Glocalized Solutions for Sustainability in Manufacturing
Jürgen Hesselbach • Christoph Herrmann Editors
Glocalized Solutions for Sustainability in Manufacturing Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011
Editors Prof. Dr.-Ing. Dr. h.c. Jürgen Hesselbach Technische Universität Braunschweig Institut für Werkzeugmaschinen und Fertigungstechnik (IWF) Langer Kamp 19B 38106 Braunschweig Germany
PD Dr.-Ing. Christoph Herrmann Technische Universität Braunschweig Institut für Werkzeugmaschinen und Fertigungstechnik (IWF) Langer Kamp 19B 38106 Braunschweig Germany
ISBN 978-3-642-19691-1 e-ISBN 978-3-642-19692-8 DOI 10.1007/978-3-642-19692-8 Springer Heidelberg Dordrecht London New York Library of Congress Control Number: 2011924877 © Springer-Verlag Berlin Heidelberg 2011 This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Cover design: eStudio Calamar S.L. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Preface „[Sein] Kampf um Klarheit und Übersicht hat ungemein dazu beigetragen, die Probleme, Methoden und Resultate der Wissenschaft in vielen Köpfen lebendig werden zu lassen.“1 1
“[His] struggle for clarity and a comprehensive view has contributed immensely to bring the problems, methods and results of science into life.” Albert Einstein on the 70th birthday of Arnold Berliner. Die Naturwissenschaften (The Science of Nature),Vol.20/51, Springer, Berlin (1932).
Globalization, rapid developments in information technology, fast process- and product innovations, changing market requirements (e.g. environmental policies, increasing energy- and raw material costs) as well as global challenges, as the growing world population and the intensive use of limited resources, determine the surrounding conditions of producing companies in the 21st century. The comprehension for the resulting complex structures of social, political, economic, technical, ecological and organizational coherences increases with growing insights gained from natural sciences and technology. “Sustainable development” describes a way of how the needs of today’s generations can be satisfied without interfering with the possibilities of future generations. In order to follow this path, “ecological” change processes have to take place in society and economy. “Sustainable economies” require innovative products and processes and a life-cycle-oriented way of thinking and acting or rather a way of thinking and acting in terms of systems, i.e. in value chains and -networks embedded in the natural environment. Only by this means the shifting of problems can be avoided and integrated solutions can be created. This way of thinking and acting does not end with the customer, but proceeds up to the disposal of products and handling of materials and products and/or product parts in life cycles. Decisions on the planning and design of products and processes also have to be made in an integrated manner. This means that technical, economic and ecological aspects have to be integrated into one approach. This should be accomplished under a ”cradle-to-cradle” view – from the raw materials extraction up to end-of-life. This view should take into account not only the manufactured products but also the equipment and auxiliary materials which are necessary for production (e.g. machine tools, cooling lubricants). For this year’s conference we chose the theme “Glocalized Solutions for Sustainability in Manufacturing”. The term “glocalization” is a combination of the words “globalization” and “localization”. It was invented to describe a product or service design developed and distributed globally and also adapted specifically to each locality or culture it is marketed in. However, “Glocalized Solutions for Sustainability in Manufacturing“ do not only involve products or services that are changed for a local market by simple substitution or the omitting of functions. We want to address products and services that ensure a high standard of living everywhere. Resources required for manufacturing and use of such products are limited and not evenly distributed in the world. Locally available resources, local capabilities as well as local constraints have to be drivers for product- and process innovations. Thus, “Best of Local” is a starting point for glocalized solutions. This means for example that the availability of fuels based on biomass is a starting point for engine development in Brazil, whereas solar energy is going to be the most important energy source for future electric vehicles in countries of the earth’s sun belt (up to 35 degrees north and south of the equator). While water-based cooling lubricants are developed in Germany, technical animal fats and used edible fats are the basis for the production of cooling lubricants in Spain. Dandelion is used for the production of rubber; thus, car tires develop from renewable resources. The crushed hard shells of fruit stones (e.g. cherry stones) serve as filling material for polymers or are used as technical abrasives for the cleaning of surfaces. Locally accumulating waste streams are locally processed into new products. Thus, old PET bottles are not only recycled into world cup soccer shirts, but also into laptop bags. However, the use of resources is always linked to the environmental impact over all stages of a product life cycle from material extraction, transport and manufacturing to usage and to the end-of-life. Even if a local scope for design is always linked to global impacts, it has the potential to reduce the impact to an ecologically compatible minimum. Future glocalized engineering solutions will have the potential to address global challenges by providing products, services and processes that take into account local capabilities and constraints to achieve an economically, socially and environmentally sustainable society in a global perspective. The CIRP International Conference on Life Cycle Engineering is a platform for this wide and complex field. It will require the efforts of all of us to bring the problems, methods and results of Life Cycle Engineering into life.
Jürgen Hesselbach
Christoph Herrmann
Table of Contents
Preface .....................................................................................................................................................................................................
v
a
Organization ............................................................................................................................................................................................ a
xiii
Keynotes Electricity Metering and Monitoring in Manufacturing Systems ........................................................................................................ S. Kara, G. Bogdanski, W. Li
1
Implementing life Cycle Engineering efficiently into Automotive Endustry Processes ................................................................... S. Krinke
11
Leveraging Manufacturing for a Sustainable Future ........................................................................................................................... D. Dornfeld
17
Sustainability Engineering by Product-Service Systems .................................................................................................................... G. Seliger
22
Solvis Zero-Emission Factory - The 'Solvis way' - Structure and Subject ......................................................................................... H. Jäger
29
Manufacturing and the Science of Sustainability ................................................................................................................................ T. G. Gutowski
32
Indian Solar Thermal Technology – Technology to Protect Environment and Ecology .................................................................. D. Gadhia
40
a
Automotive Life Cycle Engineering Assessment of Energy and Resource Consumption of Processes and Process Chains within the Automotive Sector ............. R. Schlosser, F. Klocke, B. Döbbeler, B. Riemer, K. Hameyer, T. Herold, W. Zimmermann, O. Nuding, B. A. Schindler, M. Niemczyk
45
Assessment of Alternative Propulsion Systems for Vehicles ............................................................................................................ C. Herrmann, K. S. Sangwan, M. Mennenga, P. Halubek, P. Egede
51
Concept and Development of Intelligent Production Control to enable Versatile Production in the Automotive Factories of the Future ................................................................................................................................................................................................. S. U. H. Minhas, C. Lehmann, U. Berger
57
Resource Efficiency – what are the Objectives? .................................................................................................................................. M. Gernuks
63
Comparative Life Cycle Assessment of Remanufacturing and New Manufacturing of a Manual Transmission .......................... J. Warsen, M. Laumer, W. Momberg
67
a
Automotive Life Cycle Engineering - Recycling Coordination of Design-for-Recycling Activities in Decentralized Product Design Processes in the Automotive Industry ....... K. Schmidt, T. Volling, T. S. Spengler
73
A Strategic Framework for the Design of Recycling Networks for Lithium-Ion Batteries from Electric Vehicles ......................... C. Hoyer, K. Kieckhäfer, T. S. Spengler
79
Recovery of Active Materials from Spent Lithium-Ion Electrodes and Electrode Production Rejects ........................................... C. Hanisch, W. Haselrieder, A. Kwade
85
New Technologies for Remanufacturing of Automotive Systems Communicating via CAN Bus ................................................... R. Steinhilper, S. Freiberger, M. Albrecht, J. Käufl, E. Binder, C. Brückner
90
LCM applied to Auto Shredder Residue (ASR) ..................................................................................................................................... L. Morselli, A. Santini, F. Passarini, I. Vassura, L. Ciacci
96
viii
Table of Contents
Life Cycle Design - Methods and Tools Eco-Innovation by Integrating Biomimetic with TRIZ Ideality and Evolution Rules ......................................................................... J. L. Chen, Y.-C. Yang
101
Reasoning New Eco-Products by Integrating TRIZ with CBR and Simple LCA Methods ................................................................ C. J. Yang , J. L. Chen
107
Proposal of an Integrated Eco-Design Framework of Products and Processes ............................................................................... S. Kondoh, N. Mishima
113
Development of CAD System for Life Cycle Scenario-Based Product Design ................................................................................. E. Kunii, S. Fukushige, Y. Umeda
118
Environmental Impact Assessment Model for Wireless Sensor Networks ....................................................................................... J. Bonvoisin, A. Lelah, F. Mathieux, D. Brissaud
124
Considering the Social Dimension in Environmental Design ............................................................................................................. B. Dreux-Gerphagnon, N. Haoues
130
Proposal of an Ecodesign Maturity Model: supporting Companies to improve Environmental Sustainability ............................. D. C. A. Pigosso, H. Rozenfeld
136
Environmental and Operational Analysis of Ecodesign Methods Based on QFD and FMEA ......................................................... F. N. Puglieri, A. R. Ometto
142
Synergico: a new “Design for Energy Efficiency” Method enhancing the Design of more environmentally friendly Electr(on)ic Equipments ......................................................................................................................................................................... L. Domingo, D. Evrard, F. Mathieux, G. Moenne-Loccoz
148
Improving Product Design based on Energy Considerations ............................................................................................................ Y. Seow, S. Rahimifard
154
Eco-Design Tool to support the Use of Renewable Polymers within Packaging Applications ....................................................... J. Colwill, S. Rahimifard, A. Clegg
160
A
Life Cycle Design - Selected Applications State-of-the-art Ecodesign on the Electronics Shop Shelves? A Quantitative Analysis of Developments in Ecodesign of TV Sets ........................................................................................................................................................................................................... C. Boks, R. Wever, A. Stevels
167
Simultaneous Application of Design for Sustainable Behavior and Linked Benefit Strategies in Practice .................................. J. Schmalz, C. Boks
173
Strategic Evaluation of Manufacturing Technologies ......................................................................................................................... G. Reinhart, S. Schindler, P. Krebs
179
Consideration of the Precautionary Principle – the Responsible Development of Nano Technologies ........................................ M. Weil
185
Proposal of a Design Support Method for Sustainability Scenarios 1st Report: Designing Forecasting Scenarios ................... H. Wada, Y. Kishita, Y. Mizuno, M. Hirosaki, S. Fukushige, Y. Umeda
189
a
Sustainability in Manufacturing Evaluating Trade-Offs Between Sustainability, Performance, and Cost of Green Machining Technologies ................................. M. Helu, J. Rühl, D. Dornfeld, P. Werner, G. Lanza
195
Sustainable Production by Integrating Business Models of Manufacturing and Recycling Industries ......................................... C. Jonsson, J. Felix , A. Sundelin , B. Johansson
201
Life Cycle Engineering – Integration of New Products on Existing Production Systems in Automotive Industry ....................... W. Walla, J. Kiefer
207
Managing Sustainability in Product Design and Manufacturing ........................................................................................................ K. Ioannou, A. Veshagh
213
A System for Resource Efficient Process Planning for Wire EDM ..................................................................................................... S. Dhanik, P. Xirouchakis, R. Perez
219
Increased Trustability of Reliability Prognoses for Machine Tools ................................................................................................... G. Lanza, P. Werner, D. Appel, B. Behmann
225
Table of Contents
ix
Hidden Aspects of Industrial Packaging - The Driving Forces behind Packaging Selection Processes at Industrial Packaging Suppliers .................................................................................................................................................................................................. S. S. Casell
229
Applying Functionally Graded Materials by Laser Cladding: a cost-effective way to improve the Lifetime of Die-Casting Dies ........................................................................................................................................................................................................... S. Müller, H. Pries, K. Dilger, S. Ocylok, A. Weisheit, I. Kelbassa
235
A Total Life-Cycle Approach towards Developing Product Metrics for Sustainable Manufacturing .............................................. A. Gupta, A. D. Jayal, M. Chimienti, I. S. Jawahir
240
Carbon Footprint Analysis for Energy Improvement in Flour Milling Production ........................................................................... C. W. P. Shi, F. Rugrungruang, Z. Yeo, B. Song
246
a
Sustainability in Manufacturing - Energy Efficiency in Machine Tools Modelling Machine Tools for Self-Optimisation of Energy Consumption ......................................................................................... R. Schmitt, J. L. Bittencourt, R. Bonefeld
253
Energy-Efficient Machine Tools through Simulation in the Design Process .................................................................................... C. Eisele, S. Schrems, E. Abele
258
Energy Consumption Characterization and Reduction Strategies for Milling Machine Tool Use ................................................... N. Diaz, E. Redelsheimer, D. Dornfeld
263
An Investigation into Fixed Energy Consumption of Machine Tools ................................................................................................. W. Li, A. Zein, S. Kara, C. Herrmann
268
Energy Efficiency Measures for the Design and Operation of Machine Tools: An Axiomatic Approach ...................................... A. Zein, W. Li, C. Herrmann, S. Kara
274
Analyzing Energy Consumption of Machine Tool Spindle Units and Identification of Potential for Improvements of Efficiency ................................................................................................................................................................................................. E. Abele, T. Sielaff, A. Schiffler, S. Rothenbücher
280
a
Sustainability in Manufacturing - Energy Efficiency in Process Chains Energy Consumption as One Possible Exclusion Criterion for the Reuse of Old Equipment in New Production Lines ............. L. Weyand, H. Bley, M. Swat, K. Trapp, D. Bähre
287
Optimizing Energy Costs by Intelligent Production Scheduling ........................................................................................................ A. Pechmann, I. Schöler
293
Methodology for an Energy and Resource Efficient Process Chain Design ..................................................................................... S. Schrems, C. Eisele, E. Abele
299
A New Shop Scheduling Approach in Support of Sustainable Manufacturing ................................................................................. K. Fang, N. Uhan, F. Zhao, J. W. Sutherland
305
Comparison of the Resource Efficiency of Alternative Process Chains for Surface Hardening .................................................... G. Reinhart, S. Reinhardt, T. Föckerer, M. F. Zäh
311
Synergies from Process and Energy Oriented Process Chain Simulation – A Case Study from the Aluminium Die Casting Industry .................................................................................................................................................................................................... C. Herrmann, T. Heinemann, S. Thiede
317
a
Sustainability in Manufacturing - Methods and Tools for Energy Efficiency Context-Aware Analysis Approach to Enhance Industrial Smart Metering ....................................................................................... C. Herrmann, S.-H. Suh, G. Bogdanski, A. Zein, J.-M. Cha, J. Um, S. Jeong, A. Guzman
323
Exergy Efficiency Definitions for Manufacturing Processes .............................................................................................................. Renaldi, K. Kellens, W. Dewulf, J. R. Duflou
329
State of Research and an innovative Approach for simulating Energy Flows of Manufacturing Systems .................................... S. Thiede, C. Herrmann, S. Kara
335
Modular Modeling of Energy Consumption for Monitoring and Control ........................................................................................... A. Verl, E. Abele, U. Heisel, A. Dietmair, P. Eberspächer, R. Rahäuser, S. Schrems, S. Braun
341
Architecture for Multilevel Monitoring and Control of Energy Consumption .................................................................................. A. Verl, E. Westkämper, E. Abele, A. Dietmair, J. Schlechtendahl, J. Friedrich, H. Haag, S. Schrems
347
x
Table of Contents
Sustainability in Manufacturing - Selected Applications Green Performance Map – An Industrial Tool for Enhancing Environmental Improvements within a Production System ......... K. Romvall, M. Kurdve, M. Bellgran, J. Wictorsson
353
Analysis and Quantification of Improvement in Green Manufacturing Process of Silicon Nitride Products ................................ N. Mishima, S. Kondoh, H. Hyuga, Y. Zhou, K. Hirao
359
Evaluation of the Environmental Impact of different Lubrorefrigeration Conditions in Milling of γ-TiAl Alloy ............................. G. Rotella, P. C. Priarone, S. Rizzuti, L. Settineri
365
Quantitative and Qualitative Benefits of Green Manufacturing: an Empirical Study of Indian Small and Medium Enterprises .. K. S. Sangwan
371
Preliminary Environmental Assessment of Electrical Discharge Machining .................................................................................... K. Kellens, Renaldi, W. Dewulf, J. R. Duflou
377
Development of an Interpretive Structural Model of Obstacles to Environmentally Conscious Technology adoption in Indian Industry .................................................................................................................................................................................................... V. K. Mittal, K. S. Sangwan
383
Identifying Carbon Footprint Reduction Opportunities through Energy Measurements in Sheet Metal Part Manufacturing ...... C. W. P. Shi, F. Rugrungruang, Z. Yeo, K. H. K. Gwee, R. Ng, B. Song
389
Sustainable Production Research - a Proposed Method to design the Sustainability Measures ................................................... M. K. Wedel, B. Johansson, A. Dagman, J. Stahre
395
Green Production of CFRP Parts by Application of Inductive Heating .............................................................................................. M. Frauenhofer, S. Kreling, H. Kunz, K. Dilger
401
Saving Potential of Water for Foundry Sand Using Treated Coolant Water ...................................................................................... J. O. Gomes, V. E. O. Gomes, J. F. de Souza, E. Y. Kawachi
407
a
End of Life Management - Reuse and Remanufacturing Modular Grouping Exploration to design Remanufacturable Products ............................................................................................ N. Tchertchian, D. Millet, O. Pialot
413
Development of Part Agents for the Promotion of Reuse of Parts through Experiment and Simulation ...................................... H. Hiraoka, K. Ito, K. Nishida, K. Horii, Y. Shigeji
419
Systematic Categorization of Reuse and Identification of Issues in Reuse Management in the Closed Loop Manufacturing ... T. Sakai, S. Takata
425
Approach for Integration of Sustainability Aspects into Innovation Processes ............................................................................... S. Severengiz, P. Gausemeier, G. Seliger, F. A. Pereira
431
Remanufacturing Engineering Literature Overview and Future Research Needs ............................................................................ Q. Ke, H.-C. Zhang, G. Liu, B. Li
437
a
End of Life Management - Selected Applications Effects of Lateral Transshipments in Multi-Echelon Closed-Loop Supply Chains ........................................................................... K. Tracht, M. Mederer, D. Schneider
443
Development of an Interpretive Structural Model of Barriers to Reverse Logistics Implementation in Indian Industry .............. A. Jindal, K. S. Sangwan
448
Recycling of LCD Screens in Europe - State of the Art and Challenges ........................................................................................... S. Salhofer, M. Spitzbart, K. Maurer
454
End of Life Strategies in the Aviation Industry .................................................................................................................................... J. Feldhusen, J. Pollmanns, J. E. Heller
459
Contribution of Recycling Processes to Sustainable Resource Management ................................................................................. A. Pehlken, K.-D. Thoben
465
Business Issues in Remanufacturing: Two Brazilian Cases in the Automotive Industry ................................................................ O. T. Oiko, A. P. B. Barquet, A. R. Ometto
470
A Systematic Investigation for Reducing Shredder Residue for Complex Automotive Seat Subassemblies ............................... S. Barakat, J. Urbanic
476
Eco Quality Polymers-EQP ..................................................................................................................................................................... C. Luttropp, E. Strömberg
482
Table of Contents
xi
Intelligent Products to Support Closed-Loop Reverse Logistics ....................................................................................................... K. A. Hribernik, M. von Stietencron, C. Hans, K.-D. Thoben
486
The Prospects of Managing WEEE in Indonesia .................................................................................................................................. J. Hanafi, H. J. Kristina, E. Jobiliong, A. Christiani, A. V. Halim, D. Santoso, E. Melini
492
Medical Electrical Equipment - Good Refurbishment Practice at Siemens AG Healthcare ............................................................. M. Plumeyer, M. Braun
497
a
Information and Knowledge Management Sustainable Product Lifecycle Management: A Lifecycle based Conception of Monitoring a Sustainable Product Development ............................................................................................................................................................................................ M. Eigner, M. von Hauff, P. D. Schäfer
501
Semantic Web Based Dynamic Energy Analysis and Forecasts in Manufacturing Engineering .................................................... K. Wenzel, J. Riegel, A. Schlegel, M. Putz
507
Energy Data Acquisition and Utilization for Energy-Oriented Product Data Management .............................................................. T. Reichel, G. Rünger, D. Steger, U. Frieß, M. Wabner
513
Integrating Energy-Saving Process Chains and Product Data Models ............................................................................................. G. Rünger, A. Schubert, S. Goller, B. Krellner, D. Steger
519
Challenges in Data Management in Product Life Cycle Engineering ................................................................................................. T. Fasoli, S. Terzi, E. Jantunen, J. Kortelainen, J Sääski, T. Salonen
525
Business Game for Total Life Cycle Management ............................................................................................................................... S. Böhme, T. Heinemann, C. Herrmann, M. Mennenga, R. Nohr, J. Othmer
531
Requirements Management – a Premise for adequate Life Cycle Design ......................................................................................... S. Klute, C. Kolbe, R. Refflinghaus
537
Towards a Methodology for Analyzing Sustainability Standards using the Zachman Framework ................................................ S. Rachuri, P. Sarkar, A. Narayanan, J. H. Lee, P. Witherell
543
Sustainability through Next Generation PLM in Telecommunications Industry ............................................................................... J. Golovatchev, O. Budde
549
Challenges of an Efficient Data Management for Sustainable Product Design ................................................................................ T. Leitner, M. Stachura, A. Schiffleitner, N. Stein
554
Product and Policy Life Cycle Inventories with Market Driven Demand: An Engine Selection Case Study .................................. H. Grimes-Casey, C. Girata, K. Whitefoot, G. A. Keloeian, J. J. Winebrake, S. J. Skerlos
558
A Case-Study: Finding References to Product Development Knowledge from Analysis of Face-to-Face Meetings ................... B. Piorkowski, J. Gao, R. Evans
564
a
Life Cycle Assessment - Methods and Tools CAD-Integrated LCA Tool: Comparison with dedicated LCA Software and Guidelines for the Improvement ............................... A. Morbidoni, C. Favi, M. Germani
569
Comparison of two LCA Methodologies in the Machine-Tools Environmental Performance Improvement Process ................... M. Azevedo, M. Oliveira, J. P. Pereira, A. Reis
575
Developing Impact Assessment Methods: an Approach for addressing inherent Problems .......................................................... M. Toxopeus, V. Kickert, E. Lutters
581
Developing a Conceptual Framework for UT based LCA .................................................................................................................... J.-M. Cha, S.-H. Suh
587
Towards the Integration of Local and Global Environmental Assessment Methods: Application to Computer System Power Management ............................................................................................................................................................................................ V. Moreau, N. Gondran, V. Laforest Cradle to Cradle and LCA – is there a Conflict? .................................................................................................................................. A. Bjørn, M. Z. Hauschild
593 599
xii
Table of Contents
Life Cycle Assessment - Selected Applications Environmental Assessment of Printed Circuit Boards from Biobased Materials ............................................................................. Y. Deng, K. Van Acker, W. Dewulf, J. R. Duflou Application of Life Cycle Engineering for the Comparison of Biodegradable Polymers Injection Moulding Performance ............................................................................................................................................................................................ D. Almeida, P. Peças, I. Ribeiro, P. Teixeira, E. Henriques
605
611
Using Ecological Assessment during the Conceptual Design Phase of Chemical Processes – a Case Study ............................ L. Grundemann, J. C. Kuschnerow, T. Brinkmann, S. Scholl
617
Environmental Footprint of Single-Use Surgical Instruments in Comparison with Multi-Use Surgical Instruments .................. J. Schulz, J. Pschorn, S. Kara, C. Herrmann, S. Ibbotson, T. Dettmer, T. Luger
623
Comparative Carbon Footprint Assessment of Door made from Recycled Wood Waste versus Virgin Hardwood: Case Study of a Singapore Wood Waste Recycling Plant ....................................................................................................................................... R. Ng, C. W. P. Shi, J. S. C. Low, H. M. Lee, B. Song
629
a
Life Cycle Costing A Target Costing-Based Approach for Design to Energy Efficiency ................................................................................................. A. Bierer, U. Götze
635
Life Cycle Costing Assessment with both Internal and External Costs Estimation ......................................................................... S. Martinez, M. Hassanzadeh, Y. Bouzidi, N. Antheaume
641
Environmental and Economic Evaluation of Solar Thermal Panels using Exergy and Dimensional Analysis ............................. G. Medyna, E. Coatanea, D. Millet
647
Implications of Material Flow Cost Accounting for Life Cycle Engineering ...................................................................................... T. Viere, M. Prox, A. Möller, M. Schmidt
652
a
Life Cycle Costing - Modelling Aircraft Engine Component Deterioration and Life Cycle Cost Estimation ...................................................................................... Y. Zhao, A. Harrison, R. Roy, J. Mehnen
657
Life Cycle Cost Estimation using a Modeling Tool for the Design of Control Systems ................................................................... H. Komoto, T. Tomiyama
663
Assessing the Value of Sub-System Technologies including Life Cycle Alternatives .................................................................... A. Bertoni, O. Isaksson, M. Bertoni, T. Larsson
669
Costing for Avionic Through-Life Availability ...................................................................................................................................... L. Newnes, A. Mileham, G. Rees, P. Green
675
Eco Global Evaluation: Cross Benefits of Economic and Ecological Evaluation ............................................................................. N. Perry, A. Bernard, M. Bosch-Mauchand, J. LeDuigou, Y. Xu
681
A Index of Authors ......................................................................................................................................................................................
687
Organization
CHAIRMEN Prof. J. Hesselbach PD Dr.-Ing. Christoph Herrmann ORGANIZING COMMITTEE Chief Organizers Dipl.-Wirtsch.-Ing. Mark Mennenga Dipl.-Wirtsch.-Ing. Tim Heinemann Organizing Committee Hannah Jule Schäfer, M.A.
Dipl.-Wirtsch.-Ing. Katrin Kuntzky
Dipl.-Ing. (FH) Stefan Andrew
Dr.-Ing. Tobias Luger
Dr.-Ing. Ralf Bock
Dipl.-Chem. Gerlind Öhlschläger
Dipl.-Ing. Gerrit Bogdanski
Anne-Marie Schlake, M.A.
Dr.-Ing. Tina Dettmer
Dipl.-Wirtsch.-Ing. Tim Spiering
Dipl.-Wirtsch.-Ing. Patricia Egede
Dipl.-Wirtsch.-Ing. Julian Stehr
Dipl.-Wirtsch.-Ing. Philipp Halubek
Dipl.-Wirtsch.-Ing. Sebastian Thiede
Dipl.-Ing. Mohamad Jamal Kayasa
Dipl.-Wirtsch.-Ing. Marius Winter
Dipl.-Ing. Michael Krause
Dipl.-Wirtsch.-Ing. André Zein
INTERNATIONAL SCIENTIFIC COMMITTEE Prof. L. Alting / DK
Prof. N. Nasr / US
Porf. C. Boks / NO
Prof. A. Nee / SG
Prof. B. Bras / US
Prof. R. Neugebauer / DE
Prof. D. Brissaud / FR
Prof. A. Ometto / BR
Prof. J. L. Chen / TW
Prof. S. Salhofer / AT
Prof. W. Dewulf / BE
Prof. K. S. Sangwan / IN
Prof. D. Dornfeld / US
Prof. G. Seliger / DE
Prof. J. Duflou / BE
Prof. W. Sihn / AT
Prof. T. Gutowski / US
Prof. S. Skerlos / US
Prof. M. Hauschild / DK
Prof. T. Spengler / DE
Prof. H. Kaebernick / AU
Prof. S. H. Suh / KR
Prof. S. Kara / AU
Prof. J. Sutherland / US
Prof. F. Kimura / JP
Prof. S. Takata / JP
Prof. W. Knight / US
Prof. S. Tichkiewitch / FR
Prof. T. Lien / NO
Prof. T. Tomiyama / NL
Dr. C. Luttropp / SW
Prof. Y. Umeda / JP
Prof. H. Meier / DE
Prof. E. Westkämper / DE
Prof. L. Morselli / I
Prof. H. Zhang / US
Electricity Metering and Monitoring in Manufacturing Systems S. Kara
1, 2
, G. Bogdanski
1, 3
, W. Li
1, 2
1
Joint German-Australian Research Group in Sustainable Manufacturing and Life Cycle Management
2
Life Cycle Engineering & Management Research Group, School of Mechanical & Manufacturing Engineering, The University of New South Wales, Australia
3
Institute of Machine Tools and Production Technology (IWF), Product- and Life-Cycle-Management Research Group, Technische Universität Braunschweig, Germany
Abstract Traditionally, electricity costs in manufacturing have been considered as an overhead cost. In the last decade, the manufacturing industry has witnessed a dramatic increase in electricity costs, which can no longer be treated as an overhead, but a valuable resource to be managed strategically. However, this can only be achieved by strategically gathering electricity consumption data by metering and monitoring. This keynote paper presents the latest developments and challenges in electricity metering and monitoring systems and standards in the context of manufacturing systems. An industry case is presented to emphasise the challenges and the possible solutions to address them. Keywords: Manufacturing Systems; Energy Efficiency; Metering and Monitoring
1
INTRODUCTION
Global warming and its disastrous environmental and economic effects are considered as one of the major challenges that today’s and future generations have to face during the 21 century. One of the main attribute of this challenge is due to the environmental impact e.g. Green House Gas (GWP) emission, caused during the generation of electricity from fossil fuels [1]. Therefore, one of the possible ways to reduce GWP emission is to reduce the electricity consumption, which is also enforced by national and international initiatives, e.g. Kyoto agreement. In addition, industry has a high interest in reducing the energy consumption, because energy has become a major cost driver, especially for high technology industries with their energy intensive manufacturing processes. Energy cost has long been treated as a necessary overhead cost for creating value-added products. However, more and more industrial companies are consciously shifting towards treating energy as a valuable resource, which needs to be planned and managed as a variable input for their plant. A necessary prerequisite for such energy-conscious behaviour is to be able to systematically measure energy consumption in a manufacturing plant. One of the challenges plant managers facing today is to gain transparency inside complex energy distribution networks of their manufacturing plants. A fundamental prerequisite for achieving this transparency is primarily to meter the consumed energy and its related characteristics in time. In order to gain full awareness, the metered physical values need to be monitored, interpreted and visualized in plant management systems. Electrical energy is in high favour of industry because it can easily be converted into many lower energy forms such as heat, light, compressed air, mechanical torque and many others. Consumption of electrical energy, in comparison to other energy forms, can be measured easily and precisely. Therefore, other energy forms are usually converted by sensors and transducers into electrical signals which themselves can be picked up by standard procedures of electrical signal metering techniques. In this paper the evolution and the latest development in electricity
metering and monitoring technologies are first introduced. A rapid development in measurement instruments requires up-to-date standards in order to compare and select appropriate device. After giving an overview about the most relevant international and national standards, the potentials of electricity metering and monitoring in manufacturing plants are illustrated and technical requirements for metering and monitoring systems are presented. The most important aspects that need to be considered when designing metering strategies are highlighted with a case study from an Australian manufacturing company. 2
EVOLUTION OF MONITORING
ELECTRICITY
MEASUREMENT
AND
Since the introduction of electricity distribution grids, there has been a demand for devices to measure the energy consumption in order to assist suppliers for distributing, pricing and monitoring their service. As early as during the 1880s, companies were authorized to sell electricity. One of the first patents for an electricity meter had been taken out by Pulvermacher in 1868 for an electrolytic meter [2]. Besides the electrolytic meters, there were other early inventions for measuring electricity, for instance thermal meters, clock meters and motor meters. In 1884 the Aron Meter Co. started selling the first meters of the true dynamometer type electricity meter patented by Hermann Aron. They were considered to have the highest degree of accuracy of the available meters at that time. As one form of the motor meters, the induction meter (Ferraris disc meter), had emerged to meet the needs of the emerging multiphase generation and transmission of electric power for high precision Alternating Current (AC) meters. The induction meter is still in general use today but is reaching its limits of accuracy and lacking ability to communicate its metering values. Recent developments try to meet the demands of the evolving smart grid technology calling for multi-value measurement and bi-directional communication ability [3]. The advances in semiconductor technology have led to the technological overrun of bulky electromechanical meters by smaller dimensioned, solely electronic metering devices by the early 1990s. By removing all complex
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_1, © Springer-Verlag Berlin Heidelberg 2011
1
2
Keynotes
analogue/digital converter circuitry for the basic measurements such as current and voltage
multiplier for the instantaneous measurement values
time to frequency converters for voltage and current [6].
The overall structure of measuring elements of electronic meters is basically the same. However, the applied measurement techniques are, in contrast to electromechanical metering systems, very different. The multiplication of voltage and current for example can be done by a hall multiplier, a time division multiplier or digital multiplier [5-6]. Providing at least the same functionality as hybrid meters the electronic meters aim at offering extended information to the user. This is done through digital signal processing by means of microprocessors or customized integrated circuits. The digital circuitry can perform time accurate calculation of active, reactive, apparent energy and power factor as well as frequency and harmonic distortion metering with many mathematical functionalities such as averaging, min/max detection, integration and accumulation [4, 6]. All performed metering is provided to serve for billing and controlling of the supplied and used electrical energy. The application of different measurement principles and individually designed integrated circuitry in metering devices calls for national and international standards to allow users to verify their metered value in accordance to approved limitations. As an example for international standards, the IEC 62053 and the ANSI C12.20 mandates the accuracy of static watt-hour meters and have defined four different classes: class 2, class 1, class 0.5 and class 0.2 (e.g. class 0.5 requires a repeatable meter precision of 0.5% of nominal current and voltage) [7-8]. The revised German VDE 0410 had even clustered these classes into utility measurement ranging from class 1 to class 5 and into precision measurement ranging from class 0.1 to class 0.5 for directly indicating metering devices with a scale. The VDE 0410 has been overworked in the IEC 60051, within a much more comprehensive international standard for direct acting analogue electrical measuring instruments [9-10]. The electrical meters measurement chain is subject to an error of the measurement chain, expressed in the accuracy G, indicated by the class and enabling the user to calculate the limitations of maximum and minimum deviation ∆x inside a given metering measurement range xmax-min of the instrument
∆x = G xmax-min / 100%.
(1)
Current digital electricity meters available on the market already claim to some extent, to meet the accuracy limitation of 0.1% or
PURPOSE OF ELECTRICITY MONITORING IN INDUSTRY
MEASUREMENT
AND
Electricity metering and monitoring in industrial applications address a wide range of applications, which can be divided into three broad levels of application:
Factory Level
Department Level
Unit Process Level.
In general, from the customer perspective, metering and monitoring of electricity in industry applications is done to gain transparency into electricity billing, internal electricity distribution and energy controlling. Each of the three stated levels above contains its own set of technical requirements concerning the metering equipment and the attached monitoring system. They also have their own associated potential benefits and degree of transparency requirement from the application of electricity metering and monitoring. Figure 1 shows a factory from an electricity consumer perspective with all its organisational sub-consumers within the three levels. Transparency gains through electricity metering and monitoring
Organizational level structure
• energy billing • energy contract •…
Factory
• energy accounting • identify hot-spots • transparency of energy flow within the factory • …
• energy modelling • process simulation • machine efficiency redesign • ...
Factory
3
Department 1
Machine 1
…
Department n
SubDepartment 1.1
...
SubDepartment n.1
SubDepartment 1.m
…
SubDepartment n.m
…
Machine i
Periphery System 1
…
Department
For simple kilowatt-hour metering the electromechanical meters such as Ferraris disc meters, are still considered to be the most economical solution because of its extremely long life and durability [5-6]. The metering industry has been trying to lengthen the technology life of the predominant electromechanical technology by using hybrid solutions, e.g. adding electronics to the present devices, to fulfil additional functions like maximum demand calculations as demanded by today’s suppliers to price industry and medium to large sized commercial electricity customers [6]. With the help of add-on electronics, hybrid meters can provide their users various other functions such as multi-rate registers, seasonal registers, historical value registers, maximum demand, and consumptions threshold definition. The inevitable next step in the evolution of electricity meters is the electronic meters. In contrast to electromechanical meters different principles are used to measure the basic values of electricity, from which all other values of interest are usually derived by means of electronic calculation. Basic elements for each power phase are:
lower, causing buyers to be confused because there is no existing international standard of 0.1% accuracy which a manufacturer could claim compliance to [11]. Therefore, standards need to keep up the pace of technological innovation in order to ensure that metering equipment buyers are able to compare and benchmark claimed accuracy by providing manufacturers a specified set of tests over the whole range of operation conditions of load current, power factor, temperature and harmonic distortion.
Unit Process
moving mechanical parts, the electronic meters are able to house multi-sensors within highly integrated circuits [4].
Periphery System j
Figure 1: Three levels of a factory as a consumer of electricity. The most important fact that needs to be stated is that the organisational structure rarely complies with the technical electricity distribution network, which brings additional challenges during department level electricity metering and monitoring which will be discussed later. What all three levels have in common in relation to metering electricity is the basic measurement values that all other specific information can be mathematically driven from: voltage and current with respect to time. For instance, in a general 3 phase system, the total active power PW tot can be calculated as:
PW tot = PW1 + PW2 + PW3
(2)
= U1N eff I1 eff cos φ1 + U2N eff I2 eff cos φ2 + U3N eff I3 eff cos φ3.
(3)
Keynotes
3
Where φn is the phase angle between the current In and the voltage Un of the n-th phase. In 2-phase-systems it is theoretically possible to meter only two lines because one line can be seen as the neutral. Nevertheless, it is often seen that 3-phase-systems are also equipped with three separate power meters to monitor three phases and to ensure a higher accuracy of the metering result, especially at low powers and high phase angles [12]. The accuracy of a measurement is therefore always directly related to the accuracy of the current and voltage measurement error and needs to be calculated accordingly. (a)
I1
(b)
K
L
k
l
U
V
u
v
I2
Figure 2: Current (a) and voltage (b) transformer circuits [12]. Usually a galvanic isolation, as depicted in Figure 2, is used in the current and voltage meters to prevent them from accidental overload from harming the sensitive metering equipment. The most widespread application to realize a galvanic isolation is a simple current and voltage transformer which is going into saturation if an overload is applied on the primary windings. The secondary windings are short circuited by the current meter. The transformation rate is generally dimensioned to conduct 1 or 5 A (current meter) and 100 V (voltage meter) on the secondary windings. The transformers are also regarded as part of the measuring chain and are also adding their own error in terms of accuracy limitation to the total measurement system. The IEC 60044-1 is dealing with the technical requirements of current transformers for instruments as well as their indicated accuracy classes [13]. Assuming a normal distribution of the measurement errors of the components, the total measurement systems accuracy Gtot is calculated as considering the accuracy levels of the transformer Gtrans and of the instrument Ginst:
Gtot = ( Gtrans2 + Ginst2 )1/2.
(4)
The error of a current transformer is actually consisting of a basic error, which can be very low in a good technically designed transformer, and an angular error, which is highly dependent on the applied apparent current burden [12]. The following section will
present a more specific view on the potential gains from metering and monitoring applications on the three organizational levels. Recent publications show, that the industrialized countries are facing a multidimensional pressure from the economical, ecological as well as legislative side to shift their field of actions more towards energy and resource efficient processes and structures within their company, their products and their services to stay competitive in the global market environment. 3.1
Factory Level
The electricity metering and monitoring on factory level is done on the interface between the electricity supplier and the consumer (factory inlet). Electricity is an energy resource that is demanded by the manufacturing industry ever since the light bulb was invented and the establishment of the electricity industry in the late nineteenth century. With the rising demand, quality and the continuity of the supply have become a serious concern [14]. As a result, today’s electricity grid and the supply of electric voltage are standardized within different regulations. For instance in Europe, the EN 50160 written by the European Electrotechnical Standards Body CENELEC is used [15]. The EN 50160 address the electric voltage as a good and the quality has to be ensured in the provision to the customer. Otherwise the customer would be able to claim a better product quality from the supplier. Electricity is a very unique product, being produced, delivered and used at the same instant of time [14]. Due to a high level of dependence on electric voltage supply, the industry and the public have to be sure that they can operate their electrical equipment without incurring additional capital expenditures due to a lack of quality in the electricity supply from the low (LV) and the medium voltage (MV) grids. The voltage quality can be imagined as the usability of electrical energy without interruptions. The subject of voltage quality is becoming more and more important in highly developed countries, because of the increased use of applications, which are very sensitive to disturbances of the voltage amplitude or of the voltage wave shape [16]. In order to check the quality of the electrical voltage supplied to the customer according to the given characteristics of the EN 50160, metering equipment suitable for that task needs to be deployed. Table 1 lists an extract of specifications from EN 50160 that brings in another important aspect of modern digital electricity meters – the resolution in time [4]. A high resolution of metering data can be ensured by a high sampling rate of the analogue-digital circuitry and a short settling time of any analogue components like the current and the voltage transformers and the amplification circuits.
