonlinear process planning and simulation-based shop floor control. Proc. Winter Simulation Conference, 2002. [13] Schneewind J. Entwicklung eines Systems zur integrierten Arbeitsplanungerstellung und Fertigungsfeinplanung und -steuerung ffir die spanende Fertigung. Shaker Verlag, Aachen, 1994. [14] Steinmann H and Schrey6gg G. Management. Gabler Verlag, Wiesbaden, 2000. [15] Denkena Bet al. Gentelligente Bauteile. ZWF, Hanser Verlag, Vol. 10/2005. [16] Tracht K. Planung und Steuerung des Werkzeug- und Formenbaus aufBasis eines integrierten Produktmodells. PhD Thesis, Univ. of Hannover, 2001. [ 17] Awiszus B. Integrierte Produkt- und Prozessmodellierung unformtechnischer Planungsprozesse. Aachen, Shaker, 2000.
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eels) O 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
An Advanced Engineering Environment for Distributed & Reconfigurable Industrial Automation & Control Systems based on IEC 61499 T. Strasser a, I. Mtiller a, M. Schtipany a, G. Ebenhofer ~, R. Mungenast a, C. Stinder b, A. Zoit| b, O. Hummer b, S. Thomas ~ and H. Steininger d a Robotics and Adaptive Systems, PROFACTOR Research, 4407 Steyr-Gleink, Austria b Automation and Control Institute, Vienna University of Technology, 1040 Vienna, Austria c Bachmann Electronic GmbH, 6806 Feldkirch-Tosters, Austria d kirchner SOFT GmbH, 3124 Oberwdlbling, Austria
Abstract
The manufacturing and production industry will only survive in a more and more globalised world if they react fast and flexible to new market and customer demands. In order to technically achieve the postulated flexibility support for reconfiguration is necessary. Distributed automation systems built out ofmechatronic components help coping with these demands. This paper presents a new architecture for the integrated modelling of control and reconfiguration control applications for such systems which results in a modular advanced engineering environment for distributed and reconfigurable Industrial Automation and Control Systems (IACS) based on the IEC 61499 standard. K e y w o r d s : Distributed control, reconfigurable automation and control systems, advanced engineering environments
1. I n t r o d u c t i o n
The manufacturing and production industry will only survive in a more and more globalised world if they react fast and flexible to new market and customer demands. New paradigms like "Flexible production up to small lot sizes", "Mass Customization", "Life Cycle Service" or "Zero Downtime Production" can solve the mentioned requirements but they need completely new technologies. In order to technically achieve the postulated flexibility of these paradigms, support for reconfiguration- both at the machine (physical) and at the control (logical level)- is necessary [1 ]. From the technological point of view a shift from closely interlocked, rigidly coupled and central controlled production systems to more flexible, distributed environments is required. This can be realised with reusable and closely cooperating components with standardised interfaces. Future machines, plants and their corn-
ponents are built up from flexible autonomous and intelligent mechatronic components to a distributed system. Compared to the higher complexity of such systems a number of new advantages and opportunities turn up: higher modularity, flexibility & (re-)configurability; scalabiliO~ in functionality & computing performance, simpler system design & engineering, better local real time behaviour by local computing performance, comprehensive real-time behaviour over components as well as higher system availability through systematic distribution. Therefore the automation and control concept plays a central role of its realization [2]. An approach to solve this complexity on control level is provided by the new IEC 61499 standard- "Function Blocks for Industrial Process Measurement and Control Systems" [3]. It has been developed especially as a methodology for modelling open distributed Industrial Process, Measurement and Control Systems (1PMCSs) to obtain vendor-independent system architecture. This standard
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defines concepts and models so that control software encapsulated in function blocks can be assembled and distributed to controller nodes to define the behaviour of control systems. Furthermore IEC 61499 meets the fundamental requirements of open distributed systems as mentioned in [7,8]. IEC 61499 can be seen as a reference architecture that has been developed for modelling and implementation of distributed, modular, and flexible control systems. Through the management model [5,6]-including the management interface of IEC 61499 compliant devices--the new standard provides a suitable reconfiguration mechanism. It specifies an architectural model for distributed applications in IPMCSs in a very generic way and extends the function block model of its predecessor IEC 61131-3 [4] with additional event handling mechanisms and concepts for distributed systems. Additionally it defines platform independent reconfiguration services at device level [1 ]. Up to now there exist onlyprototypic implementations ofIEC 61499 compliant Run-Time Environments and the corresponding Engineering Tools (ET) with lacks in engineering support. The principle challenge and aim of this paper is to present an approach to overcome the limitations of IEC 61499 compliant ETs and give an outlook on necessary engineering support. To begin with, chapter 2 discusses the requirements for an advanced engineering environment. Chapter 3 gives an overview of currently existing IEC 61499 compliant engineering tools for modelling of distributed automation and control systems. In Chapter 4 we propose a framework for an advanced IEC 61499 engineering tool. Chapter 5 deals with a prototypical implementation of the proposed tool. Finally conclusions are presented in chapter 6.
to face a seamless system view depending on his requirements. - Application centred engineering: Especially reconfiguration control applications have to be considered from the whole applications' point of view. A device centred engineering approach will not be manageable and will lead to complex evolution scenarios. - Engineering and configuration support f o r communication: Due to the fact that a distributed system leads
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2. Adv. Engineering Environment Requirements This section deals with requirements for an Advanced Engineering Environment. The modelling of distributed control and reconfiguration control applications 1 is getting a more and more complex task. In order to keep it manageable for control engineers the following requirements are introduced through the engineering process: - Integrated development environment (IDE): Integration of the different tools for programming, configuration, hardware settings, diagnosis, monitoring etc. to a whole engineering suite for distributed and reconfigurable control applications. The control engineer wants
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to more complexity the engineering and configuration of the communication links between different devices shall be automatic or semi-automatic configured by an advanced engineering environment (i.e. the complexity should be hidden from the control engineer). Different engineering views." Reconfiguration introduces additional views within the engineering. The original/actual state of the control application has to be displayed in comparison with the desired state. The reconfiguration control application and its interconnections to these applications and the sequence of reconfiguration have to be visualized in an intuitive manner. Reconfiguration modelling language: The modelling language for reconfiguration has to be easy to understand and maintain. Therefore a similar semantic has to be used as for control applications. Extendable SW-component library: Software components (function blocks) have to be reusable for different applications. This requires standardized interfaces and the possibility to categorize existing components. In case of a distributed development also an interchange of component libraries is required. Distributions support f o r SW-components." Distributed systems tend to more complexity. This has to be hided form the control engineer. Therefore the ET should provide a support for the management of the SW-component distribution. Import functionality: Integration of existing software components beyond the range ofIEC 61499 (e.g. IEC 61131-3, IEC 61804) should be possible for the migration of existing knowledge from control applications.
3. Related Work in IEC 61499 based ETs Within this section a review on engineering environments for IPMCSs based on IEC 61499 with focus on the above mentioned requirements is carried out. Currently there exist the following six ETs: 3.1. H O L O B L O C F B D K
1Withinthe scope of this paper a reconfigurationcontrolapplication is described best as a control application which is able to change control application during its execution. A reconfiguration control application contains usually severalbasic reconfiguration steps.
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The Function Block Development Kit (FBDK) was the frst prototypical implementation of and IEC 61499 based engineering tool and originally developed by Rock-
well Automation. This demonstration software enables a control engineer to build and test data types, function block types, resource types, device types and system configurations according to the IEC 61499 standard [3]. Furthermore it allows application centred engineering, has an extendable SW-component library and is able to download the control application to different devices. Currently the FBDK is maintained by HOLOBLOC Inc. [7] that provides customized training, expertise and technology for IPMCSs based on the IEC 61499 standard. The major drawback of FBDK is the missing support for modelling of reconfiguration control applications and a lack in the configuration support for communication. 3.2. O 0 0 N E I D A Workbench
The OOONEIDA Workbench is an open source project of the OOONEIDA Community of Common Interest [9]. Its purpose is to develop an IDE supporting the global adoption and deployment of the IEC 61499 standard for the use of software components in distributed industrial-process automation and control systems. This project was started in Sept. 2005 and is currently in a pre-alpha stadium. In the final version it is planned to support the engineering of IEC 61499 applications according to the features of the HOLOBLOC FBDK. 3.3. CORFU ESS
CORFU ESS is an IEC-compliant Engineering Support System (ESS) that extends the IEC 61499 model to cover requirements specifications through the use of UML [ 10]. It adopts a hybrid approach for the development of IPMCSs that integrates UML with the IEC 61499 Function Block concept. The current implementation integrates IBM's Rose. Compared with the HOLOBLOC FBDK it supports the engineering of IEC 61499 system configurations using function block, data and devices types. Currently it's not possible to use the resource concept of IEC 61499 in CORFU ESS. 3.4. TUT-IPE Visio T M Template
The Tampere University of Technology has developed a Function Block Editor based on MS Visio T M [ 11 ]. It supports the creation of function block types and IEC 61499 control applications. The major drawback is the missing tool support for the hardware configuration. 3.5. TORERO 1DE
The TORERO IDE is an engineering tool that includes several functionalities such as allocation of control applications to devices, support of their
configuration, deployment of the code and configuration to the devices and specification of the communication between different devices [ 12]. For the modelling of the control application the IEC 61499 standard is used. Therefore the TORRERO IDE provides a IEC 61499 function block editor. The major drawback of this IDE also is the missing support for a systematic modelling of reconfiguration control applications. 3.6. ISaGRAF F B D K
The next release of ICS Triplex's ISaGRAF version 5.0--currently in a beta testing phase--has been enhanced to also support a first set ofIEC 61499 models in addition to its present IEC 61131-3 features [ 13]. This includes the basic function block and composite function block models. These FBs can be used in programs, which reside in resources of the different ISaGRAF devices in a distributed automation system. Although it provides a so called application view, which shows where parts of the application reside in the system, the parts have to be programmed directly in the resources. The communication between the application parts is achieved through network variables. An additional problem is that the event-triggered IEC 61499 FBs are executed on top of a time triggered IEC 61131-3 runtime system, which results in a large execution overhead and therefore in a rather poor performance of the 1EC 61499 application. All of these tools excepting ISaGRAF FBDK have no import functionality to integrate existing software components and are primary developed to support IEC 61499 compliant engineering of distributed control applications. Furthermore they are not designed to support the modelling of reconfiguration control applications.
4. Approach for an Advanced Engineering Support for Distributed, Reconfigurable IACSs To overcome the limitations of existing IEC 61499 based engineering tools for distributed control systems we propose a modular and extensible tool framework supporting application centred modelling of control and reconfiguration control applications in a hardware independent form [ 14]. The top-level approach focuses on replacing state-of-the-art "ramp d o w n - - s t o p - download--restart--ramp up" methods with a simple continuous system re-configuration, which is controlled by an reconfiguration application that is modelled with components in the same way as control applications In the last step of the control engineering process SW-components (function blocks) are mapped to the corresponding embedded control devices. Fig. 1 shows
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the different modules of the framework.
control devices. The visualisation and the parameter configuration ofthese devices are based on the hardware capability description. Furthermore the actual configuration and capabilities (e.g. free processing power, flee memory...) of the embedded control devices have to be determined also by the framework and should be display by the Hardware Configuration Editor. This is very essential for the verification and validation ofreconfiguration control applications. In case of reconfiguration also the hardware layout/setup can change. This means that I/Os from actuators or sensors have to be replaced physically and have also be supported by the advanced engineering environment. 4.3. Hardware Capability Description
iili~i ~;~;~::~i!:~i~i~i iii Embedded :!i~i: !~!i~ii~i;i!~:il:~~;::~= i'Ti~m~O~er~ting ;(RTO~S! ~i~U~i~i!~i~i ~i~i~!z!i~ Controller
Fig. 1. Modules of an advanced engineering environment framework for distributed and reconfigurable IACS The central parts of the proposed framework are described below in more detail. 4.1. Control and Reconf Control Application Editor
The application architecture also for control and reconfiguration control should focus on the process instead of devices. This means that the programming should be done in an application-centred way instead of a device centric one. Application centred engineering will be supported by adapting the IEC 61499 reference model for distribution. This allows hardware independent programming. In one of the last steps of the engineering process the function blocks are distributed (mapped) to the corresponding embedded controllers. Through the mapping communication effort between the different control devices is necessary. In the engineering environment this fact will be represented graphically. The control engineer has the possibility to use preconfigured network connection parameters or he can specify parameters in an advanced network configuration view. 4.2. Hardware Configuration Editor
This editor is responsible for the visualisation of the available hardware modules (e.g. embedded controller devices ofmechatronic modules), their configuration and the visualisation of the mapped function blocks of a control or reconfiguration control application to different embedded
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A main point for tool support in regard to distributed IACS is a precise description ofthe capabilities ofthe hardware. A typical system environment is not restricted to devices of one or a very small subset of vendors. Therefore the tool has to handle different kinds of information about the devices. This leads to a description that does not only expand a description as already known from field bus nodes; the set of parameters and their description has to form a comprehensive image of the device which is very important also for reconfiguration. The hardware capability description has to include the following different aspects: - Device dependent & application independent parameters: Within a heterogeneous system environment fundamental parameters like available memory, processing capability, I/O interfaces and supported network stacks have to be mentioned, additionally features of the software (especially the runtime platform) on the device (set of supported commands and functionality, processing time for atomic control operations, capability of the scheduling algorithm e.g. real-time) are necessary. Within these parameters modular devices and the configuration and parameters of the modules also have to be mentioned. - Device dependent & application dependentparameters: On one hand information about the currently available free memory space and processing power are important to determine the current situation ofthe processing unit. On the other hand the behaviour of the active applications on the devices has to be described. This means their cyclic and also their acyclic characteristic have to be qualified by significant parameters. In case of vendor defined software modules these parameters can be defined by extensive analysis work bythe vendor, but also for user defined applications these parameters are needed. - Device independent: Additionally the position of a device within the network gives important informa-
tion to determine latency of the communication networks. For instance the number of switches within the communication of two devices has a major impact on the latency time of an Ethernet network. The hardware capability description can be used in many ways. First of all the tool gets the possibility to support the user when mapping the applications to the devices (e.g. automatic network configuration). But there are further impacts possible for verification of correctness of the application with regard to the possibilities of the hardware (e.g. execution behaviour, realtime constraints). Another point is the tool support for verification of changes of applications in case ofreconfiguration of IACS without downtimes.
5. Prototypic Advanced Engineering Environment for Distributed, Reconfigurable IACSs The following section gives an overview of a prototypic implementation of the proposed advanced engineering environment. Based on Eclipse SDK [15] and the Graphical Editing Framework (GEF) [ 15] the engineering tool is realized as a plug-in for Eclipse. Eclipse is an open source community whose projects are focused on providing an extensible development platform and application frameworks for building software which is very suitable for the proposed framework in section 4. The GEF allows creating a rich graphical editor from an existing model. Fig. 3 shows the prototypic implementation. The main parts of the tool are the application, the hardware configuration editor, the system (project) manager and the function block and device libraries and the reconfiguration editor. The Application Editor is used to draw function block networks. Usability and aid for the user are main goals of the editor. Functions like copy/paste or undo/redo are added to the tool. Checks during drawing connections whether a connection is possible help the user to avoid mistakes. Functions like zooming helps the user developing larger function block networks. The Outline View provides an overview of the developed control application. Furthermore connections are drawn by a dotted line if connected function blocks are mapped to different devices. This shows the user that communication links have to be configured (see Fig. 3). The Hardware Configuration Editor is responsible for the modelling of the communication network. The network can be split into more parts for structuring. Each of these parts can be opened with the editor. Within this editor a function block can be mapped to a device. Mapping means that a function block gets assigned to a device or to a resource if the device has more resources (execution environments for
function blocks according to IEC 61499 [3]).
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Fig. 2. Un-parameterized communication function blocks (SIFBs) in hardware configuration editor Mapping can be done either by drag-and-drop within the Hardware Configuration Editor or in the Application Editor by selecting the device/resource in the context menu of a function block. If a function block (call Service Interface Function Block SIFB in IEC 61499) needs to communicate with the process interface or the communication interface of a control device special hardware mapping connections are available. Such hardware mapping connections can be connected with hardware ports of the device as depicted in Fig. 2. Hardware ports with common properties can be grouped. The previous mentioned mapping can occur either on a single port or onto a group of ports. As a communication function block knows whether it has to communicate with the process interface or the communication interface the mapping connections are drawn either on the bottom or on top of the abstract representation of the function block. By selecting the connection and dragging the not mapped end to the defined hardware port the mapping can be executed. Mapping is only possible if the function block can communicate with the specified port.
6. Summary and Outlook Within this paper the requirements for an advanced engineering environment for distributed and reconfigurable IACS are discussed. A review of existing engineering tools based on the IEC 61499 standard shows a lack especially in engineering support for communication and also for reconfiguration control applications. Therefore we introduced a framework for an integrated engineering environment. Furthermore a first prototypical implementation of this tool was presented. The next steps in our research work is to find an intuitive way for modelling of reconfiguration control applications, the corresponding re-arrangement of embedded control device I/Os in case of an physical reconfiguration and to integrate these features in the proposed framework.
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Acknowledgements This work is supported by the FIT-IT: Embedded System program, an initiative of the Austrian federal ministry of transport, innovation, and technology (bm:vit) within the eCEDAC-project under contract number FFG 809447. Further information is available at: www.ecedac.org PROFACTOR is partner ofthe EU-funded FP6 Innovative Production Machines and Systems (I'PROMS) Network of Excellence. http://www.iproms.org
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[5]
[6]
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Iacocca Institute: 21 st Century Manufacturing Enterprise Strategy. An Industry-Led View. Volumes 1 & 2. Iacocca Institute, Bethlehem, PA, 1991. Lewis,R.W.: Modeling control systems using IEC 61499. Number ISBN: 0 85296 796 9. lEE Publishing, 2001. lEC 61499: Function blocks for industrial-process measurement and control systems. Publication, Int. Electrotechnical Commission lEC Standard, 2005. IEC 61131-3: Programmable controllers - Part 3: Programming languages. Publication, International Electrotechnical Commission IEC Standard, 2003. Brennan, R., Fletcher, M., Norrie, D., eds.: An agentbased approach to reconfiguration of real-time distributed control systems, IEEE Transactions on Robotics and Automation, Special Issue on Object-Oriented Distributed Control Architectures, 2002. Fletcher, M., Norrie, D.H.: Real-time Reconfiguration using an IEC 61499 Operating System. In: 15th International Parallel and Distributed Processing Sympo-
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sium (IPDPS'01) Workshops, 2001. Christensen, J.H.: HOLOBLOC.com - Function BlockBased, Holonic Systems Technology. URL http://www.holobloc.com, Access Date: March 2005. Christensen, James H: lEC 61499 Architecture, Engineering Methodologies and Software Tools. URL http://www.holobloc.com/papers/71 christensen.zip, Access Date: July 2002. OOONEIDA Workbench URL http://oooneida-wb. sourceforge.net, Access Date: Dec. 2005. K. Thramboulidis, "Development of Distributed Industrial Control Applications: The CORFU Framework", 4th lEEE International Workshop on Factory Communication Systems, August 2002, Vasteras, Sweden J.L. Martinez Lastra, L. Godinho, A. Lobov and R. Tuokko, "An 1EC 61499 Application Generator for Scan-Based Industrial Controllers", 3rdlEEE Int. Conf. on Industrial Informatics (INDIN), Perth, Austrial, 2005. C. Schwab, M. Tangermann, A. Lueder, "The Modular TORERO IEC 61499 Engineering Platform - Eclipse in Automation", Emerging technologies and factory automation, ETFA 2005, 10th lEEE international conference Catania, Italia, September 19-22, 2005. ICS TriplexISaGRAFInc.:ISaGRAFUser's Guide.Nov.2005. T. Strasser, A. Zoitl, F. Auinger, C. Stander: "Towards Engineering Methods for Reconfiguration of DistributedRealtime Control Systems based on the Reference Model of IEC 61499", 2ndInt. Conference on Applications of Holonic and Multi-Agent Systems, Copenhagen, Denmark, 2005 E. Gamma, K. Beck, "Contributing to Eclipse: Principles, Patterns, and Plugins", Addison-Wesley Professional; 1st edition (October 31, 2003), ISBN: 0321205758
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Analysis of wireless technologies for automation networking C. Cardeira ~, A. Colombo b, R. Schoop b a GCAR - IDMEC, lnstituto Superior TOcnico, Avenida Rovisco Pais, 1049-001 Lisboa, P T b Schneider Electric GmbH, Steinheimer Str. 117, 63500 Seligenstadt, D E
Abstract Wireless technologies are challenging automation for new products and services. Like it happened in the past with Ethernet, the growing popularity of wireless among the general public is lowering the costs of wireless equipment. In the same way that Ethernet is being more and more spread in automation networking, wireless solutions are starting to find their place in automation networking, in spite of some scepticism about their robustness in a industrial environment. In this paper we address some of the advantages and issues of wireless network in manufacturing networking, namely the ability of exiting solutions to meet real time requirements, their security and safety issues, power issues and location awareness of the wireless devices. Keywords: wireless, networking, automation.
1. Introduction Modern production systems have to cope with shorter product cycles, which often demand production to be reconfigured. Modern production systems have to exhibit large flexibility to cope with frequent changes that may involve the reconfiguration of the plant layout. To achieve a fast, reliable and flexible reconfiguration there has been a large evolution on the flexibility of device connections. 1.1 From Point-to-Point connections to fieldbus
From point-to-point connections there was an evolution toward the creation of fieldbus. Fieldbus, in spite of some dispersion among standards, where achieving a steady state but the introduction of wireless technologies has a new strong impact on industrial communications. In the 80"s and 90"s, among a lot of standardisation activities [1 ], fieldbus were appearing
as a standard way to interconnect sensors actuators and control equipment. The main goal was to abandon legacy practices of point to point connections and replace them by a standard fieldbus, taking advantage of the decreasing hardware costs. The sensors and actuators would be equipped with CPUs and network controllers and connected directly to the network. Such solution would present a lot of advantages, namely [2]: data to be transmitted would have increased noise immunity, as digital communication copes better to noisy interference. Reconfiguration would become much easier because changing the location of a controller would need much less connections to be rewired. The devices CPUs would be used to perform local pretreatment to the data. This approach promised the distribution of the system intelligence all over the plant. The distribution level would eventually dismiss the controllers, leading to a system in which the tasks could migrate among the intelligent sensors, actuators and other devices connected to the network.
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Automation systems would become distributed, autonomous, fault tolerant and reconfigurable [2]. 1.2. Now wireless !
The emergency of wireless has a strong impact of industrial communication architectures. It is really convenient to connect devices to the network, without the use of wires. Using wireless, tasks like re-cabling or installing a new device on an automation system can be made much more efficiently. But it is not just on saving costs or on the increased flexibility that wireless connections are important. Some applications need wireless connections intrinsically. For instance, when there are mobility requirements of a given device, wireless provide a good alternative to the use of sliding contacts or trailing cables. In wireless, not only the installation costs are much lower, but also the true self-reconfiguration of a system without any rewiring becomes possible as ever did before. Wireless technologies will play an important role in the future agile, wireless manufacturing plants [3].
1.2.3. R e a l Time Issues
In spite of the economical and structural advantages, some scepticism exists towards the use of wireless in industrial plants, especially in real-time systems. Wireless communications are subjectto much more path loss. The signal strength decreases with distance exponentially (the exponent is between 2 and 4, depending on the environment). Wireless communications do not support full duplex communications, because when a device transmits, it is not able to receive on the same channels. The physical layer overheads are higher than wired solutions because of extra training sequences necessary to establish communication. The probability of getting channel errors is higher as wireless communications waves can be reflected or refracted and arrive to the receptor in multiple copies that will interfere with each other [6].
2. Wireless for Automation 2.1. Wireless issues
1.2.1. Self reconfiguration
Self configurable wireless sensors networks, which are usual for other domains (military or environment surveillance) have applications in automation. In a self reconfigurable wireless sensor network, devices spontaneously assemble the network themselves without central administration, autonomously identify new devices and integrate them in the network, dynamically adapt to sensor nodes configuration, manage location of sensor nodes, etc. When placed together, sensor nodes immediately know about the capabilities and functions of other smart nodes and work together as a community system to perform co-ordinated tasks and networking functionality. Wireless networking actually increases the scalability of an enterprise providing ease of expansion with minimal implementation and maintenance costs
[4].
1.2.2. Fault Tolerance
In case of accidents or faults that might destroythe wired network, wireless devices might still be able to communicate. This increases the possibility of keeping the system work safely even in the presence of wired communication faults [5].
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Many of the wired LAN protocols for medium access control rely on collision detection schemes, as it is the case of Ethemet. However, one of the most important differences between wired and wireless LANs is that there are no known solutions to efficiently detect collisions in wireless communications. When using wireless for fieldbus, another problem arises: fieldbus messages are generally short. As wireless communications need to have more redundancy and preambles for training sequences, they are more suited to send long and not timed constrained messages, than short and time constrained messages. So the efficiency of the bandwidth decreases when dealing with typical fieldbus traffic. 2.2. Wireless LAN, PAN and WAN
There are nowadays available and under development many wireless technological solutions for Local Area networks (LAN), Personal Area Networks (PAN) and Wide Area Networks (WAN). PAN range is typically bellow some meters, LAN is in the order of tens to hundreds of meters and WAN range has an order of kilometres. Bellow we present some of the most active technologies and the importance they might have for satisfying today's requirements for automation.
2.2.1. WiMAX
WiMAX is a Wireless WAN being discussed in the IEEE 801.16 group. It uses focalised microwaves to make point to multipoint transmissions. WiMax has a long transmission range (up to 50 km), but can also be used for last mile broadband communications. Combining multiple IEEE 802.16 channels for a single transmission could provide bandwidths of up to 350 Mbps. Originally, the 10 to 66 GHz band is used but the under the IEEE 801.16a standard it will also operate on the licensed and unlicensed 2 to 11 GHz band. The interest on these lower bands is that the signals can penetrate walls and most non-metallic obstacles and thus not require a line of sight. WiMAX seems much more interesting for telecommunications operators that may use WiMAX links to access distant places and then have a local Wi-Fi signal distribution. As for automation purposes, it seems that WiMAX will not have a strong impact in the flow shop but can be interesting for accessing data in distant sites with difficult physical access. WiMAX can be an enabling technology for remote access applications like for instance, tele-operation or tele-supervision. 2.2.2. Wi-Fi
Wi-Fi standards support up to 11 Mbps (802.1 lb) or 54 Mbps (802.1 l g) with a typical indoor range of 30 m indoor or 90 m outdoor range. As they use the 2.4 Ghz unlicensed band, there can be a lot of interference among these devices as well as from microwave ovens and high-end wireless phones. The 5 GHz band of 802.11a deals with much less inference, however it incurs in more difficulty to go through walls [7]. It is expected that the standard 802.1 In will soon be available which goal is to increase the rate and range. The standard 802.11 e aims to implement the quality of service functionality and provide deterministic media access. Concerning automation, Wi-Fi devices have power consumption that, in some cases, are not suitable for the requirements of sensor/actuator networks. However it is a mature technology and is helpful for the vertical integration in automation fields. 2.2.3. Bluetooth
Bluetooth is a Wireless PAN. It is a set of protocols with the physical layer based on IEEE 802.15.1 standard. It operates in the 2.4 Ghz unlicensed band. Bluetooth requires much less power than Wi-Fi, but the area covered and data rates are also smaller.
Bluetooth 2.0 supports data rates up to 2.1 Mbps with a range that depends on the power class of the product. In most common implementations the range can be up to 1 m or 10 m depending on the power class. For automation purposes, Bluetooth use for sensor networks seems not suitable especially because of the power requirements. Actually, other technologies, like ZigBee are available to provide low cost and low power solutions (but much lower rates) that are more suitable for sensor networks. Bluetooth seems very suitable to replace serial cables for configuration and be used together with an HMI device to monitor and check and equipment for maintenance or diagnosis. 2.2.4. ZigBee
Zigbee is another wireless PAN. It is a set of protocols with the physical layer based on IEEE 802.15.4 standard. It operates in several frequencies including the 2.4 GHz band used by most Wi-Fi and Bluetooth devices. It presents a comparable or slightly higher range (10-100 metres) but a lower data rate (20-250 Kbps). The main advantages of ZigBee are lower power consumption and network self-reconfiguration. ZigBee devices are able to 'sleep' most of the time. The power consumption is reduced, making it possible to have devices that operate with a single battery for years. The standard provides star or meshed networks. In the latter case, it allows the coverage area to be extended when new nodes are added. ZigBee is an emerging technology and it is not as mature as Wi-Fi and Bluetooth, but as ZigBee fulfils the requirements of low power and low cost, it is a promising technology for sensor actuator networks. 2.2.5. IrDA
IrDA is a PAN where all the data is transmitted by modulated infrared light. These protocols had a very promising start and gathered some popularity. Nowadays, many laptops, palmtops or mobile phones offer IrDA communications in the base configuration. Data rates of 1 and 2 Mbps are available in a 1m range. However, this solution never gained a lot of support and seems condemned because it requires unobstructed line-of-sight and a specific angle range [7]. 2.2.6. UWB
Ultra-Wideband is a very promising solution for PANs. It is a technology where the communication is send by
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short-pulse electromagnetic waves, instead ofthe usual modulation ofsinewave carriers [8]. It is claimed that UWB might achieve rates up to 500 Mbps in a 2 m range (or 110 Mbps in a 10 m range) operating in the same bands as other communication systems without significant interference. The occupied band is very large (500 Mhz or 20% of the centre frequency) but the hardware will consume just a few mW of power. Currently, there are two competing UWB standards: Cordless-USB from Freescale and Wireless USB from the WiMedia Alliance. The standard for Wireless USB, IEEE 802.15.3a, was under discussion but the discussion group voted to disband and it will be the market to decide which will be the winner. For automation there seems to be a large domain of application of this technology. UWB might be a solution for demanding tasks like wireless closed control loops. 2.2.7. RFID
Radio Frequency Identification (RFID) is an electronic PAN technology for a wireless transmission of device identification. Their main goal is to replace the bar code labels. Passive RFID tags are powered by the microwave signal they receive through the antenna. They answer with a sequence of bits that defines its identification [9]. Compared to code bar labels they have the advantages of not requiring line of sight, not be limited to static data and have a longer read range. This turns them to the ideal device for product traceability. On the other hand they have the inconvenient of being more expensive (yet, a passive RFID tag will not cost more than some tens of cents). They use several frequency bands from 125 KHz to 2.45 GHz, but there are several standards driving their evolution. Their use on automation is very promising for product tracking and warehouse management. Embedded within the equipment (or on the parts of it) they can stay there forever and answer with their identification whenever asked to.
technology will have on automation, but we may consider it somewhere around the impact of RFID and Bluetooth. 2.2.9. G S M 2 G and 3G
The usual telecommunication GSM services provide larger coverage and higher rates with GPRS or UMTS. These technologies require an infrastructure of a service provider. They depend on a quality of service that cannot be always guaranteed for automation purposes. It seems that, like WiMax, these solutions are more interesting to telecommunications providers than for the automation. However, in remote installations, like water supply systems, remote RF antennas, windmills, solar power plants, where the cost of local maintenance operations is high, cost savings can be done using the GSM based networks. In these applications, the generated traffic is usually small (order of a few bytes a second or even a minute) and there is no big issue if connection is momentarily lost. In this case, the use of these networks might reduce the number of costly maintenance visits. 2.2.10. Others
The are some other technologies that are not described in this paper for several reasons, but they deserve some reference. WiBro aims to provide a high data rate wireless internet access under the stationary or mobile environment. It is primarily based in South Korea, but it is too soon to state about the success of this technology. WISA is a solution proposed by ABB, which uses it for connecting wireless proximity sensors to their robots controllers with the sensors powered by an alternating magnetic field. DECT is a well-known technology for wireless phones and some works have been carried out for their use on automation.
2.2.8.NFC
3. Power Issues
Near Field Communication (NFC) is another PAN technology where an emitter provides a magnetic field and the receiver answers by modulating this field. The speeds are limited (106, 212 or 424 Kbps). The maximum operating distance is 1.5 - 2 m, but, in practice, small distances 0-20 cm are usually considered. It is still difficult to say what impact this still immature
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The freedom to place wireless sensors anywhere in the factory plant or a building gets limited if those devices have to be connected to a main power source. Although power is generally available in the plants, it is often not provided at the precise location for the sensor placement [ 10]. There are several solutions for self-powering:
In this category we consider devices that obviate the need for a battery by exploring the energy present on the environment. This can be done, for instance, by using coils and magnets to retrieve energy from mechanical movements as in motors, pumps or fans, by using piezoelectric materials that generate power when mechanically strained or by using termocouples when a temperature differential is available [ 10], [ 11 ].
For automation purposes the location awareness can have a positive impact. Usually AGV (Automatic Guided Vehicles) guidance systems compute the AGV position by making the fusion of data from the wheels incremental encoders (which are prone to accumulate errors) with the data of an absolute position. The absolute position can be given from triangulation orthe passage by referenced places identified by sensors [13]. Recent developments turn the use of wireless into an easier solution for the AGV to recognise its absolute position. For maintenance operations it is very convenient for the operator to carry with him a wireless palmtop or similar equipment that would guide him directly to the location of the equipment that needs assistance. Using wireless technology to track products and materials in their different phases would provide more efficient management. A quasi-total integration could be achieved if a similar development is made to identify the location of RFID tags [9]. Low cost active RFID cards, probably powered by energy harvesters with a location awareness system would be important for the management of a manufacturing site. Even people location inside an area can be achieved with precision and commercial systems are already available, like the Ekahau Wi-Fi tag [14].
4. Location awareness
5. Security and safety
Wireless communications present another, somewhat unexpected, advantage: it is possible to know the position of a device by measuring and correlating the signal parameters when they arrive to the wireless access points. Wireless location awareness emerged for safety reasons for cellular phones. According to the existing FCC laws that are being increasingly adopted by other countries, mobile phone providers have to deliver the precise location of the emergency calls, within 100 m of its actual position for at least 67% of the calls. The solution of installing a GPS receiver in each device has a lot of drawbacks (cost, outdoor only, need to modify the devices). The solution found is based in measuring the time delays, angles and signal strength of the emitter and fusion all the data to have an estimation of the device location. This approach has the strong advantage of requiring no modifications in the existing cellular phones. In Wi-Fi networks, a similar approach is used to provide location of Wi-Fi devices [12]. Several new applications may arise like mobile advertising, assert tracking, fleet management, security and location sensitive billing [ 12].
All wireless technologies face a security problem. As electromagnetic waves are easy to intercept and easyto jam. Using today' s data encryption methods and spread spectrum techniques, it would be hard for a spy system to decode the protected information. Unintentional jamming can be solved changing to bands that might be free. Intentional jamming caused by criminal acts would be much harder to handle. Wireless can keep the communications working when a criminal act destroys the wired communications but is unable to perform if intentional noise is sent in all the operating bands.
3.1. Batteries.
Battery operated devices seems a natural solution, if the low power consumption of the device allows a 3-5 year battery lifetime. This solution is used in temperature sensors located along one building to reduce the costs of heating, ventilation and airconditioning systems [ 10]. 3.2. Microwave
This is the solution used by passive RFID. The power needed to operate the sensor is taken from the power of the electromagnetic communication waves [9]. 3.3. Energy harvesters
6. Conclusions
In this paper we analysed wireless solutions that are emerging and their impact in industrial automation networks. We concluded that Wi-Fi devices have power consumption that might limit their use in industrial environments at the sensor actuator level but are suitable for vertical integration. On the other hand, Bluetooth devices have smaller power consumption
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and a smaller range. With a small range, Bluetooth might accommodate more devices in the same area, thus making a better use of the available bandwidth. The same arguments apply to ZigBee, which has the advantage of even lower power consumption and might be aplicable to the emerging UWB solutions. Many of these solutions use the same public band, typically the unlicensed 2.5 GHz band. The CSMA protocols avoid many potentially destructive interference, however degradation is inevitable and several studies were already carried out to compute the throughput degradation when several of these solutions coexist [6]. Solution for self-powering the wireless devices is also under study. The classical solution is the use of batteries that might feed low power devices for 3-5 years. Other interesting solutions are arising with energy harvesters that are able to explore the energy present in the environment (e.g. mechanical or thermal). RFIDs can be consider in this class as they get the power they need to operate from the energy of the microwaves that carry the signals. The location awareness of a wireless device is a new feature of these devices. This feature may have strong impact on services where the physical location of the device is important, like tracking, logistics, security or maintenance. In conclusion we may say that there is still some scepticism about wireless networking in industrial automation. However, in spite of some drawbacks, there are many advantages on wireless networking that will provide new and innovative services and solutions for automation networking.
6. Acknowledgements The authors would like to thank the European Commission, the Portuguese Foundation for Science and Technology and the partners of the Network of Excellence "Innovative Production Machines and Systems (I'PROMS; http://www.iproms.org/)" and European Integrated project "Virtual Automation Network (VAN; http://www.van-eu.org)", for their support.
References [1] Thomesse, J.-P., "Fieldbus Technology in Industrial Automation", Proceedings of the IEEE, Vol. 93, n. 6,, June 2005, pp. 1073-1101. [2] Cardeira, C., Mammeri Z., "A Schedulability Analysis of Tasks and Network Traffic in Distributed Real-Time
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Systems", Measurement, n~ 15, 1995, pp. 71-83. [3] Filos, E.; Riemenschneider, R; Patton, R.; Drews, P.; Strake, G.; Johansson, A.; Herian, D.; Fogliazza, G.; Colombo, A.; Zuehlke, D.; Lastra, J., Workshop Report "The Agile, Wireless Manufacturing Plant", ICT for Manufacturing, FP7 Track of Workshops on Industrial ICT, http ://www.ims-noe.org/FP7.asp. [4] Colandairaj, J.; Scanlon, W.; Irwin, G.; "Under wireless networked control systems through simulation", IEE Comp. & Control Engineering, April 2005, pp 26-31. [5] Choi, S.; Kim, B.; Park, J,; Kang, C.; Eom, D.; "An Implementation of Wireless Sensor Network for Security System using Bluetooth", IEEE Trans. on Consumer Electronics, Vol. 50, No. 1, February 2004, pp 236-244. [6] Willig, A.; Matheus, K.; Wolisz, A., "Wireless Technology in Industrial Networks", Proceedings of the IEEE, Vol. 93, N. 6, June 2005, pp. 1130-1151. [7] Stallings, W., "IEEE 802.11: wireless LANs from a to n", IT Professional, Vol. 6, n. 5 Sept. 2004, pp 32-37 [8] R.J. Fontana, "Recent System Applications of ShortPulse Ultra-Wideband (UWB) Technology", IEEE Transactions on Microwave Theory and Techniques, Vol. 52, Issue 9, Part 1, Sept. 2004, pp. 2087-2104. [9] Want, R.; "RFID A Key to Automating Everything", Scientific American, January 2004, pp. 56-65. [10]Kintner-Meyer M., and R. Conant. "Opportunities of Wireless Sensors and Controls for Building Operation." Energy Eng. Journal, 2005, vol. 102, no. 5, pp.27-48 [ll]Energy Harvesters and Sensors. FerroSolutions. Roslindale, MA. Available at www.ferrosi.com/files/ FS_product_sheet_wint04.pdf. [ 12] Ali H. Sayed, Alireza Tarighat and Nima Khajehnouri, Network-Based Wireless Location: Challenges faced in developing techniques for accurate wireless location information, IEEE Signal Processing Magazine, July 2005, pp 2440. [13] Borges, J., Lima, R., Alves, R.; Pasadas, F., Cardeira, C.; "Triangulation Method for Determination of a Robot's Location"in Proc. of EUREL'98 Mobile Robotics, Leiria, Portugal, September, 1998. [14] T201 Wi-Fi tag: Quick setup & low cost deployment over standard Wi-Fi networks, Ekahau - Innovation Through Location, http://www.ekahau.com
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eels) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
ENGINEERING MODULAR AUTOMATION SYSTEMS R. Harrison, A.A. West, S.M. Lee Distributed Systems Group, Wolfson School of Mechanical and Manufacturing Engineering Loughborough University, Loughborough LE11 3TU, UK
Abstract
Automation systems often fail adequately to support required business objectives. Whilst they may offer adequate real-time performance, they are often difficult and complex to support, configure, integrate, and optimise, particularly in the face ofrapid and often unforeseen change. Research conducted at Loughborough University into the development of concepts and tools to support the global engineering of component-based reconfigurable manufacturing automation systems in the automotive production domain is described in this paper. A collaborative framework to integrate and coordinate the various engineering activities of globally distributed engineering teams involved in the design, implementation, operation and diagnosis of production machinery is also described. Keywords: Automation, Lifecycle, Modular, Reconfigurable
1. Introduction In many manufacturing industries end-users of automation systems have been very product-focused whilst careful consideration may have been given to manufacturing processes, automation and control system engineering has traditionally received less attention and has often been done on a largely ad-hoc basis. Today's automation systems are often difficult to maintain and reconfigure and are ultimately very expensive over their lifecycle. The capabilities of automation systems need to be regarded by end-users as a competitive weapon, e.g., with the potential to give them an advantage in terms of inherent manufacturing agility relative to their competitors [ 1]. However, the overall functionality required of their automation systems, in an overall business context, has received little attention, and little research has been done with representative sets of supply chain partners to enable the creation of suitable automation infrastructures.
From the end-user's business perspective, embedded automation systems of the future must not only meet local control needs but must also be conceived from the onset as part of a system-ofsystems into which control functionality can be easily and efficiently integrated. Figure 1 illustrates the ARC collaborative manufacturing management model, but very similar models exist for other embedded automation and control domains. In this context a collaborative automation system needs not only to support integration of distributed real-time automation devices but also to support integration ofthose devices with enterprise systems and with value/supply chain partners throughout the system lifecycle [2, 3]. Research at Loughborough has investigated the requirements, design and potential for implementation of new modular automation systems better able to meet the lifecycle needs of the products they manufacture by exhibiting improvements in key lifecycle performance
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Fig. 1. ARC collaborative manufacturing model [2] characteristics [4, 5, 6, 7]. For example: 9 Better machine scalability, i.e., allowing machine changes to be made more efficiently by simply adding or removing individual components at will. 9 Greater modularity and support for postponed machine build, i.e., allowing sub-assemblies, from single components up to major sub-sections, to be built and tested separately and then combined quickly and easily in accordance with a well defined machine architecture. 9 Easier process definition, i.e., provision of highlevel graphical representations of machine behaviour in a processes-related manner, which will allow process engineers to make system changes more easily. 9 Implicit support for e-service/maintenance, i.e., provision of embedded support for remote machine diagnostics and monitoring. Although a review of the state of the art or comparison of approaches is beyond the scope of this paper, several research projects are now looking at improved approaches to the lifecycle engineering of automation systems. For example: i) the project F6deral, which focuses on the integration of engineering disciplines (mechanical, electrical, etc.) in one modular reusable approach [8]; ii) the EU project TORERO, which aims at creating a total-life cycle web-integrated control design architecture and methodology for distributed control systems in factory automation [9]; and iii) the work of the University of Michigan's Engineering Research Center for Reconfigurable Manufacturing Systems (RMS), which has undertaken a range ofprojects around the theme of machine reconfigurability, mainly focusing on machining applications [10]. The reader may also wish to refer to a recent review paper which places the Loughborough work in the context of other relevant research [3].
In order to realise a modular reconfigurable automation system with the desired capabilities, it is vitally important to be able, reliably and repeatably, to construct and compose distributed embedded systems that can meet and adapt readily to ever changing user application requirements. Such systems need to be generally applicable to a broad spectrum of application domains and yet be capable of easy and precise tailoring to specific applications. The objective is not only to support application design, simulation and monitoring of real-time distributed automation components from the control perspective (control dimension) but also to support the integration of these devices with higher-level business process systems (enterprise dimension), with supply chain partners (value/supply-chain dimension) and within a lifecycle engineering context (lifecycle dimension). Figure 2 presents an example of a modular automation system composed of distributed mechatronic components and highlights its support and integration needs. Work on the research requirements for an engineering environment to support this four dimensional approach is the subject of ongoing research at Loughborough University [7]. 3. Enabling distributed engineering The focus of this paper is the realisation of a framework for distributed engineering capable of supporting the supply-chain partners involved in the creation and lifecycle support of modular reconfigurable automation systems. The concept is illustrated in Figure 3. An integrated engineering environment is needed to support the multi-perspective needs of different classes of application users in an efficient manner. The
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Fig. 3. Integrated engineering environment approach adopted was to identify reusable, configurable components, the aim being to mask complexity, maximize reuse and build domain-specific libraries of configurable components and associated services, minimising the need for new custom components for each new application. Previous studies in several industrial sectors have shown that a relatively small library of components, specifically tailored to the needs of a given automation user, could meet 80-90% of the user's needs [7]. In order to support and facilitate distributed engineering teams, a framework is required capable of hosting distributed engineering tools and supporting the collaborative engineering of manufacturing machinery from concept definition, through system development and implementation, to through-life operational support and maintenance. It is necessary that the framework support bi-directional flow of data and information between the tools used within the various engineering phases and operational levels. Two main criteria should be considered in the development of such a framework: 9
The framework is required to support the collaboration of globally distributed engineering teams throughout the entire lifecycle of manufacturing design, implementation and operation. The framework will enable a seamless
integration of and support for (i) activities on the production shop floor, (ii) design and engineering processes, (iii) system visualisation and machine operation (human-machine interface). A "common data model" to support the interworking and decision-making of distributed engineering teams is necessary to provide a consistent data representation regardless of the viewpoints and domains of expertise of the team members; the stakeholders are then able to rely on the same set of data from this common model to provide different instantiations of the machine design from their perspectives of concern [4]. The Manufacturing System Integration (MSI) Research Institute at Loughborough University has been involved in an Engineering and Physical Sciences Research Council (EPSRC) funded research programme with major automotive producers, production machine builders and second-tier component suppliers in investigating appropriate integration structures that could support the distributed engineering of automotive production machinery [6, 7]. A common data model to support the design and implementation of automation from the machine design and process specification stage to the build,
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installation, commissioning and operational stages is core to the research and development. Instead of independent engineering teams directly communicating process specifications information with each other, the common data model allows the teams to determine the specific information they are interested in from a common representation (see Figure 3). The data can be presented in different views or formats according to perspectives required or areas of concern of each user. Based on the roles of the users of the model, they are selectively allowed to add or modify specific parts of the common model. Research into the business processes involved in the current lifecycle of machine design and implementation has indicated that the activities involved can be supported by four coupled engineering environments as shown in Figure 3. They are: i) a process engineering environment, ii) a componentbased runtime environment, iii) a machine visualisation environment, and iv) a human-machine interface (HMI) environment. Underlying the four environments is a common data model, which provides a consistent data representation that acts as an integrated communication link for the distributed engineering environments. Details of the four engineering environments and information on how they can be used to facilitate lifecycle engineering processes are described in the following section.
4. Engineering environments The process engineering environment provides an engineering and design platform for the system developer to compose the component-based machine and configure the process parameters for each component in the runtime environment [11]. The control behaviour of the components is abstracted as a set of state-transition diagrams and a logic simulation engine. The component-based runtime environment consists of the physical and software elements of the manufacturing automation system i.e. the production machine and its components. Physical elements include automation hardware such as drives, actuators and sensors whereas the software elements cover embedded control application and input/output functions (I/O) associated with individual hardware. Under this paradigm automation systems are composed of basic component building blocks using the process engineering tools. A component is defined as an autonomous mechatronic unit consisting of the automation device (e.g., actuator and sensor) with its
508
own computing hardware (processor, memory, communication interface and electronic interface to the automation device) as well as control software (application programs, operating system and communication protocol). The visualisation environment is used to provide different views of the machine and its associated components. The same stream of information from the common data model is utilised but expressed in different views according to the specific needs and engineering tasks of the model user/viewer (e.g., VRML model). The visualisation environment can be used to support all phases of the machine lifecycle including, for example, remote maintenance utilising web-based connectivity [7]. The human-machine interface (HMI) environment allows the user to look at the status of the machine from the perspective of machine operator, process engineer or maintenance engineer. The HMI also enables direct control and operation of the production machine in the runtime environment. Each HMI is based on HTML (hyper-text modelling language) and, hence, can be viewed in any standard Internet browser application. The use of browser-based technologies enables remote users to access the state of the production machinery regardless of geographical locations. Figure 4 illustrates different abstractions of a typical component from a transfer-line system within an automotive engine block machining cell that has been implemented at Lamb Technicon UK, one of the collaborators in this research project.
5. Component-based automation for engine manufacture The component-based concept has been evaluated by the researchers and industrial collaborators through the implementation of a series of component-based manufacturing automation systems, including i) a fullsize automotive assembly machine at Johann A. Krause, Bremen, Germany, and ii) an engine-block transfer-line machining system in Lamb Technicon, Mildenhall, UK. A prototype software suite known as the Process Definition Environment (PDE) has been developed at Loughborough University to facilitate the distributed engineering of component-based systems [11]. The PDE hosts a set of software business objects that provide services for the distributed engineering environments as shown in Figure 3. These software objects (e.g., logic engine simulator, interlock editor and broadcaster) have been developed to run on the
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physical locations. One exercise involved the deliberate input of errors into the physical runtime system to study the effectiveness of the PDE tools to support the identification and diagnosis of machine errors by engineers situated in remote locations. Conventionally, this remote diagnosis process is typically undertaken via telephone conversations and usually requires a great deal of time on the part of the remote engineer to establish the current state of the machine so that the problem can be identified. Recovering a machine from an error state by means of verbal instructions can be highly problematic [12]. Using the PDE, far fewer conversations between the remote engineer and the machine operator were necessary. The engineer was able to visualise the state of the remote machine directly using the various visual models provided by the visualisation environment (i.e., 3D machine models, STD and remote web-based HMI) (see Figure 6). Interactions between the distributed parties focused on analysing the cause of the problem with the machine rather than establishing the current state. The remote engineer became more proactive and was able to provide specific recovery instructions to the machine operator on the shop floor as he was able to monitor the entire machine operational sequence and state progressions through the virtual models.
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Fig. 5. PDE stakeholders and typical use cases With the use of the PDE tools, the time taken to identify and diagnose a machine fault could be reduced by about 50% [ 12]. It was found that the employment of the PDE also improved the accuracy of the diagnosis and quality of the suggested machine recovery instructions. The PDE tools allowed the remote engineer performing the machine diagnosis to be active in terms of having direct access to the information via the common model instead of relying on a third party to relay this information 6. Conclusions
A component-based approach to the implementation of automation systems has been discussed, which enhances system configurability and reuse. To meet the needs of agile manufacturing, collaborative automation systems are needed capable of supporting not only real-time control requirements
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but also the business needs, supply-chain integration requirements and lifecycle support needs of each application. This paper has focused on the provision of a framework for the distributed engineering support of the automation system lifecycle for use by the supplychain partners, e.g., end users and machine-builders. Initial evaluation of this system has shown that it offers significant advantages over traditional approaches to the implementation and support of automation systems.
[ 1] Anon., "The Future of Manufacturing in Europe 2015 2020, The Challenge for Sustainability", Final Report, European Commission, March 2003. [2] Anon, Collaborative Manufacturing Management (CMM) model, ARC Strategy Report, October 2001, ARC Advisory Group, http://www.arcweb.com/. [3] Harrison R. and Colombo A.W., Collaborative Automation from Rigid Coupling towards Dynamic Reconfigurable Production Systems, 16th IFAC World Congress, Prague, Czech Republic July 4-8, 2005. [4] Harrison, R., A. A. West, R. H. Weston, and R. P. Monfared, "Distributed engineering of manufacturing machines," Procs. of the I MECH E Part B Journal of Engineering Manufacture, vol. 215, pp. 217-231,2001. [5] Harrison, R., and West A. A., Component-based paradigm for the design and implementation of control systems in electronics manufacturing machinery, J. of Elect. Manuf., vol 10 no. 1, December, pp 1-17, 2000. [6] Anon., "Common Model for Partners in Automation (Companion): Systems Integration funded project investigating issues to do with the design and implementation of a common model-based environment in the area of production automation, GR/M53042, Loughborough University 1999. [7] Harrison, R., Lee S. M., and West A. A., Lifecycle Engineering of Modular Automated Machines, presented at 2nd IEEE International Conference on Industrial Informatics (INDIN'04), Berlin, Germany, 2004. [8] FODERAL-Initiative, http://www.foederal.org/ [9] TORERO - Total life cycle web-integrated control http ://www.uni-magdeburg.de/iaf/cvs/torero/ [ 10] NSF Engineering Research Center for Reconfigurable Manufacturing Systems, University of Michigan http ://etc.en gin.urnich.edu/ [11] Thomas D.W., A. A. West, R. Harrison, and C. S. McLeod, "A Process Definition Environment for Component Based Manufacturing Machine Control Systems developed under the Foresight Vehicle Programme," presented at S.A.E. Conference, 2001. [12] Ong M.H., S.M. Lee, A. A. West, and R. Harrison, "Evaluating the use of Multimedia tool in the Remote Maintenance of Production Machinery in the Automotive Sector," IEEE Conference RAM'04, Singapore, 2004.
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All rights reserved.
Linking production paradigms and organizational approaches to production systems S. Carmo-Silva, A.C. Alves, F. Moreira Centre for Production Systems Engineering, University of Minho, Campus de Gualtar, 4700-057 Braga, Portugal ([email protected])
Abstract Manufacturing system design and operation is critical to achieve strategic company objectives. This must aim fitting manufacturing systems capabilities to the different demand market environments, having in consideration the different approaches and strategies that should be used. In this paper we develop a framework for characterizing production system conceptual models and linking them to both production paradigms and organizational approaches to production, such as lean and agile manufacturing. The conceptual models identified are useful for aiding to implement organizational approaches and fit manufacturing systems to manufacturing requirements determined by different product demand patterns. Keywords: Manufacturing systems, agile and lean, production paradigms and models 1. Introduction Production systems must be designed and managed to best fit market demand requirements. Critical to such design and management is the nature of demand. It is therefore relevant to characterize demand and, accordingly, to link this to production paradigms and these to production approaches and systems.
2. Production Paradigms
2.1. Mass Production It is common to refer as mass production the production paradigm that addresses a demand market where demand for a product is large and is kept so over long time periods, i.e. it is predictable and stable. In this paradigm, production is continuous, at a flow rate which ideally matches product demand. Production systems of the mass production paradigm have as a key performance objective meeting demand at low cost per unit manufactured.
Thus, to take advantage of scale economies not only the production system as a whole, but also their workstations, main equipment and tooling are dedicated to one product. Therefore, the life time of such a system is linked to the life time of the product to which it is dedicated.
2.2. Repetitive Production We can also envisage an evolution of the market demand to a situation of variable and less predictable demand, in lower volumes and shorter product life cycles than in mass production. Therefore a dedicated system to each product is economically unacceptable. Thus, a variety of products, repeatedly required over time, with somewhat different production requirements, may have to be manufactured in the same production system with characteristics different from those of mass production systems. This requires flexible forms of production and/or of organizing production. This organization is usually based on interlinked and relatively autonomous subsystems, usually cells of
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several types [1 ], or on function oriented production units. Flexible production is achieved through flexible equipment subject to tool changing for multi-task handling and is usually operated by multiskilled operators. Flexible manufacturing may also be used in some circumstances. Several tools can be used for designing, adjusting, managing and continuously improving manufacturing systems' configuration. These include approaches, techniques and methods associated with Lean Manufacturing, Total Quality Management, Continuous Improvement and set-up reduction. Additionally we should refer, at operations planning and control, the use of several order release and materials flow control mechanisms [2] and scheduling methods and systems [3]. Repetitive Production (RP) is based on repeatedly required products whose demand is predictable but in volumes that do not justify mass production systems. This definition is in line with what is presented by Bradford [4], and MacCarthy and Fernandes [5], but differs from the view of other authors. Two fundamental instances of RP can be identified with basis on the production flow pattern, i.e. Repetitive Uniform Flow Production (RUFP) and Repetitive Intermittent Flow Production (RIFP). In RUFP different products are manufactured together, in a mixed manner, during a given planned production period, at a uniform flow rate. This rate matches and is synchronized with demand rate for the period. We could say that RUFP attempts to mass produce a variety of products in low volumes as if they were a single product. The RUFP repetitive production paradigm instance is itself frequently referred as repetitive production [6] without including the RIFP instance. The RIFP paradigm instance is based on the repetitive but independent, i.e. not mixed, manufacture of products which were also manufactured in the past. The flow of production is not uniform but, on the contrary, intermittent, i.e. based on the flow of independent batches. It is common to refer RIFP systems as multi-model production systems in opposition to the RUFP systems which can be identified as mixed model ones [7]. Not disregarding the importance of low cost per unit, typical of mass production systems, key performance objectives of repetitive production systems are the efficient use of manufacturing resources and good customer service measured mainly in two dimensions, namely timely delivery of products and product quality. A key design feature of
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a repetitive production system is its capability to jointly manufacture, in the same production period, a variety of products required in variable but predictable demand.
2.3 Non-repetitive Production Repetitive production is still a valid paradigm in today's market environment and is likely to continue to be so for many years. However it is loosing importance and rapidly giving place to the nonrepetitive one, probably the most common paradigm in the near future. Non-repetitive production is mainly linked with unpredictable and turbulent demand markets for unique products, different from others previously manufactured, i.e. not repeated. This means that a company cannot reasonably forecast, or precisely identify, products before costumer orders are placed. This is both the result of global competition and increased and varying customer needs. Although ordering is not likely to be repeated, this does not mean that only a single or a small number of product units will be required. In fact, a customer may order a large quantity of a new, unpredictable, customized product. Non-repetitive production is, surely, associated with product customization. This means that there is the involvement of the customers in the specification or customization of products. Product customization may lead to an approach to production referred to as mass customization. The concept was initially put forward by Davies [8] and brought into Production and Operations Management area by Pine [9] who defines mass customization as the ability of a firm to produce a variety of customized products quickly, on a large scale, and at a cost comparable to mass production. A typical case of mass customization has been reported by Kotha [10] for the production of mass customized bicycles based on individual customer anthropometric measures and other customer requirements. Duray et al. [ 11 ] argues that mass customization is associated with modular product design and manufacture. This is dependent on modular options and variants, or differentiation enablers as they are called by Tseng [12]. The customer choice of differentiation enablers can be facilitated through product configurators [13]. The essence of modular concept is to design develop and manufacture parts that can be combined in the maximum number of ways [14]. Product customization can be realized at different levels. Mintzberg [15] distinguishes three: pure, tailored and
standardized. Pure means products designed and produced from scratch for each customer. In this level of customization mass customization may be not met. Standardized means the assembly of products from a set of standard components according to individual customer needs; and tailor customization is altering a basic product design to suit customer needs. Gilmore and Pine [ 16] refers four approaches to product customization dependent on the degree of customer involvement in the customization process and degree of product customizability. Mass customization has, in many instances, elements of production repeatability reason why the manufacturing organization solutions for mass customization may be based not only on the nonrepetitive paradigm but also on the repetitive one. In the non-repetitive production, production requirements can only be established after customer orders are known. In some cases, due to market unpredictability, even resources to carry out production tend to be "assembled" only after the business opportunity appears. This is typical of Virtual Enterprises paradigm [ 17]. To be competitive, companies must always aim at low cost per unit and good product quality. This also applies to non-repetitive production. However, for companies to sustain competitiveness ability under turbulent or unpredictable market demands they must be fast responsive and ensure good customer service. To achieve such performance objectives, a key feature of non-repetitive production systems is agility to easily adapt to, or accommodate, frequent changing production requirements as a result of constantly varying product demand. This adaptation requires flexible forms of work organization, system flexibility and, frequently, the ability for fast system reconfiguration Although we have identified only three main production paradigms that embrace the whole spectrum of product demand, from stable to unstable and unpredictable markets, these paradigms can lead to quite a few different production systems conceptual models. The next sections focus on such models and relate them to a range of organizational approaches to manufacturing.