Acceptable limits
Measurement Monitoring period interval
Grid Frequency
49.5 Hz to 50.5 Hz, 47 Hz to 52 Hz
10 s
1 week
Slow Voltage Changes
230 V ±10%
10 min
1 week
Voltage Sags or Dips (≤ 1 min)
10 to 1000 times per year (under 85% of nominal)
10 ms
1 year
Short Interruptions (≤ 3 min)
10 to 100 times per year (under 1% of nominal)
10 ms
1 year
Accidental, long interruptions (> 3 min)
10 to 50 times per year (under 1% of nominal)
10 ms
1 year
Temporary Overvoltages (line to ground)
Mostly < 1.5 kV
10 ms
-
Transient Overvoltages (line to ground)
Mostly < 6 kV
-
-
Voltage Unbalance
Mostly 2%, but occasionally 3%
10 min
1 week
Harmonic Voltages
8% total harmonic distortion (THD)
10 min
1 week
Supply voltage phenomenon
Table 1: Summary of electricity specifications from EN 50160 and related measurement intervals from Shtargot [4].
4
Keynotes
On the factory level, several aspects of the electric energy are essential to be metered in order to gain the certain state of transparency of the factory’s energy consumption from a holistic perspective [17-18]. Table 2 lists a summary of possible cost factors based on the electricity consumption on the factory level and possible potential benefits that can be achieved by gaining a certain level of transparency through metering key values such as listed in Table 1. By using a simple initial monitoring and controlling of consumption the electricity consumer will be able to address the problems on time and not retrospectively after receiving the bill. Electricity metering and monitoring on the factory level enables the consumer to check the quality characteristics of the supplied product and enables him to gain an important amount of transparency for time dependent controlling of energy consumption. Factory level: Cost factors
Total energy consumed, peak power demand, power factor limitation, THD feedback
Potential benefits enabled through electricity metering
Adaption of the electricity supply contract; preventing of peak charges through rescheduling of processes or events
Table 2: Cost factors and potential benefits through electricity metering and monitoring on factory level. 3.2
Department level
The department level structure usually consists of n departments which can show the functional units of the factory. Table 3 shows the major cost factors concerning electricity and related potential benefits through electricity metering and monitoring on department level. Department level: Cost factors
Specific energy consumed, peak power demand, power factor limitation
Potential benefits enabled through electricity metering
Energy intensive process scheduling; ability to deploy and track continuous improvement measures; department based energy saving targeting and benchmarking; simulative improvement of energy costing; effective utilization of secondary energy carriers produced by electricity; quantify energy savings
Table 3:
Cost factors and potential benefits through electricity metering and monitoring on department level.
The contents of Table 3 comply very much with the goals of smart metering and energy accounting, known from private households. The main goal is to try shortening the informational feedback time from the consumption of energy to the moment of billing [19]. For industry the simple monitoring is already a big leap forward to raise the corporate awareness and to motivate each individual to minimize their own share of energy consumption and their related costs. This will also lead to putting some effort into reducing their individual energy consumption without just shifting consumption from one individual consumer to the other. It has been shown that an extended holistic process and system understanding is beneficial in order to increase energy and resource efficiency measures in manufacturing sites. This is due to the fact that many sub-systems are interlinked and have indirect or direct coupled energy consumption, which are not obvious at first
sight and can cause a problem shift if efficiency measures are only applied from a narrow point of view [20]. Several researchers have already stated the basic need for reliable energy consumption data for a successful development towards more energy efficient processes and factories e.g. by use of software tools (energy aware process chain simulation, LCI of manufacturing process chains, evaluation of machine tool configuration) [21-24]. Some even put a special focus on the energy aware upper level planning and control of production, which can be defined as an interface between all three levels of a factory [25-26]. Some researchers have even tried to break down the assessed energy of the whole factory in order to allocate it to one product manufactured at the site, stating that a more efficient monitoring and control of energy used in infrastructure and technical services can help to optimise the plant level activities [27]. When planning and ultimately deploying an electricity metering and monitoring concept in a factory, it quickly becomes obvious that the electrical distribution network structure inside a factory highly varies from a simple organisational structure. This makes setting-up of a consistent metering network with proper upper level monitoring quite challenging. Especially, when the department structures are being monitored with the purpose of energy accounting based on organizational structures, the complexity of the deployed metering strategy increases dramatically. 3.3
Unit process level
Unit process level of electricity metering and monitoring is considered as the lowest hierarchical type of metering point selection. Meters are directly attached to single machines or machine components (e.g. auxiliary pumps, ventilation systems) and peripheral units such as decentralized coolant treatment or decentralized compressed air production systems. On this lowest level the most detail of electrical energy consumption can be obtained [17]. Direct monitoring of single machines may be required for energy optimized production planning around highly energy intensive processes or to conduct a deeper understanding of the energy flow distribution onto sub-components of production machines or to better understand the energetic coupling of in-line production processes [24, 28-30]. Table 4 shows the major cost factors concerning electricity and related potential benefits through electricity metering and monitoring on unit process level. Unit process level: Cost factors
Specific Energy Consumption (SEC), peak power demand, power factor limitation, THD feedback
Potential benefits enabled through electricity metering
Supplementing unit process values to machine LCI databases; energy forecasting in production design, process planning and control; energy labelling of machine tools and products; specific quantification of single efficiency measures; evaluation of technical improvements; condition monitoring as a prophylactic measure in energy and resource sufficiency
Table 4: Cost factors and potential benefits through electricity metering and monitoring on unit process level. Other publications utilized electricity metering and monitoring on unit process level by using energy and time studies to assess the specific environmental and economic impact of particular production processes which can then be used to build Life Cycle Inventory (LCI) databases [31-32].
Keynotes
5
In addition to impact assessment and efficiency improvements as planning tools, electricity metering and monitoring can also be used for condition monitoring and diagnostics of machines and processes. This enables to prevent energy and resource losses ahead of time such as tool changes, planning of maintenance cycles as well as early detection of tool wear. The international standard such as ISO 13374-1, are helping users to implement such established measures [33]. Others have also demonstrated additional benefit by combining electricity metering and monitoring data with machine control data (e.g. from programmable logic controllers) to gain beneficial additional transparency into process specific environmental impact assessment [34-35]. 4
GUIDELINE FOR ELECTRICTY MONITORING IN MANUFACTURING
METERING
AND
Rohdin and Thollander have listed in detail the barriers that especially non-energy intensive manufacturing companies are confronted with, when being faced by a decision to actually go for energy efficiency assessments and measures [36]. The study indicated that responsible staff often fears the interruption of production processes, the lack of insufficient sub-metering in the company structure to quantify and assess implemented efficiency measures and some even face a lack of technical skill to put metering and monitoring into action. Occupational Health and Safety is also critical in use and selection of measurement instruments. The IEC 61010-1 declares the general safety requirements for electrical test and measurement equipment for electrical industrial process control and laboratory equipment [37]. For easier recognition of suitable devices, the standard defines four categories (CAT I, II, III and IV) indicating the specified area of usage for the specific instrument (ranging from measurement in circuits not directly connected to the network up to measurement on overvoltage protection devices). To ensure a true comparability of measurement instruments brought into the market in the European Union, the European Parliament has issued the directive 2004/22/EC, also known as the MID [38]. The MID and related European and international standards like the IEC 60359 ensure a proper indication of performance criteria, basic functional requirements, which are a common way to indicate measurement ranges and limitations of uncertainties of measurement as well as indication of calibration results [39]. Various metering instruments and monitoring solutions are available on the market, which are able to fulfil the requested task of the user. Therefore, the challenge of the user is to define the task. Although, researchers like Schleich have already identified that a lack of information about energy consumption patterns has been found to be a barrier for energy efficiency, which can be overcome by installing metering devices and implementing energy management systems [40]; the biggest difficulty is in fact to define and execute the corresponding metering strategy. Designing a metering strategy incorporates the definition of a metering task, the goal which also describes the characteristics of the resulting measurement in terms of accuracy and resolution. A metering strategy also implies an estimation of the expected value to be metered in order to dimension the metering equipment accordingly. An over or under dimensioned metering system can result in a low accuracy and high variance of the metered value or even an overload situations with fatal errors. Only a few publications are seen in the community of manufacturing engineering that actually address how electricity metering of single devices is actually performed and which measurement instruments are recommended to be used [41].
In the following sections of the paper a guideline for electrical energy metering and monitoring will be presented. Technical and economical challenges will be addressed and specific ranges of technical specifications suitable for the three defined levels of application will be suggested. The decision of selecting suitable measurement instruments always depends on the minimum requirements due to the defined task and the economic aspect as a limiting factor for the upper range of requirements. Technical challenges: Electricity metering instruments are designed to cover a lot of measurement purposes. Some have been developed for highly accurate and real time monitoring like oscilloscopes and others have been designed to suit a variety of tasks such as multi-meters for network quality analysis. Each one of the instruments has different technical specifications that the user has to be aware of before making the purchasing decision. Against each task required, individual set of technical specifications need to be clarified by formulating a measurement strategy while giving certain ranges of specifications of the informational degree. Economical challenges: Selecting the most cost-effective metering solution requires a clear vision of the required outcome. More available options and features are always more expensive and are a quick step towards over-dimensioning. The economical challenges of each level provide some considerations that inevitably come with designing a measurement strategy and implementing electricity metering and monitoring. Factory Level: Selecting the right accuracy class is essential to be able to control electricity billing. Technical challenges: Factory level metering is done on or near the interface between the electricity supplier and the customer. The installed meters from the supplier in industry applications are usually electronic meters that do not simply meter the energy consumption in kilowatt-hours but additionally use certain register intervals in which the specific amount of energy is accumulated. For instance, the register interval in Germany is fixed to 15 minutes, which means the resulting accumulated 15 minute energy is used to charge peak loads in individual electricity supply contracts for industry consumers. The register values from the electronic meters are collected by the supplier by using remote instrument reading. The MID as well as the IEC 62053 enable the customer to select appropriate metering instruments to meter with a higher accuracy and in higher temporal resolution. As a result, breaking down the 15 minute standard register interval from the supplier to 30 second intervals can be used to gain transparency into how 15 minute peak charges occur and be a first step towards evaluation of whether load management could be used to lower the charges. Economical challenges: The investment for metering equipment on this level is considered low since only a few meters (at least one at each medium voltage transformer inlet) are needed. The resulting data volume is considered negligible. Despite this, the selection of the metering instrument is not a simple task. Scientific discussions from the early nineties up until now have stated that higher levels of sophistication in electricity metering will be essential to prepare the suppliers and customers for the inevitable utilization of the smart grid [42-43]. It should be kept in mind that the consumed amount of kilowatt-hours is not the only number that is of interest for the electricity suppliers. There are other parameters such as current, voltage, apparent power and their specific behaviour in time that demands costly improvements and maintenance actions in the distribution networks. Therefore, it might be in the interest of the customer to know in advance about these parameters in order to have transparency into energy billing. The selection of a metering device with the capability to meter active power, apparent power, power factor and the total harmonic
6
Keynotes
distortion as a quality parameter with a temporal resolution of 30 seconds up to 15 minutes with accuracies complying with the standards described earlier (as well as with the local requirements of the state legislations) is highly recommended as a quality parameter. The amount of data collected from one metering device in standard office applications will result in a data volume ranging from 280 megabytes to 8.2 gigabytes per year (depending on the selected resolution). Department level: Metering on department level is done to gain a better transparency of the energy flows inside the organisational and the technical distribution network of the factory. A certain degree of transparency enables organisational and technical energy efficiency measures. The metering strategy and the related challenges in the selection of metering equipment on department level are highly dependent on the consumption behaviour of the single substructures. This paper draws a distinction between highly dynamic behaviour, low dynamic behaviour and near static behaviour as addressed in Table 5. Highly dynamic energy consumption behaviour can be found in assembly or production departments or single lines with several inline processes and machines that perform highly variable processes. As a result, energy demand from the grid is highly variable as well. Low dynamic consumption behaviour can be found in technical building services and are represented by processes like compressed air production, technical air ventilation or facility heating. Near static consumption behaviour can be found in office complexes or in server rooms. These substructures show distinct periodic cycles over days or weeks while being not very prone to sudden changes or peak demands.
Department
Technical challenge: Table 5 presents recommendations for metering specifications for different metering strategies on department level related to the dynamic consumption behaviour of the regarded department. The recommended temporal resolution of the output data of the electronic metering devices are linked with the dynamic behaviour. A high dynamic behaviour needs high resolutions of the metering output data in order to achieve certain transparency and a satisfactory understanding of the consumption behaviour of the department. Behaviour
Resolution
Parameters
Highly dynamic
1 s – 1 min
Wh, VAh, PF
Low dynamic
30 s – 5 min
Wh, VAh, PF
Near static
1 min – 30 min
for high dynamic metering tasks, would result in a yearly data volume of 256 gigabyte (raw data) if three parameters (active power, apparent power and power factor) are logged continuously. Each sub meter added to the metering and monitoring structure adds its part of the data volume share that needs to be handled by the data processing system. The measurement instruments itself will also play a considerable role in the economical challenge. Measurement instruments with high accuracy classes and capabilities to meter THD and PF characteristics are usually too expensive to be distributed on department level metering applications. However, more and more, ultra low cost power quality meters and energy management systems with class A IEC 610004-30 compliance are emerging which will make electricity measurement possible on this level in the near future [44]. In fact, electricity metering and monitoring on department level can actually pay off very quickly as shown in a case study by Stephenson and Paun. The authors demonstrated how a small manufacturer was able to shift and reschedule some of his manufacturing machines to avoid peak charges, and power factor charges by soft starting controls for machine start-ups on Mondays and deferring electrical consumption on activities like energy intensive drying processes or chilled water production by less than two hours without affecting production requirements [45]. Unit process level: On unit process level the single process, machine, or component is being metered and monitored. As mentioned above, it might be needed to do unit process metering in applications considered as department level metering and monitoring, but the actual unit process metering is mostly considered to be research related or only short term metering rather than continuous. In the scientific community unit process metering and monitoring are often found throughout many case studies. Solding et al. have used metering data of unit processes to accumulate the fundamental data basis for energy aware production simulation in various degrees of detail [46]. Considering consumption profiles from products Elias et al. have shown the importance of electricity metering in order to evaluate the user behaviour’s influence on the product’s electric energy consumption [47]. Whereas Dietmair et al. have used electricity metering and monitoring on unit process level to analyse and evaluate machine tool design strategies to foster energy efficiency [48]. Li et al developed an empirical approach to model and predict unit process energy consumption for material removal processes [49].
Wh, PF Behaviour
Economical challenge: The department level metering has probably the highest variety of possible economic impacts that tend to be very case specific. However, general propositions can still be made to address the challenges. Metering on department level is often used to do energy accounting for a fixed sub structure, which can be an organisational structure (department), a production line for a specific product or a storage area. Each one of these clusters of defined sub consumers is drawing electricity from the internal distribution network. Since organisational structures and technical structures are mostly not same due to the building design, the consumer clusters might not be situated in the same branch of the distribution network, which will result in a high number of submeters. These meters can also be used for determining the unit process energy consumption pattern on individual processes. Complex structures of metering systems on this level requires a well structured communication and data computation system to handle the monitoring of the complex metering output data. Single meters with a data output resolution of 1 second, as recommended
Unit process
Table 5: Department level metering specifications.
Highly dynamic Low dynamic
Resolution
Parameters
10 ms – 1 min
Wh, VAh, PF, THD
1 s – 5 min
Wh, VAh, PF, THD
Table 6: Unit process level metering specifications. In all these electricity metering and monitoring applications, the accuracy is not of primary importance since no monetary value is calculated from the metered values. Moreover it is the qualitative importance of the metered values directly related to the high temporal resolution which enables an understanding into the process. Technical challenge: As in department level, the unit process metering specifications are closely related to the dynamics of the unit process’ electrical energy consumption behaviour. Highly dynamic behaviour is likely to be seen in high speed machining processes or robotic applications, whereas low dynamic processes can be seen in thermal or galvanic processes. As Table 6 shows,
Keynotes
7
the recommended temporal resolution can go down to 10 milliseconds for highly dynamic processes. On unit process level, the source of the harmonic distortion and low power factor can also be found and compensated by making use of continuous unit process metering as an input for closed loop controls. Economical challenge: The investment for metering equipment on this level is estimated to be high, because only the highly sophisticated metering systems are able to provide such high temporal resolutions and are able to handle the high data output volume from the single metering equipment. This high data volume enables real time monitoring, but at the same time makes logging applications very data intensive. Especially on unit process level, the harmonic distortion charge of the suppliers can be addressed, as the countermeasures can be applied directly at the source. Total harmonic distortion (THD) is an electrical noise feedback caused by electrical inverters and phase controlled modulators. THD does not only lower the quality of the distribution network, but also severely impacts local machines and sensitive devices. The following section of this paper addresses difficulties in selecting the right measurement equipment for a given task. 5
REVIEW OF AVAILABLE ENERGY METERING DEVICES
In the previous chapter it has been shown that within each electricity measurement task, whether to enable energy efficiency measures or to do energy accounting within organisational structures, it is always a challenge to formulate the right measurement strategy and to select the right measurement instruments for the task of metering. This section gives a brief overview of some typically metering devices found in the market as well as explaining some basic distinguishing features that must be considered in industrial and research applications. Table 7 presents a selection of most commonly used electricity metering instruments from industrial and research applications. The aim is neither to provide a complete list nor to rate the instruments in any way. The selection is just a very limited selection of some important features that distinguish the single instruments. The instrument features range from installation, which describe whether the device can be mounted in a fixed location or if it can be used in mobile applications. Mobile applications are usually found in short time measurement for quick energy assessments on factory or unit process level or in special research applications. Fixed types Measurement instrument:
Installation
of devices can be found in the long time measurement applications available on all levels. Such devices can be built-in directly into control or distribution cabinets. As the evolution of electricity metering devices has allowed user to obtain not only single measurands meters (Ferraris disc meter) but also multi measurands meters, a broad selection of possible measurands becomes available and allows more integrated applications. This enables for example single devices at factory level to provide users the information about active energy consumption for energy controlling as well as reactive energy and total harmonic distortion values for quality monitoring at the same time. The amounts of measurands that can be read from the single devices are defined by the complexity of the electronic meters and often manifest itself in the purchasing price. Table 5 and Table 6 show certain ranges of recommended resolutions of the metering points needed to perform metering applications. The output resolution presented in Table 7 should match these required resolutions in order to be well dimensioned for the metering task. The degree of output resolution is directly proportional the purchasing price of the instruments. Data loggers with resolutions of higher than a metering point per second can exceed 5000 EUR. A high output data resolution is not always a sign of quality. It is rather an indication of the possible types of information that can be gained from the metering data through analysis. As discussed earlier, a high degree of metering data resolution can quickly result into high additional costs for handling of the large amounts of resulting data if centralized monitoring is used. Selecting the right degree of detail is an essential part of the right dimensioning of a metering strategy. In large metering networks, commonly found in department level metering and monitoring, the communications interface plays a very important role as well. Metering and monitoring applications have to be able to use the same communication interface. Real-time monitoring and control applications often use industrial bus interfaces like Profibus or in near real-time applications interfaces like Ethernet. Simple applications such as monitoring for energy accounting do not rely on real-time data and are usually working with bus systems based on RS485 or even impulse signal recognition. The communication interface matching is very important when designing cost-effective metering strategies. Multi interface applications can easily lead to complex and costly software and hardware conflicts. If a holistic department level
Measurands*
Brand, series/type
Output resolution*
Communication interface*
IME, NEMO 96
Fixed type
V, A, W, VA, PF, THD
60 s
RS485, Impulse
Siemens, SIMEAS
Fixed type
V, A, W, VAR, VA, PF, THD
< 1s
Profibus, RS485
Schneider, Electrics, PM
Fixed type
V, A, W, VA, PF, THD
60 s
RS485, Impulse
Simpson, GIMA1000
Fixed type
V, A, W, VAR, VA, PF
1s
RS485, Impulse
Yokogawa,
Fixed type
V, A, W, VAR, VA, PF
<1 s
Ethernet, RS485, Pulse
AccuEnergy, Acuvim
Fixed type
V, A, W, VAR, VA, PF, THD
<1 s
Ethernet, Profibus, RS485, Impulse
Janitza Electronics, UMG 604
Fixed type
V, A, W, VAR, VA, PF, THD
<1 s
Ethernet, Profibus, Impulse
Chauvin Arnoux, C.A.8335
Mobile type
V, A, W, VAR, VA, PF, THD
1s
USB
Fluke, 434
Mobile type
V, A, W, VAR, VA, PF, THD
0.5 s
Voltech, PM3000
Mobile type
V, A, W, VAR, VA, PF, THD
<10 ms
Load Controls, PPC
Mobile type
W
National Instruments, cDAQ*
Mobile type
V, A
15 ms <1 s
USB RS232, IEEE488 Analogue 0-10 volts or 4-20 milliampere USB
*the listed features are retrieved from the datasheets of the devices and are due to change in future instrument revisions
Table 7: Selection of often found electricity measurement instruments with a selection of important distinguishing features.
8
Keynotes
6
REVIEW OF SELECTED ELECTRICITY METERING AND MONITORING SYSTEMS FOR RESEARCH PURPOSES
In this section, a special emphasis will be given to electricity metering and monitoring systems for research related purposes. As stated before, research applications most often utilize mobile systems in order to redeploy them easier. The last five instruments listed in Table 7 are mobile metering devices with different features, individual advantages and weaknesses. The two devices from Fluke and Chauvin Arnoux are highly sophisticated industrial multi-measurands instruments that are capable to log measurement values in high temporal resolution of up to 0.5 and 0.1 seconds respectively. Plug and play features allow users to perform highly mobile measurement strategies as found in quick energy assessments or simple before-and-after measurements to evaluate energy efficiency measures. Other instruments like from Voltech and Load Controls are very likely to be used in detail energy system analyses. Very high metering data resolutions in the range of hundredth of a second allow analysing transients, inrush currents and other short term electrical events. The specific characteristic of these two devices is that they need additional external digital analogue converters with high sampling rates for logging purposes of the analogue data output interface, which is additionally affecting the accuracy of the logged metering values. The last item on the list from National Instruments is a typical laboratory solution for research tasks, with an open programming platform to create individual metering algorithms. The individual algorithms and the specifications of the individual input modules define the amount of calculated measurands and their accuracy. Since such an open platform is not sufficient for energy accounting or related purposes on factory or department level, this mobile device is solely meant for research applications with a high degree of individuality and freedom. 7
INDUSTRY CASE: ONGOING CHALLENGES OF DEPLOYING A METERING AND MONITORING STRATEGY IN AN AUSTRALIAN MANUFACTURER
The presented case is derived from the analysis of a bio-medical products manufacturing company in Australia. The increasing energy bill due to both business growth and rising energy cost has been considered as one of the main issues in this company. This not only impacts the environmental performance of this company but also affects its business strategy. Reducing energy consumption would benefit the company both economically and ecologically. The main aim was to identify the areas of improvement as well as implementing energy accounting for individual functional units, processes and ultimately for the individual products. Thus, the company has decided to implement an energy management system. The first step taken is to establish a metering and monitoring system in order to achieve transparency into its energy consumption. The company initially aims to monitor the energy consumption behaviour from top factory level down to sub-department levels. Multiple energy meters were thus allocated throughout the distribution network. The factory plant is powered by an 11 kV electrical connection from the energy supplier. The voltage is decreased to 400 V three-phases by 3 transformers, which powers
3 main switch boards and then splits into different distribution boards. The energy meters attached at the three main transformers is to be able to perform parallel metering on the factory, which aims to check the accuracy of the electricity bill from the supplier. The metering devices at the distribution boards and circuit breakers were then assigned to individual department or sub-departments. A Supervisory Control and Data Acquisition system (SCADA) here allows the real time management of energy consumption. The system also allows the user to configure analysis and reports to show the previous and current energy consumptions. By performing the analysis of the demand for each department or area, management can determine the Key Performance Indicator (KPI) and apply procedures to minimise demand, which leads to energy savings and lower energy bill. The output from the power meter devices were collected with the SCADA server via RS485 communication, which requires gateways to access them to the factory Ethernet communication network. However, the deployed energy metering and monitoring system failed to provide reliable information of energy consumption of the plant. The aggregation of energy consumption measured at three main transformers did not agree with the electricity bill. The gap between measured value and billed amount far exceeded the errors due to the measurement. Assuming that the energy supplier measured the real amount of energy consumption and billed correctly, the failure to obtain similar total energy consumption reading internally may possibly due to the selection of power meters. As the energy supplier charges not only the total work load but also peak power, low power factor and THD, the current meters did not cope with the range of all the energy billing categories. The following Figure 3 shows the energy consumption profile of the factory over a year per period.
Main Transformers Plant Room
Energy [kWh]
metering strategy is to be deployed, a high amount of time has to be invested for investigating the possible existing communications infrastructure and resulting requirements for the new data communication system.
Administration Bottle Filling
Jan
Feb Mar Apr May Jun
Jul
Aug Sep Oct Nov Dec
Figure 3: Break-down of energy consumption profile over a year. The reading of the power meters at the distribution boards and circuit breakers experienced inconsistency throughout the period. For example, the recorded data for one department gave zero energy consumption for the whole year, where the plant actually runs 24/7. Another example is that some unusual spike was recorded for Administration department as shown in Figure 3, which even exceeded the total energy consumption of the main transformers. The possible reason for this failure is mainly due to the connection problem within the communication system between power meters and SCADA server. Furthermore, the layout of distribution network did not agree with the organizational structure as shown in Figure 4. In order to implement energy accounting for each organizational department, the energy monitoring system requires new metering points in the distribution network. As a result a new energy monitoring system
Keynotes
9
has been implemented, which resulted in changing some of the existing devices and installing new ones in order to extend the monitoring and measuring to individual processes. A considerable cost is thus caused to improve the energy metering and monitoring system.
Organizational Structure
Machine 1
…
…
SubDepartment
SubDepartment
SubDepartment
…
SubDepartment
SubDepartment
SubDepartment
SubDepartment
Periphery System 1
…
After these changes, company’s external reading and energy bill have started to agree with the internal measurements. Currently the company, in collaboration with the authors, has been developing a new energy oriented factory planning system in order to link the material flow to the energy flow within the factory. Currently the company’s energy bill exceeds $1M a year. Despite the initial investment with the introduction of the new system, company is expected to reduce its energy bill by about 30% as well as reducing its carbon foot-print substantially. SUMMARY AND OUTLOOK
Today’s manufacturing companies are facing a more stringent cost pressure than ever before due to rising energy and resource costs and the associated environmental impact. Manufacturing companies have come to realisation the importance of electrical energy metering and monitoring as a foundation to work out energy efficiency improvement potentials. This key note paper presented a short review of the evolution and the latest developments in electricity metering monitoring systems. A special emphasis is given the challenges of the designing of metering strategies in order to properly dimension metering instruments for a given task. In addition, the paper addressed the critical aspects of data communication and the compatibility of interfaces in relation to different application areas. An exemplary list of metering devices was presented to demonstrate how the features of the different measurement instruments can be matched for certain measurement tasks on different application levels. In order to emphasise the importance of a well designed metering strategy and selection of the right metering and monitoring equipments, a case study from a manufacturing company was presented. 9 [1]
Schwendtner, M.F. (1996): Digital measurement system for electricity meters, in Metering and Tariffs for Energy Supply Conference Publication, Institution of Electrical Engineers, No. 426, pp. 190-193.
[6]
Schwendtner, M.F. (1996): Technological developments in electricity metering and associated fields, in Metering and Tariffs for Energy Supply Conference Publication, Institution of Electrical Engineers, No. 426, pp. 240-242.
[7]
International Electrotechnical Commission (2003): 62053:2003 - Electricity metering equipment (a.c.).
[8]
American National Standards Institute (2010): ANSI C12.202010 - Electricity Meters - 0.2 and 0.5 Accuracy Classes.
[9]
Verein Deutscher Elektrotechniker (1976): VDE 0410 Regeln für elektrische Meßgeräte.
[10]
International Electrotechnical Commission (1998): IEC 60051:1998 - Direct acting indicating analogue electrical measuring instruments and their accessories.
Periphery System n
Figure 4: Factory organisational structure and the existing metering system.
8
[5]
Department
SubDepartment
Machine n
Shtargot, J. (2008): Advanced Power-Line Monitoring Requires a High-Performance Simultaneous-Sampling ADC, in MAXIM Application Note 4281.
Distribution Network
Factory
Department
[4]
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Herrmann, C.; Thiede, S.; Zein, A.; Heinemann, T. (2010): Holisitc Approaches for Increasing Energy and Resource Efficiency in Manufacturing, in: Proceedings of the 1st International Conference on Automotive Materials and Manufacturing, Pune.
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Keynotes Huang, H.; Liu, Z.; Zhang, H.-C.; Sutherland, J.W. (2010): A Proposed Method to Study the Life-cycle Energy Consumption of the Automotive Industry, in: CIRP International Conference on Life Cycle Engineering, pp. 127130. Klocke, F.; Lung, D.; Schlosser, R.; Nau, B. (2009): Energy and Resource Efficient Production – a Core Competence for Manufacturers, in: 16th CIRP International Conference on Life Cycle Engineering, pp. 209-214.
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Herrmann, C.; Thiede, S. (2009): Towards Energy and Resource Efficient Process Chains, in: 16th CIRP International Conference on Life Cycle Engineering, pp. 303309.
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Chiotellis, S.; Seliger, G.; Weinert, N. (2009): Energy-aware Production Planning and Control, in: 16th CIRP International Conference on Life Cycle Engineering, pp. 310-315.
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Müller, E.; Löffler, T. (2009): Improving Energy Efficiency in th Manufacturing Plants – Case Studies and Guidelines, in: 16 CIRP International Conference on Life Cycle Engineering, pp. 465-471.
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Rahimifard, S.; Seow, Y.; Childs, T. (2010): Minimising Embodied Product Energy to support energy efficient manufacturing, in: CIRP Annals – Manufacturing Technology, pp. 25-28.
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Herrmann, C.; Thiede, S. Heinemann, T. (2010): A Holistic Framework for Increasing Energy and Resource Efficiency in Manufacturing, in: 8th Global Conference on Sustainable Manufacturing. Klocke, F.; Schlosser, R.; Lung, D. (2010): Energy and Resource Consumption of Cutting Processes, in: 17th CIRP International Conference on Life Cycle Engineering, pp. 111115. Dietmair, A.; Verl, A. (2010): Energy Consumption Assessment and Optimisation in the Design and Use Phase of a Machine Tool, in: 17th CIRP International Conference on Life Cycle Engineering, pp. 116-121. Devoldere, T.; Dewulf, W.; Deprez, W.; Duflou, J.R. (2008): Energy Related Life Cycle Impact and Cost Reduction Opportunities in Machine Design: The Laser Cutting Case, in: 15th CIRP International Conference on Life Cycle Engineering.
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Devoldere, T.; Dewulf, W.; Deprez, W.; Willems, B.;Duflou, J.R. (2007): Improvement Potential for Energy Consumption in Discrete Part Production Machines, in: 14th CIRP International Conference on Life Cycle Engineering, pp. 311316.
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International Standardization Organisation (2003): ISO 13374-1:2003 – Condition monitoring and diagnostics of machines – Data processing, communication and presentation, Part 1: General guidelines.
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Bottene, A.C.; Franca, T.V.; Ometto, A.R.; Torrisi, N.M.; Filho, A.G. (2009): Methodology to integrate the electrical energy consumption real-time monitoring and related environmental impacts assessment, in: 16th CIRP International Conference on Life Cycle Engineering, pp. 373-376.
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Rohdin, P.; Thollander, P. (2006): Barriers to and driving forces for energy efficiency in the non-energy intensive
manufacturing industry in Sweden, in: Energy, vol. 31, issue 12, pp 1836-1844. [37] International Electrotechnical Commission (2009): IEC 610101:2009 - Safety requirements for electrical equipment for measurement, control, and laboratory use - Part 1: General requirements. [38] European Parliament and (2004): 2044/22/EC directive on measuring instruments, in: Official Journal of the European Union, L 135. [39]
International Electrotechnical Commission (2001): IEC 60359:2001 – Electrical and electronic measurement equipment – Expression of performance.
[40] Schleich, J. (2009): Barriers to energy efficiency: A comparison across the German commercial and services sector, in: Ecological Economics, Vol. 68, pp. 2150-2159. [41]
Herrmann, C.; Zein, A.; Winter, M.; Thiede, S. (2010): Procedures and Tools for Metering Energy Consumption of Machine Tools, in: 3rd International Scientific Conference with Expert Participation – MANUFACTURING.
[42] Sioshansi, F.P. (1991): Electronic metering and two-way communications – The electric power industry, in Utilties Policy, Vol. 1, No. 4, pp. 294-307. [43] Mak, S.T. (2010): Sensor Data Output Requirements for Smart Grid Applications, in Power and Energy Society General Meeting, IEEE, ISSN 1944-9925, pp. 1-3. [44] McEachern, A.; Eberhard, A. (2009): A new, ultra low cost th power quality and energy measurement technology, in: 20 International Conference on Electricity Distribution, ISBN: 978-1-84919126-5, pp. 1-4. [45] Stephenson, P.; Paun, M. (2000): Consumer Advantages from Half-Hourly Metering and Load Profiles in the UK Competitive Electricity Market, in: International Conference on Electric Utility Deregulation and Restructuring and Power Technologies, pp. 35-40 [46] Solding, P.; Petku, D.; Mardan, N. (2009): Using simulation for more sustainable production systems, in: International Journal of Sustainable Engineering, Vol. 2, No. 2, pp. 111122. [47] Elias, E.W.; Dekoninck, E.A.; Cully, S.J. (2009): Quantifying the Energy Impacts of Use, in: 16th CIRP International Conference on Life Cycle Engineering, pp. 444-451. [48] Dietmair, A.; Zulaika, J; Sultika, M; Bustillo, A.; Verl, A. (2010): Lifecycle Impact Reduction and Energy Savings through Leight Weight Eco-Design of Machine Tools, in: 17th CIRP International Conference on Life Cycle Engineering, pp. 105-110. [49] Li, W.; Kara, S. (2011): An Empirical Model for Predicting Energy Consumption of Manufacturing Processes: A Case of Turning Process, in: Proceedings of the Institution of Mechanical Engineers, Part B, Journal of Engineering Manufacture.
Implementing Life Cycle Engineering efficiently into Automotive Industry Processes Stephan Krinke Volkswagen AG, Head of Group Research Environmental Affairs Product, Wolfsburg, Germany
Abstract Life cycle assessment (LCA) is a powerful tool which supports life cycle engineering. It can be used as an environmental management instrument within the product development. For successful life cycle engineering the formal incorporation of life cycle thinking into the company policy is a necessary pre-requisite. Additional success factors which have to be met are the transformation of LCA results into measurable targets for engineers. Based on given environmental targets, such as a certain target value for greenhouse gas emissions, LCA can be used to calculate a specific technical target such as the weight of a component, the fuel consumption of a vehicle or the minimum amount of recycled content in a product. The transformation of pure LCA results into measurable target values, which can be understood by engineers, will clearly show the added value which LCA can give in terms of life cycle engineering.Even for very complex products with a huge variety of different materials and a complex value chain life cycle assessment can be performed with a reasonable time demand, with good quality and integrated efficiently into business processes. Keywords Design for Environment; Automotive; LCA; Life Cycle Engineering
Environmental management based on figures and facts
The automotive industry is since decades one of the industrial focus areas for environmental technologies and environmental protection.
Identification of hot spots for product optimization along the entire value chain
But how can the environmental performance of a complex product such as an automobile be measured? The aspects which directly or indirectly influence the environment are manifold:
LCA is an internationally accepted method and a firm basis for a dialogue with stakeholders
LCA is part of company ratings which directly influences the interest rate and financial power of the company
1
INTRODUCTION
Starting with the production of raw materials and going along the value chain the entire production affects the environment. Especially the automotive industry is one of the industry sectors with a very complex value chain, including nearly all kind of materials such as metals, polymers, glass and ceramics. The usage phase of the vehicle also effects the environment due to the combustion of fuel and the herewith linked emissions such as CO2, contributing to climate change. Other tailpipe emissions such as carbon monoxide, nitrogen oxides and hydrocarbons contribute to local air quality (e.g. summer smog). The quantity of these impacts depends on the fuel consumption and the emission standard of the vehicle. And last but not least the driving behavior of the customer, which influences the fuel consumption, has an impact on the environment. During the end-of-life phase materials are recovered or recycled from the end-of-life vehicle and can be used as secondary (raw) materials in other applications. Therefore the environmental assessment of a vehicle has to cover the entire life cycle. One of the most suitable instruments to measure the potential environmental impact of a product is life cycle assessment [1, 2]. Volkswagen started in 1991 with a research study for the life cycle inventory (LCI) of the Golf III which was published as first automotive LCI of a complete vehicle worldwide in 1996 [3]. In the following years LCIs of different vehicles of the Volkswagen Group were published [4, 5, 6, 7]. Today the Volkswagen Group has incorporated life cycle thinking as a main principle of the product development. The advantages of this approach are:
In the following chapters the environmental strategy of the Volkswagen group and the implementation of life cycle engineering into the environmental management will be explained. 2
ENVIRONMENTAL STRATEGY
The key element of the Volkswagen group strategy 2018 is to position the Volkswagen group as a global economic and environmental leader among automobile manufacturers. We intend to set new environmental standards in vehicles, powertrains and lightweight construction. 2.1
Environmental challenges
The main environmental challenges for the automotive industry today and in future are climate protection, health and local air quality and resource protection. Therefore these three items are incorporated into the environmental principles product of the Volkswagen Group. This standard is the basis for the product related environmental strategy. 2.2
Environmental strategy of the Volkswagen Group
The environmental strategy of the Volkswagen group has two main pillars: The environmental standard production and the environmental principles product. The latter addresses the three main environmental challenges (climate protection, health and local air quality and resource protection) and incorporates the life cycle engineering as a main principle in product development.
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_2, © Springer-Verlag Berlin Heidelberg 2011
11
12
Keynotes
Environmental Strategy Volkswagen Group „Individual ecological mobility“ Group Management Group Environmental Strategy Committee
Environmental Standards Product
Environmental Standards Production
Climate
- Emissions - Consumption -…
Infrastructure - Energy - Water - Waste -…
Resources
- Alternative fuels - Recycling -…
Health
Production processes
- Pollutants - Noises -…
Guidelines
Preamble
Environmental Policy
- Press shop - Paint shop - Foundry -…
Regional Environmental Conferences / Environmental-Audits ISO 14001 / EMAS Figure 1: Environmental strategy Volkswagen Group. The Volkswagen group is committed to developing vehicles and components in such a way that they have better environmental properties than their predecessors over the entire life cycle. Life cycle assessments are used to analyze and document the environmental performance of vehicles, technologies and processes.
Reliable, meaningful and measurable targets for product development
Communication strategy
3
For the Volkswagen group the environmental standards production and product are the basis for the environmental strategy. But for a successful environmental management system this is only the starting point.
SUCCESS FACTORS FOR LIFE CYCLE ENGINEERING
Whereas the first ideas of life cycle based analysis were developed 30 years ago, it still remains a challenge to implement life cycle assessment successfully as environmental management tool into business processes. In this chapter we describe the key success factors for such an implementation. The success factors are:
Integration into company policy and processes
Reasonable time demand
3.1
Integration into company policy and processes
The commitment of the top management is crucial for the success of any environmental policy and strategy.
For the Volkswagen brand we developed an environmental management scheme according to Figure 2. Responsible for the implementation of environmental aspects into the product development is the environmental officer product.
Environmental Management at Volkswagen Networking and directing worldwide activities in environmental protection
Head of the Group Research Division
Board of Research and Development
Group Environmental Strategy Committee
Environmental management officer of the Volkswagen brand
Environment
Environmental officer product
Environmental protectors for R&D Volkswagen
Environmental Affairs Strategy
Vehicles
Environmental officer for production
Environmental Affairs Product
Modules
Environmental Affairs Production
Material controlling
LCA & raw material analysis
Environmental officers at the locations Wolfsburg
Emden
Hanover
Zwickau
Pamplona
Figure 2: Environmental management at Volkswagen.
Other locations
Keynotes
13
Environmentally compatible product development Volkswagen brand Product development over time
Development stages and tasks Environmental Analysis
Environmental Control
Material controlling
Research
Advance engineering
Vehicle targets
Characteristics catalog
Performance specifications
Product description book
LCAs and strategic environmental topics Raw material analysis
Model update
Follow-up Environmental commendations
Supervision of vehicle projects Screening of environmental related laws and activities of competitors
Product communication
Environmental targets, performance specifications and standards Environmental product Environment and descriptions innovation roadmaps Global material legislations
Material controlling Material data acquisition (MISS/IMDS) Legends Tasks Instruments
Figure 3: Environmentally compatible product development for the Volkswagen brand. Figure 3 shows an overview of different tasks and instruments along the product development which usually takes around 48 months until the start of production.
sure that the entire life cycle is taken into account during the development of our products.” This target is signed by the board member for the technical development for the Volkswagen brand.
Different teams act along the product development. The team environmental analysis has the task to perform LCA and strategic raw material analysis for different components and technologies. The focus of this work is the early phase of the product development including research. Strategic raw material analysis focuses on long term risk assessment [8] such as
Supply and demand today and in future
Production costs
Geostrategic risks
Market power of the raw material suppliers
Life cycle inventory data and the herewith linked bill of materials is a powerful basis for raw material risk assessment which can not be performed without that information. The task of the LCA is to compare different technologies in the early phase of product development. The challenge of this task is that the information required for the LCA is often not fully available in the early phase of the product development. E.g. the body-in-white concept is known from the material perspective, but the exact weights of the different components are unknown. Here LCA should be applied in such a way that the unknown variables are calculated as measurable target values. In our example where we do not know the exact weight of all components the sum of the component weight can be calculated as target, which has not to be exceeded in order to achieve an environmental goal such as a maximum greenhouse gas emission potential. Another important aspect for the implementation of life cycle engineering is the formal inclusion of the suppliers. Volkswagen included in its supplier conditions that on request the supplier has to deliver life cycle inventory data for his parts and components [9]. Additionally the IMDS system (www.mdsystem.de) is a reliable data basis for information about the material composition and the bill of materials of a certain part. The most important aspect is that the Volkswagen brand included in its environmental objectives “that we will develop each model in such a way that, in its entirety, it presents better environmental properties than its predecessor. As we do so, we will always make
Figure 4: Environmental objectives of the Volkswagen brand.
14
3.2
Keynotes
Reasonable time demand
Data quality
LCA is a very comprehensive tool which offers a detailed insight in the environmental profile of a product. Performing an LCA of a complex product can be divided into two steps: The first is the data collection of the product. Here the part list of the product is required. For every single component the material composition must be quantified and the herewith linked production process chain must be defined. This step can be improved in terms of time efficiency via the implementation of IMDS data for the components and the linkage to other internal ISsystems of the company. Anyhow this still remains a time consuming step and can take even today up to 30 days. The final result of step 1 is a so called transfer file, which describes for a vehicle every single component by its bill of material and the respective production chains (see Figure 5). The second step is the transfer of this information into a LCA model. Within Volkswagen we developed an in-house solution which automatically builds up the LCA model based on the information of the transfer file and the so called correlation list [10]. The correlation list correlates each material and each production process to a respective LCI data set. The advantage of this approach is not only a huge reduction in time demand but also huge increase in consistency and quality of the LCA results. Time and Resource Demand for LCA of Cars ~ 1-30 days
transfer file
interface 1
~ 0-2 days
mapping file
other internal data sources
complete transfer file
For commodities generic data sets can be used as a first approach. For the modeling of special materials, which are new on the market or based on new technologies, a data acquisition in cooperation with the respective supplier is strongly recommended. At that point a formal requirement for data acquisition of life cycle inventory data in the supplier contracts is very helpful. For highly innovative industries, such as the automotive industry, it is of utmost importance to use high-quality inventory data in order to assess the environmental profile of new materials and new technologies. This is a challenge because the knowledge of new technologies and new materials is of course limited. But this does not mean that LCA can not be applied. Also in this case the area of unknown knowledge can be transformed by LCA into a measurable target value. If, for instance, the energy consumption of a new production process is still unknown, an LCA can be used to calculate the acceptable maximum energy consumption in order to achieve a certain environmental target. Anyhow there is a parallel between the automotive industry and life cycle assessment: High-tech powertrains need high-quality fuels – high-tech LCA need high-quality inventory data. Therefore today and in the future the provision of high-quality inventory data will remain an important and necessary task, especially for science and consultancy.
Situation today ~ 1 day
Foreground data, such as the bill of materials, information about production processes or energy consumption in the use phase, describes the characteristics of the product. From our experience this foreground data is of major relevance and has a strong influence on the overall LCA results.
interface 2
3.4 internal data sources
IMDS
specify vehicle
data base
technical drawings
product model
IMDS
close data gaps check weights check materials manual processing
map material/ process information to available data sets in database
predefined process
electronic data
Figure 5: Time and resource demand for LCA of vehicles. Therefore life cycle assessments, even for complex products with a huge variety of different parts and materials, can be performed with a reasonable time demand.
3.3
Reliable and development
measurable
targets
for
the
product
Life cycle assessment is an environmental management tool that delivers scientific sound results, not less but also not more. A good environmental management system is therefore characterized by the capability to transform LCA results e.g. in terms of a greenhouse gas profile of different technologies into a technical target which can be understood, measured and monitored by an engineer. These targets can be fuel consumption, electric power consumption or the weight of a certain component. The real challenge of life cycle engineering is to bring together two different worlds: The world of the LCA expert, who models the product in terms of environmental impacts versus the world of the engineer who develops the product and takes technical measure that influence the environment. In order to derive reliable, meaningful and measurable targets the following aspects should be considered. Stable and reliable results As LCA is a model outcome every LCA result should be tested by sensitivity analysis in order to prove whether the result, or much more important, a derived technical recommendation, is stable while varying certain model parameters or assumptions.
Communication strategy
It is most important that products with special environmental features or technological improvements are also communicated as such. A company which invests in environmental performance and technology leadership should also use these characteristics in the communication and marketing. Therefore the challenge in that area is to translate LCA results into a communication that will be understood by the respective target groups. It is notable that economical and environmental optimization oftentimes are in line, especially with regard to the fuel consumption. In the case of the automobile industry we have to differentiate between private customers and fleet customers as two different kinds of target groups. Whereas private customers mostly focus on fuel consumption and emission levels in terms of costs, they do not reflect what we call total cost of ownership (TCO). While TCO means that higher purchase prices can be amortized over life time at the customer, the private customer has in many cases a limited willingness to pay for additional environmental features, even if they offer the opportunity to get the economical break-even within a time scale of 2-3 year. In contrast to that, fleet customers base their decisions often on TCO calculations. For an OEM this means that fleet customers have a willingness to pay for environmental technologies such as BlueMotion, if the economical break even will be reached within a few years. But also other environmental aspects might be important for fleet customers, e.g. a company can improve their sustainability ranking by improving the company fleet cars. For the Volkswagen brand we developed the so-called environmental commendations (www.environmentalcommendation.com). Environmental commendations for new vehicle models and technologies highlight ecological progress compared with predecessor models and previous technologies. We use environmental commendations to inform our customers, our shareholders and other stakeholders how we are making our
Keynotes products and production processes more environmentally compatible and what we have achieved in this respect. The underlying LCA not only covers the time when the vehicle is on the road but its entire life cycle from production through to use and disposal. This reflects the fact that we assume responsibility for the entire supply chain, including the production of raw materials and parts for our vehicles. We engage in dialogue with our suppliers to identify environmental measures that can be taken. The information of environmental commendations is based on an LCA, which has been verified and certified by the technical inspection organization TÜV NORD. The TÜV certificate confirms that the LCA is based on reliable data and that the methods used to compile it comply with the requirements of the ISO standards 14040 and 14044.
4
15 vehicles steeltechnologies.
and
aluminum
lightweight
are
established
INTELLIGENT LIGHTWEIGHT DESIGN OVER LIFE CYCLE
In this chapter we show how an LCA can be applied as environmental management tool within the product development. Lightweight design measures are chosen for this example. Modern lightweight design is one of the key technologies for an efficient future mobility, because it contributes to reduce the fuel consumption and the herewith linked CO2-emissions. Based on the Volkswagen’s environmental standard for products this means that any lightweight design measure should also have an environmental benefit over the entire life cycle. Approximately one third of the fuel consumption in the NEDC (New European Driving Cycle) depends on the mass of the vehicle. Lightweight materials such as aluminum or magnesium are energy intensive in the production as shown in Figure 6.
Figure 6: Greenhouse gas emission for the production of different materials. Figure 6 also shows the range of greenhouse gas emissions for the production of different metals. The reasons for this range are manifold: The amount of recycled material, the CO2-intensity of the used energy mix, different kinds of protecting agents and their impact on climate change are only a few aspects which influence the greenhouse gas balance of such a material. It is also important to note that a comparison of materials can not be done based on Figure 6. A comparison can be done by including the entire life cycle and by assessing products which fulfill the same functions. E.g. a body-in-white with the same crash performance will have different weights depending on the material-mix applied and the design of parts. For lightweight concepts the herewith linked CO2-emissions in the production phase are often, but not always, higher than those ones for conventional construction. These additional CO2-emissions which occur in the production phase should be compensated as fast as possible during the use phase due to lower fuel consumption of the lightweight design. Only when we achieve the ecological breakeven, we speak of an “intelligent“ lightweight design as shown in Figure 7. Volkswagen has different strategies to reduce the weight of a vehicle. Besides different material concepts the integration of different functionalities contributes to reduce the number of parts. For the body-in-white construction, which accounts for up to 35% of the overall vehicle weight in the series production of passenger
Figure 7: Lightweight design and environmental break-even. The question whether a specific lightweight concept has a better greenhouse gas balance than a competing concept, can be answered only by assessing the entire vehicle. An important question is whether a lightweight measure will lead to a smaller engine or not. Of course any lightweight measure reduces the fuel consumption. But this reduction is low (0,15 l/100km*100kg for gasoline engines) compared with a fuel reduction of 0,35 l/100km * 100kg which can be achieved by additional measure of the engine. What is clear from the above examples is, that it is not possible to make general claims along the lines that „material A is always better or always worse than material B“. Whether a lightweight design measure reduces life cycle greenhouse gas emissions or not will primarily depend on the following factors: the extent of the weight savings by material and material-adopted design, whether powertrain modifications can be implemented and the quality of secondary (recycled) materials derived from the end-of-life vehicle. In practice, one and the same lightweight design measure might allow powertrain modifications to be made on vehicle A but might not, by itself, be sufficient to warrant such modifications on vehicle B. Saying this, it is often the case in practice that components or assemblies are optimized and assessed in isolation from each other, so that the significance of each measure in the causality chain that leads ultimately to powertrain modifications cannot always be clearly determined. Therefore lightweight measures should always be assessed from the perspective of the entire vehicle and not from the part perspective. As can be seen in Figure 8, the potential benefits of the various lightweight materials extend even further. The table shows the main actors – and potential actions – at the different life cycle stages. For example the material manufacturing stage offers the opportunity to significantly reduce specific CO2-emissions per kg of material produced by reducing specific energy consumption and/or through the use of renewable, low-carbon energy sources. The use of secondary (recycled) materials can likewise help to reduce environmental impacts – for example some cast alloys already use up to 90 % recycled content. On the process side, too, measures such as use of climate-friendly shielding gases as a replacement for SF6, or the reduction of offcuts and scrap, all have a part to play. In the vehicle use phase meanwhile, fuel-efficient vehicle design measures by the OEM must be complemented and maximized through the optimal use of this potential by customers e.g. by ecodriving trainings. Finally, at the recycling stage, reprocessing of lightweight materials into high-quality secondary materials will
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influence the range of potential applications of such materials and thus the associated positive environmental impacts.
Figure 8: Environmentally friendly lightweight design, aspects and measures. Seen from this overall perspective therefore, it is clear that environmentally friendly lightweight product development offers a wide range of different measures. In addition to the engineers in the automotive industry, the other stakeholders in the value chain (materials manufacturers, suppliers, recycling companies) likewise have their part to play in further improving their products and enhancing the life cycle environmental impact of vehicle concepts of the future.
5
CONCLUSIONS
As demonstrated above, life cycle assessment is a powerful tool which can be used as an environmental management instrument within the product development. For successful life cycle engineering the formal incorporation of life cycle thinking into the company policy is a necessary pre-requisite. Additional success factors which have to be met are the transformation of LCA results into measurable targets for engineers. Based on given environmental targets, such as a certain target value for greenhouse gas emissions, LCA can be used to calculate a specific technical target such as the weight of a component, the fuel consumption of a vehicle or the minimum amount of recycled content in a product. The transformation of pure LCA results into measurable target values, which can be understood by engineers, will clearly show the added value which LCA can give in terms of life cycle engineering. Even for very complex products with a huge variety of different materials and a complex value chain life cycle assessment can be performed with a reasonable time demand, with good quality and integrated efficiently into business processes.
6
REFERENCES
[1]
International Organization for Standardization (2006): ISO 14040: Environmental Management – Life Cycle Assessment – Principles and Framework, International Organization for Standardization, Geneva.
[2]
International Organization for Standardization (2006): ISO 14044: Environmental Management – Life Cycle Assessment – requirements and guidelines, International Organization for Standardization, Geneva.
[3]
Schweimer, G.W.; Schuckert, M. (1996): Sachbilanz eines Golf. In: VDI Bericht 1307 Ganzheitliche Betrachtungen im Automobilbau. Wolfsburg.
[4]
Schweimer, G.W. (1998): Life cycle inventory of 3-L Lupo. Volkswagen AG, Wolfsburg.
[5]
Schweimer, G.W.; Bambl, T.; Wolfram, H. (1999): Sachbilanz Seat Ibiza. Volkswagen AG, Wolfsburg.
[6]
Schweimer, G.W.; Levin, M. (2000): Life cycle inventory of Golf A4. Volkswagen AG, Wolfsburg.
[7]
Schweimer, G.W.; Roßberg, A. (2001): Sachbilanz Seat Leon. Volkswagen AG, Wolfsburg.
[8]
Rosenau-Tornow, D.; Buchholz, P.; Riemann, A.; Wagner, M. (2009): Assessing the long-term supply risks for mineral raw materials - a combined evaluation of past and future trends. In: Resources Policy 34, 161–175.
[9]
VW standard 91100 www.vwgroupsupply.com
[10] Koffler, C.; Krinke, S.; Schebek, L.; Buchgeister, J. (2007): Volkswagen slimLCI – a procedure for streamlined inventory modelling within Life Cycle Assessment (LCA) of vehicles. In: International Journal of Vehicle Design (Special Issue on Sustainable Mobility, Vehicle Design and Development), Olney: Inderscience Publishers.
Leveraging Manufacturing for a Sustainable Future David Dornfeld Laboratory for Manufacturing and Sustainability (LMAS), University of California, Berkeley, California
Abstract Manufacturing offers many opportunities for reducing environmental impact, utilizing resources more efficiently and, overall, greening the technology of production. These opportunities are most often related to process, machine or system improvements that impact only the operation of the process, machine or system. But, there is more potential in manufacturing enhancements to have a larger impact on the life cycle impact of the product the manufactured item is used in. This is referred to as “leveraging” and several examples of this are given, along with definitions of the fundamental terms. The potential for leveraging in manufacturing to have an impact on sustainable manufacturing and some future requirements are described. Keywords: Process; Machine; System Improvement; Life Cycle Impact; Make versus Use
1
INTRODUCTION
Manufacturing offers many opportunities for reducing environmental impact, utilizing resources more efficiently and, overall, greening the technology of production. These opportunities are most often related to process, machine or system improvements that impact only the operation of the process, machine or system. But, there is more potential in manufacturing enhancements to have a larger impact on the life cycle impact of the product the manufactured item is used in. This is referred to as “leveraging” and identifies manufacturing-based efficiencies in the product that are due to improved manufacturing capability but which, in the long run, have their biggest effects on the lifetime consumption of energy or other resources or environmental impacts. First, what is meant by the term “leveraging”? We understand a lever to be a device to increase mechanical advantage, as a bar used with a fulcrum to pry a heavy load allowing a larger load to be moved than with simple force alone. Leveraging is used as a transitive verb, usually in financial discussions such as [1]: “The use of credit or borrowed funds to improve one's speculative capacity and increase the rate of return from an investment.” The general idea is to employ resources in such a way as to insure a larger return on the effort (or in financial terms, money) than might otherwise be realized. How does this relate to manufacturing? And, in specific green manufacturing? This will depend on the component being manufactured by a machine or process and its eventual use in a product. This paper will first provide some definitions so that the use of terms like green manufacturing, sustainable manufacturing, etc. will be understood. Then, the concept of leveraging manufacturing will be explained and several examples of will be given of situations that provide leveraging along with some that do not. Finally, future directions in sustainable manufacturing driven by leveraging are suggested.
2 2.1
BACKGROUND AND TERMINOLOGY Green and sustainable
The familiar Brundtland Commission definition of sustainable development - “Sustainable development is development that meets the need of the present without compromising the ability of future generations to meet their own needs” [2] does not really speak to manufacturing but makes the key point that we need to at least “do no harm.” The US Department of Commerce defines sustainable manufacturing as “the creation of manufacturing products that use materials and processes that minimize negative environmental impacts, conserve energy and natural resources, are safe for employees, communities, and consumers and are economically sound” [3]. We define green manufacturing here as a first step towards sustainability. These first steps were proposed as green manufacturing “technology wedges” in [4] after a concept proposed by Pacala and Socolow [5] to address the big gap between the present trajectory and impact of CO2 on the atmosphere (business as usual – BAU) and a sustainable level – and how to close this gap in 50 years. They argued that, rather than trying to find one solution to correct this increasing mismatch between what is required and what is being done, we should concentrate on “technology wedges” – small advances and improvements that, when added up, have the effect of a large change. These wedges make a lot of sense in the context of manufacturing and sustainability. We can visualize sustainability as a relationship between consumption or impact as part of normal business practice compared to a “sustainable level.” For example, in California we store rainfall during the winter months as snow in the Sierra Nevada mountains. The amount of snow determines the amount of water we have to use in the next season for residential, commercial and agricultural use. If we use water at a rate that will exhaust the supply before the next rainfall – that is not a sustainable situation. We are using too much and should find a way to conserve or reduce usage. We could make the same argument for impact, for example, green house gas generation. The atmosphere has a
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_3, © Springer-Verlag Berlin Heidelberg 2011
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certain capacity to accommodate green house gases. Exceeding that risks a build up that will endanger future generations according to the predictions of atmospheric scientists. We can illustrate this as seen in Figure 1 below, from [6]. The figure illustrates the normal trend of consumption or impact over time. A small reduction of either one results in a reduced rate of impact but does not provide enough change to achieve a sustainable situation. The application of technology wedges to, collectively, bridge the gap between present rate of consumption or impact and a sustainable level is illustrated with the green triangles. With sufficient wedges, the gap can be closed.
Pollution (air, water, land)
Ecological footprint - “fair share” - footprint
Exergy (available energy) or other thermodynamic measures
To be able to understand the effect of the improvement or change being measured, these can be represented in terms of a "return on investment" - for example, greenhouse gas return on investment (GROI). Other forms of return measure include:
Energy payback time
Water (or materials, consumables) payback time
Carbon footprint
Efficiency improvement (for example, wrt exergy)
Then, a measure of the change in the T term of the impact équation can be determined. For green manufacturing these need to be linked to traditional design and manufacturing parameters. And they need to be assessed over all three scopes of ISO 14064 (1- direct emissions from on-site or company owned assets, 2- indirect emissions created on behalf of the company from energy generation or supply, 3- all others resulting from business operation including business travel, shipping of goods, resource extraction and product disposal). 2.3
Figure 1: Illustration of sustainable consumption and technology wedges, [6]. It is our role of manufacturing researchers to develop the wedge technologies. Individual wedges might be considered as “green” manufacturing steps. If there are sufficient greening steps we can achieve sustainable manufacturing. 2.2
Tracking progress
To insure that real progress is being made it is necessary to define metrics to measure change. Recall the “master equation” for impact attributed to John Holdren and Paul Ehrich [7]. This equation, sometimes referred to as IPAT, defines human impact (I) on the environment as the product of population (P), affluence (A, measured as GDP/capita), and technology (T, measured as impact per unit of GDP). Manufacturing has its impact on the T part of the equation – the impact per unit of technology. This is the impact per GDP of manufactured products. By reducing that impact, we start to “bend the curve” of the consumption or impact curve seen in Figure 1. The challenge is to come up with technology wedges that will reduce the T part of the équation at a rate sufficiently fast to offset population growth while at the same time make a dent in the impact that is already too high. Metrics are used by engineers for analyzing information and data to enable better decision making, including trade-offs among several alternatives, and for design. For green manufacturing these metrics could include:
Global warming gas emission (e.g. CO2, methane CH4, N2O, chlorofluorocarbons, CFC’s)
per capita
per GDP
per area/nation
Recyclability (or percent recycled)
Reuse of materials
Energy consumption
Leveraging
We can define two different classes of leveraging of manufacturing. The difference is due to the magnitude of the impact. That is, whether it impacts only the performance of the manufacturing process, machine or system or whether it impacts the performance of the product resulting from the application of the process, machine or system. An additional distinction must be made for products used in manufacturing – for example, machine tools. In the case of an improvement, say in energy consumption of a process, we would require that, at minimum, the “cost” of the improvement (in embedded energy, carbon footprint, etc.) would be more than offset but the reduction in energy consumption or carbon footprint in operation of the “improved” process. This is the basic definition of energy payback or green house gas return on investment. The magnitude of the impact reduction can be measured simply by knowing the number of manufactured products coming from the process over the life time of the process. This is a minimum amount of leveraging for any contemplated process improvement to insure that we are making progress. A second, more impressive, leveraging is due to process (or machine or system) improvements that have an inordinately high ability to reduce the impact of the product of the manufacturing operation (or machine or system) over the lifetime of the product use. The original process improvement may not have been made as part of a greening analysis of the process but is due to the introduction of new technology, machine capability or materials. It is this second type of leveraging that is likely to have the greatest potential for reducing the T term in the impact equation – making a larger than normal reduction in the product impact/GDP during the product’s life time. Why this distinction is important is discussed in the next section. 3 3.1
WHY LEVERAGING IS IMPORTANT Does manufacturing matter?
The base of this discussion is an assessment of whether or not manufacturing is a significant component of energy and resource consumption and the impact from this consumption, and, then, whether or not changes in manufacturing can really help overall. A review of all the data, pie charts and discussions about how much
Keynotes of the world’s energy use is attributed to manufacturing is not presented here.
19 consumption, and that is worth another 20%. Now we are down to .8% (20% of 4%).
Allwood et al [8] point out that industrial carbon emissions are predominately due to production of goods in steel, cement, plastic, paper, and aluminum. With the demand for these materials expected to double at least by 2050, during which the global carbon emissions are desired to be reduced by at least 50%, simply improving process efficiency will fall far short. Allwood suggests several strategies for industrial emissions reduction in addition to process efficiency, increased recycling, and carbon sequestration and storage, as: (1) reducing demand for materials; (2) nondestructive recycling; and (3) radical process innovations which allow shorter, less energy intensive process routes to yield the completed component. These all address the T term in the IPAT equation. But, for “general product manufacturing” is there enough that can be accomplished by manufacturing improvement? Specially if we apply this to what many of use consider our core process capabilities – like machining? 3.2
Figure 2: Primary energy consumption for VW Golf manufacture [9].
Automobile manufacturing example
If we think about where the major energy consumption associated with a product occurs we can divide the space up into two regions – manufacturing and use. We see that "things that don't move or need power to operate" like bridges, furniture, etc. are dominantly manufacturing phase consumers of resources and, by extension, impact. Things that do "move and need power to operate" like automobiles, airplanes, buildings, etc. are use phase heavy. Interesting to note are the items that are close to the break-even (imagine a 45 degree line on a plot of use vs manufacturing impact graph for products). So, what about automobiles? At a presentation at the ICMC Conference in Chemnitz in September 2010 by a representative of the automaker VW, the speaker mentioned that, by their analysis, about 20% of the impact of a typical VW Golf A4 car came from manufacturing while 80% was due to the use phase. One can find data on the GolfA3 (marketed from 1991-1999, also called the Polo) from some time ago and the comparison was similar. Figure 2, from [9], shows the energy consumption during the manufacturing phase of the GolfA3 in Gj/auto. Materials and part suppliers account for much of the embedded energy in the manufacturing phase. Machined components, such as the gear box and engine are a small percentage of the total (accounting for about 10% overall or about 25% with materials and parts from suppliers included). If one looks at the impact of the auto, including car production, fuel production and use phase, Figure 3 from [9], it is clear that the fuel production and consumption in the use phase dominates all categories of emissions to air and water with the exception of dust generated by material production and casting of some components and painting of the vehicle and biological oxygen demand impacts on water. Looking a bit closer at the data above, does this make sense in terms of reducing the impact/GDP? If we focus only on the manufacturing phase we may not be encouraged - specially if the predominant impact is in the use phase. Consider the VW Golf example of 20% manufacturing phase impact versus 80% use phase impact. If we think about the areas many in our community work in a lot, machining, and we assume about 20% of the manufacturing is machining or machining related, that gives us a potential for improvement of 20% of 20% or only 4% (and then only if we get rid of all machining!). Let's assume that some of the better technology for improving machining efficiency is employed, say some specialty tooling material that reduces machining power
Figure 3: Use vs manufacturing phase impacts for VW Golf [9]. One could argue that this is hardly worth the effort it would seem. Of course, if you are paying the electricity bill for the factory and this 8% technology wedge is added to a lot of others in machine operation it can add up to real savings. But, still not impressive compared to use phase impacts. That is, impact over the full life cycle of the auto. More recent data from Volkswagen for the Golf A4 indicates that some improvements have been made (for example reduction of primary energy used in production, use and end of life due primarily to improved fuel consumption (a 20% improvement from 8.1 liter of fuel/100 km to 6.5 l/100 km for the gasoline engine) [10]. 3.3
Accounting for more of manufacturing’s impact
The question is, then, what is the true leverage effect of manufacturing on the life cycle impact of a consumer product – one that has its dominant impact in the use phase rather than the manufacturing phase? If we are speaking of a manufacturing machinery builder, like a machine tool company, then we can argue that the machine tool has its largest impact in the use phase so that improvements in energy efficiency of the machine will been seen over its life [11] since it is the “product.” The thesis here is simple. If improvements in manufacturing yield a substantial reduction in the life cycle impact of a product, should not manufacturing get some of the “credit” for this improvement. And, by similar reasoning, can we claim this as a part of “green manufacturing” contribution towards sustainability since it is a major element in reducing the technology impact of the product – the T term in the IPAT equation? The next section gives some examples of this leveraging effect.
20 4 4.1
Keynotes EXAMPLES OF LEVERAGING Basic influences
Manufacturing has a number of fundamental effects on a product. In no particular order, manufacturing can:
guarantee a certain level of precision or accuracy of the produced component
allow the use of advanced materials (enhance strength to weight, improved surfaces, wear resistance, thermal stability, etc.)
allow reductions in process steps or sequences
combine processes for enhanced effects as in hybrid processes or mill-turn machine tools
achieve complex shapes or features to improve performance
and so on
reduction of almost 2.5 kg/km CO2. And this is over the life of the aircraft – many millions of kilometers. The next example relates to a similar impact on product use for an automobile. It is also due to enhancement in manufacturing capability due to precision manufacturing. The improvement for the Boeing aircraft example was based on tightened tolerances allowing increased structural performance by better control on dimensions - resulting in lower weight components. Looking at improvements in engine performance for automobiles we can see similar improvements. The performance (power density in kW/l) of diesel passenger car engines is shown in the graph in Figure 4 from [15]. With better tolerances, better
There are more but you can get the idea. How these manufacturing induced effects influence the life-cycle performance of the product must be clearly understood to explain the full potential of leveraging. This influence usually comes from the extension of one of the above listed effects onto the energy consumption or “environmental performance” of the product the manufactured components are used in. A simple example might be a spindle motor for a machine tool. If the production technique for the motor, using advanced magnetic materials, allows the construction of a motor that extracts more useful work from the energy supplied to it, then the manufacturing effect is leveraged over the life of the spindle. Alternatively, if the improvement in energy consumption is due to controller related performance enhancement, as the 40% reduction in energy consumption illustrated by Mori Seiki due to overall system component improvements and optimum acceleration of spindle and servo motor during machining [12], this is not due to manufacturing leveraging but, certainly, improves the life cycle impact of the machine tool – the product in this case. 4.2
Leveraging examples
Two examples are presented here that illustrate the concept of leveraging manufacturing with life cycle impacts on the product that the manufactured component(s) is (are) used in. And the life cycle impact is substantial and most of the benefits are due to manufacturing. Both of these examples relate to improved machining tolerances and their impact on product performance. On an aircraft airframe (a large one like a B747 or the A380) savings in weight correspond directly to savings in fuel. And many other aspects of an aircraft scale with weight. This is, to some extent, true also for an automobile. That is the second example If the machining process for large airframe components is under control and precision manufacturing principles applied, a reduction in machining tolerances from approximately +/- 150 microns to +/100 microns on the features of the airframe can account for a weight reduction of 4500 kg/aircraft and substantial fuel savings (8%) [13]. This allows an increase of 10% in passenger load (the engines don't need to carry as much plane), or increase in cargo payload and a substantial reduction in manufacturing cost of the aircraft (less material and improved assembly) and the accompanying reduction in scrap. And less fuel consumption means reduced CO2 impact from aircraft operation. The accumulated savings over the life of the aircraft are incredible. The fuel consumption per km is estimated as 11.88 L/km (or about 5 gallon per mile). Thus, the CO2 emission rate can be estimated at 30.64 kg/km [14]. A reduction in fuel consumption of 8% results in a
Figure 4: Change in power density over time for Diesel engines [15]. surface finishes, better control of orifice size and shape on the fuel injector nozzles (with diameters on the order of 60 microns), tighter control on cooling channels and fluid flow in the engine due to enhanced casting techniques, and so forth, the engine (still working on the same old Diesel principles) performs dramatically better. The "dog leg" in the chart above corresponds to the introduction of high performance, precision, manufacturing to the power train manufacturing in the automobile. In the years since 2000, the power density has been improved by double (in 2007) and anticipated to quadruple by 2020, Similar improvements can be seen in the transmission as well. And, with advanced sheet metai forming technologies (another manufacturing technology enhancement) and replacement of metal components with non-metallics (manufacturing and materials enhancement) more improvements would be anticipated. This is not to suggest that precision technologies had not been employed before. But, the engine and associated fuel injectors, etc. were designed to take advantage of increasing manufacturing performance and, as a result, yielded tremendous product performance as well. And that is how to reduce the technology impact per GDP. Manufacturing dramatically increased the efficiency of fuel utilization in the internal combustion engine. The small percentage of manufacturing phase improvement has a giant leverage effect on use phase impact. Since the principal element in use phase impact of the automobile, the reduction in consumption (due to increased power density of the engine), hits both the fuel production impact as well as the fuel consumption impact there is additional impact. In the Golf A3 data for emissions, Figure 3, 90% of the CO2 impact was due to the use phase (81% from driving and 9 % from fuel production). A doubling of the fuel economy, by manufacturing induced engine efficiency
Keynotes
21
improvements, by precision machining and processing will essentially halve that (same distance driven) - or account for, in the case of the Golf A3, a reduction of some 16 tons of CO2. And if, in the process of manufacturing enhancement, we save most of our 4% impact from machining, that's .4 ton of CO2. So, for our .4 ton we get a return of 16 tons (a factor of 40!).
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4.3
REFERENCES
[1]
http://www.thefreedictionary.com/leveraging, 1/16/2011.
[2]
Brundtland Commission, i.e. World Commission Environment and Development (WCED) (1983).
[3]
http://www.trade.gov/competitiveness/sustainablemanufa cturing/how_doc_defines_SM.asp; accessed June 21, 2010.
[4]
Dornfeld, D. and Wright, P., (2007): “Technology Wedges for Implementing Green Manufacturing”, NAMRI Trans., 35, pp. 193-200.
[5]
Pacala, S. and Socolow, R., (2004): “Stabilization Wedges: Solving the Climate Problem for the Next 50 Years with Current Technologies,” Science 13 August 2004: Vol. 305. no. 5686, pp. 968 – 972.
A second issue is whether or not manufacturing can rightfully claim credit for any or all of these improvements under leveraging. Traditional design textbooks outline the design process in stages with clever designs being turned into real products through manufacturing. So, for sure, the role of manufacturing as a design enabler is undisputed. In that case, we can claim the benefits of leveraging manufacturing as well.
[6]
Dornfeld, D., (2010): “Sustainable Manufacturing – Greening Processes, Systems and Products,” Proc. ICMC Sustainable Production for Resource Efficiency and Ecomobility, Fraunhofer Institute for Machine Tools and Forming Technology, Chemnitz University of Technology, Chemnitz, September, 2010.
[7]
Ehrlich, P. R. and Holdren, J. P. (1971): Impact of population growth, Science, 171, 1212-1217.
5
[8]
Allwood, J., Cullen, J., and Milford, R. (2010): “Options for Achieving a 50% Cut in Industrial Carbon Emissions by 2050,” Environ. Sci. Technol., 44 (6), pp 1888–1894.
[9]
Volkswagen AG, and Harald Florin, PE Europe/IKP-University of Stuttgart, Germany from a presentation of A. Horvath, UCBerkeley, 1995.
The fine print
There are constraints of course. The technology enhancement (the “wedge”) needed to improve precision of the machine tool to enable some of the product performance increases may not be strictly “green” (meaning there is a cost in terms of embedded energy, energy/unit product, or other measure). Trends in machine and process design are showing that one can enhance the performance of the manufacturing process and also realize reduced impacts. Recall the Mori Seiki example cited earlier. But, this needs to be carefully accounted for.
SUMMARY
This paper has proposed a view of the potential for manufacturing to play a more significant role in reducing the environmental impact of technology. The manufacturing capabilities that yield aircraft or automobile engines with dramatically reduced fuel consumption, or structural components for aircraft that allow higher payloads per unit of aircraft structure, or advanced processes that yield lower power electronics for reduced energy consumption, and so on, are examples of leveraging manufacturing. We cannot claim all benefits in product performance stem from manufacturing. An enhanced wash cycle on a home laundry that reduces water and energy consumption has likely very little to do with manufacturing technology improvements. But we should stand up for those manufacturing driven improvements that, on their own, are responsible for substantial environmental impact reductions. The challenge raised by researchers, like Allwood, pointing out the fundamental changes needed in production technologies (specially for materials processing and efficient material use) must be complimented by the tremendous potential for leveraged manufacturing. It points out, at least, the significant role that manufacturing (broadly defined and over all processes and systems) can play in creating a sustainable future. Finally, we need some numbers! The arguments presented here are based substantially on empirical observations. A more careful analysis of the tradeoffs of competing technologies with respect to potential leveraging effects must be done for several case studies. That is on the agenda for our future work. 6
ACKNOWLEDGMENTS
The author acknowledges the researchers and affiliates of the Laboratory for Manufacturing and Automation (LMAS) and partners in the Sustainable Manufacturing Partnership (SMP) for their helpful discussions and support of this research. For more information see lmas.berkeley.edu and smp.berkeley.edu.
accessed on
[10] Schweimer, G., and Levin, M., “Life Cycle Inventory for the Golf A4” posted on line at www.volkswagenag.com/.../Golf_A4__Life_Cycle_Invento ry.../golfa4_ english.pdf; accessed 1/18/11. [11] Diaz, N., Choi, S., Helu, M., Chen, Y., Jayanathan, S., Yasui, Y., Kong, D., Pavanaskar, S., and Dornfeld, D. (2010): “Machine Tool Design and Operation Strategies for Green Manufacturing,” Proc. 4th CIRP International Conference on High Performance Cutting, Gifu, Japan. [12]
Mori, M. (2010): “Power consumption reduction of machine Tools,” présentation at 2010 CIRP General Assembly CWGEREE, August, Pisa.
[13] Thompson, D. (1995): presentation at Symposium on Research Issues in Precision Manufacturing, Univ. of California, Berkeley, September, 1995. [14]
http://micpohling.wordpress.com/2007/05/08/math-howmuch-co2-released-by-aeroplane/; accessed 1/18/11.
[15]
Berger, K. (2005): Daimler, Presentation at CIRP January 2005 Meeting, WG on Burr Formation, Paris.
Sustainability Engineering by Product-Service Systems Günther Seliger Department of Machine Tools and Factory Management, Chair of Assembly and Factory Management, Technische Universität Berlin, Berlin, Germany
Abstract Product-Service Systems offer high potentials for increasing the use productivity of resources. Functionality can be provided in specification how, on place where and in time when needed for the user by modern communication and logistics. Business models change from selling products to selling functionality due to fixed costs of under utilized products being higher than additional costs for communication on demand and supply, and for transport of artifacts to places of performance. Remanufacturing, disassembly and reassembly enables for same products and components performing as required in consecutive different usage phases thus avoiding disposal of valuable resources. Methods for product design with respective components, performance supervision, maintenance of components, configuration for different usage specifications, user’s and provider’s qualification, ubiquitous access on information about demand and supply to create efficient functionality markets, all represent product related services for the functionality business thus achieving more functionality with fewer resources. Keywords: Sustainability Engineering; Value Creation; Product-Service Systems
1
INTRODUCTION
Sustainability Engineering is on exploiting the dynamics of fair competition to achieve the required sustainability of our global living conditions by processes of knowledge creation and innovation. Product-Service Systems (PSS) exploit design potentials for new business models by shaping interrelations between tangible products and intangible services. They enable for innovative function, availability and result oriented business models. These models can help in reducing resource consumption and waste generation by fewer resources providing more functionality. Supplier’s motivation is changed from selling ever higher numbers of products to the customer with ever lower costs of manufacturing to selling ever more functionality to the customer with ever lower input of resources. The old manufacturing paradigm of producing big lot sizes for low costs per piece is challenged by providing more functionality with fewer resources. Tangible resources are partly substituted and partly supplemented by intangible services. Sustainability in its three dimensions of economic, environmental and social concern helps in directing the processes of technological innovation. Economic challenge is in market competitivity of resource saving product service design. Environmental challenge is in resource efficiency and effectiveness, e.g. no longer disposing non renewable resources by consequent adaptation for consecutive different usage phases, also in substituting non renewable by renewable resources within the constraints of sufficient renewable resource generation. Social challenge is in establishing the awareness of users and developers for mankind’s threat if not adapting ways of living to fair wealth distribution within environmental constraints. How can PSS contribute to meet the challenge of sustainability by competitive offers of minimal necessary tangible products integrated with required service functionality? The threat of ignoring the conflict potentials of unequal global wealth distribution, the saving potentials in resource exploitation for useful applications is illustrated. Chances of PSS approaches for exploiting these potentials are described.