3. Production Systems Conceptual Models Production systems conceptual models can be defined and related with production paradigms to meet the fundamental requirements of production determined by market demand. Such definition and
relation, require identification of important system related conceptual variables capable of allowing a clear characterization and differentiation of each conceptual model. Moreover the relationship between any model, production paradigm and organizational production approach, such as lean or agile manufacturing, must be clear. Five such variables were selected, namely product variety, systems reconfigurability, reconfigurability environment, product repeatability and workflow continuity, each of which instantiated at two levels. Fig. 1 shows the alternative values of each variable characterizing fifteen production systems conceptual models (PCM). For example PCM 8 represents a virtual reconfigurable system simultaneously addressing production of several different products under the repetitive production paradigm and leveled mixed uniform flow production. This is a novel configuration suited to agile manufacturing [18]. Model 9 differs in that batched or intermittent production instead of leveled mixed flow is used. This particular model configures virtual manufacturing cells as defined by McLean et al. [19]. Production systems reconfigurability can be understood as a measure of the easiness of manufacturing system reconfiguration to suit manufacture changing requirements. Although system reconfigurability may be important for manufacturing agility this may be brought about by ways other than system reconfiguration. In fact this can be carried out, for example, by fast and flexible tool changing systems [20] or by the provision of several forms of flexibility related with materials flow system, workstations, people skills and management. We can think of two types of system reconfigurability environment: virtual and physical. Virtual systems reconfigurability is the ability of reconfiguring a system through temporary reallocation of available or accessible distributed manufacturing resources to a system, without physically displacing them. Distributed resources mean that they are locally or globally apart and are autonomous, i.e. control their own processes. Virtual systems may be based on company internal resources or, otherwise, be based on a wide range of resources globally available. In this case the virtual system can be seen as part of a Virtual Enterprise.
Physical reconfigurability of manufacturing systems has similarities to the virtual reconfigurability with two important differences
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Fig. 1. Conceptual variables and the fifteen production systems conceptual models set. associated with two dimensions: system and resource dimensions. First, in the reconfiguration process, the manufacturing resources can be displaced from their original locations and physically replaced to best fit changes in manufacturing requirements. Second, the resources themselves can be reconfigured to fit manufacturing requirements. Important fitting measures seek to stream line or at least simplify the work and materials flow during production. System reconfiguration can be done on a production order basis, regularly, at time intervals, or whenever important product demand changes occur.
4. Organizational Approaches to Production Five major organizational approaches to production are identified. These are: mass, batch, job, lean and agile. The relationships between these, the production systems conceptual models and production paradigms are illustrated in table 1. 4.1. Mass Production
The mass production approach is strictly related with the mass production paradigm and, therefore, implements a system which during its life time is dedicated to the production of a single product. The
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system is designed to achieve the expected maximum demand rate for the product. When production rate cannot be adjusted and synchronized with product demand rate then inventory is created. Sometimes, due to high processing similarity of a few products they can share, on a time basis, usually aider minor system adjustments, the same mass production system and, therefore, they may be seen as if they were the same product. Nowadays, due to global competition and constantly changing markets, mass production of discrete products involving assembly is uncommon. We still can encounter mass production of parts and also of products from process industries. In the former case manufactured is carried out by either automatic machines or transfer lines. PCM 1, Fig. 1, characterizes the conceptual model associated with the mass production approach. 4.2 Batch Production
In this organizational approach to production several different products are ordered and production is always carried-out in batches, in a repetitive intermittent manner. Clearly, batch production is closely linked with the RIFP instance of the repetitive paradigm. Production requirements are usually known in advance and production processes and management are carefully established to achieve both technological and operational efficiency. The nature of batch production systems have an in built ability to deal with multi-product processing requirements and may have variable degrees of flexibility. This can result from exploring the versatility of manned or unmanned workstations. In the former case we are talking about traditional batch production and in the latter one about Flexible Manufacturing Systems [21]. Therefore, some nonreconfigurable FMS may be seen as instances of this organizational approach. We can link Batch Production to three PCM models, namely PCM 5, PCM 9 and PCM 13. 4.3 Job Production
In this approach, systems are designed to deal with requirements of the non-repetitive production paradigm. This means that an enormous variety of products should be handled. For this, systems must be highly flexible, exploring the use of versatile equipments, with jobs usually visiting either stand
Table 1 Relationships between organizational approaches, production systems conceptual models and production paradigms Volume Production Organizational Market (per product P r o d u c t Flow Reconfigu Product paradigm a p p r o a c h predictability variety rability customizability ordered) None Mass Mass Stable Large None C None High Non-repetitive Agile T u r b u l e n t Medium Small C Virtual Medium Non-repetitive Agile Unpredictable Medium Small C Physical Low 4 Repetitive Lean Predictable Medium Small C None Low 5 Repetitive Batch Predictable Medium Small I None Medium 6 Agile UnpredictableOne-of-a-kind Large C None Non-repetitive Medium Job UnpredictableOne-of-a-kind Large I None 7 Non-repetitive Medium Agile Unpredictable Small Medium C Virtual Repetitive Medium Batch Unpredictable Small Medium I Virtual Repetitive High Agile T u r b u l e n t One-of-a-kind Large C Virtual 10 Non-repetitive High Agile T u r b u l e n t One-of-a-kind Large I Virtual Non-repetitive Medium Lean Unpredictable Small Large C Physical 12 Repetitive Medium Batch Unpredictable Small Medium I Physical 13 Repetitive High Agile T u r b u l e n t One-of-a-kind Large C Physical 14 Non-repetitive High Non-repetitive Agile T u r b u l e n t One-of-a-kind Large I Physical 15 F (1) Accordingto Fig. 1; Flow: C- continuous, I - Intermittent synchronize production with demand for a variety of products. alone workstation or cells, or functional sections, or The objectives and organization strategies of both, in a random manner, according to processing Lean production allow identifying this approach requirements of each job. Both flexible stand alone mainly with the PCM 4 and PCM 12 conceptual programmable workstations and manned universal models. machines are frequently used. Scheduling is critical for achieving production objectives and coordination 4.5 Agile Production of production. The typical production system configuration associated with this approach is a jobThe Agile production approach addresses shop. Systems efficiency is usually poor in these production of customized products and, in particular, systems. The PCM 7 model is the one most related of the mass customization type. with the Job Production approach. The associated Huang and Nof [25] state that enterprise agility production paradigm is clearly non-repetitive. must be accomplished through agility in business, organizational, operational and logistic systems. In 4.4 Lean Production many instances, to achieve agility production manufacturers need to interact or collaborate through It is common to say that Lean Manufacturing the internet and intranets with partners, including focuses on waste elimination and lean thinking [22]. Lean manufacturing was firstly explored in the suppliers and even competitors, as well as with customers. Toyota car factories under the name of Toyota Production System (TPS) which is based on Due to the fact that the agile approach focuses on production of customized products, the nonprinciples and techniques of Just-in-Time (JIT) production [23]. An evolution of the TPS to a more repetitive production paradigm is predominant. This advanced approach intensifying collaboration is why seven of the eight Production Conceptual between companies, from design to manufacture and Models, associated with non-repetitive production, delivery, has been referred to as Lean Extended [24]. fit the agile approach requirements as can be seen Lean manufacturing may be seen as an attempt from table 1. The only other case is PCM 8 that to apply the mass production paradigm and, more configures a repetitive system model already specifically, levelled uniform flow production, to the described in section 3 as suitable for agile repetitive production environment, from raw manufacturing. materials to delivery. An important objective is to achieve high productivity and, at the same time, Production Conceptual Model (1) 1
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5. C o n c l u s i o n s
Mass, repetitive and non-repetitive production paradigms were reviewed, clarified and extended having in mind recent developments in manufacturing strategies and approaches to fit production systems to demand markets and environments of today and tomorrow. Based on the production paradigms and a set of critical variables, relevant to system design and operation, a set of fifteen production systems conceptual models were characterized. These may be seen as reference models to implement, at manufacturing level, several organizational approaches to production. In this work such approaches were reduced to five briefly described and to a great extent coincident with some well known concepts that include lean and agile manufacturing. A clear interrelation between production conceptual models, organizational approaches to production and production paradigms was shown. A c k n owl edge m e n ts
University of Minho is partner of the EU-funded FP6 Innovative Production Machines and Systems (I'PROMS) Network of Excellence. http://www.iproms, org References
[1] Silva SC and Alves A. In: Ferreira JJP (Ed.), A framework for understanding Cellular Manufacturing Systems. e-Manufacturing: Business Paradigms and Supporting Technologies, Kluwer, 2004, pp. 163-172. [2] Fernandes NO and Carmo-Silva S. Genetic POLCAA Production and Materials Flow Control Mechanism for Quick Response Manufacturing. Intemational Journal of Production Economics (article in press). [3] Pinedo M. Scheduling- Theory, Algorithms and Systems, Prentice-Hall Inc., 1995. [4] Bradford MJ. Repetitive Manufacturing: Benefits, benefits. IIE Solutions (2001), pp. 38-43. [5] MacCarthy BL and Fernandes FCF. A Multidimensional classification of production systems for the design and selection of production planning and control systems. Production Planning and Control (2000) Vol. 11, No. 5, pp. 481-496. [6] Toni AD and Panizzolo R. Repetitive Manufacturing Planning and Control Systems: a framework for analysis. Production Planning and Control (1997) Vol. 8, No. 5, pp. 500-508. [7] Scholl A. Balancing and Sequencing of Assembly Lines. Physica-Verlag, 1995. [8] Davis SM. Future Perfect. Addison-Wesley, Reading,
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MA, 1987 [9] Pine BJ. Mass Customization: the new frontier in Business competition. Harvard Business School Press, 1993. [10] Kotha S. Mass customization: implementing the emerging paradigm for competitive advantage. Strategic Management Journal, Vol. 16 (1995) 21-42. [11] Duray R Ward PT Milligan GW and Berry WL. Approaches to Mass Customization: Configurations and empirical validations. Journal of Operations Management, Vol. 18, No. 6, pp. 605-625, 2000. [12] Tseng M Jiao J. Mass Customization. In Salvendy (ed) Handbook of Industrial Engineering, 3rd edition. John Wiley and Sons, USA, 2001. [13] Bourke KE. Product Configurators: Key enablers for Mass customization, 2000. [14] Starr MK. Modular Production a New Concept, Harvard Business Review, Vol. 43, No. 6, pp. 131-142, 1965. [15] Mintzberg H. Genetic Strategies: Towards a comprehensive framework. Advances in Strategic Management, Vol. 5, pp. 1-67, 1988. [16] Gilmore JH and Pine BJ. The four faces of customization. Harvard Business Review Vol. 75, No. 1 (1997) 91-101. [17] Camarinha-Matos LM and Afsarmanesh, H. The Virtual Enterprise Concept. In L M Camarinha-Matos and H Afsarmanesh, Infrastructures for Virtual Enterprises: Networking Industrial Enterprises Kluwer Academic Publishers, 1999, pp 3-14. [18]Hormozi, A. M. Agile Manufacturing. In 37 International Conference proceedings of APICS (APICS), San Diego, (1994) 216-218. [19] McLean CR, Bloom HM and Hopp TH. The Virtual Manufacturing Cell. In: Proceedings of the 4th IFAC/IFIP Conference on Information Control Problems in Manufacturing Technology, 1982, 105111. [20] Silva SC. Strategies and Fundamental Structures for FMS tool Flow Systems. In Camarinha-Matos LM. (Ed.) Re-Engineering for Sustainable Industrial Production. Chapman & Hall, 1997. [21] Yempelmeier H and Heinrich Kuhn, Flexible Manufacturing Systems: decision support for design and operation. John Wiley and Sons, 1993. [22] Womack JP. and Jones DT. Lean Thinking. Siman & Schuster, New York, USA,1996. [23] Monden Y. Toyota production System. Industrial Engineering and Management Press, Institute of Industrial Engineers, 1983. [24] Schonberger RJ. Lean Extended. Industrial Engineer, 2005, 26-31. [25] Huang C-Y. and Nof S. Enterprise Agility: a view from the PRISM lab. International Journal of Agile Management Systems, Vol. 1, No. 1, (1999), 51-59.
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Responsive system based on a reconfigurable structure Bo Hu, Janet Efstathiou Department of Engineering Science, University of Oxford, Oxford, OX1 3PJ, UK
Abstract
A reconfigurable modular approach is proposed to construct a responsive manufacturing system to satisfy customers' varied demand. In order to achieve this, the system employs a three-level control structure. We present the system's dynamic behavior and optimal layout, as well as performance measures. Entropic measurement of demand and factory capacity is used to determine the local optimal strategy under dynamic scheduling. We conclude this paper using cases of successful fast-responding corporations to gain insight into responsive reconfigurable systems. Key words: Responsive, RMS, Dynamic Scheduling, Entropy, Customization
1.
Introduction
With shorter product life cycles and higher levels of customization, it has become crucial for enterprises to respond to varied demand in order to survive in today's volatile market. Furthermore, maintaining speed while employing a 'lean' approach, increasing capacity usage and integrating with the market, will provide a competitive edge. But customization to individual needs does not come easily. Customization leads to variety, and variety, in most traditional manufacturing systems, is a cause of inefficiency [1 ]. Traditionally, a product is introduced with some variety; later the manufacturer will pick one suitable model and switch to mass production on DMS (Dedicated Manufacturing Systems), focusing on process innovation, while eliminating other models and reducing production costs. Nowadays, with much shorter life cycles, detailed process improvement is no longer possible. FMS (Flexible Manufacturing Systems) were introduced to adapt to various versions of the same
operation. However, FMS possess an integral architecture which means modules inside are coupled. Therefore, FMS have limited capabilities in terms of upgrading, adding functionality or adjusting capacity [2]. A market survey has shown that up to 50% of customers have problems with excess functionality or capacity, and two thirds expressed the view that FMS do not live up to their full potential [3]. When product life cycle shortens, these excess functionality and capacity can cost manufacturers greatly. Further examples are presented in Section 5.4. In order to make manufacturing systems more adaptive, the Reconfigurable Manufacturing Systems (RMS) concept was introduced [4]. In a reconfigurable system, hardware and software are divided into modules, with an open-ended control architecture, which means functionality and capacity can be adjusted as appropriate. Furthermore, managers can adjust the emphasis on different business aspects according to market needs, as will be discussed in section 2. The RMS concept has also been identified as the
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number one priority for future manufacturing, and one of the six key research challenges (National Research Council, 1999). Having reconfigurable production lines is considered an essential aspect of modem manufacturing operations.
2.
Proposed reconfigurable system structure
We propose using the inherent modularity of RMS to construct a three-level architecture, as shown in fig.1. The three levels are organized in a way that makes inter-level communication direct and efficient. On Level III, the operations level, a Demand Analyzer is introduced. It receives customer orders and decides whether to trigger Rescheduling in order to handle disruptions [5]. The Scheduling module will decide how to distribute the tasks among machine tools (further discussed in 5.1). Then each task is processed in WIP (Work In Progress) module and shipped to market. On Level II, the layout level, the Reconfiguration Module and Product Family Management (PFM) are introduced based on entropy measurement (Section 5.3). Thus, we can adjust functionality and capacity according to demand trends. PFM bases product selection decisions on demand patterns, and decides which products and services to provide. On Level I, the strategy level, the Trend Detector analyzes market demand and reports any noticeable pattern. Information is then passed to Level II to arrange for reconfiguration once necessary. Enterprises can also use pricing policy to affect demand as well as customer behavior. Level I: Strategy
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Overall, Level III and Level II correlate to each other the same way as that of adaptive control - two different feedbacks at different pace, both event-triggered. Level I supervises them and uses PFM and forecasting to make reconfiguration arrangements. This would enable dynamic scheduling as well as product transition in the job shop at relatively low cost. The system can be adaptive to customer needs and market fluctuations. But first of all, we need to discuss the feasibility and justification of implementing reconfigurable manufacturing system by defining production objectives.
3.
Balance among production objectives
Traditional performance measures are unable to justify the increasing importance of responsiveness and customization. In this paper, we group performance measures into four major objectives, namely: responsiveness, quality, variety and capacity (Fig.2. illustrative emphasis). Responsiveness means the speed at which an enterprise can reflect demand trends upon its products; quality refers to the product quality level that the manufacturer can maintain despite quick reconfiguration and reduction of ramp-up and ramp-down time; variety is defined as the different kinds of products that can be supplied without disrupting continuous flow in the job shop or having a major negative effect on profits; and capacity is the level at which a factory is making use of their resources and reducing waste. These four objectives are interconnected. Focusing on responsiveness, for example, might mean that manufacturers have to sacrifice quality or variety. Increasing capacity usage, which facilitates process flow, will increase response speed initially, but will eventually decrease it if carried too far, because the cost for "total leanness" is high. For a specific manufacturing system, it may be possible to use actual data to form a decision domain, and find the best balance for gaining profits. Figure. 2. proposes a characterization of manufacturing systems' different emphasis on the attributes of variety, responsiveness, capacity and quality. This is constructed assuming: variety has a negative effect on responsiveness given that capacity is relatively constant; improving responsiveness from a medium level will improve capacity usage, while
pushing the responsiveness to its limits will decrease capacity usage. The same results can be observed with variety. Higher capacity usage means less waste, or leaner, but pushing capacity to limits also has dramatic negative effects on variety and responsiveness. Good decisions are likely to be in the middle. With a fluctuating market and changing products, this domain is actually shifting all the time. So the objective we try to achieve using the architecture in fig. 1 is to detect such a shift and adjust to it dynamically.
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Fig. 2. Manufacturing systems' emphasis among the major business objectives: capacity, responsiveness, variety and quality It has been accepted that there is no perfect manufacturing system, and the best balance comes from processes specifically designed for the product and the target market. But unfortunately, even if the high costs are neglected, with new products emerging and market's never-ending fluctuations, dedicated design is like shooting a plane by directly aiming at it. The advantage of RMS is the ability to shift among these objectives in the decision domain, at relatively low cost and high speed. RMS can achieve local optimal results according to market needs, while traditional approaches can not adjust as freely. Although RMS is inherently adaptive, we need a robust structure and a flexible control algorithm to actually make it work. These are the two keys to successful implementation of a responsive system as will be discussed in Sections 4 and 5.
4.
Robust architecture
In constructing a responsive reconfigurable system, we propose a decision structure, which consists of three levels, namely: strategy, layout and operation. Production processes and services are modular and specific control algorithm settings are devised. Each level is connected the others, while a special link between management strategy and factory operations is proposed. This is crucial as the involvement of managers in the basic process is the key to flexibility. Through this link we can reflect market trend on to the shop floor and use shop floor options to influence the market, as will be discussed in 5.2. 9Operations level and layout level: Fault tolerance Within each production cycle, a new schedule based on current strategy and layout will be generated to achieve the local optimum based on demand. There are also monitors checking module conditions, detecting disturbances such as a machine breakdown, which was regarded as number one problem in a job shop. Rescheduling will be event-triggered and automatically configure other same-family machines to substitute for unavailable machines. The overall performance will not be largely influenced. 9Strategy level and layout level: Adaptive control When management detects a noticeable trend in the market over a longer period, a reconfiguration in the hardware and the control algorithm will be triggered, activating the adjusting of necessary functionality and capacity and also enabling product transition control. So, a shift in demand pattern will not make production unstable. 9Strategy level and incorporation with market. The entropy method is used to measure the complexity of demand as well as currently available resources. Then we decide how to use PFM (Product Family Management) and Pricing Policy to indirectly control demand complexity. The disruptive effect of market fluctuations and supply chain delays are hoped to be reduced in such a system, compared to some traditional manufacturing approaches. Once we obtain the architecture, we must think about how to devise an algorithm to control it as well as the scheduling rule on the shop floor.
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5.
Control algorithm
conditions.
5.1. Scheduling and rescheduling In factory operations level, we use a flow structure to allow direct information exchange. This is crucial to achieve fast response. It is assumed customer demand arrives randomly, but on the whole follows the market trend. Whenever a demand is received, the Demand Analyzer will make judgments about its urgency. If it's urgent, the Rescheduling Module will be triggered and this job would be added immediately to the scheduling pool. Otherwise it will wait until a job finishes and be entered into scheduling. We present the problem for simple cases consisting of three machine types A, B and C, each supporting a production process that includes possible machining and sequence configurations. A number of RMT (reconfigurable machine tools) belonging to several machine types work on the shop floor. In this case, machine A has three configurations: A1, A2 and A3; machine B has two: B 1, B2; machine C has three: C 1, C2, C3. We present some definitions in scheduling: Task: if a demand goes through a machine type under a certain configuration, it is called a task. For example, task B2 means demand needs to be processed with machine B under configuration 2. Job: a job is a sequence of tasks. A demand is fulfilled atter the job is done. For example, A1B2C3 has three tasks to finish: A1, B2 and C3. Job Set: job set is a number of jobs in the scheduling pool at one time. For example, A1B2C3, A1, A1B1, B2C2, A2C3, is a job set consisting of five different jobs. So when a job set like the one mentioned above arrives, the normal FIFO rule would construct a flow line for each job. In the mean time the other jobs have to wait. But if we allow process flow in both directions and divide each job into single tasks (A1, B2, etc), capacity use could be greatly increased. This becomes more evident when jobs are more complex and demand arrives in large volumes. In our simple numerical case, we observed that using our dynamic scheduling method from Graph Theory under ideal conditions, lead time is reduced by 31.25%-68.4% than FIFO depending on the demand distribution. Further cases need to be studied to test the feasibility of this method under general
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Fig. 3. Production routing in reconfigurable scheduling Also, in a complex network, the breakdown of one machine will not affect the whole system unless it's a bottleneck or the only machine of its kind. From the architecture in fig. 4, we can study the robustness of the responsive system in the same way.
5.2. Product family management (PFM) To reflect market changes in an RMS and to reduce the gap between manufacturing and market needs, a reconfiguration link is introduced to facilitate grouping similar products into families for the selection of appropriate families for particular reconfigurations [6]. We further this idea by identifying three types of strategies for coping with product variety: value-adding, subcontracting and undesirable. In studying the entropies of different members within a product family, we can select which customized products to provide, with regard to their probability of market demand. It is possible to identify portfolios of products to be addressed using a combination of the three strategies. Profit and complexity are to be the likely performance measures of this selection policy. Lean thinking should be adopted from the initial modular customization period, so as to better adjust factory capacity towards increasing profits.
5.3. Entropy measurement and Complexity There would be three measurement of entropy, entropy of demand ( H d ), entropy of resources ( H r ) and entropy of product family members (H m ). These are used to measure complexity in different stages of
manufacturing [7]. This entropic measure of complexity will be used in three aspects: 9 Selection among the scheduling algorithms on the reconfiguration level (as discussed in 2.) [8]. The Demand Analyzer compares the entropy of demand and the entropy of resources and decides on the most efficient scheduling rule. 9 Product Transition (as will be discussed in 5.4.). Entropy measure helps decide whether to change modules in existing family or switch to a totally different other family. 9The selection of products and services in PFM (as discussed in 5.2.). Efficiency is defined as profit over entropy, if entropy of demand exceeds entropy of resources, only products with higher efficiency will be provided; otherwise, more options may be provided to achieve higher customer satisfaction. Further technical detail is beyond the scope of this paper but will be addressed in future publications.
systems, the scenario has changed: there is no need to wait until the market saturates and the need to customize is urgent. Because it is possible to offer product variety at the beginning and use PFM to dynamically maintain products or services according to customer satisfaction and profit. Fig. 4 includes a predictive model that determines the trend. Volume and variety transition are identified between an existing product and a new product. Factory capacity will be redistributed accordingly when there is evident mismatch between factory capacity and demand. This transition is made possible by the inherent low-cost capacity adjustment of RMS. This discrepancy is spotted by a monitor module, which analyzes demand pattern. There is also a forecasting module deciding the general trend. Forecasting will help the redistribution of resources and better adjust the system for upcoming production. Product Transition Period Demand
5.4. Product transition with forecasting Product innovation, and more importantly, the transition between new and existing products, has always been a problem for large enterprises. The reason is that large enterprises have spent large sums of money and human resources in developing the production process, which is efficient and competitive. However, the system lacks the ability to adjust functionality and capacity at low cost. Often, shifting to a new process means abandoning the old one completely, and the cost is too high to make the decision lightly. Hesitation and reluctance to make such a difficult decision could result in the new market being taken by smaller and more reconfigurable companies. That is why we say "Vessels cut the waves while boats sail to the wind". The ability to maintain existing profits while exploring new horizons is important for enterprises in most industries, while in some high value-adding ones, such as biotechnology, software engineering and chip manufacturing, it is really vital. A biotech company failing to adjust capacity in a short time could lose new drug market, costing them millions [9]. A chip designer faces unimaginably high cost when they "reinvent the wheel" each time a similar demand arrives. However, with reconfigurable responsive
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6.
Challenges and perspectives
RMS with the appropriate control algorithms and integration with market could enable manufacturers to be more responsive to demand. But here are several issues worth considering:
6.1. Challenges Robustness: It's important to identify the possible causes and how to avoid deadlocks, laggards and fluctuations. In addition, a comparison between reconfigurable systems and others can be drawn in terms of tolerance of disturbances. Computing complexity: To achieve the reduction
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in manufacturing system complexity, the algorithm for design and control of such systems are likely to be correspondingly more complex. We use heuristics and entropy-based decisions to reduce real-time scheduling complexity. Further simulation could be used to test the model.
and suppliers; developing responsive control algorithm for modeling and simulation and reaching a reconfiguration strategy in the supply chain with limited perspective.
Acknowledgement
6. 2. Perspectives In fashion retailing, which is a typical fast-clock industry, response speed is vitally important. The same garment can be sold at a several times higher price in fashion than when out of fashion. Inditex (Zara) constructed three parallel design processes to allow direct information flow. Together with other measures, Zara could respond to a fashion shift within half a month while the norm for other companies is 4-6 months. Inditex maintained their competitive edge by speed, and their sales and net incomes grow 20% each year during the last 3 years while competitors experienced poor performance [10]. Other companies like Dell and BMW also benefited from fast-response and customization. The story doesn't end here. When markets go global and many enterprises are thinking about outsourcing, synchronizing supply chain, etc, it's important to think where the competitive edge really is.
7. Conclusions We propose a structure enabling responsive reconfigurable system, consisting of three levels from strategy to shop floor operations. On the shop floor (Level III), we plan to further develop a dynamic scheduling algorithm to improve responsiveness on a set of RMT (Reconfigurable Machine Tools). Then, on Level II we incorporate it with layout reconfiguration. The reconfiguration and selecting of scheduling rules are based on demand trend. This would enable the system to adjust functionality and capacity according to feedback from demand and market fluctuations. Then, entropy measures are used to calculate the complexity of demand and manufacturing systems. PFM and pricing are used to influence demand variety towards higher profits. We illustrate in Section 6.2 with enterprise examples on how to gain competitive edge in today's market. Future research involves studying the interaction among agent groups of customers, manufacturers,
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University of Oxford is a partner of the EU-funded FP6 Innovative Production Machines and Systems (I'PROMS) Network of Excellence. The authors would also like to acknowledge the funding from Clarendon Scholarship and ORS (Overseas Research Scholarship).
References [1] Hopp W. J. and Spearman M. L. Factory Physics. McGraw Hill, 2000. [2] Mehrabi M. G, Ulsoy A. G. and Koren Y. Reconfigurable manufacturing systems and their enabling technologies. International Journal of Manufacturing Technology and Management, (2000) 1(1), 113-130. [3] Mehrabi M. G, Ulsoy A.G, Koren Y. and Heytler P. Trends and perspectives in flexible and reconfigurable manufacturing systems journal of intelligent manufacturing, 13, (2002), 135-146. [4] Koren, Y., Heisel, U., Jovane, F., Moriwoki, T., Pritschow, G, Ulosy, A.G and Van Bruseel, H. Reconfigurable manufacturing systems. Annals of the CIRP, 2, (1999) 1-13. [5] Huaccho Huatuco L, "The role of rescheduling in managing manufacturing systems' complexity", DPhil Thesis, University of Oxford, 2003. [6] Abdi, M. R., Labib, A. W. Grouping and selecting products: the design key of Reconfigurable Manufacturing Systems (RMSs). International Journal of Production Research, 2004. [7] Sivadasan S., Efstathiou J., Calinescu A., Huatuco L. H. Advances on measuring the operational complexity of supplier-customer systems, European Journal of Operational Research, 2004. [8] Zhang, T. and Efstathiou, J. The complexity of mass customization systems under different inventory strategies, International Journal of Computer Integrated Manufacturing, 2006. [9] Pisano CL P. The development factory. Harvard Business School Press, 1997. [10]Ferdows K., Lewis, Machuca M. A., Jose A.D., Rapid-Fire Fulfillment. Harvard Business Review, Vol. 82 Issue 11, p 104-110, 7p, 3c Nov 2004.
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhfi and A.J. Soroka (eds) 9 2006 CardiffUniversity, Manufacturing EngineeringCentre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Towards Reconfiguration Applications as basis for Control System Evolution in Zero-downtime Automation Systems C. Stinder a, A. Zoitl a, B. Favre-Bulle a, T. Strasser b, H. Steininger c, S. Thomas d a
Automation and Control Institute, Vienna University o f Technology, 1040 Vienna, Austria b Robotics and Adaptive Systems, PROFA CTOR Research, 4407 Steyr-Gleink, Austria c kirchner SOFT GmbH, 3124 OberwOlbling, Austria d Bachmann Electronic GmbH, 6806 Feldkirch-Tosters, Austria
Abstract Industrial Automation and Control Systems will evolve towards downtimeless, adaptable, distributed real-time systems. Especially the reconfiguration of control applications is not sufficiently solved by State-of-the-Art technology. This paper gives an overview of the use ofreconfiguration applications for downtimeless system evolution of control applications on basis of the standard IEC 61499. This new methodology combines the need for close interaction with the application/physical process and the existing knowledge about the modelling of control applications. By use of a representative example the structure, the required instruction set and the methodology for the downtimeless system evolution utilizing a reconfiguration application are described. The special requirements, which concern to the topics verification, failure handling and the runtime platform, are presented.
Keywords: reconfiguration application, system evolution, IEC 61499
1. Introduction A survey of the technological State-of-the-Art in the area of distributed industrial system design [1 ] resuits in a set of requirements that have to be fulfilled by future engineering tools. A total life-cycle approach is required in order to take into account features like high degree of heterogeneity, different communication protocols, validation of distributed applications, maintenance, configuration and reconfiguration. The component-based reference architecture introduced by IEC 61499 [2] for distributed industrial control systems features first concepts to reach these goals. This standard inherently includes a distribution model and offers a basic management interface that enables reconfiguration of control logic represented as function block diagram. Especially the aspect ofa runtime envi-
ronment for reconfiguration of real-time distributed control systems based on IEC 61499 is part of current research activities [3], [4]. The TORERO project [5] aims at creating a total life cycle web-integrated control design architecture with the main focus on the development of a self-configuring, self-maintaining and automatically distributed control system. The described engineering process makes use of IEC 61499 for programming control logic. But during the reengineering process the system has to be stopped before code is deployed to the devices [6]. That does not correspond with the requirement of downtimeless systems as stated above. On the other hand, current engineering tools based on IEC 61131-3 [7] already enable online code exchange for single devices. For instance, reference [8] enables fast online modification of control programs including transfer of variable values. But
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there are some fundamental problems concerning the reconfiguration process: 9 The switching point in time can not be determined because of the cyclic way of execution and the lack of information about the state of the system or application. 9 The reconfiguration of one task of an application interferes with all tasks of this application since all tasks have to be stopped because of the asynchronous cyclic execution of tasks. This leads to jittering effects. 9 The lack of fine granularity (task level) introduces high complexity according to communication, memory management and re-initialization. 9 The reconfiguration of elements may lead to inconsistent states, e.g. deadlocks or token-proliferation in Sequential Function Charts (SFC). 9 New elements start with their cold start initial values. The principle challenge and the aim of this paper is to overcome the restrictions mentioned above by use ofreconfiguration applications based on the reference model oflEC 61499. To begin with, chapter 2 presents an overview of the standard IEC 61499. Chapter 3 presents the concept ofreconfiguration applications illustrate by an example; chapter 4 presents a summary of requirements for enabling the use of reconfiguration applications.
2. IEC 61499
The new standard IEC 61499 [2] serves as a reference architecture that has been developed for distributed, modular, and flexible control systems. It specifies an architectural model for distributed applications in industrial-process measurement and control systems (IPMCS) in a very generic way and extends the function block model of its predecessor IEC 61131-3 (Function Block Diagram FBD) [7] with additional event handling mechanisms. The function blocks (FBs) of the standard IEC 61499 have both an event and a data interface. A FB only executes if it receives an event. Distributed applications are handled from the top-level functionality point of view, so called application centred engineering, with late mapping to concrete hardware components. The standard builds a good basis to overcome the above mentioned problems according to reconfiguration processes in current IPMCSs. In the following the concept of the management interface as fundamental issue of IEC 61499 is described that makes this standard suitable as reference architecture for building
524
zero-downtime IPMCS by using the concept ofreconfiguration applications.
Management interface of IEC 61499 devices: The configuration of a distributed automation and control system based on IEC 61499 can be enabled by the use of management functions which can be included in each device. For this purpose the standard defines a management application, represented by a management FB (the generic interface is depicted in Fig. 1). By using this FB combined with a remote application, access between different IEC 61499 compliant devices is possible. The IEC 61499 Compliance Profile for Feasibility Demonstrations (available from [9]) describes a concrete interface of a management FB and an appropriate remote application. The following standardized management functions can be used to interact with a device ([2], Tables 6 and 8). For illustration examples of possible actions are added. 9 CREATE: FB instance, data or event connection 9 DELETE: FB instance, data or event connection 9 START: FB instance, application 9 STOP: FB instance, application 9 READ: FB instance data outputs 9 WRITE: FB instance data inputs 9 KILL: FB instance 9 QUERY: FB types, FB instance, data or event connection Especially the management of software components (FBs) regarding to their execution is a very important item in reconfiguration processes. A FB instance operates according to a state machine ([2], Figure 24) that includes an initialization and operation state controlled by management commands: Create, Start, Stop/Kill, Delete.
3. Reconfiguration Applications
The process of evolution of an automation system without downtimes sets high demands on the underlying concepts and methodologies: Applications within the automation system have to work without disturbances; the reconfiguration process has to be adapted to the special environmental conditions of the affected application part; any failure during the reconfiguration EVENT EVENT BOO,
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process has to be managed at least to such a degree, that the system is left in a defined state. As described in chapter 2 the standard IEC 61499 already includes basic management commands for configuration and reconfiguration of applications. But the standards lacks for an engineering methodology for the reconfiguration process. In current available IEC 61499 compliant system implementations, the startup phase of applications is done by use of these management commands. The main idea of this new methodology is to control the system evolution of control logic by an application, the so called reconfiguration application. This should make use of the management interface of IEC 61499 devices to control another application. The basic commands have been described in Chapter 2. Further the reconfiguration application can use any event and data flow to recognize the current system state of the application. For instance, the reconfiguration application may realize that the process has reached an idle state and starts the system evolution. The event driven approach of IEC 61499 supports such a synchronization with the control logic in a very good manner: 9 The reconfiguration application can be located on the local device. This enables direct interaction to the concerned device/application without time delays due to communication networks; real-time requirements on reconfiguration sequences can be fulfilled. 9 The reconfiguration application has to interact directly with the corresponding application to react on the current system state and to coordinate the reconfiguration process to the application behaviour. These interactions can be modelled by event and data connections between the application and the reconfiguration application. 9 The verification ofreconfiguration applications is a main point for enabling secure execution ofreconfiguration sequences. Therefore the existing concepts for verification of IEC 61499 control applications (e.g. [ 10] uses Net Condition Event Systems) can be applied to reconfiguration applications; especially additional inputs are needed to enable the consideration of important details for system evolution (see chapter 4). 9 Failure handling maybe introduced directly within the reconfiguration application. A main requirement to the reconfiguration process is to leave the system within a defined state, even in the case of unexpected failures during the reconfiguration process. 9 The reconfiguration process splits up into atomic steps that represent typical sequences of commands and interaction. Based on these steps the development of design patterns will help the user to simplify the application of reconfiguration applications. The engi-
neering process will take place by composition ofthese reconfiguration steps. Of course adaptations to the special needs of the application are necessary to a certain extent. 9 Distributedreconfiguration applications are needed to model the interaction of the engineering tool and the devices and of course to synchronize reconfiguration sequences concurrently on several devices. The reconfiguration application needs a set of commands to enable this proposed functionality. The following section gives an overview of the instruction set specially needed for reconfiguration and its interrelation to the standard IEC 61499. The example of a closed-loop control circuit will be used to demonstrate the structure and behaviour of a reconfiguration application.
3.1. Access of Reconfiguration Applications to the Device Management A reconfiguration application consists of several commands that influence another application. In the scope of IEC 61499 such commands are capsulated as function blocks. Fig. 2 depicts the situation within an IEC 61499 device. The device includes several resources (MGR, Resource A, Resource B) that execute function block networks. Further the device includes an interface to the physical process (process interface) and to communication networks (communication inter.face). A central component is the device management, which is responsible for management of resources and applications within the device. A special resource (MGR) includes an instance of the management function block (Fig. 1) that enables communication to an engineering tool. Current implementations, e.g. the Function Block Development Kit (FBDK) available from [9], make use ofthis conceptto connect to the devices and download applications. As depicted in Fig. 2 by the dashed arrows, the engineering tool connects to the management FB, which builds the interface to the device management. The device management processes the different commands and influences the appropriate resources within the device. Of course an acknowledgement is returned to the engineering tool. Within Resource A the behaviour ofreconfiguration applications according to the device management is presented. The Reconf-Application is depicted to do some reconfiguration of Application 1. Therefore several function blocks are used that have direct access to the device management (dash and dot arrows). That way the reconfiguration application manipulates other
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3.2. Instruction Set for Reconfiguration Applications As described in chapter 2, IEC 61499 already defines a basic set of commands to enable management of resources, function blocks or connections. Based on the generic interface of the management function block a set of specialized function blocks should be available for modelling ofreconfiguration applications. But this set is not sufficient and has to be enlarged. The following gives an overview of missing instructions: 9 Query of all internals of function blocks: For instance the currently active Execution Control Chart (ECC) state or the value of an internal variable may be needed. 9 Setting of all internals of function blocks: A management FB should be able to set internal variables or to force the ECC to a dedicated state. In case of the latter action it must be possible to choose whether the corresponding algorithms or output events should be executed or not. 9 Generation ofevents: The occurrence of an eventat a function block input has to be controlled by a command for selective operation sequences. 9 Real-time execution of dedicated commands: Especially for the purpose of exchanging a function block the relocation of the output connections is time critical. 9 Access to the execution behaviour of the runtime: In case of timing conflicts the reconfiguration application has to be able to control the application execution according to its demands.
526
3.3. Example." Reconfiguration of a Closed-Loop Control Circuit within one device The modelling and the behaviour ofa reconfiguration application for the exchange of the controller without disturbance of the active control circuit are described by use of a simple closed-loop control circuit. The whole function block network containing both the application and the reconfiguration application is depicted in Fig. 3. The application is marked as shaded grey FBs with bold-faced lines within the figure (lower part). The control cycle consists of four steps: write the output value of the controller to the physical process (Set Value), read the current value of the control variable (Get Value), build the difference to the set point (summingpoint) and calculate the control algorithm (Controller). The additional FBs are used for generation of the control clock (clock), receiving of the set point (Get_Setpoint) and generation of an initial event for initialization (START). By use of the reconfiguration application the Controller-FB will be exchanged by a new instance (NewContr). This may be caused by a software update, bug fix or a new control algorithm. In this example, the new PI controller additionally includes saturation. The reconfiguration application uses the output fitting method for a smooth transition to the new controller [ 11 ]. Therefore the integral term has to be read from the old controller, adapted to the new gain and limitations and transferred to the new controller. The switching point is defined by the spare time between the end of the calculations for one cycle and the starting time of the next cycle. The reconfiguration application can be split into three parts" O the startup sequence, 19 the reconfiguration sequence and 9 the closing sequence. The startup sequence O is not time critical and includes the following parts: 9 Download of the reconfiguration application to the device (this includes all white shaded function blocks) 9 Initialization of the reconfiguration application: First of all the new FBs have to be started, then an event triggers the INIT of the first FB (Starting point) 9 Execution of the Startup-FB: Within this function block management FBs are included for the creation of the new instance of PI Controller2 (NewContr), creation of the input connections, writing of input parameters and starting of the execution of NewContr. 9 Checking for correctness: When Startup_CHK FB is triggered it checks whether the previous commands have been executed successful. Then it triggers the next sequence. Fig. 3 represents this by the event connection from Controller. CNF to Startup_CHK. CLK.
different algorithms are needed for failure handling. In this example ErrorHandlel includes countermeasures if an error occurred during the startup sequence, ErrorHandle2 reacts on a failure during the reconfiguration sequence: Either the relocation of connections has to be cancelled immediately, or the whole sequence has to be undone in the same manner.
The reconfiguration sequence 19 includes the time critical part within the reconfiguration application. The output connections have to be relocated to the new controller NewContr. The following commands have to be executed within the idle time of the closed-loop control circuit (needs to be assured by real-time constraints to the runtime environment). 9 Read integral term from Controller (Getlnternal) 9 Calculate transition of the integral term (Transi-
4. Requirements Specification for System Evolution
tion) 9 Write integral term to NewContr (Set_Internal). 9 Relocate the output connections from Controllerto NewContr. This is done by a sequence of management commands within the Relocate FB. 9 Checking for correctness: In this case two checks are provided: First the correct execution of the previous commands is checked; second the ReconfCHK FB observes the output value for 30 cycles to check the error-free operation of the control. The reconfiguration process ends with the time uncritical closing sequence O: 9 Stop execution of Controller FB 9 Delete input connections of the Controller FB 9 Delete function block instance Controller After the Closing_Seq FB has been operated successfully, the whole reconfiguration application can be stopped and deleted from the device. As indicated in Fig. 3 the reconfiguration application uses check points to trigger failure handling mechanisms. Depending on the application behaviour c~ ~
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Although the reconfiguration process can be split up into small parts and often non critical reconfiguration processes with less interaction to the application may be used the reconfiguration application introduces a certain complexity. [12] presents an engineering cycle for system evolution, the transition from an old system state to a new one by downtimeless reconfiguration, which is the central topic of the research project eCEDAC [13]. In the following a set of requirements are presented to the hardware platforms as well as the engineering environment.
Verification of Reconfiguration Applications: The offline verification ofreconfiguration applications represents a very important part of the engineering process. Because the reconfiguration process must not influence the stable operation of the control system, failures during reconfiguration have to be avoided. Additional to logical correctness the capabilities of the underlying hardware (processing power, free memory, ...) and the behaviour of active applications have to be
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527
taken into account.
Requirement Specification for the Runtime Environment: The runtime environment used for the devices within the system needs to fulfil distinct conditions additional to the compliance to IEC 61499: 9 Support for enhanced management commands: Chapters 2 and 3.2 present an overview of commands to enable reconfiguration applications, further commands are needed to enable full acquirement of system state including hardware capability. 9 The usage of many management FBs demands an advanced device management capable of handling multiple influences, e.g. engineering tool and reconfiguration application. 9 Real time constraints have to be possible for both, control applications and reconfiguration applications. 9 At execution time, the runtime has to recognize exceeding of time limits; the reconfiguration application has to react on it. Failure Handling and S a f e t y : In the case of failure during operation of the evolution control application the system needs to be at least left in a defined state. That means the reconfiguration process has to recognize error messages and for instance react on a failure situation. The evolution engineering has to mention different problems that may occur and include fall back strategies, e.g. an undo-path or the splitting up into alternative evolution paths, within the reconfiguration application. Another aspect regards the process that is controlled by the application, for instance a robot arm. In this case the special requirements of the process have to be mentioned for the progress of the reconfiguration application. The reconfiguration application has to be coordinated in a very tight manner with the control application.
6. Conclusion The paper presented the methodology of reconfiguration applications for downtimeless system evolution. The methodology is based on the reference model of IEC 61499. An application is used to control the reconfiguration process. Enhancements were proposed and discussed in accordance to the example of the exchange of a closed-loop controller. The usage ofreconfiguration applications demands an advanced support from hardware platforms and the engineering environment: open requirements have been presented.
528
Acknowledgements This work is supported by the FIT-IT: Embedded System program, an initiative of the Austrian federal ministry of transport, innovation, and technology (bm:vit) within the eCEDAC-project [13] under contract number FFG 809447. PROFACTOR is core member of the I ' P R O M S consortium, www.iproms.org
References [1] A.S. Prayati et. al., Engineering Tools to Support Interoperabilty in the Development and Maintenance of Heterogeneous Distributed Real-Time Control Systems, Proceedings of the 23rd Int. Conference on Distributed Computing Systems Workshops (ICDCSW'03), 2003 [2] International Electrotechnical Commission--IEC 614991: Function Blocks, Part 1-Architecture, International Standard, Geneva, 2005 [3] G. S. Doukas, K. C. Thramboulidis, A real-time Linux execution environment for function-block based distributed control applications, Proceedings of 3rd IEEEe Int. Conference on Industrial Informatics (IND1N'05), 2005 [4] gCrons project, Micro Holons for Next Generation Distributed Automation and Control, www.microns.org [5] Ch. Schwab, M. Tangermann, L. Ferrarini: Web based Methodology for Engineering and Maintenance of Distributed Control Systems: The TORERO Approach, Proceedings of the 3rd IEEE Int. Conference on Industrial Informatics (INDIN'05), 2005 [6] K. Lorentz, TORERO Newsletter No 2, January 2004, www.uni-magdeburg.de/iaf/cvs/torero [7] International Electrotechnical Commission--IEC 611313: Programmable Controllers, Part 3-Programming Languages, International Standard, Geneva, 2001 [8] kirchner SOFT GmbH, www.kirchnersoft.com [9] J.H. Christensen: HOLOBLOC.com- Resources for the New Generation of Automation and Control [ 10] V. Vyatkin, H.-M. Hanisch: A Modeling Approach for Verification of IEC1499 Function Blocks using Net Condition/Event Systems, Proceedings of IEEE Int. Conference on Emerging Technologies and Factory Automation (ETFA'99), 1999 [ 11] M. Guler et. al., Transition management for reconfigurable hybrid control systems, IEEE Control Systems Magazine, vol. 23, issue 1, Feb. 2003 [ 12] T. Strasser et. al., A Distributed Control Environment for Reconfigurable Manufacturing, Proceedings of 1st I'PROMS Virtual Conference, Elsevier, 2005 [13] ~CEDAC project, Evolution Control Environment for Distributed Automation Components, www.ecedac.org
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldttkhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Acoustic source localization for design of tangible acoustic interfaces L. Xiao, T. Collins, Y. Sun Department of Electronic, Electrical and Computer Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
Abstract
Acoustic source localization is important to the design and implementation of intelligent environments such as computer-human interaction systems. Different methods for multiple source localization are discussed and compared in this paper. The mathematical model of the method based on SRP-PHAT is presented for multiple sources localization under reverberant conditions. The PHAT are applied to suppress the reverberation from the environment. Global searching method and clustering technique are used to find the positions of multiple sources. MATLAB simulations and practical tests were carried out under real reverberant conditions. The simulation and test results show that the method is suitable for two acoustic source localization in reverberant environment.
1. Introduction
Source localization is used in speech separation for hands-free communication devices, security systems and intelligent environments. Spatial parameters obtained in the localization process can be used in a variety of applications such as in the design of computer-human interaction (CHI) systems. In CHI systems offering tangible acoustic interfaces (TAI), it is necessary to determine the physical position of users. Without precise knowledge of the spatial location of users in an environment, the interface would not be able to react correctly to the needs and behaviors of the user. For single source localization, the method based on time delay estimation (TDE) applying phase transform (PHAT) is quite effective and robust in environment with moderate reverberations and the majority of practical acoustic source localization systems are TDE-based [1][2]. However, TDE based methods are useful only for single source
localization. Under certain circumstances, it is necessary to localize multiple sources in an environment with reverberations. The method based on multiple signal classification (MUSIC) algorithm is a quite popular method for multiple source localization [3]. Also, the incremental multi-parameter (IMP) algorithm can be used for multiple source localization [4]. Although other statistical signal processing methods such as particle filters have also been used, they are integrated with or based on the methods such as those beamforming methods[5]. Nevertheless, the MUSIC method is not effective for acoustic source localization under reverberant conditions [6]. The most successful and prevalent one for multiple source localization in reverberant environment have generally been based upon the idea of maximizing the steered response power (SRP) applying PHAT (SRP-PHAT) [7].
529
2. General review and comparison of multiple source localization methods
2.1 The MUSIC method The multiple signal classification (MUSIC) method is a subspace based direction of arrival (DOA) estimation method. Generally, the basic MUSIC method can only be used for narrow band signal application. If it is used for wide band signal processing, several notch-filters should be used to extract signals at those specified frequencies. The mathematical model of the MUSIC method can be described by the following equations. To search for the locations of sources, a orthogonality-measure spectrum is constructed by p(r,O,(p) =
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2.2 The IMP algorithm The IMP algorithm is one of the beamforming methods [4]. Theoretically, it can be used for multiple source localization under reverberant conditions and it is claimed that it can achieve higher resolutions than MUSIC methods under reverberant conditions [4]. The mathematical model of the IMP method is a H (r,O,(,o)QXX HQa(r,O,(p) (3) p(r,O, (p) = a H (r,O,(p)Qa(r,O,(,o) where p(r,O,(p) is the output power spectrum of the array, X is a matrix of the sampled signal from the microphone array and a(r,O,(p) is also a steering matrix as equation (2). Q is a matrix defined as Q = I-an(r,O,(p)(a~(r,O,(p)an(r,O,(p))-la~(r,O,(p) (4) a n = [ a l , a 2 ...an_l] (5) The source positions can be obtained by searching the peaks of the output power spectrum p ( r , O , ( p ) . MATLAB simulation results in an acoustic environment with some reverberations showed that it is effective for multiple source localization and practical test in an anechoic chamber also validated its effectiveness. However, if the method is verified with practical test results under moderate reverberation conditions, it failed to localize single source correctly.
Here, a(r,O,(p) is a (LxN) steering matrix, L is the number of the transducers and N is the number of sources. Zkn is the distance between the n th source and the k th transducer..~ is the wavelength of the n th impinging source. The peaks of the spectrum, p ( r , O , ( p ) are the locations of sources. Theoretically, the MUSIC method can not be used for coherent source localization, i.e., in acoustic environment with reverberations. However, simulation results have shown that if it is used for wideband signal application, as the number of frequency fins is increased up to 5, its accuracy can be greatly increased. MATLAB simulation of MUSIC has been carried out in an acoustic environment with some reverberations and the method can locate passive acoustic sources correctly and practical tests showed that it can localize multiple sources in an anechoic environment. However, when the method is applied to practical environments with moderate reverberations, the method could not even localize single passive source correctly.
530
3. Mathematical model of SRP-PHAT
Since both MUSIC method and IMP algorithm are not suitable for multiple source localization in a practical environment with moderate reverberations. Steered response power method based on phase transform (SRP-PHAT) is therefore investigated for multiple source localizations under reverberant conditions. 3.1 Mathematical model of GCC-PHAT Consider any two microphones, i and j. Let the signals received by these two microphones be x / ( t ) and x j ( t ) , and they are represented as y i ( f ) xj(f)
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o r - o~;o~/ and a ; - 2 7 f . Suppose the noise signals are uncorrelated to the source signal and each other, the last three terms do not make contributions to the cross-correlation computation. PHAT is a special form of general crosscorrelation (GCC) based method proposed to suppress reverberation. Here, a pre-filter is added to process xi(t ) and xj (t) before they are crosscorrelated. Suppose the transfer function of the prefilter in the frequency domain is Hi(f) for xi(t ) and Hi(f)
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Therefore, under the assumption of completely un-correlated noise, the spread caused by the autocorrelation of incident signal disappears and a much sharper delta function for the cross correlation can be achieved. Because the transform tends to enhance the true delay and suppress all spurious delays. It turns out to be effective in moderate reverberation. One disadvantage of PHAT is that it tends to enhance the effect of frequencies that have low power when compared to noise power. This can cause the estimates to be corrupted by the effect of uncorrelated noise. Therefore, additional measurement should be taken such as pre-processing signal by removing those signals over a certain frequency and those signals whose amplitude is lower than a certain threshold, and pre-processing the signal with smoothed coherence transform (SCOT) to suppress the background noise. [1] [8]. 3.2 Mathematical model of SRP-PHAT It should be noted that when calculating O~,x, (f) in (8), it is assumed that ~i and o~j of (6) are time invariant. This is not true if there is reverberation. Therefore, ideal results of (12) cannot be achieved in a practical acoustic environment. The GCC-PHAT method yields good results in low to moderate reverberant environments. The combination of the GCC-PHAT with the SRP method can improve the robustness. The SRP beamformer is given by [9] L
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531
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Fig. 2 Comparison between the practical and estimated source positions from practical test In this research, the global searching method plus clustering technique is used to find the multiple source positions. Suppose N is the source number. The maximum value and positions of N sources are initially preset and a cluster is preset surrounding each source position. When a value which is bigger than the smallest maximum of the N sources is found during the searching process, firstly, it is determined whether it is belong to any one of the clusters. Then, if it is not belong to any of the preset cluster, a new cluster will be set around this new maximum and replace the cluster which is corresponding to the smallest maximum. The method searches the whole related area where the acoustic sources would be positioned. The disadvantage of the global search is the heavy computation load. Therefore, it is not suitable for real-time implementation. The shortcoming of the clustering technique is that a suitable area should be preset for each cluster in order to remove the effect caused by the noise and reverberation. If the preset area is too small a maximum caused by noise or reverberation would be misjudged as a real source position. On the contrary, if the preset area is too big, the resolution of the estimation would be reduced. If two or more sources are allocated close to each other, only the position of one source can be estimated correctly, the correct position of other sources can not be obtained. Another disadvantage of the clustering technique is that the source number should be pre-known. In most practical situations, the source number is not known beforehand. Therefore, other method has to be used to estimate the source number before the source positions can be
found applying global search and clustering method.
4. Simulation results
In order to verify the SRP-PHAT method and its application to multiple acoustic source localizations in a real acoustic environment, MATLAB simulations were firstly carried out in a practical meeting room. The dimension of the room is 7.4 m •
4.2 m x 3.1 m. The measured reverberation time of the room is about 130 ms. The acoustic performance of the room and the reverberation are modeled by the acoustic image method [11]. The background noise is white noise, the signal to noise ratio (SNR) is between 4-20 dB depending on the source level and position of the microphone. The acoustic sources are some practically recorded passive acoustic sources such as human
Table 1 Comparison of MATLAB simulation results between practical and estimated source localizations Sources
xp(m)
Xest(m) error(m)
yp(m)
Yest(m) error(m)
Zp(m)
Zest(m) Error(m)
1
1.52
1.46
-0.06
2.03
2.02
-0.01
1.08
1.08
0.0
2
2.56
2.58
0.02
0.53
0.54
0.01
0.55
0.54
-0.01
3
5.81
5.78
-0.03
1.04
1.06
0.02
2.05
2.01
-0.04
Table 2 Test results comparison between practical and estimated source localizations Sources
Xp(m)
1
2.37
2.30
2
3.05
2.96
Xest(m) error(m )
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Yest(m)
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3.36
3.44
0.08
0.48
0.42
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-0.09
1.39
1.44
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0.41
0.52
0.11
voice, hand clapping, and percussive noise. Three acoustic sources are positioned at point 1, 2 and 3 respectively as in Fig. 1. The simulation results are shown in Fig. 1. The practical coordinates of the acoustic source, represented by footnote 'p', and the estimated coordinates, represented by footnote 'est' are compared in Table 1. The errors between the practical value and the estimated value are also listed in the table as italics. The origin of the coordinate is chosen at one corner of the room as shown in Fig. 1, the x coordinate, y coordinate and z coordinate represent the length, width and height of the room respectively.
5. Experimental tests
After the method was verified by the MATLAB simulation results, practical tests were carried out. The experimental setup and the configuration of the
z p ( m ) Zest(m) Error(m)
microphone array are shown in Fig. 2. The SNR ranges from 6 dB to 14 dB in the practical tests depending on the strength of the source, its position and the positions of the microphones. The test results are shown in Fig. 2, and the practical and estimated results are compared in Table 2. Two acoustic sources are positioned at points 1 and 2 respectively. From Fig. 2 and Table 2, it can be observed that good agreements were found between the practical coordinates of the passive acoustic sources and those from estimation.
6. Discussion and conclusions
A general review and comparison have been made on different multiple source localization methods and it is verified that the MUSIC and IMP method are not suitable for multiple source localization with moderate reverberations. The mathematical model of SRP-PHAT is described in the paper for multiple acoustic source localization.
533
Results from MATLAB and practical tests are presented in the paper. Test results showed that the method can localize two sources correctly. However, except for the great computation requirement of the SRP-PHAT which limits it from real-time implementation, other problems were also found in the research. During the MATLAB simulation, if one or two sources are close to the sensor array and the other sources are comparatively farther away from the sensors, only the positions of those sensors close to the array can be estimated correctly. Arranging the sensors evenly in the localization area can improve the performance of the method. The same situation happens when the signals from one source is apparently stronger than those from other sources. Moreover, during the MATLAB simulation, if four or more sources are to be localized by the SRPPHAT method, not all the positions of the sources can be determined correctly by the SRP-PHAT method. The reason, perhaps is that if there are more sources positioned in the area, more local maximum of SRP-PHAT is caused by reverberations and the value of those local maximums would be bigger than those maximums corresponding to the real source positions and therefore, the real source positions can not be obtained by the SRP-PHAT method. This situation also happened during practical test. When three sources are tested, in most occasions, the SRPPHAT method can not localize the position of all three sources correctly. In order to make the SRP-PHAT method suitable for real-time implementation, firstly, more effective searching method should be found to reduce the heavy computation load of global searching. Secondly, it is necessary to investigate other signal processing techniques to alleviate the effect caused by reverberations.