2
CHALLENGES OF SUSTAINABILITY
Predominantly all over the world industries are still working in source-sink economic patterns relying on resource availability without limitations. Non renewable resources are often exploited for only one usage phase with consecutive disposal. However, there are huge potentials of recycling of products, components and materials. Also, substituting non renewable by renewable resources within their limits of regeneration can help avoiding upcoming shortages in resource supply for a growing population with expectations on higher standards of living. Earth’s resources are limited. Out of the present global population of about 6.7 billion people -9.5 billion are prognosed for 2050-, less than one billion belong to the industrialized world in Europe, North America, Japan, South Korea, Australia, and few islands of wealth. China with its population of 1.3 billion and India with 1.1 as well as other developing and emerging countries are striving to catch up. If the lifestyles of the upcoming nations are shaped by the existing, predominating technologies, then the resource consumption will exceed every accountable economic, environmental and social bound. The question arises, which production technologies can serve as basis for dealing with this growth, in an economically, environmentally and socially responsible manner. Independent of the exact limits of access to virgin non-renewable resources, alone due to the increasing material demands of more people with increasing standards of living, non-renewable resources worn out after usage phases of products must not be disposed any more but regained in product or material cycles. Due to limited availability and increasing demand an increasing increase in prices for non renewable resources as aluminum, copper and iron can be observed. Between 2006 and 2009 the costs for import of raw materials to Germany have grown from 31 to 86 billion Euros, including 16 billion Euros for metals. The price for copper e.g. increased from 3300 Dollars per ton in the beginning of 2009 to 6000 Dollars per ton in August 2010, an increase to 10,000 Dollars per ton is expected for 2011. Expanding application areas
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_4, © Springer-Verlag Berlin Heidelberg 2011
22
Keynotes for copper are expected in electrical vehicles and infrastructure for electrical energy supply. Rare earth materials are required for luminous diodes, batteries and photovoltaics, all areas with expected market growth in applications but limitations in raw material purchase. Recycled material becomes the only source of raw material available in the long term [1]. Already 56 percent of copper applied for manufacturing in Germany has been recycled. Keeping ownership on materials to avoid consequences of fluctuating prices for a stable functionality business might be a competitive approach for innovative PSS. Energy availability is a fundamental premise for any material wealth creation. Nearly one third of primary resources for global energy conversion presently stems from crude oil. Estimates on remaining reserves lie between 1000 and 3000 billion barrels. The date of peak-oil, i.e. the date from when on oil production remains constant or even decreases, is expected for 2020 at latest. Obviously saving primary resources for energy consumption in global dimensions is a huge challenge in responsible management of resources. The amount of primary resources for energy consumption worldwide has increased from 2000 to 2008 by 25% to over 500 EJ 18 15 (EJ = 10 Joule) or over 140 PWh (Peta Watt hour = 10 Wh). Germany with 80 million people accounting for one percent of the world's population of 6.7 billion consumes energy of 14 EJ or 4 PWh accounting for about three percent of world consumption. Crude oil with 30%, natural gas with almost 20% and coal with 25% have taken about three quarters of primary resource contribution for energy consumption and caused CO2 emissions of nearly 30 Gt 9 (10 t). Biomass with over 10%, nuclear with 6% and renewable energy are contributing another quarter of world primary resources for energy consumption [2] [3]. Figure 1 shows a gross quantitative assessment of the global energy flow without losses in transformation from primary resources to useful applications. Passenger and goods transport each account for one eighth of consumption in final application, industrial production for one third, and two fifth are for infrastructure with substantial proportions for building, heating and food production. Between primary resource input and final consumption, there are many different processes and production facilities. The key elements of this transformation are the direct fuel use in machinery and equipment with approximately three fifths and electrical generation with two fifth shares. The movement of vehicles and machinery is generated with about one third, and process heat with about half of primary energy use. The consecutive steps and different paths in conversion of primary resources to energy driven operations offer big potentials for
23 increasing efficiency and effectiveness. Often these potentials are already baled out within the balance frame of respective entrepreneurial institutional activities. By integrated product and service innovation PSS independently from traditional balance frames can create new business models of providing more functionality in application by less investment in instrumental facilities. E.g. providing customers with energy saving equipment thus saving electricity consumption can be cheaper than investing in additional power stations. Diesel engines directly driven by fuel might save conversion losses of electrical power generation required for electrical drives. Overcoming grown thinking, living and working habits requires new means of convincing people by teaching and learning, by softly integrating innovative artefacts in existing living environments. Mutual dependencies of tangible products and intangible services must be thoroughly considered for successful implementation [4]. The ecological footprint represents a measure for the consumption of renewable resources. About one quarter of earth’s surface, accounting for 11.3 billion hectares, can be considered as biologically productive area contributing to the regeneration of resources. The average amount of biocapacity per capita on earth is calculated by dividing the productive area by the number of people on earth, what results in 1.8 global hectares biocapacity per capita. Since approximately 1985, resource consumption on a global level is higher than the ecological capacity. For 2050, an impossible biocapacity of two globes would be required if the trend of increasing renewable resource consumption would be not stopped. Half of global population is still living without access on electricity and telecommunication, earning less than two Dollars per day. One fifth of global population disposes of four fifths of global wealth. Environmental damages caused by unbalanced resource exploitation and considerable lacks in human education lead to social conflicts and terror. Presently there are 25 local wars on the earth. Culture and education for billions of people have become the dominant social challenge for human survival on earth. PSS approach seems to be promising means to cope with this challenge. Also the global population growth must be stopped by better standards of living without increasing resource consumption. Social stability can only be achieved if mankind is able to create jobs and living conditions of human dignity worldwide and not only in the technologically developed regions of East Asia, North America and Western Europe. Rapid technological development, diversity, creativity, entrepreneurship and dynamism in competition and cooperation
Figure 1: Quantitative assessment of global energy conversion in primary resource shares without losses in transformation.
24
Keynotes
shape the living environments for the global citizens from the regions of prosperity, but also bring an irresponsible waste of resources. The responsibility of the wealthy minority enables for technological development based on criteria of sustainability in order to harmonize the quality of life and resource use. This challenge offers with great opportunities in sustainable services, products and processes for value creation. Sustainable development with social innovation is also important to open the hungry market of more than 5 billion people who still have not enough purchasing power.
sustainability. Referring to requirements of communication, to training and educaton, to maintenance and repair, to knowledge creation and information access for continuous improvement, to setting up competitive offers for resource saving functionality markets, PSS can considerably contribute to sustainable value creation.
Due to the unbalanced development along developed, emerging and developing countries, it is difficult to reach a new agreement after Kyoto Protocol to reduce the carbon emission. Carbon emission relates with the economic development and environmental protection. Governments are conflicting about individual national wealth development, access on resources and responsibility for climate change. But all of them are concerned about how to reduce the CO2 emissions to protect the environment. The rationally required sustainable global development can stimulate the forces of human initiative and creativity. Sustainable global development could eliminate the unfair distribution of opportunities by technological and social innovation [5]. Mankind’s wealthy minority has the responsibility to identify innovative paths for sustainability engineering. Great opportunities are opened in sustainable services, products and processes for value creation. Social innovation can help to open the new markets of more than 5 billion people. Technologies from developed and emerging countries can empower help for the poor people to help themselves. Product-Service Systems (PSS) offer chances to fulfill the challenge of sustainable development. The potentials of PSS in resource saving and qualification may succeed to develop economies and civilization in the global discourse. 3
VALUE CREATION
Value creation can be modelled considering both, actual entrepreneurial activities in globalized markets and requirements of sustainable development. Market dynamics can be powerful drivers in achieving sustainable develoment for global mankind along economical, evironmental and social criteria by technological innovation. Figure 2 describes an architecture for sustainable global value creation. Value creation factors are integrated in modules to be designed along economic, environmental and social criteria of sustainability. Cooperation and competition among entrepreneurs drive for horizontally and vertically integrating modules thus constituting value creating networks. There are different levels of hierarchy in value creation. From manufacturing tool and operation via value creating cells and systems, whole factories to national and international entrepreneurial conglomerates or knowledge generating educational communities. Each of them from top-down pespective is considered as a network consisting of modules and from a bottom-up perspective as a module together with other modules contributing to a network. Modules or networks can be set up under different infrastructural conditions in industrialized, emerging or developing countries respectively regions within countries. Value creation in an engineering perspective addresses the development of artefacts for useful applications thus shaping areas of human living. From the module view in a bottom-up perspective entrepreneurs try to get their specific value contribution integrated in higher level networks, whereas, from the network view in a top-down perspective entrepreneurs try to purchase original elements enriching their product system from lower level modules. The dynamics of demand and supply in this mutual dependency offers chances for directing global value creation to pathes of
Figure 2 : Architecture of Sustainable Value Creation.
4 4.1
POTENTIALS OF PRODUCT-SERVICE SYSTEMS Business Model
Product-Service Systems (PSS) are an integrated product and service offering that delivers values in industrial applications. PSS must be understood as a new product consisting of integrated product and service shares, which comprises the integrated and mutually determined planning, development, provision and use. PSS includes the dynamic adoption of changing customer demands and provider abilities. The partial substitution of product and service shares over the lifecycle is possible. There are different models for PSS, the individual business models can be differentiated by further criteria [6]. They differ in the responsibility of production result, personnel, service initiative and finally the ownership of the products, e.g. machine tools. This means the provider takes over responsibilities in the production process by delivering service personal, initiating services and ensuring the production quality, see Figure 3.
Figure 3: Specification of innovative use models [6].
Keynotes
25
A competitive provider offers product functionality, availability or a result in quality, time and location as required by the user. Multiple usage phases make a PSS competitive by maximizing the utilization of resources and can be achieved by disassembly, component adaptation, and reassembly. The opportunity to optimize the use phase is the key for success in consequence of more freedom of business and engineering development. The PSS business model takes as its starting point the goal of achieving an integrated functional solution to meet client demands, moves away from phase based servicing and discrete resource optimization, to system resource optimization which is utility based. The resulting PSS can produce synergies in profit, competitiveness and environmental benefits. The potential eco-efficiency of a PSS relies on system optimization in resource use and emissions because of the stakeholders’ convergence of interests [7]. Further PSS enables an equipartition for the use of technology. It allows the adoption of the right technology and a competitive production also for low-budget small medium enterprises (SMEs) and emerging countries at the time, place and in the specification which is needed. PSS can lead to reduced resource use and waste generation. The increase in sales of services can balance reductions in sold products. Employment lost in manufacturing can be balanced by jobs created in services. As a business concept, PSS have the potential to improve access to technology worldwide. With PSS, consumers worldwide would have less need to buy, maintain, dispose of, and eventually replace a product. In fact, the quality of the service, and thus consumer satisfaction, may improve with PSS because the service provider has the incentive to use and maintain equipment properly, increasing both efficiency and effectiveness. The incentive also exists for producers to design closed-loop systems for equipment based on designs for higher durability and recyclability [7]. To be competitive on the market the PSS providers have a strong interest in using a minimum of production resources, which means maximum utilization and usage of products and components. Therefore a provider will use production equipment out of a PSS in a new or other PSS. Resources will have several life cycles. This is possible because of the shift of the ownership in PSS and leads to a cycle economy. Human beings in the phases of planning, development and delivery of PSS play a major role; especially the human-machine interaction in the phase of delivery is in the focus. Different qualification of technicians and company-spanning cooperation in the field of industry demand a specific support concept during the delivery of services. Figure 4 addresses the economical, environmental and social motivation and technical progress, being sustainability contributions of PSS.
Figure 4: Contribution on Sustainability by PSS.
In developed countries, which already have a large environmental footprint arising from a high rate of per capita resource consumption, PSS can facilitate the transition toward a more service-oriented, sustainable society. The service industry gets the chance to find new and increasing market opportunities. Other benefits include reduced dependance on externally produced resources and reduced load on waste disposal facilities. For emerging countries, PSS may represent a more promising and environmentally sound path to economic development since it enables them to bypass the development stage characterized by individual ownership of goods. A cycle economy is not only environmentally reasonable but also a chance for new businesses. Selling functionality instead of selling products is advantageous once additional costs for information processing and logistics are less than costs for underutilized capacity (Figure 5).
Figure 5: Selling functionality instead of selling products. From the point of qualification the use of technology is limited. Especially in developing countries the qualification level of worker and technicians is not comparable to the one in the developed world. However, the provider has to take care that his products are usable, as promised in the contract. The challenge is to qualify the local worker and technician from the customer or contractors and supervise their work from distance. 4.2
Knowledge Management
The close co-operation with suppliers and service producers as well as with final consumers can cope with these gaps. While relationships with suppliers are addressed by ISO 14000-series standards and environmentally conscious purchasing practices, downstream practices are addressed by extended producer responsibilities and Product Stewardship concepts. Integrated Chain Management (ICM) specifically addresses the issue of involving several actors in order to improve the environmental performance of products. However, problems associated with ICM are also going to be relevant for PSS due to a similar value chain basis that is extended in PSS into a value creation network. These problems include trade-offs between co-operation and internal environmental management; the problem of choosing wrong actors who do not have the power or knowledge to change or influence events; information sharing and transparency, and barriers from material flows crossing borders and a variety of regulatory frameworks in different countries. By sharing information the provider will be enabled to identify the customer needs and enhance his business relation with new features satisfying the customer needs. By this a long relationship can be established, which has to be seen as a partnership. Due to the shift in the ownership of products PSS business models give a platform to collect information from every PSS which earlier
26
Keynotes
was only accessible for the customer or by feedbacks of service personnel. This offers providers opportunities to learn more about the PSS behavior and usage by the customer and give the chance to use it for PSS design or redesign. Knowledge generation as well as knowledge and information management is necessary. A definition of knowledge is shown in Figure 6. The adaptation of products and services to the continuously changing technical requirements, application areas and user demands is crucial for the competitiveness of the PSS business model. Different configurations have profound impact on the performance of the system in terms of reliability and productivity, product quality, capacity scalability, and costs. Adapting the functions to the customer needs, the design to reduce idle and operation costs and an ongoing adjusting of service times requires knowledge about the system behavior and prognostics about the system conditions. This knowledge needs to be generated and managed.
system that supports the supply chain and communication processes in the whole PSS life cycle with knowledge. The VLCU acquires data from the usage phase, communicate and process it with the help of data mining algorithms to knowledge about the system behavior. This includes rules about the process flow of the PSS and its operations, as well as prognoses and classification of the PSS health condition or its components [6]. The processing to knowledge can be done by using data mining algorithm. These algorithms can be used to formulate rules about the PSS system behavior, e.g. a decision tree model. Other algorithms like Support Vector Machines or Statistical Pattern Recognition can be used for condition prognostics based on the actual system condition and wear [9]. In this concept the generated knowledge is available for all actors in the PSS supply chain. Beside the VLCU concept there are product accompanying information systems, supervising the product or components condition and allow condition based maintaining [6][5]. This is crucial for a PSS as it also facilitates to reduce unseen breakdowns and so a breach of contract, e.g. the promised availability. 4.3
Figure 6 : Definition of knowledge by North [8]. The development of PSS has the challenge to handle the increased complexity of the integrated PSS products and services. To ensure an efficient development of a PSS and to provide decision support, knowledge out of the usage phase or experience from previous developments and PSS provision phases should be used. To support a competitive design of PSS, knowledge needs to be generated about correlations between product and service shares in order to fulfill the required needs and demands. This knowledge is crucial for a successful combination of product and service shares, whose influences are very complex and not always known. The PSS life cycle data, especially from the operation phase, is the source to discover this knowledge.
Performance Supervision and Maintenance
In the PSS of aircraft turbines there is a continuous condition supervision to ensure the availability, but also to schedule maintenance planning at the place and the time it is needed. Data from the engine in use are transmitted to the provider. Input factors are planned future running times and available parts on the following destinations of the engine, respective aircraft. While in earlier times the engine was owned by the airline, spare parts and technicians needed to be stationed on every airport from every airline. Nowadays costs could be reduced by having just one team and depot for every airport. An aircraft engine is a very complex machine, the knowledge about it is best on the side of the engine company, e.g. Rolls Ryoce, GE or Pratt&Whitney. Those decide best when wear parts need to be replaced and because of the PSS business model they run today, they maximize use by condition based maintenance (CBM) methods or multiple usage phases, e.g. by running the engine on different aircrafts. Figure 7 shows the typical parameters measured for engine health management on a Rolls Royce Trent Engine. Aircraft Communications Addressing and Reporting System (ACARS) digital data-link systems are used as the primary method of communication. This transmits the Aircraft Condition Monitoring System (ACMS) reports via a VHF radio or satellite link whilst the aircraft is in-flight. A worldwide ground network transfers this data to the intended destination [10].
Due to the modern IT based engineering, business communication and documentation, most data along the life cycle of a PSS are already available in digital form and worldwide accessible via internet, e.g. machine datasheets, service protocols or personal database. Companies offering machine services already document plenty of detailed data about the service processes. Currently this is being used as an information base to compare new situations with historical data. The challenge lies in accessing, acquiring, communicating and finally processing it to knowledge. The continuous supervision of products and processes as well as processing of data by intelligent algorithms is an enabler to do prognostics of the system condition. Following the Business Intelligence idea an integrated approach for all participations or companies in a supply chain network of a PSS is required. An integrated concept has been developed – the so called Virtual Life Cycle Unit (VLCU). It is an information technology
Figure 7: Engine health management sensors RR Trent Engine [11].
Keynotes 4.4
27
Education
The PSS connect the providers and customers in multifaceted work places at which people determine during their operations the sustainable impact of products and processes. Worker or technicians need knowledge to have the ability in an application do something within the required time and quality. The integrated form of services in the PSS business model also includes the humans. To get access to technology the worker or technicians need to be qualified to do this. Constant availability, different qualifications of users and company-spanning cooperation in the field of industry demand a specific support concept during the delivery of services. Therefore easy and online education systems are required. In order to cope the challenge on qualification the use of modern IT systems offer great solution potentials. One way is the support of less and insufficiently qualified users. As a part of a support concept, e.g. in a maintenance scenario, the creation of a “shared vision” can be a possibility for the diagnosis and solving of a problem (Figure 8).
generate stimuli in people of different age, cultural background and qualification to activate interest and therewith initiate a learning cycle. The state-of-the-art of information and communication technologies (ICT) offers further innovative teaching concepts. By supervising the workers action and give direct instructions to him. So called Teachtools are enablers to incorporate physical and with technical information means to coach and train skills in technological, social, environmental and managerial fields. The learning motivation is addressed and creative competencies are fostered through practical application and project oriented problem solving at the workplace. This environment is ideal for forms of action-orientedand discovery-learning, which guide the learner through a selfdefined learning process. The learner on his learning path meets different kinds of teaching objects, like pictures, drawings, movies or texts. The teachtool suggests learning objects and the learner chooses which teaching objects suites his interest best. His journey will provide him with additional information for a more sustainable design and usage of the products and processes which he is working with. The learning processes can take place in all kinds of value creating activities done in a manual workplace, for example assembly, agriculture or teaching workplaces. The regional infrastructure, education level and values of various participants are to be hereby observed, in order to indentify deficiencies in the qualification of persons. A variety of best fitting teaching material or objects can be identified, which raises the chance that the learning processes will be performed at a high level of efficiency. Web-based intelligent agents are used to identify the qualification gap of the people and to support the learner with the appropriate teaching contents and highly effective methods. In [12] such a teachtool has been presented. An image based recognition of manual work processes in combination with an expert system has been developed and verified, see Figure 9.
Figure 8: Shared Vision System [5]. During the production of a work piece the machine informs the user about a breakdown. After a detailed inspection the user realizes, that her/his qualification for the removal of the failure is insufficient. To minimize downtimes of the machine, expensive journeys of service staff and last but not least mistakes in the problem diagnosis, the user can establish an audio-visual contact to a remote service expert of the machine manufacturer by a sharedvision system. Now this service expert can analyze the problem in time. Furthermore he can guide the user to a solution of the problem. As the gaze direction shows the focus of attention the visualization of the gaze direction of both involved persons can enhance the communication. Perceiving the mutual gaze movement therefore means an additional intuitive source of information. To ensure the quality of e.g. a service, the technician might have to be supervised in a way, that his actions are documented and in case of a mistake he/she immediately be informed. This can be done under the umbrella of technician security, but of course also to ensure the quality, economically and resource consumption of the PSS. A wrong installed ball-bearing might wear faster, which means it results in higher lifetime costs because of higher resource consumption. This is not sustainable in economic and environmental way. Novel tools for education and innovation dissemination guide people during their work to reflect their operations, to gain knowledge and to generate and adopt innovative ideas. They
Figure 9: Image-based recognition of a manual work process [12]. With the help of teachtools and shared vision systems, PSS providers are enabled to qualify every person to do services on their machines or use them. This means, that the technology is available to be used for everyone, especially the in the beginning mentioned 5 billion people, having not access to such technology nowadays. 5
SUMMARY
Sustainability engineering meets the challenges of economic competitivity, of responsibly managing environmental resources and of developing social competencies for worldwide wealth generation. Technological innovation can be directed along these guidelines of sustainability. The dynamics of value creation in the framework of global markets enables for network integration of partners under different infrastructural conditions. Product-Service Systems are means to cope with the challenge of more functionality with fewer resources. Selling functionality by service integration substitutes the traditional approach of selling tangible products in innovative business models. Knowledge management and education by
28
Keynotes
modern information and communication technology helps in implementing the new paradigms of resource efficiency and effectiveness. 6
ACKNOWLEDGEMENT
We express our sincere thanks to the Deutsche Forschungsgemeinschaft (DFG) for funding this research within the Collaborative Research Project SFB/TR 29 on Industrial ProductService Systems – Dynamic Interdependencies of Products and Services in the Production Area. 7
REFERENCES
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Angerer, G. et al. (2009): Rohstoffe für Zukunftstechnologien: Einfluss des branchenspezifischen Roh-stoffbedarfs in rohstoffintensiven Zukunftstechnologien auf die zukünftige Rohstoffnachfrage, ISI-Schriftenreihe Innovationspotenziale, Fraunhofer IRB Verlag, Stuttgart.
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Cullen, Jonathan M., Allwood, Julien M. (2010): The efficient use of energy: Tracing the global flow of energy from fuel to service, in: Energy Policy Vol. 38 (2010), 75-81, Elsevier.
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VDI. (2010): Stellungnahme Energiepolitik, Berlin.
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Herrmann, C. (2010): Ganzheitliches Life Cycle Management: Nachhaltigkeit und Lebenszyklusorientie-rung in Unternehmen, Springer, Berlin.
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Seliger, G. (2007): Sustainability in Manufacturing, Springer Verlag Berlin Heidelberg.
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Meier, H., Roy, R., Seliger, G. (2010): Industrial ProductService Systems – PSS, in: CIRP Annals - Manufacturing Technology, Vol. 59, Issue 2, pp. 607-627.
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United Nations Environment Program. (2002): ProductService Systems and Sustainability: Opportunities for sustainable solutions.
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North, K. (2005): Wissensorientierte Unternehmensführung: Wertschöpfung durch Wissen, 4. Auflage, Gabler Verlag, Wiesbaden.
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Lee, J., Ni, J., Djurdjanovic, D., Qiu, H., Liao, H. (2006): Intelligent Prognostics Tools and E-Maintenance, Computers in Industry 57, pp. 476-489.
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und
[11] http://www.rolls-royce.com/Images/ehm_1_tcm9225476.jpg. [12] Postawa, A., Kleinsorge, M., Krüger, J., Seliger, G. (2010): Bildgestütztes Erkennen manueller Arbeitsprozesse Automatisierte Erkennung manueller Verrichtungsschritte im Remanufacturing von Lichtmaschinen, in: wt-online, vol. 92010.
Solvis Zero-Emission Factory – The ‘Solvis way’ – Structure and Subject 1
Helmut Jäger 1
SOLVIS GmbH & Co KG, Braunschweig, Germany
Abstract The philosophy of Solvis is simple: The optimal use of solar energy with mature technology and as little use of the environment as possible – for the good of everyone. The central pillar of the product range of this employee-run company is the SolvisMax solar boiler. It combines a solar stratified storage tank with a heating boiler in one unit, thus providing hot water and supporting heaters. To produce ecological products also in an environmentally-friendly building, Solvis moved 2002 into its zero-emissions plant. The concept and structure of this new plant are described within this keynote-paper. Keywords: Sustainable Building Concepts, Zero-Emission Factory
1
INTRODUCTION
To produce ecological products also in an environmentally-friendly building, Solvis moved 2002 into a zero-emissions plant (Figure 1). The Energy consumption was reduced by 80 percent over conventional in-dustrial plants. 100 percent of the supply of heat and power comes from regenerative energy, which is hence CO2-neutral. With a floor area of nearly 15,000 m², the building is the largest zeroemissions plant in Europe. The building has already received several awards, among them the European Solar Prize 2002 and the worldwide Energy Globe 2003 and also as the “the most energyefficient commercial property” of Germany with the Energy Performance Certificate for buildings according to the German Energy Conservation Regulations 2007.
Figure 1: The Solvis zero-emission factory
2
THE 'SOLVIS WAY' - STRUCTURE AND SUBJECT
The administrative areas included in the production site at Grotrian Steinweg Strasse personify the situation of the building's main access and an enclosed 'atrium' as a high-quality external space within the building volume, that is otherwise optimised with regard to the A / V relationship. The lengthways opening-up of the building is carried out as a central internal opening-up; internal movements of persons meet the product lines here. The internal central way, as a themed lengthways axis of the building, became the central situation of the building, the 'backbone' of the overall system, acquired its conceptual, contextual significance as 'Solvis way'.
The constructional realisation of the Solvis way and connected administrative areas was carried out as a reinforced concrete construction, for reasons of thermal storage capacity, in the direction of the nocturnal cooling possibilities of the building masses, and for reasons of optional fire safety classification. On the ground floor, all the necessary ancillary rooms for the functions and sanitary installations are enclosed in the 'Solvis way' and the complete building technical installation can be seen on the first floor.
Figure 2: Solar collectors above the atrium The Solvis way gives the building concept its specific identity, as a space for coordinated organised building technical installations, and is the identity-forming central area for Solvis, the manufacturer of building technical installations. The production and storage areas were conceived as a widespanned lightweight wood construction over 27.50 m connected on both sides to the primary reinforced concrete construction of the Solvis way, taking the primary energy contents of the building materials. The connections of the internal lengthways axes of the wooden construction to the massive construction enabled all horizontal loads from the wide-spanned roof construction to be absorbed. The two external lengthways axes of the wide-spanned wood construction were implemented as column constructions with nonbearing wooden element facades, alternatively as high-temperature insulated wooden frame constructions.
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_5, © Springer-Verlag Berlin Heidelberg 2011
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The goals of the planning stage were high quality workplaces with the same standard with short communication paths and production without emissions of climatic gases. Because of the production processes, emissions from the energy requirements for electricity and heat are the only potential sources of pollution, the conception of a zero-emissions factory is therefore above all an energy concept for reducing the electricity and heat requirement and a concept for a CO2-neutral energy supply. Thanks to the external supporting framework of the wooden truss construction of the production areas with the 27.50 m span, the building volume that was to be heated and ventilated was reduced by 1.20 m headroom (15 % of the complete enclosed space). Loading zones for trucks connect to the production and storage areas. These zones were integrated into the thermal shell of the production areas. The solar energy plant is built on the loading zone that is oriented to the southwest. The supports for the hall's external supporting framework hold the thermal collectors. The high-quality thermal protection and the compactness of the cubage of the overall project enable a heating requirement of < 30 kWh/m²/a. The sun (PV, collectors) is available as the supplier of energy without CO2-equivalent; as a regenerative supply of energy rapeseed oil is used as a supplementary source of electricity and heat, and a rapeseed oil fired block-type thermal power station is in use. With energy supply from excess energy from a PV generator and the substitution of electricity from conventional generation, CO2neutral energy supplies can be achieved with internal electricity generation even with conventional rapeseed oil cultivation. The target values for the heat with 40 kWh/m²/a and for the electricity consumption for the building's technical installations with 20 kWh/m²/a result from the tresholds of the federal economics ministry's subsidy concept for solar-optimised buildings. In addition, restrictions to 20 kWh/m²/a heat and 12.5 kWh/m²/a electricity for the building's technical installations and operations result from the objective of CO2-neutral energy supplies and the maximum available space of 600 m² for the installation of PV modules and solar collectors. The target values demonstrate that, together with a very low heating requirement, in particular the electricity requirement for the building's technical installations should be very low. For this reason, the main focuses for improvements are on energy-efficient ventilation, a good supply of daylight and adapted lighting, and integration of the solar energy supply. 4
ROOM AIR CONDITIONS AND THERMAL INSULATION IN SUMMER
Thermal insulation in summer in the offices is guaranteed by an external two-part heat protection. A reduction of the g-value (total energy transmittance) is achieved by using triple heat protection glass. In addition, the opening parts of the windows are designed as wooden panels with vacuum insulation, so that a reduction of the heat yield in summer is achieved without increasing heat losses. These are reduced considerably by means of a consistent reduction in internal loads, e.g. by performance management in electronic data processing and by demand-oriented lighting controls. Because of the dense layout, it is necessary to lose heat in summer through nighttime ventilation (change of air: 3/h). There is a mechanical ventilation system to make the building safe from breakins. There are 245 working hours with temperatures in excess of 25°C; this is less than 9% of the working hours.
5
HEATING REQUIREMENT
A low heating requirement is achieved on the one hand by a good thermal installation standard and on the other hand by an efficient ventilation plant with heat recovery in the production areas. Precondition for achieving the heat supply level of >75% is a very airtight building shell. The building achieves a very low change of air volume of 0.22 1/h at 50 Pa partial vacuum/excess pressure. This value was verified by a blower door measurement. A heat requirement of 220 MWh/a results for the building (determined by dynamic building simulation); this is 27 kWh/m²/a in relation to the net floor area with internal loads of 150 Wh/m²/d. 6
DAYLIGHT USE AND LIGHTING
The production halls receive an average daylight quotient of 3% through the fanlights, so that there is a supply of daylight adjusted to requirements. The lighting is dimmed automatically in dependence on the daylight by an external brightness controller; on the one hand this reduces the costs in comparison with a decentralized controller and, on the other, the dependence of the sensors on the reflection properties of the surfaces in the inside of the building is avoided. The offices are well supplied with daylight (daylight quotient in the area of the workplaces average 4.5%, daylight autonomy: 77% in 0.75 m room depth, 47% in 2.75m room depth); as in the production halls the lights are grouped with a daylight-dependent controller and dimmer. 7
ELECTRICITY REQUIREMENT
Even now, a major part of the electricity (approx. 55%) is required for lighting and computers/communications. For this reason, in the main 'conventional' energy saving measures were carried out in the field of electricity supply. These include lighting controllers, TL5 fluorescent lights, flat screens, low-energy actuators for pumps and ventilators and energy-saving operations for the computer systems. Thanks to a vacuum drainage system the water requirement is reduced by 80% in comparison with conventional drainage systems. The remaining wastewater is fed into the municipal sewer, because the clarified sludges are processed further in a block-type power station. 8
SOLAR ENERGY SUPPLY
The heating is supplied by a rapeseed oil factory heating and power station (180 MWh/a.), a collector plant (20 MWh/a.) and by the heat waste from the development department (20 MWh). The current demand is covered by a 60 kWh PV-plant (45 MWh/a.) and via the rapeseed oil factory heating and power station (115 MWh). Thus the energy supply is provided by regenerative energy sources. The primary energy demand for heating and current is 700 MWh/a., which equates to 90 kWh/m² p.a. The collector plant and the PVGenerator achieve a solar contribution of 22%. Additional expansion of the photo-voltaic current generation is limited from an economic point of view and also because of the lightweight construction of the hall. The non-insulated sprinkler tanks set up in the building serve as a buffer store for the 150 m² thermal collector system and thus as low-temperature radiant heating system. The waste heat from the burners in the development area is fed via a busbar to the buffer store of the rapeseed oil factory heating and power station. In winter, the waste heat from the EDP central office serves as a heating support for the warehouse, in summer a circulation fan is used here.
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Figure 3: Solar collectors on top of the Solvis plant
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BUILDING EXTENSION
By firm growth a building extension became necessary of 5.400 m². The new building part is used completely as central storage depot. By four lowerable stages it is possible to load at ground level up to 30 trucks daily. With a new automated production line, in which the smaller compact collectors are manufactured, Solvis is in the position to produce altogether annually up to 300,000 m² collector areas. Three laser welding machines make a capacity with absorber production of up to 500,000 m² possible. Additionally further offices and a training area on approx. 1,000 m² extended. For the measures the photovoltaic-plant was supplemented around 2.000 m², a thermal long term storage with 100.000 litres of volume supports the heat supply. With these additional activities Solvis invested 10 million Euros. 10 CONCLUSION The target values for heating consumption of 40 kWh/m²/a. and a current consumption of 20 kWh/m² p.a. for the building facilities are considerably undercut, the primary energy consumption being 90 kWh/m²/a. The excellent thermal insulation as well as the consistent planning and implementation of low-energy building facilities means that the new production building of the company Solvis can be completely supplied using regenerative energy sources and in the future will become a zero-emission factory with CO2-neutral rapeseed oil production. Through the minimisation of its regenerative energy resource requirements, the Solvis zero-emission factory has also become a model of how buildings could be supplied with energy within industrial and commercial construction in the future.
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Manufacturing and the Science of Sustainability Timothy G. Gutowski Massachusetts Institute of Technology, Cambrige, MA 02139
Abstract A new area, called Sustainability Science, is engaging large system scientists to address the challenges that face the future of human society on planet earth. In this paper, the methods and frameworks of diverse disciplines are reviewed and compared with those of the manufacturing community. The results show significant differences between disciplines, including the level of urgency expressed. Synthesizing these divergent viewpoints, this paper makes suggestions for needed research on “sustainable manufacturing”. The main message is that manufacturing needs to significantly increase the boundaries of its analysis to be able to understand its effect at the global scale. Keywords: Weak Sustainability; Genuine Investment; Eco-Systems Ecology; Panarchy; Resource Accounting; Triple Bottom Line; Scale; Human Well-Being
1
INTRODUCTION
Recent papers have identified an emerging area of research called sustainability science. This new area of study looks at the complex interactions between the methods of society and eco-system services and human well-being, over long time scales and at global dimensions. The earliest manifestations of this new science involve the natural sciences (biology, ecology, etc.) on the one hand, and the social sciences (sociology, economics etc.) on the other, with systems modelers of many persuasions in the middle [1], [2], [3], [4]. Manufacturing, as one of the major sub-systems that connects eco-systems services to human well-being has an important role to play in this new emerging field [5],[6], [7], [8]. In this paper, we review various approaches to understanding the concept of sustainability and compare them to sustainability initiatives in the manufacturing sector. 2
wealth, called genuine investment, must be equal to or greater than zero. Genuine investment is the sum of the values of all capital stocks including manufactured capital, human capital and natural capital. The accounting is done in dollars which means that economic equivalents of these different types of capital must be obtained. The difficulties in establishing prices for components of natural capital are acknowledged, and a representative calculation is made for countries, regions, similar economic groups, and the world, and announced yearly in the World Bank’s publication The Little Green Data Book. Figure 1 gives the recent accounting for the World [11]. The results (given in percent of gross national income – GNI) indicate that the world’s manufactured capital assets grew at 7.9 %, the human capital assets (represented by education expenditures) grew at 4.2 % and the natural capital assets declined at 5.0 %. The result is greater than zero (+7.2 %; note the rounding error) and so by this calculation the world is sustainable.
ALTERNATIVE VIEWS OF SUSTAINABILITY
A major thesis of this paper is that until there is reasonable agreement on a working idea of what sustainability means, it will be difficult or impossible to measure progress or describe a way forward. In this section, brief summaries of alternative views of the concept of sustainability within recognized scientific disciplines are offered. The reviews include; 1) economics, 2) eco-system ecology/resilience, 3) resource accounting, and 4) the business approach called the triple bottom line (TBL). 2.1
Economics
Starting from the concept of sustainability as defined in the UN document “Our Common Future” [9], economists in collaboration with ecologist have put forth an operational scheme for estimating society’s sustainability [10]. In this paper, sustainability is defined as the requirement that our so-called inter-temporal social welfare must not decrease over time. The inter-temporal social welfare is calculated as the present discounted value of the flow of utility from consumption from the present to infinity. Under certain conditions, this is equivalent to the more transparent requirement that genuine wealth per capita must not decline. Hence the change in genuine
Figure 1: Results from the 2010 Little Green Data Book produced by the World Bank indicate a positive genuine investment. This is the so-called weak form of sustainability which allows substitution between capital stocks. In other words, depletions in natural stocks can be compensated by additions to manufactured and human capital stocks. Substitutability, and its implied value
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_6, © Springer-Verlag Berlin Heidelberg 2011
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system and technical feasibility, is an issue for any aggregate measure of resources, and is not unique to economics. Economics, however, seems to take it to the farthest extreme by allowing for the compensation of lost eco-system services, with human engineered products and institutions. This is not only an extremely optimistic statement about human abilities, but also implies a vision of the future without nature, or at least with much less natural capital than we now enjoy. Because many people are uncomfortable with this vision and because the technical feasibility of substituting for ecosystem services on a global scale is in doubt, many people criticize the weak form of sustainability. See for example Daly and Daly and Farley [12], [13]. At the same time, the idea to aggregate resources in an attempt to measure some aspect of sustainability seems potentially worthwhile, and should be further explored [14]. In fact, the economists carry this approach much further than outlined here, by attempting to include the contributions to our economic productive base provided by human institutions [10]. See Dasgupta for a short, concise description of this calculation [15], and for more detail see [10], [16]. 2.2
Ecosystems Ecology
Ecosystems ecology brings a very different perspective to the issue of sustainability, one that may not be familiar to manufacturing engineers. Overall, ecosystem ecologists do not see sustainability as an equilibrium state, but rather as a process that naturally includes phases of decline and recovery. The central question is, can the system accommodate change and still retain controls on function and structure [17]. This inquiry is closely aligned with the new study of resalience, see for example the website for the resalience alliance [18]. A cornerstone of this effort is a systems dynamic conceptualization called panarchy [19]. Panarchy is a stylized representation of the dynamics of an adaptive system based upon observations from ecosystems ecology. It is hypothesized that other adaptive systems (including ecological, social and economic systems) may go through similar transitions. It places a useful emphasis on dynamics and transitions, identifying four crucial stages in the prototypical transition: 1) growth, 2) conservation, 3) release, and 4) reorganization. The scheme is shown graphically in Figure 2. The authors identify two metaparameters that can indicate where one is in this scheme; “potential” equivalent to the “y” axis in Figure 2, and “connectedness” equivalent to the “x” axis. A third variable, out of the plane of Figure 2 (the “z” axis) is “resilience”. If a system is not resilient it may move off of the pattern shown in the figure to a new pattern or to a dead end or “trap”. The researchers have identified two important traps. One is the so called “Rigidity Trap”. As described by Gunderson and Hollings, “Rigidity traps occur in social-ecological systems when institutions become highly connected, self-reinforcing and inflexible”. The other is the so called “Poverty trap”. As described in the resilience literature, “this is a situation in which connectedness and resilience are low and the potential for change is not realized” [19]. Ecosystem ecologists offer many examples of these behaviors in nature and speculate on the application of this framework to other systems. For example, in Gunderson and Holling, a testable hypothesis is proposed for applying the panarchy framework to industrial systems, for example the Bell telephone Co. in the U.S. For those who study business cycles, there seems to be some similarity with concepts such as the Kondratiev long wave, see [20], [5]. Overall the resilience literature is highly integrative bringing together scientists from different fields of study. Much of the earlier work however is mostly conceptual, such as the panarchy framework. A recent paper identifies specific global limits for several ecological problems described in the next section [21].
Figure 2: Graphic representation of an adaptive cycle for a complex system. 2.3
Resource Accounting
Resource accounting is the physical equivalent of the economics approach to counting identified resources needed to maintain some aspect of sustainability. The accounting is done in physical units, rather than monetary units, and usually employs some version of a “sources” and “sinks” view of the planet. That is, human activities interact with the planet by extracting energy resources, materials, biological entities and other sources, process them, and then deposit the residuals back to the planet that acts as a sink capable of absorbing a certain amount of these wastes. This approach may be done with varying degrees of rigor depending largely on how well the system is defined and the tools employed. Thermodynamics would be among the most rigorous physical accounting approaches [22], [23], [24], [25]. While using physical units greatly limits the degree of aggregation one can usefully accomplish, large categories of natural capital type resources are commonly counted. Prominent examples include: Primary energy resources measured in units of energy; climate change gases, measured in CO2 equivalents; water use measured in weight or volume; acidification potential measured in hydrogen ion equivalents; material resources measured in weight, and biological extinction measured in rates of species loss. In fact, all scientific investigations of sustainability ultimately resort to some form of resource accounting to state a problem or measure progress. And it is the resource accounting arguments which ultimately make the strongest and clearest statements concerning the current unsustainable practices of humanity. Examples of unsustainable trends come from a variety of sources, of particular importance are the effects on global eco-system services addressed in the International Panel on Climate Change [26], and the Millennium Ecosystems Assessment [27], [28]. These reports and others point to a broad array of disturbing and potentially disastrous trends including climate change, ocean acidification, nitrogen and phosphorous overloading, freshwater depletion, biodiversity loss and land system change. See also [21]. The most notable features of the resource accounting approach are; 1) that attention is directed predominantly to the natural environment, and 2) that much of the news is quite concerning if not alarming. This contrasts starkly with the much more optimistic view given by Arrow et al [10] and the seemingly non-committal view of Gunderson and Hollings [19]. In particular, an optimistic case can be made that humans can employ forward looking mechanisms and institutions to anticipate potential future disruptions, and plan accordingly [19]. At the same time, humans can game these very same mechanisms (as the recent financial crisis clearly illustrates) and fail to perceive potential precipitous decline. This potential
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inability to adapt would be an example of the so-called “Rigidity Trap” described by Carpenter and Brock [17] and others.
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2.4
Here we propose three questions concerning sustainable behaviors. The questions are intended to expose the limitations of simple notions concerning sustainability.
Triple Bottom Line
The triple bottom line is a business response to the need for corporate performance measures that go beyond shareholder value, and include social responsibility and sustainability. The term is generally attributed to John Elkington writing in the California Business Review in 1994. Triple Bottom Line accounting includes not only corporate financial performance, but also an evaluation of potential impacts on people and planet. While it may appear to include some of the same topics as in the genuine wealth calculation mentioned earlier, the focus is on the firm and the accounting has not been standardized. In practice, it is more a portfolio approach, where the different spheres of activities are treated separately. While this method is indeed a step forward in terms of corporate acknowledgement of social and environmental issues, it is clearly a work in progress and whether it could actually have an impact on sustainability needs to be tested. The main problems are that the accounting is limited in scale, the methods may not be fully disclosed, and business as usual can often be dressed up to look like a new contribution to sustainability. Later examples will show this explicitly. 2.5
Summary
This brief synopsis of alternative views of sustainability is offered to illustrate the range of views on this topic and the uncertainty that faces the development of a science of sustainability. It seems that in a situation where we would all want clarity, it is not to be had. The problem resides not only in the vast complexity of the global environmental-social-economic system, and the weakness of the “unsustainable signal” as experienced by the average inhabitant of the planet, but also in the hugely ambitious agenda contained in the simple word “sustainable”. Even some of the most fundamental concepts are seen quite differently by the various disciplines engaged in this study. For example, consider the test question, would the addition of many more people to the planet improve or decrease the sustainability of human society? Those who see resources as limited will divide finite resources by a growing population and tend to answer in the negative. However a subset may say the opposite. The difference depends on whether one sees additional people as a potential resource, or primarily as consumers of the resources. An interesting contribution in this area is offered by the Harvard economist Michael Kremer who has shown that the rate of technological progress correlates very closely with population levels [29]. That is, the more people there are, the more new ideas and opportunities for development, and so the more we advance. This view is discussed in a popular book by Harford [30] to illustrate how contradictory the solution to sustainability might be. However to many, this view seems to deny any biophysical limits to growth. In fact, it may sound like a Malthusian mistake with the opposite sign. Indeed, those who come to this problem from a natural resource accounting point of view, would probably cast humans first in the role of consumers, and secondly as those who could modify the consumption process. The alternative views presented here are in stark contrast. This result, that even a most basic question, such as the role of human beings in sustainability, has no simple uncontestable answer, is not unique. Other basic questions, often the foundation for action plans in “sustainable manufacturing” are equally complex. The result is this; there is plenty to study concerning manufacturing and the science of sustainability and the sooner these produce results, the sooner manufacturing will be able to clearly address the problem.
THREE QUESTIONS PRACTICES
ON
SUSTAINABLE
INDUSTRIAL
Gulf Oil Spill in the U.S. A simple question might be to ask how would one evaluate the performance of the company BP during the recent oil spill in the Gulf of Mexico? Indeed by any measure this was a lose-lose-lose proposition. The company lost economically, the environment lost (although we do not know the extent of the damage yet) and indeed many local people lost by losing income and potentially their livelihood, and having their environment damaged. This was the largest oil spill in U.S. history, and at about 200 million gallons likely the largest of its kind in world history. At the same time BP has been known for promoting renewal technologies and providing investment funds for many of these. Also some may have seen BP’s response as proactive. So how would you rate BP’s behavior? Are they sustainable? The easy answer to this question is “no”, but as this paper is being written the investigations into the roles of the various players is currently in progress, and the extent of the ecological damages may take years to understand. Hence, in this case we may look to the bigger picture proposed by both the ecosystem ecologist and the economists and ask a larger question - not how did BP perform, but how did (or will) the overall system perform? The answer to this question may lie more in how the institutions which control deep water drilling and are charged with guarding ecosystems, while at the same time providing energy resources, respond to this disaster. Similarly we may ask the question how do the consumers, who ultimately drive the need for deep water drilling, respond? Are they aware of the consequences of their actions or is the signal too weak to produce a response? We leave the question unanswered but use it as an example of how we have to expand our analysis framework to get ultimate answers to these questions. Solid State of Lighting Consider the question – should an improvement in the energy efficiency of lighting be considered a engineering contribution to sustainability? This topic is currently of great interest because of significant new improvements to solid state lighting and recent studies concerning the life cycle energy use of solid state lighting [31], [32]. Anyone who owns an LED flashlight already knows that this form of lighting is very efficient because the batteries last for a very long time. However, it is also true that solid state devices are made by semi-conductor type manufacturing process which can be very energy intensive. So the question is, when viewed over the product life cycle i.e. manufacturing and use, is solid state lighting more efficient than incandescent and/or florescent lighting? Recent LCA studies indicate that the answer to this question is yes, the solid state devices can provide an equivalent amount of illumination for a much smaller amount of energy – something like 3 to 5 times less energy depending upon the exact nature of the comparison [31]. This would seem then, one face value, to be a very significant improvement and certainly a candidate for being called a contribution to sustainability. But there is more to this story. A further study of this issue looked more into the nature of our demand for lighting, and explores the question – “just exactly how much lighting do we want?” That is, will we be happy with what we got, and continue to use the same amount of illumination and therefore save energy with solid state lighting, or will we take advantage of this efficiency improvement and actually increase our amount of illumination and thereby potentially offset some or all of our expected savings? [33]. This is
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the kind of question that economists can address, using economic growth models and other models to connect our demand for illumination to other factors. As it turns out, the result to our question can be surmised roughly from available empirical data. That is, observations from over 300 years, for various geographic units, indicate that the demand for lighting per capita, , is directly proportional to per capita gross domestic product (gdp) divided by the cost of lighting CoL [33], as given in equation 1.
~
gdp CoL
(1)
This result, and one that can be derived from it, giving the total power used on lighting, are shown in Figure 3 [33]. The data show two things; 1) the wealthier we are, the more lighting we want, and 2) the lower the cost of lighting, the more lighting we want. Most notably, the graph shows no sign of saturation. We may saturate in the future, but that is a debatable point (and one that is discussed further in the reference [33]). The evidence so far however is not good for energy savings, and in fact the paper (using a more complex economic argument) predicts more energy use with solid state lighting not less. Efficiency data from an earlier paper puts this into perspective. Figure 4 shows historical improvements in the efficiency for two technologies: steam engines and lamps plotted on a “linearized” logistics curve [34]. Here we are interested in lamps. Current solid state lighting would appear roughly in the upper right hand corner of the figure. What becomes apparent from this figure is that while solid state lighting indeed represents a significant improvement in the energy efficiency of lighting, it is not, from a historical perspective, unique. That is, going from paraffin candles to Edition’s first lamp, or from tungsten filament incandescent lamps to sodium vapor lamps where also significant improvements in there own right. We return then to our original question, should an improvement in the energy efficiency of lighting be considered an engineering contribution to sustainability? Or does it look remarkably like business as usual? This seems as a core question for those who want to study manufacturing and sustainability. Remanufacturing and Energy The third question is, does remanufacturing save energy? In many references one can find comments that remanufacturing is the best option for end-of-life product. The obvious benefits are that remanufacturing can generally save some (usually large) portion of the invested energy used in both the materials production as well as the manufacturing. (The assumption is usually made that the
(a)
(b)
Figure 3: a) Empirical data showing the correlation between illumination and GDP divided by the cost of lighting b) Converts the data to show energy usage by illumination see [Tsao 2010].
Figure 4: Historical efficiency data for steam engines and lamps from Ausubel and Marchetti 1997. remanufactured product is a substitute for a new product.) But a recent study of remanufacturing of eight different products reveals that energy saving may not always be in favor of remanufacturing [35]. The result depends heavily on whether the product has an energy intensive use phase. That is, if the product has a power cord or an internal combustion engine attached to it, then the use phase of the life cycle will very likely dominate the energy use. Since it is a major product design trend to power-up previously “passive” products, the use phase is coming to dominate energy use for many products. Furthermore, since the first powered design will likely be inefficient, the ironic implication is that future energy efficiency improvements in the design could act to undermine remanufacturing. The study found that in 25 case studies, 8 showed clear energy savings for remanufacturing, 6 showed clear energy savings for buying new, and 11 cases were too close to call. The results depended heavily upon use phase energy efficiency trends and could change dramatically with time. For example, while it made sense from an energy savings point of view, to replace a damaged compressor and extend the life of a refrigerator in the 1960’s, in the 1990’s it was better to buy a new refrigerator. Ultimately however, if products can move asymptotically to a steady highly efficient state, the case where remanufacturing saves energy could be restored. It remains to be seen if such a scenario obtains. 4
RESEARCH QUESTIONS
A review of the sustainability science literature shows considerable uncertainty in regards to a definition of sustainability, disagreement concerning the roles of major players in this problem, and the lack of a clear path forward. This contrasts sharply with many of the ideas put forth as “sustainable manufacturing”. These are primarily programs of self improvement (and to some extent self preservation) and technology development to address market opportunities. They appear to be based upon a much clearer vision of the mission, usually involving resource accounting arguments, (but addressed at a relatively small scale). A resource accounting framework provides a clearer enunciation of the problems, and a basis for measurement and hypothesis testing. At the same time however, it must be recognized that even if one accepts the resource accounting approach, this must be woven into a larger picture that addresses how these resource depletions at various scales might interact and ultimately how they provide for human needs. Furthermore one must study the problem at a sufficient scale so that actions at the manufacturing level can be followed to higher levels, ultimately to a global scale. One can even go farther in the analysis to anticipate the temporal pattern of these actions. It appears that even when we do good, the scale of our actions can lead to new effects not anticipated. For example, consider the substitution of MTBE for TEL, and HCFC’s for CFC’s. In each case
36 an improvement on a unit basis is accompanied by an expansion in use, and a realization of yet new problems. Note that at the time of its introduction, CFC’s could have been easily characterized as “green”, an improvement, or even “sustainable”. See Allenby’s comments in [36]. The problem is that the scale of human activity is so large that whatever we do it will affect the environment. The study of sustainable manufacturing needs to reorient itself so that this concept is at the center of the discussion. Several critical areas for sustainable manufacturing research are identified below. 1. Scale – Manufacturing must expand the boundaries of analysis if it wants to understand its impact on sustainability. This does not mean just to distant components of manufacturing, but also to social and environmental effects. There are indeed many dimensions to this expansion, several of them are mentioned in the following points. See [37], [38], [39]. 2.Measuring Human Well-Being – The ultimate goal of sustainability is to maintain some level of human well being extending indefinitely into the future. While human well being is admittedly a complex topic, there are many credible studies that have explored this topic and proposed various measures. Examples range from the happiness index to the GINI coefficient of inequality, to the UN’s human development index (HDI), the index for sustainable economic welfare (ISEW) and the genuine progress indicator (GPI) [39], [16], [40], [41], [42] [43]. Additionally, some researchers are working on the development of a so-called Social Life Cycle Assessment [44]. While each of these can be challenged in some way, they all appear to offer more or additional sensitivity to the human condition compared to the usual default measure, which is the per capita gross domestic product (GDP) or some similar financial measure. In fact many of these human well being indicators show that while per capital GDP may be rising in many developed countries, measures of human well being are staying flat or even declining. These measures should be central to the sustainability discussion and need further and vigorous development. Even in their current state of development, application of these measures to the stake holders involved in manufacturing’s actions would be enlightening. The results should show both the benefits and disadvantages of manufacturing. Furthermore, they may provide important insights that would allow one to differentiate between various manufacturing activities. Given the global nature of manufacturing, one would have to address how these large geographical boundaries would be treated. 3. Measuring Resources – Resource Accounting is based upon the widely held premise that there are certain types of resources that need to be maintained in order to provide for the sustainable development of human society. These identified resources generally correspond to certain natural capital stocks and ecosystem services or the accounting is done on the anthropogenic emissions or actions that degrade and threaten these services. While sustainability issues can exist at all scales for society, primary concern must be focused on those problems that exist at the global scale. Several publications attempt to list these major global scale challenges. See [45], and [21]. Resource accounting approaches can use alternative accounting schemes. Two extreme cases would be highly aggregated measures (such as the genuine investment accounting scheme of the economists) or by considering individual levels of specific resources and emissions. Both methods have advantages and disadvantages. When using highly aggregated methods, one would need to acknowledge potential interactions or stay away from these in the accounting scheme. Highly aggregated measures would have significant advantage in modeling and developing conceptual frameworks for future evolutionary paths of society. On the other
Keynotes hand, identifying specific resource issues allows for a much more in depth analysis. These could address in greater detail potential limits of the global system and complex interactions between the various dimensions of the problem. In the aggregate resource accounting method, the issue of substitutability would need to be addressed in some detail. Note that the application of the genuine wealth calculation or some variation on this theme to manufacturing could result in the differentiation between the benefits provided to society by different products, for example infrastructure products versus consumer goods. While this is a very value laden issue, there is wide agreement in the psychological literature that all needs are not equal. That is, one could attempt to link product, to need, to sustainability. For engineers a particular attractive aggregate accounting scheme could be based upon the consequences of the second law of thermodynamics. These would include estimating exergy losses and entropy production at the global scale. See [22], [46], [24], [25], [23]. Concerning accounting schemes that address individual global scale problems, a recent paper by Rockstrom et al identifies 9 potential problems and makes a first estimate of quantified global limits for seven of them. That list includes; 1) climate change, 2) ocean acidification, 3) ozone depletion, 4) nitrogen and phosphorus cycle overloading, 5) global fresh water withdrawals, 6) land system change, 7) biological diversity, 8) atmospheric aerosol loading, and 9) chemical pollution. The paper claims that three of these, climate change, nitrogen and phosphorus cycles overloading and biological diversity, have already transgressed the safe operating space for the global environment. The obvious challenge for manufacturing is to connect their effects on these problems from the manufacturing scale to the global scale. 4. Mechanisms of Interdisciplinary Study – Increasing the scale of analysis will inevitably involve crossing interdisciplinary boundaries. How to do this gracefully and rigorously is an important challenge. In the review of sustainability science literature it seems that the ecologist and economists have started the process of successful interdisciplinary studies. This issue strongly affects the professional development of young academics. 5. Subdivisions by Topic Area – Several major themes emerge that both span the breath of the sustainability research area, but at the same time provide a focus which allows measurement and modeling. These area include: 4.1
Energy resource use and efficiency
The effect of energy efficiency on conservation and growth has been discussed since at least 1865 when Stanley Jevons published his book on coal. Since then it has been measured (the direct rebound effect), analyzed and debated primarily in the economics literature, and proposed as the driver of economic growth by Ayres. See [47], [48], [49], [50], [51]. This topic is among the most important that needs to be understood. Proposals to increase energy efficiency without an understanding about how society would use those advances could lead to surprisingly different results than expected, as illustrated in the earlier sections in this paper. 4.2
Materials use and efficiency
Materials connect manufacturing to the environment both as a source of raw materials and as a sink for the residues. And materials connect manufacturing to people by providing for their needs and quality of life. Current trends show a growing need for more materials as the world develops, the use of more elements in the periodic table (leading to complex mixtures) and increased needs for higher purity materials. Furthermore, materials are energy intensive and newer materials generally have even higher energy
Keynotes
37
requirements. While the materials with the largest use have been around for some time leaving only finite opportunities for energy and CO2 efficiency improvements, newer materials may present major opportunities. How materials are used, substituted and recycled is central to sustainable manufacturing. From the manufacturers point of view: how will materials use in future products be affected in light of potential restrictions, reporting requirements and standards, constrained supplies, fluctuations, and potential increases in prices and recycled content? See [52], [53], [54], [55]. 4.3
Technology innovation
development,
business
practices
Clark, W.C., Dickson, N.M., (2003): Sustainability science: The emerging research program, PNAS, Vol. 100, No. 14, pp 80598061.
[3]
Levin, S.A., Clark, C.W., (2009): Toward a Science of Sustainability, Report from Toward a Science of Sustainability Conference,held in Airlie Center – Warrenton, Virginia, funded by National Science Foundation.
[4]
Fiksel, J., Graedel, T., Hecht, A.D., Rejeski, D., Sayler, G.S., Senge, P.M., Swackhamer, D.L., Theis T.L., (2009): EPA at 40: Bringing Environmental Protection into the 21st Century, Environ. Sci. Technol. No. 43, pp 8716-8720.
[5]
Jovane, F., Yoshikawa, H., Alting, L., Boer, C.R., Westkamper, E., Williams, D., Tseng, M., Seliger G., Paci, A.M. (2008): The incoming global technological and industrial revolution towards competitive sustainable manufacture, CIRP Annals – Manufacturing Technology, No. 57, pp 641-659.
[6]
Seliger, G., Kim, H-J., Kernbaum, S., Zettl, M. (2008): Approaches to sustainable manufacturing. Int. Journal of Sustainable Manufacturing, Vol. 1, Nos. 1/2, pp. 58-77.
[7]
Gutowski, T., Murphy C., Allen, D., Bauer, D., Bras, B., Piwonka, T., Sheng, P., Sutherland, J., Thurston, D., and Wolff, E. (2001): WTEC Panel Report on: Environmentally Benign Manufacturing (EBM),” International Technology Research Institute, World Technology (WTEC) Division: Baltimore, Maryland, USA, and Environmentally Benign Manufacturing: Observations from Japan, Europe and the United States, Journal of Cleaner Production, Vol. 13, pp. 117.
[8]
Murphy, C.F., Allen, D., Allenby, B., Crittenden, J., Davidson, C.I., Hendrickson, C., Matthews H.S. (2009): Sustainability in Engineering Education and Research at U.S. Universities, Environ. Sci. Technol. Vol. 43, pp 5558-5564.
[9]
UN Our Common Future, Report of the World Commission on Environment and Development, (1987): World Commission on Environment and Development.
and
Many people are banking on innovating our way out of the sustainability problem. This is a major paradigm for technology optimists. Given that we have only first begun this journey, as we focus our attention many new developments can be expected. Skeptics on the other hand will counter that new technologies have never really been evaluated from a global perspective before. The game is changing, and the hurdles to success may be much higher. New technology needs to be encouraged and guided by informed social and environmental analysis. For manufactures this will be similar to concurrent engineering and the quality movement. We must move away from pampered products that only perform well in a highly constrained environment. There will be many new opportunities here for manufacturing. The bottom line is that to connect manufacturing to the new Science of Sustainability, much larger boundaries of analysis need to be considered. While an evaluation at the level of the firm is a desirable goal, without a credible framework that connects the firm to the planet the local evaluation risks being meaningless.
Kates, R.W., Clark, W.C. Corell, R., and 20 additional authors (2001): Environment and Development: Sustainability Science (Policy Forum), Science, Vol. 292, No. 5517, pp. 641-642.
[2]
Measurements, Metrics and Tools
The measurement of anthropogenic outputs and ecological and social responses are an area of considerable potential for sustainable manufacturing. Manufacturing engineers can develop these and this activity does not necessarily depend directly upon the definition of sustainability. Further manufacturing can contribute to the analysis of the data, and interpretation and use of it in models. While it is rewarding to see that LCA has grown in application, many tough problems remain concerning allocation, boundaries standardization and accuracy. At the same time tools that move beyond single product evaluations are a critical need for manufacturing. Indeed many modeling problems exist at many levels. 4.5
[1]
Geography and supply chains
The spatial arrangement of supply and demand presents a challenging and as yet largely unexplored area for sustainability research. In a recent book David Mackay analyzes the energy needs and the renewal energy resource potentials for England and comes to the conclusion that it cannot supply its own needs [56]. It must be engaged in some kind of trade to do so. This is a sobering and useful conclusion that highlights the problem; what does a sustainable world look like? Who trades with whom and for what reasons? For example, a recent study suggests that if the price for carbon goes to $100/tonne CO2, laptop and notebook computer manufacturing for the U.S. market should move back from China to the U.S. [57]. From the manufacturers point of view, how will supply chains be affected in terms of changing labor rates, shifting markets, materials availability and centers of manufacturing, and potentially increasing energy prices and carbon taxes imperfectly applied in different countries across the world? 4.4
5. REFERENCES
[10] Arrow, K. Dasgupta, P., Goulder, L., Daily, G., Ehrlich, P., Heal, G., Levin, S., Mäler, K-G., Schneider, S., Starrett, D., Walker, B., (2004): Are We Consuming Too Much?, Journal of Economic Perspectives, Vol. 18, No. 3, pp. 147-172. [11] The Little Green Data Book, World Bank, 2010. [12] Daly, H., (2005): Economics in a Full Economy, Scientific American, pp 100-105. [13] Daly, H.E., Farley, J. (2004): Ecological Economics – Principles and Applications, Island Press, Washington, Covelo, London. [14] Ayres, R.U., van den Bergh, J.C.J.M., Gowdy, J.M., (1998): Viewpoint: Weak Versus Strong Sustainability, Tinbergen Institute Discussion papers No. 98-103/3, available at: http://ideas.repec.org/p/dgr/uvatin/19980103.html. [15] Dasgupta, P., (2007): Economics – A Very Short Introduction. Oxford University Press, New York. [16]
Dasgupta, P. (2001): Human Well-Being and the Natural Environment, Oxford University Press.
[17] Carpenter, S.R., Brock, W.A., (2008): Adaptive Capacity and Traps, Insight, Ecology and Society, Vol. 13, No.2, pp.40.
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Gunderson, L.H., Holling, C.S., (Editors), (2002): Panarchy – Understanding Transformations in Human and Natural Systems, Island Press.
[20] Grubler, A. (1990): The Rise and Fall of Infrastructures – Dynamics of Evolution and Technological Change in Transport, Physica-Verlag Heidelberg. [21]
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Rockstrom, J., Steffen, W., Noone, K., Persson, A., Chaplin, F. Stuart III, Lambin, E., Lenton, T.M., Scheffer, M., Folke, C., Schellnhuber, H.J., Bykvist, B., de Wit, C.A., Hughes, T., Leeuw Sander van der, Rodhe, H., Sorlin, S., Snyder, P.K., Costanza, R., Svedin, U., Falkenmark, M., Karlberg, L., Corell, R.W., Fabry, V.J., Hensen, J., Walker, B., and Liverman, D., (2009): Planetary Boundaries: Exploring the Safe Operating Space for Humanity, in Ecology and Society 14(2): 32. Szargut, J. (2005): Exergy Method – Technical and Ecological Applications, WIT Press, Southampton, U.K., Boston, USA.
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Ausubel, J.H., Marchetti, C., (1997): Electron: Electrical Systems in Retrospect and Prospect, in Technological Trajectories and the Human Environment, Ausubel, J.H., Langford, H.D. (Editors), National Academy Press, Washington D.C.
[35] Gutowski, T G., Sahni, S., Boustani, A., Graves,S.C. (2011): 'Remanufacturing and Energy Savings, submitted to Environmental Science and Technology. [36]
National Research Council (2006): Sustainability in the Chemical Industry – Grand Challenges and Research Needs, The National Academies Press, Washington D.C.
[37] Costanza, R., Cumberland, J., Harman, D., Goodland, R., Norgaard, R. (1997): An Introduction to Ecological Economics, St. Lucie Press, Boca Raton, Florida. [38]
Daly, H., Townsend, K.N. (1996): Valuing the Earth – Economics, Ecology, Ethics, The MIT Press, Cambridge, MA., and London England.
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[23] Wall, G., M. Gong, (2001): On Exergy and Sustainable Development, Part I: Conditions and Concepts. Exergy, An International Journal, Vol. 1, No. 3.
Jackson, T., (2006): The Earthscan Reader in Sustainable Consumption. Earthscan U.K. and U.S.
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Nordhaus, W. and J. Tobin, (1972): Is growth obsolete? Columbia University Press, New York.
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Bakshi, B.R., Gutowski, T.G., Sekulic, D.P. (2011): Thermodynamics and the Destruction of Resources. Cambridge University Press.
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Daly, H., Cobb, J., 1989. For the Common Good. Beacon Press, Boston.
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]Gutowski, T., Sekulic, D. P., Bakshi, B.R., (2009): Preliminary Thoughts on the Application of Thermodynamics to the Development of Sustainability Criteria, IEEE International Symposium on Sustainable Systems and Technology, Tempe, AZ. May 8-20.
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Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M.C., Avery, K., Tignor, M., Miller, H.L.J., editors. International Panel on Climate Change (IPCC) (2007): Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, UK.
[42] ]Diefenbacher, H. (1994): The Index of Sustainable Economic Welfare in Germany, in C. Cobb & J. Cobb (eds.), The Green National Product, University of Americas Press, 1994. [43] Hamilton, C. (1999): The Genuine Progress Indicator: methodological developments and results form Australia, Ecological Economics, vol. 30, pp.13-28. [44] Jorgensen, A., Le Bocq, A., Nazarkina, L., Hauschild, M., (2008): Methodologies for Social Life Cycle Assessment, Int. J. LCA, Vol. 13, No. 2, pp. 96-103. [45] Graedel, T.E., Allenby, B.R., (2003): Industrial Ecology, second edition, Prentice Hall.
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Millennium Ecosystem Assessment (MEA). (2005a). Ecosystems and human well-being:synthesis. Island Press, Washington, D.C., USA.
[46] Hermann, W.A., (2005): Quantifying global exergy resources, Energy, No. 31, pp. 1685-1702.
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Millennium Ecosystem Assessment (MEA). (2005b). Ecosystems and human well-being: biodiversity synthesis. Island Press, Washington, D.C., USA.
[48] Herring, H., Sorrell, S. (2009): Energy Efficiency and Sustainable Consumption – The Rebound Effect. Energy, Climate and the Environment Series, Palgrave, Macmillan.
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Kremer, M., (1993): Population Growth And Technological Change:One Million B.C. to 1990. Quarterly Journal of Economics, Vol. 108, No 3, pp. 681-716.
[49] Sorrell, S., (2010): Energy, Growth and Sustainability: Five Propositions, SPRU Electronic Working Paper No. 185, University of Sussex, SPRU – Science and Technology Policy Research. Brighton, U.K.
[30] Harford, T., (2008): The Logic of Life – The rational economics of an irrational world, Random House, New York. [31]
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OSRAM Report (2009): Life Cycle Assessment of Illuminants A Comparison of Light Bulbs, Compact Fluorescent Lamps and LED Lamps, Executive Summary by OSRAM Opto Semiconductors GmbH, Regensburg, Germany and Siemens Corporate Technology, Berlin Germany. Matthews, D.H., Matthews, H.S., Jaramilo, P., and Weber, C.L., (2009): Energy Consumption in the Production of HighBrightness Light Emitting Diodes, IEEE International Symposium on Sustainable Systems and Technology, Tempe, AZ., May 18-20. Tsao, J.Y., Saunders, H.D., Creighton, J.R., Coltrin, M.E., Simmons, J.A., (2010): Solid-state lighting: an energyeconomics perspective, Journal of Physics D: Applied Physics, No. 43, 354001, IOP Publishing Ltd.
[47] Jevons, S., (1865): The Coal Question, Macmillan, London.
[50]
Ayres, R.U., Warr, B. (2009): The Economic Growth Engine: How Energy and Work Drive Material Prosperity, Edward Elgar Cheltenham, U.K., Northampton, MA. USA.
[51] Ayres, R.U., Ayres, E.H. (2010): Crossing the Energy Divide: moving from fossil fuel dependence to a clean-energy future, Pearson Education, Inc., Wharton School Publishing. [52] Graedel, T.E., Cao, J. (2010): Metal spectra as indicators of development, PNAS, Vol. 107, No. 49, pp. 20905-20910. [53] Allwood, J.M., Cullen, J.M., Milford, R.L. (2010): Options for Achieving a 50% Cut in Industrial Carbon Emissions by 2050 Environ. Sci. Technol, 44 (6), pp 1888–1894. [54] Allwood, J.M., Ashby M.F., Gutowski, T.G., Worrell, E. (2011): Materials Efficiency, a White paper, accepted for publication, Resources, Conservation and Recycling.
Keynotes [55]
Gutowski, T.G., Liow, J.Y.H., Sekulic, D.P. (2010): Minimum Exergy Requirements for the Manufacturing of Carbon Nanotubes , IEEE/International Symposium on Sustainable Systems and Technology, Washington D.C., May 16-19.
[56]
Mackay, D. JC. ((2009): Sustainable energy – without the hot air, UIT Cambridge Ltd. England.
[57]
Colon-Jimenez, E. (2010)” CO2 Price Impact on Dell’s Supply Chain: A Framework for Carbon Footprint Economic Analysis, Master of Science and Master of Business Administration Thesis, Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA.
39
Indian Solar Thermal Technology – Technology to Protect Environment and Ecology Deepak Gadhia 1
1
Green Force Enviro Pvt. Ltd., India
Abstract Rising fuel costs and global warming are pushing the development of renewable energy supplies. Solar energy is most promising as unlike wind it is evenly and uniformly spread and more predictable. 1 % of the solar energy received on earth would meet the total energy requirements of the world. It has been calculated that even if only 2 % of Indian deserts were to be used for putting up solar concentrators that would suffice to produce all present India’s power need i.e. 140,000 MW. In the near future, solar energy may become part of the total commercial energy basket with its share rising in proportion to the new challenges. This study report shall provoke readers to act in their limited “circle of influence” by using technologies that have already delivered results worldwide. India is a leader among the nations in having a special Ministry for the promotion of Renewable Energy (MNRE). Even though there is no lack of funds and ideas, the results are still not commensurate with the potential that exists in India. This paper introduces a case study for the establishment of renewable energy supplies in India. Sustainable entrepreneurship as well as potentials and challenges from a life cycle perspective are described. Keywords: Solar Concentrators; Sustainable Business Models; Renewable Energy 1
INTRODUCTION
In the 30 member countries of the OECD, where electricity markets are well developed and the economical situation as well as the size of populations are more or less stable, the growth of electricity generation is evidently slower than in the non-OECD countries. Considering the strong economic growth that is projected for the developing non-OECD countries such as China and India, where a high percentage of the population does not even have access to electricity yet, a substantial increase of electricity generation will be needed to meet the industrial as well as the residential demand. The World Energy Outlook 2008, published by the International Energy Agency (IEA), projects that China and India will account for more than half of the growing total energy demand until 2030, considering that India is the second largest country in the world and has a significant economic growth of about 8% with over 1 billion inhabitants [1]. Relying on existing patterns and the strong use of fossil energy carriers to cope with the resulting energy demand would unfavourably influence global climate conditions. India’s government recently presented an action plan which specifically focuses on an increasing usage of renewable energies to reduce environmental impact without disturbing economic growth. Certainly a continuous energy supply is crucial for long term economic growth. Having in mind that 60% of India’s rural population are not or not sufficiently connected to electricity underlines the importance of decentralized and renewable energy supply structures. Solar energy is specifically addressed by the government and with over 300 sun days a year the country is predestined for this renewable energy source. Therefore India has a special Ministry for New and Renewable Energy (MNRE). The Ministry has been working hard to develop renewable energy, widen the base of manufacturers and providing incentives for users to make renewable energy products affordable and accessible. Recently, the Ministry committed itself to the aim that by 2012 more than 10 % of India’s power shall come from renewable energy. At present, the share is about 5 %. For the first time in Indian history, the share will be higher than that of nuclear energy.
Till now, major contribution in renewable power comes from wind energy. To promote Solar Concentrator Technology, Indian government through MNRE provides subsidy of Rs 5,000 (approx 90 Euro) per square meter of Concentrator reflecting area to NGOs and non-profit institutions as well as accelerated depreciation benefits and subsidies of Rs 3,500 (approx 55 Euro) to the industry. The only drawback of going for government support and subsidies at present is that to get subsidies the buyer has to undergo a tendering procedure. The matter with MNRE has been taken up to do away with tendering as it causes delays and forces the companies to buy from someone who may quote low but has never proven his supplying capacities. In addition to the government support, the industry can sell carbon credits under the Clean Development Mechanism (CDM) of the Kyoto Protocol. 2
SOLAR CONCENTRATOR TECHNOLOGY
Solar Concentrators are one example for decentralized and renewable energy supply structures as they are demanded in India and have been known for centuries. Everyone knows that fires can be ignited using magnifying glasses. But only recently, technologies for its commercial use are being developed. There are various types of solar concentrators that can be broadly classified into Line Concentrators, Trough Concentrators, Heliostats, and Parabolic Solar Concentrators. At present, due to high costs and the complex curved mirror technology, Line Focus Concentrators are viable only in larger capacities. The same is true for Heliostats as the cost of the Central Tower is high and thus a minimum size of MW is needed. Parabolic Solar dishes can be of any size and thus having modularity can be of smaller sizes enabling its use for cooking, process heat and process cooling while they may also be scaled up to the range of a MW Power Plant. In this technology, parabolic dishes concentrate solar rays at a point achieving high temperatures. The temperature in the focus can be 500 to 2000 degree centigrade depending on the Concentration Ratio (CR). Of all solar technologies, Parabolic Dish Solar Concentrators have made maximum impact in India and have relevance and future.
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_7, © Springer-Verlag Berlin Heidelberg 2011
40
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41
There are four applications (technologies) in Solar Concentrators that have found their foothold and made mark in India:
For Domestic Cooking: Seifert Parabolic Solar Concentrator (SK 14) are small size parabolic dishes of 1 meter or 1.4 meter diameter in which blackened cooking vessels are put in its focus and due to temperatures above 250 degree centigrade food gets cooked fast, conveniently and free. There are thousands of such solar cookers being used in rural India.
For Community Cooking: Scheffler Concentrators (Figure 1) are concentrators made out of segments of parabola with flexible curvatures to achieve a stationary focus and thus the light can be reflected in the kitchen and cooking can take place in comfort. These advantages led to their being used for community cooking preparing food for 50-100 people.
Institutional Cooking, Process heating, and Cooling applications: With the help of the German company HTT GmbH, a solar steam generating system was developed. Now there are about 50 large systems in India that provide cooking energy at institutions for 500 to 25,000 people twice a day.
Scheffler Concentrators have also been successfully installed and tried out by Gadhia Solar for uses like solar air-conditioning ( a 100 TR= 350 KW system has been successfully installed at a Hospital by us in India), laundry and ironing, sludge drying, wastewater evaporation, incineration, food processing, desalination, and others. With this, the Solar Concentrator Systems are bound to spread and newer applications are being developed.
Figure 2: Installed Scheffler Concentrators, Project from 2001 Gadhia Solar Energy Systems is an innovative solar thermal energy company, focused on providing energy solutions by using parabolic concentrated technologies. Since its inception, Gadhia Solar has been technologically, solution focused company driven by strong passion for environmental and social contribution. Gadhia Solar has implemented some of the world's largest solar thermal systems in the last two decades (Figure 2, Figure 3). It offers cost effective, reliable hot water solutions, solutions for drinking water, water evaporation and desalination system of industrial scales, centralized solar air conditioning and heating systems, as well as complete customized solutions for industrial applications from pharmaceuticals to chemical to food processing industries [3].
Figure 3: Installed Scheffler Concentrators, Project from 2008/09 Figure 1: Scheffler Solar Concentrators.
3
ESTABLISHMENT OF TECHNOLOGY IN INDIA
SOLAR
CONCENTRATOR
After studying Process and Environmental Engineering and doing Post Graduation in Energy Consultancy and Energy Management at TU Berlin after his return to India Deepak Gadhia founded the company Gadhia Solar Energy Systems Pvt. Ltd. He discovered the potential of solar energy more by chance than by plan. Mr. Gadhia came into the field to help his wife Dr. Shirin Gadhia who runs the NGO Eco Center ICNEER and who was looking for a solution to offer to poor when she told them not to cut forest and was confronted with their question: “Than how do we cook?.” On getting in the field Deepak Gadhia realised that 50% of the world’s population cook on open fire and that pollution in the kitchen is the third largest killer. Thus he saw business opportunities and started his company that evolved and grew [2].
The company Gadhia Solar with the help of GTZ registered its Solar Kitchen Projects under the CDM Gold Standard and the CER (Certified Emission reduction Certificates) arising out of use of Solar kitchens would be purchased by GTZ for German Ministry to compensate for the CO2 emission of the Solar Meet held in Bonn making it a Carbon Neutral Conference. 3.1
Products
Systems from Gadhia Solar are built up of dishes (in sizes of 9 m², 16 m² or 32 m² depending on specific application) that incorporate the basic principle and therefore the advantage of high efficiency of the parabolic dish concentrators. However there is a major difference regarding the functional principle. While parabolic dish concentrators like dish-Sterling systems have to move the absorber unit connected to the dish during sun tracking, the Gadhia system can use stationary mounted receiver units. That means that receiver constructions and piping systems are significantly easier to design and the linking of several dishes for efficient energy generation systems is enabled [3].