7. Acknowledgements This work is funded by the EC 6 th framework program Tai-Chi project
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References [1]
Silverman HF, Yu Y, Sachar JM and Patterson WR. Performance of real-time source-location estimators for a large-aperture microphone array. IEEE Trans. Speech and Audio Processing, SAP-13(2005) 593606. [2] Xiao L and Collins T. In-air passive acoustic source localization in reverberant environments. IPROMS 2005 conference on Intelligent Production Machines and Systems. 2005 [3] Schmidt RO. Multiple emitter location and signal parameter estimation. IEEE Trans. AP. AP-34(1986) 276-280. [4] Mather J. The incremental multi-parameter algorithm. 24th Asilomar conference on Signals, Systems and Computers. 1990 368-372. [5] Ward DB and Williamson RC. Particle filter beamforming for acoustic source localization in a reverberant environment. Proc. ICASSP-02. Orlando FL, 2002, 1777-1780. [6] Chen JYn Yao K and Hudson RE. Source localization and beamforming. IEEE Signal Processing Magazine, SPM-19(2002) 30-39, 2002. [7] Dibiase J, Silverman HF and Brandstein MS. Robust localization in reverberant rooms, in microphone arrays: signal processing techniques and applications. Springer-Verlag, New York, 2001, pp. 131-154. [8] Knapp CH and Carter GC. The generalized correlation method for estimation of time delay. IEEE Trans. Acoust., Speech, Signal Processing. ASSP-24(1976) 320-327. [9] Mungamuru B and Aarabi P. Enhanced sound localization. IEEE Trans. Systems, Man and Cybernetics- Part B: Cybernetics, SMC-34(2004) 1526 -1540. [10] Aarabi P. The fusion of distributed microphone arrays for sound localization. EURASIP Journal on Applied Signal Processing, EJASP-4(2004) 338347. [11] Allen JB and Berkley DA. Image method for efficiently simulating small room acoustics. Journal of the Acoustical Society of America. JASA65(1979) 943-949.
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
A m b i e n t i n t e l l i g e n c e in m a n u f a c t u r i n g I. Maurtua a, M.A. P6rez a, L. Susperregi a, C. Tubio A. Ibarguren a a Fundaci6n Tekniker, Av. Otaola 20, 20600 Eibar, Spain
Abstract There are several references to projects linked to the AmI concept in technical publications. These are mostly centred on applications linked to everyday situations concerning individuals: at home, in the street, in the car, in public places and relating to leisure. The AMILAB research group at TEKNIKER is analysing the technological, human and social needs and implications for AmI in the manufacturing field. For this aim we have set up a laboratory to spread the model of AmI to the area of manufacturing. This paper presents that Lab, the agent based architecture we have implemented as well as the application of machine learning techniques to identify the activities a worker is doing.
Keywords: Ambient Intelligence, context, agents, manufacturing
1. Introduction Ambient Intelligence (AmI) [1] is a vision of the future of the Information Society that has attracted the attention of numerous research groups. AmI stems from the convergence of three key technologies: Ubiquitous Computing, Ubiquitous Communication, and Intelligent User Friendly Interfaces. All of them collaborate to create an intelligent environment that identify people, get adapted to them and with which interact in a natural way. Ambient systems need to address some key issues: 9 Context awareness: have a predictive behaviour based on knowledge of the environment. 9 Natural Interaction: relates, in a natural manner, to the users by means of multi-modal interfaces, movements and gestures, images, etc. 9 Adaptation: adaptation to users and context in an autonomous way. 9 Integration and ubiquity: technology will
become invisible, embedded in our natural surroundings, present whenever we need it, offering services regardless of where the user is located, of the position from where the user demands the services and the artefacts available at that particular moment. 9 New services: produce new services in fields such as entertainment, security, health, housework, the work environment, access to information, computing, communications, etc., to improve the quality of life by creating adequate atmospheres and functions. There are several references to projects linked to the AmI concept in technical publications. These are mostly centred on applications linked to everyday situations concerning individuals: at home, in the street, in the car, in public places and relating to leisure. The opportunities for its application in the Industrial and Productive environment are frequently cited, although it is mainly the office environment that is mentioned. The key technologies in the future of
535
manufacturing identified in two of the most important studies relating to manufacture [2,3] are clearly linked to the vision of AmI: flexible manufacturing and control systems, decision support systems, improved human-machine interfaces, equipment, re-configurable and adaptable processes and systems, distributed computation, etc. Potentially, the adoption of this vision could affect all stages in the manufacturing process: the design of the plant or the product, engineering, the organisation and management of production, process control, quality control, maintenance, logistics and the management of the product throughout its life cycle, including its re-cycling. The AMILAB research group at TEKNIKER is analysing the technological, human and social needs and implications for AmI in manufacturing. In the following sections we present some of the real industrial applications Tekniker is developing. From the technological point of view we describe the most important achievements: (1) an AmI environment in the manufacturing field, (2) context aware agents able to offer contextual information and to anticipate user needs, and (3) a system that can identify the activities a worker is doing.
Fingerprint identification system. Instrumentalised gloves Traditional applications/functions in a manufacturing environment, such as monitoring and operating the machine, production follow-up, maintenance. These applications have been re-designed and implemented in the form of Agents on the JADE platform [4], incorporating the concept of intelligent interfaces, context sensitivity and automatic learning
Fig.1. AmlLAB Laboratory 2. AmlLAB Tekniker has set up a laboratory to spread the model of AmI to the area of manufacturing. The main research objectives of the laboratory are: 9 To support complex tasks with a minimum of human-machine interaction. 9 To enable mobile professionals to keep their attention focused on the interaction with the work environment. 9 To investigate the user acceptance of wearables, as well as different methods for user interaction. 9 To identify processes suited to wearables in industry. The l a b o r a t o r y - see figure 1- reproduces an industrial environment equipped with machines (highspeed milling machine and 6 axes robot) and various devices and applications: 9 Interface technologies: voice recognition systems, head mounted displays and data gloves. 9 Location and tracking system based on RFID tags and sensor networks. 9 Wearable and portable computing.
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3. Agent based architecture One of the aims is to create applications that make use of context information to provide users the information they wish, when and how they need it. The definition of"context" refers to any relevant fact in the environment. To reach this objective a multi-agent system, called AMICO, has been developed. It creates an integrated AmI environment in manufacturing. AMICO is able to support and follow users along the Laboratory, offering them the information needed at anytime in the most suitable device available. The activity has been focused on several user profiles: the machine operator, the maintenance operator and quality manager. The main functions provided by AMICO are: 9 To show contextual information taking into account three criteria: user profile, the most suitable device, and the user location. 9 To allow the access to machine functionalities according to the user context. 9 To learn and adapt to the user. Figure 2 shows the components of the AMICO
prototype and the communication schema among the agents. To sum up, different agents co-habit in different devices (PDA, Smartphone, Xybernaut, NC .... ), these are: Broker agent: maintains a model of the context for the rest of agents, services and devices and decides which information and to which device should be delivered. This Broker agent acts as a middleware with external applications that are not implemented as agents, such as real time machine information retrieval, centralised voice recognition system, etc. Tracker: is in charge of tracking and reporting the position of users and devices in the laboratory. An RFID system provides geographical information of all users and devices in the laboratory. Mobile Agent: is the "user interface" agent. It offers the user context aware information, i.e. that information need for the user according to his preferences, the activities he is doing or the interfacing devices he can access to. Register: registers all the devices in the agent platform and manages the main characteristics of them. Input Agent: is a data input agent called UbiKey (Ubiquitous Keyboard and Mouse). This mobile Agent can be called anytime by users, the Broker agent decides which keyboard equipped device is near the user and sends Ubikey to it. From that moment on the user can use that device' s keyboard or mouse as if they were physically attached to his wearable computer in order to input data to other Agents running in it. l~:.! ~" :;;~i~i!:
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of the machine axes, etc. 9Execution of certain instructions: execution of a program, sending programs, etc The agent is executed on the Fagor 8070 NC, which offers the functionalities of a PC and which is part ofthe JADE platform. The NC Agent calls up the different functions of the API supplied by the NC manufacturer to interface with it.
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The Java Agent DEvelopment Framework, JADE1, is used to develop and run the system. A JADE-LEAP ("JADE powered by LEAP") version is used which provides a runtime environment for enabling FIPA agents to execute on lightweight devices such as cell phones running Java. This free platform (under LGPL license) supports multi-agent implementation following standards defined by FIPA, Foundation for Intelligent Physical Agents. Furthermore, it offers a reduced version compatible with mobile devices that we use on Symbian (smartphone), besides the versions for Windows (NC and Xybernaut) and Pocket PC (PDA). JADE includes two agents that facilitate agent management: "Agent Management System" (AMS) and "Directory Facilitator" (DF). The first one provides information of all agents registered in the platform and the DF offers a yellow page service with the services registered in the platform. There is a central server or "main-container" where the Broker, Tracker and Mobile agents are initialised. Besides each active device in the laboratory defines its particular container, where mobile agents might migrate when required. Communication, coordination and cooperation are 1 http ://jade.tilab.com/
537
key points for a multiagent system. All information exchange among agents is done using FIPA ACL standard and JADE provided mechanisms. Regarding Ontologies, the ones used for this multi-agent system are the mobility Ontology implemented by JADE and the Device Ontology defined by FIPA. 4. Context detection
The definition of "context" refers to any relevant fact in the environment: 9The user position and related parameters: indoor or outdoor, temperature, humidity, date and time, background noise, user's physiological status, activity, etc. 9Devices at hand: telephone, PDA, printer, etc. operating system and communication and interaction capabilities, display size, memory, battery status, etc. Accessible networks, pricing and bandwidth. 9User's preferences: voice versus text messages, favourite devices, font size, ... 9Available services and people. In short, the context could be any data available when interacting with the system, being specially relevant in a mobility environment. One of the factors that offer a richer source of information is the activity carried out by a person, which in the case of a productive environment acquires a particular relevance. Once the activity developed by a worker is known, the possibilities offered by an AmI environment are unlimited: making the interaction with information systems easier, offering the necessary information each time, monitoring the observance of security measures, controlling the observance of the manufacturing procedures, etc.
and facilitate the human interaction with deaf-mute people has been the objective of several researchers. Waleed [5] tried to recognise the 95 signs of the Australian Sign Language (Auslan), the language used bythe Australian Deaf community. Fels and Hinton [7] have developed a system that allows controlling a real time speech synthesiser using data gloves and neural networks. More recently, within the wearIT@work2 project, researchers from ETHZ and UMIT have used several in-body sensors to identify activities during the assembly of car's front-lights in Skoda production facilities [6]
4.2.Methodology Several Machine Learning paradigms have been used in order to test their behaviour with this specific problem. The three methods selected were: one based on instances such as the K-NN [8], the widely used C4.5 [9] for creating decision trees and finally the probabilistic method known as Naive-Bayes [8]. Genetic Algorithms [ 10,11 ] were additionally used for some specific optimisation tasks. These algorithms allow to obtain almost optimal solutions with problems where the search space is too wide. In this way, it is possible to get a good solution within a reasonable time. Several experiments were set up using the glove, starting with learning from data coming from a single person, and later, carrying out more complex tests in order to arrive at a learning procedure based on data from several people. The objective of the experiments is simple: try to ascertain what the object being used by the person wearing the glove i s - see Fig.4.
4.1.Instrumentalised data gloves There are different alternatives for the characterisation of a person' s activities, from the use of sensors to the inference from the interaction with the systems. However, the alternatives are neither equally valid, nor even acceptable in some situations. One of the research areas we are working on in our laboratory is the identification of the activities a worker develops based on the information given by gloves he wears during his activity. Using instrumentalised gloves to identify gestures
538
Fig.4. An operator wearing data gloves at work
2 http ://www.wearitatwork.com
In order to achieve this, it was decided that predictions were to be made on the basis of 10 classes: the motionless hand, the hand in transition (picking or leaving an object), and handling of 8 different objects used often in the workshop context: a hammer, screwdriver, PDA, wrench, pliers, tester, drill and a bolt that was screwed manually.The data sent by the glove was read while different persons used the objects. 5 readings with each object were done with each person: 3 using the object and another 2 doing transitions (starting with the motionless hand, picking up and then using the object, and finally setting down the object and the hand at rest). In this test, the algorithms used were the already mentioned 1-NN (K-NN, with K=I), the Naive-Bayes and the C4.5, which represent different families of classifiers. The validation was done with the 10-fold cross-validation method. Once this first experiment was done, and the results seen, we started a second stage where we tried to make the predictions based on data from different individuals; from now called interpersonal learning. The idea of being able to predict which object a person is using based on the data of other individuals is very interesting. In fact, physical characteristics of human hands and the different ways of handling objects introduce an extra degree of complexity.This is the concept that we found behind what we call interpersonal learning or leave-one-person-out: trying to predict the objects without having previous data of the person wearing the glove. In order to simulate this idea, we took readings from 5 persons. We used the readings of one of them for the test and we trained our model with the data of the other persons. In this way, during the training, there was no access to the cases used in the test. We carried on this process for the data of each one of these 5 persons. 4.3.Results
In the first experiment it was observed that, once an individual's data are gathered, it is possible to create a robust and reliable classifier with a 99% recognition rate that predicts the different objects that this person may be using- see Fig. 5. We have created an application capable of predicting the different objects on-line, once the training data has been gathered. Every 15ms, data are read from the glove and predictions are made by using a C4.5 classifier. In order to stabilise these predictions
we use a buffer that records the last 30 readings and makes the predictions on this data within a reliability limit set by the user.
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Table. 1. Recognition rate in interpersonal learning It is clear that this inadequate rate makes it impossible to use the algorithm in real systems. In order to increase the success rate in interpersonal learning, we decided to make a selection and weighting of variables by using a Genetic Algorithm: 9 Selection of Variables: to completely eliminate the variables that have a negative influence on the predictions. 9 Weighting of Variables: to give the different variables their due importance. In order to do so, we applied changes to some of them for "weighting" the data they contained and decreasing its importance. We carried this out by raising all the values of the variable to an integer value. In our case we used the following formula: X Y (1) where X is the value of the variable and Y an integer value between 1 and 6 The use of the Genetic Algorithm for selection and weighting of variables has achieved an increase in this rate to 35% - see Table 2 -, but even these success rates are far from being usable within any application.
539
Person Rate
1 16,4
9
2
3
53,89 44,92
4 41,4
8
5 39, I
1
Mean 39,18
Table. 2. Recognition rate with interpersonal training using variable selection and weighting 5. C o n c l u s i o n s a n d f u t u r e w o r k
In the area of manufacturing, AmI is not only going to affect the way in which processes develop, but will also provide new ways of working and doing business. The development of new products and services and the shift in the focus of attention of the worker from the machine to their immediate working environment will be the immediate consequences of the adoption of AmI vision. In no case this is proposed as a new manufacturing paradigm but, whatever models are followed, these will have to take into account these changes. Not only several technological challenges such as miniaturisation, inter-operability and energy management have to be addressed by research teams across the world in the present decade, but also a strong focus on the social and organisational aspects of AmI has to be taken to overcome barriers to its realisation. On the other hand, in the manufacturing field there is a great amount of already existing legacy systems that should be integrated in a Manufacturing AmI environment for which there is a need of integration mechanisms Regarding human aspects, cost, risk of intrusion, potential employee resistance, legal and ethical restrictions, and risks associated with high system complexity can be main drawbacks in the widespread adoption of AmI. The AMICO prototype offers a new work environment providing high quality information and content to any user, anywhere, at any time, and on any device. The AmILAB group aims to continue the research in different topics: 9 To seek to achieve a more intelligent contextual information management. 9 To create new agents that will be added to the AMICO platform. 9 To integrate Machine learning techniques in order to adapt to the user and the context. 9 To extend the AmI Laboratory by introducing new interaction technologies. Relating to activities' identification, the challenge
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is trying to increase the recognition rate of what we call interpersonal learning. We are creating more complex classifiers that will be capable of solving the evident problem derived from the diverse physical characteristics of human hands and the different ways of handling objects. The laboratory created and operating in Tekniker is a good test bed where research on technology and human factors is done in a holistic way. Acknowledgements
This research is possible thanks to the Basque and Spanish Government research programs. The wearIT@work 3 project is funded by the European Community FP6. Fundacion Tekniker is partner of the EU-Funded FP6 Innovative Production Machines and Systems (I'PROMS) 4 Network of Excellence. References
[ 1] K. Ducatel, M. Bogdanowicz, F. Scapolo, Leijten J., and J.C. Burgelma. Istag: Scenarios for ambient intelligence in 2010. ISTAG 2001 Final Report, 2001. [2] Expert Group on Competitive & Sustainable Production and Related service Industries in Europe in the Period to 2020, EU Commission, "Sustainable Production: Challenges & objectives for EU Research Policy", 2001 [3] Visionary Manufacturing Challenges for 2020. Committee on Visionary Manufacturing Challenges, National Research Council. NATIONAL ACADEMY PRESS,Washington, D.C. 1998 [4] JADE. Java agent development framework, 2004. URL: http ://jade.cselt.it/index.html. [5] Mohammed Waleed Kadous. "Machine Recognition of Auslan Signs Using Power Gloves: Towards LargeLexicon Recognition of Sign Languages", 1996 [6] T.Stiefmeier, Paul Lucowicz, "Showcase platform architectural design and specification- A technical document". Internal deliverable wearIT Project, 2005 [7] S. S. Fels and G. Hinton. Glove-talk: A neural network interface between a data-glove and a speech synthesiser. IEEE Trans. on Neural Networks, 4(1):2 {8, 1993. [8] T. Mitchell. "Machine Learning", McGraw-Hill, 1997 [9] J. R. Quinlan. "C4.5: Programs for Machine Learning". Morgan Kaufmann, 1993 [10] D. Goldberg. "Genetic Algorithm in search, optimisation and machine learning". Addison-Wesley, 1989 [11] T. B~ick. "Evolutionary Algorithms in Theory and Practice". Oxford University Press, 1996
3 http ://www.wearitatwork.com 4 http ://www.iproms.org
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All rights reserved.
Localisation of impacts on solid objects using the Wavelet Transform and Maximum Likelihood Estimation D. T. Pham, Z. Ji, O. Peyroutet, M. Yang, Z. Wang, M. Al-kutubi Manufacturing Engineering Centre, Cardiff University, Cardiff CF24 3AA, UK
Abstract The Time Difference Of Arrival (TDOA) method, often used for sound source localisation, is not suitable for locating the source of dispersive waves. It is difficult to establish the actual time of arrival of a dispersive wave because of the dependency of its velocity on its frequency. To overcome this timing uncertainty, two novel approaches for the localisation of an impulsive acoustic source in a solid object are proposed in this paper. The Wavelet Transform is utilised to extract different frequency components from the recorded acoustic signals for estimation of the group velocities of the various frequencies. Maximum Likelihood Estimation (MLE) is introduced to improve the accuracy and reliability of the localisation. In the paper, three localisation methods based on these techniques are introduced and compared.
Keywords: Wavelet Transform, Maximum Likelihood Estimation, computer interface, human computer interaction
1. Introduction An impact on an elastic surface can produce energy in the form of acoustic vibrations. The objective of the experiments presented in this paper is to utilise the information carried by such vibration signals to localise the position of the vibration energy source arising from the tactile interaction. Using this method, an object surface can be transformed into an interactive computer interface. Acoustic localisation has been adopted in a number of applications to implement Human-Computer Interfaces (HCIs)[1], [2], [3], [4], [5], [6]. There are two basic techniques developed for insolid acoustic source localisation: Time Difference Of Arrival (TDOA) and Location Pattern Matching (LPM). TDOA is a technique which estimates the
location of an acoustic source by computing the differences in the arrival times of acoustic signals at a number of sensors. LPM uses previously recorded acoustic signal patterns to match an unknown acoustic signal pattern so as to identify the position of the associated impact. In this paper, discussion is concentrated on different TDOA-based approaches.
2. Background
2.1. Theory Acoustic wave propagation in solids is much more complicated than in air. This is because tapping on an object can cause it to vibrate and generate complicated particle movements. These are governed
541
by different wave modes. There are two Lamb wave modes relevant to the experiments, Extensional wave (symmetrical) and Flexural wave (asymmetrical). In the low frequency domain, the flexural wave is dominant. The propagation velocity of a flexural wave in plates is dependent on its frequency. This is called the dispersion property, which should be correctly analysed and taken into account in the localisation algorithm to achieve precise localisation. In-air acoustic source localisation using time difference estimation is a well developed area [7]. There has also been great interest in non-destructive testing and evaluation (NDT & NDE) using ultrasound to locate source positions in solids. However, the significant difference between in-air and in-solid acoustic localisation is the complexity of wave propagation in solids. Consequently, the traditional time difference method is unsuitable for applications requiring precise localisation. In this work, the Continuous Wavelet Transform (CWT) was used to estimate the group velocities of dispersive signals for improving the accuracy of the localisation. The development of a statistical method of estimation of maximum likelihood in conjunction with CWT is proposed to improve the accuracy and reliability of the localisation.
2.2. Source location in a plane The principle of the TDOA method is to use the measurement of time differences between arrivals of signals to estimate the source location. These time arrival delays result from the differences in distances from the source to the sensors which are placed at known geometric positions. Fig. 1 presents a typical TDOA system comprising a plate, four sensors installed at the four corners, signal conditioning hardware, a data acquisition card and a PC. A knock on the plate can be detected by the sensors. The sensor outputs are amplified and filtered, then digitised and finally processed by the PC to obtain the coordinates of the position of the knock. In the case where four sensors are used for the localisation of an impact (theoretically three sensors will be sufficient for localisation), the location at(x,y) has the following relationship with the time differences of the sensor signals: ~/(X -- Xl) 2 +(y--y1)
(1)
542
2 --~/(X X3) 2 + (Y-- Y3)2 = vAtl 3 -
~/(X--X2) 2 + ( y - - y 2 ) 2 --~/(X--X4) 2 + ( y - - y 4 ) 2 = vat24
(2) where (xl, yl), (x2, y:), (x3, y3) and (x4, y4) are the coordinates of the four sensor positions, v is the acoustic wave propagation velocity in the plate. Atl3 is the measured time difference for the sensor locations (xl, yl) and (x3, y3), and At24 is the measured time difference for the sensor locations (x2, Y2) and (x4, y4). The above equations can be expressed in the general form
~,j-~/(x-x~) ~+(y_ y~ _~/(x_x~)~+(y_ y ~ (3~ where i and j represent the sensor indices. The source location can therefore be calculated by minimising the following expression: 4
E = Z(vAtl,i Adl,i)2 -
i=2
(4)
where At1,i represents the time difference between signal arrivals at the 1st sensor and the i th sensor. It should be pointed out that the determination of the source location in a plane is based on the hypothesis of acoustic waves travelling in the plate at a constant velocity.
ill
Compute the p ~ n p(x,y)
Fig. 1 TDOA hardware block diagram
2.3. General TDOA techniques A conventional method used to determine the arrival time of a signal is to detect its rising edge. The time difference is then calculated atter each rising edge of the arrived signals is detected and recorded. In this work, it was found that the time of arrival directly extracted from the rising edge varied
widely due to the dependency of wave velocity on wave frequency. In practice, the value of the threshold should be tuned experimentally in order to achieve accurate localisation results. In addition to the selection of an appropriate threshold, the accuracy and the reliability of the localisation can also be improved by low-pass filtering and normalisation of the signals [ 1]. Due to the dispersion of waves in solids, it is difficult to use the rising edge detected by different sensors to calculate the correct arrival time of the wave. This is because of the dependency of wave velocity on frequency and this timing uncertainty impairs system accuracy. Wave attenuation is another factor that makes the rising edge method unreliable. Moreover, this method is not suitable for continuous acoustic signals, as there is no distinct front to detect. Finally, the rising edge detection method is prone to ambient noise, particularly for weak signals. Cross-correlation is also used in time difference estimation [5]. The maximum of the crosscorrelation coefficients of two signals indicates the time lag between them. However, it was found that, if sensors are placed close to the boundaries, results of the cross-correlation can be affected by the multiple reflections of acoustic waves from object boundaries. The reflected waves disperse the pattern of the incoming wave from the source to the sensors. As a result, ambiguous peaks will appear in the correlation coefficients causing abnormal results (see Fig. 2).
their ability to obtain the time-frequency representation of a signal simultaneously. It would be beneficial to know the time intervals between different spectral components travelling in a solid object for the purpose of correct and precise measurement of the time differences of acoustic signals. The Continuous Wavelet Transform of a signal f(t) is defined as: 1 WT/(a,b) - ; f (t)-~a g* ( t - b )dt
(5)
where a represents the CWT scale, which is related to the frequency, and b denotes the shift parameter in the time domain. The relationship between the scale variable a and frequency o; is co= (Oo/a, where o)0 is the central frequency of the wavelet
~(t). 3.2. Velocity estimation with time-frequency analysis Considering two harmonic waves propagating in the x-direction with a unit amplitude and different frequencies (q and (o2: U ( x , t ) = e -i(k~x-rqt) + e -i(k2x-c~
(6)
where u(x,t) is the superposition of the two unit waves, and k 1 and k 2 are the wave numbers.
05 0.4
Introducing
0.3 "~ 02
g
0.1
(kl +k2)/2=k~, (kl-kz)/2=Ak,
o
( 4 + ~ ) / 2 = (~176
' 0 .0.1
(~
= At~
(7)
-0.2
equation (6) can be simplified as
-0.3
0
(ms)
2
4
6
8
u(x,t) = 2 cos(kkx- A ~ ) e -i(kcx-c~
Fig. 2 Cross-correlation coefficients with ambiguous peaks
3. Continuous Wavelet Transform Method
3.1. Continuous Wavelet Transform To overcome the problem of timing uncertainty, Wavelet methods have been investigated to exploit
(8)
Equation (8) represents a modulation process. The carrier has frequency o)C and phase velocity
coc/k~. The modulated wave cos(kkx-Ao~t) has frequency Ao~ and propagating velocity Ao~/Ak, which is the group velocity when Ak -+ 0. With CWT, the signal u(t) is transformed into the following form [8]:
543
W T ( x , a, b) - ~ a {e-i(klX-~
the impulse response hi(t ) and the source signal
(a601 )
+ e-i(kzX-C~
(aO)2 ) }
(9)
the external noise and the time delay 9 Hence, the probability can be expressed with the Gaussian distribution as:
The magnitude of CWT can be obtained as
[WT(x,a,b) = ~ a {~(aco1)2 + gY(ao~ )2 + 2~r(a~ )~(ao) 2) cos(2Akx - 2Aob)} 1/2
s(t-"ci) at the i th sensor, ni(t ) and Z"i represent
(10)
P ( X l , X2, X3,
X41 L, s )
- I-I P(xi
The value of A g is assumed to be small 9 Therefore, it can be neglected after CWT giving
[L,s)
i=1
~(agq ) = ~ ( a ( o 2) = ~(a(Oc). After simplification,
~'I
m
the magnitude of CWT can be written as:
e
(14)
_ fo+W [xi(t+ri)-s(t)] 2 %-w
2or2
i=1
I WT,(x,a,b) = ~ a I ~(aO)c) [ 9[1 + 2 cos(Acab -
~)]1/2
(11)
With an assumption of non-dispersive signals, the estimated ~(t) can be obtained using maximum likelihood estimation as follows:
The right side of Eq. 11 reaches its maximum value at a = 6o0 / 6oc and b = (Ak / A(o)x = x / Cg . SO the maximum of CWT of scale a with time shift b indicates the arrival time at the group velocity.
s(t)
--
1s -4- i=1
Xi(t+Ti
)
(15)
The likelihood is thus yielded [9] as: 4. M a x i m u m
Likelihood Estimation (MLE)
log P ' - 2
Likelihood Estimation can overcome problems of ambiguities in time difference estimation, which may happen with techniques such as crosscorrelation or CWT peak detection as described above 9 This method searches for a target position over all possible positions in the workspace instead of just looking for the precise time difference of the arrived signals. The highest likelihood determines the target presence 9 The likelihood distribution can be described with Bayes' rule that the posterior probability of a source at location L is expressed as [9]: P(Xl,X2,X3,X 4 [L,s)P(L,s) P(L,slxl,x2,x3,x4) = P(Xl,X2,X3,X 4)
where |
544
N - 1
X
VE
(16)
where 3
4
rt0 +W
- Z Z J o_wXi(t + r )xj(t + rj)clt i=lj=i+l
17)
is the cross-correlation of all possible pairs of signals with the calculated time delay z", and 4 rt0 +W 2 V E - i ~ = l J t o _ w X i (t +
ri)dt
(18)
(12)
In the simplified condition, the signal model at the i th sensor is assumed as:
xi(t ) = hi(t ) | s(t - r i) + ni(t )
-VC-
(13)
denotes the temporal convolution between
is a constant value representing the summation of signal energy over all the channels and does not affect the final result. It should be noted that the MLE used here is based on the assumption of non-dispersive signals. As discussed above, CWT can provide the timefrequency representation of a signal. At individual scales or frequencies, CWT can be understood as
band-pass filters that extract narrowband information from signals. Due to the frequency-velocity dependency of flexural waves, CWT is effective in dealing with different frequencies separately. Although the arrival time at a particular group velocity can be extracted at the corresponding scale, it was found to be unreliable because the assumption of two unit harmonic waves does not hold. Nevertheless, any individual scales of CWT can be regarded as narrowband signals with a central frequency co0 . One main feature of the crosscorrelation of narrowband signals is the presence of distinct local peaks with time intervals equal to the period of the central frequency. One of the local peaks can represent the group delay at a specific frequency bandwidth. Fig. 3 illustrates a typical example of CWT of the vibration signals from two channels. The correlation at one scale is shown in the lower part of Fig. 3. In this case, the phase shifts between frequencies are assumed to be small.
Signal of channel I
0.2
5. Experimentation 5.1. The three tested methods MLE with cross-correlation. This method (defined in Eq.16) is suitable for non-dispersive waves. In practice, a digital high-pass filter is applied before the cross-correlation. MLE with cross-correlation in CWT domain. This is similar to the above approach, but crosscorrelations are computed in the CWT domain on some specific scales. Another difference is that the time delays Z" defined in Eq. 15 are extracted from the group velocities. MLE with peak extraction in CWT domain. This method uses the maximum value after CWT as the indication of the arrival time at the corresponding group velocity directly. This is similar to the method of beam-forming [10], which computes the energy of the back-propagated signals; instead, the maximum value of the summed signals is taken as the probability of target presence.
Signal of channel 2
5.2. False alarm removal
"0'02 -0 04!
~
{
-0.1 -0.2[
,-uu 406 BOO 800 0 200 400 8]0 800 Wavelet transform of channel I at frequency 1454Hz Wavelet transform of channel 2 at frequency 1454Hz
In noisy environments (e.g. doors slamming, people shouting), the system may detect unwanted ambient signals. It is possible to remove these false alarms by looking at the 'consistency' of the detected target position. The position consistency is defined as the product of the ratios r
-0.
-0.2
-0,2 . . . . 0 200 400 600 8130 Time,frequency representat on of channel t
0.4 0 200 400 600 800 Time,frequency representation of channel 2
l,, pair of sensors
(i, j),
- max(Cu ) for the Co(to.)
where max(C0. ) is the
maximum of the cross-correlation function C o. and C o. ('c o.) is the value of this function for the time lag 100 200 300 400 500 600 700
100 200 300 400 500 600 700
Cross-correlation of wavelet transform at frequency 1454~ 1 , .
/'# corresponding to the estimated target position (x, y). Therefore, P o s i t i o n C o n s i s t e n c y - N r~,/ 9 i,j
o .....
o
J~J~
6oo
WW", ........
1000
1600
Fig. 3 Wavelet transform of typical signals from two channels. The graph in the lower part is the crosscorrelation of the signals CWT at a frequency of 1454Hz.
A large value of this function indicates a high probability that a received signal is a false alarm. Thus, a simple threshold can be applied to classify the consistency for identifying a real tap. 5.3. Experimental hardware The experiments were carried out on a thin Medium Density Fibreboard (MDF) board of dimensions of 1200x900x6 (ram3). The sensors are located at the four corners of the central
545
experimental area of 600x400 (mm2).
5. 4. Velocity determination When an acoustic source position is known, the wave propagation velocity can be determined using the following expression:
Adl,i
ci=~,
Atl,i
i-2,3,4
With CWT, the group velocities calculated for each frequency of individually.
(19)
can be interest
5.5. Comparative results The experiments were carried out with the source positioned at (40, 30). Figures 4, 5, and 6 show typical results for the three methods. A brighter region indicates a higher probability of the target presence. The experiments have shown that the first method using cross-correlation (Fig. 4.) and the second method using cross-correlation in CWT (Fig. 5) have similarly good performance. However, the third method produced a more blurred image (Fig. 6). The first method can be further improved with a high-pass filter. Cross-correlation in the CWT domain was found sensitive to specific frequencies when different types of objects were used to produce impacts. It is also a method of high computational demand. However, the main advantage of the second and the third methods is their ability to separate frequencies of dispersive waves with CWT for a precise localisation. Compared with the third method, the second method of MLE with crosscorrelation in the CWT domain has shown greater reliability.
5 10 15 E 2O u 25 3O 35 4O
10
20
30
40
50
60
crn
Fig. 6 Example of MLE with peak extraction in CWT. The source is at (40, 30).
6. Conclusion
This paper has discussed two conventional TDOA-based methods for the localisation of an impulsive source. Taking into consideration the frequency-velocity dispersive property of Lamb waves, CWT has been applied in order to deal with narrower band signals in different bandwidths. Maximum likelihood estimation was used to overcome problems of ambiguities of precise time lag determination. Finally, three localisation methods have been proposed and evaluated experimentally.
A c kn owl edge me n ts
This work was financed by the European FP6 IST Project "Tangible Acoustic Interfaces for Computer-Human Interaction (TAI-CHI)". The support of the European Commission is gratefully acknowledged. The MEC is the coordinator of the I'PROMS Network of Excellence being funded by the EC under its FP6 Programme.
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References [ 1] Pham D T, A1-Kutubi M, Ji Z, Yang M, Wang Z, and Catheline S. Tangible acoustic interface approaches. Proceedings of IPROMS 2005 Virtual Conference, Elsevier, Oxford, July, 2005, pp 497-502. [2] Rolshofen W, Yang M, and Wang Z, Acoustic holography in solids for computer-human interaction. Proceedings of IPROMS 2005 Virtual Conference, Elsevier, Oxford, July, 2005, pp 475-480. [3] Crevoisier A and Polotti P. Tangible acoustic interfaces and their applications for the design of new musical instruments. NIME, 2005, pp 97-100. [4] Camurri A, Canepa C, Drioli C, Massari A, Mazzarino B, and Volpe G. Multimodal and cross-modal processing in interactive systems based on tangible acoustic interfaces. Proceedings of International Conference sound and music computing, 2005. [5] Checka N. A system for tracking and characterising acoustic impacts on large interactive surfaces. MS Thesis, MIT, 2001. [6] www.i-vibrations.com [7] Special issue on time delay estimation. IEEE Transactions on Signal Processing, vol. 29(3), 1981. [8] Jeong H and Jang Y-S. Fracture source location in thin plates using the Wavelet transform of dispersive waves. IEEE Transactions on Ferroelectrics and Frequency control, vol. 47(3), 2000. [9] Birchfield S T and Gillmor D K. Fast Bayesian acoustic localization, IEEE International Conference on Acoustic, Speech, and Signal Processing, 2002. [10] Chen J C, Kung Y, and Hudson R E. Source localization and beamforming. IEEE Signal Processing Magazine, vol. 19(2), 2002.
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Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Modelling elastic wave propagation in thin plates D. Rovetta, A. Sarti, S. Tubaro, G. Colombo DEI- Politecnico di Milano, Piazza Leonardo Da Vinci 32, 20133 Milano, Italy
Abstract In this work we propose an in-depth study of elastic wave propagation in thin plates, based on the theory of Viktorov. We show that at the frequency range of interest and for modest plate thicknesses, the only waves that can be excited and propagate in the structure are guided waves (also called Lamb waves). As the elastic properties of the panel and the finger touch signature are usually unknown, therefore we propose two different methods for estimating them through simple experimental procedures (calibration). The first is an active method based on the use of a transducer, while the second one is a passive original method which infers the elastic properties of the board from the information given by a single tactile interaction. The obtained estimates are then used to simulate the propagation in the boards. Our approach is to implement the general solution of the elastic wave equation for infinite plates, and introduce the boundary conditions afterwards using a real-time beam tracer. We finally prove the effectiveness ofthe approach by comparing the predicted response of a finger touch with the measured one on a MDF (Medium Density Fiberboard) plate, showing how the active and the passive calibration procedures give comparable results.
Keywords: elastic waveguides, Lamb waves, tactile interaction.
1. Introduction We refer to a tactile interaction on a thin panel where the vibrational signals transmitted by a finger touch (the source) and propagated like elastic waves in the board, are acquired by some receivers. In this paper we show how to calculate the received signals, knowing the elastic properties of the board and the exact position of the touch (modelling problem). To achieve this purpose the study of the elastic wave propagation in plates is required: at the frequencies of our interest and for small thicknesses of the panels, the only waves that can be excited and propagate in the medium are guided waves (also called Lamb waves). The propagation follows the theory of Viktorov [ 1]. Moreover, in our experiments, the elastic properties of the panel and the finger touch signature
548
are unknown and so we have to face the problem to estimate them from the data. We propose two different techniques for active (based on signal injection) and passive (based on finger touch) evaluation of the elastic parameters of the board on different materials. Finally the estimates of the plate elastic properties and of the transmitted signature are used to simulate the propagation in the boards. Our approach is to implement the general solution of the elastic wave equation for infinite plates and to introduce the boundary conditions afterwards using a real-time beam tracer [2-3]. The experimental equipment (Fig. 1) is made up of a panel and an acquiring system, composed by some receivers (sensors), a semi-professional audio card and aPC. We assume that the boards used for the tests are characterized by homogeneity, isotropy and relatively
thin, layered, plate-like geometry. We found different common materials with these requirements, for example plexiglass (PLX) or Medium Density Fiberboard (MDF). This latter is a composite wood product similar to particleboard, made up of wood waste fibers glued together with resin, heat, and pressure. They exhibit large attenuation coefficient in the high-frequency range. In this paper we show results for a MDF panel, whose dimensions are 1=152 cm, w = 106 cm, t=0.5 cm and whose elastic properties are unknown.
t~PgNSI~ ,OF~BU ! ~._"!_..P~LATIvE T0,,3K!,!~
Sens igivity .(.at. 1 Ktlz~_
Fig. 3. Frequency response of the BU-1771 receivers.
Fig. 1. The experimental equipment of the tests. The receivers are used to acquire the signals transmitted by the finger touch and propagated in the board. Several kinds of sensors have been tested, principally piezoelectric devices and the best results have been achieved with the BU-1771 (Fig. 2). i!i!iNi!~i~i~!~i~ii~!i~il~i~i~i~i~i~i~i~iiiiiiiiiiiiii~i~i~!i~ii!iii!ii~i~iiii~iiiiiiii!i!i!i!~8!iZ 4i~i~i!~ii~ iiiiiiii!ii | i~iiiiiiili
'~iiii!i!i'~i'~':i::::::::::::::::::::::::::::::::::::::::::::::::
N~!i~i~iiii~ii'~i~i ..
:~:'!iii,'!i
: ::
: :::::'::::: ::
ii
Fig. 2. Piezo-sensor BU-1771 applied to the MDF board. They are based on a piezoelectric transducer followed by a FET, which ensures low output impedance and high output level. Moreover, their small dimensions make them suitable for this application. These receivers are sensitive only to displacements along the z-axis (normal to the plate) and have a large bandwidth (10 kHz). Their frequency response is shown in Fig. 3 where it is evident that the acquired signals have to be low-pass filtered at a frequency ranging from 5 kHz to 8 kHz to compensate the undesired effects of the non-linearity.
The sensors are coupled to the plate surface using double sided adhesive tape and are connected to the audio card using the three wires method (signalpower supply-ground). All the tests and measuring campaign are taken using the standard semi-professional M-Audio Delta 44 audio card with an external box for analog connections (all audio connectors are 1/4" jacks). It is modified to have the power supply for the microphones on the input sockets. In fact the balanced inputs are transformed in unbalanced, using the ring contact to carry the 5 V power supply. The PC used for the signal processing is a Pentium IV (3GHz, 1GB RAM, Win2003 Server Operating System).
2. The general solution of elastic wave equation for infinite plates Nicholson at al. [4] analyzed the elastic wave propagation in panels considering two different types of geometry: a semi-infinite solid half-space with the transducers on the free surface, and infinite solid plates, with thickness of 10 and 5 ram, with the transducers on the upper free surface. In the semi-infinite solid half-space the situation is clear: we can recognize the longitudinal (P- wave) and shear (S-wave) wavefronts, the Rayleigh wave localized close to the surface and the lateral or head wave, shown as the component of the longitudinal wave propagating at, and parallel to, the surface [5]. In the infinite solid plate with thickness of 10 rnrn the situation is much more complex, due to the presence of guided waves generated by the interaction of the different wavefronts with the surfaces of the plate.
549
Finally for infinite solid plates with smaller thicknesses, only guided wave arrivals are visible. Tucker [6] showed that the previous behaviour can be found in other wood-based composite panels, like MDF. The boards are "perceived" by the wave as homogeneous (through the thickness), orthotropic plates as long as the wavelength remains much larger than the panel thickness. The "perception" of the material greatly reduces the complexity of the equations needed to describe the wave propagation [ 1]. Plate wave propagation occurs when the wavelength, 2, is much greater than the thickness, t, of the plate (2 >> t). Some authors recommend the wavelength be ten times greater than the thickness (A > 10 t), while others propose less stringent wavelength requirements [6]. The remaining dimensions (length, I and width, w) of the plate must be much greater than the wavelength. Wavelength is calculated from the phase velocity, v, and the frequency, f as: 2= v f
(1)
In our experiments the requirements 2 >> t, 2 << l and A << w are always satisfied and thus a plate wave propagation occurs. The theory of Viktorov, governing the wave propagation in thin plates (also commonly termed Lamb or guided wave), is documented in [ 1] and in this section we only consider the main concepts. An ideal bulk wave is a spherical disturbance that originates from a point source and propagates through an infinite medium. A plate wave may be thought as a two-dimensional representation of a bulk wave bounded by an upper and a lower surface. There are two distinct types of plate waves: symmetric (s) and antisymmetric (a), each of which have an infinite number of modes (So, sl, s2..... sn and ao, al, a2, ..., an) at higher frequencies. The lowest modes are also called extensional (so) and flexural (ao) Lamb modes. Plate waves are dispersive by nature, meaning that different frequencies travel at different speeds (phase velocities). The phase velocity v is the fundamental characteristic of the Lamb wave and once it is known we can determinate the wave number and calculate the stresses and displacements at any point of the plate, v can be found by numerically solving the following characteristic equations [1 ]. If t = 2d is the thickness of the plate, k/~ is the S-wave number, va and vp are the P-wave and S-wave
550
velocities and is, a is the phase velocity of the Lamb waves, then the characteristic equation for the symmetrical modes is'
tg(d41_ ~2 ) 4;2 41_ ;2s ~/~2 _ (2 :0 tg( ~ : 2 --C 2 )+ (2(s2__ 1)2
(2)
and for the antisymmetrical modes:
tg(d~
!
-- ( a ) +
4(a~l--
where d : k~d , (s2,a:
( 2 ~ : 2 -- ( 2
2
?
Vs,a
=0
(3)
2
and ~:2_ m v~ .~ 9
Vo~
Many authors have performed calculations of the phase velocities and their dependence on the plate thickness and frequency (dispersion curves). To achieve this purpose the elastic Properties of the medium (in our tests the velocities v~ and v/~ of the P-wave and S-wave in the board) are necessary. In the next section we show how to estimate these properties and then how to calculate the dispersion curves for the So and ao main modes. Extensional and flexural modes should be distinguished in the same received signal but it can be proved that the extensional mode does not propagate below certain frequencies. Rose and Tucker [6] offer an explanation: as the frequency-thickness product increases, the structure of a Lamb wave changes. For extensional waves, the out-of-plane displacement increases with the frequency, for a given thickness. As normal contact transducers are only sensitive to out-ofplane motion, the phenomenon observed is explained and the flexural mode may then be easily isolated at low frequencies.
3. Estimation of the panel elastic properties" active method (AM) vs. passive method (PM)
3.1. Active method The panels here analyzed are characterized by large attenuation coefficient in the high-frequency range. This attenuation forces the method described in this section into the low-frequency, long-wavelength region, where the prominent wave propagation modes are plate waves.
A transducer T converts electrical energy into mechanical energy, propagated through a thin panel in form of elastic waves. Two sensors Rx~ and Rx2, placed a known distance apart, can be used to receive the signals associated to these waves, to calculate their phase difference and thus to measure their phase velocity (Fig. 4). Then different phase velocity observations can be used to estimate the elastic properties (va and v/~) of the panel. T
panel xo
Rx 1
Rx 2
T
T
2000
&x
The transducer T generates sinusoidal bursts ranging from 1500 H z t o 5 0 0 0 H z for the MDF, with a step of 250 Hz. At these frequencies only ao Lamb mode can be excited: extensional plate waves are negligible, due to their small out-of-plane motion at lower frequencies. A short number of sinusoidal bursts (4 to 6 cycles) aids in reducing the time duration of each wave mode. Moreover the observed phase velocities are obtained by averaging several realizations (also varying Ax and Xo) to reduce the effect of noise. For each frequency, the difference between the theoretical vo, c,l and the experimental V,,oh~,phase velocities is computed and optimal values for P - w a v e and S - w a v e velocities, v~ and v~, can be obtained by minimizing the data residual Rj:
- v .... ,(v~,v~) ~
>~
1 ;t
Fig. 4. Technique to calculate the dispersion curves of a thin panel with the active method.
R.(v~.,v~)-IV..o~
X 104
(4)
The 2D objective function Rd is shown for different values o f v a a n d v~ in Fig. 5. The minimization of Rd can be achieved by using a grid search technique but it is difficult due to its flatness: an additional constraint is necessary to reach a solution with a good accuracy. As suggested by Tucker and Viktorov [6] we can constraint the solution of the minimization problem by fixing the value of the Poisson ratio v of the board.
2500
3000
3500
4000 4500 V [m/s] c~
5000
5500
6000
Fig. 5. Data residual as a function of the P-wave and S-wave velocities for the MDF. A confidence interval for v in the MDF is obtained by using the commercial software CES selector 4.5: v - 0.2 + 0.3. With a fix value of the Poisson ratio, the data residual becomes a 1D function. It is shown in Fig. 6 versus the P - w a v e velocity for three meaningful values of v. 3.5f
5
xlO
/
v=0.3 9
-
-
v=0.2
v=0.25
2.5
-o rr 1.5
1
i
0.5
1000
i
2000
3000
4000
v 0~ [m/s]
r
5000
6000
Fig. 6. Data residual as a function ofthe P-wave velocity for the MDF. The minimization problem is now simple and choosing v = 0.25 the estimated elastic properties of the MDF board are va = 2 9 0 0 m/s and vfl = 1600 m/s. Using the estimated elastic properties of the boards and the plate wave propagation theory, the phase velocity at different frequencies can be computed ( c a l c u l a t e d data) for different materials. Observed data and calculated data for MDF are shown in the range of frequencies of the measurements in Fig. 7.
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very good agreement in a wide range of frequencies.
360-. 340
o
-tObservedData
I
.
.
.
.
.
-]
o.iI
320
0.05
300
~'
SI 0 I
80
-0.05 4
>'~ 260 240
i
0.1I
220
0.05
200
$2 0I
180 1500
2000
2500
3000
f[Hz]
3500
4000
4500
5000
Fig. 7. Phase velocities of the ao mode in the MDF board: measured and calculated data with the active method (AM). The good agreement between the observed and the computed curves confirms the accuracy of the solution of the optimization problem.
8
i
,
6
8
t Is]
10
12
14
16
18 x 10-3
r
,
10
12
r
-0.05
I 2
0
I
4
t Is]
14
16
18 x 10-3
Fig. 8. Signals induced by a finger touch and acquired by two different receivers. The first arrivals are well visible. 340
t o
320
3.2. P a s s i v e m e t h o d
6
,
t
,
3000
3500
Observed Data ]
300 280
The passive method is based on the frequency band subdivision of the spectrum of the acquired signals induced by a tactile interaction. At the central frequency of each band, the phase constant value ofthe signals acquired by some receivers (at least two), located at known positions, are computed. An estimation of the elastic properties of the board is then achieved with the same procedure described for the active method. In the practice the passive calibration procedure is complicated by the difficult problem of the first arrival extraction, which is solved in the time domain by using a proper Tukey window. In Fig. 8 we show the signals induced by a finger touch (XT = 91 cm, y v = 45.5 c m ) acquired bytwo receivers (xR1 = 1 3 6 cm, YRJ = 23 cm; XR2 = 16 cm, Yn2 = 83 cm). The corresponding first arrivals are well visible. The estimated elastic properties of the MDF board are va = 2 7 0 0 m/s and vp = 1 6 0 0 m/s. Observed data and calculated data for MDF are shown in the range of frequencies of the measurements in Fig. 9. The boundary effects, due to the time windowing, do not affect the accuracy of the solution. 3.3. C o m p a r i s o n s
We now compare the results of the MDF elastic properties estimation obtained with the active and passive calibrations. As shown in Fig. 10 there is a
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~- 26o 24O 220 200 t 180 1
6~)1500
2000
2500
f [Hz]
Fig. 9. Phase velocities of the ao mode in the MDF board: measured and calculated data with the passive method (PM). 4. Estimation of the excitation signal In this section we propose a simple scheme to estimate the finger touch signature. Let us consider a receiver Rx, located at the centre of the board, and the corresponding acquired signal s. This latter is not affected by problems of overlap between the direct arrival and the signals reflected from the borders of the panel ( e d g e reflections). If the position of the touch (xv, yT) is known, the transmitted signature can be estimated by i n v e r s e p r o p a g a t i n g s of the exact distance between source and receiver. The inverse propagation is obtained by filtering and the filter is designed in the frequency domain by using the knowledge of the plate wave theory and of the estimated elastic properties of the panel. In an experiment on a MDF panel with the
thickness of 5mm, the estimated signature of the finger touch, after inverse propagating the signal s, is shown in Fig. 11. It is impulsive with a time duration of about 6 ms.
--
Estimated Wavelet
0.2 0.15 0.1
320
I
300
>k.
I
Calculated Data wilh A M Calculated Data wilh P M
._ Q..
<E
0.05 0
-0.05 Z
26O >~ 240
J
220
./
J
/
-0.1
J
-0.15 -0.2 0
2
4
6
8
10
12
14
16
18
20
t[ms]
Fig. 11. Estimated signature of the finger touch in MDF. 1%00
2000
2500
3000
3500
4000
f [nz]
1
0.9
Fig. 10. Phase velocities of the ao mode in the MDF board: simulated data with both active (AM) and passive (PM) methods.
0.8 0.7 E" 0.6 0.5
5. Prediction of the board response
0.4 0.3
The knowledge of the transmitted signature and of the propagation model allows the calculation of the direct arrivals acquired by all the receivers (simulated or recalculated data): the transmitted signature is forward propagated of the exact distance between source and receivers. As we want to simulate the complete elastic wave propagation in the plate, in order to compare the observed response with the calculated one, we have to take into account the edge reflections. A fast beam tracer [2, 3] can be used to achieve this purpose. We can therefore compute the complete board response as the result of the sum of the signals due to the direct arrival and to the most energetic reflected rays. Let us consider an experiment, whose configuration is shown in Fig. 12. We calculate the direct arrival, corresponding to the ray directly linking the source with the receiver (bold line) and the first four delayed arrivals, corresponding to the path of the rays reflected only once by the borders of the plate and linking the source with the receiver (black lines).
0.2 0.1
0
0
0.2
0.4
0.6
0.8
1
1.2
x-axis Ira]
Fig. 12. Direct arrival and the first four reflected rays.
0.3 - -
0.25
Observed Data Recalculated Data - A M Recalculated D a t a - P M
0.2 0.15 0.1 0.05 0 -0.05 -0.1 -0.15 -0.2
0
5
10
15
t [ms]
Fig. 13. Comparison of the observations with the simulated data only considering the direct arrival, with both the active (AM) and the passive (PM) methods.
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0.3
0.3 Observed Data t Recalculated Data - AM Recalculated Data PM
0.25 [.
0'2t.
0.15 -o
Recalculated D a t a - A M Recalculated Data - PM
0.2
0.1 0.05
o
o
-0.05
-0.05
-o.1
-0.1
-0.15
-0.15
-0.2
t --(3bservedData
0.15
0.1" 0.05
<
,
0.25
0
5
t [ms]
10
15
Fig. 14. Comparison of the observations with the simulated data considering the direct arrival and the first reflected ray, with both the active (AM) and the passive (PM) methods.
-0.2
0
5
10
t [ms]
15
Fig. 16. Comparison of the observations with the simulated data considering the direct arrival and the first three reflected rays, with both the active (AM) and the passive (PM) methods.
0.3 '
0.25
O!::[~udtDec~tData _ A M
....................... Recalculated
0.2
0.3
D a t a - PM
0.15
0.2
o.1
0.15
0.05 <
- -
0.25
()bserved Data Recalculated Data A M Recalculated Data - PM -
o.1
0 -~D,,,,"~,,=~
0.05
-0.05
-0.05
-0.1 -0.15
-0.1
-0.2
-0.15
0
5
t [ms]
10
15
Fig. 15. Comparison of the observations with the simulated data considering the direct arrival and the first two reflected rays, with both the active (AM) and the passive (PM) methods. First we compare the observations with the simulated data only considering the direct arrival (Fig. 13). There is a good agreement before the arrival of the reflected waves (about 6 ms). Moreover there are not any significant differences between the board response calculated with the active calibration and the one obtained with the passive calibration. The more reflected rays we consider in the computation of the simulated signal response (Figs. 14-17), the more the agreement between observations and calculated data is good. Finally the active and passive calibration procedures give comparable results.
554
-0.2
0
5
t [ms]
10
15
Fig. 17. Comparison of the observations with the simulated data considering the direct arrival and the first four reflected rays, with both the active (AM) and the passive (PM) methods. 6. Conclusions A study of the elastic wave propagation in thin plates has been conducted, following the formulation of Viktorov. Estimates of the panel elastic properties and of the signature transmitted by a finger touch allow to simulate the propagation in the panels. The reflections from the borders of the panel can be also considered in the modelling by using a beam tracer. We compared the predictions ofa MDF plate response with the observation, showing a good agreement on a wide range of situations and proving that the active and the passive calibration procedures
give comparable results. This allows to estimate the elastic properties of a plate with the passive method, which is faster and cheaper, without any loss in the accuracy of the board response predictions. References
[1] I. A. Viktorov, Rayleigh andLamb Waves, Plenum Press, New York, 1967. [2] F. Antonacci, M. Foco, A. Sarti, S. Tubaro, "Fast Modeling of Acustic Reflections and Diffraction in Complex Environments Using Visibility Diagrams", Proc. DAFX-02, Hamburg, Germany, September 2628, 2002. [3] M. Foco, P. Polotti, A. Sarti, S. Tubaro, "Sound Spatialization Based on Fast Beam Tracing in the Dual Space", Proc. DAFX-02, Hamburg, Germany, Sept. 26-28, 2002. [4] Nicholson et al., "Guided ultrasonic waves in long bones: modelling, experiment and in vivo application", lOP, 2002. [5] K. Aki, P. G. Richards, Quantitative Seismology, W. H. Freeman and Company, San Francisco, 1980. [6] B. J. Tucker, Ultrasonic Plate Waves in WoodBased Composite Panels, Washington State University, Department of Civil and Environmental Engineering, 2001.
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Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Pattern Matching for Tangible Acoustic Interfaces D T Pham, M A1-Kutubi, M Yang, Z Wang, Z Ji Manufacturing Engineering Centre, Cardiff University, Cardiff CF24 3AA, UK
Abstract In this paper, a coherence function technique is introduced for Tangible Acoustic Interfaces (TAI) using Location Template Matching (LTM) approach. A simple workflow is proposed for the purpose of evaluating performance of different TAI techniques and investigating the effect of various parameters based experimental data. Performance analysis shows that the method of magnitude squared coherence outperforms traditional method of using time-domain cross-correlation in terms of both resolution and reliability.
the the on the
Keywords: Tangible acoustic interface, Computer interface, Template matching
1. Introduction Interfaces in current computer human interaction technology are dedicated devices made from integrated elements for location sensing. These devices can be categorised as passive and active. A passive device requires no external energy source. Examples include resistive touch screens, where the surface is layered with specific touch sensitive materials reacting to any physical impact on it. In active devices, like touch screens using surface acoustic waves, the surface is excited by an ultrasonic source, to be used as a location reference for changes in the waveform when the surface is disturbed by contact. In the last two decades, work has been carried out to implement audio and video analysis for computer human interaction [1]. There has been some recent research on employing acoustic vibration analysis to localise an impact in solid objects [2, 3]. This technique has the potential of allowing the conversion of virtually any solid object into an interactive surface. This would provide freedom in choosing the material, location and size of the interactive object as well as
556
performing the physical interaction naturally without the need for any specific hand held device. With acoustic signal analysis, there are two main approaches for localising impacts in solid objects. One estimates the physical coordinates of the source from the Time Difference of Arrivals (TDOA) [4] using either cross correlation or rising edge detection [5, 6]. The other approach is Location Template Matching (LTM), which finds the index mapped to a predefined location rather than the actual coordinates [7,8]. The TDOA approach has the advantage of estimating the source location at any point on the surface. It requires information on the wave velocity and sensor geometry using a minimum of three sensors. However, TDOA works best in a uniform medium with minimal reverberation. TDOA has been extensively investigated particularly for in-air applications such as in a video conferencing room, where there is a need to locate the speaker and direct the camera at him. On the other hand, although the LTM approach works for limited predefined locations and requires registering each location before it can be used, it has the unique advantage of being able to work in
non-homogeneous media of any shape, using a minimum of one sensor. In the following sections, the LTM approach is explained and a new matching technique is proposed. A workflow for performance analysis using experimental data is introduced. The workflow is used here as a means to compare the performance of different techniques in terms of resolution and reliability and to investigate the performance achievable with multiple sensors.