42
Keynotes
The design bases on the idea of the Scheffler dish, which was invented by Mr. Wolfgang Scheffler in Kenya in 1983. The basic idea that did lead to the development of the Schefflerreflectors was to make solar cooking as comfortable as possible [4] [5]. At the same time the device should be build in a way that allows it to be manufactured in any rural welding workshop in southern countries after a certain period of training with locally available materials. To make cooking simple and comfortable the cooking-place should not have to be moved, even better: it should be inside the house and the concentrating reflector outside in the sun [5]. The technology to achieve these desired requirements is based on changing the shape of the parabolic dish concentrator and led to the term fixfocus principle which shall be explained hereafter (see also Figure 4).
differing and has to be adjusted as well for a sharp and small focal area. This is only possible by shaping the reflector after another parabola for each seasonal inclination-angle of the sun, i.e. for each day of the year. This means the reflector has to change its shape at regular intervals. The reflector-frame is build for equinox. By inclining and elastically deforming the reflector-frame all other parabolas can be achieved with sufficient accuracy [5]. The dishes were already used in diverse applications in the context of decentralized cooking (with just one dish), water treatment or steam generation in systems with more than 100 dishes e.g. for cooking in pilgrim shelters or women's refugees. Besides basically a wide range of promising fields of applications is possible which typically involve larger amounts of solar concentrators to run the system efficiently, e.g. steam generation for process heat (for industrial purposes), co-generation of steam (process heat) and electricity (approx. 1 MW), and generation of electricity (power plant with at least 5MW) [3]. 3.2
a b
c
Figure 4: Gadhia Solar concentrator - functional principle (a), concentrator (b) and system with several dishes (c) [3]. The reflector is a small lateral section of a much larger paraboloid. The inclined cut produces the typical elliptical shape of the Scheffler-Reflector. The direct beam radiation falling onto this section of the paraboloid is reflected sideways to the focus located at some distance of the reflector. The concentrator system is tracking the suns’ daily movement one the one hand and the seasonal movement on the other hand. The axis of daily rotation is located exactly in north-south-direction, parallel to earth axis and runs through the centre of gravity of the reflector. The focus is located on the axis of rotation to prevent it from moving when the reflector rotates. The distance between focus and centre of the reflector depends on the selected parabola. During the day the concentrated light will only rotate around its own centre but not move sideways in any direction. That way the focal area or hot spot stays fixed and is therefore called fix focus. After sunset the system has to be set back into morning position. As previously mentioned, the unique feature of the concentrator system is the fixed hot spot near the ground. The adjustable paraboloid dish can concentrate the radiation onto a defined target area for all positions of the daily and seasonal sun movement. Adjustment of the dish shape is necessary because in the course of the seasons the incident angle of the solar radiation varies in relation with the perpendicular to earth-axis. The paraboloid has to perform the same change of inclination in order to stay directed at the sun. Changing only the inclination of the dish is not sufficient to obtain a sharp focal point. Even though the position of the focus stays fixed the focal length is
Potentials and Challenges from a Life Cycle perspective
As the previous chapters show the current and predicted economic background in context to solar thermal electricity generation is quite positive in India as well as worldwide. Gadhia Solar as leading and well known company for solar thermal solutions in India naturally wants to take part in these developments. Their products are characterized by unique technological properties like the fixed focus dishes which may give them technological advantages in terms of efficiency. However, until now Gadhia Solar has not installed power plants for large scale electricity generation yet. Quite obviously there are some challenges that have to be solved when establishing business with large scale solar power plants. Based on previous statements two critical success factors can be derived [6]:
be able to cope with high production volumes to face capacity increase and upscaling of systems
reducing costs of electricity generation to encounter high cost pressure in competition with other energy sources
Considering economies of scale and scope it is obvious that both factors are closely connected to each other. Against this background the following paragraphs shall give a more detailed look on challenges along the life cycle of a solar thermal power plant based on Gadhia Solar concentrators (Figure 4). Thereby, while India being the home country and also primary market of Gadhia Solar it is absolutely crucial to consider the specific conditions there.
Figure 4: Life cycle of solar concentrator systems [6]. Product Design Whereas the influence on costs is mainly given in the design phase, in the sense of a “Design for X” it is naturally necessary to consciously consider all other life cycle phases already here. In order to cope with high production volumes and cost pressure specifically design for manufacturing and assembly (DFMA) is of major relevance. In this context it is important to take a look at improving the product structure, increasing the amount of common parts and the careful definition of interfaces with appropriate selection of subassemblies and joining technologies. Main objectives of DFMA are the reduction of product costs assembly
Keynotes time (enables faster product throughput/higher capacity) and the amount of parts (e.g. fewer parts in stock) both resulting in lower product costs (labor and material). Studies underline that reductions of about 50% in all categories compared to the initial state are realistic [7]. Hereby it is crucial to take certain specific critical conditions or restrictions regarding transportation (e.g. road conditions, space on trucks), qualification of personnel (e.g. easy activities, safety issues) and usable technologies (e.g. no electricity on installation site) into account. Critical points in mechanical and electrical system design in the considered system of Gadhia Solar include parts like the steam pipeline (design of all components for optimizing the system to produce superheated steam for power generation), the mirror surfaces (foils, adhesive bonding of reflective coated glass, improvement of the long time behaviour of the mirror surfaces, cleaning, weather proof coatings), the frame (redesign for light weight and carbon foot print, using of space frame, computation and optimization of the welded structure), the day time sun tracking and the seasoning adjustment (e.g. design for automatical use in power plants). [6] Production As described above the proposed fields of applications using the Scheffler solar concentrator involve a very significant increase (factor 5-10 of current production capacity per year) of the necessary number of concentrators. This larger production volume is a totally new requirement for the whole production process chain which consists of the rather manual manufacturing of parts and subassemblies in a production plant (in Valsad, Gujarat) and their final assembly on the actual installation site. Significantly higher production volumes need a redesign of the production system. However, connected technical (e.g. production system design /material flow, selection and dimensioning of specific production processes / machines) and organisational (e.g. planning and control of production, organisational structures, qualification) measures to adapt the production system will lead to economies of scale with positive effect on product costs. Again, while being quite different compared to e.g. European countries Indian specific conditions and requirements (e.g. qualification of employees, given infrastructure, cost structure/ prices for machinery and raw materials, integration of local content) need to be considered. As a final result the production process chain based in India shall be able to timely deliver as many qualitatively sound solar concentrators as needed for the proposed fields of application – under consideration of additional targets like competitive costs and also low environmental impact. [6] Product usage and End of Life As a long term investment good with a desired life span of at least 10-15 years the use phase naturally has a major impact regarding the ecological and economical performance of solar concentrators over the life cycle. Whereas current solutions considering systems with approximately 100-200 mirrors totally different approaches are needed to cope with the desired shift to power plant system with several hundred dishes. Critical aspects in this context are the significant effort needed to ensure efficient functionality of the whole system through adjusting / control (e.g. daytime and seasonal adjustment of the mirrors, control of system regarding heat usage / storage et cetera) or maintenance measures (e.g. ensuring high availability, cleaning of mirrors, strategy for monitoring and controlling of the system states) despite being strongly exposed to the elements. While large amounts of concentrators are in use, it is important to be able to appropriately dispose them after use (e.g. no hazardous materials). Additionally, aspects like reuse or material recycling should be considered in product design (e.g. common parts as spare parts) to gain advantages over the life cycle. [6]
43 4
MOTIVATION FOR FURTHER ACTIVITIES
Like all countries, India too has rolled out a very ambitious 20 GW Solar Energy Mission. This will fuel the demand but also create an increased demand for engineers, skilled workers, technicians, installers, after sales service providers, marketeers, and consultants. To overcome the shortage that will emerge Green Academies must be developed with the support of the International Labour Organisation (ILO). Muni Seva Ashram, an Indian NGO in India in which Deepak Gadhia is a trustee, plans to start a Green Academy called ASPIRE (Academy for Sustainable Practices, Innovations in Renewable Energy) to meet that demand. Green Industries and Green Jobs are the future. They offer great opportunities to Entrepreneurs who are willing to enter into fields like solar energy, biogas etc. Besides the incentives of subsidies and carbon credits for renewable energy and of saving fuel many industries and institutions are purchasing renewable energy systems with the aim to “Go Green” and contribute to the protection of the environment and to reducing the problems of global warming. They understand the need to act. While some time ago all the decisions were taken considering economics only people are now willing to pay a bit more if that helps to protect the environment and the ecology. For the survival of mankind, it is imperative that economy and ecology go hand in hand. 5
SUMMARY AND ACKNOWLEDGEMENTS
The paper presents a case study for the establishment of renewable energy supplies in India and its underlying and enabling sustainable entrepreneurship. While being a promising approach for the future, probable developments demand manufacturers of solar thermal systems to be able to cope with high production volumes and significant cost pressure. This was underlined by the case study of Gadhia Solar where additional influencing parameters were also presented. This paper was developed in context of a cooperation between Gadhia Solar Energy Systems Pvt. Ltd. And the Technische Universität Braunschweig. 6
REFERENCES
[1]
World Energy Outlook, 2008, International Energy Agency (IEA), www.worldenergyoutlook.org.
[2]
Gadhia, D. (2010): Solar Concentrators in India - Technology to Protect Environment and Ecology, in: TU INTERNATIONAL, No. 66, pp. 12-13, Berlin, Germany
[3]
Gadhia Solar Energy Systems Pvt. Ltd., 2009, company information, e.g. www.gadhia-solar.com.
[4]
Tyroller, M., 2004, Solarsterilisator für Entwicklungsländer, München.
[5]
Scheffler, W., 2009, Solare Brücke Reflektoren, www.solare-bruecke.org.
[6]
Herrmann, C., Thiede, S., Kuntzky, K., Böhm, S., Frauenhofer, M., Gadhia, D. (2009): Indian Solar Thermal Technology: Potentials and Challenges, in: Proceedings of the 7th Global Conference on Sustainable Manufacturing, pp.169-174, Chennai, India.
[7]
Boothroyd Dewhurst Inc., 2009, Design for Manufacture and Assembly (DFMA), www.dfma.com.
-
Die
Scheffler
Assessment of Energy and Resource Consumption of Processes and Process Chains within the Automotive Sector 1
1
1
2
2
2
R. Schlosser , F. Klocke , B. Döbbeler , B. Riemer ,K. Hameyer , T. Herold , 3 4 5 6 W. Zimmermann , O. Nuding , B. A. Schindler , M. Niemczyk 1
Laboratory for machine tools and production engineering (WZL) of the RWTH Aachen University, 2 Institute of Electrical Machines of the RWTH Aachen University, 3 4 5 6 Daimler AG, Robert Bosch GmbH, PE International GmbH, Effizienz-Agentur NRW
Abstract Within this paper a methodology for the assessment of energy and resource consumption within manufacturing processes is described and two case studies from the automotive sector were evaluated. On basis of the Life Cycle Assessment approach the energy and material flows within single manufacturing processes were acquired concerning the two selected case studies. With the generated knowledge about energy and material use it was possible to show up optimisation potentials for the reduction of the ecological impact of the determined products within the manufacturing phase. The work shown in the paper was conducted within the project BEAT which is kindly financed by the Federal Ministry of Education and Research in Germany (BMBF). Keywords: Manufacturing Process Evaluation and Characterisation; Total Process Efficiency; Sustainability
1
INTRODUCTION
Due to the scarcity of resources and the increasing global demand to use these resources for products and power generation, the commodity prices on the international energy and commodity market are increasing heavily as shown in Figure 1. Although the prices on the markets crushed during the last world economic crisis, as highlighted in grey in Figure 1, they are constantly growing again. This vast increase in prices emerges to one of the central problems which manufacturing industry has to face [1][2].
200 150 100 50 0
Ja n 0 Ja 0 n 0 Ja 1 n 0 Ja 2 n 0 Ja 3 n 0 Ja 4 n 0 Ja 5 n 06 Ja n 0 Ja 7 n 0 Ja 8 n 0 Ja 9 n 10
Price trend based on 2005 / %
250
crude oil (import price index) natural gas (import price index) energy (manufacturing price index for special contracted companies) steel (manufacturing price index) copper (manufacturing price index) Figure 1: Development of energy and commodity prices. In economic as well as in ecological perspective, the resource consumption of products has to be planned to be more sustainable. Therefore products have to be designed to be resource and energy efficient along the total life cycle. To achieve this aim, it is possible
to influence the energy and resource consumption in one or more product life cycle phases. The change of one product life cycle phase might also effect the energy and resource consumption in another phase in a positive or in a negative way. For this reason it is essential to evaluate all changes across the whole life cycle, to guarantee an overall reduction in energy and resource consumption [3]. The necessity for this can be shown by the following example: The manufacturing industry will primarily focus on the resource efficiency during the manufacturing process. The customers of energy or material consuming goods, like a machine tool on the other hand, intend to have low resource consumption during the use stage. The analyse of the whole life cycle including preproducts production, manufacturing itself as well as scenarios for use stage and end-of-life is therefore essential for the manufacturers of these goods [4]. With the increasing impact of material and energy costs in the use stage of a machine tool even in high wage countries, it is important to identify and optimise the energy and material demand. Former studies identified that manufacturing processes can be influenced most, if the processes with the highest average power consumption are optimised. Therefore heat treatments are generally considered with a high optimisation potential. Within this paper further investigations of the project BEAT (“Bewertung der Energieeffizienz alternativer Prozesse und Technologieketten”, engl.: Assessment of the energy efficiency of alternative processes and process chains), which is kindly supported by the Federal Ministry of Education and Research (BMBF) in Germany, are presented and it will be shown that this assumption is not sufficient for machining operations in the automotive sector. 2
A LIFE CYCLE ASSESSMENT APPROACH FOR THE EVALUATION OF THE ENERGY AND RESOURCE CONSUMPTION OF PROCESS CHAINS
Throughout recent years, the objective of manufacturing processes was to reduce the manufacturing costs. Due to a lack of data it was
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_8, © Springer-Verlag Berlin Heidelberg 2011
45
46
Automotive Life Cycle Engineering
assumed that the major cost drivers are staff and machine costs. The detailed analysis of energy and material was not in the focus of optimisation potential. This can be proven best on the basis of the available inventory data in Life Cycle Assessment. Until today the data on machining of metal parts is poor. A model for the prediction of the material and energy consumption with reasonable preciseness during a typical machining process like milling or turning is not yet available [5]. It is common practise within companies that costs for energy are assigned towards the machine tools rather by the amount of square meters used by the machine tool than by the real usage data. It is generally known that the best procedure to compare different production processes with respect to their energy and material consumption and the associated environmental impacts is done by a Life Cycle Assessment according to DIN EN ISO 14040/14044. Within the Life Cycle Assessment material and energy flows of all life cycle phases are determined (compare Figure 2) and the environmental impacts are calculated by means of using scientifically based characterisation factors. Only the relative comparison of the results for the different impact categories enables the evaluation of the values [6][7].
Energy Resource
Waste Raw material production
Energy Resource
Waste Production
Energy Resource
Emissions Waste
Use
Energy Resource
Emissions
Emissions
Detailed measurements on the machines will identify “hot spots” within the processes and help to develop options for savings even in today’s state of the art processes.
Better understanding on the material and energy consumption of different machining processes, which means the development of ecodesigns for whole process chains.
An additional benefit is a better understanding of hidden costs, in most cases declared as overhead, which also contain energy and auxiliary material (range from 5% to 50%). 3
INDUSTRIAL SECTOR
CASE
STUDIES
IN
THE
AUTOMOTIVE
Within the BEAT Project the following two industrial case studies of the partners Bosch and Daimler were investigated. The case studies were chosen due to their high volume relevance of the parts. 3.1
Gear manufacturing
The research object of the Daimler AG is a production line for the manufacturing of the 4th gear wheel (idler gear) of the front-shift transmission (type FSG) in the Rastatt (Germany) plant. The FSG is the manual transmission of the A and B class, with an annual production volume of 100,000 units. The used process chain is displayed in Figure 3. The basic structure of the process chain is soft machining of the forged part and hardening followed by hard finishing processes, such as hole and gear grinding. Within the project the process chain from turning to gear grinding is captured and evaluated as this part of the process chain is done within the Rastatt production plant.
Forging
Waste Recycling/ Disposal
Emissions
Turning Hobbing
Figure 2: Balance shell for Life Cycle Assessment with in- and outgoing energy and resource flows. As stated above, even from the cost perspective machining operations have not been in focus of the Life Cycle Assessment studies so far. Assumptions and estimations have to be used due to missing data for these processes. Recent studies have shown, that the costs of energy used in machining operations are as high as the costs for tools [8]. For this reason it is absolutely crucial to further analyse and measure the energy and material consumption of typical machining processes. Alternative processing routes may become interesting on a cost perspective as well as for ecological reasons. So far optimisations of technologies and process chains have been driven by the lowest production costs as a sum of machine costs and staff costs. This may change in future due to rising costs for material and energy. A detailed data collection for process chains with reference to the considered products is necessary. This allows the extension for comparisons to alternative process routes and for a “technical” cost calculation. While the work load for such an inventory analysis may seem to be very high, its effect may be even bigger. This approach will help the companies to reduce the consumption in two ways.
Deburring Shaping Washing Carburisation Quenching Washing Tempering Hole grinding Tooth flank grinding Washing Figure 3: Case study gear manufacturing with its process chain.
Automotive Life Cycle Engineering 3.2
47
Valve injector manufacturing
The research object of the Robert Bosch GmbH is a production line for the manufacturing of the valve injector of a magnetic common rail injector in the Bamberg (Germany) plant. Common rail systems are produced since 1997 and the total volume is clear about 50 million systems so far. The applied process chain is displayed in Figure 4. The basic structure of the process chain is soft machining of bar material and hardening followed by hard finishing processes.
Turning, Drilling Washing Heat treatment Eroding Rounding Flow Rate Check Laser marking Grinding Deburring Figure 4: Case study valve injector manufacturing with its process chain. 4
ENERGY AND RESOURCE CONSUMPTION ALONG THE PROCESS CHAIN
For the evaluation of the process chain it was essential to define the balance shell first. It is not useful to respect only the energy and resources which are needed for the process itself, as the enabling energy and resources for a machine tool cannot be neglected for the process. Within the BEAT project, the machine tools were chosen as the relevant balance shell for the evaluation. Besides it has been decided, that the first level of central systems (directly connected to the machine tool) is also measured and converted on the single machine tools by the consumption of functional units of each machine tool. Nevertheless it is not practical to measure even more layers of central systems as the influence and therefore the relevance of these systems is very small in comparison to the direct energy and resource flows. As in most manufacturing plants the real energy and resource flows in the machine tool are not known, a first step within the BEAT project was the qualitative detection of all flows in and out of each machine tool and central unit. An extract of the example of the gear manufacturing can be seen in Figure 5. Electrical energy is a major flow as it is required almost everywhere. Therefore its correct data acquisition is important and will be further investigated in the next chapter.
Figure 5: Energy and resource flows within gear manufacturing.
48 5
Automotive Life Cycle Engineering TECHNOLOGY REQUIREMENTS FOR DATA AQUISITION OF ELECTRICAL ENERGY
In general converter-fed motors are applied to modern machine tools to allow the required dynamic operation. From the electrical point of view a machine tool can be simplified as done in Figure 6, including the converter and one electrical machine, e.g. a speed variable main spindle.
the load (machine tool) and is not converted into mechanical power. The reactive power is defined as
Qn S n2 Pn2 ,
(3)
with the apparent power:
S n U rms ,n I rms ,n ,
(4)
and the root-mean-squared values:
1 2 xt dt , T T
X rms
x u, i .
(5)
The calculation of the root-mean-squared values has to be synchronised by the zero crossing of the voltage or the current. This synchronisation fails if the sample frequency of the measurement instrument is too small. By this reason it is not possible to use power quality analysers for power measurements at pulsed voltages behind the converter. Figure 6: Simplified electrical representation of a machine tool. For a significant data acquisition of the electrical energy flow, the voltage and current shapes have to be considered. The nearly sinusoidal input voltage of the converter is rectified in the first module. The converter feeds the connected motor with voltage pulses of a switching frequency of 2-16 kHz. These voltage pulses lead to a current flow which can be controlled in magnitude and frequency as it is required by the process. Due to the switched voltage the current has a high fraction of higher harmonics beside the fundamental wave. For a reliable data acquisition the sample frequency of the measuring instrument has to be adapted to this higher harmonics. Electrical power measurement devices can be differentiated into power analysers and power quality analysers. A major difference is the sample frequency. Modern power analysers have sample rates up to 3 MS, resulting in a sample frequency of 500 kHz per channel for a 3-phase power measurement. This sample frequency allows a power measurement at the input and at the output of the converter. Power quality analysers, which have an extensive advantage in price compared to power analysers up to a factor of 5-10, are generally used for long term power quality measurements of power grids. They have sample rates of 128 - 512 samples per cycle, resulting in a sample frequency of 6.4 kHz - 25.6 kHz for 50Hz grids [9]. To understand the impact of the sample frequency on the measurement accuracy some basic principles of electrical power measurement have to be considered. The instantaneous input power of an electrical load is given by:
1 un t in t dt. T T
7000
(1)
The phase voltage un(t) and the phase current in(t) are sampled with the sample frequency of the measurement instrument. It has to be considered that the error of the measurement will increase for a decreasing sampling frequency. The instantaneous power can be separated into active and reactive power. The active power less the internal losses of the electrical drive, e.g. friction, switching and ohmic losses, is dissipated into motion. The active power for each phase n is defined as
Pn
8000
(2)
For a holistic energy analysis the reactive power has to be regarded as well. The inductive behaviour of electrical machines causes a reactive current flow leading to additional ohmic losses onto the supply conductors. Reactive power oscillates between the grid and
6000 active power [W]
pn t u n t in t .
Beside the sampling frequency, the method of the current measurement has an arbitrative influence on the quality of the measured data. The use of serial connected high-precision shunt resistances necessitates an unacceptable disconnection of the machine tool from the grid. This interruption of the machining process can be avoided by using current probes. Current probes are often based on the principle of a transformer. Their accuracy is lower compared to shunt resistances due to stray fluxes and external influences. Compared to power analysers, which use generally shunt resistances, power quality analysers are equipped with current probes. To quantify the ability of power quality analysers to acquire the energy consumption of machine tools, a comparative measurement is performed. Using a simple configuration as depicted in Figure 6, the energy consumption is measured at the terminals of the converter. The process of a machine tool is simulated using a controlled asynchronous machine as a main spindle connected back to back to a controlled permanent magnet synchronous machine. The power analyser measures the current with Hall Effect current sensors with an accuracy of 0.3 %. The sample rate is adjusted to 500 kHz per channel. The power quality analyser works at sample frequency of 6.4 kHz per channel and uses current probes with an accuracy of ±0.7 % ± 2 mA and a phase angle error of ± 1.5º. The rms-values are averaged over a period of 1s. Figure 7 and Figure 8 depict the measured active and reactive input power at the converter terminals for different applied loads of the asynchronous machine.
5000 4000 3000 2000 Power Quality Analyser Power Analyser
1000 0
0
50
100
150 200 time [s]
250
300
350
Figure 7: Energy measurement of the active power by a power analyser compared to a power quality analyser.
Automotive Life Cycle Engineering
49
Epart, indirect:
6000
The direct manufacturing energy per part is the sum of the energies of the single manufacturing processes
reactive power [VAr]
5000
m
E part , direct E part ,man j
4000
Epart,manu_j:
1000
Power Quality Analyser Power Analyser 0
50
100
150 200 time [s]
250
300
350
The relative deviation between the measured active energy is 0.14 %. The reactive energy has a relative deviation of 0.16 %. These matching results of the comparative measurement demonstrate the usability of power quality analysers at the terminals of machine tools within the project despite the lower sample frequency and the less accurate current measurement. Power demand and energy consumption in a turning process
Figure 9 displays the composition of power demands, forming the overall process energy depending on the process times. During one cycle several different power demands can be distinguished. The electrical energy can be divided in energy which flows directly into the balance shell, in other words in the machine tool, and in indirect energy from the periphery (central unit systems) which is necessary to enable the manufacturing process. On the one hand there is a constant portion containing periphery (illumination, air conditioning ...) and machine aggregates (basic demand = standby demand). On the other hand the variable portion depends largely on the actual process as well as on auxiliary and feed movements [10].
Power /kW12 10
direct
Process
indirect
Idle
process energy consumption energy consumption during idling
Esb,j:
energy consumption in standby mode
Eoff,j:
energy consumption while machine tool is switched off
If the idling and process mode cannot be separated, both energy consumptions can be calculated by the mean process and idling power multiplied with the time per unit. The energy consumption of the other machine tool state can be proportionally added.
E part , man j Pprocess, mean t process t process Psb , mean
Figure 9: Power consumption in cutting processes. The energy consumption per part in manufacturing can be calculated as the sum of the direct and indirect energy per part.
energy consumption per part
Epart, direct:
direct energy consumption per part
poff p process
psb p process
(9)
t process Poff , mean .
Pprocess, mean:
mean process power
Psb, mean:
mean standby power
Poff, mean:
power consumption while the machine tool is switched off
tprocess:
process time
psb:
proportion of standby
poff:
proportion of the machine tool is switched off
pprocess:
proportion of the productive time
m
i 1 j 1
Standby Illumination, air conditioning, compressed air, central coolant lubricant supply… Time /s
Epart:
(8)
Eidle,j
n
E part E part , direct E part , indirect
in
Eprocess,j
E part , indirect
4
0
part
The indirect energy from the periphery has to be estimated by defining a functional unit (FU) of each auxiliary energy and resource flow. A functional unit for compressed air is for example Nm³.
6
2
per
E part ,man j E process , j E sb , j Eidle , j E off , j
Figure 8: Energy measurement of the reactive power by a power analyser compared to a power quality analyser.
8
direct energy consumption manufacturing process j
The energy of a single manufacturing process consists of the real process energy, the energy consumed in the idling mode and a proportional part of the energy consumed in standby mode and while the machine tool is switched off.
2000
5.1
(7)
j 1
3000
0
indirect energy consumption per part
(6)
Eaux ,i FU machine tool j , part FU total ,i
(10)
Eaux,j:
energy consumption of auxiliary device j
FUtotal:
total amount of functional units
FUmachinetool, part:
amount of functional units used on a machine tool for a product
The following chapter will show the results of measurements of the direct energy for the valve injection process chain, which were generated up to now. Further research work is outstanding. 5.2
Energy consumption within valve injector manufacturing
Corresponding to the described proceeding above, every process was measured and calculated within both case studies. In the following Figure 10 and Figure 11 the results are shown for the valve injector process chain. The results are especially interesting if
50
Automotive Life Cycle Engineering
the average power consumption of the single processes is compared with the influence on the energy per part. As it can be seen, the extensively used assumption, that the optimisation of the highest power consumers would bring the best benefit, does not really make sense, if the energy per unit is taken into account.
7
ACKNOWLEDGMENTS
The authors gratefully acknowledge the financial support of the Federal Ministry of Education and Research (BMBF) and the project supervision of the Project Management Agency Forschungszentrum Karlsruhe, Division Production and Manufacturing Technologies (PTKA-PFT) for the project BEAT - Bewertung der Energieeffizienz alternativer Prozesse und Technologieketten (Assessment of the energy efficiency of alternative processes and process chains).
Turning Washing Heat treatment Eroding Rounding Flow Rate Check Laser marking Grinding Deburring
Figure 10: Distribution of power consumption along the process chain. 8
REFERENCES
[1]
Herring, H., (2000) Is Energy Efficiency Environmentally Friendly? Energy & Environment 11, No. 3, pp. 313 - 325.
[2]
Herrmann, H., Zein, A., Thiede, S., Bergmann, L. Bock, R., (2008) Bringing sustainable manufacturing into practice – the machine tool case, in: Sustainable Manufacturing VI, Global Conference on Sustainable Product Development and Life Cycle Engineering, Pusan, Korea, pp. 272 - 277.
[3]
Graedel, T., (1996) Industrial Ecology: Defnition and Implementation, in: Socolow, R. et al.: Industrial ecology and global change, Cambridge University Press, pp. 23 - 42.
[4]
Figure 11: Distribution of influence of the processes on the required energy per part.
Gerschwiler, K., Jahns, P., Klocke, F., Lingg, G., Lung, D., Schlosser, R., Werner, A. R., Zeller, R., (2008) Ressourcenschonende Produktion, in: AWK 2008 – Wettbewerbsfaktor Produktionstechnik, pp. 81 - 124.
[5]
The reason why high energetic processes, as heat treatment, in the shown case studies didn’t have such a big influence on the direct electrical energy per part is that in the heat treatment and washing process much more parts are treated at the same time than on a machine tool.
Büdicker K., (2003) Reduktion und Verwertung von Abfällen anhand ausgeführter Beispiele der Wälzlagerherstellung, VDI Berichte Nr. 1779.
[6]
N.N., (2009) Environmental management – Life cycle assessment – Principles and framework, DIN ISO EN 14040.
[7]
N.N., (2006) Environmental management – Life cycle assessment – Requirements and guidelines, DIN ISO EN 14044.
[8]
Bode, H.-O., (2007) “Einfluss einer energieeffizienten Produktion auf Planungs- und Produktprämissen am Beispiel der Motorenfertigung”; in XII. Internationales Produktionstechnisches Kolloquium, Berlin, pp. 299-305.
[9]
N.N, (2009) IEEE Recommended Practice for Monitoring Electric Power Quality, IEEE Std 1159-2009.
Turning Washing Heat treatment Eroding Rounding Flow Rate Check Laser marking Grinding Deburring
6
SUMMARY AND OUTLOOK
This paper presented and discussed research work within the framework of the BMBF supported project BEAT. It was possible to show a suitable proceeding for the assessment of energy demands of manufacturing processes with the focus on the energy per unit. The case studies showed that the energy per unit is quite different from the average power consumption of a process, as the units in a process might have an even bigger impact on the energy per unit as the amount of power consumption. Nevertheless, for a comprehensive balance of manufacturing processes further information are still missing and have to be generated in the future.
[10] Bhattacharya, A., Das, S., Majumder, P., Batish, A., (2009) Estimating the effect of cutting parameters on surface finish and power consumption during high speed machining of AISI 1045 steel using Taguchi design and ANOVA, In: Production Engineering, Vol. 3, No. 1, pp. 31 - 40.
Assessment of Alternative Propulsion Systems for Vehicles 1
2
1
1
Christoph Herrmann , Kuldip Singh Sangwan , Mark Mennenga , Philipp Halubek , Patricia Egede 1
1
Technische Universität Braunschweig, Institute of Machine Tools and Production Technology, Product- and Life-CycleManagement Research Group, Braunschweig, Germany 2
Birla Institute of Technology & Science, Pilani, India
Abstract Concern for the environment, the approaching end of fossil fuels and the dependence on oil exporting countries has revived the interest in alternative propulsion systems for vehicles. However, an assessment of alternative propulsion systems with respect to sustainability is very complex due to the variety of technologies as well as unsecure and conflicting economic, ecological and social consequences. This study provides a framework for assessing alternative propulsion systems for vehicles using the AHP methodology. A criteria catalogue is derived from existing approaches and the analysis of propulsion systems, their market situation and surrounding conditions. Its application is illustrated by the assessment of two exemplary propulsion systems. Keywords: Alternative Propulsion Systems; Technology Assessment with the Analytic Hierarchy Process; Criteria Catalogue
1
INTRODUCTION
The need and demand for automotive mobility is continuously rising. Today, around 800 million vehicles are in operation worldwide. This number has been reached during the last 100 years but could double in the next 30 years as the vehicle production augments extensively each year. Between 2002 and 2015 the number of produced vehicles is estimated to rise by 33% from 57 to 76 million vehicles a year [1]. The primary reason for these numbers is the economic development and growth of countries like Brazil, Russia, India and China. A comparison of the distribution of vehicle ownership of these countries with industrialized countries shows the present potential. For example, in Germany 566 cars are registered for 1000 people [2] whereas in India only 7 vehicles are registered [1]. Today, 90 % of the 800 million cars in service are operated with conventional engines [3]. However, concern for the environment, the approaching end of fossil fuels and the dependence on oil exporting countries has revived the interest in alternative propulsion systems [4]. The use of propulsion systems other than the fourstroke combustion engine and fuels other than diesel and petrol is not a new idea. In the beginning of the automobile history different directions were explored and pursued. For example, the original diesel engine developed by Rudolph Diesel was designed to run on vegetable oil [5]. Furthermore, the first automobile with an electric drive was introduced in 1892 [2]. However, eventually the petrol and diesel engines superseded the other options because their fuel was cheap and their efficiency superior [3]. Innovative propulsion systems (e.g. for electric or hydrogen vehicles) are expected to create a more sustainable mobility. An assessment of these systems with respect to sustainability has to cover the variety of alternatives as well as unsecure and conflicting economic, ecological and social consequences from multidimensional perspectives (e.g. consequences coming from the propulsion system itself or the energy and infrastructure supply during usage). Today, precise statements on the effectiveness and efficiency of individual propulsion systems are not yet possible.
2
OVERVIEW ON ALTERNATIVE PROPULSION SYSTEMS
Propulsion systems consist of an engine and an energy source. An overview of alternative propulsion systems is shown in Figure 1. A variety of engines is available other than the well established diesel and petrol engine. One option is the exclusive use of an electric motor with either a fuel cell or a battery as source of energy. Another option is the use of alternative combustion engines with either an external or internal combustion. Finally, two different engines can be combined to form a hybrid. The most common type is the combination of an electric motor with a combustion engine, alternative or conventional, building a hybrid electric vehicle (HEV) [1] [6]. Alternative fuels are those other than the conventional fuels based on mineral oil (petrol and diesel). They can be categorized according to the primary energy from which they originate. Two groups are distinct: non-renewable and renewable energy. Nonrenewable energy includes the fossil resources crude oil, natural
Figure 1: Alternative propulsion systems [1], [4], [6].
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_9, © Springer-Verlag Berlin Heidelberg 2011
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gas and coal along with nuclear power. Fuels converted from fossil sources like Compressed Natural Gas (CNG) are known as fossil fuels. Solar, wind and water energy as well as biomass are considered renewable. Corresponding fuels like biodiesel or biogas are referred to as renewable fuels [4].
Third, the time horizon regarding the assessment of propulsion systems can differ from system to system. Thus, considerable know-how of the development of individual technologies and markets is required when conducting the assessment.
The term alternative propulsion system refers to the use of alternative fuels or alternative engines. Petrol, (bio) diesel, (bio) gas, Liquefied Petroleum Gas (LPG), synthetic fuels and alcohols can be combined with combustion engines and hybrid engines whereas electricity can be combined with electric and hybrid engines. Hydrogen can be used for all three types of engines.
4
3
CHALLENGES OF THE ASSESSMENT
The assessment of different propulsion systems with respect to global sustainability and the suitability for a local area, e.g. an urban or rural area, is a challenging topic. In addition to the complex technical assessment, different stakeholder perspectives, life cycle phases as well as evaluation dimensions and time horizons have to be considered. First, the assessment has to incorporate different views on assessment criteria. For example, when evaluating propulsion systems regarding costs, different types of costs are relevant for different stakeholders. Development costs are important for manufactures of propulsion systems, while costs for infrastructure development, e.g. the costs for energy supply, are crucial for the participating governments. Costs occurring during the operational use are relevant for customers. Thus, an assessment method has to reflect the preference profiles of different stakeholders. Second, the impact of different propulsion systems has to be considered regarding their impact in all life cycle phases with respect to the economic, ecological and social dimension. To structure the complex life cycle orientation, it is useful to refer to frameworks, such as the Model of Total Life Cycle Management and to include the different life cycle phases from product idea to disposal in the assessment [7]. Furthermore, life cycle orientation includes the intersection of two different life cycles such as the engine and the energy source in the case of propulsion systems (see Figure 2). In the use-phase of the engine the impact of the energy source has to be considered as well, e.g. impacts resulting from the production of electricity when assessing electric vehicles. life cycle of the fuel life cycle of the engine
ASSESSMENT METHODOLOGY
An assessment of alternative propulsion systems requires methods in which different criteria can be incorporated. For that the field of Multi Criteria Decision Making (MCDM) provides Multi Attribute Decision Making (MADM) and Multi Objective Decision Making (MODM) methods. MADM methods are suitable for problems with a given number of alternatives, MODM methods are used for an infinite number of alternatives [8]. Within this study the MADM method AHP has been chosen to identify a propulsion system which is suitable for a distinct area. AHP was developed by Saaty [9] and relies on a five step procedure: (1) construction of a hierarchy, (2) pairwise comparisons of all elements, (3) determination of priorities, (4) verification of consistency, and (5) aggregation of the results. If the result of step (4) is not satisfactory the process must be revised starting from (2). Thereby comparisons should be reconsidered and possibly more information should be requested [10] [11]. Step 1 – Construction of a hierarchic structure The elements related to the decision making problem are arranged in a hierarchical structure resulting in a composition made up by the central goal, the assessment criteria and the alternatives to be ranked. Figure 3 shows an example of such a hierarchy. Starting with the goal at the top of the hierarchy tree, the decision problem is broken into clusters of criteria and sub criteria on the lower levels concluding with the alternatives at the bottom. The depth of the structure has to be chosen to suit the problem as it is the goal to give an overall view of the decision problem and to allow a comparison of elements of equal importance. Step 2 – Pairwise comparisons of all elements Pairwise comparisons are conducted by the decision maker to generate matrices of which the principal eigenvector is determined to derive priorities for each element. These comparisons are supposed to be made with the scale shown in Table 1. Comparisons are conducted on all elements of a cluster with respect to the parent criterion using integers or fractions. For a criterion the pairwise comparison indicates its importance with respect to the other criterion. For alternatives the comparison expresses the ability to fulfill the criterion. Whenever possible, the comparisons of alternatives should be based on reliable data which is proportional to desirability. If such information cannot be obtained then personal judgments can also be conducted using SAATY’s assessment scale.
Figure 3 : Hierarchy with four levels [9]. Figure 2 : Considering the impact of primary and secondary product [7].