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Localisation in the LTM approach is accomplished by creating a library of templates of signals generated by exciting each point of interest on the interactive surface with an impact, for example, a finger tap, and mapping the different templates to the corresponding points. During operation, an impact is localised by finding the index of the template signal that best matches the pattern of the signal received under test. A TAI system employing the LTM approach consists of an interactive surface which can be on any solid object such as glass, metal or wood, a sensor or sensors, normally piezoelectric connected to signal conditioning hardware, with a data acquisition card and a PC. The localisation software implements the matching algorithm to identify the location of the impact on the interactive object. A matching technique commonly used to find the similarity between two signal patterns is crosscorrelation. This has had various applications, including image localisation [9] and medical diagnosis [10]. In LTM localisation, crosscorrelation can be interpreted as a focusing operation in time-reversal theory [1 1]. The crosscorrelation coefficient of two signals s(t) and g ( t ) is given by
signals
s(t)
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2.1. Coherent Spectral Analysis The coherence function is another technique used in acoustics for signal analysis in the frequency domain [12]. The coherence function quantifies the linear relationship between two signals at frequency 05. The magnitude squared coherence between signals s(t) and g(t)is given by 2
and Ag are the auto-correlation of and g ( t ) respectively at lag zero.
- ....................... ,................... ........ ,................... ,~
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R (co)R==(col
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possible to rank the similarity of the two timedomain signal patterns.
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where
Rsg ((0) is the cross-spectral density and
Rss (co) and Rgg (co) are the auto-spectral density functions of s(t) and g(t)respectively. Equation 2 produces a real number between 0 and 1 that represents the degree of matching between the two signal patterns in the frequency domain. Rather than detecting the peak of F , the mean
squared coherence with the normalised crosscorrelation using the same data. The same workflow is adopted to investigate the effect of using multiple channels, achievable resolution and reliability. It can also be used to investigate the influence of various factors such as different signal filtering methods or a different sensor type.
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value of 7"2 is computed and used as matching criterion:
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where A co is the range of frequencies for which the
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power spectrum is above a threshold level. A typical example of the cross-correlation coefficient and the coherence between two signals generated by nail clicks at the same location on a glass surface is presented in fig. 1. When the signal is multi-dimensional (for example, a signal made up of components picked up by different sensors), the best matched pattern is identified by determining the average of the matching results for the different signal components.
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Fig. 2. Experimental layout for data generation
3. Performance Analysis A problem arises when evaluating algorithms and examining different parameters using experimental data when the data changes for each evaluation. With experiments that involve generating signals by applying natural impacts like finger taps and nail clicks, it is not possible to reproduce the same data. Using different data may result in incorrectly biased evaluations. It is, for example, difficult to know if a location was incorrectly detected as a result of a deficient algorithm or because of different signal strengths or a shifted impact location. It is therefore important to use the same database for each evaluation or comparison. Also, for reliable results, it is useful to employ the average from multiple sensor channels using a large number of data samples. A simple but practical evaluation workflow has been devised to compare the performance of the mean magnitude
558
The proposed evaluation process operates as follows. Two signal databases are created with signals obtained from regulated impacts, one for generating the templates and the other for testing the method. For better reliability, the template is formed from the expected value of the signal at each location which is found from the ensemble average of five impacts. With reference to the layout shown in Fig. 2, M impacts are applied sequentially at each location
Lj
(j=l to J). Each
received signal is mapped to the corresponding source location. The process is repeated to generate the database for nail clicks in the same manner. The evaluation workflow is performed as illustrated in Fig. 3 by applying the assigned algorithm to match each signal gnm from the test data with all signals s j in the template data. The location is estimated from the template signal index
9
Jo associated with the signal pair
and s/,,that produces the maximum degree of matching above a threshold level. Incorrect location estimation is detected if no is different from Jo. After this process has been completed for all test signals, the 'confidence' is calculated as the percentage of all test impacts that are correctly localised. g .....
be repeated for new data extracted from the available data by spatially sampling the latter to include signals from locations at multiple distances of dx and dv denoted in Fig. 2. For example, if the resolution (dx, dy) equals (20mm,20mm) in the available data, then new data can be formed by skipping one location in each direction making (d~,dy) equal to (40mm,40mm) without repeating the experiment. This is important to have an unbiased comparison as explained earlier. The reliability of the algorithm, that is its ability to localise impacts of types different from those generated for the template, can also be evaluated using the same workflow. This is done by measuring confidence using the same test data but with a template generated from different types of impacts or vice versa.
4. Experimental Results
u]
Fig. 3. General performance evaluation workflow With this workflow and using the same data collected experimentally, confidence is measured for each algorithm tested and for each individual channel. The same process can be repeated for multi-channel estimation and for any combination of input channels. To investigate the resolution at which confidence is satisfactorily high, the workflow can
Experiments were carried out on a 700x600 mm 2 glass sheet of 4mm in thickness as shown in Fig. 4. The sheet had four piezoelectric sensors attached near the edge at arbitrary locations and connected to a four channel data acquisition card to receive sensor data simultaneously once triggered by an impact. The impact locations are marked in the centre with a mesh of 12x9 squares each of dimensions 20mmx20mm that fit a fingertip. Impacts were applied by tapping 10 times at each location sequentially from top left to bottom right, going through all the 108 locations producing 4320 signals from all four channels. This signal database was used for the evaluation according to the workflow in Fig. 3. From the results presented in table l a, the coherent spectral algorithm with a single sensor, say sensor c, correctly localised 939 impacts, which is 86.9 % of the total 1080 impacts versus 75% using the cross-correlation algorithm, a 17 % improvement on average per channel. To examine the effect of employing multi-sensor decision estimation, the four input channels were used with both algorithms. Confidence attained was 98% and 92% for the proposed and the conventional algorithms respectively. Similar improvement was obtained when nail click impacts were tested instead of finger tap impacts, as can be seen in table lb. A comparison between tables l a and l b indicates that nail clicks were better localised than finger taps. This can be due the contribution of the
559
higher frequency components, which is the significant difference between the two types of impacts as can be seen from a sample in Fig. 5, showing the signals for a finger tap and nail click at the same location on the glass surface.
sensor a
sensor b
sensor c
sensor d
average
correct
correct
correct
correct
% of correct
wrong 824
wrong 710
wrong 810
wrong 803
256 936
370 895
270 939
277 926
144
185
141
154
sensor a
sensor b
sensor c
sensor d
Averag e
correct
correct
correct
correct
% of correct
wrong 891
wrong 823
wrong 879
wrong 803
189 1036
257 978
201 1031
277 1025
44
102
49
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Algorithm Cross Correlation Coherent Spectral
72.8
85.5
(a)
Algorithm Cross Correlation Coherent Spectral
Figure 4. Glass sheet, sensors and locations marking used in the experiment.
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96.3
(b)
Table 1. Results oflocalising 1080 impacts at 12x9 locations at 20mm resolution for four channels, a) taps, b) nail clicks.
Figure 5. Signal samples o f - - nail click.
finger tap,
5. Conclusion
An example of testing different resolutions using the same data is given by selecting signals from the available database that are only corresponding to locations at 40mm apart from each other. At this resolution, both techniques achieved a similar performance of 92.5% on average with a single sensor. For the same resolution, the reliability of the algorithms was investigated with respect to the impact type. The template was replaced with another one generated from nail clicks while keeping the same test signals used earlier, which was generated from finger taps. Results show that 75.6% of the impacts were localised correctly using a coherent spectral technique while only 39% of locations were found correctly using the cross-correlation technique.
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A novel technique has been proposed for an LTM approach using coherent spectral analysis and a simple workflow introduced for evaluating the performance as a flexible means to examine various parameters. It has been shown that the proposed algorithm has achieved higher resolution and is more reliable under different types of impact compared to the conventional cross-correlation algorithm. The resolution of localisation can also be enhanced by employing multiple sensors with both techniques. Although the coherent spectral technique is computationally more expensive than the crosscorrelation technique, it provides an alternative to using multiple sensors for a comparable resolution.
Ac k n o w l e d g e me nts
This work was financed by the European FP6 IST Project "Tangible Acoustic Interfaces for Computer-Human Interaction (TAI-CHI)". The support of the European Commission is gratefully acknowledged. The MEC is the coordinator of the EC-funded FP6 I'PROMS NoE.
References
[1] Paradiso JA, Hsiao K, Strickon J, Lifton J and Adler A. Sensor systems for interactive surfaces. IBM Systems Journal, Vol.39, Nos.3&4, 2000, pp 892914. [2] Paradiso JA, Leo CK, Checka N and Hsiao K. Passive acoustic knock tacking for interactive windows. ACM CHI 2002 Conference, Minneapolis, Minnesota, 20-25 April 2002. [3] http://www.i-vibrations.com (last accessed 16 August 2006). [4] Checka N. A system for tracking and characterising acoustic impacts on large interactive surfaces. MS Thesis, MIT, 2001. [5] Ding Y, Reuben RL and Steel JA. A new method for waveform analysis for estimating AE wave arrival times using wavelet decomposition. NDT & E International, Vol.37, 2004, pp 279-290.
[6] Knapp CH and Carter GC. The generalized correlation method for estimation of time delay. IEEE Trans. Acoustic, Speech and Signal Processing, Vol.24, 1976, pp 320-327. [7] Fink M. In solid localization of finger impacts using acoustic time-reversal process. Applied Physics Letters 87, 204104, 2005. [8] Pham D T, A1-Kutubi M, Ji Z, Yang M, Wang Z, and Catheline S. Impact Localization Techniques for Tangible Acoustic Interfaces. Proceedings of IPROMS Virtual International Conference, 4-15 July 2005, Elsevier, Oxford, pp. 497-501. [9]O'Hagan R and Zelinsky A. Finger Track- A Robust and Real-Time Gesture Interface. Advanced Topics in Artificial Intelligence, Tenth Australian Joint Conference on Artificial Intelligence Proceedings, 475-484, Dec. 1997. [10]Bousseljot R and Kreiseler D. Waveform Recognition with 10,000 ECGs. IEEE Computers in Cardiology Proceedings, 24-27 S e p t 2000, Cambridge, MA, 331-334. [11]Fink M. Time-reversal mirrors. J. Phys. D: Appl. Phys. 26 (1993) UK, 1333-1350 [12] Khong, A W H, Naylor, P A. Stereophonic Acoustic Echo Cancellation Employing Selective-Tap Adaptive Algorithms. IEEE Transactions on Audio, Speech, and Language Processing, Vol. 14, No. 3, May 2006, pp. 785-796.
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Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Tracking Target Using Wideband Cox Comb Signals for Human Computer Interaction Y. Sun, T. Collins, L.Xiao Department of Electronic, Electrical and Computer Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
Abstract
This paper introduces a novel waveform-the wideband Cox comb waveform for the target tracking in air. It can provide an alternative estimation of the range and Doppler of the target as a new means for human computer interaction. The ambiguity function and matched filter are employed for the active signal analysis and processing. The simulation shows that the designed waveform, the cox comb waveform, can achieve a reliable result in terms of reverberation processing gain. The tracking system is combined time of flight technique and the Doppler tracking algorithm. The initial experimental results show that further requirement to improve the reverberation gain by the pulse design with the slow moving target.
1. Introduction
The next generation of human-computer interaction (HCI) is determined by a number of new contexts and challenges [ 1]. They are rooted in new, emerging technologies as well as in new application areas asking for new approaches and visions [1]. A common problem with the tangible devices, such as keyboards, mice, is the restriction of the mobility of the users into the certain area. The tangible acoustic sensing technology becomes an interesting subject to solve the problems for the human computer interaction recently [2]. This paper present a novel approach based on active Doppler processing to simultaneously track the range and the Doppler of the target. The relative Doppler shift A is defined as the ratio of the source relative velocity (vm) to the speed of sound (c). For a single transmitted frequency fo, the Doppler effect can be expressed as the frequency scaling
562
f ' = f 0 ( l + A) (1) This is often used as the approximation of the Doppler for narrow-band signals. The whole signal spectrum is translated by the same frequency as the carrier, such as the Doppler sensitive CW (continuous wave), applied in [3]. The major disadvantages of this approach are the poor range resolution of such pulses leading to poor reverberation processing with low Doppler targets [3]. For wideband signals, Doppler translates each frequency component by a different amount. The Doppler effect can be modelled as the complete time scaling (stretching or compressing) of the transmitted waveform, r(t) = s((1 + A)t) (2) The advantage of wideband systems is that they allow a larger processing interval which results in greater gain, better noise immunity and increased range resolution. Several new classes of pulse design have been proposed to provide a superior reverberation
processing to CW pulses, such as Newhall trains, sinusoidal frequency modulated pulses (SFM) and geometric comb waveforms [4, 5, 6, 7]. Through the theoretical comparison and the experimental verification, the geometric cox comb waveforms is the only one transmitted which could successfully resolve the range and velocity of the target without ambiguity [4,8], superior to the SFM (or a Newhall train). To summarize, the focus of this paper is to develop a novel wideband active acoustic approach to accurately estimate the range and the Doppler simultaneously. In this paper, section 2 presents the ambiguity function of the wideband signal for analysis and the matched filter theory for the Doppler estimation. The properties of the designed cox comb signal are discussed in Section 3. Section 4 provides details of the experimental results. Finally, conclusions are drawn in Section V.
2. Ambiguity function and matched filters
2.1 Ambiguity function
output of the matched filter gives a measure of how well the hypothesised signal (also known as the replica) matches the received signal as a function of a set of parameters, the range and velocity of the target. The matched filter is the optimum detector for a point-like fluctuating target of unknown range and Doppler [12]. Its response in time domain can be defined as the cross-ambiguity function 2'(~', r]), ifthe received signal plus noise is r(t)
Z(r, Jl)-
Where y(t) is the hypothesised signal as a function of time delay r and scale factor 1/. To estimate the Doppler shift in r(t), we must search in 11 to find the appropriate point of the signal matched filter output envelop. In practice, the search area can be constrained within the anticipated Doppler range, determined from the maximum relative velocity encountered and the sound speed in air. Figure 2 shows an illustrative example as a bank of discrete correlators (matched filter) with different Doppler-shifted replicas of the transmitted waveform.
The ambiguity function is widely used to estimate the performance of the transmitted pulse for the sonar systems. It provides a starting point for waveform synthesis. For narrow-band signals, the ambiguity function 2",,,('g',~)is a two-dimensional function of correlator output power against time delay r (related to the target range) and Doppler frequency shift (related to velocity) assuming that the Doppler shift is constant across the pulse spectrum [9]
/~,s"(r, O) -- ;~ S(t)S* (l Jr-~-j2Ka dl
(3)
The narrow-band approximation is inappropriate for many wideband sonars. The effect of target velocity cannot be approximated by a simple 'shift' in frequency. Therefore, the wideband ambiguity function is defined as a function of time delay r and Doppler scaling factor 1/[9]
x,
j[,(,),"
Where 11=(1+Vre/C)/( 1-%~/C).
<5)
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9
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~\ \
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9 9 Corr~-htor3
'\
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~-~ ~teciol
,j.f
../'
Fig 1 Matched filter structure The range resolution of a pulse is defined as the 3dB width of the main-lobe ofautocorrelation function of the transmitted signal. The Doppler resolution of a pulse is defined as the complete ambiguity width at3dB along the Doppler axis. The range and Doppler resolution of this technique depends on 1) received signal-to-noise ratio 2) time-bandwidth product of the signal (BT) 3) shape of the signal's ambiguity function
2.2 Matched filters The Matched filtering [10, 11] is central to active sonar signal processing. Fundamentally, the matched filter is a correlator which compares the received signal with a hypothesised signal (or a set of signals). The
3. Designed Cox comb pulse
An alternative Doppler sensitive pulse, Cox comb, gives an improved range resolution and reverberation
563
processing gain in comparison with the conventional CW pulse [4]. It has the form
s(t)- w(t)~ exp[jZlf, n--I
(t + a')]
where fn=fn_~+sr n-z, s being the spacing between the two lowest frequencies and r being a number slightly greater than one, it defines the degree of nonuniformity of the sequence. And w(t) is the window function. Particularly, it is called a geometric cox comb when r - 1+s/f1. Figure 2 shows an example of a Cox comb. Figure 2a illustrates the time plot of the 10 tones Cox comb. The centre frequency is set as 7500Hz, the bandwidth being 5000Hz, time duration is 0.1s.The spectrum of the pulse is plotting in Figure 2b. The minimum frequency difference is 481.5Hz. The largest frequency difference is 827 Hz. The non-uniform spacing of the Cox comb pulse is to spread and reduce the ambiguous peaks of the uniform comb. time plot: geometric comb waveformwith hamming window (N=10,R=1.07 D=450Hz)
range ambiguity function at zero Doppler. The attractive of the cox comb is its fine range and Doppler resolution of the pulse, primarily due to its good autocorrelation function. Figure 3 shows the autocorrelation of the hamming windowed cox comb. The hamming window is designed to achieve sidelobe reduction when using the Fourier transform for spectrum analysis. It can be seen that the main lobe width is approximately 1/B=l/5000=0.0002s and the sidelobe is -9dB lower than the peak of the autocorrelation. 20
autocorrelation
10 0 -10 rn -20 -o -30
-40 t
-50 -
-60
-0.01 -0.008 -0.006 -0.004 -0.002 0 0.002 0.004 0.006 0.008 0.01 time delay
Fig 3 the autocorrelation function of the cox comb
3.2 Ambiguity function The resolution properties of a give sonar waveform can be visualized by the wideband ambiguity function, described in (3). The wideband ambiguity function of the pulse mentioned earlier is shown in Figure 4, as a three dimensional plot of the point target response. Fine Doppler resolution achieves good signal-tointerference level by rejecting interference at all other Dopplers while admitting echo energy at the target Doppler [6]. The good range resolution improves signal-to-reverberation ration by reducing the effective size of the patch of scatterers. It is noted from Figure 4 that the important feature is its fine Doppler and range resolution, theoretically.
time (s) Normalized Frequency content of y
0 -20 -40 -60 -80
/ ~
-100
-120[~ -140
-160[. -180 0
0.5
1 1.5 frequency (Hz)
2
2.5 x 104
Fig.2a time plot of the cox comb 2b the spectrum plot 3.1 Autocorrelation function The autocorrelation is important because it is the
564
I
=o
I
I
4
jJ
O.
,,= o. -5 o. _Q
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V e l o c i t y [m/s]
S1 (6.5cm, ll9cm, 74cm) $2 = (77.1cm, 0cm, 72cm) $3 = (12.5cm, 0cm, 37cm) $4 = (72.5cm, 0cm, 37mm). The transmitter is placed at T(45cm,57.5 cm,8cm). The target is a metal ball with the diameter of 15cm, giving the theoretical target strength of approximate 30dB. The metal ball will be driven by the human along the linear tracking line. The established 3D position of the receivers and the transmitter are plotted in Figure 5. The experiments were carried out in a reverberation room.
Time [ms] _- .......... .....
Fig4 the 3D ambiguity function of the cox comb with a hamming window
_
l
GEl
~4-1
The prototype [3] of the Doppler tracking system was developed at the early stage of this project. It is using the narrowband CW pulse. If the wideband pulse is used, the range can be detected at the same time. Therefore, time of flight (TOF) technique can be used to estimate the position of the target. The relative distance D is measured according to the time t that acoustic sound travels, assuming that the sound travels at the direct path with the constant speed. D=Dt+Dr,=ct. The time delay t is determined by the matched filtering response of the pulse. By combining both the TOF and Doppler tracking systems, improved quality 3-D tracking of the dynamic targe can be achieved if the merits of each subsystem (TOF and Doppler) are exploited optimally. The experimental work consists of two phases, (i) system setup (ii) initial experimental results. 4.1 System setup
Accurate location evaluation from acoustical signals requires at least 4 positioned receivers with the relative co-ordinates of each receiver, the (x, y, z) coordinates of the four sensors are measured by:
-....
,
~:~:
-] .....
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:
:
......
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......
:
/
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4. Experimental tracking system
i
r. _. _ ._ ._ ..
. ..... ,,--
q'-
,3,-...................~..... .~
In a practice scenario, the reverberating scatterers will generally consist of a volume of many targets forming the equivalent of a large extended target. The following section will present some experimental results of the designed pulse.
.......
L; "-"
50
y
(cm)
~
.... i............ i.......... i .i_-....................i
i
-;"........ ~
-'----. -- ><........ ..... --~;--------~ 213 0
-! ...... ~-.....i
0
60
40
x
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Fig5 3D plot of the receivers and the transmitter respectively
4.2 Initial experimental results As discussed in [3], the narrowband Doppler tracking system can only measure the velocity of the moving target, and cannot measure its initial absolute position. Therefore, determination of instantaneous absolute distance between the target and the receiver and the transmitter requires the knowledge ofthe initial displacement. This can be determined by the combined TOF and the matched filter response of the pulse. Figure 6 shows the time plot and spectrum of the received waveform. The note is that the frequency contents of several tones of the cox comb degrades seriously due to the low performance of the speaker in the certain frequency. The frequency equaliser is used to improve the response the sound source after the calibration using Behinger ECM8000 measurement microphone. However, only a little improvement ofthe sound source can be achieved.
565
distinguish the target at a r o u n d - 3 0 d B level due to a number of scatters act as the targets as well. The Doppler velocity was buried by the high sidelobe level of the designed pulse. It is because that the target travels with the relatively low speed compared with the Doppler resolution. The center frequency, time duration and the bandwidth plays a crucial role in the pulse design, which need further investigation.
0.8 0.6 0.4 0.2 O -0.2 -0.4 -0.6
5. Conclusions
-0.8 -1 4.8
4.85
4.9
4.95
5
505
5.1
5.15
Wideband geometric comb waveform is very attractive for providing the range and Doppler estimation simultaneously from simulation. The TOF and Doppler tracking algorithms can be combined together to provide an optimal estimation of the target movement. In practice, the initial experiment shows that this pulse need further designed due to the large reverberation environment and slowly moving targets. The future work will include the statistical analysis of a more controllable target and more investigation of the pulse against the reverberation level and interference.
5.2 X 104
-IO
-2o rn -30
-40
-50
-60
0,2
0.4
0.6
0.8 1 1.2 frequency (Hz)
1,4
1.6
1,8
2 x 104
6. Acknowledgements
Fig6 the time plot and spectrum of the received signal by one of the sensor
.................
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il
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References
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iiiiiili!iiiiiiiiii!iiiiiiiiiii!iiiiil, .................ii~i!iii 0.5
1
1.5 2 relative range (m)
2.5
3
Fig 7 Ambiguity function formed from a Cox comb transmission Figure 7 presents the matched filter response of one received signal. It is quite obvious that the peak of the main lobe is approximately at 1.5m relatively to the receiver and the transmitter. But it seems difficult to
566
This work is funded by the EC 6 th framework program Tai-Chi project
[ 1] Streitz.N, et.al, Roomware: torward the next generation of human computer interaction based on an integrated design of real and virtual worlds, access website: http ://www.ipsi.fraunhofer.de/ambiente/paper/2001/Strei tz-etal-update.pdf. [2] Singh Paradiso JA, Leo CK, Checka N and Hsiao K. Passive acoustic knock tacking for interactive windows. ACM CH12002 Conference, Minneapolis, Minnesota, 20-25 April, 2002. [3] Sun Y, Collins T, Xiao L, 3D active Doppler tracking in air using Kalman filter, lASTED conf. Sig. Proc., Pattern Recognition, and Apps., Innsbruck, Austria, 15-17 Feb. 2006, p. 520-097. [4] Collins T, Atkins P, Doppler-sensitive active sonar pulse designs for reverberation processing, lEE proc-radar, sonar Navig, Vol. 145, No.6, December 1998 pp347-353 [5] Alsup J, Whitehouse H, Hermite functions and regularized deconvolution in sonar waveform Design and Processing, Proceedings of the 34th Asilomar conference on Signals, Systems, and Computers, vol.1 29Oct-
1Nov,2000,pp673-679 [6] Alsup, J, Comb waveforms for sonar, Proceedings of the 33th Asilomar conference on Signals, Systems, and Computers, vol.2, 24-27 Oct, 1999, pp864-869 [7] Cox H, Lai H, Geometric comb waveforms for reverberation suppression, Proceedings of the 28 th Asilomar conference on Signals, Systems, and Computers, vo12, 31Oct-2Nov, 1994, pp1185-1189 [8] Collins T. Active Sonar Pulse Design. D.Phil. Thesis, University of Birmingham, UK, 1997. [9] Lin Z, Wideband ambiguity function of broadband signals, J. Acoust. Soc. Am. 83(6), June 1988, pp21082116 [10] Turin. GL, An introduction to matched filters, IRE
Trans on information theory, vol.6, 1960, pp311-329 [11] Lerner RM, A matched filter detection system for complicated Doppler shifted signals, IRE Trans on information theory, vol.6, 1960, pp373-385 [12] Doisy Y, etal, Target Doppler Estimation Using Wideband Frequency Modulated Signals, IEEE Trans on signal processing, Vol.48, No.5, May 2000, pp 12131224
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Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhd and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
An Intuitive Teaching Method for Small and Medium Enterprises Ch. Meyer a, R.D. a
Schraft a
Fraunhofer Institute for Manufacturing Engineering and Automation, Nobelstrasse 12, 70569 Stuttgart, Germany
Abstract In contrast to highly automated industrial companies small and medium enterprises need a more intuitive programming method for robots than the teach pendant or offline programming tools. A solution for some tasks is the known procedure of walk-through teaching, but improved in several ways: post processed to identify dominant points, enriched with sensor data and usable via a multimodal user interface. The article introduces the method of Intuitive Teaching with focus on the post processing of trajectory data. One application is described. Keywords: Walk-Through-Programming; Teaching; Human-Machine-Interface 1. Introduction
1.1 Deficit: small lot size production
Industrial robots are widely used in companies producing mass products in high lot sizes. Body shell work in the automotive industry for example mainly consists of robots handling, machining and joining the sheet metal parts. Besides the further development of these applications, industrial robots are currently intruding other markets: in a few years small and medium enterprises will benefit from industrial robots as much as the automotive industry does today.
In small and medium enterprises robots are not commonly found. One of the reasons, the high investment, is rapidly vanishing: the cost of a robot system has fallen to 25 percent of the costs of 1990 (quality adjusted) [IFR04]. Another reason is the necessary environment, especially the programming capabilities. To work with today's systems, the SMEs need to set up a robots department with programming engineers and trained service personal. These financial efforts do not pay. In the upcoming part of this contribution, we will focus on the area of programming.
1.2 Definition of niche
Figure 1: Welding of steel beams
568
Common industrial robots are programmed by a teach panel in lead-through programming, or with an offline programming system. These and other programming possibilities have e.g. been described by Biggs and McDonald [Biggs03]. Both methods only pay for high lot sizes, they need a long time and much experience. Figure 2 shows some connections between lot size, degree of automation and programming method. For high lot sizes offline programming tools are used, especially for production lines with several robots interacting. A teach panel can be used to program single robots. Offline programming systems will also be used for production lines in the mid or low lot size, single robots will be programmed with the teach panel. In this lot size area human workers can be more efficient than an automated cell or line.
degree of automation
offline programming
I teach pad / offline programming point related:
(D
offiine programming O
path related: intuitive Teaching
Often foundat SMEsiteswithno robotexperience Transit to achieve
Figure 2" Definition of niche Single work pieces will normally be produced by human workers. We propose, to get a transit of manual production to automated robot cells for small lot sizes as depicted in Figure 2. The main problem, the complex and time consuming teach-in shall be done with the Intuitive Teaching method.
2.3 Proposed solution." intuitive teaching We propose to use a walk-through attempt to provide a tool for fast and effective teaching of industrial robots in this niche. The user guides the robot with a handle that is equipped with a force torque sensor. The robot moves actuated by an admittance control strategy [AlbuSchaeffer02]. The trajectory guided by the human is recorded and can be replayed. Before replay, parameters like velocity, position and orientation can be adopted. This programming approach is not new, it has been used e.g. with early painting robots. But today, it is not in use anymore. Our goal is to solve the problems, that prevent the usage of this intuitive teaching approach:
2.4 Defiances 9 Precision of path regarding position and orientation.
Today's robots used in industry rely on lead-through programming with a teach panel, or the offline programming with complex tools. The walk-through programming is commonly not used in the industry, but there exist several companies with products in this area. The Barrett arm can be guided by the user, trajectories can be wor recorded [Leeser94]. Additional _ functionalities like virtual walls add value. The robot is actuated on the order base of motor current measurements. Manutec robots can get equipped with mz robotlab controls, these are able to conduct force sensitive processes like grinding or deburring, also they can be programmed by guidance [Zahn06]. The motion control is done using the measurements of a force torque sensor. KUKA robots can be ordered equipped for safe handling, then the robots can be guided using a 6-DOFjoystick. 3 Conducted experiments
In the labs at Fraunhofer IPA a simple gluing scenario has been set up to get first impressions of how to interact with the robot system. A Reis RV40 robot forms the base of the robot cell; the robot gets motion commands via an XML Ethemet connection from an industry PC. This PC provides interfaces to the force torque sensor, the PDA and dialog system and a graphical user interface (Figure 3). The robot implements a complex safety concept to allow the user in the robot cell while the robot is working in automatic mode. Using the standard Reis safety controller in the robot controller velocities and motion areas can be supervised according to Category 3 of EN954. It is also possible to adapt the robot cell to the new ISOI0218:2006 that will be published soon. The PDA used is ruggedized to conform to industrial environments. With its touch screen the user can command the robot, have a 3-D visualization and define a velocity profile (see Figure 4, bottom).
The user cannot guide the robot within a tenth mm or degree, the precision has to be achieved in post processing. 9 Adaptability of the trajectory." Errors in the teaching process have to be easily overcome, changes should be possible. 9 Human Machine Interface." The user needs multimodal and intuitive interaction. 9 Safety. Robot and human come closer together - this interaction has to be safe. 2. State of the art
The American Occupational Safety and Health Administration defines three means of programming a robot: lead-through programming, walk-through programming and offline programming [OSHA06].
ii!~:~!
Figure 3." Set up of human-machine interface 569
In this section we will present possibilities to compress the path data and generate an abstraction of the path in an automatic way. Afterwards several means of interaction with this path segments will be defined. Figure 5 shows a visualisation of the recorded path, the segments generated by this path and an example for a deviations metric. At this time the presented processes only deal with three of the six degrees of freedom the robot can move in. The orientation has to be included in the further work.
4.2 Data compression As a first step the data complexity has to be minimized. In this application the Douglas-Peucker algorithms [Douglas73] is used. Known from the area of cartography this recursive weeding algorithm detects so called dominant points. These dominant points are chosen in a way, that a zigzag line approximates the original path within a defined tolerance. The algorithm is defined by three steps: construct a connecting line between start and end point search point with maximum distance to this connecting line
perpendicular
if distance > g then add point and return to 1. With the two resulting connection lines. If distance < g then end.
4.3 Segmentation Figure 4:Manual guidance (top), PDA for definition of velocity projqle (bottom) 3.1 Experiences The investigations with the robot systems are still running. First impressions support the anticipated objective that a very fast programming should be possible. By means of guiding the robot and simple graphical interaction interfaces working robot programs can be defined very fast. The guiding of the robot is more simple with less degrees of freedom, so with only translation or only orientation. On our interfaces simple means are included to provide a transition from orientation to translation and vice versa.
4 Metric for trajectories
4.1 Path adoption To meet the goals of robot programming the recorded trajectories have to be adopted. Several reasons are responsible for this need of manual interaction: 9 The path guided by the human worker cannot get into tolerances needed by some processes, e.g. tenth of a millimetre in welding applications. 9 There should be the possibility to change the path e.g. for a new, almost identical work piece. 570
After the compression geometric elements are fitted in between the dominant points. Currently lines and NURBS are used to approximate the recorded path. The algorithm for deciding what geometric primitive maps best to the according points is under further research.
4. 4 Means of interaction The user has several possibilities to interact with the segmented path through the 3-D environment, but it is not intended to generate an interface as complex as an offline programming system. The interaction should be simple and easy to understand. Several possibilities are available up till now: 9 Move a dominant point with the mouse 9 Change a dominant points character, from edge to smooth and vice versa 9 Add a dominant point at a specific position on the recorded path 9 Delete a specified dominant point With these means of interaction a recorded path can be adopted, further means of interaction are related to the definition of a velocity profile via a graphical user interface.
abstraction by lines or NURBS, but there is an error: in this example the deviation between both trajectories amounts to 678.9 mm 2 (without artificial deviations). 5 Conclusion and future work
Figure 5." Visualisation of the path adoption 4.5 Deviations metric Between the recorded and the segmented path there will be always deviations. Also, and even more interesting, there will be deviations to a commonly programmed robot program. To get an indicator for these deviations a metric has been implemented. The simplest form of a metric is the sum over all deviations between a new and an old frame: g - 1/ e - 1/ ~
frame
.... - f r a m e o ; ~
We presented the old idea of teaching a robot while guiding him through the cell and discussed the defiances that prevented the application of this method so far. Problems like the human machine interface, the safety of the human, and the adaptability of the trajectory can be overcome with state of the art technology. More challenging is the question how to reach the necessary precision. Sensors are needed, but also information about the users intentions. We will further conduct experiments to verify the intuitive teaching method; we will put much effort in the development of HR-interfaces and safety systems. On the area of the precision we will proceed with experiments. Acknowledgment This work has partly been funded by the European Commission's Sixth Framework Programme under grant no. 011838 as part of the Integrated Project SMErobot. 6. References
This metric can be applied when dealing with an equal number of points and two points with the same number describing the same position. In our problem both conditions do not meet. In [Teh89] two different Metrics are defined, the integral square error (a) and the maximum error (b):
(a) E 2 - 2 . , e , 2 ' i=1
(b) E
- maxe i l
The error e i is the perpendicular distance from point i to the according line. But also these metrics only make sense in chain-code applications. We propose the computation of the area between the two trajectories. This error value can get obtained by a triangulation of the area in between the two curves. Of course the result will not be an indicator for the quality of the trajectory; several problems arise: To provide a quality measure a reference path has to be known. This is only the case for tEw training applications. In this graphical representation only six degrees of freedom are represented, orientation is not included. The user does not necessarily know which trajectory is the best. Figure 5 shows a trajectory and the proposed metric. Artificial deviations have been introduced to the segmented path in order to show the triangulation that takes place between the hand guided path and the segmented path. Deviations of the hand guided path are overridden by the
[Albu-Schaefer02] Albu-Schaefer, A.; Hirzinger, G.: Cartesian Impedance Control Techniques for Torque Controlled LightWeight Robots. In: Proceedings of the 2002 IEEE International Conference on Robotics and Automation (ICRA). Washington, 2002. [Biggs03] Biggs, G.; MacDonald, B.: A Survey of Robot Programming Systems. In: Proceedings of the 2003 Australasian Conference on Robotics and Automation (ACRA). Auckland, 2003. [Douglas73] Douglas, D.; Peucker, T.: Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. The Canadian Cartographer 10(2). 1973. [Heiligensetzer05] Heiligensetzer, P.: Safe Operation - Safe Handling. 4. OTS-Workshop. FpF - Verein zur F6rderung produktionstechnischer Forschung, Stuttgart, 2005. [IFR04] World Robotics 2004. UNCE, IFR. United Nations, Geneva, 2004. [Leeser94] Leeser, K., Donoghue, J., Townsend, W.: Computer Assisted Teach and Play. In: Proceedings of the Robotics International/SME/Fifth World Conference on Robotics Research. Cambridge, MA, 1994. [OSHA06] http://www.osha.gov/dts/osta/otm/otm_iv/otm iv 4.html. Homepage of the Occupational Health and Safety Administration, U.S. Department of Labor. April 2006. [Teh89] Teh, C. H.; Chin, R. T.; On the detection of dominant points on digital curves. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11., 1989. [Zahn06] http://www.robolab.de. GmbH, March 2006.
Homepage of mz robolab
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InteUigent Production Machines and Systems D.T. Pham, E.E. Eldukhfi and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
From robotic arms to mobile manipulation: on coordinated motion schemes V. P a d o i s t, J.-Y. F o u r q u e t * a n d E Chiron* t Stanford Artificial Intelligence Laboratory, Stanford, California Laboratoire G6nie de Production,ENIT, Tarbes, France
Abstract The task definition for mobile manipulators is presented. Then, a generic formulation of global instantaneous kinematics is proposed for wheeled mobile manipulators. It is compared with the classical kinematic modelling of robotic arms. In particular, it is shown that many tools of classical manipulation can be re-used in this new framework. Finally, simulation results and experiments are presented and the remaining industrial challenges are mentioned. So, this article provides comprehensive bases for the use of such systems in an industrial context.
Keywords: mobile manipulation, instantaneous kinematics, nonholonomy, motion generation
1. Moving products and robots Mobile manipulation is an activity human perform every day. So, it is very unnatural to separate locomotion and manipulation in human tasks. On the other side, automation has been historically organized by explicitly separating the motion of products and the action of manipulators with fixed basis. The main drawbacks of this kind of organization are due to the inherently bounded workspace of classic robotic arms or automatic machines. Finally, their geometric reachability is extremely limited. So, when dealing with relatively small products (car, electronics, etc.), this paradigm leads to move the products inside a fixed bank of robotic manipulators by any kind of conveyors. At least two cases are not really consistent with this kind of organization: 9 operations (painting, stripping, welding, etc.) on large products like ships, planes or cranes need to move the tools around them since moving a plane or a ship requires much space and energy. 9 More generally, in a context where the products change quickly or need updates or customiza572
tion, fixed basis robotic manipulators impose hard constraints on the production cycle, and possibly waste of ressources. So, it became natural to consider robots that have motion and manipulation capabilities. The most famous are humanoids. Nonetheless, the problems they give rise in terms of complexity, cost or stability, disqualify them for a genuine use in manufacturing or production solutions in the near future. Instead, autonomous wheeled vehicles already are in the factories and some laboratories have worked on the coupling of these wheeled mobile bases with industrial robotic arms. Even more, some cases of industrial use of this wheeled mobile manipulators appeared. These compound systems have some common features: 9 Mobile base provides an infinite workspace. 9 The combination of both subsystems generally give rise to a global system possessing more actuators than required locally by the task. 9 Wheeled vehicles are nonholonomic and are difficult to feedback stabilize. Moreover, when they are not omnidirectional, they impose manoeu-
vers that have to be taken into account at the environment design stage. 9 Since reference frame supporting the tool is moving, real-time calibration is necessary and requires exteroceptive sensors (laser rangefinder, ultrasonic sensors, vision) and associated feedback laws. 9 Since mobile manipulators are compound systems, one of the question concerns the coordination of motions of subsystems: is it necessary to use actuators of both subsystems at the same time? Do some tasks need only the motion of the arm or the motion of the mobile base'? What are the advantages and drawbacks related with coordination or hierarchization strategies'? The objectives of this paper are to clarify what kind of tasks can be defined in weeled mobile manipulation and then how to perform these tasks. First, it is shown how the manipulation task and the environment constraints can be taken into account. Then, the need for coordination and the models underlying each strategy are discussed. The coordinated motion strategy is emphasized together with the models derived from the classic models of wheeled mobile platforms and robotic arms. Finally, experiments and simulation show the effectiveness of the approach. Concluding remarks are devoted to the gap between these laboratory experiments and industrial implementation.
2. Mobile Manipulation Tasks A Manipulation Task as it is defined for robotic arms is only related to the motion of the end-effector and to the torque/force exerted on it. It is imposed by the user in the so-called operational ,space. A point in this space is the location of the end-effector (EE) denoted by the m • 1 vector ,~. It is characterized by a set of operational coordinates that correspond to the value of the position and the orientation of a frame attached to the EE at a particular point of this EE. Both values are measured with respect to a fixed reference frame. The tasks are mainly of two types: regulation or tracking. In a task of regulation, the goal is to reach a desired value of the EE location. In a task of tracking, one needs to realize a given velocity of this location, i. e. a given operational velocity, to follow a prescribed operational motion. Remark that when force/torque are imposed by the task, the same representation is used. So, imposing location of the EE defines equality constraints. Of course, other constraints due to the environment and to construction limits (articular bounds) define inequality constraints. Generally, in automatic motion generation, cluttered environments are tackled by a planning process defining forbidden regions or, more often, intermediate passing configuration points with a given clearance. Reachability constraints due to construction limits are
considered by taking locally lower bounds on reachable space. There are few works devoted to redundant manipulators, and also few redundant industrial manipulators since they are more expensive to build and because the whole organization scheme of production lines did not require them. In mobile manipulation, redundancy is the rule and decomposing the task by considering subsystems may appear as a useful recipe. The first experiments, in laboratory ([1] e.g) and industry, explicitly break down the task into sequences of pieces esssentially devoted to locomotion (motion of the platform) or to manipulation (motion of the arm with platform keeping a fixed location). Of course, this static decomposition does not allow to use all the capabilities of these systems. It is necessary to consider tasks for which all the actuators are used at the same time in order to provide a synchronized motion of the mobile manipulator. Moreover, due to the environment and to the inherent manoeuvering nature of the vehicles, a question arises naturally: is it necessary to impose a path for the vehicle - as equality constraints - to avoid obstacles? Or is it possible to solve the problem by the sole consideration of the inequality constraints the environment and the articular bounds of the arm define? So, even in a synchronized motion of the mobile manipulator, there are two main ways to define the task: 9 hierachy: defining a subtask for the platform and then a subtask for the arm, 9 coordination: defining a main task for the endeffector without explicitly imposing equality constraints on the motion of the platform.
2.1 Hierarchy Here one reference motion is given as equality constraints for the platform. The task for the platform consists in moving its location and the task for the arm is relative to the frame attached to the platform. Here, it is necessary to have two models: the first one that link actuators of the platform to its location evolution on one side, and the second that link end-effector evolution with respect to the first body of the arm to its actuators. First, remember that nonholonomic platform cannot follow any path by definition. Thus, the first problem is to define a feasible path for the platform, or to define path for which it is ensured that the mobile base will remain in a given neighborhood of it. Then, the arm must adapt its motion. This hierarchical decomposition emphasizes nonholonomic constraints of the platform. In this approach, let us mention the works concerning transverse functions that allow to bound the region the platform traverses [2,3]. In these works, it can be observed that decomposition and hierarchization does not provide an easy to use solution and is of limited efficiency.
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2.2 Coordination On the contrary, it can be assumed that the non feasible directions of the wheeled platform will be compensated at the end-effector level by the degrees of freedom of the arm. Such an approach requires a global modelling and the definition of a main task for the end-effector without explicitly imposing equality constraints on the motion of the platform. In that case, it is necessary to have global models that link the end-effector evolution to all the actuators of the compound system. In this approach, it is supposed that the platform will "follow" the end-effector. So, it is necessary to avoid large of the platform, platform collisions and to insure that tool is never at the boundary of its workspace relative to the base of the arm. All these inequality constraints can be taken into account as secondary tasks the whole redundant system will satisfy. When looking at the global mobile manipulator, the kinematic constraints of the platform may be compensated by an adequate generalized velocity of the arm to realize a prescribed end-effector task. In that case, nonholonomy of the platform is somewhat hidden in the global instantaneous kinematics. This approach is presented in the following sections.
Figure 1. H2bis: a 3D mobile manipulator.
3.1 Mobile manipulator kinematics The configuration of such a mobile manipulator is completely defined using vector q = [ qb qp IT where qb = [ qbl ... qbnb ]T and qp = [ Or O1 xop YOp z9 ]T respectively represents the arm configuration and the platform configuration (see Fig. 2). Its end-effector location (i. e. location of ~ E E (OEE,:~EE9 flEE, ~EE) in the reference frame Tr = (O, Z", if, Z)) can be described using a minimal set of parameters ~ = [ ~1 ... ~,~]T. ~ is expressed as a non linear function =
of q. Differentiating it, the relation between ~ and/t is given by: = J(q)~t (1) with J ( q ) a m • n matrix and n = nb + 5.
3. Global Modelling Recently some contributions concerning modelling and control of generic nonholonomic mobile manipulators ([4,5]) have been proposed. Based on these proposals, it is now possible to consider modelling and control of mobile manipulators on a unified basis and a comparison can be made with classical manipulation. Manipulation and mobile robotics literature both provide modelling tools to solve this problem. On one hand, kinematics and instantaneous kinematics of robotic arms with a fixed base are now a very classical material. The associated notions of redundancy, singularity, and manipulability [6,7] are also parts of the classical background of manipulation. On the other hand, wheeled mobile platforms were properly described and modelled by [8]. Though less classical and less used in the robotics community, these notions are of great interest in the case of wheeled mobile manipulators. For the sake of simplicity, the case of mobile manipulator composed of a mobile platform with two independent driving wheels and a serial manipulator with nb joints is considered. Such a system is depicted on figure 1 in a 3-dimensional version used for experiments or on figure 2 in the planar version used in simulation. Some kinematic modelling results regarding mobile manipulators based on [5,9,10] are sum up first. 574
In addition, the components of ~1 are constrained by the nonholonomy of the platform (i. e. the wheels cannot slip). Then, one can define a vector u = [ ub Up] of fi independent parameters (i. e. taking the nonholonomic constraints into account) such as: /t = T(q)u.
(2)
J(q) = J(q)T(q),
(3)
Defining J(q) as:
equation (1) becomes : = J(q)u.
(4)
Equation (4) completely describes the mobile manipulator kinematics. When m < fi, the mobile manipulator is said to be kinematically redundant. This property provides the capability to choose a particular kinematic control vector u among those giving the prescribed endeffector velocity ~j by using the relation: u = J(q)~ + (I-
j(q)~j(q))z,
(5)
where ](q)~ is any generalized inverse of J ( q ) and z any fi x 1 vector. Access to the kinematic redundancy of the system is given by the second right-hand term of equation (5)
~
where T ( q ) + denotes the pseudo-inverse of T(q).
E
When the task imposes end-effector force, the global model can be used for adapting classic scheme generally used for holonomic arms. For example, hybrid speed / force controller used for the following experiments is a modified version of the well known work presented in [12]. Once the contact is established, the robot's endeffector cannot independently exert a displacement and a force in the same direction. The control vector is then calculated as the sum of three terms: 0
g
~o v
(1
U --- Us + U f + U r ,
(9)
Figure 2. A planar mobile manipulator. also called the internal motion control term since it does not provide any end-effector motion. Remark that practically, it is sufficient to choose the generalized inverse among the so-called weighted pseudo-inverses [11] for having any interesting behavior. Finally, at kinematic level, global system modelling leads to a model that has the same form as the one used for fixed redundant manipulators 1 So, the same main control techniques can be used and adapted.
with: us-
3V(q)~S~s,
uf - J(q)~(Iu~ - ( I -
S)~f,
3V(q)~J(q))z.
(10) (11) (12)
~s and ~f are the control vectors whose simple versions are given by:
3.2 Operational kinematic controllers and: Given ~* and (* desired end-effector speed and location to track, and a positive definite weighting matrix W~g, the control vector defined by: u - J(q)~(~* + W.r~g(~* - ~)) + ( I - J ( q ) ~ J ( q ) ) z ,
(6) ensures an asymptotic decreasing of e = ( * - ~ toward 0. In order to take advantage of the kinematic redundancy of the system, z can be chosen such as to minimize a scalar function 79 (q), also called potential function. The "gradient descent" local optimization method consists in choosing ~t such as: dl + Wg~,dV79(q) = 0,
(7)
where Wg,.~d ~ 7~~ • is a positive definite weighting matrix and V79(q) is the gradient of P ( q ) . This choice ensures an evolution of the system configuration tending to locally minimize 79(q). However, Cl components have to be independent which is not the case for a mobile manipulator. Thus, one have to adapt this method by choosing z as: z
=
-T(q)+WgradV79(q).
(8)
1 When the platform features steering actuated wheels, it can be shown that the model is slightly different but keep its main properties. At dynamic level, many analogies that allow to use known techniques are also obtained by an adequate choice of variables [ 10].
~f - W~gs (f* - f),
(14)
where W~o~ and W~os are two positive definite weighting matrices, and 5' is a m x m diagonal selection matrix where ones or zeros are placed on the diagonal respectively to indicate whether the component of ( corresponding to the line in S is velocity or force controlled.
3.3 Use of redundancy Here a set of functions to optimize using internal motion is presented. Many other functions may be used but these ones are relevant according to the presented results.
3.3.1. Manipulability maximization The manipulability notion was first introduced for manipulators (cf [7] for a detailed presentation of this notion) but was also extended to mobile manipulators in [5]. The different manipulability measures are quantitative indicators representing the ease to instantly move the endeffector in any direction. Maximizing any of these indicators tends to avoid singular configuration of the system and thus to avoid high joints speed. Arm Manipulability measure is also useful for avoiding configurations close to workspace boundary of the arm. 575
3.3.2. Impact force reduction During the transition tasks between free space motion and contact motion, it is interesting to re-configure the mobile manipulator using internal motion so as to give it good inertial properties. Results concerning holonomic mobile manipulators are presented in [13]. Using the dynamic model of the global system (i. e. the model establishing the relation between physical effects of motion, actuating torques and contact forces at end-effector level), it can be shown that the magnitude of the impact force is configuration dependent. So, redundancy can be used to provide convergence toward a configuration that minimizes this impact force magnitude. 3.3.3. Collision avoidance Techniques to avoid obstacles have extensively been studied in the case of mobile robots. However, the problem to solve here depends on the nature of the task to perform. Is the goal to follow an obstacle (like during a writing task on the wall) or to go around an obstacle (like in free space end-effector motion)? Different potential functions have been defined depending on what kind of collision avoidance is expected.
Figure 3. Go to the wall and follow it
4. Simulation and experiments Simulation and experiments have been conducted on two different mobile manipulators : 9 The first one is depicted at the figure 2. It has been used in simulation in order to illustrate numerous strategies based on redundancy resolution on the basis of local/reactive potential function. 9 The second one, H2bis, has been used for demonstrating a complex mission realization including free-space and contact tasks.
4.1 Simulation framework The simulator has been developed using Matlab and Simulink. The robot is modeled at kinematic and dynamic level. In particular, load torques due to dynamic effects of motion or due to contact, saturations of the actuators, low level digital PID, noise and inaccuracy of the end-effector force sensor together with its limited bandwidth are taken into account. Contact between the end-effector and the environment is locally modeled either as a {spring//damper} system (i. e. Kelvin-Voigt visco-elastic model) or as a spring system and the values of the parameters characterizing the contact are unknown or poorly estimated.
4.2 Simulation results 4.2.1. Simulation 1: go to the wall among obstacles and follow it applying a normal force The task to 576
Figure 4. Interactive real-time end-effector setpoint realize consists in two main phases: first, the endeffector motion is imposed along a straight line and the environment may comprise low obstacles the robot will sense as motion progresses; in the second stage, the end-effector must follow a line on the wall and apply a given normal force (see figure 3). Here the end-effector motion is imposed and the mobile base has to avoid low obstacles reactively. There is only a local information about the location and number of obstacles. The dynamic sequencing scheme switchs automatically between indices: manipulability, obstacle avoidance, inertia, etc. The imposed endeffector motion is made of the two blue straight lines and the path of the middle of the rear axle of the mobile base is depicted in red. ~ is chosen as the endeffector position and u - [ 0~ 01 c)bl Ob2]T 9Once the contact is established, the normal direction is force controlled whereas the tangential direction is velocity controlled.
4.2.2. Simulation 2: interactive joystick Here, the control scheme is used interactively since the endeffector velocity is imposed in real time. Again, the real-time imposed end-effector motion is in blue whereas the path of the middle of the rear axle of the mobile base is depicted in red (see figure 4). This
'4,
9 organizational: a fleet of mobile manipulators will, for example, totally change the rules of production sequencing, of reorganization on small series. All is to be redefined, including PLM software tools!
Acknowledgment: This work is partially supported by the French CNRS Robea program within the project +
="f2.~
9
Figure 5. Coordinated Writing on a board simulation shows the robustness of the control scheme and the ability to realize totally reactive behaviour with a nonholonomic mobile manipulator. In particular, cusps or manoeuvers are automatically generated. This mode shows how simple is to control the wheeled mobile manipulator in real time. In particular, use by human operator for cooperative tasks is particularly easy.
4.3 Experimental results The mobile platform is actuated using two independent driving wheels. The manipulator is a 6R serial arm called GT6A. The robot is endowed with localization devices such as ultrasonic sensors, a telemeter and a black and white camera, an odometer system, the manipulator's incremental coders and a 6 - a x i s GIROBO force / torque sensor. Control algorithms are implemented using Genom, a generator of software control modules developed, as well as the robot, in the RIA team of the LAAS laboratory. The task simulated in 4.2.1 has been tested experimentally and the end-effector has been equipped with a pen so as to visualize the path of the end-effector and the action of the normal force. The figure 5 shows the end of the mission obtained by global coordination of mobile manipulator including a transition phase during which configuration is adapted for impact force minimization.
5. Concluding remarks It is shown in this paper that classic control schemes can be revisited for wheeled mobile manipulators by choosing adequate global modelling and coordinated control. At present, the industrial use of these robots is limited by at least two factors, technological and organizational: 9 technological: challenges are on real-time precision on geometric measures relative to the location of the robot in the environment.
Dynamic sequencing of multi-sensor based tasks for complex motions execution in mobile robotics and the experiments have been performed at LAAS-CNRS in Toulouse, France. LGP-ENIT is a partner of the Innovative Production Machines and Systems (I'PROMS) Network of Excellence funded by the European Commission under the Sixth Framwork Programme (Contract n ~ FP62002-500273-2).
References [1] K. Nagatani and S. Yuta. Door-opening behaviour of an autonomous mobile manipulator by sequence of action primitives. Journal of Robotic Systems, 13(11):709-721, 1996. [2] V. Padois and P. Chiron and J-Y. Fourquet and A. Carriay, Coordination and partial decoupling in tracking control for wheeled mobile manipulators, Proceedings of the 35th International Symposium on Robotics, Paris, France, 2004. [3] M. Fruchard, R Morin and C. Samson. A framework for the control of nonholonomic mobile manipulators. Activity report INRIA 5556, 2005. [4] K. Tchon and J. Jakubiak. Extended Jacobian Inverse Kinematics Algorithms for Mobile Manipulators. Journal of Robotic Systems, 19(9):443-454, 2002. [5] B. Bayle, J-Y. Fourquet, and M. Renaud. Manipulability of wheeled mobile manipulation: application to motion generation. The International Journal of Robotics Research, vol. 22(7-8):565581, July 2003b. [6] L. Sciavicco and B. Siciliano. Modelling and control of robot manipulators. Springer, 1999. [7] T. Yoshikawa. Foundations of robotics - Analysis and Control. The MIT Press, 1990. [8] G. Campion, G. Bastin, and B. D'Andr6a Novel. Structural Properties and Classification of Kinematic and Dynamic Models of Wheeled Mobile Robots. IEEE Transactions on Automatic Control, 12:47-62, February 1996. [9] B. Bayle, J-Y. Fourquet, and M. Renaud. Kinematic modelling of wheeled mobile manipulators. In IEEE International Conference on Robotics and Automation, Taipei, Taiwan, September 2003. [10] V. Padois. Encha~nements dynamiques de tfiches pour des manipulateurs mobiles gt roues. PhD thesis, LGP-ENIT, Tarbes, France, 2005. [11] K. L. Doty, C. Melchiorri, and C. Boniveto. A theory of generalized inverses applied to Robotics. The International Journal of Robotics Research, 12(1): 1-19, February 1993. [12] M. H. Raibert and J. J. Craig. Hybrid position/force control of manipulators. ASME Journal of Dynamic Systems, Measurement and Control, 1981. [13] S. Kang, K. Komoriya, K. Yokoi, T. Koutoku, and K. Tanie. Utilization of inertial effect in damping-based posture control of mobile manipulator. In Proceedings of the 2001 IEEE International Conference on Robotics and Automation, pages 1277-1282, Seoul, Korea, 2001.
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Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Fuzzy and Neuro-fuzzy Based Co-operative Mobile robots D T Pham, M H Awadalla and E E Eldukhri M a n u f a c t u r i n g E n g i n e e r i n g centre Cardiff University Cardiff CF24 3AA, UK
Abstract
This paper focuses on the development of intelligent multi-agent robot teams that are capable of acting autonomously and of collaborating in a dynamic environment to achieve team objectives. A biologically-inspired collective behaviour for a team of co-operating robots is proposed. A modification of the subsumption architecture is proposed for implementing the control of the individual robots. The paper also proposes a fuzzy logic technique to enable the resolution of conflicts between contradictory behaviours within each robot. Furthermore, the paper proposes a neuro-fuzzy based adaptive action selection architecture that enables team of robot agents to achieve adaptive cooperative control to perform two proof-of-concept co-operative tasks: dynamic target tracking and boxpushing. Simulated and real experiments have been conducted to validate the proposed techniques. Keywords: Multiple Mobile Robots, Behaviour Coordination, Fuzzy Logic technique, Neuro-fuzzy Technique.
1. Introduction
Biological agents, for example social insects, have been manifestly successful in exploiting the natural environment in order to survive and reproduce. Scientists are interested in understanding the strategies and tactics adopted by such natural agents to improve the design and functionality of computer-based artificial agents (robots). They observe how these social insects locally interact and co-operate to achieve common goals. It seems that these creatures are programmed in such a way that the required global behaviour is likely to emerge even though some individuals may fail to carry out their tasks. In this paper, a biologically-inspired group behaviour for a team of co-operating of mobile robots is proposed. Since co-operation among a group of
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robots working in an unknown environment poses complex control problems, it is necessary to obtain solutions that achieve a suitable trade-off between the objectives of the robots that can potentially conflict [ 1]. This highlights the problem of deciding what action to select next as a major issue in the design of systems for control and co-ordination of multiple robots. For this purpose, a new approach of fuzzy logic technique for behaviour coordination in co-operative target tracking is proposed. Achieving adaptive co-operative robot behaviour is more challenging. Many issues must be addressed in order to develop a working co-operative team; these include action selection, task allocation, coherence, communication, resource conflict resolution, and awareness. Therefore, a neuro-fuzzy based action selection architecture is proposed that enables these
robots to achieve adaptive cooperative control despite dynamic changes in the environment and variation in the capabilities of the team members. The remainder of the paper is organised as follows. Section 2 outlines the background related to the theme of the paper. Section 3 presents collective behaviour of social insects and co-operating mobile robots. Section 4 describes fuzzy-logic-based dynamic target tracking behavioural architecture. Section 5 describes adaptive co-operative action selection architecture. Section 6 shows simulation results. Section 7 describes mobile robots hardware and real experiments. Finally, Section 8 gives the conclusion.
2. Background The research that closely relates to the topics presented in this paper includes that of [2], they presented a collective robotics application whereby a pool of autonomous robots regroup objects that are distributed in their environment. A team of real mobile robots that co-operated based on the ant-trail-following behaviour and the dance behaviour of bees is presented [3]. An interesting example of decentralised problemsolving by a group of mobile robots is given [4]. A collaboration in a group of simple reactive robots through the exploitation of local interactions is investigated [5]. New methods for tracking ball and players in soccer team and team coordination approaches are proposed [6]. Mobile robot navigation and co-operative target acquisition examples are given, in which the principles of multiple objective decisionmaking (MODM) are demonstrated [7]. Desirability functions as an effective way to express and implement complex behaviour coordination strategies were espoused [8]. An action selection method for multiple mobile robots performing box pushing in a dynamic environment is described [9]. A multi-channel infrared communication system to exchange messages among mobile robots is developed [10]. A description of LALLIANCE architecture that enables teams of heterogeneous robots to dynamically adapt their actions over time is given [11 ].
3. Collective Behaviour of Social Insects and Cooperating Mobile Robots The collective dynamic target tracking task investigated here is based on the emergence of collective strategy in prey-predator behaviour, where the predators co-operate to catch the prey or the prey
co-operate to defend themselves. The term collective is used in the sense of the collective motion ofdefence or attack. The dynamics of predator-prey interactions where the predators surround the prey to catch it using local sensor-based interactions among them have been implemented in the task of dynamic target tracking. In this paper, the subsumption architecture is modified to comprise more than one behaviour module within one layer run in parallel and have the same priority and to allow information exchange between the layers as shown in figure 1. The design of the targettracking controller begins by specifying the sensing requirements for the task. Collision free movement will require an obstacle sensor; to follow other robots needs a robot sensor; tracking the target will require a target or goal sensor. The lowest priority default behaviours are the "search" and "listen for messages" behaviours. "Search" directs the robot to advance along its current path. Simultaneously, "listen for messages" makes the robot receptive to messages sent by other mobiles. The above default behaviours can be suppressed by the "follow message sender" behaviour if a message has been received from another robot (by means of the robot sensor on the current robot). "Follow message sender" causes the robot to move to its nearest sensed neighbour. The "send message" and "approach goal" behaviours are activated by the goal sensor. "Send a message" makes the robot issue a "target intercepted" message to the other mobiles and "approach goal" directs them towards the target. "Approach goal" causes the robots to turn a number of degrees towards the target while the goal sensor is active. The task is accomplished once several robots collectively have captured the target. The highest priority avoid behaviour becomes active and remains active as long as the obstacle sensor has detected an obstacle. Avoid behaviour turns the robot a fixed number of degrees away from the sensed obstacles at each simulation time step prevents collisions.
4.
Fuzzy-Logic-Based Dynamic Target Tracking Behavioural Architecture
Even though the modified subsumption architecture allows more than one behaviour to run simultaneously, however only a behaviour requires to activate the robot actuators modules will get the control of robot actuators at a time. The question arises here is how to control robot actuators when several main behaviours are activated simultaneously. To address this issue, an approach based on fuzzy sets operations
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Fig. l" target - tracking robot architecture is proposed here that takes into account the recommendations of all applicable behaviour modules. Behaviour coordination is achieved by weighted decision-making and rule-based (behaviour) selection. The weights used for weighted decision-making are the degrees of confidence placed on the different behaviours. They are empirical measures of applicability of particular behaviours. The fuzzy-logicbased architecture for mobile robots, in the context of a dynamic target tracking system, consists of several behaviours, such as target following and obstacle avoidance. Multiple behaviours could share a common fuzzy inference module. Fuzzy control recommendations generated by all behaviours are fused and defuzzified to generate a final crisp control command. The basic algorithm executed in every control cycle by the architecture consists of the following four steps: (1) the target following behaviour determines the desired turning direction; (2) the obstacle avoidance behaviour determines the disallowed turning directions; (3) the command fusion module combines the desired and disallowed directions and (4) the combined fuzzy command is converted into a crisp command through a defuzzification process. 5. Adaptive Action Selection Architecture
To maintain a purely distributed co-operative control scheme which affords an increased degree of robustness, individual agents must always be fully autonomous, with the ability to perform useful actions even amidst the failure of the other robots. An adaptive action selection architecture based on neuo-fuzzy technique (figure 2) is developed to be fully distributed, and giving all robots the capability to determine their own actions based upon their current
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Commands1 Actuators Fig.2: Adaptive action selection
architecture
situation, the activities of other robots and the current environmental conditions. The monitor function, implemented within each robot, is responsible for observing and evaluating the performance of any robot team member (including itself) whenever it performs a behaviour. A neuro-fuzzy technique has been used to finetune the fuzzy rules and minimise the total error between the desired output and the fuzzy controller output. The structure of the neuro-fuzzy system is shown in Fig.3. The network structure contains six layers. Nodes in layer one are input nodes that represent input linguistic variables. Nodes in layer two are input term nodes that act as membership functions to represent the terms of the respective N input linguistic variables. The nodes in layer three are rule nodes, where each node associates one term node from each term set to form a condition part of one fuzzy rule. The nodes in layer four are output term nodes that act as membership functions to represent the output terms of the respective L linguistic output variables. The number of nodes in layer five is 2L, where L is the number of output variables, i.e. there are two nodes for each output variable. The function of these two nodes is to calculate the denominator and the numerator of a quasi Centre of Area (COA) defuzzification function. The nodes in layer six are defuzzification nodes. The number of nodes in layer six equals the number of output linguistic variables. The structure of the neurofuzzy system is created in three steps. The first step is to specify the input and the output variables of the network. The second step is to divide the input-output universes into a suitable number of partitions (fuzzy sets) and to specify a membership function for each partition. The third step is to generate fuzzy rules to perform the input-output mapping of the fuzzy logic system.
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Fig.3: Neuro-fuzzy structure perform the input-output mapping of the fuzzy logic system. Following this construction phase, the system then enters the parameter learning phase to adjust its free parameters. The adjustable free parameters are the centre (mij s) and width (cYijs) ofthe term nodes in layer four as well as the link weights in layers two and six. A supervised learning technique is employed in conjunction with the back propagation (BP) learning algorithm to tune these parameters. 6. Simulation
The objective of the developed simulation tool is to test the proposed architecture based on the context of the co-operative tasks of dynamic target tracking and box pushing.
6.1. Dynamic target tracking task For this task, a simulated environment has been designed to model a large population of robots (a few thousand), different obstacles and one target. Two kinds of sensors were simulated: obstacle detection sensors and target detection sensors. Three ultrasonic sensors were modelled to provide information on obstacles to the left and the right, and in front of the robot. The same models were used for the ultrasonic sensors fitted to the moving target. Target detection was simplified by using an infrared source at the centre of the target and infrared target sensors mounted on the robots. Two actuators were modelled, one for each motor (left and right). Experiments were run with different numbers of robots and different obstacle densities. Each experiment on a collection of robots was performed thirty times and the results were averaged. The first experiment analysed how varying the number of robots affected the time required to track
(capture) the target. This experiment took place in a limited arena containing one small target and no obstacles. The second experiment differed from the first only by the addition of obstacles in the arena. Figure 4 shows that increasing the number of robots reduced the time required to track the target. However, robot collision and interference tended to degrade the performance. Adding more robots did therefore produce a proportional increase in performance. The first and second experiments were repeated to investigate the application of fuzzy logic technique for behaviour coordination. Fuzzy logic enables to solve conflicts between the contradictory behaviours by selecting an action that represents the consensus among the behaviours as shown in figure 5.
6.2. Box-pushing task The objective in this task is to find a box, randomly placed in the environment, and push it across a room. The box is so heavy and long that one robot cannot achieve this alone. The Webots simulation tool [12] was used to implement this task. This software operates in three dimensions and enabled the modelling of robots, sensors, actuators, and obstacles, as well as a set ofbehaviour modules in order to map sensor inputs to actuator outputs. Experiments in [11 ] are repeated for comparison. In the first experiment, two robots cooperate to find a box and push it across the room, with no obstacles in the environment. In the second experiment, obstacles are added that obstruct one of the two robots to investigate how the other one dynamically reselects its actions in response to changes in the mission situation. The robots are initially situated randomly in the environment and they then start to locate the box (figure 6a). After both of them have reached the, it is assumed that the robot at the left end of the box starts to push first (figure 6b). 4O cluttered - o -
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From sensory feedback and acquired knowledge (learned off line [13]), the box has then to be pushed from the right end. The robot at the right end starts to push and broadcasts that action to the robot at the left end. During the expected time for that action, the robot at the left end monitors the performance of its team mate. In the first experiment, the robots completed the task. In the second experiment, one of them is stuck because of obstacles in the environment while the other has reached the box. Because there is no progress from the other robot, the robot that reached the box starts to push the box from one end. Then it moves to the other end to push (figure 6c). It continues its back and forth pushing executing both pushing tasks as long as it fails to hear that another robot is performing the push at the opposite end of the box. 7. Mobile Robots Hardware and Real experiments
Fig. 6.a: Initial environment .
.
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Fig. 7" The robots and target
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Small radio-controlled toy cars and a small radiocontrolled toy tank were adapted to provide the mechanical structures for the mobile robots and moving target, respectively (see figure 7). The control system for the robots and target was purpose designed for this application as the existing radio-operated controllers in the toy cars and toy tank were not suitable. A robot can detect another robot approaching it from either side of it because one of the side sensors of the first robot will be activated by the signal emitted by the approaching robot. A robot can also distinguish between a target and another robot located on either side of it because of the different signals they emit. Experiments were run with two or three robots, different obstacles and one target. The first set of experiments analysed how varying the number of robots affected the time required for tracking the target. This experiment took place in a limited arena containing one target and no obstacle. The second set of experiments differed from the first set only by the addition of obstacles in the arena. Figure 8(a) depicts an intermediate stage of three robots are tracking the target. Figure 8(b) shows the final stage when the robots have cooperated and captured the target. It was found that the time required to track and capture the target using three robots was approximately 2 minutes. With only two robots, the required time was about 4 minutes. In the case where three robots and obstacles were included, the time was 7 minutes.
M., (2000). Blazing a trail: insect-inspired resource transportation by a robot team. In Proc. of the fifth Int. Symposium on Distributed Autonomous Robotic Systems (DARS), Knoxville, TN, pp. 111-120. [4] Kube, C. R. and Bonabeau, E. Cooperative transport by ants and robots. Robotics and Autonomous Systems, 2000, Vol. 30, pp. 85-101. Fig. 8(a): Intermediate stage
[5] Ijspeert, A.J., Martinoli A., Billard A. and Gambardella, L.M. Collaboration through the exploitation of local interactions in autonomous collective robotics: the stick pulling experiment, Autonomous Robots. 2001 ,Vol. 11, No.2, pp 149-171. [6] Weigel,T., Gutmann, J., Dietl, M., Kleiner, A., Nabel, B., Freiburg, C. Coordinating Robots for Successful Soccer Playing. IEEE Trans. On Robotics and Automation, 2002,18(5) 685-689.
Fig. 8(b): the robots captured the target 8. Conclusion
The use of fuzzy logic enabled the resolution of conflicts between contradictory behaviours by selecting an action that represents the consensus among the behaviours and that best satisfies the decision objectives encoded in them. Furthermore, the proposed co-operative robot architecture has been shown to allow robot teams to perform real-world missions over long periods, even while the environment or the robotic team itself changes.
Acknowledgement
The authors are members of the EU-funded FP6 I'PROMS Network of Excellence. References
[1] Hu, H. and Gu, D. Hybrid Learning Architecture for Fuzzy Control of Quadruped Walking Robots, Int. Journal of Intelligent Systems, 2005, Vol. 20, No. 2, pp. 131-152. [2] Chantemargue, F., and Hirsbrunner, B. A collective robotics application based on emergence and selforganisation. In Proc. of Fifth Int. Conf. for Young Computer Scientists, Nanjing, China, 1999, pp. 1-8. [3] Vaughan, R., Stoey, K., Sukhatme, G., and Mataric,
[7] Pirjanian, P. and Mataric, M. Multi-robot target acquisition using multiple objective behaviour coordination. In Proc. of the 2000 IEEE Int. Conf. on Robotics and Automation, San Francisco, CA, 2000, pp. 101-106. [8] Saffiotti, A., Zumel, N.B. and Ruspini, E.H. Multirobot team coordination using desirabilities. Proc. of the sixth Int. Conf. on Intelligent Autonomous Systems (IAS), Venice, Italy, 2000, pp. 107-114. [9] Yamada, S. and Saito, J., (1999). Adaptive action selection without explicit communication for multirobot box-pushing. 1999 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS'99), pp. 14441449. [ 10] Hu, H., Kelly, I. and Keating, D. Coordination of multiple mobile robots via communication. In Proc. of SPIE'98, Mobile Robots XIII Conf., Boston, 1998, pp.94-103. [11] Parker, L. E. Distributed algorithms for multirobot observation of multiple moving targets. Autonomous Robots, 2002, Vo|. 12, No. 3, pp. 231255. [12] Michel, O. Webots: Symbiosis between virtual and real mobile robots. In Proc. of the first Int. Conf. on Virtual Worlds, VW' 98, San Mateo, CA, 1998, pp. 254-263. [13] Awadalla, M. Adaptive Co-operative Multiple Mobile Robots. PhD thesis, Manufacturing Engineering Centre, Cardiff niversity, 2005.
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Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Multi-agent snake-like motion with reactive obstacle avoidance G.I. Birbilis a, N.A. Aspragathos ~ a
Department of Mechanical Engineering & Aeronautics, University ofPatras, Panepistimioupoli Rion, Greece
Abstract
A geometric constraint satisfaction approach to serial linkage (chain) motion and reactive obstacle avoidance, based on a multi-agent architecture, is presented. Application of this architecture to achieve real-time motion planning for serial manipulators and mobile robot snake-line swarm formations is suggested. A hierarchy of"Master- Slave" relationships is used, with the event of an autonomous motion of a controller agent propagating to its two neighbouring ones in the formation chain and progressively further on towards the two endpoints of the chain. At each propagation step, a constraint preservation mechanism enforces the respecting of minimum and maximum distance constraints between pairs of consecutive chain agents. The emergent behaviour of the multi-agent system equals to having the moving part of the chain push or pull the two subparts ofthe chain it connects. To cater for fixed base manipulators, and to allow replaning in case some slave part of the chain formation can't adapt to its master's motion, being trapped in some obstacles or malfunctioning, the notion of a "Master- Vetoable slave" relationship is used, where a slave part can object (veto) to the motion of its master part. Keywords: Reactive snake-like motion, Redundant manipulators, Swarms, Obstacle avoidance, Geometric constraint satisfaction, Multi-Agent systems, Master- Vetoable slave relationship, Push-Pull-Rotate behaviour
I. Introduction
In this paper we consider the usage of a geometric constraint satisfaction approach to calculate a virtual serial linkage (chain) motion, which could be a serial manipulator or a mobile robot snake-line swarm formation, with its environment possibly containing unknown stationary or moving obstacles. It is supposed that each part of the chain receives sensor feedback concerning its proximity to obstacles, in order to react accordingly. Many approaches to the problem of motion planning in a time varying environment have been proposed, most of which are thoroughly examined in Hwang and Ahuja [1 ]. The case of unknown moving obstacles existing in the environment greatly favours
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local planning methods, since global re-planning would be too expensive, especially in the case of many obstacles or when having to plan for a system with many degrees of freedom (highly redundant), as pointed out by Chen and Hwang [2], and Challou et al. [3]. The more degrees of freedom (DOFs) a system has, the higher its flexibility is, so we mostly value an approach that easily scales up to highly redundant systems, while still supporting those with less DOFs. A top-down centralized approach would have increasing complexity and cost as the number of DOFs rises, so a bottom-up, modular approach is suggested, modelled as a multi-agent system, using the multi-agent system design concepts presented by Liu [4]. In the mobile robotic swarms [5] field, Dorigo et
al. {6], while evolving self-organizing behaviours for their "swarm-bot", experimented with eight robots connected by flexible links to create a snake formation and observed that they were able to negotiate a unique direction to produce coordinated movement along it and collectively avoid walls. Given the use of flexible links, the swarm-bot tends to change its shape during coordination phases and during collision with obstacles. Also, because swarm members tend to maintain their direction of movement, the system displays an ability to go through narrow passages, eventually deforming its shape according to the configuration of the obstacles. In the robotic manipulators' field, almost all the approaches based on a multi-agent platform to address the motion planning problem are doing motion planning on each subpart of the manipulator structure and combine the solutions of those sub-problems into a solution for the global problem. In most of the cases, composition is implemented in real-time, via interaction (cooperation or contention) of the respective agents that control the various parts of the manipulator. Overgaard et al. [7], proposed a multiagent system comprised of both joint and link agents, to control a 25-DOF snakelike robot in an environment with obstacles which are modelled using an artificial potential field, pioneered by Khatib [8]. Bohner and Lfippen [9], applied a similar concept on a 7-DOF robot, considering only the robot joints as agents. They are doing both sensor-data integration and motion planning on a per-agent basis, thus decomposing the problem and reducing its complexity to the sum of its subproblems. In our previous work [10], we proposed a multiagent system where each software agent controls a specific part of a planar manipulator' s joint-link chain, and agents interact with each other towards the adaptation of the manipulator configuration to external events and changing situations in real-time. We presented a geometric constraint satisfaction approach that reduces the motion planning of a manipulator to motion planning of a single part of it, for instance the manipulator tooltip, having the rest of the manipulator chain parts react and adapt or object (veto) to the moving part' s motion. In this paper, we slightly revise the agent interactions and implement the proposed architecture in both the 2D and 3D space, for either physical chain linkages (manipulators) or virtual ones (swarms of mobile robots), presenting new findings on the potential applications of this approach.
2. Problem definition We're considering the case of a 3D serial chain structure where every two consecutive chain elements have to maintain a given minimum and maximum distance constraint between them, and every three consecutive elements have to maintain a minimum and maximum angle constraint. The chain can be a serial manipulator which can have both rotational and translational joints, in any combination. The base of the manipulator may be fixed or able to move around either freely or on some predefined path. Alternatively, the chain can represent a formation maintained by a swarm of mobile robots, with each two consecutive robots in the chain maintaining a directional communication link (e.g. short range optical or microwave), or using vision in order for a follower robot to be able to detect the motion direction of its leading robot relative to itself. The environment may contain obstacles, either stationary or moving with a speed comparable to the manipulator joint motors (or to the mobile robots in the 2 nd case) speed. The case of moving obstacles includes the case of other entities (mobile robots, manipulators, humans) also acting in the same environment, with which no communication channel or common protocol exists for coordination. No a-priori knowledge of obstacles is assumed and incoming collisions can be detected at each part of the chain using either touch sensors or range sensors.
3. Multi-Agent approach Liu et al. [11 ] define an agent as an entity that is able to live and act in an environment, is able to sense its local environment, is driven by certain objectives and has some reactive behaviour. A multi-agent system is defined as a system that has an environment, that is a space where the agents live, a set of reactive rules, governing the interaction between the agents and their environment (the laws of the agent universe) and of course a set of agents. In our multi-agent system model, we control the chain's motion in a way that mimics the motion of a chain of rods, interconnected at their endpoints with connectors (potentially constrained universal joints). The chain's two endpoints are connectors, while some rods could be resizable, having a minimum and maximum length instead of a fixed one. Any part ofthe chain that initiates a motion "'pushes" or "pulls" the
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other parts of the chain so that the constraints defining the chain structure are kept. Given a high number of rods and respective connectors, this model behaves like a snake crawling amidst obstacles.
of M a s t e r - Vetoable Slave relationships, where the first entity is the master of the second entity, the second entity is the master of the third entity and so on.
3.3 Change event propagation 3.1. Mapping kinematic to conceptual constraints In the case of a manipulator chain, the base connector (that links to the environment) is used to cater for a potentially mobile (free or partially/totally constrained) manipulator base. A rotational joint and its outgoing link map directly to the first connector of a fixed-length rod, whereas a translational joint and both its incoming and outgoing links are represented by a connector and an expandable rod. Each connector agent has a position property and each rod agent has a length property, such that the length bounds of a rod are respected by the positions of the connector agents placed at the rod's two endpoints. The values of joint parameters can be calculated geometrically from the connector positions at any time, using the mapping shown in Fig. 1. In the case of a mobile robot swarm formation, the rods represent distance constraints between pairs of consecutive robots, while the connectors represent angular constraints between triplets of consecutive robots in the chain formation. Mapping between conceptual and kinematic model in swarms robot chains is done the same as described above for manipulator chains.
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State change events at the head of a master-slave relationship chain propagate towards the tail of it, since each slave tries to react and adapt to its master's state change event, changing its state and thus causing its own slave to react and try itself to change its state and so on, as shown at the left side of Fig. 2. Any part of the chain and not just the chain endpoints can initiate the chain motion, acting autonomously to avoid some obstacle or receiving its own motion commands from a planner module or a human operator. The chain is treated as two sub-chains, both having as head the part that initiated the motion, and for tails the head and the tail of the original chain respectively, as shown at the right side of Fig. 2. To cater for fixed base manipulators, and support replaning in case some slave part of the chain can't adapt to its master's motion because it's trapped in some obstacles or malfunctioning, a slave part of the chain can object (veto) to the motion of its master part if it cannot move to adapt to the master's motion while still preserving the chain model and environmental constraints. At the right side of Fig. 2, a manipulator with a fixed base is shown, where the connector agent at the manipulator's fixed base always objects to any relocation of it. 9 )
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3.4. Reactive agent ruleset
We define a Master- Vetoable slave relationship between two entities, a "master" and a "slave", to be such that the slave entity listens for changes of the master entity's state and reacts to them either by changing its own state or by objecting to its master changing state (veto). The slave entity of such a relationship can still take part as a master in other relationships, having its own slave entities. So, given a finite number of entities, one can define an open chain
Each slave entity follows a predefined rule set that defines how it reacts to the changes of its master entity's state. That rule set defines reactions that try to preserve some constraints imposed on the slave object and its relation to the master one, either by design (internal, hardware constraints) or decided on the fly at runtime (environmental constraints, obstacles or failure of subparts). The reaction rules we employ are exposing two
586
basic model constraint preservation behaviours, named Push and Pull, with the former one caused when the master-slave connectors' distance exceeds the minimum length of the rod that connects the two connectors, and the later one caused when that maximum length is exceeded. When a master connector is "pushing" a slave connector, an immediate neighbour of it on the chain, the effect is that the slave connector moves on the guiding line defined by the new position of the master and the old position of the slave respectively. The slave is pushed away on the guiding line only if its distance from the new position of the master is less than the minimum length of the (potentially shrinkable) rod that connects it to the master connector. A similar situation exists when a moving master connector is "pulling" a slave one. The slave connector is pulled on that same guiding line as before, but only if its distance from the master exceeds the maximum length of the (potentially expandable) rod that connects it to the master connector, as illustrated in Figs. 3-4. The pushing and pulling actions of a master connector on its slave connector neighbours are obtained via the respective slave rods that connect the slave connectors to their master. pull
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During the reaction of a slave connector to the motion of a master connector towards a new position in the free space, a potential obstacle collision for the rod that connects the master and slave connectors may block the realization of the new placement of the slave connector that is obtained using the push or pull behaviours described at section 3.4. In such a case, the rod rotates around the master connector as if the motion of the master connector was causing a rotation (at the rod's obstacle collision point that is closest to the master) of the guiding line on which the rod would
have lied if the obstacle wasn't there, as shown in Fig. 4. We name this environmental constraint preservation behaviour as Push-Pull-Rotate. I
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For navigation of the motion initiating agent (controller) we extended a wall following algorithm with target seeking and corridor passage behaviours, having the following steps: 1) A reading of the left-front and right-front side (laser scanner) sensors is acquired to get the closest distance to visible obstacles at the left and right side of the controlling agent respectively, as shown in Fig. 6. 2) Then, based on the sensor readings above, the mover's position adjustment is calculated. To achieve a minimum and maximum distance from obstacles (we experimented in simulation with 0.7 and 0.9 meters respectively), we define the mover's reactive behaviour using a stack of subsumption layers [12]. The lower subsumptive layers potentially replace the behaviour exposed by the higher layers, with the collision avoidance being the lowest behavioural layer. 2.1) KEEP-MOVING: The highest layer dictates the start and continuiment of motion, keeping the current mover direction. If a specific direction the mover should follow to a target is available (for example if the environment is characterised by a force field gradient function), that direction could be used instead. 2.2) TARGET-CHECK: This layer checks for target reach in order to stop the motion, so that any target handling behaviour can then be initiated. 2.3) FAR-RIGHT: Checks if the right sensor gives a distance reading above the maximum distance and turns the mover to the right. Tactile pressure or IR and Sonar sensors could also be used; sensing touch or close collision with an obstacle, instead of trying to keep some predefined minimum distance from the obstacles. 2.4) FAR-LEFT: Checks if the left sensor gives a distance reading above the maximum distance and
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above the right sensor reading (that was acquired at the previous layer) to turn the mover to the left. We avoid overriding the previous layer's behaviour if the mover is very far from obstacles at both the right and the left side, for optimization reasons. In hardware based multilayer implementations one might better choose to not exchange much information between subsumptive layers or read the right sensor again at this layer and thus override the previous layer's behaviour and go towards the left if the mover is far from obstacles at both the left and the right side. 2.5) CLOSE-RIGHT: A check is done if the right sensor gives a distance reading below the minimum distance and to avoid an eminent collision the mover must turn to the left. 2.6) CLOSE-LEFT: At the lowest layer, an eminent collision check is done using the left sensor, turning to the right if needed. Again here we do an optimization as in layer 2.4 above, which could be skipped at a hardware implementation for simplicity in the layers' design and to allow easier exchange of the subsumptive layers' order to experiment with the emergent behaviour of the system (the layer stack).
The above algorithm enables passing through narrow corridors (even when they have high curvature) which was the main goal behind its design. Also it should work with a dynamically changing environment and moving obstacles, as long as they have a speed comparable to the mover's speed.
4. Experiments A Pascal and Logo scriptable testbed application for 3D simulations has been developed using the Object Pascal programming language and RemObjects PascalScript scripting engine, combined with an implementation of a Logo to PascalScript transcoding compiler. The proposed multi-agent architecture has been implemented as a Pascal language script in this environment, shown in Fig. 5, achieving snake-like realtime motion at numbers of 450 and more agents.
Table 1 Subsumptive layers a Priority: Layer
Trigger condition Output
6: KEEP-MOVING Always vl--vr=s 5: TARGET-CHECK Target detection vl, vr to target 4: FAR-RIGHT Leaving right wall vl=s, vr=0 3: FAR-LEFT
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Fig. 5. Snake-like manipulator motion in the 3D space. A 2D version of the same architecture has been implemented at the MobotSim mobile robots simulator for a swarm of mobile robots using SAX Basic script, where obstacle avoidance in narrow curved corridors has been achieved for a wandering controller robot equipped with a laser scanner and 15 slave robots following it in a snake like formation using simpler close-collision detection sensors, as shown in Fig. 6.
avr: right (virtual) wheel speed, vl: left (virtual) wheel speed, square brackets denote optional trigger optimization. The output after the final one of the above layers is the resulting mover behaviour. At that point, regarding turning, the mover can turn left or right immediately by a prearranged number of degrees, or turn more depending on the error between the current distance and the offended distance limit (be it min or max distance that needs to be preserved), or even use the notion of a left and right wheel speed setting (be it real or virtual wheels depending on the problem) and thus turn gradually as time elapses.
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Fig. 6. Swarm evasion from highly curved corridor.
operator that is controlling it, since the rest of the chain can't follow its motion.
5. Future work
We plan to implement collision detection at the 3 D simulator as well, where given the modular design we used, minimal changes should only be required to the implementation of the constraint preservation rules, having the rest of the multi-agent system and event propagation design kept intact. Also, we wish to investigate further and measure the performance of this system in acquiring solutions for the inverse kinematics problem of highly redundant manipulators and physical or virtual (e.g. snake-like moving swarms) reconfigurable chain linkages in general (see Fig. 7). Currently the 2D simulation has been observed to automatically provide an inverse kinematics solution and reconstruction of a chain that had been manually broken into scattered or even to fewer parts during a simulation pause, with the reconstructed solution seeming to respect the relative formation of non-broken parts at the two ends of the chain where possible.
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6. Conclusions
Our architecture reduces the problem of path planning for chain formations to that of planning for only a master part of the chain (usually the tool-tip or leading mobile robot). The other chain parts adjust in real-time to the master's motion and at the same time react to avoid sensed obstacles. Best application should be at highly redundant or reconfigurable manipulators and swarms with high number of robots trying to move through narrow corridors. At such settings it can scale up efficiently, since an agent only needs to sense obstacles locally and interact with its two neighbouring agents in the control chain. This architecture isn't concerned with the situation where a veto from a slave agent back-propagates to the original motion initiating agent. In that case, the motion originator should replan its motion if it's an autonomous mover, or let the veto propagate further to a planner module or human
Acknowledgements University of Patras is partner of the EU-funded FP6 Innovative Production Machines and Systems (I'PROMS) Network of Excellence. http://www, iproms, org
References
[1] Hwang, Y., and Ahuja, N. Gross Motion Planning- A Survey. ACM Computing Surveys, no 3, vol 24 (1992) 219-291. [2] Chen, P., and Hwang, Y. Sandros, a motion planner with performance proportional to task difficulty. Proceedings of IEEE Int. Conf. on Robotics and Automation (1992). [3] Challou, D., Boley, D., Gini, M., Kumar, V., Olson, C. In: Kamal Gupta and Angel P. del Pobil (Eds.) Practical Motion Planning in Robotics: Current Approaches and Future Directions, 1998. [4] Liu, J. Autonomous Agents and Multi-Agent Systems: Explorations in Learning, Self-Organization, and Adaptive Computation. World Scientific, Singapore (2001). [5] E. Bonabeau, M. Dorigo, and G. Theraulaz. Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York, NY, 1999. [6] Dorigo, M., Trianni, V., Sahin, E., Labella, T., Grossy R., Baldassarre, G., Nolfi, S., Deneubourg J-L., Mondada, F., Floreano D., Gambardella, L.M. Evolving Self-Organizing Behaviours for a Swarm-bot. Swarm Robotics special issue of the Autonomous Robots journal, 17(2-3) (2004) 223-245. [7] Overgaard, L., Petersen, H., and Perram, J. Reactive Motion Planning: A Multi-agent Approach. Applied Artificial Intelligence, no 10 (1996) 35-51. [8] Khatib, O. Real-time obstacle avoidance for manipulators and mobile robots. International Journal of Robotic Research, no. 5 (1986) 90-98. [9] Bohner, P., and Lfippen, R. Reactive Multi-Agent Based Control of Redundant Manipulators. Proceedings of the 1997 IEEE Int. Conf. on Robotics and Automation, Albuquerque, New Mexico (1997). [10] Birbilis, G. and Aspragathos N. In: Lenarcic J. and Galletti C. (Eds.) On Advances in Robot Kinematics. Kluwer Academic, Netherlands, 2004, pp 441-448. [11] Liu, J., Jing, H., and Tang Y., Multi-agent oriented constraint satisfaction. Artificial Intelligence no.136 (2002) 101-144. [ 12] Brooks, R. A. A robust layered control system for a mobile robot. IEEE Journal of Robotics and Automation, RA-2, April (1986) 14-23.
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Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Path planning in weighted regions using the Bump-Surface concept E.K. Xidias a, N.A. Aspragathos a a
Department of Mechanical & Aeronautical Engineering, University ofPatras, Patras 26500, GR
Abstract In this paper, we introduce a new method based on the Bump-Surface concept for global, near-optimal, motion planning of a robot moving in 2D weighted regions with arbitrary shape and size. The proposed method generates a near-optimal path from a start point to a destination point, conformal to the objectives and criteria of motion planning, by performing a search on the modified Bump-Surface. Extensive experimental work shows the efficiency and the effectiveness of the proposed method in a variety of complex weighted environments. Keywords: Path planning, 2D weighted regions, Bump-Surface
1. Introduction Path planning is an important problem in robotics [1] with many applications in other research areas such as computer graphics and geometric information system. In most approaches a planner is required to determine the shortest path between a start point and a destination point, according to a user defined metric on paths. A real robot environment consists of different regions such as hazard regions, obstacles, grass-land, etc. So it is natural to associate each region with a weight for specifying the "cost per unit distance" of moving in that region. It must be noticed that, a weighted region is an area where the "cost per unit distance" does not vary with the direction of the motion. Several researchers introduced methods to solve the problem of motion planning in 2D weighted region terrains [2-7]. They use a discretization of the planar space, which is subdivided into polygonal regions, based on edge subdivision and Dijkstra's [8] or similar algorithm to determine an optimal path in the graph induced by discretization. These methods can guarantee to determine a near-optimal path for
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any two points s (start) and g (goal) in the space. However, the discretization size and therefore the time complexity depends on the number of Steiner points inserted on the discretization scheme and on the total number of vertices of the weighted regions. In addition, the resulted path is polygonal which is an unpleasant feature because the robot is forced to make a complete stop each time it meets a vertex of this polygonal path or a further processing of the path is necessary to smooth the comers. In this paper, we study the problem of determining an optimal path in a 2D environment subdivided in weighted regions with arbitrary shape convex or non-convex, polygonal or no-polygonal. Our method is based on the Bump-Surface concept [9]. The Bump-Surface represents the entire 2D environment through a single mathematical entity using a tensor product B-Spline surface [9] embedded in 3D Euclidean space, where the obstacles are represented by "bumps" and the free space by "valley". The final path is represented by a B-Spline curve satisfying the given optimization criteria and constraints. The proposed method using the Bump-Surface concept determines a near-optimal path on the
modified Bump-Surface using a tensor product BSpline surface where the weighted regions are represented by "tablelands" with a height representing the region's weight. The final motionplanning problem is formulated as a global optimization problem which is solved using Genetic Algorithms [11] with variable length chromosomes. In this way one is able to determine both the robot's path and the optimal number of control points that define it. The searching time of the introduced methods does not depend on the complexity of the workspace namely the number of weighted regions vertices since the search space is one entity, the modified Bump-Surface. In addition, the determined path can adopt easily more qualitative characteristics like the smoothness or some constraints on the curvature of the path [12]. This paper is organized as follow. The problem description is given in Section 2. Section 3 describes the motion-planning for a mobile robot, while Section 4 presents the experimental results. Conclusions in Section 5.
2. Problem Statement
Assume a mobile robot, which is modeled as a point moving in a 2D environment subdivided into weighted regions which have arbitrary shape. It is required to determine an optimal path for this robot from an initial position to a target position. The aim is to compute a path which satisfies the following criteria and constraints: i. The robot should travel from the start point to the destination point with minimum cost. ii. The path should be smooth with the minimum curvature. Further objectives can be included in the above criteria and constraints based on the requirements of the actual motion-planning problem. The robot's path is represented by a B-Spline curve defined in the 2D environment in order to take advantage of the local attributes and the well-known numerical stability of the respective computational implementations [9]. It must be noticed that, each region assigned a weight we [1,woh] where, a weight equal to 1 is assigned for the free regions and weight equal to Woh which is a sufficiently large constant for the obstacles.
3. Motion Planning
A new method is described in this section for global, near-optimal path planning of a mobile robot moving in 2D weighted region terrain satisfying the aforementioned criteria 2.i and 2.ii. We use the modified Bump-Surface construction to represent the entire 2D weighted terrain through a single mathematical entity using a B-Spline surface. The overall motion planning problem is formulated as a global optimization problem which is solved using Genetic Algorithms with variable length chromosomes. First we give the notion of the modified BumpSurface and then the application of the proposed method to resolve the motion-planning problem for a mobile robot moving on a 2D weighted region terrain. 3.1. The modified Bump-Surface concept
In the present paper, each 2D weighted region is represented by a closed boundary clockwise oriented curve which can have any shape convex or nonconvex, polygonal or non-polygonal. We consider that the robot environment is enclosed within a square [0,1]•
2 which in the following is
called normalized space. The normalized space is divided into equally spaced subintervals along the two orthogonal directions u and v, respectively, forming a grid of points Pij - (xij, Y o , z i j ) e [0,1]3 ,where i, j e [ 0 , N - 1] and N denotes the grid size. We assign to the z 0 coordinate of each grid point P0 the weight of the corresponding region where the point belongs. For example, if a region has a weight equal to w~ then the z 0 coordinate of each grid point which belong in this region take a value equal to wl , where Wob is Wob
the weight with the higher value. It must be noticed that, the z 0 coordinate of a grid point which lies on the boundary of two or more regions, takes the value of the weight which corresponds to the region with the lower weight. Using the grid derived from the above process, a mapping S :[0,1] 2 ---->[0,1]3 is constructed using a tensor product B-Spline surface with uniform knot vectors [9]. In the present implementation, the
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modified Bump-Surface is given as a B-Spline surface: N-1 N-1
S=S(u,v)-ZZii,p(U)ij,q(V)p
O.
(1)
i=0 j = 0
where, Mi, p (u) and mj,q(V) are the basis functions and p,q are the degrees in the u and v parametric direction of the surface, respectively. For example, fig. 2a illustrates a parametric space divided by two weighted regions where, the white region has weight equal to 1 and the grey region has weight equal to 5. Fig. 2b illustrates the corresponding modified BumpSurface.
the algorithm used to determine if a point is internal to a region [13]. In addition, the efficiency of the proposed method depends of the density of the grid size, increasing the density the search space becomes more detailed allowing solutions of higher accuracy. However the required time is very short compared to the searching time. The robot's path is given by a B-Spline curve defined in the 2D parametric space, K-1
C(t) = (u(t), v(t)) = Z MiA(t)Pi' 0 < t < 1 (2) i=0
where, K is the number of control points p/, Mi,d is the B-Spline basis function and d is the curve degree. Our variational curve design problem is focused in the definition of the ( K - 2 ) control points P i such that curve C satisfies the motionplanning conditions 2.i-2.ii. The first and last control points, namely P0 and p/(_l, are fixed to the initial and the target position of the robot.
(a)
3.1.1. Satisfying the objectives 2.i and 2.ii A valid path where the robot can travel from the initial position S(P0), which is the image of the P0 on S, to the destination position S(px_ 1), which is the image of the P x-1 on S , expressed by the following function: 1
1
0
0
where, S z (C(t)) denotes the z-coordinate of the (b) Fig. 2. (a) The initial 2D weighted environment. (b) The corresponding modified Bump-Surface. In the present implementation we use a (2,2)- degree B-Spline surface in order to represent the weighted planar regions with high accuracy and the normalized space is the actual parametric space of the modified Bump-Surface. It is clear that, the computational time required to construct the modified Bump-Surface depends only on the algorithm which is used to determine the control points which define the B-Spline surface. Thus, the construction of Bump-Surface depends on
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image of the C(t) on S and the first part of Eq. (3) approximates the weighted arc-length of C when the image of C onto S crosses the "flat" areas, in other words the areas where the z-coordinate of the modified Bump-Surface has constant value. The second part of Eq. (3) expresses the requirement that the robot should travel in regions with small weight. In addition, is in Eq. (3) in order to "help" the algorithm to give a faster solution which satisfies the condition 2.i. The minimizer of Eq. (3) with respect to Pi, (1 < i < K - 2 ) leads to optimal curve which satisfies the condition 2.i. The condition 2.ii expresses the requirement for a smooth path without sharp corners and loops. This
requirement is expressed by the minimization of the following Equation" 1
Yllc"(,)l ,
(4)
o
with respect to P i, (1 _< i _
(5)
is a continuous collision-free path which satisfies the motion-planning objectives 2.i-2.ii. The scalars a~ + a 2 = 1, 0 < a I , a 2 < 1 are weight factors. For example, higher values for a~ enforce the optimization method to search for shorter paths ignoring to some degree their smoothness and vice versa. 3.2. Searching the optimal path
min(a,W* + a2Curv.* ) Pi
(9)
We have used GAs in order to solve the optimization problem expressed by the equation (9). We have adopted GAs motivated by the fact that GAs have been successfully applied to optimization problems with large and complex search spaces and their ability of reaching a global near- optimal solution even if the search space contains multiple local minima. In order to apply GAs one has to select the "genetic operators" which include crossover, mutation and selection. In the proposed method we use uniform crossover, a Gaussian mutation operator and the Roulette Wheel selection. In addition, since GAs are designed to provide a near-maximum of a given function we use the following fitness function in order to minimize E* : 1 F : ~ E*+Q
(10)
where, Q is given by:
Following the process from [9], the path C approximated by N , - 1 sequential chords, thus,
-cp
Q - R e R and
the j-th point of C(t) is given by, Np-2 K-1
(6)
C/--ZNi,d(tj)Pi i=0
where,
0 _<j <_N, - 1 .
R-
j=0
D Thus,
Equation
(3)
is
replaced by, Np-2 j;=O
(7)
Np -1
+ Z Sz(.:,~:) j=O
The Eq. (4) is replaced by, Np -1
Curv.*- ~ 1 j=0
Ic,,Ji
(8)
Taking the above into consideration the final global optimization problem is written as:
(11)
where, cp is the maximum number of control points (which is constant) and D is the weighted Euclidean length of the straight line connecting the start point and the destination point. It must be noticed that the function Q is incorporated in the fitness function (Eq.(10)) in order to allow the algorithm to determine the optimal number of control points that define the robot's path. Each chromosome represents a possible path for the robot as a sequence of control points which define the B-Spline curve of Eq. (2).
4. Experimental results In this section we present a series of experiments using the introduced methodology for solving the problem of motion-planning for mobile robots in 2D weighted regions.
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The overall method is implemented on Pentium IV 3.2 GHz PC using Matlab. The grid size is set to 80x80 and the general GA's control parameters are defined as follows: population size 100, number of generations 90, crossover rate 0.7 and probability of boundary mutation 0.0075. Finally, in all cp=lO and experiments we set N p = l O 0 , a 1 = 0.7,
02 = 0.3
in
order
to
increase
the
importance of the first two motion-planning objectives. Finally, we use a 2-degree B-Spline curve and a (2, 2)-degree B-Surface. Test Case I: Figure 3, shows a 2D environment subdivided into seven weighted regions which have convex or non-convex polygonal shape. Each region is associated with a weight which varies 1 to 7. The robot's path is defined by 5 control points and tends to be a straight line in areas with the constant weight avoiding jerky motion. The processing time is 5.46
Fig. 4. The initial 2D environment and the solution path.
Test Case IlL" Figure 5, shows a 2D weighted region terrain subdivided into four regions which have non-polygonal boundaries. The robot is moving only in the area with the lower weight and its path is defined by 3 control points. The processing time is 6.79 sec.
see.
Fig. 5. The initial 2D environment and the solution path.
Fig. 3. The solution path and the initial environment.
Test Case II: Figure 4, shows a 2D weighted environment subdivided into three non-convex polygonal regions. The robot passes through the region with the highest weight in order to move on a path with the minimum cost according the motionplanning criteria and constraints 2.i-2.ii. The robot's path is defined by 3 control points. The processing time is 3.65 sec.
Test Case IV." Figure 6, shows a 2D environment subdivided into four weighted regions with arbitrary shape. The robot is moving mainly in the area with the lower weight and passes very close to the regions with high weight. The robot's path is defined by 6 control points. The processing time is 8.145 sec.
Fig. 6. The solution path and the initial environment.
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Experiments have shown that the computational time does not depend on the weighted regions shape, which are not necessary polygonal but on the density of the grid size and on the actual path difficulty. It is also experimentally proven that the construction of the Modified Bump-Surface follows a linear graph versus the density of the grid. The computed paths are always smooth and satisfy all the motion planning criteria and constraints 2.i-2.ii. In addition, we noticed that in regions with the constant weight the robot tends to move almost a long a straight line.
5. Conclusions
In this paper, a new method is presented based on the Bump-Surface concept for motion-planning of mobile robots in 2D environments cluttered with weighted regions which have arbitrary size and shape. Using the modified Bump-Surface the solution to the motion-planning in weighted regions is searched in a higher dimension. The inverse image of the solution onto the initial environment provides the final path. Computer simulations show that the introduced method works efficiently in environments with high complexity. The computed path is always smooth, conformal to the motion-planning objectives and the computational time is quite low and it does not vary considerably versus the complexity of the scene. In the future work a systematic complexity analysis should be carried out.
A c kn owl edge me n ts
This work is supported by the Research Committee of the University of Patras as a part of the research project "An Optimal Motion Planning for a Robot based on Computational Geometry" under the K. Karatheodoris research program. University of Patras is partner of the EU-funded FP6 Innovative Production Machines and Systems (I'PROMS) Network of Excellence.
subdivisions. Proceedings of the 13th Annual Symposium on Computational Geometry, Nice, France, 1997. [3] Voros Jozef. Mobile robot path planning among weighted regions using quadtree representations. Proceedings on Computer Aided Systems Theory, pp 239-246, 1999. [4] Joseph S.B. Mitchell and Papadimitriou C.H. The weighted region problem: Finding shortest paths through a weighted planar subdivision. Journal of ACM, 38:18-73, 1991. [5] Joseph S.B. Mitchell. Shortest paths among obstacles in the plane. Proceedings of the 9th Annual ACM Symposium on Compuatational Geometry, pp. 308317, 1993. [6] Lanthier M., A. Maheshwari and J. Sack. Approximating weighted shortest paths on polyhedral surfaces. Proceedings 13th ACM Syrup. Computational Geometry, pp. 485-486. ACM Press, 46 June 1997. [7] Chert D., O. Daescu, X. Hu, X. Wu and J. Xu. Determining an optimal penetration among weighted regions in two and three dimensions. Proceedings 5th SCG'99, Miami Beach Florida. [8] Dijkstra. A note on two problems in connection with graphs. Numerische Mathematic, pp. 269-271, 1959. [9] Azariadis, P. and Aspragathos N. Obstacle representation by Bump-Surface for optimal motionplanning. Journal of Robotics and Autonomous Systems, Vol 51/2-3,129-150, 2005. [10] Piegl L., T. Wayne. The NURBS Book. SpringerVerlag Berlin Heidelberg, 1997. [11 ] Goldberg D.E. Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley Publishing Company, 1989. [12] Elias k. Xidias, Philip N. Azariadis and Nikos A. Aspragathos. Motion planning for car-like robots using the Bump-Surface concept. Accepted in journal CAGD'06. [13] Hormann K., A. Agathos. The point in polygon problem for arbitrary polygons. Computational Geometry 20 (2001) 131 - 144.
References
[1] Latombe J. C. Robot motion planning. Kluwer Academic Publishers, Boston, 1991. [2] Cristian S. Mata and Mitchell J.S.B. A new algorithm for computing shortest paths in weighted planar
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Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All rights reserved.
Self-Organising Locally Interpolating Map for the control of mobile microrobots H. Htilsen a, S. Fatikow a, D.T. Pham b, Z. Wang b a
Division of Microrobotics and Control Engineering, University of Oldenburg, 26111 Oldenburg, Germany b Manufacturing Engineering Centre, Cardiff University, Cardiff CF24 3AA, UK
Abstract The paper presents a learning controller that has been designed for the control of mobile microrobots, which belongs to the class of nonlinear, time-variant systems with ambiguous inverse behaviour. The so-called SelfOrganising Locally Interpolating Map (SOLIM) consists of a continuous nonlinear support vector-based map generating control commands from desired system states, and a learning algorithm that approximates the map to a smooth inverse model of the system behaviour with help of the measured system output. The learning algorithm uses a new self-organising rule that has been specifically designed to learn a controller mapping even when succeeding sensor values are correlated. Experiments show that the controller can learn to control the velocity of a mobile microrobot by only observing its behaviour.
Keywords: self-organising map, learning control, mechatronics
1. Introduction Mobile microrobots are cm3-sized robots that can position with gm- and sub-pm-accuracy and that can move within a large working range [ 1]. Their applications range from microassembly, over handling ofbiological cells [2], handling of silicon-lamella for the semiconductor industry [3 ] to handling and manipulation of nanoobjects like CNTs and nanowires [4]. Irrespectively whether the microrobot is positioned automatically or via tele-operation the positioning accuracy is limited by the actuator resolution, the global sensor accuracy and the controller accuracy. This paper describes a controller that can learn to con-
596
trol mobile microrobots in the context of a micro- and nanohandling station. The actuator-sensor behaviour of the mobile microrobot shows some properties that complicate the controller design. First, it is difficult to extract an analytical model with sufficiently exact parameters. Even worse, most microrobotic systems change their behaviour over time (wear, environmental influences, drift) [5]. Secondly, most microrobots have nonlinear motion behaviour [6], and finally, the mobile microrobot platform used here is overactuated, i.e. it has six control channels for three degrees of freedom.
g~ .~
topology space C
input space G
Fig. 1. Self-supervised learning [7]. Taking these properties into account the controller mapping should be nonlinear and based on data. In addition, the mapping should be learned by measurement data and should include a criterion to resolve ambiguities due to overactuation. The authors propose to use a Self-Organising Locally Interpolating Map (SOLIM) [8,9] that uses the basic structure of the SelfOrganizing Map (SOM) from Kohonen [10] and the Local Linear Map (LLM) from Ritter et al. [11]. The mapping and the learning of the LLM and its successor, the Continuous Self-Organizing Map (CSOM) [12], are based on local linear models described as Jacobians of the inverse system behaviour. This means that the controller behaviour cannot be easily interpreted or initialised with a-priori knowledge. In addition, the Kohonen learning rule used here can be problematic during online learning in robotic applications [13]. Another related work is the Parameterized SelfOrganiszing Map (PSOM) from Walter [14], being essentially a continuous version of the SOM. The PSOM models the system behaviour and extracts the controller mapping during operation with an optimisation algorithm. This allows resolving ambiguities in a very flexible manner but for the price that the whole system behaviour (i.e. not the controller) must be modelled and that the controller is not guaranteed to be continuous. In the following sections the SOLIM algorithm with a new self-organisation algorithm that replaces the standard Kohonen self-organising rule is described, as well as its application to the control of microrobots.
2. SOLIM" Self-Organising Locally-Interpolating Map
~nput g~
_
g~g~ g~ g~g~ input support vectors
. . . . . C~ C3 C4 topology vectors
output space P
I
Ct
Cs
output P~ . . . . v P~ P2 P:~ P4 P~ output Support vectors
support vector pairs
Fig. 2. Input space, topology and output space of a 1D network. 2.1. Structure
The structure ofthe SOLIM is similar to the structure of the LLM. The network N consists of INI nodes ni = (gi,ei,p;,Bi) that are arranged in a predefined topology, i.e. each node has a set ofneighboured nodes B i (Fig. 2). Associated to each node is an input support vector gi e G , e.g. velocities, a support vector e i in the topology space C and a corresponding output support vector Pi 9 P , e.g. signal amplitudes. 2.2. Mapping
The mapping task G + P is to find an output vector p~ that has the same relation to the network of output support vectors as the input vector gd has to the input support vectors. First, for each node n i an influence weight f with respect to the input vector gd is calculated (Fig. 3). The output vector p~ is then the linear combination of all output support vectors Pi and their extrapolation components Px;, weighted by f , which are the weights f normalised to not exceed 1.
P~- Z f'(Pi+Px,) i=I...IN I
The SO LIM is a framework that performs two tasks in a control context (Fig. 1): 9 Map from a desired state gd of a system, e.g. a desired velocity of the microrobot, to actuation parameters p~, e.g. voltage amplitudes.
1 P~
--
k=l...]N]
fi
(1)
(2)
otherwise
9 Update the mapping by using the desired state gd, the measured state g~ and the applied actuation parameters p~.
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SOLIM approximation
/ P4 or "o 9 e--
i
pa~ .....
-13 p31 N I'D P2 o1
"",
F"'*'..
S -... Q + rl/" ++ J,"+~
Pa ..... _
g2
g3 ge
g4
input space G
gd
Fig. 3. Piecewise linear interpolation with a 1D network.
~9 P,,i "a
opposite neighbours of w.,
/+/,
gl
Pi
T
X i " Phi
"*" o
system behaviour
gm
G
G
Fig. 5. Left: SOLIM approximates an inverse of the system behaviour. Right: SOLIM self-organises according to the given topology. 2.3. L e a r n i n g
,,
!
There are two goals for the learning task: The SOLIM mapping approximates an inverse system behaviour and the input and output support vectors are both arranged according to a predefined topology, such that similar input vectors are mapped to similar output vectors and the mapping thus becomes smooth.
s Pa
gi g,i
~-
gd x~ "g,,i ....
w,-
input space G Fig. 4. Linear extrapolation with a 1D network. The influence weights f/ are measures of how close the input vector gd is to the corresponding input support vectors gi. The influence weight is 1 for gd - g i and decreases (mostly linearly) to 0 at the in-
2.3.1. Approximation Approximation is done by updating the output support vector belonging to the node nwm = (gwm'Cwm 'Pwm 'Bwm ) that has the highest influence
fluence limits, which are defined by all neighbours of node n i . When the grid is placed in a 1D input space
with respect to the measured system output gm (Fig. 5,
the limits are points, in a 2D input space the limits are lines and in a 3D input space the limits are planes, etc. Details on how to calculate the influence are given in [8,9]. The extrapolation components in the input and output space for node n i are computed as
system output gm an estimate Pe,a for Pwm can be
Pxi
--
xi " Pni ,
(3)
where x i is a weight that defines the distance of the input vector gd from the border input support vector gi in relation to gni (Fig. 4). gni is the mean difference vector between gi and its limiting neighbourhood
left). With the system input P a and the corresponding found by solving eq. (1) for moved towards
Pi:wm [9]. Pw~ is then
P e,a"
p<.ew) = p~+d> + (Pe,a -P<~Wm Wm m , =
-
~ ._____2_5 1
l---dc+l
.( _
(P)
w ) m
(P) .e;+
(4)
e~,p) is a constant approximation learning rate. e~,p) is an additional factor that is c~,p) --- 1 for good estimates,
B i and, analogously, P,i is the mean difference vector
where n w~ had full influence fCwm = 1 on P a, and de-
between the output support vector P i and its limiting
creases to e~,p~ > 0.5 for smaller fCWm. 1/(1 + d c ) is the
neighbourhood B i .
smallest influence weight for a winning node within a net with topology dimension d c .
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2.3.2. Self-organisation in input space Self-organisation in the input space is often not necessary because in typical robotic applications the input support vectors can be predefined according to a topology. E.g. the input support vectors can be placed on a 3-D grid covering the velocity range of the mobile microrobot platform. If this is not the case they can be learned with the Kohonen self-organisation rule [ 10] and the input vectors gd as attractors. Special attention
must be taken when succeeding input vectors gd are strongly correlated as it is the case in many robotic applications [ 13]. The classic rule will then let the input support vector network contract along the gdtrajectory, if the learning rate is not small enough.
approximation
I
~
I
d/'
thus combining the approximation and the selforganisation task. But here again, its application to online learning in robotics can be critical as the output support vector network tends to contract along the Pe,~-trajectory. In this paper the separation of approximation and self-organisation is proposed and a new self-organisation rule is introduced, which does not interfere with nodes that already have a small approximation error or that are arranged according to a given order. The rule states that all neighbour pairs of the winner node n that are in-line in topology space are changed in the output space to be in-line, too (Fig. 5, right). Each of these neighbour pairs consists of a reference node n = ( g r , c ,p~,Br), which has the
the larger approximation error and is updated according to ,
" P ...... - P c
9
For each candidate node an estimate for its output support vector is calculated as
I
,t1-+'11 dT'
Fig. 6. Relative changes after one approximation step and u self-organisation steps.
Also the learning rate for the self-organisation is calculated for each candidate node and depends on the learning rate e~p) for the approximation and on a ratio ~,,~
-- d(P)s / d(p)a (7)
Each node n, has
(5)
u = [Bill 2 neighbour pairs such
that, averaged over time, its output support vector is moved u times towards a self-organisation estimate P e,, when it is moved one time towards an approximation estimate pe,~ (Fig. 6). These relative changes can be expressed with help of the corresponding learning rates and solving r = d~ p~ /d(P)a for gP~, then yields eq. (7). The ratio r,~ is used as parameter of the selforganisation and depends on the degree of disorder 6 of the three nodes n , n~ and % , and on the approximation error Ac of the candidate node n
rstp> =max ,a
smaller approximation error and remains unchanged, and a candidate node % - (go, %, Pc, Bc ), which has
+
1_+: I
s~ p> - 1 - ( 1 - r , a "gP~) ~/'a
2.3.3. Self-organisation in output space In most related works [10,11,12,14] for selforganisation in the output space of a map the Kohonen self-organisation rule is applied with p~,~ as attractor,
-pc
self-organisation
.ipt A /
I,z~,t, A ~,~,r,,a/, tttA (p> ',,~,h ,~c ), ( g g r (p> r (p~ g~ ~,"l ' "h '
(8)
s , a , l ' s,a,h ' " ]
where y = t ( x i , x h, Yt, Yh, x) describes a linear transition from Yt to Yh between x~ and x h . Simulations have shown that A t / = 0 . 1 , ~h -- 9 0 ~
'
rs,a,l (p) -- 0 and
r s,a,h (p) --
A,h=l.0,
6~=30 ~ ,
0.7 yield a good learn-
ing behaviour. The degree of disorder 6 is measured as the angle between d ~ (p>, w - P ~ - P . . . . and d (p) . , r - P .... - P r . Theap-
g wm ~ g c
g~ -gw,,,
proximation error is measured in topology space, where e~ / e m and exi are computed analogously to Pa and px,, making the measure relatively independent of scale differences between the dimensions
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-20 0 map output outputPal Pal map
Fig. 7. Set-up of the micro- and nanohandling station.
Cd: Z
fii(gd)'(Ci+Cxi).
Cm -- Z
f(g.,)'(Ci
i=I...INI
i=I...[NI
0 40""~ 4o
~_ -2020 mapmapoutputoutput PazPa2
Fig. 8. Network with the first three of six amplitudes of the signals applied to the mobile microrobot's piezo disks. 100
(9)
~-Cxi )
10-!
3. Experiment -~
The SOLIM algorithm has been implemented and applied to the actuation control of a mobile microrobot platform.
i0.2 0
500 1000 1,500 2000 2500 iterations v
Fig. 9. Development of the velocity error, relative to the maximum velocity.
3.1. Set-up
plitudes p, - (v 1 v2 ... The test set-up is part of a micro- and nanohandling station [3] (Fig. 7). The mobile platform can move on a glass plane and has integrated a highresolution micromanipulator carrying the end-effector. The platform is driven by segmented piezo disks and is used for coarse positioning. The movement of the platform can be measured with help of a CCD-camera mounted below the platform or with help of a light microscope or scanning electron microscope (SEM). The training software uses a part of the control software of the station, mainly consisting of a vision server that extracts the microrobot's poses from the camera's or microscope's images and a control server that controls the signal generator to actuate the microrobot.
5x5x5 nodes has been initialised with ordered input support vectors and random output support vectors within their predefined ranges of[-lmm/s... 1mm/s] for .+ and .9, [-3~176 for ~ and [-80V...80V] for vL.6. There is no more information required for the initialisation and operation of SOLIM since all learning rates are fixed or adapted according to the network state. The following procedure has been repeated 2500 times (see also Fig. 1 and Fig. 5, left): - A random desired velocity vector gd is gener-
dom (DoF) g = (k
600
.9
~) to the actuation signal am-
ated. SOLIM maps from the desired velocity gd to actuation parameters p .