Automotive Life Cycle Engineering Scale
53
Definition
Explanation
1
Equal importance
Two activities contribute equally to the objective
3
Moderate importance of one over another
Experience and judgment favor one activity over another
5
Essential or strong importance
Experience and judgment strongly favor one activity over another
7
Very strong importance
An activity is strongly favored and its dominance demonstrated in practice
9
Extreme importance
The evidence favoring one activity over another is of the highest possible order of affirmation
2, 4, 6, 8
Intermediate values between the two adjacent judgments
When compromise is needed
Table 1: Assessment Scale by Saaty [9]. Step 3 – Determination of priorities Three different types of priorities can be distinguished: local, global and total (or final) priority. Local priorities represent the importance of elements with regard to the next higher level and are obtained by solving the eigenvector problem of the matrices found in step 2. Global priorities give the importance of an element in the overall context of the hierarchy. To determine the global priority of an element, the local priorities of all elements leading directly up to the goal have to be multiplied. The final priority expresses the ranking or the score of the alternatives. Step 4 – Verification of consistency A distinguishing feature of AHP is the verification of the consistency of the information entered by the decision maker. This step ensures coherent data. Due to the comparisons, a consistent matrix is improbable when applying AHP even if only a few criteria are compared. In order to verify the inconsistency of a matrix, SAATY defined a ratio that expresses the percentage to which the result equals the outcome of a probability distribution. According to him an inconsistency ratio of 10 % is acceptable because it allows new information that might be added to have an impact on the decision. Step 5 – Aggregation of results In order to obtain the total priority, every path from the goal down to the alternative has to be considered. Starting at the top the global priority of the parent element is multiplied with the local priority of the element below. These products are added up until the last level is reached. By aggregating all results from each path, the final priority of each alternative can be determined. The ranking of the alternatives represents the preferences of the decision maker. 5
CRITERIA CATALOGUE FOR ASSESSING ALTERNATIVE PROPULSION SYSTEMS
To assess alternative propulsion systems with AHP, a criteria catalogue is needed. The presented criteria have been derived either from existing approaches used in previous assessments or from lists of criteria illustrating future requirements of fuels and engines, such as [1], [3], [4] and [12] to [18]. It includes the characteristics of alternative fuels and engines, the market situation and surrounding conditions in order to cover the wide range of influences and aspects of alternative propulsion systems. The criteria catalogue is presented in Table 2. It comprises the identified main and sub criteria which are relevant for the decision making process as well as a brief description. The structure of the assessment criteria includes four levels: At the top of the hierarchy is the goal of the decision process, i.e. the assessment of alternative propulsion systems. The second level
Assessment criteria
Description
Safety Fire safety Explosion safety Electrical safety Toxicity safety Environmental impact
Risks regarding fire Risks regarding explosion Risks regarding electric shock Risks regarding toxicity
Sound / noise Global warming Photo-oxidant formation Eutrophication Acidification Resource consumption Land use Economic Impact Cost of the propulsion system Costs for infrastructure development Economic benefit Availability Resource availability Infrastructure availability Staff availability Food availability Performance & capability Cruising range Performance Torque Speed Life span Temperature sensitivity Fuel Sensitivity User acceptance Useable space Intuitive operation Refuel rate
Noise (sound), which are harmful or disturbing by their volume and structure to humans and the environment. Increase in the average temperature Contamination of the atmosphere Overfertilization of an eco-system Reduction of ph-value in an eco-system Type and number of resources consumed Need of land Total costs of the development, production, distribution, usage, redistribution, recycling of the propulsion system, etc. Total costs of gas stations, supply networks, workshops, training, roads, reverse supply chains, etc. Employment, gross domestic product, welfare, etc. Access to technical and / or economically recoverable resources Existence infrastructure to bring the resources to the place of usage (gas stations, roads, workshops, …) Existence staff (workers, engineers, …) Conflicts regarding food Range per volume, range per kilogram Technical capability Max. torque Max. speed Deterioration, max. life, max. charge cycles, etc. Performance at different ambient temperatures and operating temperatures Performance with different fuels Possible reduction of cabin or trunk size Simple use, repairable Duration of charging, duration of the refueling
Table 2: Criteria for assessing alternative propulsion systems. contains six main criteria: safety, environmental impact, economic impact, availability, performance and capability as well as user acceptance. Each of these criteria is divided into sub criteria. For example, the main criterion environmental impact includes noise, global warming, photo-oxidant formation, eutrophication, acidification, resource consumption and land use. On the fourth level, these effects are divided into the different life cycle phases of a propulsion system (see Figure 4) as mentioned in chapter three. On this basis, decision makers start the weighting of the criteria according to their preferences as described in chapter four. Decision makers can be individuals as well as interest groups on the one hand or a group of people evaluating simultaneously on the other hand. The scoring of different propulsion systems is evaluated with respect to their characteristic regarding each individual criterion. This requires expertise of the decision maker or wellgrounded data on the performance of different propulsion systems. 6
CASE STUDY: ASSESSMENT OF ALTERNATIVE PROPULSION SYSTEMS IN INDIA
The proposed criteria catalogue will be used for the assessment of two propulsion systems for CNG and biodiesel made from jatropha as both fuels are currently promoted in India. As an example, the view of the Indian government is elaborated in detail. The perception is obtained from public available literature as indicated in the following section. Likewise, the views of the customers and manufacturers in India are derived from [20], [21] and [23].
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Automotive Life Cycle Engineering
Safety Environmental impact Economic impact Availability Performance & Capability User acceptance
Safety
Environ. impact
Econ. impact
−
1/5
1/4
1/3
9
9
5
−
2
4
9
9
4 3
1/2 1/4
− 1/2
2 −
9 9
9 9
1/9
1/9
1/9
1/9
−
1
1/9
1/9
1/9
1/9
1
−
Avail.
Perf. and Cap.
User accept.
Table 3: Weighting of main criteria by government. Figure 4 : Four level structure of the criteria catalogue. Problem description – CNG vs. biodiesel in Dehli The urban traffic situation in emerging countries faces major challenges. Problems like environmental and noise pollution, fatalities and injuries as well as congestion and other mobility problems are much more severe compared to Northern America or Europe. Several reasons contribute to this situation. The rapid growth of the population combined with often uncontrolled urbanization has overpowered many cities. The demand for transportation by far exceeds the capacity which can be provided. Choosing a non-motorized mode of transportation like walking or cycling is often very difficult and dangerous urging people to choose motorized vehicles [19], [20]. Even though the city of Delhi still faces difficulties, improvements have been made. In 2003 the Indian capital was awarded the Clean Cities International Award by the U.S. Department of Energy because of the immense attempt to decrease pollution [21]. In the beginning of the 1990s India’s Ministry of Environment & Forests started a series of efforts to ameliorate air quality. More rigorous emission standards for vehicles, the withdrawal of old, ill-maintained vehicles, the introduction of CNG and higher quality fuels as well as the construction of the Metro Rapid Transport Service are some of the changes introduced [19]. To make further progress and improve air quality the next step would be to change the current fleet to newer, less polluting vehicles. However, the negative impact of banning older and more polluting vehicles could be immense. Many residents depend on their mobility to reach their place of work and would not be able to afford newer vehicles [20]. Against this background biodiesel is currently also promoted in India. The government targets a blend of 20% biodiesel and 80% fossil diesel by 2011-2012. In this process the plant jatropha has been identified as a potential feedstock for biodiesel because it has several advantages. Jathropha is a shrub which makes harvesting easier in comparison to trees. The plant has a short ripening period which makes it less time consuming to grow. In addition, the seed collection is not in the monsoon period like most other plants. Thus, it allows farmers to generate an income in a time during which they usually have less work and less income. It is furthermore very robust and not edible. Hence, animals do not feed on the plant [22]. The question arises whether the use of both fuels, CNG and biodiesel from Jatropha, is meaningful or if one alternative is much more favorable than the other. Pairwise comparisons of criteria In this case study CNG and a diesel blend of 20% biodiesel made from jatropha and 80% fossil diesel are compared. In order to demonstrate the weighting of criteria, pairwise comparisons of the main criteria are displayed for the governmental view in Table 3. As stated above the measures which have been taken so far show that the government is willing and able to invest in the transport sector to decrease impacts on the environment. Thus, a special
interest in environmental concerns can be deduced [21]. The automotive industry has also been identified as an important part of the nation’s economy and the Automotive Mission Plan sets goals for the years of 2006 to 2016 to strengthen this sector of the economy. Hence, it can be assumed that economic aspects of the automotive industry are of high importance to the government. The availability of resources and labor play a crucial role in the success of economic ambitions and are ranked accordingly [23]. Safety issues of propulsion systems are currently not targeted essentially as many other sources of danger are of larger amplitude. The number of fatalities is rising because the traffic system in general is mal controlled [20] [23]. The criteria performance & capability as well as user acceptance are assumed to be of marginal importance for the government. Likewise pairwise comparisons are conducted for all sub criteria in order to derive the local priorities for each element. Pairwise comparisons of alternatives To finalize the assessment, the two alternatives CNG and biodiesel must be compared with regard to all sub criteria. For reasons of clarity the sub criteria are reduced to the second level in this case study. Thus, the assessment includes a life cycle consideration whenever possible and reasonable. Table 4 shows the results of the pairwise comparisons with respect to each sub criterion from the governmental point of view. Regarding safety, the danger of fire is higher in the case of liquid fuels because it spreads on surfaces and cannot evaporate as fast as gas. In contrast, gas is more likely to explode which requires special security measures. With respect to the electrical hazard there is no indication that one propulsion system is more dangerous than the other. However, the two substances differ significantly with respect to their hazardous effect. On the one hand natural gas has no active toxic effects on humans but it can cause affixation because it lowers the oxygen content of air [24]. In addition, it cannot be seen or smelled if no odorants are added. On the other hand the diesel blend is toxic even though biodiesel is less toxic than fossil diesel [25]. In total, CNG and the diesel blend are considered equal with respect to toxicity. Regarding the environmental impact, the combustion of the diesel blend is considered better than the use of CNG in the categories global warming and the use of resources [26] [27]. A gas engine has less harmful effects in the categories eutrophication, acidification[26] [27], photo-oxidant formation [27] [28] and land use. Regarding the category noise, only differences in the use phase are considered. A gas engine is considered less noisy than a diesel engine [29]. With respect to the costs of the propulsion system, it is assumed that differences occur only in the operational use as the engines are comparatively similar and equally mature. The cost for CNG in Delhi is lower compared to a blend of fossil diesel and jatropha diesel [22] [30] while the costs of the vehicles are very similar [31]. Thus, a natural gas vehicle is considered superior. For the development of the infrastructure elaborate programs are necessary. For example, the government plans to establish 2500 nurseries for jatropha
Automotive Life Cycle Engineering
55
plants. Furthermore, farmers must be educated and the genetic properties of the plant must be improved [22]. In contrast, natural gas is readily available [32]. Hence, it can be concluded that the cost of infrastructure development is higher for biodiesel because the required measures are much more elaborate and comprehensive. This implies that the economic benefit for the country is higher because it offers new possibilities for farmers and support for research and development [22]. With respect to the use of resources natural gas is preferred. Natural gas is made completely out of fossil resources, the diesel blend to 80%. However, natural gas is preferable because India has much larger supplies of natural gas than of crude oil [33]. Also the production of jatropha diesel is not on a commercial level, yet [22]. With regard to the availability of the required infrastructure and staff the diesel blend is preferred because it can use all systems already present. Both fuels do not compete with food as long as jatropha is not cultivated on land that is intended for food. The technical properties of the two fuels are very alike [1], [34]. However, comparing the cruising range and fuel sensitivity an engine using diesel blend is superior. The cruising range of a CNG vehicle lies in between 200 and 300 km [3], whereas the diesel blend reaches values very similar to fossil diesel. Diesel blends of different rations can be used in the same engine without alterations allowing flexibility in the choice of fuels [35]. With regard to the criterion user acceptance, it can be assumed that the diesel blend is more user friendly than the gas engine. Both can be operated intuitively but the CNG tanks reduce the usable space in the vehicle. Additionally the refuel rate with gas is slower [3].
Criterion
Diesel blend: CNG
Safety
Criterion
Diesel blend: CNG
Availability
Fire safety
1/3
Resource
1/2
Explosion safety
3
Infrastructure
4
Electrical safety
1
Staff
4
Toxicity safety
1
Food
1
Environmental impact
Performance and Capability Cruising 4 range
Noise
1/3
Global warming
3
Power
1
Photo-oxidant formation
1/2
Torque
1
Eutrophication
1/9
Rotational speed
1
Use of resources
5
Life span
1
Acidification
1/7
Use of land
1/7
Economic impact Cost of propulsion system Cost of infrastructure development Economic benefit
1/4 1/3 5
Temperature sensitivity Fuel sensitivity User acceptance Intuitive operation Useable space Refuel/ reload rate
1 4
1 4 3
Table 4: Comparison of the performances of biodiesel and CNG.
Aggregation of results
7
In order to obtain the total priority every path from the goal down to the alternatives has to be considered and the values have to be aggregated. The resulting ranking of the alternatives is presented in Figure 5. It represents the preferences of the stakeholders regarding CNG and the diesel blend. The values of all different groups have been calculated in the same way as demonstrated.
Within this study an assessment of alternative propulsion systems using the AHP methodology was presented. The underlying criteria catalogue was derived from existing approaches used in previous assessments and the analysis of alternative fuels and engines, the market situation and surrounding conditions. Thereby, a wide range of influences and aspects of alternative propulsion systems are covered. The criteria catalogue provides the possibility to assess alternative propulsion systems from economic, ecological and social perspectives on a qualitative level. Inherent in AHP is the possibility to consider preference profiles of different stakeholders. Furthermore, the impact of propulsion systems across the entire life cycle and the impact of secondary products are considered as well. The presented methodology is usable for all presented alternative propulsion systems. Its application was shown by an assessment of two different propulsions systems.
Figure 5 shows that CNG is preferred slightly by consumers and the government whereas the manufacturer has no preference regarding the alternatives. Indian consumers prefer CNG as they are very price sensitive and the operational costs for CNG are lower in comparison to the diesel blend. From the governmental perspective the main reason for preferring CNG is smaller local environmental impact regarding photo-oxidant formation, eutrophication, acidification and use of land. However, all values alternate around the value of 0.5. Thus, it can be assumed that no alternative dominates the other. The result of the comparison shows that the diesel blend can be an alternative when considering global environmental impact as it reduces the global warming potential in comparison to CNG. However, neither of the two alternatives will lead to a break through regarding the challenges of urban traffic in India. Thus, further alternatives need to be considered and assessed to make further progress in the areas of sustainability and air quality in Delhi.
Total priority
CNG
diesel blend
0,5
SUMMARY AND OUTLOOK
As stated above (see chapter 3), the assessment of propulsion systems with respect to different time horizons is challenging. It is conceivable that an assessment of propulsion systems for 2015 differs from an assessment for 2025. This leads to uncertainties and requires considerable know-how regarding the development of technologies and markets. Although AHP allows for conducting sensitivity analyses these uncertainties are not reflected adequately with the proposed method. A distinct project focus of the BMBF funded project STROM, Strategic options of the automobile industry for the migration towards sustainable propulsion systems in established and emergent markets (www.strom-sustainability.de), is therefore to incorporate insecurities such as the maturity of the technology in the decision making process. For this purpose, methodological support will be developed in future.
0
8
Figure 5: Total priorities of CNG and Biodiesel.
The presented research work is partially financed by the German Federal Ministry of Education and Research (BMBF) and the German Academic Exchange Service (DAAD). The authors would
Government Manufacturer Customer
ACKNOWLEDGEMENTS
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Automotive Life Cycle Engineering
like to acknowledge the support of the BMBF for funding the research project “STROM - Strategic options of the automobile industry for the migration towards sustainable propulsion systems in established and emergent markets” under the reference 01UN1006A and the DAAD for funding the research cooperation “Lean and Green – A new passage to India” under the reference 50082745. 9 [1]
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Concept and Development of Intelligent Production Control to enable Versatile Production in the Automotive Factories of the Future 1
1
1
Sarfraz Ul Haque Minhas , Christian Lehmann , Ulrich Berger 1
Chair of Automation Technology, Brandenburgische Technische Universität, Cottbus, Germany
Abstract The automotive industry is being confronted with numerous challenges among which the increased product customization, short product life cycles and faster time to market are inducing a lot of complexities in controlling the current production systems. Consequently, the production systems are incapable of handling changing product variety and volume in a productive way and often involve intensive redesigning and reconfiguration processes. This paper introduces the concept of versatile production setups, an intelligent approach for selecting appropriate resources and optimal parameter settings to enable fast changeability as well as for reducing quality assurance time to achieve productivity in production processes. Keywords: Versatile Production Setups; Plug and Produce; Reconfigurability Ease
1
INTRODUCTION
The European manufacturing industry particularly the automotive industry has entered a new era in which they have to compete in a global economy. This has led them to confront many challenges like fast globalization, unpredictable and heterogeneous markets, increased product customization [1] [2], short innovation and product life cycles [3], reduced development time and faster time-to -market. This situation has induced a lot of complexities in controlling the current production systems [4] by imparting negative influence on productivity. Moreover, the increase in derivatives of vehicle models and growing trend towards the integration of new materials in the car Body-in-White development to enable reduction in weight and fuel consumption as well as the eco-efficient design, the quest for new and advanced material processing and production technologies has increased. This trend can be observed from the fact that multifunctional materials [5] as well as light weight materials [6] are being integrated into the car Body-in-White to make the manufacturers more competitive. Consequently, the future automotive production systems have to handle a huge variety of materials as well as various products of individualized geometries. The current production systems are quite flexible in switching from one variant to another however they are not capable of handling their high variations in a productive manner. The main reason is that the changing product design and integration of new materials are pushing the manufacturers to redesign/modify their production setups. It has led to a situation in which they will have to rely heavily on the system developers/suppliers for redesigning/upgrading their production facilities. This increased reliance will hinder them to achieve higher production rates. This is because the production system redesigning/upgrading is highly time consuming and cost intensive process. Currently about 75% of the vehicle production and 50% of automotive research and development is being done by suppliers and technical consultants [7]. The rapid customization and reduced product life cycle [8] demands from the manufacturers to configure their production system in the shortest possible time to enable fast ramp up of materials and technologies. It also requires achieving smooth and
effective production control in order to reduce production costs and time substantially. Moreover, it also demands them significant improvement of their production processes as well as the product quality. 2 2.1
AUTOMOTIVE PRODUCTION CONTROL: LIMITATIONS AND ANALYSIS Mass Customization: A challenge for the Production
Nearly every European automotive manufacturer is experiencing increased product complexity and reduced lifecycle time, whilst manufacturing a larger number of variants within shorter time-tomarket. A dramatic shift from production-push and economy of scale towards customer-pull and economies of scope is a solution to these problems. The current situation of product customization in the automotive industry is mainly attributed to the creation of new production segments and enrichment of existing ones with more possibilities for their individualization. The idea of platform sharing among different models has enabled the automotive manufacturers to offer a much wider range of product families, with different appearances and styles. On contrary to that, the migration principle [9] is helping certain automotive manufacturers to produce many models with additional variants from different OEMs. In this context, the shift towards development of Body-in-White on modular and scalable concept aims at offering broader range of customized products whilst maintaining low development costs. It concludes that the product development process is mainly accompanied by design reuse i.e. by taking more carryover of parts or by platform sharing for various vehicle segments. Additionally, the product development and process planning of diversified products is enabled with the help of powerful CAD/CAM and PLM systems. However, the handling as well as production of these products in the production setups is still a bottleneck. This situation occurs due to frequent variation in product features and materials, and the respective inappropriate adaptation, thereby generating internal complexities in the production systems. The state of the art automotive production process comprises of the technologies for
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_10, © Springer-Verlag Berlin Heidelberg 2011
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manufacturing diversified components and technologies for joining/or assembling them to complex customized products. A clear analysis of their production setups shows that they are mainly dedicated to the process (machining/joining/assembly) technology for which they are designed. A typical example is the production setup for welding automotive Body-in-White components. It is dedicated to weld automotive components of specific material combinations and certain range of dimensions only. Similarly, the production setups for joining technologies other than Welding e.g. adhesive bonding is dedicated for joining automotive components of specific material combinations and dimensions. The increasing multifunctional and light weight materials (as mentioned already) with changing Body-in-White shapes have influenced significantly, the automotive production setups by integrating various forming, joining and assembling technologies/techniques. It has not only increased the size of automotive production setups but also the complexity in its control. The increase in size of automotive production setups has compelled the automotive manufacturers to expand their production facilities which are hindered by limited and costly space requirements. The best option left in this situation is to allow reconfiguration of production setups by making them adaptable for other technologies and to achieve systematic reconfiguration process from their design to the job sequencing at the shop floor thereby implementing intelligent production control. 2.2
State of the Art Production Setups
The state of the art automotive production setups are comprised of various but distinct production/manufacturing cells connected to single/various lines. These production setups are quite flexible in handling various variants in their respective production setups. There exist two innovative categories of the production setups known as modular production setups and cellular production setups respectively. The modular production setups are relatively advanced setups that are designed to improve throughput by increasing the efficiency of parallel joining cell/subassembly lines feeding into the final assembly lines. They involve joining /assembling separate vehicle modules such as chassis, interior, body on their dedicated assembly lines connected with process dedicated joining cells [10, 11]. Another category refers to cellular layout based automotive production setup [12] which is considered as one of the major steps in accessing JIT. They are comprised of machines and robots gathered together to carry out multiple tasks such as material handling and welding typically run by single operator or a multi-person work cell. Each one of these setups exhibits the following complexities during their adaptation/reconfiguration:
Limited flexibility in programming of industrial robots due to the manufacturer dependent programming environment, Offline verification of industrial robot programs which in high variation would limit the adaptation and consume lot of time, leading to high production time and cost,
Complicated synchronized implementation,
Large number of interfaces within the production cell and noncompatibility issues with the centralized controller,
Fixed but highly branched program (spaghetti code) execution corresponding to the sensor data inputs and
Large volume of measured data and the resulting difficulty in database connectivity with certain Human Machine Interfaces such as Personal Digital Assistants (PDAs) for fast and remote interaction.
simulation
of
robots
and
its
In addition to all these aforementioned complexities, there are also limitations to reuse the automotive production setups dedicated to one process technology for any other technology. For example, the
production cell dedicated for adhesive gluing of car windows is inflexible to incorporate other joining technologies. It implies that in future automotive factories, the extinction of any of the process technology will require redesigning of the associated production cell to be used for some other process technology. The upgrading process will be highly cost inefficient and time consuming which may last from several weeks to several months. Moreover, the input coming to the production system in terms of nature of jobs will be of very distinct nature that will involve distinct processes/operations for distinct Body-in-White components. As a consequence, the production ramp-up time as well as the setup time will rise sharply resulting in decrease in the production throughput and increase in time-to-market. A comprehensive approach is therefore needed to resolve those complexities that the future automotive production setups are foreseen to experience. It also requires eliminating those limitations that the current production setups are having. 2.3
Derivation of Requirements Production Control
for
future
Automotive
From the discussion above, it can be further added that the market conditions and required production efficiencies of today are keeping mass production an acceptable business model for the present automotive factories. This trend is being diminished in favor of mass customization. Therefore the future automotive production setups need to be highly reactive and productive in managing change. This change is characterized by variations of the product volume (high, medium, low) as well by variations in terms of materials, forms and dimensions. It is a noteworthy fact that these variations influence greatly on the design, planning and control of these production systems. Moreover, as the high product variety is the main complexity driver in planning and controlling of the production systems, the method for sequencing of the production tasks (jobs) to achieve optimal configuration of the production setups is needed considering reduction in ramp-up time of materials and technologies. Also, an intelligent concept for production control is required to achieve reduced setup time, fast throughput, quality assurance time and their associated costs. From the requirements, it is deduced that the production system must be designed on versatile architecture where it can support the intelligent production control. It is particularly important for optimal reconfiguration of production setups using the same resources for distinct processes or operations. The intelligent production control also requires the sequencing and reordering of jobs to attain high reconfigurability ease in reconfiguration of production setups. 3 3.1
CONCEPT FOR INTELLIGENT PRODUCTION CONTROL Concept for Versatile Production Setups
The concept of versatile production setups for automotive factories of the future is derived from the idea presented in [13] which identifies their structuring into seven levels. Moreover, the factory should be designed considering the changeability enablers (modularity, scalability, adjustability, universality, compatibility, mobility, convertibility etc.). The production design based on these enablers will address the impact of product variations and frequency (volume per certain amount of time) on the design of production setups. Therefore, the versatile production setups must be characterized by their adaptable functionality and scalable capacity. It can add, remove, and change/replace software and hardware modules to support a broad product range with required cost and quality. These hardware and software modules can be related to the cells, machines, process equipments, sensors, actors, handling and fixation units etc. Figure 1 highlights the essential
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features/attributes needed to enable versatile production based on reconfigurable control. Web
Automotive manufacturer
Production planning
Configurator interface Product sequencing
Layout definition
Product Design
Design rules, components/modules libraries, control programs
Digital Simulation
Production metrics (Production throughput, cycle time, collisions, offline robot programs)
Production setup controller (Parameter Settings) Production control
Product design, rules
Actual production metrics Control parameters of the defined layout
Technology data catalog
End customers
Production Setups (Process applications: Machining/Joining/Assembly)
Figure 1 : Modules and subsystems of versatile production setups. These essential features/ attributes are the following: i)
ii)
Integration of customer in product design process to analyze their demands and offer them best matching products. It can be achieved through web based collaboration or by employing other methods for analyzing the end user requirements. Introduction of an intelligent production setup configurator as an interface between the product design phase and the production planning phase named as Configurator interface. This configurator helps in optimizing the components and modules of the production system and the features that these production setups should contain and the functions that they should perform.
iii) Integration of knowledge base with the digital simulation to analyze the production related metrics well before the production layout is configured helps in achieving the production goals. iv) Introduction of production setup controller to enable configuration or reconfiguration of the production setups based on optimal scheduling of jobs intelligently. The optimization of production resources carrying the adaptable functionality (e.g. responsive to variable range product features, various materials) as well as the scalable capacity (adaptation to different range of tasks, production volumes) against a definite customization is mathematically modeled using the Axiomatic Design approach. Compared to other available designs approaches like TRIZ, axiomatic design methodology offers a generic framework and hierarchical structure to achieved precise and simple optimization of production resources. The methodology is used to analyze a certain range of product characteristics and the selection of corresponding optimal processing technology e.g. joining technology/assembly technology. The precision in optimal selection can be achieved provided the pure knowledge base approach is adopted. However, this approach will have limited implementation due to the complexity and level of effort needed to acquire domain knowledge as well as design knowledge. So a knowledge reuse up to an achievable level is extremely important to assist the automotive manufacturers in preventing any errors that may generate in the selection process. The technology data catalog is therefore introduced to serve this purpose. It acts as an intelligent subsystem of the versatile production system that stores the application-specific knowledge and provides the best practice settings in the reconfiguration process.
The concept of versatile production setups is implemented by considering the innovative joining processes in automotive Body-inWhite development. The concrete reason behind this consideration is the increasing integration of advanced materials besides aluminum and steel to support eco-efficient design of vehicles. Foreseeing this trend, the increase in material pool will also increase the number of joining technologies with limited scope. Currently, welding process e.g. laser welding is widely used automated joining technology in Body-in-White development and is delivering best quality joining. However, new materials are limiting its scope as it is applicable only to joining of similar materials. This in turn, has enhanced the exploitation of adhesive bonding in future joining applications. Therefore, versatile production setups must support various joining processes to be carried out using the same resources with minimum changeover. This changeover can be made in process monitoring, handling systems and the process equipment while executing the sequenced tasks using versatile production setups. 3.2
Concept for Production Planning and Control
The production systems of the future will experience immense level of complexities in production planning due to distinct characteristics of individualized products. Therefore, the production system needs to be reconfigured efficiently and precisely. The reconfigurable production planning and scheduling is divided into two phases. In the first phase, the reconfigurable production setup layout is to be defined followed by the allocation of production resources and their reconfiguration in the product sequencing phase. In the layout definition phase, the structuring of the resources is made in a way to accommodate a broad range of products using the same resources. This phase takes the input from the simulation and analysis module (see Figure 1) which also gives the evaluation metrics about the overall cycle time as well as the allocation to make the layout definition phase more precise. These metrics are evaluated to achieve high throughput by minimizing the buffer and transportation time of each part. The second part is the product sequencing phase where the products are sequenced optimally using intelligent algorithms. Each mass customized product has to be produced in the given product layout, therefore jobs specifications are analyzed and sequenced in a way to achieve high reconfigurability ease in the reconfiguration process. The high reconfiguration ease is dependent upon the variation in the product specifications that are to be manufactured at the particular production setup. A similar issue but from the other domain has been addressed in [14] for generating an optimized work plan based on feature technology in NC machine tools. A further extension of this work is already implemented and mentioned in [15] for planning and scheduling of machine operations. The perspective planning and machining operations are transferred into a directed graph. The results acquired after the implementation of the Floyd-Warshall algorithm [16] (Jungnickel, 1990) deliver "cost-efficient" sequencing order of the NC operations [17]. A similar methodology is adopted for the production domain i.e. in joining and assembly processes to achieve high reconfigurability ease. It is required that the intelligent production control implements the optimal parameter settings in versatile production setups and executes the tasks in a generated sequence from the planning module. The job sequencing based on high reconfigurability ease helps reduction of setup time and ensures high throughput from the production system. Additionally, it should also assist in reducing production costs and high process reliability and product quality.
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Automotive Life Cycle Engineering DEVELOPMENT OF INTELLIGENT PRODUCTION CONTROL
4.2
Development of Versatile Production Setups
Development of Intelligent Production Planning and Control
The development of versatile production setups is initiated after the optimal selection of production setup components. The crucial factors that drive the optimal selection process are the desired production goals and set of customer demands. The optimization can be done through mathematical design approach i.e. the Axiomatic Design that translates customer requirements from the customer domain into the functional requirements (functional domain) and the designed parameters. The mapping process between functional requirements (FR) and design parameters (DP) is expressed mathematically in terms of the characteristic vectors that define the design goals and design solution.
After defining the versatile production setups, the optimal sequence and ordering of the jobs is done. In this process, the Technology Data Catalogue (TDC) which acts as a knowledge base provides all relevant applicable technologies corresponding to the joining tasks. The sequencing system has manifold access to the PPR (process, product, resource) hub to get information about the produced mass customized product and other relevant information such as materials, joining technique applicability and tolerances. The sequencing (see Figure 3) classifies joining cells as:
FR1 X FR2 0
0 DP1 X DP2
(1)
Some of the functional requirements from the functional domain are the following:
Capability: At this joining cell, the joining task can be executed
Incapability: At this joining cell, the joining task cannot be executed due to certain constraints (productivity issues)
Non-designated: The assignment of tasks at this particular joining cell is not possible at the desired allocation time; therefore this alternative branch will be assigned higher weight.
Selected: This particular joining cell is selected for sequencing along with parameter settings.
ܴܨଵ = respond to market quickly (delivery time)
Joining Layout
ܴܨଶ = perform joining operations for Body-in-White with high accuracy ܲܦଵ = short setup time
Sequencing & Parameter Settings (PS)
ܲܦଶ = reconfiguration of the joining cell
I) Identifying the relevant process incapability
II) Determining the allocation
Joining cell with three alternative parameter settings Selected
III) Selecting the joining cell with parameter settings
Capability
Non-designated
Incapability
Figure 3 : Sequencing of product workflow in production layout.
Figure 2 : Versatile joining cell (conceptual and virtual model). The functional requirements are further decomposed at different levels to determine the detailed design parameters. The information axiom of the axiomatic design selects the appropriate components of the versatile production setups. The versatile production setup (versatile joining cell) is developed on modular and scalable architecture with plug and produce components to enable quick and error-free configuration/reconfiguration of the cell (see Figure 2). These components are related to process equipment, handling and process monitoring systems as well as actuators. For versatile joining cell, the changeover can be made from one joining technology to another, similar to the CNC machines where different machining operations are carried out by changing the tool. It means that the adhesive dispenser is replaced by laser welding head upon requirement considering the safety standards. The adhesive dispensing nozzles can be exchanged during the joining operation if adhesive beads of different forms and dimensions are to be applied. Variant flexible gripper adapts itself in case it has to handle parts of distinct forms and sizes.
The product information as well as the joining task is taken into the processes of the joining layout (see Figure 3(I)) with the help of the variant process planning documents which are stored in the PPR hub. At this point, the system can determine the capable joining cells that can contribute in accomplishing various joining tasks. The branches with the incapable joining cells are marked as nonsequenced. The capable joining cells with different parameter settings are then assigned. In the second step (see Figure 3(II)), the designation of each joining cell is checked for the desired allocation time. If the cell is not allocated, then the effort of the corresponding branch will be increased sharply provided that the other alternatives are incapable of doing the same task. In the third step, the effort of the remaining joining cells with concrete parameter settings is designated (III) with the help of introduced metrics. Thus, each parameter settings of every remaining joining cell are assigned with individual effort. The application of Johnson’s algorithms known from graph theory determines the sequence of joining cells with configuration that has in total the least sum of efforts (high reconfigurability ease). The sequenced work plan is used to process the mass customized products in the selected joining setup. The calculation of this optimal work plan can be done in realtime, because of the low runtime of algorithms.
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In the versatile joining cell, each of the joining tasks (JT) can be split into more than one sub tasks (ST). This is however, mainly dependent upon the type (form) of joining interface (JI) as well as its dimensions. Joining tasks (JT) can be mathematically described as n
JT JSTi
(2)
i 1
A subtask in this particular case is defined as an activity performed in which no alteration is made in the configuration (related to hardware/software) of the subsystems of the joining cell. The joining sub task is a set of 7-tuple and can be mathematically described as JST= {M, T, P, HU, F, S, JI}
(3)
Where M= manipulator, T= technology, P= path, HU= handling unit F= fixture, S= sensoric, JI= joining interface It is important to mention here that every JST (3) provides the parameters of the joining process and can be named as changeover (reconfigurability) parameter setup. The production is controlled by quick reconfigurability (high reconfigurability ease) by taking up the products with minimum change in JT tuple parameter values between two consecutive tasks in the sequence as well as delivery time of the product. This concludes that the reconfigurability effort is strongly dependent upon the minimum change in JT tuple and the delivery time of the product. By this approach, the setup time of the joining cell is reduced significantly. 5
KR16 takes the subassembly floor module components and moves them under the stationary dispenser for adhesive application. The KR15 robot takes the glued components from the turn table and places it outside the joining cell (in a heating oven or conveyor). To achieve efficient reconfiguration, distributed control architecture is adopted in which the cell controller is separated from an additional controller. This additional controller is directly connected to the process sensors and actuators and handles the measurement data from the joining process separately to enable quick reconfiguration of process sensors and actors. The Cell controller is mainly responsible for controlling the sequential operation of the cell components. It is a standardized embedded controller SIMATIC S7 and is connected to the cell components over Profibus/ProfiNet as a standardized interface. The measurement controller is a programmable automotion controller from National instruments that provides the PC based functionalities. Its user friendly graphical programming environment i.e. Labview, is particularly suitable for analyzing measurement data as well as developing real time control programs in a short time. Hence, the fast configuration/reconfiguration of the cell is achieved. The on-process quality assurance is a highlighting topic of this development to control the adhesive bead form and position in real time scenario. The sensor actor head unit as a plug and produce device (see Figure 4 (b)) is an independent solution for online correction of the robot path and therefore can be reconfigured quickly.
TECHNICAL IMPLEMENTATION: VERSATILE PRODUCTION SETUPS AND PRODUCTION CONTROL
The scenario for the implementation of versatile production setups and respective reconfigurable control is based on production cell development for joining automotive subassemblies. The cell setup (hardware and software) is grouped into modules to achieve versatility in the setups. This means that the cell can be configured from adhesive bonding to laser welding in a very short time depending upon the material combinations selected as well as the required quality. The commonality between cell components required for both processes is determined to enable easy and fast configuration. The current state of the art joining cell is flexible in handling and fixturing systems only. The innovative aspect of the implementation is on versatile production setups in which different cell subsystems such as the process equipment, manipulators, handling units as well as monitoring unit are divided into modules and sub modules to allow easy replacement of components during reconfiguration. To lower complexity in first place, a semi automatic and modular adhesive bonding cell (see Figure 4(a)) is implemented to allow joining multi-variant subassemblies of automotive Body-in-White floor module components. The cell consists of KUKA KR 16 and KR 15 robots assembled around a turn table KUKA KPF1-MB1000. The KUKA KR 16 robot can be configured or reconfigured for two purposes. It can be a part of handling system to take the sub assembly (adapter and seat cross members) under the stationary process application head i.e. either adhesive dispenser or laser welding head. It can also be configured by either taking the adhesive dispenser or laser welding head to enable joining of parts. The process application unit i.e. adhesive dispenser can be reconfigured by exchanging various different nozzles (round, triangular, semi rectangular), allowing application of adhesive beads of variable forms and dimensions, thus enabling a wide range of subassemblies to be glued together in the same facility. The second robot KR15 can be used for transporting the material in or out of the cell as well as to the next cell. A turn table transports the subassemblies from one robot to the other. Additionally, it also serves as an extra axis to the robot to achieve application of adhesive on complex surfaces. The KUKA robot
Figure 4 : (a) Joining cell setup (b) Sensor-actor head unit. The independent solution in this context means that it can be attached to any robot type or installed on the stationary dispenser to influence the adhesive bead position and form. This solution saves time and associated costs involved in post visual checking processes that are currently carried out in adhesive and sealing applications in the automotive industry. Thus, there is a significant reduction of quality assurance time in Body-in-White joining processes in future. It is imperative to highlight that this particular setup supports the foreseen layout of the future automotive production setups in which production will be carried out in the reconfigurable cells. Moreover, the role of automated guided vehicles for material transport will be expanded as they will replace current conveyor systems. It will not only make them smart but also scalable to allow reconfiguration of factory space. 6
CONCLUSIONS AND OUTLOOK
The increasing new materials integration in automotive vehicles as well as the related processing technologies is reducing the life span
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of automotive production setups. Furthermore, the reduced product life cycle and the faster delivery are also influencing negatively to the ramp-up of technologies and processes in the automotive industry. Therefore, the automotive industries of the future need versatile production setups to cope with the huge investment and the time involved in redesigning them. These setups are developed on modular and scalable hardware and software components. These components act as plug and produce devices and modules respectively, thereby enabling quick reconfiguration. The production setups design needs to be optimized based on the product requirements from the market. Axiomatic design methodologies through knowledge exchange from the technology data catalog can optimize the design of the versatile production setups. The intelligent production control approach handles changing product variety and volume in a productive way. This is done by intelligent planning of the resources to achieve optimize layout in the production system. This is followed by the product sequencing with minimum reconfigurability effort combined with optimal parameter settings of the versatile production. This concept is implemented on the versatile joining cell for joining Body-in-White components, achieving lower ramp-up time, setup time as well as quality assurance time, thereby elevating productivity. An extension to this work is of much wider scope in context of resolving mass customization induced complexities in future automotive production systems. The work aims at reaching an efficient level of decentralization in production. Therefore, the productivity will be enhanced by configuring the production system through web based collaboration mechanism. 7
ACKNOWLEDGMENTS
The work reported in this paper is regarded as a part of dissemination activity and is supported by European Commission FP6 NMP Program, "Integration Multi-functional materials and related production technologies integrated into the Automotive industry of the future" (FP6-2004-NMP-NI-4-026621). A further development that is currently being made on this work will be supported in future by EC FP7 Factories of the FUTURE Research Project e-Custom "A Web-based Collaboration System for Mass Customization" (NMP2-SL-2010-260067), to control production systems through web based collaboration among its participating actors. 8
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[10] Marinin K. J., Davis T. R.V. (2002): Modular assembly strategy in international automotive manufacturing, International Journal of Automotive Technology and Management, vol. 2, no. 3-4, pp. 353-362. [11] Fredriksson P. (2002): Modular assembly in the car industry an analysis of organizational forms’ influence on performance, European Journal of Purchasing & Supply Management, vol. 8, no. 4, pp. 221-233. [12] Kirk S.,Tebaldi E. (1997): Design of robotic facilities for agile automobile manufacturing, Journal of Industrial Robot, vol. 24, no. 1, pp. 72-77. [13] Wiendahl H.-P., ElMaraghy H.A., Nyhuis P., Zäh M.F., Wiendahl H.-H., Duffie N., Brieke M. (2007): Changeable Manufacturing - Classification, Design and Operation, CIRP Annals - Manufacturing Technology, vol. 56, no. 2, pp. 783809. [14] Cai, J., Weyrich, M., Berger, U. (2005): Ontological Machining Process Data Modelling for Powertrain Production in Extended Enterprise, Journal of Advanced Manufacturing System (JAMS), vol. 4, no. 1, pp. 69-82. [15] Berger, U., Kretzschmann, R. (2007): Development of a holistic guidance system for the NC process chain for benchmarking machining operations, Proceedings of the 12th IEEE Conference on Emerging Technologies and Factory Automation. [16] Jungnickel D. (2008): Graphs, Networks and Algorithms, 3rd ed., Springer. [17] Hamelmann S. (1996): Systementwicklung zur Automatisierung der Arbeitsplanung (System development for the automation of work planning), Fortschriftberichte VDIReihe 20 Nr. 195, VDI Verlag Düsseldorf.