-
The actuation parameters p, are applied to the
-
robot for some time. The velocity gm is measured with help of the
-
vision system. SOLIM is trained with the 3-tuple
3.2. Learning SOLIM has been used to learn the mapping from the microrobot's local velocity in three degrees of free-
v6) . The 3D-networkwith
po
The network after 2500 iteration steps, which took about two hours, is displayed in the first three dimensions of the output space in Fig. 8. The nodes are arranged in an ordered manner, although it is not obvious from the figure. The velocity error related to the velocity ranges can be found in Fig. 9. It can be seen that after about 700 steps the error is typically below 0.1, i.e. within _+0.1mm/s and +0.3~
References [1] [2]
[3]
4. Conclusion The paper has presented a learning algorithm that has been designed to control nonlinear, time-varying systems with ambiguous inverse behaviour. Its main features are as follows: 9 Only the topology and input / output limits are needed for initialisation. 9 A-priori knowledge can be used for initialisation. 9 The mapping is continuous, nonlinear and exact with respect to the support vectors. 9 An inverse system behaviour, i.e. a controller can be learned during operation. 9 A new self-organising algorithm ensures that the map is learned such that neighboured input vectors are mapped to neighboured output vectors and the mapping therefore is "smooth". This is an inherent criterion to resolve approximation ambiguities. On the other hand, it must still be proven that the mapping can be learned online when the microrobot moves along a trajectory, i.e. when succeeding input vectors are highly correlated. Also the number of influence limits and thus the necessary processing power for a mapping increases with the factorial of the number of topology dimensions. The introduction of hierarchy could help reducing the processor load, but would put some constraints to the placement of input support vectors. Growing and shrinking topology structures would reduce the sometimes difficult task of knowing the topology size beforehand.
Acknowledgements Parts ofthis work are based on the cooperation between the University of Oldenburg and the University of Cardiff. Financial support in the framework of the ARC-initiative of the German Academic Exchange Service (DAAD) and the British Council is gratefully acknowledged (Grant-N~ 313-ARC-XVIIID/0/40828).
[4]
[5]
[6]
[7]
[8]
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[ 13]
[14]
Kortschack, A., and Fatikow, S. Development of a mobile nanohandling robot. Journal of Micromechatronics 2, 3 (2004), 249-269. Trtiper, T., Kortschack, A., J~ihnisch, M., Htilsen, H., and Fatikow, S. Transporting cells with mobile microrobots. IEE Proc.-Nanobiotechnol. 151, 4 (August 2004), 145-150. Fatikow, S., Wich, T., Htilsen, H., Sievers, T., and J~ihnisch, M. Microrobot system for automatic nanohandling inside a scanning electron microscope. In Proc. of Int. Conference on Robotics and Automation (ICRA'06) (Orlando, FL, U.S.A., May 2006). Wich, T., Sievers, T., and Fatikow, S. Assembly inside a scanning electron microscope using electron beam induced deposition. In Proc. Int. Conference on Intelligent Robots and Systems (IROS'06) (Beijing, China, October 2006). submitted. Zhou, Q., Chang, B., and Koivo, H. Ambient environmental effects in micro/nano handling. In Proc. Int. Workshop on Microfactories (Shanghai, China, October 2004). Scherge, M., and Schaefer, J. Microtribological investigation of stick/slip phenomena using a novel oscillatory friction and adhesion tester. Tribology Letters, 4 (1998), 37-44. de A. Barreto, G., and AraOjo, A. F. R. Identification and control of dynamical systems using the selforganizing map. IEEE Transactions on Neural Networks 15, 5 (September 2004), 1244-1259. Htilsen, H. Design of a fuzzy-logic-based bidirectional mapping for kohonen networks. In Proc. Int. Symposium on Intelligent Control (ISIC'04) (Taipei, Taiwan, September 2004), pp. 425-430. Htilsen, H., and Fatikow, S. Extrapolation with a selforganising locally interpolating map. In Proc. Int. Conference on Informatics in Control, Automation and Robotics (ICINCO'05) (Barcelona, Spain, September 2005), pp. 173-178. Kohonen, Y. Self-Organizing Maps, 3. ed. Springer, Berlin, Germany, 2001. Ritter, H., Martinetz, T., and Schulten, K. Neural Computation and Self-Organizing Maps: An Introduction. Addison-Wesley, Reading, M.A., U.S.A., 1992. Aupetit, M., Couturier, P., and Massotte, P. FuO~tion approximation with continuous self-organizing maps using neighboring influence interpolation. In Proc. Neural Computation (NC'2000) (Berlin, Germany, May 2000). Jockusch, J., and Ritter, EI. An instantaneous topological mapping model for correlated stimuli. In Proc. Int. Joint Conference on Neural Networks (IJCNN'99) (1999). Walter, J. Rapid Learning in Robotics. Cuvillier Verlag, G6ttingen, 1997. http://www.techfak.unibielefeld.de/walter/.
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Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Spectral Characterization of digital Cameras using Genetic algorithms Ioannis Chatzis, Dimitris Gavrilis, Evangelos Dermatas Department of Electrical & Computer Engineering, University of Patras, Rio Patra 265 00, Hellas.
Abstract
In camera characterization a number of techniques is applied to minimize the impact ofdifferent hardware and software implementation in image acquisition systems, and preserve colour distortion between devices. In this paper, a new method for spectral response estimation is presented and evaluated based on genetic algorithms. The optimization criterion minimizes the maximum difference between a mixture of Gaussian functions and the real spectral response. A genetic optimization process estimates the parameters of the Gaussian mixture, implemented in Java using the tournament selection method. The experimental results show significant improvement ofthe proposed spectral estimation method over the well-known PCA method using the first six to ten most significant eigenvectors in the presence of three additive noises: the noise which is statistically independent to the intensity level, the signal dependent noise, and the digitization noise. K e y w o r d s : Spectral characterization, Genetic algorithms, mixture of Gaussian
1. I n t r o d u c t i o n
Digital colour images are widely used in medical imaging, visual inspection, communication, and reproduction applications. During image acquisition an extremely strong data-reduction process is taking place: the spectral distribution of the light captured by the device photo-detectors (CCD or CMOS technology) is reduced to three integer numbers, the RGB values [1]. Different spectral distributions are mapped to the same RGB values, a phenomenon referred as metamerism. Moreover, the great number of imaging systems produces device dependent images due to different spectral sensitivity, which makes the reproduction and images comparison process difficult. In camera characterization, a number oftechniques is applied to minimize the impact of different hardware
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and software implementation and preserve colour distortion between devices. Among the most accurate methods for device characterization, the estimation of the acquisition channels' spectral response from images has received recently a considerable attention [2-5], especially in cases where complex optical systems and filters are used, or the image acquisition chip is unknown. Direct estimation of the camera spectral responses [13] requires expensive hardware. Many researchers introduce model-based methods to reconstruct the camera spectral response from multiple images using various methods such as, principal component analysis (PCA) [11 ], set theoretic estimation [2,12], quadratic programming [3,10], Wiener estimation [4], and parametric model fitting [5]. Based on the recovered spectral sensitivity ofa colour scanner, Shi and Healey
[6] proposed a characterization method that uses a high-dimensional linear reflectance model (LRM). DiCarlo and Wandell [7] introduced absolute and relative-scale sub-manifold estimation methods to improve further the spectral characterization results, when the training colour sample set systematically deviates from a normal distribution. The spectral reflectance of art paintings are estimated by Haneishi et. al. in [8] by taking into account the noise distribution and the subdivision of sensor response space. Imai and Berns [9] comparatively investigated the accuracy of spectral reflectance in various spaces by use of principal component analysis. In almost all these techniques [6-9] it was assumed that the spectral sensitivities of the imaging system were measured or mathematically recovered accurately. However, for a real scanner, as the spectral sensitivity may depart considerably from the linear reflectance model, one cannot ensure that these techniques work in spectral characterization when the mathematically recovered sensitivity is not accurate enough. This paper proposes a new model-based method to estimate the spectral response from multiple images of uniform painted patterns and known spectral illumination. Assuming that the spectral response of each colour channel can be approximated by a mixture of Gaussians (the approximation accuracy is controlled by the number of mixtures), the maximum difference between the predicted and the real channel value is minimized using genetic algorithms, taking into account the presence of types of noise occuring through the image formation process. The accuracy of the approximated spectral response is estimated in simulation experiments and compared to the PCAbased method as proposed by [ 11 ]. In other approaches proposed in the bibliography an extended number ofparameteres must be infered by the user, while the restrictions imposed are causing possible exclusion of the best solution. The optimization has non-linear nature, as it also has in our case, but the final solution is dependent on the selection of the initialization parameters. The genetic algorithm approach was selected for various reasons. The spectral characterization of a camera is a task that can be executed off-line. So the time limitations of the optimization search procedure are of minor importance. Moreover the proposed model is of comparable simplicity on the number of user defined parameters as in the PCA-method. The genetic algorithm approach as a global optimization method is not limited by nature from this artifacts. The structure of this paper is as follows. In section 2 a detailed description of the spectral response of
typical imaging devices is given, followed by the presentation of the proposed genetic estimation method. A presentation of the simulated noise components and the experiments are given in sections 4, and 5. The experimental results conclude this paper.
2. Spectral response of typical imaging devices The spectral response of an imaging system can be given by the following equation [1 ]:
B - IC~ (2)R(3~)I(A)d2,
(1)
where, Bc is the pixel value ofthe c-channel, R isa uniform colour patche's spectral reflectance, C~(2) is the spectral response of the cth channel cE [ 1,M], and 1(2) is the spectral power distribution of the illuminant. If the spectral space is digitized into M distinct areas, eq. (1) becomes: (2) 2=4 Assuming that a set of N different pixels intensity Bc., under the same illuminant and camera setting, are available then, according to eq. (2), the following set of equations is valid:
(3)
/~M
Be, - ~ C c (2)R (2)I()L),
n - 1 ..... N.
In a matrix form, eq. (3) leads to: [R(2)I(/J,)](N,M)[Cc(/~)](~,)
-- [Be, ](xx,) ,
(4)
[R,,(A)]IN~It[diag(I(fc))](M~M)[Cc(fc)](M~,) = [B,.]IM~, 1. (5)
If the spectral reflectances (R,(2)), the spectral distribution of the illuminant (1(2)) is known, and the number of available colour samples (N) is greater than the number of spectral spaces (M), the estimation of the unknown spectral response of the camera (C(2)) can be estimated in the over-determined linear problem
603
using the pseudo-inverse matrix [ 13]. In practice, the matrix of the spectral data
[R (2)1(2)]
is rank deficient and the estimation of
the pseudo-inverse matrix is extremely sensitive to additive noise, which is present in the photo-electronic conversion. One popular solution is to incorporate a number of restrictions to the linear problem, reducing the solution space by removing the a-priori known deficient solutions. In this type of methods, two problems appear: An efficient method to define the optimum set of restrictions has not been proposed up now, a direct solution of the pseudo-inverse matrix cannot be applied in this case. Thus the camera spectral response is derived recursively, and. Usually, rigid restrictions eliminate the optimum solution, and soft restrictions produce multiple local optimum solutions. While the noise is present in the imaging process and has been shown that affects strongly the accuracy of the spectral response estimation, only few researches introduce more robust methods for spectral characterization of the colour channels using noise models. Radiometric noise models have already been proposed in the literature [14]. The noise of the imaging process at each pixel can be divided into two components. The noise related to the light intensity level (shot noise, thermal shot noise), and the electronic noise (amplifier noise, dark current, reset noise, digitization noise). Assuming additive behavior for both types of noise, fixed integration time, camera gain and imaging conditions, a more detailed description of the camera response is given by the following equation: &
B. = ~-'C (/~)R (3.)I(2)+S(B.)+Dc+Q ,
(6)
where, S(B,) is the total signal correlated noise, Dc is the sum of all noise components not influenced by the light intensity, and Q, is the pixel round-off error introduced in the ADC process. 3. Genetic estimation of spectral response
A novel error function between the estimated model-based spectral response and the actual value derived from the channel intensity, based on the maximum absolute difference in any spectral space: Z0(C)=max n
604
~C(2)R.(2)I(2)+ S(B.)+Dc+Q.-B. [ (7)
The typical mean square error (MSE) function favors solutions that present greater fluctuations of errors over the test space. A number of restrictions are also embodied in the channels spectral responses assuming that a mixture of Gaussian can approximate the real spectral response of typical channels. The proposed channel spectral response model becomes:
C(3.):~--'p~G(A,~,q),
k=l,...,K, and a~ ~9"f
(8)
k=-I
The proposed approach has several advantages compared to solutions proposed in the literature. The optimization function decreases the maximum error between the measured camera responses and the estimated response of the camera under the same input using the estimated spectral response for each channel, not the mean error as traditionally used. Moreover, the number of unknown parameters is reduced significantly. Although different basis functions can be used to approximate the real spectral response, in this paper the mixture of Gaussian is studied reducing the number of unknown parameters to 3*K. Among the most efficient optimization methods for functions with strong non-continuity areas in their searching space, the genetic optimization methods combine stochastic, deterministic and random searching techniques to obtain a robust local maximum/minimum solution. In the specific minimization problem, the search space for all unknown parameters can be easily defined: the alpha parameters (a~), depends on hardware characteristics, which is restricted in a typical space for all imaging systems, the mean value of the Gaussian distribution (m~) determined by the channel wavelengths, and the variances (~2k) which control the slope of the spectral response. Each chromosome consists of K genes, each one representing a Gaussian function. Each gene, is represented by three floating point numbers (the parameters found in Eq. 8). 4. Noise Models
The stochastic components of the error function
S(B), Dc and Qn can be easily simulated in genetic optimization algorithms. After the theoretic camera responses were calculated using the image formation equation, noise was added. In our approach, the noise is approximated by two independent Gaussian stochastic processes and a uniform probability density
function (pdf) respectively. The noise parameters could be infered after a noise characterization preprocessing step as mentioned in [14]. In the related bibliography the signal correlated noise which is related to the photon shot noise has been proved to follow a Poison distribution. However as the photon shot noise is not the only signal correlated noise source in the imaging pipeline the selection of a Gaussian approximation of the correlated noise distribution can be considered as a fair choice. It must be mentioned that the Gaussians used for the noise simulation are not related with the Gaussian basis functions N ( o ) used for the modeling of the spectral response. That is because of the integration procedure that describes the image formation process. Thus, the pdf of the signal dependent noise is given by the following equation:
(12)
(n - 2 ) q < V ~ < (n + l ) q ,
The value of Vp is rounded to a digital value
D=nq, where n is an integer: O
b -1,
(13)
and b is the number of bits encoding the pixel brightness in the discrete space. Under reasonable assumptions described in [15], Q, can be shown to be a zero mean random variable that is independent of signal amplitude B, with a uniform probability distribution over the range [ --2q'-2q 1 1 ] and a variance q2 12 "
S(B) - N(x,O, o'2), S
(9) q
2
where the noise power (0, 2) is proportional to the pixel intensity value Bc:
Q, - U(x,O,-i-2).
o~2 - aBc .
5. Experiments
(10)
The pdf of the signal uncorrelated noise is:
2 D c - N ( x , mDc, crz)c ) .
(11)
The mean value of the signal uncorrelated noise
mDc depends on the integration time and is highly temperature dependent. In our case is set as a proportion of the maximum brightness value, considering the stable set of conditions previously referred. The variance is typically set in a percent value of the corresponding mean. The analog signal produced by the imaging process must be quantized in magnitude to generate a digital image. The ADC procedure causes the final pixel value to be an approximation of the analog voltage Vp, with error that depends on the maximum number of quantization steps q. Consequently each value of Vp, satisfies:
(14)
The proposed genetic method is evaluated in a large scale of simulation experiments. A total number of 1289 spectral reflectances from the color chips of the Munsell Book of Color (download from [17]) where used to create the RGB values of the CCD Imager Kodak KAC1310e [ 16]. The Munsell Book of Color is a well-known database and has already been used in a great number of publications to evaluate reflectance reconstruction methods. The spectral characteristics of the CIE standard illuminant-A are obtained from [18]. The stochastic components are created by a typical random generator following the noise models presented in previous section. The mean of the dark current noise (mo,) was set to 1% of the maximum brightness value. The standard deviation of the dark current was set to 3% of the corresponding mean value. The parameter value b for the Qo noise was set to 8 and the parameter a of the Sb noise parameter was set to 0.1, representing typical CCD imager noise behavior. In this paper, six real spectral responses are estimated in a level of noise controlled by the above mentioned parameters.
605
The genetic programming algorithm is implemented in Java using the well-known tournament selection method. A double precision number represents each gene in the chromosome, and a bit mutation method was implemented. Each chromosome represents a mixture of Gaussians. Each genes describes a mixture component, the linear coefficient a, the mean and the variance of the Gaussian pdf. Thus, a mixture of five Gaussians is represented by a chromosome of 15 genes in length. In all experiments the maximum number of generations was 1000 and the population size was set to 300. The mutation rate was set to 0.05, the crossover and selection rate was set to 0.95 and 0.1 correspondingly. All these parameters were experimentally derived to minimize the optimization criterion. The wheel selection variant of the GA was also implemented and evaluated but it is not recommended due to poor results. The fitness used by the genetic algorithm was the absolute maximum error found in all training samples. The MSE of the training samples is also calculated. A variation of the algorithm that could produce variable-length chromosomes was also implemented but is not recommended due to poor results mostly because of the growth of the search space. The proposed method is compared to the most popular spectral estimation method which is based on the PCA. The complete set of images was used to estimate and select the most important eigenvectors according to eigenvalues: the significant eigenvectors has the greatest eigenvalues.
6. Experimental Results In Table 1 the percent maximum error rate between the real and the estimated spectral response of the five mixture of Gaussian mixtures for each channel in the complete set of six channel-CCD Imager Kodak KAC1310e is given. In these errors, two components are included and cannot be distinguished: (a) Even in case of accurate estimation of model parameters, the real spectral response is slightly different to the optimum estimation of the five Gaussian mixtures, (b) the generic algorithm converge to a local optimum solution, controlled by the nature of the optimization function and the algorithm parameters. In Table 2 the maximum and the mean absolute error between the real and the estimated from the first six to ten best eigenvectors are shown. The complete noise components have been included in the estimation problem. In Table 3 the maximum and the mean absolute error between the real and the estimated from the first six to ten best eigenvectors are shown. In this
606
Table 1 Maximum absolute error between the real and the five Gaussian mixtures spectral response in percent rates for six channels using genetic estimation of channels spectral response and all noise components. Channel
Dark noise
1
11.475
3
10.869
2
Without noise 4.988
13 . 1 2 1
4
11.262
5.701
10.869
5
6.833
ii. 3 7 4
6
4. 5 9 4
Ii . 8 1 0
4.404
Table 2 Mean and maximum absolute error at each channel using PC A-based estimation of channels spectral response, the first 6 to 10 eigenvectors, and all noise com )onents. Ch. 1 Ch. 2 Ch. 3 CK4 Ch. Ch. 5 6 Mean absolute error in Percent rates 6 8
0.84 0 83
0.56 0 58
0.48 0 38
6.40 6.12 I0 . O0
10.48 13.02 16 . 13
8.63 12.18 14 9 29
0.40 0.41
10 1 O0 0 71 0.591 0.59 Maximum absolute error in Percent rates 6 I 8 ]
0.76 0.71
O.agl
[ 7.51 [ 5.40 [ i0 43 ] 5 . 6 9
0.66 0.64
O.aS 4.94 6.85
Table 3 Mean and maximum absolute error at each channel PCAbased estimation of channels spectral response, using the first 6 to 10 eigenvectors, and two noise components. [Ch. 1 I ch. 2 [Ch. 3 [Ch. 4 [Ch. S I Ch. 6 Mean absolute error in Percent rates 6 o.o7olo.16olo.29olo.oso o.m2olo.o~o 8 10
0.040 0 005
[ 0.120 0 074
I 0.08010.030 0 053 0 029
0.040 0 030
[ 0.040 0 050
1.20 0 64 0 25
0.93 0.97 0.40
Maximum absolute error in Percent rates 6 8 i0
0.80 0 52 0 46
2.76 2 27 1 19
5.06 1 09 0 70
0.90 0 33 0 44
experiment only the signal correlated noise (S(B)) and the quantization noise (Qn) was used to distort the signal. As it can be expressed from the results even in the case of a limited number of iterations, the results provided by the genetic algorithm are superior to the results provided by the use of the PCA. This is for the case that the signal uncorrelated noise parameter is absent. When additive uncorrelated noise is used, the results provided by the genetic algorith are inferior. This is due to the fact that the DC component cannot be expressed adequately by the sum of a limited number of gaussians while preserving low values for
the objective function. The uncorrelated noise can be subtracted from the measurements by a dark flame subtraction preprocessing stage. In addition, better results are expected in the case that the genetic algorithm model includes a DC parameterer. In all cases, from the results obtained from the PCA estimation, the maximum absolute error is significantly greater that the mean absolute error. This is due to the fact that the maximum absolute error is a more strict error criterion for spectral response estimation. Moreover the PCA-based estimator does not use the absolute error minimization criterion. It is optimized to minimize the MSE criterion. Therefore, any uncorrelated noise, when present, should be removed by dark flame subtraction in order to obtain more accurate estimation results when principal component analysis is used. If the uncorrelated noise is absent, the error rate for the PCA-estimator decreases monotonically as the number of used eigenvectors increases as shown in Table 2. This is expected as the remaining noise parameters have zero mean. Although the absolute maximum error seems to be very harsh (and it is rarely used) it is found to be more challenging. The proposed error function provokes a more stable performance of the spectral estimation results over the test set.
7. Conclusions In this paper it was shown that the use of a genetic algorithm approach for the estimation of the spectral response of a camera is superior to the use of the PCA approach in the case where the uncorrelated noise has been removed through a preprocessing step. As the results are promising the performance of an enhanced genetic algorithm formulation will be compared in future with the performance of algorithms already proposed in the related bibliography.
2. Sharma G., Trussell, H. J., Set theoretic estimation in color scanner characterization. J. Electron. Imaging 5, (1996), pp. 479-489 3. Barnard K., Funt, B." Camera characterization for color research. Color Res. Appl. 27, (2002), pp. 152-163 4. Vora, P. L., Farrell, J. E., Tietz, J. D., Brainard, D. H." Image capture: simulation of sensor responses from hyperspectral images. IEEE Trans. Image Process. 10, (2001), pp. 307-316 5. Thomson, M., Westland, S., Colour-imager characterization by parametric fitting of sensor responses. Color Res. Appl. 26, (2001), pp. 442-449 6. Shi M., Healey, G." Using reflectance models for color scanner calibration. J. Opt. Soc. Am. A 19 (2002) 645656 7. Dicarlo, J. M., Wandell, B. A." Spectral estimation theory: beyond linear but before Bayesian. J. Opt. Soc. Am. A., 20, (2003), pp. 1261-1270 8. Haneishi, H., Hasegawa, T., Hosoi, A., Yokoyama, Y., Tsumura, N., Miyake, Y." System design for accurately estimating the spectral reflectance of art paintings. Appl. Opt. 39, (2000), pp. 6621-6632 9. Imai, F. H., Berns, R. S.:A comparative analysis of spectral reflectance estimation in various spaces using a trichromatic camera system. J. Imaging Sci. Technol. 44, (2000), pp. 280-287 10. Finlayson, G.D., Hordley, S., Hubel, P." Recovering device sensitivities with quadratic programming. IS&T/SID Sixth Color Imaging Conference. Color science, systems and applications. Scottsdale Arizona USA, (1998), pp. 90-95 l l. Hardeberg, J. Y., Brettel, H., Scmitt, F.:Spectral characterization of electronic cameras. Electronic Imaging: Processing, Printing, and Publishing in Color. Vol. 3409 Proc. SPIE, (1998), pp.100-109 12. Sharma, G." Targetless scanner color calibration, J. Imag. Sc. Tech. 44, (2000), pp. 301-307 13. Vora,P.L.,Farrell,J.E.,Tietz,J.D.,Brainard, D.H.:Digital color cameras- 2 - Spectral response HPL-97-54, (1997) 14. Healey, G. E., Kondepudy, R." Radiometric CCD camera calibration and noise estimation. IEEE PAMI 3 (1994), pp. 267-276 15. Oppenheim, A., Schafer, R." Digital signal processing. Prentice, Englewood Cliffs New Jershey, (1975)
16.http://www.kodak.com/global/plugins/acrobat/en/digital/c
cd/products/cmos/KAC 1310LongSpec.pdf 17. http://www.cs.joensuu.fi/-~spectral/databases/ 18. http://www.cis.rit.edu/mcsl/online/cie.shtml
Acknowledgements This work was supported by the General secretariat of Research and Technology project PENED2001 No. 3049: "XROMA" References 1. MacDonald, L. W., Luo, M. R., Color Imaging: Vision and Technology. Wiley Chichester UK ,(1999)
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Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All rights reserved.
Towards more Agility in Robot Painting through 3D Object Recognition A. Pichler, H. Bauer, C. Eberst, C. Heindl, J. Minichberger Robotics and Adaptive Systems, PROFACTOR Research, 4407 Steyr-Gleink, Austria
Abstract Building to order implies responding to individual customer requests, not simply producing large numbers of goods for stock and encouraging their sale by promotion and discounting. The paint shop is one ofthe biggest bottlenecks in production today. Adapting state-of-the-art robotized painting lines to new variants is time-consuming and leaving the line in a non-productive state. To overcome the limitations of state-of-the-art robotizes painting system - that are economically not viable at small lotsizes, the Flexpaint project (2000 - 2002) did develop a new "what you see is what you paint" approach. This approach scans unknown parts being transported to the painting cabinet, reconstructs their geometry and painting-relevant features, and automatically plans the painting strokes and executable - collision free - trajectories. Finally robot code is automatically generated and executed by the painting robots. The Flexpaint system poses a new paradigm in the flame of agile manufacturing. Resorting to 3D sensing technology in a precedent step to the task planning process proved the fitness of that concept for small volume high variant painting in experiments. This paper presents an extension of the "what you see is what you paint" approach by means of dynamic vision and 3D object recognition that is able to complement missing data of scanned parts byuse of CAD information ofrecognized and localized parts. Keywords: Automatic robot programming, industrial robotics, 3D computer vision.
1. Introduction Production on demand, mass customisation, rapid reaction to market changes and quick time-to-market of new products and variants at small batches are neededat low cost and high quality. As investments in automatic painting lines are considerably high and as the painting line otten is the bottleneck in production, it is imperative to prevent non-productive times and maximize the use of the expensive equipment. Aforementioned shrinking volumes and increasing variances challenge the state of the art. Highly flexible, scalable and user-friendly production equipment is needed, including robotic systems for painting - a common process in production. The presented work responses to the need to overcome current
608
limitations. This paper presents an extension of the "what you see is what you paint" approach by means of dynamic vision and 3D object recognition that is able to complement missing data of scanned parts by use of CAD information of recognized and localized parts. The proposed system consists of a self-programming robotic cell with embedded simulation and planning that autonomously generates the robot programs based on CAD and sensor-data or even pure sensor data. Thus, it can operate within the digital factory while at the same time compensating challenges as (1) in-available data, (2) large disturbances and deviations (shape, position, or even part type) and (3) even very small lot-sizes. From control perspective, flexibility and robustness ofproduc-
//3D~R--ange"",~.~ ~ Detect "~ \ Data J ~ Part GeometryJ i
... ~ Library ~ L~r. . . . ~ene,,,o
! Procedure b.,---,.,l~ Painting m f _Libra~ - ~ Trajectory J
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Fig. 1. System overview of the sensor based robot painting system tion is to a large extent defined by (1) needed efforts or speed for task planning and programming (teach in, OLP), (2) by its level of communication with respectively its integration into the digital factory, its scheduling capabilities in dynamic environments, and (3) by its autonomy to react in real-time for initially unforeseen part tolerances and position deviations. Bottleneck task programming: Modern CADbased off-line programming (OLP) tools partially solve deficiencies of industrial standard teach-in and iconbased based programming such as the insufficient speed for programming and/or the (permanent or partly) blockade of the equipment in an unproductive state. Automatic planning and programming speeds up this process further. Started with milling and rapid prototyping applications [15,22,6], research in automatic path planning is mostly focusing on products consisting of geometrically and technologically ideal parts and their topological relations. Randomized road maps or hierarchical best-search (A*-) algorithms [2] reduced complexity and improved planning, and led to the recent introduction of the first automatic planning tools. Research in automatic planning is now exceeding the level of linear planning in order to cope well with disturbances and a high-variant, lowbatch production. For parts with very limited surface curvature and with available full 3D models, the SmartPainter project follows a novel approach to plan painting motions: (1) virtually folding out the surfaces to be painted, (2) plan the painting motion, (3) folding back the surfaces and the painting motions [7,18 ]. Automatic generation of robot programs for welding and painting has been presented in [ 10,14]. Challenge uncertainties: In opposite to high-level uncertainties (incl. non- stable and non-deterministic production disturbances such as rush orders) that challenge scheduling algorithms within the (digital) factory, low-level uncertainties can widely be managed locally at the equipment by incorporation of sensory information. While vision-based control to compensate small pose deviations (usually in 2D or 2 1/2D) and calibration er-
rors are state of the art, large deviations in object shape or 3D/6DOF pose corrections at complex objects are beyond. The range image processing to compensate missing data or pose uncertainties described in this paper includes segmentation, feature extraction and recognition/localisation. Related work on segmentation and is presented in [9], on finding features with defined geometric properties in [3,16]. Recognition and localisation of objects in 3D based on range images has been described in [20]. Related approaches to compensate large uncertainties using planning based on sensory data have been presented in the original "FlexPaint" project [1 ], the "Fibrescope" Project (Flexible inspection of Bores with an robotic Endoscope) and in [21 ], where a sensory approach is used to deal with uncertainties in turbine-blade repair was introduced. There are are numerous 3D object recognition methods that are either global, like eigenpictures [17] or eigenshapes [4], or that rely on an initial segmentation of the object [8,5]. Those methods obtain good results on noise free images, but there deficiencies on global properties which makes them vulnerable to occlusions. A more generic way of approaching the object recognition problem pose spin images [12], which have been shown to yield good results with cluttered or occluded objects. As this method is based on finding correspondences between image and model regions, it is rather time intensive, though. [ 11 ] gives a good overview about current global and local approaches on range images. The first part of the paper describes the Flexpaint system, its evaluation in industrial manufacturing lines and its existing limitations at highly complex scenes. The second part describes the extension of the approach by dynamic vision and object recognition and localization and demonstrate its impact on compensating incomplete data and disturbances that are induced by the industrial environment. 2. Sensor based Robot Paining
2.1. System Description The main concept foresees to derive the paintprocess programming automatically even if the product is unspecified [19,23]. The proposed robot-system analyses product sensor data of the goods to be painted. Process critical regions of work pieces are identified by tailor-made feature detectors, adopting human painting knowledge to meet the required quality of the paint process. Established mappings between a particular feature of
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Fig. 3. Left top: part geometry is acquired by 3D laser scanner; right top: registrated 3D points cloud; left bottom: reconstructed surface model; right bottom: determination of process specific features. this pre-processed (interpreted and filtered) data and a process model allow deriving paint strokes by a processplanning system that is based on an inverse approach. Feature-specific paint-strokes for the feature-sets and sequences are converted to collision free tool and robot motions and complemented with airmotions to a full speed-optimized paint-application. Automatically generated executable programs are first simulated and than executed on the paint-robots.
2.2. Geometry Detection A 3D sensor cell that scans the parts to be painted during transportation, before entering the painting-cabinet allows to significantly reduce the constraints on data completeness and positioning of the parts to be painted. The concept foresees a full integration into the factory as it provides both rapid task-programming and task-simulation prior to execution (commonly) shorter than execution time. The principle approach to programming the painting robots is an inverse approach or a "wysiwyp" (what you see is what you paint) approach. The part to be painted is scanned while transported to the paint-cell. Range images of the part scanned during their transport on the conveyor are taken based on the sheet of light technique. Static mounted laser-profiling sensors are used and the conveyor motion is employed to provide the feed needed to reconstruct the third dimension. Moving equipment is avoided. The cameras are triggered by the encoders. Wavelength-filters and protection against direct sunlight raise robustness at surfaces and shapes with problematic properties. The sensor data of all cameras are filtered and registered over time to generate a full 3D model of the part or the parts. Data from part(s) and the hooks need to be separated. The sensor data must be reduced to a level that is handable for the following steps and interpreted according to elementary geometry types that reflect the constraints of the painting process. The part surface/shape is to be categorized into a genetic
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Fig. 2. FlexPaint clone exposed on PaintTech 2004 (trade fair for painting and powder coating technology) surface fraction that can be reached optimally by the paint-fan with the gun oriented in defined orientation to the surface and the paint-stroke. Cavities and rips and customer-specific features are recognized in parallel as they need to be handled differently by the paintprocess and thus the planning tool.
2.3. Collision free paintpath generation Next the idea of the employed paint-process planning is to link elementary geometries to a process model. This link is established with a flexibility that considers that the precise painting strategy that is mapped to the geometries may vary from customer to customer. Scheduling of individual strokes follows specific criteria, as cycle time or others. Next, the sensory retrieved pose of the part are employed by the AMROSE collision avoidance SW to plan colli-
Fig. 6. Examples of parts hanging in a skid in an industrial setup. The scenario is challenging since the complexity of several parts lead to large occlusion and parts are oscillating
sion-free robot paths the paint trajectories planned bythe INROPA paint-planner. The reconstructed shape of the part(s) and the hooks and the representation of the entire work-cell and active components are used to simulate the collision-flee path prior to execution. If the proper execution can be guaranteed, a generic robot program is generated.
Finally, the generic program is parsed and converted to the robot-specific program and executed. 2.4. E x p e r i m e n t a l evaluation - S e n s o r b a s e d r o b o t
task between two surfaces. The proposed 3D object recognition scheme is based on spin images which do not impose a parametric representation on the data, so they are able to represent surfaces of general shape. Finding
painting
correspondences of spin images between model and scene points. A loss function of the correlation coeffi-
Sensor data acquisition includes reconstruction, sensor-data fusion, and extraction of process-relevant features. Results ofthe individual steps are visualized in the following. As can be seen in the Figure 3, all painting-process critical features of the gear-box (cavities and other parts of the object that are hardly reachable) have been detected despite imperfect sensor data. A remaining challenge is the handling of regions of the part which are completely invisible to the sensors because of(i) occlusion or (ii) surface properties (as at the front ofthe gearbox). Automatic generation of the programs for controlling the robot and the cell is achieved within 60 seconds to 300 seconds, depending on the complexity and the numbers of the objects and the (density of the) structure of the environment.
cient is used as measure of similarity. Finding correspondences using the correlation coefficient is computationally expensive, and therefore, a different way of managing the information conveyed by the spin images is needed. In order to make the process of matching effi-
Fig. 5. Left: simulation of the planning trajectories; right: execution of paint task OFFLINE ........................
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As mentioned in the above section following the approach w h a t y o u see is what y o u p a i n t has its limitations to meet paint requirements for highly complex shaped parts and non separated parts due to laser and camera shadows. Another issue waiting in the wings are the goods hanging and oscillating in the conveyor and their dimension. To counter these issues a main innova-
Fig. 4. Planned tool paths for the painting process
tion has been added to the whole robot paint system: a robust 3 D object recognition procedure to retrieve CAD models from fragmented 3D laser scans which is presented in the paper. As seen in Figure 8 complete object models are searched for in a scene. The outcome of the algorithm provides the correspondence between scene and model. Generally the recognition task is considered as matching
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Fig. 8. System diagram of object recognition approach cient, dimensionality reduction was achieved by projecting spin images represented as n-tuples to a space of dimension d < n , using principal component analysis (PCA). Spin images provide a comfortable way of handling generic 3D data. Unfortunately in spite ofmatching PCA compressed 3D data becomes an extensive task when dealing with hundreds of 3D models as common in industry. Furthermore spin images are quite redundant and tend to provide ambivalent results. These known issues in mind the algorithm has been extended by a key point detection algorithm to find the most representative points of a 3D model. These key points are interest or salient points which can be distinguished from each other. According to the definition such features have maximum saliency. The term saliency features has already been used by many other researchers [ 13]. Finding most salient features in 3D spin image data
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has been approached by measuring the information complexity resorting to computing the entropy of each 3D point. Having detected the key points of 3D models a database is indexed. A database contains a set of models. Each model is represented by vector ofn key points. Recognition consists of finding the model which correspondents to a given scene that is the model which is most similar to this scene. Randomly selected points are taken from the scene and nearest neighbors are evaluated in the model database. If the distance of a model to a scene point is below a threshold the corresponding model gets a vote. The idea of the voting algorithm is to sum the number of times each model is selected. The model that is selected most often is considered to be the best match. In a verification step all selected models are tested against the scene by matching labeled scene points to corresponding models using Iterative Closest Point. The Hausdorffdistance between scene and model points has been used as measure of the quality of the recognition result.
result of this physical effect the frame is more or less shredded. Hooks have a quite flimsy geometry and barely reveal a dense point cloud. In our application, the main purpose of the object recognition algorithm is to robustly label 3D scene point data with the identification numbers of the 3D models kept in a database. In a subsequent step the corresponding models are matched against labeled scene data to retrieve position and orientation of CAD models related to the world coordinate space which is of importance for the robot application. Figure 10 depicts the recognition result for each of the test objects. Apparently, the position and orientation has been estimated subsequently. The robustness of the object recognition algorithm strongly depends on the quality of the sensor data. Much care has been taken to generate a smooth surface model suppressing most of the outliers as surface normals determine the quality of the basic feature spin image [ 12].
2.4. Experimental evaluation- 3D Object Recognition
The "what you see is what you paint" approach has proven to be a promising alternative to conventional teach in and O LP based programming and has shown feasible especially for high variant, low volume parts. However, highly complex shaped, non separated and oscillating parts cause incomplete data (by occlusion) and disturbances. As pointed out incomplete surface models hinder the paint planner to generate paint strokes resulting in a more predicable paint coating thickness. Furthermore, collision free motion planning requires some safety distance from incomplete objects preventing the system to paint more concave shaped objects. Robust 3D object recognition and pose estimation contributes to the realization of small lotsize robotic painting applications if part or scene complexity is very high. Future work will focus on increasing the robustness of the recognition algorithm against cluttered sensor data.
The recognition algorithm and the position estimation were tested in combination. A set of 25 objects hanging in conveyor flame was selected. It basically contains 4 different industrial objects hanged up in various positions and orientations. While the conveyor was moving around 55 scans have been taken by the sensor dynamically adapting its own kinematic configuration to cover the entire flame. Taken scans are registered and transformed to a surface model (Figure 9). Apparently, the objects in the flame causing a vast amount of occlusions due to their proximity. A next issue which is often encountered in industry is the loose hanging of objects in the flame resulting in tumbling objects. Different colors of the objects to be scanned heavenly affect the outcome of a 3D laser sensor system. Particularly, in the test set used for the experiments, the flame and the hooks are black and shiny absorbing most of the laser light. As a
4. Conclusion
Acknowledgements
Fig. 9. Reconstructed parts hanging in a skid
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The authors would like to thank the European Commission and the partners of the Innovative Production Machines and Systems (I'PROMS) Network of Excellence for their support under the Sixth Framework Programme (Contract No. 500273). PROFACTOR is core member of the I'PROMS consortium. Further information is available at: www.iproms.org
References [1] "Flexpaint." [Online]. Available: www.flexpaint.org [2] Autere, "Resource allocation between path planning algorithms using meta a*," in ISRA, 1998. [3 ] N. W. C. Robertson, R.B. Fisher and A. Ashbrook, "Finding machined artifacts in complex range data surfaces," in Proc. AC DM2000, 2000. [4] R. J. Campbell and P. J. Flynn, "Eigenshapes for 3D object recognition in range data," pp. 505-510. [Online]. Available: citeseer.ist.psu, edu/137290 .html [5] O. Camps, C. Huang, and T. Kanungo, "Hierarchical organization of appearance-based parts and relations for object recognition," 1998. [Online]. Available: citeseer.ist.psu.edu/camps98hierarchical.html [6] E.Freund, D. Rokossa, and J. Rossmann, "Process-oriented approach to an efficient off-line programming of industrial robots," in 1ECON 98: Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society, 1998. [7] P. Hertling, L. Hog, L. Larsen, J. Perram, and H. Petersen, "Task curve planning for painting robots - part i: Process modeling and calibration," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 5, pp. 324-330, April 1996. [8] R. Hoffman and A. K. Jain, "Segmentation and classification of range images," IEEE Trans. Pattern Anal. Mach. Intell., vol. 9, no. 5, pp. 608-620, 1987. [9] A. Hoover, G. Jean-Baptiste, X. Jiang, P. J. Flynn, H. Bunke, D. B. Goldgof, K. K. Bowyer, D. W. Eggert, A. W. Fitzgibbon, and R. B. Fisher, "An experimental comparison of range inlage segmentation algorithms," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 7, pp. 673-689, 1996. [Online]. Available: citeseer.csail.mit.edu/hoover96experimental.html [ 10] N. Jacobsen, K. Ahrentsen, R. Larsen, and L. Overgaard, "Automatic robot welding in complex ship structures," in 9th Int. Conf. on ComputerApplication in Shipbuilding, 1997, pp. 410-430. [ 11 ] R. J.Campbell and P. J. Flynn, "A survey of free-form object representation and recognition techniques," Comput. Vis. Image Underst., vol. 81, no. 2, pp. 166-210,2001. [12] A. Johnson and M. Hebert, "Using spin images for efficient object recognition in cluttered 3d scenes," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 5, pp. 433 - 449, May 1999. [13] T. Kadir and M. Brady, "Scale, saliency and image description," International Journal of Computer Vision, vol. 45, no. 2, pp. 83-105,2001. [ 14] K.K.Gupta and A. D. Pobil, "Apartical motion planning in robotics: Current approaches and future directions," 1998. [ 15] K. Kwok, C. Louks, and B. Driessen, "Rapid 3-d digitizing and tool path generation for complex shapes," in IEEE International Conference on Robotics and Automation, 1998, pp. 2789-2794. [ 16] D. Marshall, G. Lukacs, and R. Martin, "Robust segmentation of primitives from range data in the presence of geomet-
Fig. 10. Visualization of recognition result: Reconstructed sensor data (green shaded) and recognized matched CAD model (grey wire frame). ric degeneracy," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 3, pp. 304-314, 2001. [ 17] H. Murase and S. K. Nayar, "Visual learning and recognition of 3-d objects from appearance," Int. J. Comput. Vision, vol. 14, no. 1, pp. 5-24, 1995. [18] M. Olsen and H. Petersen, "Anew method for estimating parameters of a dynamic robot model," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 17, no. 1, pp. 95-100, 2001. [19] A. Pichler, M. Vincze, O. M. H. Anderson, and K. Haeusler, "A method for automatic spray painting of unknown parts," in In IEEE Intl. Conf. on Robotics and Automation, 2002. [20] F. R.B., F. A.W., M. Waite, O. M., and E. Trucco, "Recognition of complex 3-d objects from range data," in CIAP93, 1993, pp. 509-606. [21] X. Sheng and M. Krmker, "Surface reconstruction and extrapolation from multiple range images for automatic turbine blades repair," in IEEE IECON Conference, 1998, pp. 13151320. [22] W. Tse and Y. Chen, "A robotic system for rapid prototyping," in IEEE International Conference on Robotics and Automation, 1997, pp. 1815-1820. [23] Bauer, A., Eberst, C., N6hmeyer, H., Minichberger, J., Pichler, A., Umgeher, G., "Self-programming Robotized Cells for Flexible Paint-Jobs", International Conference on Mechatronics and Robotics 2004, Aachen, Germany.
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Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Key Technologies and Strategies for Creating Sustainable Manufacturing Organisations. A.J. ThomasaandB. Grabotb a
Manufacturing Engineering Centre, Cardiff University, CF24 3AA, UK bLGP-ENIT, 47 avenue d'Azereix, BP 1629, F-65016, F
Abstract. Manufacturing has changed radically over the course of the last 20 years and rapid changes are certain to
continue. The emergence of new manufacturing technologies, spurred by intense competition, will lead to dramatically new products and processes. New management and labour practices, organizational structures, and decision-making methods emerge as complements to new products and processes. This paper is aimed at providing an overview of the current State of the Art technologies, systems and paradigms currently operational in industry today, with a special emphasis on production and operation management. It identifies the drivers which instigate a step change in manufacturing developments. The paper then goes onto describing some of the Key Enabling Features (KEFs) which are deemed necessary for European based manufacturing industries to remain productive and profitable in 2020. K e y w o r d s : State of the Art, Key Enabling Features, Technologies, Strategies
1. I n t r o d u c t i o n
Manufacturing has changed radically over the course of the last 20 years and rapid changes are certain to continue. The emergence of new manufacturing technologies, spurred by intense competition, will lead to dramatically new products and processes. New management and labour practices, organizational structures, and decision-making methods also emerge as complements to new products and processes [ 1]. In support of the above statement, the UK DTi Foresight panel report into manufacturing 2020 [2] highlight the importance of manufacturing to the UK economy and describe the changing nature of the modem manufacturing environment as one that 'is redefining itself as a provider of lifetime service around a manufactured product'. It goes onto state that the internet is a major enabler and will initiate a paradigm shift in manufacturing.
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These issues are further supported by the EU's 'FuTMan' report [3] and the NRC's 'Visionary Manufacturing Challenges' report [1] in which it highlights that the development of new and innovative technologies are critical to meeting the six grand challenges for manufacturing in 2020. Whilst these visionary themes provide a clear view of how industry should adjust in the future, there is increasing evidence to show that many manufacturing industries throughout the EU do not have the necessary technologies and strategies to even compete let alone meet the visionary manufacturing challenges of the future. Armed with these issues, European manufacturing industry must grasp the opportunities which new and advanced manufacturing technologies provide in order to achieve a step change in manufacturing performance. One approach to achieving this is through the development of an underlying technical foundation through research by industry, academia,
and government institutions, which must be guided by a clear vision of manufacturing in the next century and an understanding of the fundamental challenges that must be met to realise this vision [3]. This paper is aimed at providing an overview of the current State of the Art technologies, systems and paradigms currently operational in industry today. It identifies the drivers which instigate a step change in manufacturing developments. The paper then goes onto describing some of the Key Enabling Features (KEFs) which are deemed necessary for European based manufacturing industries to remain productive and profitable in 2020.
2.
Technological and Strategic Systems
The type of technology employed by companies varies extensively from one organization to the next. However, in order to meet the requirements of a economically sustainable manufacturing environment of the future, we must concentrate on the innovative technologies which will provide the opportunity to give European manufacturing industry the competitive advantage required through providing products that meet the Quality, Cost, Delivery and Flexibility criteria [4], [5], [6], [7], [8]. From this, it can be shown that a series of generic technology categories that can be identified, these are shown in Table 1. In this modern manufacturing era where the requirement for mass customization of products and the increasing demand for companies to improve their product and process flexibility, delivery speed, and innovation, the development of advanced and more capable technology is becoming a major requirement. D'Aveni [9] used the term "hypercompetition" to describe the condition of rapidly escalating competition that is now changing manufacturing landscape for many industries. The resultant change in the manufacturing landscape requires companies to reconfigure both their business and manufacturing strategies in order to cope with the new pressures. However, there is little evidence to suggest that companies are realigning their business strategy with their manufacturing strategy. Brown and Bessant [ 10] provide evidence that companies remain stuck in past paradigms of strategy- most notably on the over dependence of an 61ite strategy-making group at the top of the organisation's hierarchy [11], [12]. They go onto state, that as a result of this, a state of strategic dissonance [13] occurs not only between the firm and its chosen markets but also within the firm itself, in the mismatch between strategic intent and operations capabilities [14]. They suggest that the reason for this is in the changes of
manufacturing/operations processes over time and how these became divorced from the firm's strategic process [15], [16]. It is therefore clear that in order to meet the new manufacturing challenges, a company must ensure that it aligns its business strategy (i.e. its corporate objectives) to its manufacturing strategy (which includes the manufacturing process elements and the technology used in the manufacturing processes). Kim and Lee [ 17] define manufacturing strategy as "supporting corporate objectives by providing manufacturing objectives including costs, quality, dependability and flexibility to offer a competitive advantage and focus on a consistent pattern of decision making within key manufacturing resource categories. The objective of a manufacturing strategy is to create "operationally significant performance measures' in which the competitive dimensions comprise cost, quality, dependability and flexibility". Swink and Way [ 18] define manufacturing strategy as the decisions and plans affecting resources and policies directly related to the sourcing, production and delivery of tangible products. A basic premise of the more recent literature, therefore, is that elements of manufacturing strategy, such as technology management, are most effective when their performance and function relative to the business-level strategy are measured. Swamidass and Newell [19] define manufacturing strategy as "the effective use of manufacturing strengths as a competitive weapon for the achievement of business and corporate goals". Manufacturing's strengths are developed and sustained by a "pattern of decisions" [20]. These are taken in a set of decision areas which encompass manufacturing strategy and are aimed at achieving manufacturing goals that align with business and corporate goals. Brown and Bessant [10] extend the connection between manufacturing strategy and business strategy in their work in which they investigate the development of agility within the automotive industry. They state that the ability to become agile and to manufacture mass customised products can only be achieved by combining skills, technologies, knowhow, processes and alliances with other players, brought about by strategies being in place. The need for manufacturing strategy may seem axiomatic for some academics. However, as we shall see, devising and implementing manufacturing strategies - in particular as a precursor to pursuing mass customisation - is difficult for some firms.
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3. Production and Operation management issues
planning and scheduling [27], knowledge management, competence management, new information and communication technologies for production and operation management.
Two main factors have dramatically changed the industrial context in the manufacturing area: specialization, leading to globalization on one hand, and the technological changes which have recently occurred in the Information Technology area. The consequence is that nowadays company have to take into account the following challenges: o adapt their organisation to a double opening towards their customers and suppliers, o master the information technologies allowing to keep control of the internal and external flows in a distributed context, o be able to efficiently manage mass customised products, i.e. products adapted to the specificity of each customer, but which components are standardised. develop change management methods allowing to permanently follow the evolution of their environment, efficiently manage knowledge and competence of their workforce, allowing the human resources to remain efficient in this ever changing context while decreasing their level of stress. These points mainly impact the following areas of production and operation management: o workshop layout and organisation, with a new orientation on multi-site layout [21] [22], o performance under uncertainty, o supply chain design and organisation, o reliability and maintenance of distributed manufacturing system [23], o methods of production management, o demand management [24], o planning and scheduling [25], [26], [27], o inventory control [28], o control and execution of production plans, o distribution. New areas have also recently emerged in correlation with these challenges: o design/interoperability of distributed information systems, o design of advanced decision systems, o knowledge and competence engineering, o industrial partnerships, o customer relationship [29]. o In order to address these needs, important research axis have been identified in the following areas: o "fit" manufacturing [30], o holonic layout and organisation [31 ], o distributed and cooperative approaches for
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Taking into account these orientations, the following challenges are expected to influence the research themes related to production and operation management: o define a new balance between centralised and distributed organisations (for instance using multi-agent or holonic approaches), o integrate the concept of "intelligent product" in the production management field, o improve the communication between the tactical (sales and operation planning) and the operational (execution) level of production management, o suggest new approaches for dealing with uncertainty (demand, resource availability etc.), for instance using rough sets or fuzzy logic when expert knowledge is available, o improve the management of highly customized products, o develop methods for building reconfigurable manufacturing systems, o improve the integration of industrial information systems (ERP, APS and MES) in companies, using user-oriented methodologies, o address the problem of information sharing in Supply Chains (security, confidentiality...), o investigate the social and legal aspects of cooperation in Supply Chains, o investigate how new information technologies (GPS, wireless internet, virtual reality...) could lead to totally new production management methods and practices, o improve the management and capitalisation of operational knowledge, o create bridges between this knowledge and the existing information systems. 4. Roadmap The evolution of the industrial context naturally leads to an evolution of the research themes, most of them being well-known by the research community. Taking into account more diverse objectives and improving distributed production systems performance take advantage of: a) b)
the maturity of powerful resolving tools (optimisation tools, constraints propagation, etc.), paradigms making easier the implantation of techniques (multi-agent systems, fractal
e)
enterprise, holonic enterprise etc.), efficient means of data gathering and transmission (intranet, internet, etc.).
At the level of control architectures, the need to obtain a better balance between centralization and distribution of the systems led to prefer approaches based on decentralized control, typically with multi-agents systems. Even if such approaches may provide good local results, they remain poorly consistent with the ongoing evolution of industrial information systems, using computer networks and ERP's data processing power for a more centralized management. Both approaches (centralized and distributed) have advantages and suggestions allowing to make these approaches co-operate are certainly needed. New techniques for hierarchical planning, consistent with industrial tools, could allow to better manage the needed compromise between concentration and decentralization. According to ERP editors, integrating planning and execution levels is an important problem. Intelligent, autonomous and parametric follow-up systems should rapidly allow drastic improvements for workshop control. Multi-agent technologies are also promising: intelligent agents can use the industrial networks to access the numerical commands of modern machinetools, allowing to perform real-time follow-up for the ERP. An "intelligent" product could take part in the management of its own route in the supply chain, then of its life cycle, including maintenance and recycling issues, the later being scarcely studied in production and operation management research. High levels of production management (sales and operations planning) are seldom studied in management research. A major improvement of industrial management tools is nevertheless to allow the integration of operational constraints at a tactical level. A "flows and resources management" point of view on these tactical levels could complete the presently dominating "management" point of view. Product diversification induces a constant increase of demand uncertainty, and as a consequence makes more difficult the use of statistical methods which are still mainly used. New approaches concerning the processing of uncertainty seem to be necessary (fuzzy logic, possibility theory, Bayesian networks, etc.). Functionality of production management tools is considerably enlarged in the last ten years. The multiplication of "niche" demand led to suggest products specifically adapted to individual needs. This issue motivated an important development of studies on product configuration, this functionality being now integrated, more or less efficiently, in every ERP.
Product diversification can also rapidly lead to the necessity to reconsider periodically the manufacturing system layout. The notion of reconfiguration of production systems appeared and became the subject of many research projects. Production management levels should, sooner or later, efficiently integrate product configuration, which should lead to fundamental changes in the planning methods. ERP and APS implementation, utilization and optimization have become a major industrial field of research. In this promising topic, research is oriented on various domains like continuous progress management, man-machine interfaces or competence management. It is interesting here to begin to suggest missing concepts and methodologies allowing to address this challenge, together with other communities (Sociology and Management for instance). Supply chain is one of the major themes which emerged lately in production management. Flow management through the supply chain is now intensively investigated, with approaches often based on multi-site planning, including distribution resources. These approaches often neglect the problem of information sharing, and as a consequence consider implicitly the supply chain as being composed of multiple sites of a single enterprise. The foreseeable extension of supply chains to independent companies, with similar importance, obliges this theme to consider co-operation between autonomous entities belonging to multiple supply chains. In this context, answers should be proposed for contractual, legal and social problems. Interdependence between technical aspects and enterprise management aspects leads the production and operation management community to take part in these researches. Similarly, fundamental aspects of supply chain, like benefits sharing or optimization under constraints of cost confidentiality, cannot be ignored by researchers. A paradoxical problematic appears for the interconnection of the information systems of different companies (interoperability), together with the necessity to keep confidential part of the available information. Enterprise organization does not seem anymore to be a very active domain of research: it seems now to be often limited to explaining the reasons of existing industrial evolutions. As a matter of fact, innovation is made more difficult by the researcher's concern of being credible and applicable by companies. As a consequence, suggestions of organizations totally breaking with existing ones are most of the time coming from companies, consultants or software editors willing to stay competitive. Fundamental considerations on innovating organization modes should also come from the production management
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community. As an example, it is clear that we have not reached the limit of the specialization of the companies, and an open field is certainly the opportunist design of supply chains by means of modular enterprises providing either workshops, or information systems, or decision system, or capacity purchase to others having a specific knowledge on product design. Similarly, new information and communication technologies' bursting has essentially been limited to make communication faster and easier, allowing sometimes to remove intermediaries (enterprise portals, e-business...). Re-designing the production management systems according to the capabilities of these tools is still a quite far issue. For instance, allowing a customer to plan his own order on his supplier schedule should already be possible. Realtime visualization of the costs induced by his choices of organization, planning, etc. could even be considered. Wireless internet and the miniaturization of interfaces with the information system should sooner or later considerably change the role of human actors in management decisions with reference to his interaction with the physical processes. Finally, it is clear that human resources will obviously be more and more valorized in the manufacturing system. The management of operational knowledge and its exploitation by the information system have a high potential for the enterprises' progress. Knowledge engineering techniques provide a range of mature tools which should be integrated in the present information systems. Competencies development is often only considered separately from the daily work. It is now clear that explanatory functionalities could be added to the information system, allowing to progressively increase the level of competence of the actors in fields like online diagnosis of machines breakdowns but also for problems related to flow management. From an organizational point of view, a plant centered on the human being appears to be more credible nowadays than the "plant without men" which was considered twenty years ago as being the future of the manufacturing systems. 5. Conclusions and Recommendations
There is little or no strategic guidance available to companies on the long-term development of strategic manufacturing paradigms. This often leads to many companies especially SMEs having no appreciation of the strategies and technologies
618
which are available to allow them to compete effectively in eth marketplace. The development of a roadmap provides a quick and effective approach for a company to develop its long-term manufacturing strategy. The roadmaps provided in this paper identify six key strategic paradigms. It provides a gap analysis, a risk analysis and a outline timescale to implementation which a company can use to decide on whether the strategy is in an acceptable form for implementation into their businesses. Acknowledgements The authors would like to express their appreciation to the I'PROMS, POM cluster members for contributing the State of the Art Review and the Roadmap deliverables on which this paper is based. References [1] 'Visionary Manufacturing Challenges for 2020', Committee on Visionary Manufacturing Challenges Board on Manufacturing and Engineering Design Commission on Engineering and Technical Systems National Research Council, National Academy Press, Washington, D.C. 1998 [2] 'UK Manufacturing - We Can Make It Better', Final Report, Manufacturing 2020 Panel. Foresight Manufacturing 2020 Panel Report. [3 ] The Future of Manufacturing in Europe 2015-2020, The Challenge for Sustainability, EU FP6 Funded Programme. [4] Dale, B., 1996, "Sustaining a process of continuous improvement: definition and key factors", TQM Magazine, 8, 2, 49-51. [5] Vernadat, F.B., 1999, "Research agenda for agile manufacturing", International Journal of Agile Management Systems, 1,1, 37-40 [6] Putterill, M., Maguire, W, Sohal, A.S, 1996, "Advanced manufacturing technology investment: criteria for organizational choice and appraisal" Integrated Manufacturing Systems; 7, 5. [7] Burgess, T.F.; Gules, H.K.; Tekin,M; [1997] "Supply-chain collaboration and success in technology implementation", Integrated Manufacturing Systems; 8,5. [8] Small, M.H; [1999]"Assessing manufacturing performance: an advanced manufacturing technology portfolio perspective", Industrial Management and Data Systems; 99, 6. [9] D'Aveni, R., 1994, "Hyper-Competition: Managing the Dynamics of Strategic Manoeuvring", Free Press, New York, NY. [ 10] Brown, S., Bessant, J., 2003, "The manufacturing strategy-capabilities links in mass customisation and agile manufacturing - an exploratory study", Int. Journal of Operations & Production Management; 23, 7.
[ 11 ] Hayes, R.H., Wheelwright, S.C., "Restoring Our Competitive Edge", John Wiley, New York, NY, 1984. [12] Brown, S., 1998a, "Manufacturing strategy, manufacturing seniority and plant performance in quality", Int. Journal of Operations & Production Management; 18, 6. [13] Brown, S., 2000, Manufacturing the Future Strategic Resonance for Enlightened Manufacturing, Financial Times/Pearson Books, London. [14] Hamel, G., Prahalad, C., 1989, "Strategic intent", Harvard Business Review, May-June. [15] Lazonick, W., 1990, Competitive Advantage on the Shopfloor, Harvard University Press, Cambridge, MA. [16] Lazonick, W., West, J., 1995, "Organizational integration and competitive advantage: explaining strategy and performance in American industry", Industrial and Corporate Change; 4, 1. [17] Kim, Y., Lee, J., 1993, "Manufacturing strategy and production systems: an integrated framework", Journal of Operations Management, 11. [ 18] Swink, M.,u Way, M.H., 1995, "Manufacturing strategy: propositions, current research, renewed directions", Int. Journal of Operations & Production Management, 15, 7. [19] Swamidass, P.M., Newell, W.T., 1987, "Manufacturing strategy, environmental uncertainty and performance: a path analytical model", Managt Science, 33, 4. [20] Skinner, W., 1969, "Manufacturing- the missing link in corporate strategy", Harvard Business Review, May-June. [21 ] Rhim H., Ho T., Karmarkar U., 2003, Competitive location, production, and market selection, European Journal of Operational Research; 149, 1. [22] Norman B.A., Smith A.E., 2005, "A continuous approach to considering uncertainty in facility design", Computers & Operations Research, In Press, Corrected Proof, Available online 30 December 2004. [23] Yu R., Iung B., Panetto H., 2003, "A multi-agents based E-maintenance system with case-based reasoning decision support", Engineering Applications of Artificial Intelligence; 16, 4. [24] Keskinocak P., Tayur S., 2004, "Due Date Management Policies", in: Handbook of Quantitative Supply Chain Analysis Modeling in the E-Business Era, Series: Int. Series in Operations Research and Management Science, 74, Simchi-Levi, David; Wu, S. David; Shen, Zuo-Jun (Eds.). [25] Ruiz-Torres A., L6pez F.J., 2004, "Using the FDH formulation of DEA to evaluate a multi-criteria problem in parallel machine scheduling", Computers & Industrial Engineering; 47, 2-3. [26] Anglani A., Grieco A., Guerriero E., Musmanno R., 2005, "Robust scheduling of parallel machines with sequence-dependent set-up costs", European Journal of
Operational Research; 161, 3. [27] Hao Q., Shen W., Wang L., 2005, "Towards a cooperative distributed manufacturing management framework", Computers in Industry, 56, 1. [28] Agrawal N., Smith S.,2002, "A bayesian framework for inventory replenishment and allocation for retail chains with non-identical stores", Conference on Optimization in SCM, Gainesville, Florida, USA. [29] Buckinx W., Van den Poel D., 2005, "Customer base analysis: partial defection ofbehaviourally loyal clients in a non-contractual FMCG retail setting", European Journal of Operational Research, vol. 164, n~ [30] Thomas A.J., Pham D.T., 2004, "Making industry fit: the conceptualisation of a generic 'Fit' manufacturing strategy for industry", Proc. 2nd IEEE Int Conf on Industrial Informatics, INDIN 2004, Berlin, June, R. Schoop, A. Colombo, R. Bernhardt and G. Schreck (eds). [31 ] Tharumarajah; A., Wells, A.J., Nemes, I., 1996, "Comparison of the bionic, fractal and holonic manufacturing system concepts", Int. Journal of Computer Integrated Manufacturing; 9, 3.
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Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 CardiffUniversity, ManufacturingEngineering Centre, Cardiff,UK. Published by Elsevier Ltd. All rights reserved.
An Integrated Approach to TPM and Six Sigma Development in the Castings Industry. A.J. Thomas a, G.R. Jones b, P. Vidales ~ a
Manufacturing Engineering Centre, Cardiff University, CF24 3AA, UK b Wall Colmonoy, Pontardawe, Swansea, SA1 3DE c Ecole d'ingeniear CESI, France
Abstract
Both Total Productive Maintenance (TPM) and Six Sigma are key business process strategies which are employed by companies to enhance their manufacturing performance. However, whilst there is significant research information available on implementing these systems in a sequential manner, there is little information available relating to the integration of these approaches to provide a single and highly effective strategy for change in companies. This paper proposes an integrated approach to TPM and Six Sigma which was developed as a result of work undertaken in the castings industry. The effectiveness of the approach is subsequently evaluated highlighting the benefits the host organization received through this new approach by measuring the effects of implementation against the seven Quality, Cost and Delivery (QCD) measures. Keywords: TPM, DMAIC, QCD Measures
1. Introduction to TPM Approach
and the Six Sigma
Total Productive Maintenance (TPM) is a maintenance program which employs a strategy for maintaining plant and equipment to its optimum level of operational effectiveness. Primarily the TPM approach links into the 'Lean' concept and aims at reducing waste due to poorly maintained machinery and provides for value added inputs by way of ensuring machinery remains in productive operation for longer periods of time [ 1]. Maintenance procedures and systems are designed so that they are easier to accomplish and this is achieved through machine redesign and modifications in order to facilitate this process.
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Six Sigma can be considered both a business strategy and a science that has the aim of reducing manufacturing and service costs, and creating significant improvements in customer satisfaction and bottom-line savings through combining statistical and Business Process methodologies into an integrated model of process, product and service improvement [2]. Although both strategies have similar aims, those of improving productive effectiveness, the way in which these strategies are implemented into companies varies greatly. Traditionally Six Sigma employs a structured five-phased DMAIC methodology. Six Sigma teams are created to tackle specific problems to reach Six
Sigma levels of performance [2]. TPM implementation on the other hand is seen to be implemented in a range of different ways, although attempts have been made to formalise the TPM strategy [3], [4], [5], there is still no formally defined approach that can be considered as an industry standard approach to TPM implementation. However, when considering TPM, it is worth noting that the basic principles of the TPM strategy have very close links to the Six Sigma approach. In TPM the ultimate aim is to achieve significantly reduced breakdown levels through developing autonomous maintenance teams. Employing therefore a standard operational framework for implementing both approaches is seen as an obvious and necessary step for companies to achieve simultaneous benefits from the TPM and Six Sigma strategies. To this end the DMAIC process is used as the main operational approach for the implementation of TPM. The following section highlights the application of the DMAIC process in the implementation of TPM in a castings company. 2. Introduction to Wall Colmonoy Wall Colmonoy is a manufacturer of specialist castings. The company is based in South Wales and manufactures its products to a world wide market. Over the years the company has experienced increasing competition from the far-east where product unit costs have been dramatically reduced. This has brought about major changes to the company operations and has raised the need for the company to become leaner and more responsive to customers if they are to remain as serious competitors in their market.
Over the past two years the company has embarked on a 'Lean' manufacturing program. As part of the Lean approach, TPM and Six Sigma are seen as essential strategies for success. However, the company is concerned that the separate implementation of such approaches means the requirement of large scale human, financial and technical resources as well as the associated problems of running competing projects in the company. The company requires a simple yet effective operational framework that can be used as a standard approach to adopting both strategies in the company. The company expects that worker 'buy in' will be easier if one common operational approach is adopted
was decided that the DMAIC process would form the basic foundation for the TPM strategy and hence the standard approach for adopting the major stages of the TPM project. Each stage is explained in detail in the following section of the paper.
3.1 Define A benchmarking exercise was undertaken into the major product lines operated by the company. The product lines were benchmarked against 'on-time delivery' and 'right first time' quality levels. A gauge R+R study was undertaken in order to ensure that the measuring equipment was suitable for measuring the outgoing quality from the processes. From the analysis of the key casting processes within the company, the investment casting process was highlighted as the area requiring greatest attention with scrap rates in excess of 4% and on-time delivery at only 65%. The definition stage triggered the development of a TPM team within the company. This involved the training of team members in the principles of TPM as well as the implementation of a 5S program* aimed at piloting autonomous cleaning and teamworking prior to specific and targeted TPM projects being undertaken within the investment casting area.
3.2 Measure Overall Equipment Effectiveness (OEE) was calculated on each of the machines within the investment casting area. Also, the company measured parts throughput (parts per hour) through the cell in order to identify whether the inefficiencies were due to the machinery or to the operations surrounding the machinery or both. As an example, OEE calculated for one machine was calculated at 75% however parts throughput in the cell where the machine operated in was 43% less than the theoretical throughput for that cell. Further analysis of the cell indicated that the process surrounding the machine was at fault rather than the machine itself. One casting cell was measured as having a throughput at 36% of its theoretical value and an OEE value of 30% for the wax making machine. A process mapping exercise confirmed that the wax making machine was the major cause of the low cell throughput and so this machine became the focus of the remainder of the project.
3.3 Analyse 3. DMAIC at Wall Colmonoy The Six Sigma strategy concentrates on a simple five phase methodology called DMAIC. DMAIC is an acronym of the major steps within the methodology namely Define, Measure, Analyse, Improve, Control. It
The OEE value was split down to its constituent parts namely; Availability, Performance and Quality.
* 5s - A systematicprocess of workplace cleaningand maintenance. Sort, Sanitize, Stabilize, Systematize, Sustain
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The results of this analysis showed that machine availability was lowest at 34% compared to performance at 94% and quality at 96%. This clearly indicated that machine breakdowns and major stoppage problems were the causal point for the poor OEE value. A Fault Tree Analysis (FTA) was therefore carried out by a team of engineers from within the company in order to ascertain the root cause(s) of high machinery failure. The FTA is shown in Fig 1 and lists the failure routes identified from the brainstorming session. Following the FTA, the engineering team progressed to creating Failure Modes and Effects and Criticality Analysist (FMECA) on each of the areas identified from the failure routes on the FTA. The FMECA allowed the company to identify the potential causes of failure, assess its effect on the machine and process and also, and most importantly, allow for corrective actions to be identified. The engineering team did not follow normal FMECA convention at this stage and decided to employ individual FMECA sheets for each potential failure mode. The benefit this gave the team was that each sheet could be given to the maintenance teams in turn in order to apply the corrective action specified in the documents. In order to prioritise the issuing of the FMECA sheets to the maintenance teams, a Pareto analysis was constructed of the Risk Priority Number S (RPN) from each FMECA study with the higher ranked RPNs being tackled first.
3.4 Improve Three levels of TPM were adopted in the company in order to improve the machine' s reliability. Level 1 was the introduction of shop floor autonomous maintenance teams. These teams applied basic maintenance practices including regular daily cleaning regimes as well as undertaking sensory maintenance tasks (smell, sound, sight, feel etc). However, prior to this level being undertaken, it was essential that major machinery and equipment was completely overhauled in order to revert the machinery to its original level of reliability. This was considered to be Level 2 in the TPM system and the work undertaken by the maintenance department. Level 3 involved the engineering department becoming more pro-active in the development of preventive maintenance practices including machine modification and enhancement strategies that allow for easier maintenance etc. Level 3 work also included the monitoring of maintenance
activities and concentrating primarily on approaches towards increasing Mean Time Between Failures (MTBF) so that higher machine availability is achieved. The aim here is to systematically extend the mean time between failure so that the machinery can remain productive for longer thus providing greater return on machine performance. Table 1 shows the work undertaken at each level in the TPM system. Table 1 TPM Levels and Work Definition Levels of TPM Operation and Typical Activities Level 1 Level 2 Level 3 Basic Cleaning Machine Machine redesign overhaul Machine care Major MTBF analysis & extension plans Maintenance Sensory Level 1 Level 2 maintenance Monitoring Monitoring
3.5 Control The work undertaken by the pilot TPM work was measured for its effectiveness before being rolled out through the company. Machine maintenance schedules and plans were formalized and attached to each machine. All operators were trained to undertake the maintenance schedules and to report any issues to the maintenance teams. As a control mechanism, it is the responsibility of the maintenance department to monitor the work of the operators and to rectify any issues raised by the shop floor personnel. The engineering department in turn monitored the outputs from the maintenance department in order to identify recurring failures and issues that could be redesigned in order to prevent future failures. The engineering team provided the technical and financial support to the maintenance department in order to facilitate the high level maintenance activities undertaken at the level 2 stage. Fig 2 shows the autonomous team approach at each of the TPM levels in the organization and how each level integrate with each other.
TPM Autonomousteams
I
Operators Team Leader
t FMECA - An advanced planning technique aimed at systematically assessing all the potential failures of a machine and the potential impact (criticality) of the failure on a human and/orthe system. $ RPN- Risk PriorityNumber. A numericalmethodof analysingthe failure mode and its effect on the system. RPN = Severity x Occurrence x Detection.
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I
Maintenance Team Leader
0
Engineers Team Leader
~-~1 Complexityofmaintenancefunction ~-~ v
Fig 2 Autonomous Team Structure
4. Evaluation
As part of the company's approach to improving their Quality, Cost and Delivery targets, it was decided to measure the QCD [6] outputs as a direct result of the TPM project. Table 2 shows the improvements made in each QCD area. In this case, the benefits gained from undertaking the TPM project may be considered idealistic when comparing the large benefits gained from a relatively small initial financial outlay. However, the costs incurred in continuously controlling the input variables and factors means that the monitoring costs can be large and greater than first expected. This in turn can affect the true savings achieved from the project. The issue of cost analysis and control is as a key consideration and its correct analysis and interpretation is key to providing credibility to the TPM strategy within the company. 5. Conclusions
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A TPM pilot study was undertaken in order to improve the Quality, Cost and Delivery measures ofthe company. In all measures, the TPM project achieved significant improvements. This relatively simple application of TPM using a structured DMAIC technique should allow for increased use of the methodology for tackling many maintenance issues. Likewise, the results can also provide the stimulus for the wider application of the technique to create process improvements at relatively lower costs. The application of the TPM approach to the wax machine area at Wall Colmonoy achieved savings in excess of s for an initial outlay of less than s in experimental and project costs. The development ofthe TPM approach developed a culture towards continuous improvement and the systematic implementation of the system throughout the organisation. The application of the TPM approach allowed the company to develop advanced systems mapping and analysis techniques and to become generally more 'technical' in their approach to problem solving.
writing of this paper: Wall Colmonoy, I'PROMS, Cardiff University Innovative Manufacturing Research Centre. References
[1] Jostes R S, Helms M M. Total Productive Maintenance and Its Link to Total Quality Management. Work Study Journal. (1994), 43,7. [2] Breyfogle, F.W. Implementing Six Sigma, Smarter Solutions - Using Statistical Methods, (1999).John Wiley & Sons Inc. [3] Blanchard, B S.An enhanced approach for implementing total productive maintenance in the manufacturing environment', Journal of Quality in Maintenance Engineering.(1997), 3,2. [4] Jens,O, Riis J, Luxhoj T, Thorsteinsson U. A situational maintenance model' International Journal of Quality & Reliability Management; (1997). 14, 4. [5] Raouf A, Ben-Daya M. Total maintenance management: a systematic approach', Journal of Quality in Maintenance Engineering; (1995) 1,1. [6] "Achieving Best Practice in Your BusinessQCD Measuring Manufacturing Performance". Department of Trade and Industry Brochure, www. dti. gov.uk. (2002).
Acknowledgements
The authors would like to express their appreciation to the following organisations for their support during the development of the project and the
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A u t o Wax Machine 1
OFE:35,5%
SCRAP
pqm not working
Fig I
An PTA o f the possible factors influencing machinc performance
Table 2 Quality, Cost Delivery - The Seven Measures
People Productivity Improvement
Basic Measure: Units per direct operator hour ie
Scrap / Defect Reduction
Number if good units made Number of direct operator hours
Basic Measure: % Quantity of defective units Total quantity of units supplied
Example of Improvement Measure Previous State 30 Units per Hour New State 60 units per Hour
i mprovemem = .
['- 60-30 -] [ 30 9/
I
50%
Example of Improvement Measure Previous State 5% New State 1% Improvement=
I ]~
i.e a 80% / reduction in defects (reported as a positive , number)
1
~
Improved Space Utilisation
Basic Measure: s per m2 Sales turnover of model area Number of square metres of area
:Example of improvement measure Previous State s per m2 New State s per m2 Improvement =
On Time Delivery Improvement
Increase in Stock Turns
Basic Measure: % delivered correctly and on time
ie
Previous State New State
Number of not on time deliveries
Improvement =
Sales turnover of product Value of (Raw material + WIP + Finished Goods)
50,000 - 30,000 30,000
90% delivered on Time 99% delivered on Time I
99% - 90% 90%
Basic Measure: % i.e. Availability % x Perfomance % x Quality %
Basic Measure: s / person i.e.
Output value - Input value Number of employees
96% - 80% 80%
=20%
Example of Improvement Measure Previous State 30% effective New State 66% effective Improvement = Increase
(Gross) Value Added per Person
:10%
Example of Improvement Measure Previous State 4 Stock Turns New State 5 Stock Turns Improvement = Increase
Overall Equipment Effectiveness
=67%
Example of Improvement Measure
Number of planned deliveries - Number of not on time deliveries
Basic Measure: Number of turns
~_
F L
66% - 30% 30%
-]i:120%I
Example of Improvement Measure IPrevious State s per employee New State s per employee Improvement = ncrease
s
-s
=67~
s
625
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Characterising SME Attitudes to Technological Innovation. A.J. Thomas and R Barton Manufacturing Engineering Centre, Cardiff University, CF24 3AA, UK
Abstract
The way in which UK manufacturing companies operate is changing rapidly. The requirement for mass customisation of product lines along with the need to ensure high product quality, low product cost and consistent and reliable delivery is placing increasing pressure on UK manufacturing industry. Many larger manufacturing industries in the UK are responding quickly to these pressures by developing leaner, flexible and technologically advanced and intelligent manufacturing systems. The problem however, is that there is much evidence to suggest that SMEs do not implement and subsequently develop Advanced and Intelligent Manufacturing Technologies (AIMT) at a rate that will enable them to remain sustainable in the future. This paper identifies the major operational characteristics of the SMEs surveyed and provides a classification system based on the attitudes and abilities ofthese companies to implement Advanced and Intelligent Manufacturing Technologies. The paper then describes the reasons associated with the poor level of AIMT implementation within SMEs. The findings of a three-year survey into 300 UK manufacturing based SMEs is used in order to provide a broader explanation for the poor level of AIMT implementation. Keywords: Advanced Manufacturing Technology, Intelligent Technologies, Technology Implementation, Survey.
1. Introduction
Manufacturing has become increasingly competitive in nature. The changing face of manufacturing industry is forcing companies to evolve at an unprecedented rate [1]. The number and type of small manufacturing enterprises that operate in specific areas of the country vary greatly. There is a common need however to identify the type of technology currently in place within these companies and how this technology meets customer requirements now and in the future. The requirement to keep abreast of technological developments in order to improve productivity, quality,
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range of products and other performance measures is now paramount [2]. Despite the clear evidence for a need to acquire technical skills and implement new and effective technology into SMEs to ensure survival and sustainable growth, many companies are reluctant to move towards major investment. Furthermore, many companies are especially reluctant to invest in technology pertaining to Automated, Intelligent and Computer Aided Engineering systems. The reasons for this are many. Primarily, companies are initially deterred by the extensive capital investment required to develop such technologies and secondly, the capabilities of the technology and the advantages it
brings to the average SME are not fully appreciated by the companies concerned. This, along with the fact that many SMEs do not have the technical and manufacturing infrastructure to support AIMT severely limits their success in various technology transfer initiatives [3]. It is important to note that the development and introduction of AIMT into SMEs is not to be perceived as a panacea for all production problems. The survey shown in this paper into 300 manufacturing based SMEs identifies that many companies are neither interested or require AIMT in order to remain competitive. Likewise, other SMEs are highly advanced and have developed robust and highly responsive manufacturing systems based on a continuous culture of technology implementation and have reaped the benefits of being 'high-tech' in that they have greater market share and are as a result, economically and technologically sustainable. It is therefore critical that an in-depth study into the characterization of SME operations is undertaken so that essential information relating to a company's operational infrastructure and its future strategic focus is made. Failure to consider the company's strategic needs can result in the development of technology implementation initiatives that, whilst meeting the company's short-term needs, do little to develop its longer-term requirements [4].
2. A Methodology for Characterising SME Manufacturing Dynamics If SMEs are to become sustainable then the correct selection, purchase and implementation of AIMT is essential [4]. Firstly the technology must meet the company's overall strategic vision and direction. Failure to do this often results in technology being under-utilised due to the lack of demand for its capabilities or skills shortages inherent within the company that fail to exploit its full capabilities. Likewise, the technology can fail to extend the company's manufacturing capabilities in that it is has too little technical capability thus providing only modest improvements in company performance. Also, the SME must have the drive and top down management commitment towards Technology Implementation (TI) so that they are able to maximise the opportunity available to them. An essential step in understanding the technical capacity and capability of SMEs to incorporate AIMT involves the undertaking of a detailed study into characterising SMEs operational performance and the identification of technological development within these companies. This approach towards categorisation whilst quite generic in nature, allows for an
understanding of company dynamics to be gained and helps identify the level of technology that SMEs will be able to implement. 300 manufacturing based SMEs were targeted for assessment over a three-year period. A questionnaire was devised that identified accurately the current technological platform of the SMEs as well as defining their aspirations towards developing their operations, company infrastructure, financial strength, skills base etc. It was decided that each SME would be visited by a project engineer rather than to rely purely upon questionnaire feedback since this allowed for a more realistic analysis of the company's operations. The following section of work attempts to analyse the research results. Table 1 initially characterises SMEs into three distinct areas based on their business type, technological strategy and management approach adopted by each company. Table 2 goes onto highlighting the typical organisational attributes ofthe SMEs. Table 3 identifies the benefits that arise from the adoption of AIMT by analysing the experiences gained from before and after TI. Finally, Table 4 shows the detailed manufacturing characteristics of Category 3 companies showing the infrastructures required for successful TI. Figure 1 shows the approach adopted: Figure 1 Research approach adopted Design Survey to include: No's of companies, questionnaire details etc Establish Measures for Categorisation
Undertake survey through company visits Company Characterisation Table 1 Analysis of Company Attributes Table 2 Benefits of TI in terms of attributes. Table 3 Framework of characteristics to achieve successful TI Table 4 3. SME Categories As a result of the survey, it was possible to develop a series of categories that best described companies. These categories and their associated characteristics
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are shown in Table 1. The information highlighted in Table 1 shows the typical attitudes and technological infrastructures within the SMEs surveyed. By far the greatest area of potential for new technology implementation lies with Category 2 companies with 65% of SMEs surveyed were considered to have the potential to implement new technology but had not actually taken up the challenge. The categorisation further highlights the problems with both financial and technical support for these companies and more importantly shows in general terms the problems faced with how the organisational structure within these SMEs prevents the implementation of new technologies. Table 2 shows the analysis made of the organisational issues within SMEs that affect TI. Table 2 clearly reinforces the issue that SMEs especially in Category 2 lack the technological skills to both appreciate the capabilities and advantages that AIMT can bring to the company as well as lacking the technology management skills to implement and develop the technology once acquired. Without a clear appreciation of what new technology can do for a company, it is very difficult to drive forward a culture of change within an organisation. Of course the benefits of new technology is not only seen by way of increased manufacturing output, reduced manufacturing costs and improved design to manufacture lead times etc. The benefits can be seen throughout the organisation and are exemplified in Table 3. Whilst undertaking the survey, it was seen that a number of companies had undergone some form of technology implementation and technology transfer. Table 3 identifies the typical changes in the organisational structure and technological development of the company as a result of TI. More importantly it highlights the changes that occur within the organisation at a technological viewpoint as well as a human resource viewpoint. It was felt that analysis of this area of work is important since it can help provide a mechanism towards highlighting to companies the wider benefits that can be gained from TI especially where the area of limited workforce capabilities was cited as being a major impediment towards TI. Table 3 clearly shows the improvement in job satisfaction and the importance gained from allaying fears of new technological systems as implementation progresses. Finally the survey enabled a detailed and accurate analysis to be undertaken of the typical characteristics of category 3 companies. This was considered essential to the study where again it enabled category 2 companies to appreciate the systems design issues that result from the implementation of advanced and intelligent manufacturing technologies.
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Table 4 shows not only the issues associated with systems integration of technology and the need for effective communication systems as well as complex team working dynamics, it also shows the need for effective human resource development in order to ensure the workforce to suitably trained and rewarded for their skills. Although not all category 3 companies displayed the characteristics shown in table 4, the table provides a composite set of issues and a'requirements list' that could be used to monitor progress of a company embarking on a TI programme. 4. Conclusions and Recommendations
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The implementation of AIMT in order to meet customer requirements or perceived market advantage and improved business performance is not fully appreciated by SMEs. Of the companies that had implemented AIMT, all stated that the implementation phase was the most problematic area of the process and that they had not considered that the selection and planning stages of the TI process had such an important bearing on the implementation phase. This view is supported by Van Der Merwe [5] in which he states that strategies do not fail when they are being analysed or when the objectives are being set but during implementation and, more particularly, due to the lack of proper project management. A number of studies cite the lack of monitoring by SMEs of expenses/costs, internal benefits or otherwise and outcomes of TI, although external costs (e.g. consultants or training) are generally monitored. Sources of advice in the area of technology implementation is mixed and varied. Most information relating to the relevance or otherwise to TI in SMEs is based on anecdotal claims or oneoff/small sample instances and personal preference. There is a distinct lack of research into formal TI models for SMEs. Current systems approaches do not aid in distinguishing between SMEs and larger companies hence systems become generic with large variations in systems performance often depending on what resources the company has to support such initiatives. There is a distinct lack of top management commitment combined with an unrealistic expectation about the implementation time-scale and the associated cost of TI. This leads inevitably to a failure to develop and sustain a technologyoriented culture. Several organisations are viewing technology as a panacea and a cure-all for every problem. This often leads to disillusionment when the system does not return the perceived returns on investment. This is further supported by work of Minaro-Viseras, et al [6] where their studies
showed that companies embrace the approach without understanding its impact on the long-term management practices of their organisations. They then fail to achieve lasting improvements. On a wider scale, micro-SMEs (1-10 employees) have limited financial and human resources capable of supporting complex technological systems. However, the survey highlighted the primary concern as being that companies did not feel that they possessed the knowledge base to support TI. These are critical issues that need to be considered but are not covered in more generic technology systems. Larger and more technically advanced SMEs were found to have had better resources but still employed consultants to undertake and manage the TI process. Skills and knowledge pertaining to the project management and implementation process were lost however once the consultant had left the company.
delivered and managed by the Manufacturing Engineering Centre, Cardiff University.
References
Williams B.R [ 1995] 'Manufacturing for Survival' Addison Wesley Publishing Company .
Quarashi Z.N, Polkinghorne M.N, Bennett J.P [1998] 'The Economic Impact of the Teaching Company Scheme on the Local Manufacturing Base' Plymouth Teaching Company Centre. Thomas A.J [2002] 'The College Business Partnership Scheme and its Impact on the Technical Development of SMEs in South Wales', Proceedings 18th NCMR, Leeds Metropolitan University Beatty, C., [1990] "Implementing advanced manufacturing technology", Business Quarterly, 55, 2, pp 46-50. Van Der Merwe, A.P., [2002], "Project management and business development: integrating strategy, structure, processes and projects", International Journal of Project Management, 20, 401-11. Minarro-Visera, E., Baines T, Sweeney M,. [2005], "Key success factors when implementing strategic manufacturing initiatives" International Journal of Operations & Production Management,25, 2 pp. 151-179.
Acknowledgements The survey described in this paper was supported by the Manufacturing Advisory Service (MAS), I'PROMS and SUPERMAN an ESF funded project
629
Table 1
~
CAT.1 tech’ SMEs with static progress (22% of companies surveyed)
,OW
CAT.2. Growing SME profiting from basic technology development (65% of companics Surveyed)
CAT.3. nnovative S M E with iigh tech’ profile and culture ( I 3% of companies Surveyed)
machine
shops
Table 2 - organisational attributes shown by SME category
Characterisation of SMEs
are
MANAGEMENT STRATEGY SMEs m craft activities with slow technical progress, limited profits and not very attractive. Happy with current client base, often with highly skilled labour, which impacts on profits. New technology 1s resisted at times due to company feeling that newly trained workers could he lost to competitors. Financial burden is seen as too much for company to risk. Low risk technologies favoured Business with a relatively antiquated modc of production. Generally older companies that have not kept abreast with expansion and tcchnicai progress hut are waking up to the need to advance the company in order to survive. See that technology implementation as the key to success but no rcal idea oC how to implement. Financial risk is secn as an issue hut not onc that will prevent TI. lndependcnt innovative SMEs (e.g. precision engineers), manufacturing companies focussing on in order to process manufacture the product with high quality. Tend to he able to keep workforce due to job satisfaction etc. Workforce develops unique skills that keep company ahead of competitors. Culture of continuous performance improvement 1s seen.
Multiple qualified enginccrs Ability to take strategic view of technology Able to develop own sophisticated technology Some budgetary discretion within dep’ts Previous experience of TI Focuscd on technology and know what they want
Category .2. Companies
Jategory .l.Companies
Limited numbers of qualified and experienccd engineers Able to adopt and adapt small low tech packaged solutions Need assistance with implementation Have limited technology experience Have little understanding of what technology is right for the company. Severely limited by budgets, technology must work first time No meaninghl tcchnological capability See no requirement to have such technology Happy with a markct niche Maintain bottom line by shedding workers when going gets tough. Do not see technology ab a means to improvcment Generally fearful of Technology
Table 3 - A comparison of SMEs attitudes before and after TI due to TI BEFORE T.1
CAT .1.
Table 4 detailed characterisation ofcategory 3 company inhstructures ~
MANUFACTURING CHARACTERISTICS
CAT.3.
CAT.2.
Lhrector, 5ed thc need fur using n e ~tcchnology iii company flaw auhimed PIIcccss 111 drveloplng technology Not afraid to
develop tcchnology wntiiiuous haaii.
uii
ti
+
+ +
+ 4
+ 4
+ A larger range 01. mginewr ( .3, < l o ) nith particular skills wch as CAD, Streas I FFA cic Mu51 also be flexible hut are atiractd l u the compmv due to the \.,iriei) uf Ihc job u h i l s l keeping a grasp on tcchnology ?pccialiim.
+ + + + 4
+ + CAT.3. maintain technology de\elopmeni cycle and w e technology to maximum extent Bccomes a leading company ~n thc dewlopmmt of technology and guides own suppliers in TI Company extends and advanccr technology and in its contnhutes development Nnt just an
4
+
Greater Integration of the operational processes Greatcr integration of manufacturing task? Responsibilities concentrated in the hands o f a managcr Ica\,ing MD free to look a1 qtrategic issues Integration ordecision-malung throughout the team Venical compression of processes and deccntralisatian of decisions Parallelism of thc processcs. Simultaneous approaches to m u f a c t u r e of products Establishment of snnultaneour t a s k or operations De-standardisation of processes and tasks to conslder m u f a c t u r e In the mosl productivc manner. Re-localisation of work according to natural logic and cfticiency. Cellulai approachcs to manufacture Greater integration of manufaclunng processca and the urganisation Redistribution of work acroab the company wth rcsponsibilit) being re-focusacd or specific team Minimstion o f t a s h involved in integration of processes bctneen team? Reduction of impection checks Greater confidence in process Establishment of off-line checks and more concentration on process control Relaxation of supervision andgreater tmst in workforce Ophmum exploilation ofnew Information technologies Specialisation ofdivisions according to fields of shlk
Directors
automated syqtems can be installed using local
end user any more
+ 4
+ 4
+
+ + +
+ +
Stratcgy IS one oi malnialnlng current lwek and workforce continually developing their skills Wider range or high tech' rkllls gained by wnrkcrs
normally
~ a t i s b thcir immediate need\ Feel as 11 they are doing a 'rcal' engineering job
+ + 4
+ +
Evoluhon of working UNE: from functional dep&ments to teams responsible for i process bolution of posts: from simple t a s k to multidlmensional work Evolution of roles. from supclvised posts to posts wlth autonomous responsibility Evolution of crafts: from trainmg to education Evolution of the criteria for remuneration and perfornmcc: paynent by results Evolution o f the cnteria for promotion: from performance to ability Evolution of values: from perfecnoiusm to vcrsatility Evolution o f managers: from supervisors to encouragers and lcaders by example. Evolution of organaation: from hcrarchcal to flatter structures Evolution of directors: from extension of working team to ?trategists and Ion! tenns planners. Information is available aimultaneonsly wherever it is needed due to effective team work Generalists can do the work of specialists due to reduced operatlonal complcxity Bisinesses profit fiom the combined advantages of centralisation and deccntralisation of workplace Contact with clients isinoved from peraonal contact to nwre efficient means (Intcmct. e-mail, c-commerce .etc) Certam manufacturing operahons necd no i less supemision (rccognltion tcchnologes, automatic monitoring) Plans are reviewcd immediately on an ongoing hasis
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Maximising the Effectiveness of Introducing Advanced Technologies
R. Barton
and A.J. Thomas
Manufacturing Engineering Centre, Cardiff University, CF24 3AA, UK
Abstract
In the face of increasing competition, manufacturing companies have to consider new methods of increasing the value of their products. Improved production efficiency to reduce costs, and higher product specification to raise levels of quality are needed to maintain existing business. It is often perceived among the inexperienced SMEs around the UK that investment in new technology and intelligent systems is the easiest, yet the most expensive, solution. However, this is o~en not the case when, and only when, a company undertakes extensive work to research, plan and manage technology implementation it is possible to select equipment which will enhance almost every aspect of a company's performance. However, without this work, new technology can not only confound a previously well organised operation but also incur high running costs and a long payback time. This paper outlines a generic approach to implementing new technology, identifies the keys to its success and how further sustainable growth can be achieved when new technology implementation is considered part of a company-wide strategy. Keywords: New Technology, Intelligent Systems Implementation, Sustainable Growth.
1. Introduction
The increase in competition from foreign markets has forced manufacturing companies in the UK to establish new or unique competitive advantages to ensure that they can offer more value than just a narrow margin in total acquisition cost over the cheaper foreign alternative. Many larger companies are successfully managing the change in requirements by taking on the characteristics that have made SMEs profitable in the past, namely; mass customisation [1], specific improvements in functionality and rapid development of new products and processes. However, they are still restricted by the rigidity of the organisation's structure and policies, hence it is important that while SMEs retain their traditional core capabilities and strengths, they must remain 632
competitive by further improving on lead-times and product costs while maintaining high levels of quality and more advanced product designs.. In addition, a vital part of ensuring continued success and sustainable growth is to make the company not only an integral part of the customer's supply chain, but also a key part of the new product implementation procedure both for existing customers, and whenever possible those of the competition. Underlying the achievement of these measures is the need for continued evolution in technological capability in order to enhance product specification and production efficiency. The degree to which reducing costs is more important than improving product quality, specification or delivery performance is a balance depending on each company's specific product, industry, customer base and competition. Continuous
redevelopment of a company's strategy and modifying its operational practices has to be based on these unique, distinctive requirements, the success of which depends on the degree to which the supply company is capable of meeting these demands.
2. 'Fit' Manufacturing
'Fit' manufacturing is a recently conceived manufacturing paradigm that is being constantly developed in an effort to encompass all of the characteristics of a manufacturing strategy. 'Fit' proposes an integrated approach to the use of Lean [2], Agility [3] and Sustainability to achieve a level of fitness that is unique to each company. 'Fit' does not only develop a company's latent potential to meet new market requirements, it actively encourages companies to seek new market areas and to operate in unfamiliar areas knowing that the technological, human and financial aspects of the company are robust enough to enable the company to achieve market breakthrough. There are benefits inherently gained by implementing a 'Fit' system, which are seen firstly in terms of the operational metrics, but which also directly improve the product characteristics. A 'Fit' system actively promotes knowledge and skills alignment. Through the continual enhancement of a company's technology will come the need to ensure that a company's workforce is suitably trained. However, this does not extend simply into the manufacturing aspects of the company. Since 'Fit' promotes the continual development of new and innovative products in order to attract new markets. A company's design and engineering team must also be continually trained to meet market needs. This in turn brings new knowledge and understanding of the technology required and available, perpetuating the next stage of the company' s development. It is seen then, that in the context on new technology implementation, 'Fit' manufacturing is both a driver of, and driven by, improved capability and new opportunities. This is demonstrated in Figure 1, which shows how the direct value chain is supported by many other areas of business improvement, a few of which are shown. It is also representative of the fact that the different supporting functions contribute directly to the value stream, as well as managing
the centrepiece of demonstrable improvement; that of new technology implementation. One specific requirement for new technology employed within a 'Fit' company, must be to increase product and production flexibility through highly reconfigurable supply chains and manufacturing systems in line with the company's strategic position [4] Reconfigurability is a key enabler in the 'Fit' paradigm and is not simply limited to readily adaptable machine systems but includes the need to reconfigure the complete company, its manufacturing system including its design system, technology, logistics, and supply chain [5] so that optimum responsiveness to customer demand is achieved. Therefore, the ability of a company to balance its demand requirements with its supply capabilities is critical to 'Fit'. To achieve this, and hence maximise the effectiveness of the technology, the supply chain's constituent companies must be flexible enough to support the changing demands. Naturally this introduces an additional level of complexity to the project management of the technology implementation, which the authors suggest is best controlled as part of the 'Fit' strategy. As such, there is a further potential for improvement throughout the supply chain in terms of technology and knowledge transfer. Direct use of suppliers or customers skills, facilities and technologies improves working relationships and communication but most importantly exposes all parties to new contacts, industries and different tiers in a supply chain. This subsequently gives rise to new opportunities and promotes sustainable growth. If this were to be adopted across a whole industry or technology base then it could be seen that a particular group of companies, whether it be dictated by geographical location or relative product group, would gain a significant advantage in the worldwide market for their product. 3. Effective use of technology
Investment in new technology is often perceived as a way to achieve one-off step change improvements in a company's capabilities, providing access to new markets, new customers, increased sales and profits. However, it is often seen that poorly managed new technology can not only confound a previously well organised operation but also incur high running costs and a 633
long payback time endangering existence of the company!
the
very
It is only when technology becomes integrated to a company's daily operation that these problems will be overcome and equipment will start to return on its investment [6]. Further to which the more lucrative sustainable growth and business development occurs after the integration is complete, i.e. only when the understanding is good enough to work on more complex products or offer the facility as a sub-contract service. It is clear then, that when investing in new technology these implementation and integration stages must be as short as possible and wherever possible take place before the money has been spent! An effective preparation and understanding of what the equipment has to do, and how the required results are to be achieved, will pre-empt many of the implementation difficulties that are normally experienced. Obviously, technology that will benefit multiple products or processes, maximises the return on the investment, but as part of the work described above it is just as important to identify the effects on the processes that are not improved by implementation of new technology. If there are operations required to prepare a piece of work which can subsequently be completed twice as quickly, how must the preparation be improved to match that of the finishing operation? There is also the additional opportunity of making new technology available to external users, either for manufacture of different product or for other companies to use themselves, renting the facility during unused shift time. This will have the effect of recovering investment more quickly and reducing ancillary costs such as power and property costs, all of which will make the original products more profitable. Could it even prove to be cost effective to buy and use production machinery in this way even though it does not directly increase production efficiency of the original product? Questions such as this are a considerable part of the long term strategy for a business' growth which is closely linked to the skills and development of the individual areas of the business [7]. This conceptual strategy has to extend as far as five years to enable the understanding of the common goal and allow for much of the preparatory work described above.
634
This process is clearly laid out in figure 2, which demonstrates the relationship between some of the described elements of 'Fit' manufacturing and the effective use of a company's technological capabilities.
4. Case Study Orangebox is a manufacturing company based in South Wales, assembling contract seating and office furniture. Over recent years it has experienced just the sort of increase in competition that has been described, and has sought to maintain its strong market position by offering a wider range of high quality products while controlling production costs through waste reduction and process improvements. The areas of production common to each unit being produced are the foam manufacture, fabric cutting, some sewing operations, fixing of foam to fabric and an assembly operation which is unique to each unit but all dependent on a stores input. Technological improvements have been implemented in each of these areas, in the following chronology: 1.
2.
3.
4. 5.
6.
Motorised turntables, automated mould heating systems and new foam machines. Investment in a CNC cutting machine to cut digitised patterns from multiple layers of fabric, hugely reducing cutting time of cushion covers. Continuous small improvements in sewing machine capabilities as specified by the development staff, who are seen to be increasing quality standards through introduction of new techniques and skills to the sewing operators. Also, the development of SMED achieved quicker and more responsive changeover times and assisted in rapid process reconfiguration. Investment in MRP system. Change to the gluing operation to use water based adhesive that does not need a long curing time or secondary finishing operations, which has been improved through investment in different gluing guns and extraction equipment. Purchase of CAD facilities and training to bring the design facility in-house and
control product development. Opening a huge range of opportunities for new product development and introduction. Development of the fabric spreading machine supplying the CNC cutting machine. Investment in software to streamline the design-production interface for new cut covers and foam cushion development. Bar-coding system to reduce the administration of the stores control and improve response times. 10. The technical development of local suppliers in order to achieve a agile and highly responsive supplier base which integrated with the reconfigurable systems development within the company. .
It can be seen that the chronology of the improvements is a reflection of the changing attitude towards the development of the manufacturing operation. Innovation and the development of innovative concepts to achieve a lean technology implementation process are shown points 1, 5 and 7. A multi-disciplinary team was developed at Orangebox whose objective was to create an innovative culture towards process enhancement. Also, points 3, 5 and 10 show the move towards achieving rapid reconfigurability within the company through quick changeovers, process time reduction and improved supplier responsiveness. Improvement 1 and 2 are the basic starting blocks of producing a chair, and improving these facilities efficiency resulted in large amounts of WIP as the subsequent operations had not been improved. Improvement points 3, 4 and 5 are the resulting requirements to make use of the improved facilities. These resulted in large gains in productivity, lower levels of stock-holding and cost reductions. Many lean practices including; Value Stream Mapping, 5S, TPM, Poka Yoke etc were used at this time to realise the improvements, as part of which reorganisation of the shop floor and operational workforce were required, making this step a lengthy process. On the basis of having a more robust manufacturing system, point 6 opened the door to huge potential growth, strength in its own market and exposure to new markets gave the company truly sustainable development opportunities. Aggressive sales activity produced a boom in growth.
However, a combination of a slow-down in the market and limited flexibility forced Orangebox to focus again on its internal operation. Improvement points 7, 8 and 9 have been the missing parts of the jigsaw and have ensured that all improvements, past and future, can be maximised and more quickly and effectively implemented. They have provided the opportunity to reduce the time to market of new product, reduce the costs of special product and reduce batch size without compromising the efficiency of the batch process of foam manufacture and fabric cutting. In essence, points 7, 8 and 9 have increased the agility and flexibility of the company, while reducing the fixed costs of virtually every product. The 'new and improved' Orangebox has developed a 'Fit' manufacturing system, and through the structure of the improvements made has facilitated virtually any improvement for the future. This tendency towards continuous improvement will now ensure that wherever a requirement is becoming evident, the knowledge and flexibility of the workforce will allow for reconfiguration or adaptation to prevent small issues needing the attention of management who are working on future improvements in technology to realise the next step change in business performance.
5. Conclusions
It is imperative that SMEs invest in new technologies and intelligent systems in order to improve their productive capabilities and product range whilst achieving reduced product costs and greater product customisability. It is through the development of new technologies that the service provided to existing markets can be enhanced, as well as enabling the company to compete in completely new higher value markets. However, the implementation of such technologies will only be successful as part of a planned and controlled development programme containing clearly defined project management stages that have realistic project management targets as well as a comprehensive integration strategy where the technology fits effectively into the manufacturing system of the company. The result of such an approach will enable a company to achieve sustainable growth through a lean NTI process.
635
The flexibility and the reconfiguration of the working environment and the supply chain in supporting the NTI process is essential in order to help maximise the effectiveness of the new technology. Alongside this is the need to consider the issues of developing a Lean and highly agile manufacturing system as a result of such technologies. The achievement of manufacturing fitness should be the aim of companies investing in new technologies and its is suggested that the degree of effectiveness of technology implementation is correlated to the degree of fitness of the company. Therefore, if this is the case then 'Fit' can be considered as a driver for new technology implementation in a company. The selection and subsequent implementation of new technologies is critical to a company's future sustainability. The selection of weak and ineffective technologies does not enhance company operations and incurs additional costs whilst the selection of technologies considered too advanced for a company limits its use and hence its return on investment. The selection of such technologies must be a business orientated decision and not purely an engineering or manufacturing issue. The development of a close working relationship between the key areas of a business and the technological requirements of a manufacturing process will dictate the degree of fitness of the manufacturing operation.
Recommendations Further work is required to identify and characterise the key 'Fit' measures and how technology implementation and development impact on these measures and their relative effect on that particular company's manufacturing fitness levels. This in turn will help to define which areas require the greatest level of support and improvement, thus defining the investment strategy fir the purchase of new technology.
Acknowledgement The MEC at Cardiff University is the lead partner of the Innovative Production Machines and Systems (I'PROMS) Network of Excellence funded by the European Commission under the Sixth Framework Programme (Contract No. 500273). www.iproms.org
636
6. References [ 1] McCarthy, I., 'Manufacturing Fitness and NK Modeling', 2nd International Conference of the Manufacturing Complexity Network, University of Cambridge, 2002, pp 27 [2] Bicheno, J., [2000] "The Lean Toolbox", Picsie Books, 2000, P27 [3] Christopher, M., Towill, D.R, [2000] 'Supply chain migration - from lean and functional to agile and customized' Supply Chain Management: An International Journal, Vol 5. Number 4 .pp. 206- 213 [4] Thomas A.J., Pham, [2005], "Fighting Fit Factories: Making Industry Lean, Agile and Sustainable", IEE April 2005. [5] Thomas, A.J., Webb, D. (2003), "Quality systems implementation in Welsh small- to medium-sized enterprises: a global comparison and a model for change". Proceedings of the I MECH E Journal of Engineering Manufacture, Vol. 217, No 4, 573-579 [6] Small, M.H, [ 1999] "Assessing manufacturing performance: an advanced manufacturing technology portfolio perspective", Industrial Management and Data Systems; 99, 6. [7] Deeprose D.,[2002], "Project Management", Capstone Publishing, 2002.
Figure 1. Integration of New Technology Implementation (NTI) to a successful 'Fit' organisational layout
~~~~W~s~e9
service & Sales
Supply Chain I Manageme agility
~x,
Process ~ Product development Improveme~/-..~_.,~
~ p r ~ . uction ~ ~ i s
e
Special/~ d
"~
Logistics
~'- ~ ~ / quality ~ , /
VALUE ">TREAM
/
Figure 2. Flow chart demonstrating a successful implementation of new technology, leading to increased and subsequent new capabilities Identify weaknesses in the value chain Determine areas / recent developments that will benefit from improvement, and lead to new opportunities. Identify appropriate developments Identify appropriate technologies that are appropriate to as many operations as possible 9
I
No
Yes Project management Plan and instigate the product, layout and work schedule changes and skill requirements to support the technology Implement the process improvement
Cost analysis Based on target improvements, and/or new potential of that particular product or process
Install / Implement new technology, monitor changes in the system and develop relevant controls
Develop the business to maximise the effect of the technolo~v Redefine the ideal inputs/outputs, now seen from experience. Develop new products & processes.
637
Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
On the importance of maintenance costing H. Wong and N. Rich Innovative Manufacturing Research Centre, Cardiff University, Cardiff CFl O3EU, UK
Abstract This paper presents the use of different approaches to costing management activities and relates these for the purpose of maintenance costing. A combination of the Activity-Based Costing methodology with discrete event simulation is introduced to attain more accurate estimations concerning the allocation of shared indirect maintenance cost to an individual machine and thereby the overall allocation of overheads to the product cost of outputs from the machine. The importance of maintenance costing is underlined by showing its relevancy in the context of the total cost of ownership analysis, product costing, and maintenance improvement. Keywords: Activity-based costing, maintenance costing, total cost of ownership, product costing
1. Introduction As manufacturing businesses in Northern Europe 'automate' or emigrate to low cost labour regions in the quest to remain competitive, the role of effective maintenance management has never been so important. For businesses that remain at their current operating site, competitive pressures and new business change programmes, such as lean, agile and mass customisation, all place a premium upon operational effectiveness but ignore, to a greater extent, any real treatment of cost of maintenance and potentially ignore the point at which a business is better to acquire new assets than continue with old technology. Such old technology is a major concern in an era when product lifecycles are shortening and therefore assets and their associated costs represent key areas of contemporary operations management effectiveness and also as organisational and inventory buffers have been reduced to near zero over the past decade. Cholasuke et al. [ 1], in their survey on the status of maintenance management practice in UK manufacturing organisations, show that only few
638
organisations seriously consider good maintenance management practices and realise the full benefits. The academic and theoretical treatment of maintenance costing issues has also been quite limited with most papers dealing with planning and engineering issues rather than the practice of management and the financial evaluation ofperformance. Mirghani ([2],[3]) proposed a framework for costing planned maintenance that is based on the techniques of job order costing and activity-based-costing. With such a gap in the body of knowledge, an underlying theme of this paper is to call attention to the importance of maintenance costing. First we propose the use of Activity-Based Costing (ABC) combined with discrete event simulation to make estimation on how the maintenance resources are consumed by maintenance activities including planned (preventive) maintenance and unplanned (corrective) maintenance. Further, we underline the importance of maintenance costing by the showing how accurate maintenance costing is important in the context of maintenance management itself and other business analysis including the total cost of ownership analysis and
product costing.
2. Maintenance costing: An ABC approach 2.1. Introduction to ABC approach
Activity-Based Costing is a cost accounting system used by countless commercial and noncommercial organisations today. Many authors have presented the ABC concept including Ittner [4] and Cooper and Kaplan [5], just to mention a few. The ABC approach traces costs from resources (personnel, raw materials and other supplies) to activities and then to outputs (product and services). ABC offers three significant benefits over more traditional forms of overhead and maintenance cost allocation namely the general averaging of maintenance cost over all the assets employed or through arbitrary depreciation methods. The first benefit lies in the identification of activities consuming an organisation's resources and through awareness of these activities, the potential to improve these business processes. The second benefit lies in the estimation of the cost of the activities by measuring the cost of resources consumed to perform different activities. By doing this, management is able to evaluate the efficiency of the business processes. The third benefit is a clear and logical manner in tracing costs to the outputs, which is obtained by determining the amount of an activity required for an organisation's products and services. The design and application of an ABC system have received considerable attention especially from cost accounting academics and researchers and we do not intend to cover this here in detail. 2.2. Combining ABC with simulation
In what follows we present a general framework for maintenance costing based on ABC methodology and due to the limited space available, we do not describe the technical aspects of the methodology in details (See Kaplan [5]). Mirghani ([2,],[3]) has presented the use of ABC for maintenance costing but his work is limited only to planned maintenance. Applying ABC for costing planned maintenance is rather straightforward as it recurs with known (predictable) regularity. Different from his work, in this paper we propose the use of ABC for both planned as well as unplanned maintenance and it should be noted that sporadic failures do impact upon the conduct and frequency of planned activities. As equipment failures are stochastic in nature, unplanned
maintenance actions are more difficult to predict. Discrete-event simulation modelling with a built in ABC methodology can be used for obtaining better cost estimate of unplanned maintenance. We follow the method presented by Von Beck and Nowak [6] that merge discrete event simulation with ABC for cost estimation in manufacturing environments. An ABC methodology is basically comprised of resources, activities and cost objects with resource drivers linking resources and activities and activity drivers linking activities and cost objects. In the maintenance context, resources may include maintenance and support crew, spare parts, maintenance tools and materials and maintenance floor space. Major activities may include planned (preventive) maintenance and unplanned (corrective/restorative) maintenance. The main objective of applying ABC approach in maintenance is to obtain accurate information on how the shared indirect maintenance cost is assigned to each of the production machines. This is particularly relevant for manufacturing companies operating different types of machines and, in Section 3 we discuss how such a cost allocation is useful for the total cost of ownership and product costing analysis. Figure 1 shows the logic of ABC model applied in maintenance for systems consisting of different machines. Discrete event simulation can be used to estimate more accurately the values of resource drivers when the use of resource in performing activities is probabilistic. Resource drivers, such as the percentage of a maintenance engineer' s time spent on planned and unplanned maintenance activities for each machine can be more accurately estimated by running a simulation model. Some resources such as spare parts, however, may be assigned straight to a particular machine, and therefore there is no need for running a simulation. A general process of discrete event simulation for a maintenance system is depicted in Figure 2. The necessary inputs may include estimated distribution of times between failures, repair and replacement times, spare parts inventory level, the number of maintenance crew, and service mechanism or priority rule. The simulation outputs may include any maintenance performance measures such as availability, average crew response time, and statistics of resources utilisation such as spare parts utilization and personnel utilization. The main advantage of a simulation approach is it can model highly complex scenarios involving a large number of machines and all maintenance activities including planned maintenance and unplanned maintenance with a multitude of probabilistic events. One can predict the impact of
639
changes such as those resulting from modifying spare parts inventory policy or introducing new maintenance scheduling algorithm. Furthermore, combining ABC
with simulation enables us to obtain cost variance information (in addition to point estimation) which is useful for carrying out sensitivity analysis.
nned Maintenance Mach. A " ~ I
Machine A
TotalResource Cost of Maintenance Mach. A
]
I"
Resource Cost of Planned Maintenance Mach. B
Resource Cost of Unplanned Maintenance Mach. B l .................................................................................................... i
Machine B
i
Total Resource Cost of Maintenance Mach. B
Resource Cost of Planned Maintenance Mach. C
Resource Cost of Unplanned Maintenance Mach. C Total Resource Cost of Maintenance Mach. C
Fig. 1. ABC methodology in maintenance
Fig. 2. Discrete event simulation process in maintenance
3. The benefits of accurate maintenance costing
3.1. Total Cost of Ownership In recent years, Total Cost Ownership (TCO) has become a particular focus of interest across a wide range of commercial and non-commercial communities concerned especially with the purchase of technology. TCO should be viewed as an integral part of the new concept of Asset Life Cycle Management that covers comprehensive processes to gain greatest lifetime
640
effectiveness, utilisation and return from purchased assets (Schuman and Brent, [7]). It is arguable that effective management of physical assets plays a strategic role in sustaining business profitability by avoiding the purchase of assets with long lives that have low initial purchase prices but large maintenance cost burdens. As the name implies, TCO takes into account all costs including purchase price and all other costs that occur during the entire life cycle of the equipment (Shank and Govindarajan, [8]). As shown in Figure 3, in general the life cycle of purchased
equipment is comprised of four phases, namely: purchasing, installation, utilisation or operation, and disposal with costs associated to each phase of the life cycle. As maintenance of equipment is required to keep the equipment in a full operational mode, maintenance costs are mainly incurred during the utilisation phase of the life cycle. Several authors have identified TCO analysis as a way to improve purchasing (e.g. Ellram [9], Degraeve and Roodhooft [ 10], and Degraeve et al., [ 11 ]). A more efficient organisation of the purchasing process will strengthen the competitive position of most firms since purchasing may account for 60% to 70% of total expenditures in manufacturing (Herbeling, [12]). Those authors have shown significant improvement on the efficiency of purchasing process using TCO analysis. As maintenance is one of the activities in the value chain of the firm that relate to the purchasing
i
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i i
ii!i!i~!!~i!S~!iiiii!!!ii! i ilill i!iiiii!iiiii
policy, accurate information on maintenance cost will contribute to making a better purchasing decision. For manufacturing environments operating different types of machines, making an accurate estimation on the maintenance cost allocated to each machine is not trivial. The traditional cost allocation methods use allocation bases to distribute maintenance costs among machines, such as a uniform based allocation or price of the machine. Unfortunately, these allocations can overestimate the costs of 'more expensive' machines and may not capture the complexities of 'less expensive' machines. The ABC approach presented in this paper enables managers to trace costs to specific activities undertaken for maintenance purposes. Additional benefit of the ABC approach is that it provides managers with much richer insight into why a particular machine costs more than another in the maintenance context.
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Fig. 3. TCO analysis - the life cycle of a purchased equipment
3.2. Product costing As competition in the global manufacturing environment has greatly intensified, accurate costing becomes very important as it may lead to better decisions made by companies to survive and maintain profit margin. One of the most problematic issues in cost management is the product costing, which deals with assigning shared or indirect costs associated with production support services (e.g. procurement, maintenance, quality control) to individual products. This is especially true for manufacturing environments that produce a wide variety of products with a wide range of volumes. An accurate product costing system will help managers come up with better product profitability analysis and product pricing decisions. In parallel with what we have explained earlier and with regard to the use ABC approach in maintenance, ABC can provide more accurate product costing than the traditional costing approach. Rather than using volume-based cost allocation, ABC maintains a logical manner in tracing costs to the
products through quantifying the resources consumed in all the activities required for making the products. As ABC enables us to allocate the total maintenance cost to all the machines and the machines are associated with production activities for making the products, the ABC applied in maintenance can be viewed as a subset of the bigger ABC framework associated with product costing. Figure 4 shows the link between maintenance costing and product costing. 3.3.
Maintenance
Improvement
Apart from its contribution to TCO and product costing analysis, an accurate maintenance costing will also provide a clear metric for maintenance improvement and a means of demonstrating how maintenance improvement activity contributes to the competitiveness of the firm. Figure 5 shows a continuous maintenance improvement cycle that basically consists of three activities: identifying opportunities for cost reduction, generating best practice initiatives, and measuring the improvement.
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The worthiness of an accurate maintenance costing lies in its ability to evaluate maintenance improvement program activities by determining the true cost of maintenance (' w e a r e n o t a b l e to c o m p a r e b e f o r e w e k n o w h o w to c o s t ' ) . Further, ABC in maintenance provides employees a clear picture of how the maintenance activities perform. This way, employees can better understand the costs involved and identify opportunities for consuming resources in the most cost-effective manner. i~i~i!i!i~i!~!i!~i~!ii~i~!ii~i!i!i~i~i~iiii~i~iii~i~i~i!iii!i~ii~i~i~i~!~ii~i~!i~i~i~!ii~ii~i~i!i~i~!!~iii~i~i~i~i~i!ii~
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4. Conclusions
In this paper we have presented the use of different approaches to costing management activities and related these for the purpose of maintenance costing. In an era where engineering skills are in short
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supply and incorrect purchasing decisions can lead to a large financial burden in terms of spares and human resources needed to maintain production efficiency, this paper is timely and relevant to the practical needs of the professional. This paper presents an overview and synthesis of processes that could be used to improve maintenance control routines and improve the ability to detect the point of uneconomic decline whereby an asset is better replaced than maintained any further. The proposed approach fits into the framework of intelligent manufacturing systems as it combines the Activity-Based Costing methodology with discrete event simulation that enables managers to attain more accurate estimations concerning the allocation of shared indirect maintenance cost to an individual machine and thereby the overall allocation of overheads to the product cost of outputs from the machine. The use of simulation allows us to trace the costs associated with planned as well as unplanned maintenance and to obtain cost variance information which is useful for sensitivity analysis purposes. We have also highlighted the importance of maintenance costing by pointing out its relevancy with the total cost of ownership analysis, product costing, and maintenance improvement program. As this research continues and the gap in research knowledge is operationalised through field research, the research team are actively seeking industrial partners to test longitudinally the proposed approach in a series of actual manufacturing environments. It is likely that these environments will be purposively selected based upon the commercial impact of maintenance failures. The latter selection would
involve businesses where customers impose penalty clauses for poor on-time in full performance and where inventory buffers are small at one extreme businesses that carry high levels of maintenance spares/tooling to assess the impact of maintenance costing in a highly flexible manufacturing environment and finally the engagement of this cost approach to different operations management typographies. The latter positioning of cases would include the use of the cost approach within a process industry (such as oil refining) but of equal importance the application of the approach to the jobbing environment within which product volumes are much lower and subject to erratic demand. We believe that, by conducting this applied research and creating a robust approach will benefit new generations of researcher as it will be of practical use to professional managers charged with the maintenance of operational assets in the era of time compression, cost reduction and buffer-less production.
[9]
Ellram LM. Total cost of ownership: an analysis approach for purchasing. International Journal of Physical Distribution and Logistics Management, Vol. 25 (1995), pp 4-23. [10] Degraeve Z and Roodhooft F. Improving the efficiency of the purchasing using total cost of ownership information: The case ofheating electrodes at Cockerill Sambre S.A. European Journal of Operational Research, Vol. 112 (1999), pp 42-53. [ 11 ] Degraeve Z, Labro E and Roodhooft F. Constructing a total cost of ownership supplier selection methodology based on Activity-Based Costing and mathematical programming. Accounting and Business Research, Vol. 35 (2005), pp 3-27. [ 12] Herbeling ME. The rediscovery of modern purchasing. International Journal of Purchasing and Materials Management, Vol. 29 (1993), pp 48-53.
References [1]
[2] [3]
[4] [5] [6]
[7]
[8]
Cholasuke C, Bhardwa R and Antony J. The status of maintenance management in UK manufacturing organisations: results from a pilot survey. Journal of Quality in Maintenance Engineering, Vol. l0 (2004), pp 5-15. Mirghani MA. A framework for costing planned maintenance. Journal of Quality in Maintenance Engineering, Vol. 7 (2001), pp 170-182. Mirghani MA. Application and implementation issues of a framework for costing planned maintenance. Journal of Quality in Maintenance Engineering, Vol. 9 (2003), pp436-449. lttner CD. Activity-based Costing concepts for quality improvement. European Management Journal, Vol. 17 (1994), pp 492-500. Cooper R and Kaplan RS. The Design of Cost Management Systems (2 nd edn). Prentice Hall, New Jersey. Von Back U and Nowak JW. The merger of discrete event simulation with activity based costing for cost estimation in manufacturing environments. Proceedings of the 2000 Winter Simulation Conference (2000), pp 2048-2054. Schuman CA and Brent AC. Asset life cycle management: towards improving physical asset performance in the process industry. International Journal of Operations and Production Management, Vol. 25 (2005), pp 566-579. Shank JK and Govindarajan V. Strategic cost management: the value chain perspective. Journal of Management Accounting Research, Vol. 4 (1992), pp 179-197.
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Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
Roadmapping as a Strategic Manufacturing Tool. A.J. Thomasaand G. Weichhart b a Manufacturing Engineering Centre, Cardiff University, CF24 3AA, UK b Profactor Produktionsforschung GmbH, Im Stadtgut A2, A-4407 Steyr-Gleink, AT
Abstract
The way in which European industry competes and operates is changing rapidly. The drivers which force this change can come form many different sources, these may include a customer's need for mass customised products where the highest quality, lowest product cost and consistent and reliable delivery is expected as the norm, through to the strategic pressures exerted by low labour cost countries where product quality is generally good but unit costs are significantly lower. In this instance it can be difficult for a company especially an SME, to decide which strategic direction to take in order to remain sustainable in the future. The aim of this paper is to provide an overview to academics and practitioners of the key, current and future manufacturing management technologies and strategies that will enable European manufacturing industry to compete on the world stage and to remain sustainable in the future. This review will be facilitated through the development of a conceptual Roadmap which clearly highlights the critical deficiencies in current manufacturing systems and provides an approach to resolving such problems through active research into technological and strategic management systems. Keywords: Strategies, Technologies, Roadmap, Gap Analysis
1. Introduction
UK manufacturing industry is experiencing significant changes in its strategic and operational systems [1]. The competition posed by low labour cost countries along with the mass customisation requirements of a more consumer conscious society is forcing UK industry to become increasingly leaner and more responsive to customer requirements [2]. It is clear that in order to survive, manufacturing industries must be able to develop and introduce increasingly sophisticated products more quickly and frequently to the marketplace through reduced product design and development times, to rapidly reconfigure their manufacturing systems as well as being able to ensure high product quality and low manufacturing costs. In order to achieve these aims however, the development and implementation of new and intelligent technologies is critical to the success of a company [3].
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Meeting the current challenges al of EU manufacturing industry, it is crucial to consider the requirement to develop innovative manufacturing technologies to support future societal demands. The UK DTi Foresight panel report into manufacturing 2020 [4] highlights the importance ofdevelopingnew and innovative technologies to support the requirement of'developing a lifetime service around a manufactured product'. It goes onto highlight that it considers internet technologies as being the major enabler that will initiate a paradigm shift in manufacturing. These issues are further supported by the EU's 'FuTMan' report [5] and the NRC's 'Visionary Manufacturing Challenges' report [6] in which it highlights that the development of new and innovative technologies are critical to meeting the six grand challenges for manufacturing in 2020. Whilst these visionary themes provide a clear view of how industry should adjust in the future, there is
increasing evidence to show that many manufacturing industries throughout the EU do not have the necessary technologies and strategies to even compete let alone meet the visionary manufacturing challenges of the future. The number and type of manufacturing enterprises which operate in specific areas of the EU vary greatly. There is a common need however to identify the type of technology currently in place within these companies and furthermore, how this technology meets customer requirements now and in the future. The requirement to keep abreast of technological developments in order to improve productivity, quality, range of products and other performance measures is now paramount [7]. Despite the clear evidence for a need to acquire technical skills and implement new and effective technology into companies, many these are reluctant to move towards major investment. Furthermore, many companies are especially reluctant to invest in Advanced Manufacturing Technologies and studies have shown [8] that this can be attributed in part to the lack of a coherent technical and manufacturing infrastructure to support economic growth which in turn, can be attributed to the lack of strategic vision and direction within such companies. These studies have identified the need to provide industry with a simple yet effective tool which highlights the current state of manufacturing industry and allows for a mechanism to show industry where it needs to move and align its strategic and technological systems in order to become sustainable in the future. The following chapters show the development of a manufacturing systems roadmap which will identify the current gaps in performance and highlight the key enabling strategies which allow a company to sustain growth.
2. A Methodology for Developing the Roadmap As a starting point for the development of the Roadmap, the paradigms, strategies and technologies considered essential for creating sustainable manufacturing industries have been identified. Previous work centered around the State of the Art review [9] identified a number of issues related to strategic manufacturing requirements. These issues can be grouped according to two macro-themes: 9 Cost-effective and rapid reconfiguration of the Manufacturing System using intelligent technologies 9 Integration of human and technical resources and Knowledge Management These two themes are triggered by an increase in the dynamics and complexity of today's business and
social environment. To cope with the dynamics and complexity, network structures are set up: Agents (human agents and organisations) are (globally) distributed and often appear redundant within networks but their purpose is to provide the overall system with a greater amount of flexibility. The agents have a certain amount of autonomy. This is in contrast to "older" paradigms used in manufacturing, where typically hierarchical structures were used to cope with complexity. The older paradigms and approaches where focused on efficiency. While efficiency is still an important matter, current research focuses much more on sustainability in economical, environmental and social terms. This requires a new way of thinking at management level. By increasing autonomy at "bottom level" the network becomes decentralized. Decentralisation includes a distribution of control; heteriachical network structures allow agents to act locally with a decreased response time. The first theme identified in the state-of-the-art deliverable is about enabling organisations, whether from an individual or a collaborative point of view, to achieve more flexibility so as to cope with dynamics, while maintaining efficiency and stability reaching order to achieve sustainability. Within this theme the "Virtual Enterprise" and "Holonic Enterprise" topics take a look at the organisational network level, which provides an organisational and ICT infrastructure. This in turn, provides and maintains flexible structures to cope with the dynamics and complexity of today's world. The "Individualised Manufacturing" and "Fit Manufacturing" paradigms are much more focused on individual organisations (but still not neglecting that these are embedded within networks). Here research work is done to provide the tools and techniques that help companies to become more agile and flexible while maintaining traditional goals like efficiency. The second theme focuses on humans and their needs to enable them to cope with the dynamics and complexity of a modern manufacturing environment. This thematic area includes the topic "Knowledge Management" where work is done to support humans to gain access not only to "raw" data but meaningful information through the support of semantics by ICT. This ICT helps to exchange and access knowledge between humans who work separated in time and/or space. Eventually a human has to collaborate with one from a different cultural area. In addition to the semantics issue, pragmatics is needed to take care that the individual reaches her/his desired goal in an efficient manner by supporting decision-making. This includes capability management in terms of assembling the right team for a job but also supporting lifelong 645
learning to enhance the capabilities of individuals. The topic "Integration of human and technical resources" will require ICT to support humancomputer interaction. This is likely to take the form of multimodal/multimedia knowledge-based user interfaces, with active knowledge access and adaptive, dynamic knowledge presentation. From this overview, the key strategic paradigms are therefore identified. These are: 9 Fit Manufacturing paradigm 9 Virtual Enterprise paradigm 9 Holonic Enterprise paradigm 9 Individualised Manufacturing paradigm 9 Integration of human and technical resources 9 Manufacturing Knowledge Management
its development cycle and furthermore, allows a company to identify which strategies and technologies which are close to their implementation stages so that appropriate adjustments and restructuring can be undertaken in the company in order to allow for its implementation. 3. The Roadmaps
For each of the key strategic paradigms individual roadmaps have been researched. This section gives a brief overview of the results obtained.
3.1 Fit Manufacturing
Technologies, Approaches
Basic Applied Time
implementation
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Impact of the work on Sustainability
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Figure 1 Roadmap approach adopted A roadmap can now be developed around each of the above key strategic paradigms. A State of the Art review is undertaken for each of the areas before a Gap analysis is undertaken to identify the work that is required to bring each paradigm towards an implementable system for industry. A time-horizon and the potential limits and risks are listed so as to enable companies to envisage the potential timeframe to implementation. Information is also given on the expected impact to society and industry from the development of each paradigm. The conceptual development of the roadmap is shown in Figure 1. This figure shows the three timescale phases in bringing a strategic idea to its implementation phase. This is a defining feature of the roadmap since it allows a company to see where each paradigm is on 646
Figure 2 depicts the "Fit Manufacturing Roadmap. Creating a Fit manufacturing enterprise calls for strategic consideration of three major operational areas of systems development. These are: (i) the development of a company's supply chain system to ensure high quality, highly responsive and dependable supply of raw material and sub-contracted products (ii) the development of a lean, technologically driven and highly agile production system that is designed to convert customer requirements to finished products quickly and efficiently and (iii) the development of systems that enhance sustainability by supporting and continually improving the perfQrmance of the product, the logistics and the manufacturing systems.
Lean, Agile, Six Sigma, Total Quality Mgt
Risk & Limits Low, ideally suited to quick and effective implementation
Vision
Reconfigurable, robust manufacturing systems
Gap Lack of systems integration of key business process strategies
Time Horizon
2010
Work Required
Systems Integration, need to integrate Lean, agility and sustainability
Figure 2 Fit Manufacturing Roadmap
3.2 Virtual Enterprise The Virtual Enterprise paradigm is expected to serve as a "vehicle" towards, in the limit, a seamless "perfect" alignment of the enterprise with the market. Organisations join to form an Intelligent Virtual
Enterprise to meet a customer's demand [ 10]. To do
so a breeding environment is needed which provides services necessary to find partners and connect business processes of these [ 11 ]. After providing the service to the customer the VE dismantles itself. In order to reach the required dynamics strong support from Information and Communication Technology (ICT) is necessary. Figure 3 summarises the VE Roadmap Impact Integrated, responsive, flex ible manufacturing supply chains of SMEs
State of the Art Large Organ isations, Enterprise Modeling, Data-exchange
+ Vision Flexible Network of Organisations, integration of ICT systems / processes
[
Risk & Limits uncertainty, context dependent, complexity, trust issues
State of the Art Reducing Operational Complexity; Algorithm based optimisation
Impact Novelty of products, organizations and behaviour
Vision Chaordic System Thinking (CST) and using Structural System Complexity
Risk & Limits Knowledge and knowledge management, paradigm shift
Gap Limited System flexibility, innovation, and responsiveness; Lack of multidimensional
Time Horizon 2010
Work Required Optimisation and Education of the use of Complexity; Development of communication models and tools supporting CST
Figure 4 Holonic Enterprise Roadmap 3.4 Individualised Manufacturing
Gap Missing Support tbr integration and interoperability of People and Organisations
Time Horizon 2010
Work Required Enabling a decentralized Semantics and Pragmatics based Collaboration infrastructure
Figure 3 Virtual Enterprise Roadmap
3.3 Holonic Enterprises
Under the topic of the "holonic enterprise paradigm" research is done to investigate intelligent dynamic networked systems where it is tried to not reduce the complexity of the system, but use its complexity. Termed by Athur K6stler [ 12] a Holon is a part and the whole in one. The term itself consists of the greek "holos" - the whole and the "on" which indicates a particle like in "electron". Holonic thinking considers the internal/external view and the individual/collective view. This paradigm can be linked to Chaordic Systems Thinking (CST) [13]. CST is a complexity framework for seeing and interpreting organizational patterns anchored in the meta-praxis of Chaos. Figure 4 summarises the Holonic Roadmap:
Within the topic of "individualised manufacturing" research takes place that focuses on the means to enable the production of individualised goods at costs similar to mass produced goods. Conventional centralised approaches face difficulties when it comes to adaptation to this paradigm. For individualised manufacturing systems flexibility, scalability and robustness are more important than finding the global optimum of production. The companies' business and technological processes have to be integrated with respect to the agility and flexibility of these processes themselves. Highly reactive and fault-tolerant scheduling and control systems are needed. Moreover, the natural shift towards decentralised systems encourages the development of intelligent architectures based on autonomy and co-operation of the production facilities [14]. Figure 5 summarises the roadmap. State of the Art Design to Costs; Design methodologies for Mass customization
Vision Semi-automated customization of products at a cost and timescale similar to mass produced items
Impact Customers enabled to create novel products< companies sensitive to customer preferences
Risk & Limits Human understanding of requirements; Lack of competitiveness< Implementation costs
V Gap New methodologies for product design; Incomplete interface between customers and designers
Time Horizon 2010
Work Required ICT interface to support end-users in complex design tasks; Methodologies to improve, usability and acceptability
Figure 5 Individualised Manufacturing Roadmap
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3.5 Integration of human and technical resources Research within this POM topic focuses on improved man machine interfaces. Man Machine Interfaces have a long history of research. Recently for example within the realm of Ambient Intelligence (AmI) research on personalised support for the human being working with ICT is done [15,16]. The integration of human and machines within the manufacturing domains is of special interest as in this domain a large number of SMEs are working. SMEs have not the financial resources to employ and educate technical specialised personal; still advanced machines need to be operated. Figure 6 shows this roadmap.
State of the Art Augmented Reality for simple Manufacturing Steps
Impact Human motivation and satisfaction; Support for the Aging Population
V Vision Direct machine/user interfaces that enhance human performance and promote intelligent input
Risk & Limits Cost of Virtual Reality Equipment & Maintenance; Business and Personal data security
Gap Individualisation of the interface to the Collaborative Work Environment; Full scale AmI system
Time Horizon 2010
Work Required Build the basic Aml infrastructure and a manufacturing related application; Semantics and Pragrnatics enhanced Workenvironment supporting personalised styles of working and a personal language
Figure 6 Integration of human and technical resources Roadmap 3.6 Manufacturing Knowledge Management Care has to be taken about emerging behaviours on network level when increasing the autonomy of individual network members. While distribution of control raises trust, security issues, there are issues of gaining access to knowledge. In any case, the distribution of manufacturing across different companies within collaborative networks demands new forms of knowledge management that takes the distribution and decentralisation into account. Figure 7 shows this roadmap.
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State of the Art Paper-based knowledge; Data mining and management
Impact possibility to broaden worker's knowledge
V Vision Knowledge management, knowledge engineering and organizational learning
Risk & Limits Possible high turnover of workforce Investment in learn ing
V Gap No adaptable tools for converting data into knowledge; Lacking
Time Horizon 2010
Work Required Manufacturing-oriented and Innovationoriented knowledge management and engineering New teaching and learning methodologies, Further work on the interactions between individual learning collective learning.
Figure 7 Manufacturing Knowledge Management Roadmap. 4. Conclusions and Recommendations
There is little or no strategic guidance available to companies on the long-term development of strategic manufacturing paradigms. This often leads to many companies especially SMEs having no appreciation of the strategies and intelligent technologies which are available to allow them to compete effectively in the marketplace. The development of a roadmap provides a quick and effective approach for a company to develop its long-term manufacturing strategy. The roadmaps provided in this paper identify six key strategic paradigms. It provides a gap analysis, a risk analysis and a outline timescale to implementation which a company can use to decide on whether the strategy is in an acceptable form for implementation into their businesses. Acknowledgements
The authors would like to express their appreciation to the I'PROMS, POM cluster members for contributing the State of the Art Review and the Roadmap deliverables on which this paper is based References
[1] Williams B.R [1995] 'Manufacturing for Survival' Addison Wesley Publishing Company [2] Thomas, A.J and D T Pham- "Making industry fit: the conceptualisation of a generic 'Fit' manufacturing strategy for industry", Proceedings 2"d IEEE Int Conf on Industrial Informatics, INDIN 2004, Berlin, June 2004, R Schoop, A Colombo, R
Bernhardt and G Schreck (eds), pp 523-528, ISBN 07803-8513-6 [3] Pham, D.T, and Thomas, A . J , - "Fighting Fit Factories", IEE Manufacturing Engineer, April 2005. pp 24-29 [4] 'UK Manufacturing- We Can Make It Better', Final Report, Manufacturing 2020 Panel. Foresight Manufacturing 2020 Panel Report. [5] The Future of Manufacturing in Europe 20152020, The Challenge for Sustainability, EU FP6 Funded Programme. [6] 'Visionary Manufacturing Challenges for 2020', Committee on Visionary Manufacturing Challenges Board on Manufacturing and Engineering Design Commission on Engineering and Technical Systems National Research Council, National Academy Press, Washington, D.C. 1998 [7] Quarashi Z.N, Polkinghorne M.N, Bennett J.P [1998] 'The Economic Impact of the Teaching Company Scheme on the Local Manufacturing Base' Plymouth Teaching Company Centre. [8] Thomas A.J [2002] 'The College Business Partnership Scheme and its Impact on the Technical Development of SMEs in South Wales', Proceedings 18th NCMR, Leeds Metropolitan University [9] Grabot, Bet al (2005) State of the Art Review for Production Organisation and Management (POM), I'PROMS deliverable D7.2. [10] Cunha M. M., Putnik G. (2006) Agile Virtual Enterprises: Implementation and Management Support, IDEA Group Publishing, Hershey, PA, USA (to appear in the first trimester 2006) [11] Camarinha-Matos, L. M., Afsarmanesh, H., Garita, C., & Lima, C. (1997). Towards an architecture for virtual enterprises, Journal of Intelligent Manufacturing, 9(2). [12] K6stler, A.: 1976. The Ghost in the Machine. The Danube Edition. Hutchison & Co., London. [13] Einjatten, F., Putnik, G. (Eds.), 2004. Special Issue: Chaordic System Thinking for Learning Organizations, The Learning Organization- An International Journal, Emerald, Volume 11, N ~ 6. [14] IMS NoE Consortium and SIG coordinators, 2003, Report/Observatory, Industrial Needs, Trends and Roadmap on Intelligent Manufacturing Systems. [15] IST AG: Information Society Technologies Advisory Group Reports 1999-2004 http://www.cordis.lu/ist/istag-reports.htm, 1999-2004 [ 16] Shadbolt, N. (2003): Ambient Intelligence. IEEE Intelligent Systems, July/August, 2003
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Intelligent Production Machines and Systems D.T. Pham, E.E. Eldttldari arid A.J. Soroka (eels) 9 2006 Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. All fights reserved.
The design of a sustainable manufacturing system" A case study of its importance to product variety manufacturing R.Jayachandran*, S.Singht, J.Goodyer, K.Popplewell Department of Manufacturing and Management, Coventry University, Coventry, CV1 5FB, UK
Abstract A key challenge for manufacturers is to not only design but manufacture products using a sustainable approach. Manufacturing industries have started recognising that it is their responsibility to design a sustainable manufacturing system which has less environmental impact and social disruptions and promotes wealth. This paper presents a case for adapting current manufacturing system design methods to include environmental issues. A case study is presented which uses an environmental process selection method to demonstrate how companies can transform into sustainable practices in a large product variety environment. One of the key results is that the technology capability and economic risk are the two main factors which prevent a company to adopt sustainable manufacturing. Keywords: Manufacturing system, product variety, process selection.
1. Introduction In terms of sustainable development, manufacturing industry is often cited as a source for environmental degradation and social problems, but it is the major source of wealth generation [1]. According to the Lowell Centre for Sustainable Production, sustainable production is defined as the creation of goods and services using processes and systems that are non-polluting, conserving of energy and natural resources, economically viable, safe and healthful for employees, communities, consumers and socially and creatively rewarding for all working people [2]. Sustainable development consists ofthree
structural pillars namely society, environment and economy, whilst at the same time it also involves operational aspects such as consumption ofresources, natural environment, economic performance, workers, products, social justice and community development. The concept of sustainable production emerged at the United Nations conference on environment and development in 1992; the conference concluded that the major source for environmental degradation is unsustainable production and consumption patterns [2]. Although the concept of sustainable development was developed in the last decade, most manufacturing
Corresponding author: Tel: +44-2476-88-7088, Fax: +44-2476-88-8272 E-mail address: *[email protected],]" [email protected]
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companies are still looking for improving environmental performance in their activities. The last two decades of environmental consciousness focused on end of pipe solutions i.e. reducing the amount of hazardous emissions and substances after manufacturing [3]. The focus has shifted from controlling emissions to elimination or prevention at source, which is a proactive approach. Firms adopting a proactive approach consider the environmental challenge as a competitive business opportunity rather than as an obstacle. They integrate environmental aspects in all functions of the business and the goal is zero waste. This paper presents the importance of concentrating on sustainable issues during the Manufacturing System Design (MSD) phase. There are many stages in the design of a manufacturing system, typically covering process selection; capacity planning, facility layout, etc. (see Fig. 1). It is the intention of this paper to focus on one of the key stages of MSD, which is process selection. A case study is used to demonstrate how companies can move to sustainable manufacturing practices in a large product variety environment. The tools and methodologies developed at each of the key areas of a manufacturing system will transform the current manufacturing system into a sustainable manufacturing system. 2. Literature review
Product variety is defined as the number of different versions of product offered by a firm at a single point of time. Variety within the product arises by varying the values of attributes from one product to another such as material, dimensional, aesthetic
and performance attributes [4]. Increasing product variety has implications over the operational performance (production cost or outsourcing cost), so from a firm's perspective a trade-off exists between the product variety and operational performance. It is also essential to design a manufacturing system that can manufacture the new version of the product in a sustainable way. Fig. 2 demonstrates how, by focusing on sustainability not only in the product design phase but also in the manufacturing system design phase, environmental impact can be minimised during manufacturing and at the end of a product's life. This is essential as the relationship between manufacturing strategies and environmental performance has come under close scrutiny. The increase of environmental consciousness of the public, regulations due to environmental policies and pressures from organised groups all sway companies to adopt an Environmental Management System (EMS). These systems are formulated to help an organisation to evaluate the effectiveness of the activities, operations and services [5]. However, EMS's have been widely criticised by many authors for being another standard and often yield only subtle improvements. The study of product variety has been looked at from various perspectives such as economics, marketing and manufacturing. Despite the environmental drive from regulations or pressures from stakeholders, none of the previous work emphasises implications of product variety on the environment [6]. As mentioned earlier, a trade-off exists between the product variety and operational performance. To overcome this trade-off, companies migrate towards modular design where the final product configuration is obtained by mixing and matching of standard components. The modularity of the product architecture has been accepted as a viable solution to the product variety problem. At a component or a part level, this is done by designing products to an optimal near net shape, where variants are generated from the optimal near net shape. An increase in variety of product would likely result in an increase in a variety of raw material and resource procurement. The product variety is often assumed to yield high revenues and offer a competitive advantage to the firm. However achieving competitive advantage through increased product variety is highly dependent on aligning marketing and manufacturing strategies [7]. In the last few years, there has been increased focus on consideration of environmental issues during the product design and
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Product stewardship, Sustainable consumption, Environmental practices Regulations, Corporate strategies, Green supply chain, Design for X, LCA.
Sustainable product design and development
Market requirements
Product design
Sustainable manufacturing system design
Manufacturing system design Sustainable manufacturing Manufacture
l
Manufacturing waste
I Product use
Product disposal
I Product use
Minimises environmental impact
I
Sustainable Manufacturing System Design 9 Manufacturing system requirements 9 Manufacturing process selection 9 Selection and design of equipment 9 Manufacturing system configuration 9 Manufacturing system implementation 9 Manufacturing system reconfiguration 9 Recoverable/Reverse manufacturing
Product end of life
Maximises recovery, reuse, and substitute for raw materials through sustainable practices
Traditional product development
Sustainable product development
Fig. 2. The importance of designing products and manufacturing systems for sustainability. development leading to the development of new paradigms such as Design for X (Environment, Recycle, and Reuse etc), Life Cycle Assessment (LCA), EMS, Cleaner Production tools etc. However EMS and Cleaner Production tools play a crucial role once the product, manufacturing processes and manufacturing system have been designed. In the few exceptional cases where companies adopt concurrent engineering, the time of identification of environmental aspects vary depending upon the concurrency of the product development process. As most manufacturers are moving towards offering high product variety to their customers, developing manufacturing systems to meet the objectives such as economy, flexibility, lead time, delivery, etc. is a challenging task. The decisions during the design of manufacturing systems for high product variety should consider not only the operational and economic issues but the environmental performance as well. As been outlined previously in Fig.l, a typical approach to MSD does not include any environmental issues. This paper proposes that typical MSD methods can be adapted to include environmental issues at each of the key stages of MSD so that sustainable manufacturing systems can be achieved. In the interest of brevity, all key stages are not discussed in this paper. However, this paper focuses on just one of the key MSD stages, i.e. process selection, which is outlined in Fig. 3. Here environmental issues such as consumption of material, energy, water, use of toxic materials in the process and emissions from the process are
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considered along side traditional issues such as product characteristics, production environment capabilities, etc.
3. Case Study Company A is the collaborating organisation for this research project and is a large multinational automotive component manufacturing company. Company A utilises powder metallurgy and sintering technology to produce a variety of automotive components. The case study with Company A analysed the introduction of a new product variety (Product 'X', see Fig. 4). The new product is a circular plate with a specified thickness and a specific bore diameter. This component would be part of an assembly for the powertrain system. However, it was noted that within this range, many product variants will be developed over the next few years to penetrate different markets whereby, the range will expand by varying the stepped bore coupled with varying inner, outer diameters and thicknesses as shown in Fig. 4. The traditional process (selected without the consideration of environmental issues)ofproducing a variety of these products is by centrifugal casting. A long bar is produced by centrifugal casting followed by machining to achieve the final dimensions. These two processes produce high levels of metal waste such as casting defects resulting in scrapping the entire length of the bar which is up to one metre and machining wastes such as swarf, and defects. The machining processes involved are parting, two turning and one boring operation and the respective waste for each operation is shown in Table 1. The
9 Product characteristics 9 Production environment capabilities 9 Facility tasks 9 Hierarchy priorities 9 Manufacturing constraints 9 Environmental issues (material waste, equipment energy consumption, landfill costs, waste disposal costs, by-product material reuse and byproduct material contamination)
9 Capacity planning 9 Product design 9 MSD
Process selection
9 Capable processes 9 Potential processes 9 Preferred possesses
1
9 Process sequence 9 Production device matrix
Fig. 3. Process selection stage of manufacturing system design. environmental impacts of manufacturing the product are high energy used in the casting process, defect rates in the casting process, machining wastes and swarf produced for every product. Among all the metal swarf from the machining processes, the volume of steel and cast iron swarf produced has a significant impact over the environment and cost. The metal swarf requires appropriate storage space, involves transportation cost and the volume ofswarf generated in company A is tremendous. As the swarf is coated with a thin layer of oil particles, re-melting of swarf without processing provides low efficiency and also generates pollution due to burning of oil in the swarf. Finally the low value of steel does not enable metal manufacturers to recycle steel swarf economically. These factors forced manufacturers to dispose the swarf as a solid or hazardous waste depending upon the legislative requirements. Furthermore, the rising cost to landfill affects the disposal of wastes, such as swarf. Apart from these wastes, the company is uncertain about the production volume of each of the product which forces it to stock a wide range of raw materials utilising a large amount of space and energy. In an attempt to reduce the environmental impact and to transit towards sustainable manufacturing, the same product variety is analysed using the process selection stage of the Manufacturing System Design (MSD) shown in Fig. 3.Though it can be inferred that much of the environmental impact occurs actually in the manufacturing phase, the decisions on various manufacturing activities of a product is made at various levels of the MSD process such as process planning, capacity planning, etc. By applying the process selection methodology, capable, potential and preferred
Circular Plate Q~ Variable
V~ -.] ~Bore Q V a r i a b l e / ]
" ' S t e p p e d Height/Angle Variable
Fig. 4. Variable sizes for product 'X'. processes are determined. Capable processes are defined as the processes suitable for manufacturing with product material and volume as the inputs, and may be identified by the PRIMA matrix developed by Swift and Booker [8]. The capable processes are shell moulding, ceramic moulding, centrifugal casting, closed die forging, cold forming, powder metallurgy, electrochemical machining, electron beam machining, laser beam machining, chemical machining. Potential processes are identified from the capable processes by correlating the product attributes to the performance characteristics of respective process. Processes such as cold forming and shell moulding are eliminated as they do not meet the product attribute criteria. Preferred processes are selected based upon the correlation of economic, environmental, technical, facility requirements and capabilities, production rate etc. The potential processes are selected using a process knowledge base and preferred processes are selected based upon the importance of each requirement to the company. The final preferred process alternatives are centrifugal casting, powder metallurgy and machining. By utilising the powder metallurgy technology, the component could be manufactured to a near net shape. The process of producing the product by powder metallurgy consists of pressing, dewaxing, sintering and machining. The powder is first pressed into shape by a press and then de-waxed to remove the binding agents. The product is then sintered at very high temperatures. The facing operations of the traditional process have been replaced by a high-volume grinding operation to save machining costs. Finally the turning operations are performed to achieve the specific dimensions. Therefore, by generating products with a near net shape before machining by powder metallurgy process, significant material can be saved for each component which otherwise would have been disposed of in to the environment. In this case, apart from general powder waste during pressing or
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Table 1 Machining waste in traditional process
Parting Boring Turning 1 Turning 2
Material waste xl03 mm3 8.3 6.6 1.35 1.1
Total material waste per part
17.35
Operation
scrapping of the part due to defect (as opposed to entire bar as in casting), the waste generated during the machining process is shown in Table 2. By utilising the powder metallurgy process, there is a significant reduction in material waste. By comparing the material waste generated from two different processes (see Table 1 and Table 2), it is evident that powder metallurgy process produces 76% less waste than the casting process. Furthermore, it was also suggested that by reducing the material waste, the tool life on the machining centres would be increased, thus reducing the need for frequently disposing worn out tools. However, due to the high cost of powder metal and the sintering process, the cost price per piece of product 'X' was found to be three times higher than cast material. This process was favourable from an environmental (material waste) viewpoint. However, in the current economic condition, the new piece price was too high to justify. 4. D i s c u s s i o n
For company 'A', the business decisionmaking factors and steps taken to introduce product variety in the company were essential to penetrate newer markets and to gain competitive advantage. Similarly, in today's competitive manufacturing scenario, environmental considerations are also essential to yield intangible benefits and to add credibility to the business. An increase in product variety levels would mean more waste would be generated by the increase in raw material usage, more machining operations and high levels of waste generated to produce the final product. The high manufacturing cost of the powder metallurgy process is due to cost of raw powder material, cost of mixing the powder to the correct specification, cost of carbide tooling for pressing and cost of running the de-wax and sintering furnace (highly significant). The total cost of the product includes the manufacturing cost, waste disposal cost, raw material cost and holding cost. With uncertainty in demand and high product variety in place, the company has to stock more raw material variety and volume with the
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Table 2 Machining waste in powder metallurgy process Operation Grinding Boring Turning
Material waste xl03 mm3 0.216 3.534 0.392
Total material waste per part
4.142
traditional process. However, with the suggested powder metallurgy process, the total variety of raw material (material composition) is less. Furthermore, the rising cost of landfill poses a stiff challenge to control the total cost of the products, however with the powder metallurgy process, waste powder is sieved and reused, while the volume of swarf generated is substantially lower. The traditional casting process also has an implication on field failures due to density imperfections and it has also generated huge amount of material waste during casting, which increases the raw material cost. Although the powder metallurgy process results in reduced material waste, the energy consumed in the sintering process is significantly high. However, when the production volume of the product X and its varieties are significantly higher, the economics of scale allows operating sintering equipment of larger capacities to reduce the energy cost per product. To further reduce the cost of the powder metallurgy process a proposal was made to convert the swarf generated by company A to a useful powder material after reprocessing. As the swarf is processed and converted as a powder material the alternative process has low material waste per product when compared with the traditional process. There have been many applications of use of metal swarf for producing metal components and blanks using the powder metallurgy process [9, 10]. The advantage of powder metallurgy is generation of the part to its near net shape. It is estimated that 50% of the production cost is spent on geometric shaping which also involves large material wastes such as swarf, defects, rework, though the parts produced by the powder metallurgy requires machining to achieve the final dimensions, the volume of machining is substantially less which makes powder metallurgy a prospective manufacturing technology. As the swarf is contaminated with oil and other metals, the value of the swarf is very low. To improve the value of swarf recovered, strategies such as improved swarf management by reducing contamination with other metals, breaking up into small fragments, conveying and cleaning swarfhave been developed.
Due to the availability of limited data, a detailed cost analysis was not carried out to determine the optimal production volume required by the sintering process which has an equivalent unit price that of the traditional process. Furthermore it is highly difficult to compare the process based on the environmental impacts, because the material waste generated in the traditional process is replaced by high energy consumption in dewaxing and sintering. However, with the availability of modern equipment, heat loss can be substantially minimised. Moreover with the increasing landfill cost, the company may sooner choose this process as a viable option. 5. Conclusion This paper focussed on a case study from a British company and demonstrated how a company can move towards sustainable manufacturing by looking into alternative processes. As the described case study goes beyond the current industrial best practices and approaches, it is also necessary to look at the barriers which hinder a company in moving towards sustainable manufacturing. First of all there has always been a trade-off between environmental impact and other factors such as quality, cost, performance, etc. Generally companies favour cost as a predominant factor unless the environmental impact of the product or the process is regulated by legislation. It is clearly evident there is less scope for improving sustainability (reducing environmental impact, cost etc.) once the process has been selected and the manufacturing system has been designed. For instance, the case study outlined that the traditional manufacturing process produces high material waste and with the total production volume of all the varieties is expected to be in millions per annum, the material waste is highly significant in terms of sustainability. There are process models that exist in practice and literature to analyse the trade-offs such as volume, cost, defects, etc. but these models lack the analysis between the environmental impacts, energy, cost, etc. The study also indicates that technological capabilities and economic risk are the two main factors which prevent a company to adopt sustainable manufacturing. An environmental oriented methodology to process selection has been shown in the case study. The powder metallurgy generates low material waste but the production cost is significantly higher compared to the casting process which makes this alternative impracticable in current economic conditions. Although in this case, it is not economic
to use the powder metallurgy process; this would need to be reviewed against anticipated increase in energy and landfill cost. It is also anticipated that at high volumes (either due to individual product volume or cumulated volume of all the varieties) and with the use of energy efficient sintering equipment, the cost of the powder metallurgy process can be significantly reduced. The proposed sustainable manufacturing system design method forces manufacturing engineers to consider additional environmental factors in process selection such as material waste, tool change or disposal, raw material consumption, landfill costs, waste storage and disposal costs, byproduct material reuse and by-product material contamination. References [ 1] Legarth JB. Sustainable metal resource management-the need for industrial development: efficiency improvement demands on metal resource management to enable a (sustainable) supply until 2050. Journal of Cleaner Production. 4 (2) (1996) 97-104 [2] Veleva V and Ellenbecker M. Indicators of sustainable production: Framework and methodology. 9(2001) 519-549 [3] Johansson G. Success factors for integration of Ecodesign in product development. Environmental Management and Health. 13(1 ) (2002) 98-107. [4] Randall T and Ulrich K. Product variety supply chain structure and firm performance: Analysis of the US bicycle industry. Management Science. 47 (12) (2001 ) 1588-1604. [5] Emilsson S and Hjelm O. Implementation of standardised environmental management systems in Swedish local authorities: reasons, expectations and some outcomes. Environmental Science and Policy 5(6)(2002) 443-448. [6] Tang E and Yam R. Product Variety Strategy- An Environmental Perspective. Integrated Manufacturing Systems 7(6) (1996)24-29. [7] Berry WL and Cooper MC. Manufacturing flexibility: method for measuring the impact of product variety on performance in process industries. Journal of operation management. 17 (1999) 163-178. [8] Swift KG and Booker JD (Ed.). Process selection: From design to manufacture. 2nd edition. Butterworth Heinemann, Oxford, 2003,pp 13-105. [9] Costa CE, Zapata WC and Parucker ML. Characterization of casting iron powder from recycled swarf. Journal of Materials Processing Technology 143-144 (2003) 138-143. [10] Velasco F, Ruiz-Roman JM, Cambronero LEG,. Torralba JM and Ruiz-Prieto JM. P/M Steels manufactured from cast iron swarf to powder. Journal of the Japan Powder and Powder Metallurgy Association,41 (10) (1994) 1282-1287
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Intelligent Production Machines and Systems D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) 9 2006 CardiffUniversity, ManufacturingEngineering Centre, Cardiff, UK. Publishedby Elsevier Ltd. All fights reserved.
Traceability Requirements in Electronics Assembly M. Ford a, J. D. Triggs a a Sony
TiMMS Development Centre, Sony Europe, Pencoed Technology Park, Pencoed. CF35 5HZ, UK
Abstract
The requirement for traceability in Electronics PCB (Printed Circuit Board) assembly is reviewed, together with an analysis of what traceability means to manufacturers and the associated cost. A solution is found showing the potential for traceability to not be a burden, but a net benefit to the manufacturing operation. Keywords: Electronics, Manufacturing, Traceability, Lean
1. Introduction
2. W h y Is Traceability so Important?
No longer confined to automotive, medical or military, traceability of printed circuit board manufacturing is rapidly becoming a requirement in all manufacturing sectors. Traceability carries with it a multitude of issues. One key issue is cost, that is, the net effect to the manufacturing operation as a whole. Another is the practical detail of the implementation, to satisfy both the immediate and long term requirements. There are requirements that determine the depth, breadth or "level" of traceability that will be required. With intense competition in the manufacturing industry any additional constraint or cost is difficult to accept. A more acceptable solution would be for the manufacturing operation to experience a net operational benefit, preferably even with a very persuasive return on investment. With the application of Lean Thinking to the whole manufacturing environment [ 1], such a solution is possible. This has positive effects on the manufacturing operation in terms of quality, delivery and cost performance. We will investigate here to see how this can work as a practical solution applicable to all areas of printed circuit board manufacturing and for all sizes of companies.
Traceability at its worst is insurance, though it can also be the mechanism to ensure compliance to operation standards and materials content. The insurance aspect relates mainly to being able to prove that manufacturing was executed correctly thereby correctly attributing responsibility for defects. Litigation especially in the United States related to manufacturing defects is quite common. With the increase of the expectation of good quality of electronic products, traceability also becomes an important aspect of quality control. The compliance aspect relates mainly to recent environmental legislation such as lead-free and RollS. The individual factors affecting the importance of traceability are as follows;
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2.1 Responsibility
Traceability has been a requirement for many years for example in the automotive industry due to the potential that defects in products can affect safety. Manufacturers face the risk of significant compensation claims if it is shown that a defect in their product has caused or contributed to an injury or other loss. It is often assumed that manufacturing is the cause of most defects found in products in the market. Proving where the responsibility for the defect actually was, whether design, materials, distribution or in fact
within the manufacturing process is a critical factor to avoid incorrect assignment of blame. Traceability can be used to prove that the manufacturing process was performed correctly and completely with correct materials used. The issue becomes more important for contract manufacturers where it is now common for clauses to be built into their contracts making them responsible for losses as a result of manufacturing defects. Traceability becomes the essential tool for manufacturers to protect themselves. As an example of how this works, where defects are detected in the market, the traceability data can be used to identify areas of commonality, such as if the defects were related to one particular reel, batch or vendor of materials, a certain time period in which a design change occurred, or a particular condition of a manufacturing process. In this way, the manufacturer can drill down into the data to understand the conditions in which the problems happened, and under which conditions the problems did not happen, thereby correctly identifying the responsibility. This can only be achieved by knowing the precise history of the manufacturing process and materials used.
2.2 Cost of Recall Establishing responsibility does not actually solve the effects ofthe defects. Defects may be found after some time in the market, or may be identified as part of the "life testing" of a product which can extend well beyond the time that the product itself has been introduced into the market. Either way, there will be a significant cost to manage the correction. This may simply involve repair or replacement under warranty. In some cases however where the defect represents a significant safety risk, products may have to be recalled in order to prevent serious consequence. Contributing to these costs are the processes to find and communicate with the customers, shipping, inspection, repair and re-test of the product. Traceability enables products that have been manufactured under conditions which the defect can occur to be precisely identified, for example all products containing a component from one particular batch of materials that was found to have a high incidence of failure. Being able to restrict the scope of product recall to a positively identified group of serial numbers as opposed to a range of products manufactured during the period in which the material may have been used, can reduce the scope and cost of the recall by an order of magnitude. An additional consideration is the associated cost to the reputation of the company. Using an automotive example, recalling positively identified cars from positively identified
customers can be done with a letter offering a "free service and check-up" as opposed to a publicly announced recall program.
2.3 Quality Over the last few years, the expectation from the consumer of the quality of electronic products has changed considerably with little or no acceptance of defects. Manufacturers and material vendors have done a very good job to improve the quality, though defects still occur. These remaining defects are much more difficult to resolve, appearing to be quite random. There is of course a reason for each and every defect that occurs, i.e. there is something unique about the product that failed as compared to others that did not. Six-Sigma methodology suggests that this is caused by some critical variations in the manufacturing process. With traceability we can look at the precise events that took place during this time including every process through which the product went and where every component came. Working together, traceability and tools like Six Sigma can provide the next level of quality management. There are also specific issues related to electronics manufacturing that impact quality. Some materials are sensitive to the environment in which they are stored or used. Examples are the affects of heat, moisture contamination and electrostatic discharge. Ensuring that appropriate guidelines are followed, that is measurement and control of the environment, tracking and control of material movement and usage has a major impact on the overall quality of the finished product. Customers should experience that manufacturing operations employing traceability will be able to produce far higher quality product than those without. 2.4 Environmental Legislation Legislation such as WEEE [2] and RollS [3] imposes a responsibility on manufacturers to be in control of the materials they use, specifically with regards to hazardous content. Each component on a circuit board is a contributor to the overall content of a product. Any one of these components if not compliant can invalidate the conformance of the whole product. It is essential then to control exactly which components have been used especially in environments where mixed RollS and non-RollS products are being manufactured. This issue is further complicated for PCB manufacturing since for each component there can be a choice of material and for each material a choice of vendors. Ensuring that each material used
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conforms to the requirement of the product and that there is record of the certification of conformance for each of the materials is a key driver for traceability. This includes materials consumed on placement machines, test & repair processes, scrap disposal, as well as after market repair operations.
if occurring in the middle of the night when materials support is not available, this is a major issue. Discipline and flexibility are key factors but often in conflict with each other. The nightmare for the materials group is when materials are taken without proper procedures being followed. These causes further stock discrepancies and non-conformance risk.
3. The Traceability "Team"
3.3 Engineering
Effective traceability involves all areas of the manufacturing organization working together. It is important that the concepts and procedures for traceability are understood and adhered to by everyone in the organisation regardless of their priorities or roles. The major risks occur when there are conflicts between the priorities of different responsibilities. A key factor in this is that not all divisions have the same working hours. Manufacturing typically runs 24 hours, though supporting divisions may only work 16 or 8 hours per day. Introduction of traceability therefore should be a trigger for the review of these roles and responsibilities to form a traceability "team". The key members of this team are as follows:
The Engineering group are the link between the manufacturing group and materials group, responsible for the machine programs which determine which materials will be required at each process for each manufacturing job, and the manufacturing plan which governs the order in which jobs are produced. If there are unexpected material shortages, engineering can help to provide information about the use of substitute or alternative materials. This can help the manufacturing flow situation, but can also further complicate material allocations.
3.1 Manufacturing The manufacturing group are responsible for making sure that the products are manufactured properly and delivered on-time. Their priorities are that the processes run at the most optimum performance levels. From their perspective the correct and timely materials supply should be a given but in reality material availability can be the biggest cause of disruption. Automated mounting machines run at very high speeds placing very small components, some no bigger than dust particles. Many components can be "lost" during this complex process. This can quickly reach significant levels if machine programming data is not precise or there is a variation in sizes of components. This ultimately leads to shortages and stopped processes. The lack of just one component causes the entire PCB to be useless since it cannot be completed. Often the need for continuous manufacturing conflicts with the procedures for material allocation and movement, resulting in the risk of non-conformant materials being used.
3.4 Information Systems Group Computer systems have become the backbone of the manufacturing organization. MRP / ERP systems are generally not able to cope with the intricacies of the PCB manufacturing environment. Computer systems in the manufacturing areas have often therefore been "point" or "technical" solutions developed and maintained or procured by engineers outside the realm of the Information Systems group. One essential aspect of traceability itself is the accuracy and integrity of the data. This is often outside the scope of the engineering based systems. When considering the requirements for and scale of traceability it is likely that the Information Systems group will become an essential part of the team. The introduction of a traceability system will initially be seen as a burden by each of these four groups. The solution will require all four groups to work together and is an essential part of the process to make sure that the traceability system provides a net benefit for the organization.
4. Defining Traceability 3.2 Materials (Supply Chain) The supply chain operation is a critical factor that "runs the business". Materials have to be labelled, stored, allocated to manufacturing jobs, tracked and checked. If manufacturing members make changes to their schedules or need additional material, especially
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Traceability is often used as a general term with a wide scope in terms of depth and breadth. Quite often, the actual requirements from the customer are not clear which leaves a great deal to be interpreted or assumed by the manufacturing organisation. This carries with it a large element of risk. The insurance aspect of
traceability may throw up problems at the worst possible time when detailed information is actually required in the event of serious defects found in the market. It is necessary to understand the complete scope of traceability and its many aspects, each of which need to be balanced with the actual needs of both the customer and the manufacturer. The factors affecting scope are described below: 4.1 Material Identification Identifying the materials that have been used to manufacture a product is probably the most important aspect of traceability. Virtually all electronic components are already labelled by the supplier to show their part number, batch number and other batch related information. Recording this information about the materials as they are assigned to production jobs enables the manufacturer to provide the customer with a list of the materials that have been used. This is often used as a "low cost" solution which in many cases can satisfy the customer's perceived requirement. There are several issues with this however. The data from the supplier will probably not match the part numbering system used by the manufacturer, requiring some form of indexing system and may be prone to manual error. There are likely to be many materials with exactly the same supplier data which gives no way to trace issues related to each particular material, for example for materials on reels, specifics of handling, environment, contamination or damage. Ifa problem like this occurs, a much larger quantity of components than necessary will be suspected to be defective. The alternative to this is to provide a unique identification of each reel, tray or box etc. This can be achieved through the application of a unique id code, for example bar-code or RF-ID tag to each unit of materials. This typically would be done as part of the materials receiving process. In this way, the granularity of data is increased enabling issues to be resolved down to the material unit level. A key advantage to this use of unique identification is to establish a materials database. Many key attributes can be assigned to each unit of material allowing tracking, verification, quantity management (JIT delivery) etc. all of which can be used to enhance the manufacturing operation. These same attributes are also required for environmental traceability, such as the control of moisture sensitive materials which have a time-critical element in their usage profile according to the environment in which they are stored and used.
of information the lower the cost of recovery. At the lowest level, data can be recorded against a particular production lot. Materials are recorded as they are assigned to the lot, during the preparation of the materials "kit" or as required by the machines. The fundamental limitation of this is that there is often more than one unit of materials required of a particular component to build the entire lot. The materials allocated may have come from different supplier batches or even different suppliers. If a defect is found and traced back to the lot, there is no way to know which unit of materials was at fault. All materials involved are therefore potentially defective. All products that may have been manufactured with any one of those materials in turn are therefore potentially defective. In practice this can often happen, and is sometimes outside of the expectation of the customer. Another limitation of this lot-based operation is as with the material identification, if problems are occurring on an individual PCB basis, such as the settings or environment of a process at a particular time, these will be impossible to identify and resolve. The alternative therefore is to increase the resolution of the information down to the individual PCB. In this way, unique conditions for the PCB can be stored. If materials are also uniquely labelled and checked on to the machines, then in the event of a suspected material defect the quantity of potentially defective PCBs will be minimized. The traceability data will show all ofthe individual boards that have been manufactured with any specific material. This does not completely solve the problem however. A significant error can occur where materials exhaust during the manufacture of a single board. The materials supply is replaced, meaning that for all of the individual components of that type on the PCB, some may have been placed from the exhausted material and some from the new replacement material. If a defect is then found on one of those components on the board, it will not be clear from which unit of material it came. The units of material may have been from different batches or even different suppliers. It may not be possible to prove one or the other, which, if the traceability data is being used to establish responsibility for a critical safety issue can be enough to discredit a claim to a material supplier. The ultimate solution is therefore traceability information down to individual component level. This is possible for some mounting machines that create event-based information that can be collected in realtime and cross referenced with the machine program sequence.
4.2 Information Resolution In the event of critical defects, the higher the resolution
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significant consequences.
4.3 Process Traceability At the lowest level, process traceability is the simple matter of ensuring that PCBs go through all of the proper processes in the right order. This ensures that all required testing and inspection processes have been completed. If a key process were to be skipped it can cause a significant cost of rework. In the case of a test process this could impact on the quality of the finished products. Without the unique identifier for each PCB, it is difficult to manually manage the flow of PCBs through the processes since typically, at any one moment in time different PCBs from any particular lot will be going through several different processes. Process traceability is only realistic where individual PCBs have been uniquely labelled. The PCB IDs are read before and/or after each process. As well as checking and tracking the PCBs through the processes, this also provides the opportunity to gather data about the process, such as materials changes, process settings and measurements. WIP at and between processes can also be monitored.
4.4 Process Coverage
5.2 Reliability Once a manufacturing operation is running with a traceability function, the operation is dependant on that system. There is a very high requirement for 100% system availability. If the system is not functioning for any reason then effectively the manufacturing processes must stop otherwise data integrity is lost. The application software will need to run on a continuous basis with little or no down-time. Systems in the manufacturing area which cause significant manufacturing downtime will ultimately be rejected. It is recommended that traceability systems run on systems dedicated to traceability so that there is a reduced risk of issues arising. 5.3 Longevity There is a requirement to keep the traceability data accessible for many years. The format of the data therefore must be guaranteed to be readable, or, the software used to access the data must have guarantee of support for as much as seven or even ten years. The media on which the data is stored also must be reliable and flexible. Data formats change and media degrades, so the system must be flexible enough to cope with having the data moved from time to time.
Material traceability is usually considered as part ofthe materials placement operation. Process traceability is usually considered as part of the inspection, test and repair operation. In doing this, some possibilities are overlooked which can lead to a loss of integrity of the data. For example, after the board has been assembled, a test process may fail and require the change of a component. Unless the traceability data is extended to include the fact that the particular reference was changed any defect occurring for example in the market cannot be guaranteed to have occurred by the original materials used for manufacture. The traceability data is therefore of little use. Traceability of materials therefore has to be extended to any process where any material can be changed. This may include distribution and after-sales service and repair.
Quite often the areas of manufacturing requiring high levels of traceability are those areas where access to sensitive data must be restricted. The use of an open system for these databases can be difficult to secure and are large potential targets for viruses. It is recommended that traceability systems remain as isolated as possible from non-essential users or access by external systems. Quite often, networks should be isolated, DVD and CD drives removed, USB ports physically disabled and the desktop locked down to prevent unauthorised access.
5. Traceability: Data Issues
5.5 Data Storage
The traceability data itself poses some very complex issues for any IT system that may be adopted. The key issues are as follows:
The way in which the data is stored will significantly determine the size requirement. For an average size manufacturing site using high performance lines of mounting machines, a typical database system for traceability can fill a modem high capacity hard disk every week. Optimizing the storage method to reduce the size is very important but must also take into account the accessibility needs. The key for success is to have a data storage system which is very concise and uses an intelligent way to ensure rapid access
5.1 Integrity The traceability data can be rendered useless if it is not absolutely correct or if it is missing. Either one ofthese conditions can mean the success or failure of a lawsuit or assignment of responsibility. Any flaw in the procedures to collect and record the data can have
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5.4 Security
when required. The export of traceability data is also important. A contract manufacturer may be required to assemble all of the traceability data related to each PCB or lot which can be delivered together with the finished products to the customer. The customer may need to import this data into their own systems. In such cases, a standard configurable data format such as XML can be employed to effectively transfer the data. 6. The Cost of Traceability The costs of a computer system designed to provide traceability can vary widely. The costs are generally dependant on the depth and breadth of the support capability. In reality however, the cost of the operation of the system can be far more significant. Each additional operation required at each process or material handling point introduces additional cost. Minimising this cost by reducing the depth of traceability or effort for data integrity can have serious consequences should issues with products appear in the market. 6.1 The Potential Benefits of Traceability At the higher end of traceability functionality, having unique identifiers for materials and PCBs many opportunities are created to use the traceability data for other purposes such as control and improvement of the overall manufacturing operation. This can in effect act as an encouragement and incentive for manufacturing operations to look more deeply into the traceability functionality, avoiding the risk of under-specification and gaining the benefits from the opportunities presented. 7. Solutions There are very few traceability solutions available on the market. Developing an effective solution is very much dependant on the exact mix of process equipment and way in which the manufacturing site operates. Solutions therefore tend to be very specialist and relatively expensive to buy, or, rather generic involving manual operation. In manufacturing sectors where traceability has been required for many years, it is common to find manually based recording systems still in place today. These represent the most costly form of traceability, since they require continuous manual labour. 7.1 Internal Systems Many of the internally developed systems today are based on the manual recording systems that have existed for many years. Computer database technology
has enabled engineers to put together systems to record data to suit the needs of their organisations. Typically several systems in different areas will be combined. This usually involves a great deal of customization and often places constraints on the manufacturing processes. These systems commonly suffer from data integrity issues, high cost of data collection, as well as issues such as system reliability, data storage and accessibility. 7.2 Generic Solutions There are many companies that offer generic shopfloor data capture and traceability packages. Though generally more professional than internally developed solutions, due to their generic nature, the vast majority of the data is entered, maintained and organised manually. Several smaller companies are now offering simple database solutions for lower levels of traceability. Many MRP/ERP solution providers also now offer traceability functions, though again, these are generic since they typically cannot directly interface with the specific manufacturing processes. 7.3 Integrated Solutions There are extreme examples of internally developed systems in some larger manufacturing environments typically consisting of multiple manufacturing sites world-wide. In some cases the concepts and needs for advanced quality and manufacturing management in a real-time environment have been established. One example is from within Sony Manufacturing. A solution has evolved which has become a commercially available product. Starting as an internal project within Sony in Japan in the late 1980s, TiMMS (Totally Integrated Manufacturing Management System) has evolved [4] into an acknowledged global leader for material control and traceability specifically focused on the manufacturing of circuit boards. TiMMS is a modular system capable of providing a fully functional, stable, efficient and cost effective solution. TIMMS was conceived and developed by Sony' s manufacturing engineers taking part from sites all over the world. Over the years, TIMMS has developed into a very effective and sophisticated integrated manufacturing system utilising the principles of Lean thinking. Without direct market pressures the internal TiMMS team was able to focus very clearly on the then current and future needs of manufacturing. A fully integrated set of engineering tools, a real-time manufacturing environment and a management system for material management including high resolution traceability evolved within
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the TiMMS system framework. TiMMS has been working inside Sony manufacturing sites world-wide for both consumer and non-consumer products for many years. TiMMS has become recognized as a leading Lean Manufacturing solution, and a benchmark for the best performance from manufacturing. Sony released TiMMS onto the open market in October 2000. Since then, TIMMS has been successfully fulfilling the needs of many manufacturers to achieve Lean operation and full traceability.
8. Conclusion
Traceability is something that is here to stay. There are many ways in which manufacturing companies will feel the pain of traceability. There will be those companies who do not adopt traceability solutions who face decreasing opportunity within the market. Other companies may adopt minimal solutions and face the consequences of insufficient data or issues with data integrity, either of which can result in costly legal settlement or re-work costs. There will also be companies that recognize the need for traceability, go strongly into implementation without understanding the net effects to their business in terms of the operational cost of generic systems. A final group of companies will be those who see the synergy between traceability and business improvement. They are the ones to whom traceability is not a burden, but actually a net benefit to their organisation. Using systems founded on Lean principles, Six Sigma advanced quality management with close integration of all key manufacturing functions and operations, these companies will be the future generation world-class standard. References
[ 1] Feld W. F. Lean manufacturing : tools, techniques, and how to use them. Boca Raton, FL : St. Lucie Press ; Alexandria, VA : APICS, 2001 [2] European Commission. Waste Electrical and Electronic Equipment (WEEE) Directive (2002/96/EC), Official Journal of the European Union, 13.2.2003, L37/24 [3] European Commission. Restriction of Hazardous Substances Directive (2002/95/EC), Official Journal of the European Union, 13.2.2003, L37/19 [4] Sony TiMMS website http://sonytimms.com
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