Resource Efficiency – what are the Objectives? Marko Gernuks 1
1
Volkswagen AG, Group Research Environmental Affairs Product; Wolfsburg, Germany
Abstract This paper shows that resource efficiency pursues the three objectives reducing environmental impacts, improving supply security and saving costs. Firstly, a differentiation is established between objectives and measures to achieve them. Secondly, methods to assess improvements relating to these objectives are discussed and applied to the case study of vehicle lightweight design. The presented example of substitution of steel by aluminum illustrates that trade-off between objectives may occur. Thus, a reliable assessment of measures to enhance resource efficiency must consider impacts on each objective separately. An aggregation to only one resource efficiency indicator lacks transparency and is not appropriate. Keywords: Resource Efficiency; Life Cycle Assessment; Methodology
1
INTRODUCTION
There is general agreement regarding the importance of resource efficiency, and almost every company can point to resource-efficient products. But more detailed discussion reveals that resource efficiency has many different meanings, which vary depending on the particular standpoint. In a range of presentations and publications, techniques such as substitution, recycling, lightweight design etc. are presented as resource-efficient, even though no commonly accepted definition of the term exists, as yet. This paper does not intend to provide a definition of resource efficiency, but aims to clarify the objectives of resource efficiency. The term "resources" as used here refers to non-energetic raw materials. Furthermore, the challenges of defining appropriate assessment indicators for resource efficiency are described and aspects that have to be taken into account are discussed. 2
OBJECTIVE OR MEASURE?
In the absence of general agreement on what resource efficiency really is, no differentiation has yet been established between resource efficiency objectives on the one hand and measures to achieve them on the other. Taking recycling as an example, it is first necessary to ask the question – why recycle? Why is it desirable to strive for closed-loop material use? Essentially the assumption is that closed-loop recycling will have a reduced environmental impact compared to primary production. Cost savings are also expected, as well as enhanced resource supply security. It follows from this that recycling is a measure to achieve the objectives:
reducing environmental impacts,
improving resource supply security, including lowering import dependency, e.g. for metals, and
cost savings in production.
Besides recycling, other potential measures to achieve the above-
mentioned objectives include substitution, extended product lifetime or use of renewable raw materials. But none of these measures are objectives themselves, as it makes no sense to recycle material without reducing environmental impacts, improving supply security or saving costs. Taking the proposed differentiation between objectives and measures as a basis, as a next step a framework for the assessment of resource efficiency can be developed. I.e. measures can be assessed with regard to their efficiency in achieving improvements with regard to the three objectives. The following sections discuss challenges with regard to the respective assessment procedures and provide proposals as to how to tackle these. 3
ASSESSMENT OF ENVIRONMENTAL IMPACT
An important aspect in assessing environmental impacts is the definition of system boundaries. For raw materials often only the impact of the production phase is considered when performing such assessments. However, materials choices may significantly influence environmental impacts during the product use phase as well. This can be demonstrated by the practical example of lightweight vehicle design (cf. Figure 1). The aim of the car manufacturers is to reduce vehicle weight in order to reduce fuel consumption and thus also CO2 emissions. In this context the substitution of steel by the lightweight material aluminum is a promising measure. Depending on the specific vehicle part that is substituted by aluminum, a weight reduction of 10 to 40% compared to steel is possible [1]. At first sight, this might be thought to represent a big improvement in resource efficiency. Looking at the CO2 emissions during production, however (Figure 1), it becomes clear that aluminum causes significantly higher CO2 emissions than steel at this stage, even if the full lightweight design potential of 40% is realized. If the system boundaries were restricted to material production, steel would clearly be the preferable alternative to aluminum. However, a
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_11, © Springer-Verlag Berlin Heidelberg 2011
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[kg CO2-eq] 20
Production
Use phase
15
10
5
0
Aluminum Aluminium Steel Stahl Steel (1 kg)
Al Al Al (1 kg) (0.8 kg) (0.6 kg)
0 km
150,000 km
Figure 1: Greenhouse gas emissions of lightweight vehicle design based on replacing steel with aluminum (data source: GaBi 4.3). broader view taking into account the entire life cycle, including production and the product use phase, reveals a different picture. A 40% weight reduction would result in the additional CO2 emissions from aluminum production (compared to steel) being offset in the course of the product use phase. If only a 20% weight reduction were implemented, a break-even would not be achieved within the assumed lifetime of 150,000 km. From this example two conclusions can be derived: 1. Materials that cause higher emissions in production can nevertheless be environmentally beneficial overall, due to advantages during the product use phase. 2. No material is inherently "good" or "bad". It depends on the specific application of the material, and thus a case-specific analysis is indispensable. In this example the realized weight reduction when substituting steel by aluminum is crucial. Apart from the definition of system boundaries, for the assessment of environmental impacts it must be decided what impacts to take into consideration. As well as looking at greenhouse gas emissions (measured in kg CO2-equivalent), cf. Figure 1, further impacts such as acidification, photochemical ozone creation or land use should also be taken into account for a full assessment of environmental performance. For all these topics the ISO standard 14040 and 14044 for Life Cycle Assessment [2] provides very sophisticated and clear guidelines. Certainly, LCA studies based on ISO 14040 require a considerable effort, but the procedure is indispensable for a reliable assessment of the environmental impacts of raw materials and their applications. 4
ECONOMIC ASSESSMENT
As with the assessment of environmental impacts, for the economic assessment too system boundaries must be defined. This is very much dependent on perspective. For companies, the economic assessment might at first sight appear straightforward. Raw materials prices are readily available. Taking the example of aluminum versus steel, aluminum results in
significantly higher costs: steel prices recently stood at around € 0.60 per kg [3] whereas aluminum cost about € 1.7 per kg [4]. Taking the 40% weight reduction into account, 0.6 kg aluminum would still cost € 1. In this simple economic comparison steel would be preferable. However, again this is only part of the story. In car manufacturing, further economic aspects must be taken into consideration – for example the cost impact of different body-in-white assembly technologies. In addition to production costs alone, depending on the perspective of the analysis more comprehensive approaches may be appropriate when making the economic assessment. For instance, the Total Cost of Ownership (TCO) approach could be of interest from the standpoint of customers. The probable higher price of a car with more aluminum parts might be economically acceptable if lower costs during utilization, due to lower fuel consumption, would lead to lower total costs over the life cycle as a whole. 5
ASSESSMENT OF RESOURCE SECURITY
In the current debate on resource efficiency, it is often claimed that it is necessary to reduce resource consumption due to the increasing scarcity of raw materials. However, geologists from various European countries who were part of the European expert group on defining critical raw materials concluded that in general there is no geological scarcity of raw materials [5]. Nevertheless, raw materials prices have increased enormously over recent years and the reasons must be examined. In cooperation with the Federal Institute for Geosciences and Natural Resources (BGR) Volkswagen has developed a methodology to assess raw materials risks [6]. This comprises 5 risk indicators:
Current supply / demand situation,
Raw material production costs,
Geostrategic risks,
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Risk through mining company concentration, and
6
The future trend in supply / demand.
For the implementation of resource efficiency in corporate management systems a common understanding of the term itself, and also clear objectives, are crucial.
Figure 2 illustrates the criteria for the geostrategic risks indicator. The x-axis shows country concentration, the calculation of which is based on the number of countries in which a particular raw material is produced. The highest possible value of 10,000 would mean 100% of a raw material is produced in only one country. On the yaxis the result of weighted country risk, based on the World Bank Index (WBI), is displayed. The WBI consists of six criteria, such as a corruption index, which are aggregated to a risk value for each country. The country risk for a raw material is then weighted according to the respective country's share of world production.
CONCLUSIONS
As a first step this paper establishes a clear differentiation between objectives and measures to achieve them. As a second step, assessment methods for the objectives reducing environmental impacts, improving supply security and costs savings are discussed. The discussed methods are in common use and often already part of corporate management systems. Although the use of these methods might be time-consuming and requires expert knowledge, they are indispensable for the reliable assessment of resource efficiency. A further key conclusion of this article is that measures aimed at enhancing resource efficiency may involve trade-offs. The presented case study of steel substitution by aluminum to reduce vehicle weight shows potential reduction of environmental impacts on the one hand, but possible negative economic effects on the other. An aggregation of the assessment results for the three objectives to a single indicator, which is the practice currently followed in the resource strategy of the German government [8], is not meaningful, due to the resulting lack of transparency about possible negative effects.
Taking again the example of aluminum versus steel, the criteria used in figure 2 reveal no significant risk for either metal, be it with regard to country concentration or country risk. However, a disaggregated assessment taking into account all five indicators mentioned above might reveal some specific risks for the metals in question. To summarize, although there is generally no risk of geological depletion of raw materials, industry's access to them could nevertheless be subject to a variety of other risks. Therefore, a range of risk indicators should be applied to identify specific risks for the raw material under consideration. Given the diversity of risks, a corresponding variety of specific measures is necessary to reduce the supply risk. The risk may be reduced through measures such as hedging, diversifying suppliers, substitution or enhanced recycling. However, there is no universal solution; appropriate measures must be considered on a case-specific basis for each raw material.
Before any reasonable regulatory measures on resource efficiency can be introduced, it will first be necessary to achieve general agreement on objectives, measures and their effects. This should be a main task of further research work. Companies will have to decide if a specific management system for resource efficiency is reasonable, especially if they have already established sustainability monitoring instruments which pursue almost similar objectives.
-1.5
Weighted country risk of production (based on World Bank Index)
high
metal (refined)
metal (ore)
-1.0
Sn
0.0
Au
Ag Ni Ni
0.5
Cu
1.0
1.5 400
REE
Sn
-0.5
Mn Zn Cd
Fe Bauxit Al Pb Pb Zn Cu
Te
Co Bi FeCr Cr Si Mo
V
W
Sb Mg Nb
Pd Pt
Zr
Ti
Bi
Ta Li high
low 1,000
2,000
Country concentration (Herfindahl -Hirschman Index of production 2008) Figure 2: Supply risk criteria, based on [7].
10,000
66 7
Automotive Life Cycle Engineering REFERENCES
[1]
International Organization for Standardization - ISO 14040 (2006): Environmental Management Life Cycle Assessment – Principles and Framework. 2nd ed. Geneva: International Organization for Standardization.
[2]
Krinke, S., Koffler, C., Deinzer, G., Heil, U., (2010): An integrated life cycle approach to lightweight automotive design, in: ATZ, Volume 112. pp. 36-42.
[3]
Metal Bulletin (2010): EU domestic prices for hot rolled coil, Metal Bulletin, 6 December, Number 9178, p. 24.
[4]
Metal Bulletin (2010): LME settlement prices for Aluminum high grade, Metal Bulletin, 6 December, Number 9178, p. 20.
[5]
European Commission (2010): Report of the Ad-hoc Working Group on defining critical raw materials, Version of 30th July 2010.
[6]
Rosenau-Tornow, D., Buchholz, P., Riemann, A., Wagner, M. (2009): Assessing the long-term supply risk for mineral raw materials – a combined evaluation of past and future trends, in Resource Policy 34 (2009), pp. 161-175.
[7]
Steinbach, V.: Verfügbarkeit von Hightech-Rohstoffen. Presentation of the BGR at the Euroforum conference Technologiemetalle. Frankfurt, 21-22nd September 2010.
[8]
Statistisches Bundesamt (2010): Nachhaltige Entwicklung in Deutschland – Daten zum Indikatorensystem 2010. Statistisches Bundesamt, Wiesbaden, p. 7.
Comparative Life Cycle Assessment of Remanufacturing and New Manufacturing of a Manual Transmission 1
1
Jens Warsen , Marlisa Laumer , Wolfgang Momberg 1
2
Volkswagen AG, Group Research Environmental Affairs Product, Wolfsburg, Germany 2
Volkswagen AG, Remanufacturing Powertrain, Kassel, Germany
Abstract A newly manufactured 5-speed manual transmission is compared with a genuine remanufactured transmission of identical design. The Life Cycle Assessment covers the entire product life cycle from production through to disposal. In all the environmental impact categories considered, the remanufactured transmission performs significantly better than the newly manufactured unit. The environmental improvements are attributable to the substantial reduction in material and energy consumption. Transport of used transmissions to the remanufacturing facility accounts for only a small fraction of total life cycle environmental impacts. Energy consumption is reduced by 33 % for the remanufactured transmission compared with a newly manufactured transmission. Keywords: Comparative LCA; Remanufacturing; Transmission
1
INTRODUCTION
Volkswagen is committed to developing vehicles and components with better environmental properties than their predecessors over the entire life cycle. Life Cycle Assessments (LCA) are used to document the environmental performance of vehicles, technologies and processes. We have been using Life Cycle Assessments to enhance the environmental compatibility of our cars and their individual components since 1996. As part of an integrated product policy, the LCA considers not only individual environmental aspects such as the driving emissions of the vehicle but its entire life cycle. The Life Cycle Assessment issued by Volkswagen are in accordance with ISO 14040/44 [1, 2] and are verified by independent experts, e.g. by TÜV NORD. Volkswagen today has a 60 year history of remanufacturing. The so called „Exchange Parts Programme“, a take-back scheme for used vehicle components, was launched back in 1947 in order to meet raw material shortages in the post-war era. Initially the scheme was confined to five product groups: fuel pumps, clutch pressure plates, steering boxes, carburettors and cylinders with front and rear axles and industrial engines following during the subsequent years. Being extremely successful, the programme saved substantial amounts of raw material and energy as a large part of the components usually can be directly reused. For these component parts the complex production process of smelting, casting and forming is saved. Nonetheless genuine part quality is guaranteed for all remanufactured components. The aim of this LCA-study is to analyse and compare the environmental impacts of a newly manufactured transmission model with a genuine remanufactured transmission of identical design. More detailed information about this analysis can be found at www.mobility-and-sustainability.com. 2 2.1
MATERIALS AND METHODS Data origin of LCI analysis
The data used for preparing the Life Cycle Assessment can be
subdivided into product data and process data. “Product data” describes the product itself, and among other things includes information on parts, quantities, weights and materials. “Process data” includes information on manufacturing and processing steps such as the production of materials and semifinished goods, fabrication and the provision of electricity and other consumables. This information is either obtained from commercial databases or compiled by Volkswagen as required [3, 4]. The data selected are as representative as possible. This means that the data represent the materials, production and other processes as accurately as possible from a technological, temporal and geographical point of view. For the production processes carried out at the Kassel plant chiefly primary data is used. Upstream supply chains and external production steps are modelled using published industrial data. As the transmissions are remanufactured centrally by Volkswagen in Kassel, as far as possible data related to Germany is applied. Where German data is not available, European and occasionally global conditions are reflected. The Life Cycle Assessment model for transmission production was developed using Volkswagen‘s slimLCI methodology [5]. The transmission parts list was used as data source for product data, and data on weight and materials was taken from the Volkswagen material information system (MISS). This information was then linked to the corresponding process data in the Life Cycle Assessment software GaBi. 2.2
Data quality
Material inputs, processing procedures and the selection of data in GaBi are standardised to the greatest possible extent, ensuring that the information provided by VW slimLCI is consistent and transparent. In sum, all information relevant to the aims of this study was collected and modeled completely. The modelling of components on the basis of transmission parts lists ensures that the model is complete, especially with respect to the manufacturing phase. In addition, as the work processes required are automated to a great extent, any differences in the results are due solely to
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_12, © Springer-Verlag Berlin Heidelberg 2011
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changes in product data and not to deviations in the modelling system. 2.3
Impact assessment
The Impact Assessment is based on CML methodology [6]. The assessment of environmental impact potentials in accordance with this method is based on recognized scientific models. A total of five environmental impact categories were identified as relevant and were then assessed in this study:
key technical data of this transmission. The two transmissions are comparable as the remanufactured unit is guaranteed to have the same quality as a genuine newly manufactured gearbox. 5-speed front wheel drive car transmission Torque
250 [Nm]
Max Power
132 [kW]
Length
370 [mm] 41.3 [kg] , with oil
eutrophication potential (EP)
Weight
ozone depletion potential (ODP)
Design
Twin-shaft gearbox
photochemical ozone creation potential (POCP)
Synchromesh
3rd to 5th gears: single-cone Borg-Warner,
global warming potential (GWP) for a reference period of 100 years
acidification potential (AP)
3.1
CASE STUDY Objective and target group of the assessment
The objective of this Life Cycle Assessment was to compare the environmental profiles of newly manufactured and remanufactured transmissions. For this purpose, a manual 5-speed MQ 250 transmission, currently Volkswagen`s highest-volume transmission, and a corresponding remanufactured unit have been assessed. 3.2
Reverse gear Reverse brake (input shaft) Housing
Besides this, primary energy demand has been also recognized. These six categories were chosen because they are particularly important for the automotive sector, and are also regularly used in other automotive related Life Cycle Assessments [7, 8, 9]. 3
1st and 2nd gears: triple-cone Smith
Function and functional unit
The functional unit for the Life Cycle Assessment was defined as the manufacture of a Type MQ 250 transmission. Table 1 shows the
Diecast aluminium
Table 1: Technical data of MQ 250 transmission. 3.3
Scope of assessment
Figure 1 shows the scope of this Life Cycle Assessment. It was defined in such a way that all relevant processes and substances are considered, traced back to the furthest possible extent and modeled at the level of elementary flows. The material fractions generated during recycling are the only exception. The manufacturing phase was modelled including all manufacturing and processing stages for all transmission parts and components. The model includes the transport of the old transmission to the plant, the dismantling, cleaning and testing of the old transmission, the production of replacement parts and the reassembly of the transmission. The model therefore covers all steps from the extraction of raw materials and the manufacture of semifinished products right through to assembly.
Figure 1: Scope of Life Cycle Assessment.
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Figure 2 shows a more detailed plan of the transmission remanufacturing process. The model includes the transport of the old transmissions from main distribution warehouses of the individual markets to the central remanufacturing plant at Kassel. The average distance travelled by an old transmission is 1,086 km. The national collection process is not considered as details of workshops dealing with transmissions in the individual countries are not known and the environmental impact of transport is, in any case, negligible (cf. Figure 5). The model reflects the means of transport mainly used in the markets and the distances concerned. The average distances for each means of transport are shown in Table 2.
Truck Train Ship (overseas)
The service life phase is identical for both transmission types as remanufactured transmissions are reprocessed to genuine component quality and therefore meet the same technical requirements as newly manufactured units. Within the differential approach adopted for the Life Cycle Assessment, the service life phase is therefore not relevant and is outside the scope of the assessment (see Figure 1). The recycling phases of the two transmission types are also identical and are therefore also excluded from the scope of the assessment. This applies both to the separate consideration of a new and a remanufactured transmission which have reached the end of their useful lives and are consigned to recycling and to such transmissions which are remanufactured.
Overall share in transport (rounded)
Distance considered [km]
35.6 %
387
4
170
4.1
529
Figure 3 presents the Life Cycle Inventory results for selected parameters during the manufacturing phase of the production of a new transmission and the remanufacturing of a transmission. The emissions of all the gases considered are significantly reduced by remanufacturing. In absolute terms, the emission values are very different, but the relative reductions are of the same order of magnitude. The greatest savings are achieved with respect to carbon monoxide (a reduction of 47 %), which is a key factor in local air quality (photochemical ozone creation potential). Regarding the emission of carbon dioxide the remanufacturing leads to a reduction of 73 kg CO2
15.7 % 48.7 %
Table 2: Average transport distance for a returned transmission. The model is based on the data records of the GaBi software. As regards the respective assumptions concerning capacity utilisation, consumption, etc., the parameters described in the software documentation [10] apply. On average, experience indicates that the parts which are found to be reusable on inspection account for at least 50 % of the mass of a transmission. The remaining parts are consigned to recycling and replaced by new parts. These new parts include a large number of small wear parts, which are replaced on all transmissions as a standard part of the process.
RESULTS Life Cycle Inventory
Figure 2: Transmission remanufacturing process.
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Figure 3: Life Cycle Inventory Values. 4.2
Life Cycle Impact Assessment
On the basis of the Life Cycle Inventory data, the Life Cycle Impact Assessment is drawn up for all the environmental impact categories. The interactions of all the emissions recorded are considered and potential environmental impacts are determined based on CML methodology. Figure 4 compares the absolute environmental impacts of the production of a new transmission with those caused by transmission remanufacturing. The relative reductions in environmentally relevant emissions are also indicated. For example, global warming potential can be reduced by 28 %, or 63 kg of CO2 equivalents, on average by the reuse of components in a remanufactured transmission. The greatest savings are in the area of eutrophication potential (minus 42 %), followed by local air quality (represented by photochemical ozone creation potential) with minus 41 %). Energy consumption is reduced by 33% for the remanufactured transmission compared with a newly manufactured MQ 250. Figure 5 compares the environmental impacts of the various stages in the production of a new transmission and transmission
remanufacturing. The environmental impacts shown in each of the categories have been normalised using the normalisation factors in Table 3. The environmental impact of transmission remanufacturing is due mainly to the energy and materials required for producing new parts to replace components of the old transmission which cannot be reused. The remanufacturing process itself, including dismantling, cleaning, testing and reassembly, accounts for a substantially lower share of the environmental impacts. Environmental category
Unit
EP
15906
ODP
31
PO4 equivalents R11 equivalents
POCP
7228
C2H4 equivalents
GWP
4440050
CO2 equivalents
AP
21553
SO2 equivalents
Table 3: EU 15 normalisation factors (in thousand metric tons).
Figure 4: Environmental impacts of transmission production.
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Figure 5: Normalised environmental impacts of the various production stages. 5
CONCLUSION
This study demonstrates the benefits of the remanufacturing of transmissions with regard to the environmental profile of the components. The remanufactured transmission performs significantly better than the newly manufactured component in terms of all environmental impact categories considered. The savings are chiefly due to lower power consumption and material requirements as a result of the reuse of components. The remaining environmental impact of transmission remanufacturing is largely determined by electric power and material requirement for the manufacture of new replacement parts. Thus, for further optimization, subsequent analyses and activities will have to focus on these two aspects. 6 [1]
REFERENCES International Organization for Standardization (2006): ISO 14040: Environmental Management – Life Cycle Assessment – Principles and Framework, International Organization for Standardization, Geneva.
[2]
International Organization for Standardization (2006): ISO 14044: Environmental Management – Life Cycle Assessment – requirements and guidelines, International Organization for Standardization, Geneva.
[3]
Arning, S. (2005): Identifikation Ergebnisbeeinflussender Größen bei Getriebe-Ökobilanzen, Diploma thesis, Volkswagen AG, Wolfsburg.
[4]
Laumer, M. (2011): Vergleichende Umweltbilanz gemäß DIN ISO 14040 über die Wiederaufbereitung eines PkwSchaltgetriebes gegenüber der Neufertigung, Diploma thesis, Volkswagen AG, Wolfsburg.
[5]
Koffler, C.; Krinke, S.; Schebek, L.; Buchgeister, J. (2007): Volkswagen slimLCI – a procedure for streamlined inventory modelling within Life Cycle Assessment (LCA) of vehicles. In: International Journal of Vehicle Design (Special Issue on Sustainable Mobility, Vehicle Design and Development), Olney: Inderscience Publishers.
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[6]
Guinée, J. B.; Lindeijer, E. (2002): Handbook on Life Cycle Assessment: Operational guide to the ISO standards, Dordrecht [et al.], Kluwer Academic Publishers.
[7]
Krinke, S.; Bossdorf-Zimmer, B.; Goldmann, D. (2005): Ökobilanz Altfahrzeugrecycling – Vergleich des VW-SiConVerfahrens und der Demontage von Kunststoffbauteilen mit nachfolgender werkstofflicher Verwertung. Volkswagen AG, Wolfsburg.
[8]
Krinke, S.; Nannen, H.; Degen, W.; Hoffmann, R.; Rudloff, M.; Baitz, M. (2005): SunDiesel – a new promising biofuel for sustainable mobility. Presentation at the 2nd Life-Cycle Management Conference Barcelona.
[9]
Schmidt, W. P.; Dahlquist, E.; Finkbeiner, M.; Krinke, S.; Lazzari, S.; Oschmann, D.; Pichon, S.; Thiel, C. (2004): Life Cycle Assessment of Lightweight and End-Of-Life Scenarios for Generic Compact Class Vehicles. In: IntJLCA (6), pp. 405416.
[10] http://documentation.gabi-software.com
Coordination of Design-for-Recycling Activities in Decentralized Product Design Processes in the Automotive Industry 1
1
Kerstin Schmidt , Thomas Volling , Thomas S. Spengler 1
1
Institute of Automotive Management and Industrial Production, Technische Universität Braunschweig, Braunschweig, Germany
Abstract Design processes in the automotive industry are distributed over various companies. If inappropriate contract structures are used within such collaborations, inefficiencies in the design process occur due to existing uncertainties and differing objectives of the partners. To coordinate design-for-recycling activities in collaborative design processes a mathematical model for the analysis of those processes is developed. We apply the model to a fixed-price and an incentive contract. The analysis shows that fixed-price contracts lead to inefficiencies, while incentive contracts can reduce inefficiencies. We demonstrate the use of this model based upon an illustrative example to show its applicability. Keywords: New Product Development; Design-for-Recycling; Automotive Industry
MOTIVATION
Recycling is an important issue for a sustainable economic management in the automotive industry. In recent years, 8 to 9 million tons of waste from used cars were generated in the European Union. On average, 25 % of this waste could not be recycled and needed to be brought to landfill. Furthermore, it is expected that the amount of waste will increase to somewhere between 14 and 17 million tons until 2015. [1, 2] In order to improve the management of end-of life vehicles, the European Union adopted the Directive on end-of life vehicles [3] in 2000. Among other things, the directive aims at setting specific targets on the 1 2 3 reuse , recycling and recovery of waste from end-of life vehicles for 2006 and 2015. For 2006, reuse and recovery rates of at least 85 % and reuse and recycling rates of at least 80 % are required. From January 2015 these rates shall be increased to a minimum of 95 % and 85 %, respectively. The greatest impact on the reusability, the recyclability and 4 recoverability of a vehicle is given in the design phase of this new vehicle. For this reason, a variety of Design-for-Recycling activities exist in the automotive industry. The legislator tries to impair the product design as well: in 2005 the European Union adopted the Directive on type-approval of motor vehicles with regard to their reusability, recyclability and recoverability [4]. Due to this directive, since December 2008 automotive manufacturers (OEMs) are obligated to demonstrate the statutory rates when the type-approval of a new vehicle is done. While the OEM bears responsibility for the compliance with the recycling rates, his influence is often limited. In current practice, 1
‘Reuse’ means any operation by which components of end-of life vehicles are used for the same purpose for which they were conceived. [3] 2 ‘Recycling’ means the reprocessing in a production process of the waste materials for the original purpose or for other purposes but excluding energy recovery. [3] 3 ‘Recovery’ means the use of combustible waste as a means to generate energy through direct incineration with or without other waste but with recovery of the heat. [3] 4 In the following, for shortening, we only speak of ‘Recyclability’. The same holds for ‘recycling rates’ and ‘recycling-relevant’.
design processes are distributed over various companies. Figure 1 shows the increasing relocation of the development performance to suppliers over the last ten years. OEM 2000 2005 *2010
Supplier 67% 57% 49%
33% 43% 51%
*Estimation
1
Figure 1: Relocation of development performance to suppliers. [5] Hence, the components of a vehicle are not designed centrally at the OEM, but at a variety of specialized suppliers. For this reason, the recycling rates depend on the specifications of each component of the vehicle and thus on the design effort of the suppliers. To ensure the compliance with the recycling rates, the collaboration between OEM and suppliers is based on contracts. These contracts include recycling-relevant specifications of the components the suppliers develop. The design of appropriate contracts for these collaborations poses a challenge. Reasons can be found in differing objectives of the legally and economically independent companies. Furthermore, uncertainties exist due to the fact that the contracting is done at the beginning of the design process, when no accurate assertions about the future components’ specifications are available. If inappropriate contracts are used in these collaborations, inefficiencies can occur. On the one hand, statutory recycling rates can be exceeded, resulting in additional design effort for the suppliers, without any additional utility for the OEM. On the other hand, recycling rates can be violated, which means that the new vehicle cannot be certified to the intended date, resulting in a loss of utility for the OEM and additional design effort of suppliers. Thus, the economic risk for suppliers and OEM increases. Overcoming these difficulties requires an improved coordination between the partners before and during the design process. A formal modeling approach for the analysis of contracts and their coordination ability in distributed design processes, to best of our
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_13, © Springer-Verlag Berlin Heidelberg 2011
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knowledge, is still missing. The economics of contracts have been studied in a variety of quantitative academic contributions [6, 7]. Particularly in supply chain management settings of independent OEMs and suppliers are analyzed. A comprehensive review of contract analysis in this field is provided by [7]. In the context of supply chain management, a central phenomenon causing inefficiencies is uncertainty. In these situations the flexibility to change a chosen course of action is usually associated with better performance [8]. Accordingly, uncertainty and thus risk is being shared with respect to the contractual agreement. Of particular interest for the research presented are flexible contract types [8, 9]. However, differences between supply chain management and design processes currently prevent an easy adoption of the mentioned flexible contracts. First approaches to the flexibilization of contracts in design processes are given by [10, 11, 12]. Since these contributions focus on a general analysis of the effectiveness of incentive contracts, a formal modeling approach for the analysis of contracts and their coordination ability in distributed design processes is still missing. To this end, a mathematical model for the analysis of decentralized design processes in the automotive industry is presented and applied to the case of the efficient compliance with recycling rates. The rest of the paper is structured as follows. After modeling and analyzing decentralized design processes in the next section, we present an approach to improve decentralized design processes in section 3. In section 4 we demonstrate the use of this approach based upon an illustrative example to show its applicability. Finally, we draw our conclusions and give a short outlook on our future research in section 5.
vehicle are fully detailed until they have the readiness for start of production (SOP). [13] Since the development of a new vehicle is distributed between the OEM and his suppliers, the suppliers need to be integrated into the OEM’s design process. The integration of the suppliers as well as the conclusion of the contracts is done in the first phase of the design process (see Figure 2). At this early point of time, the contracting is done although only first assertions about the product specifications and thus about the component specifications are available. For this reason, uncertainties arise in the design process, since the component specifications, resulting at the end of the design process, cannot be predicted with certainty. To support the cooperation between the OEM and his suppliers during the design process, at fixed points in time the development status of the components is evaluated, necessary coordination is done and decisions are made [14, 15]. At the end of the design process, in the fourth phase, the integration of the suppliers’ components is done (see Figure 2). Based on the realized specifications of the developed components the determination of the recycling rates is conducted. The compliance with the statutory recycling rates is an important precondition for the conferral of the type-approval for the new developed vehicle and thus for its market launch. The determination of the recycling rates is based on the four steps of an assumed recycling process: pre-treatment ( pt ), removal ( re ), metal removal ( mr ) and treatment of non-metallic residue ( nm ). In each step, the sum of the masses m is determined, which are considered to be recyclable. Thus, simplified the recycling rate R rec results from the following equation [16]: R rec
2
MODELING AND ANALYSIS OF DECENTRALIZED DESIGN PROCESSES
In this section we study current decentralized design processes in the automotive industry. We focus on the characteristics of current design processes in the first step and present a model for the analysis of these processes in the second step. In the third step we concentrate on the analysis of decentralized design processes. 2.1
Design processes in the automotive industry
Design processes in the automotive industry mainly consist, with the exception of company-specific deviations, of four phases: goal alignment, concept, preliminary development and serial development (see Figure 2). Integration of suppliers, conclusion of contracts Goal alignment Integration of components, Concept Type-approval of new vehicle Preliminary development Serial development 84 78 72 Month before SOP
60
48
40
36
30
24
20
12 10
6
SOP
Figure 2: Illustrative design process in the automotive industry. [13] In the first phase (goal alignment), the vehicle parameters as well as the vehicle targets are to be defined as part of the product specifications. In the second phase (concept), first vehicle designs are developed based on the product specifications. In the third phase (preliminary development), vehicle designs from the previous phase are analyzed and compared. A final vehicle design is selected and refined. Furthermore, legal requirements like recycling rates are taken into account. At the end of this phase, the prototype construction of the vehicle begins. In the fourth phase (serial development), the engineering drawings of all components of the
m pt mre mmr m nm mvehicle
100
(1)
This method cannot only be applied to the calculation of the recycling rate of the vehicle itself, but also to the calculation of the recycling rate of the components. Therefore, the sum of the recyclable masses mi of each step of each component is divided by the mass of the component itself. The recycling rate of the entire vehicle R rec then results from the following equation, with Rirec indicating the recycling rates of n different components, i 1,..., n , under the assumption, that the vehicle consists of n components: R rec
m1 R1rec m2 R2rec ... m n Rnrec m1 m 2 ... m n
(2)
The recyclability of a vehicle does not depend on all components to the same degree. For example, all fluids, batteries, oil filters, tires, catalytic converters and airbags are considered to be 100 % recyclable, when calculating the recycling rates. The recycling of the metallic parts of end-of life vehicles succeeds by the effective separation in the shredder and in the subsequent sorting steps (magnetic separators, eddy current separators, float-sink systems) to about 97 %. Currently, the high-quality recycling of plastics, vehicle glass and shredder light fraction, resulting from the shredding process, is improvable. For example, the recycling rate of the shredder light fraction is only at 54 %, despite a weight percentage of 20 % on end-of life vehicles. Another challenge is the specific removal and recovery of the increasingly occurring precious-metalliferous car electronic components. [16, 17] Thus, the highest influence on the recyclability of a new vehicle is given by the components with a high share of polymer and composite materials and electronic components. In particular, components like the instrument panel and the underbody protection play a decisive role for the recyclability of a new vehicle. Due to these facts, the high complexity of current automotive design processes can be reduced by concentrating only on those
Automotive Life Cycle Engineering - Recycling components, which have a high influence on the recyclability of the vehicle, when modeling decentralized design processes in the next section. 2.2
Model description
We consider three independent actors with full information: one OEM and two suppliers. The two suppliers S i each develop one component with a recycling-relevant specification s i [ s imin , s imax ] , with i 1, 2 indicating the two different suppliers/components, of a new vehicle for the OEM. For example, the recycling-relevant specification si of a component can be equal to a specific recycling rate Rirec . Since the OEM and his suppliers are legally and economically independent companies, the collaboration is regularized by contracts. In the automotive industry, contracts are usually based on unit price and amounts, while the development costs of the suppliers are included into the unit price. Since we firstly want to concentrate on the design phase, we assume that the OEM pays a lump-sum transfer payment t i to the suppliers at the end of the design process. The transfer payment functions can differ depending on the contract type that regularizes the collaboration. For example, the transfer payment functions can have a step-wise, a linear or an exponential shape. The compliance with the statutory recycling rates for the new vehicle, the OEM bears responsibility for, depends on the recyclingrelevant specifications si of the two components. Different combinations of the recycling-relevant specifications si lead to the compliance with the statutory recycling rates. We assume that these combinations, can be described approximatively by the functional relationship s 2 r (s1 ) .
75 density function P ( s1 , s 2 ) and integrating on the two intervals, with the interval limits depending on the efforts of the suppliers. This results in the following expected utility function n( w1 , w2 ) of the OEM, with d specifying the slope of the turning point, d : n ( w1 , w2 ) s1max
The expected utility of the OEM depends on the compliance with the recycling rates of the new vehicle and thus on the expected specifications s i of the suppliers’ components. If the recycling rates are met, the utility of the OEM is one, otherwise it is reduced by the costs related to a deferred start of production. To model this stepwise character of the utility in a continuously and differentiable manner, it is approximated by a Sigmoid-function, which is normalized to the interval [0,1] . Through the turning point of the Sigmoid-function the function r ( s1 ) is running, so that the substitutional interdependencies between the two component specifications are taken into account. Since the efforts wi of the suppliers are stochastically independent, the existing uncertainties get included by multiplying the Sigmoid-function with the combined
1 P ( s1 , s 2 ) d ( s 2 ) d ( s1 ) 1 exp( d r ( s1 )) exp( d s 2 )
(3)
An overview of the model is given in Figure 3. s1 n(w1,w2)
OEM s2
S1
c1(w1)
S2
c2(w2)
t1 t2
Figure 3: Model of decentralized design processes. Figure 4 shows the course of events within the model for decentralized design processes: The OEM determines the transfer payment functions t i in the first step. Based on the transfer payment function the suppliers decide about their optimal effort wi and start the development of their component in the second step. Due to the chosen effort the interval limits for the component specification are realized and thus the expected component specifications s i are resulting in the third step. Dependent on the realized component specifications the transfer payments t i ( s i ) paid to the suppliers by the OEM as well as the utility of the OEM are resulting in the last step. ti ( s i )
The recycling-relevant specifications si of the two components, resulting at the end of the design process, depend on the design effort wi of the suppliers. Furthermore, the design effort wi quantifies the development costs ci ( wi ) of the suppliers, which are normalized to the interval [0,1] . Since uncertainties exist in the design process, the resulting recycling-relevant specification s i achievable with a specific design effort wi cannot be predicted with certainty. We assume, that the probability f ( si ) of achieving a certain specification s i is uniformly distributed in the interval [ s imin , simax ] . However, the suppliers may influence the expected component specification s i with their efforts wi . Due to practical relevance it has to be assured that the expected component specification is zero, if the effort is zero. Furthermore, the interval nearly shall remain the same size and the component specification shall converge to a technological threshold with increasing effort. According to these requirements, the coherence between component specification and effort is implemented as follows: The interval limits depend on wi : simin ln( ai wi 1) and simax ln(bi wi 1) , with bi > a i > 0. Thus, a greater effort shifts the interval limits to the right, so that an increase in effort leads to a higher expected value s i .
s 2max
s1min s2min
OEM: Decision about transfer payment function
wi
si
Supplier: Decision about effort
Supplier: Realization of component specification
wi
si
OEM: Resulting transfer payment and utility
ti ( si )
t i ( si )
Figure 4: Course of events within the decentralized design process. Based on this model, an illustrative model analysis is done in the next section. 2.3
Illustrative model analysis
Centralized setting In the centralized setting ( c ) one decision maker, who has full information, develops and integrates both components (see Figure 5). s1 n(w1,w2)
OEM s2
S1
c1(w1)
S2
c2(w2)
t1 t2
Figure 5: Decision situation in the centralized setting. Hence, his aim is to maximize the total profit of the design process. His profit function results from the expected utility n minus the development costs ci as follows:
( w1 , w2 ) n ( w1 , w2 ) c1 ( w1 ) c 2 ( w2 )
(4)
The optimal efforts wi ,c as well as the resulting expected component specifications s i ,c result from the analytical determination of the extreme point of the profit function .
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Automotive Life Cycle Engineering - Recycling
These results provide as a benchmark for the analysis of the coordination ability of different kinds of contracts in the decentralized setting. Decentralized setting In the decentralized setting, the decision situation of the suppliers is considered when collaboration is based on contracts (see Figure 6). s1 n(w1,w2)
OEM s2
S1
c1(w1)
S2
c2(w2)
t1 t2
tih,iin
ŝi
si
Figure 7: Fixed-price contract.
hi
simin
simax
s max
ˆ
i
i
(5)
To analyze the decentralized setting with fixed-price contract ( fp ) the decision situation of the suppliers is considered. We assume that the OEM chooses the required component specification sˆi according to the optimal specification s i ,c in the central case. The ˆ profit functions is,i fp of the suppliers are based on the transfer payment functions minus their development costs. The suppliers then determine their effort wi by maximizing their profit function: ˆ
ˆ
is,i fp ( wi ) t is,i fp ( wi ) ci ( wi )
(6) ˆ
Since the realized specifications, and thus the resulting transfer payments, depend on the suppliers’ effort, the expected transfer payment function arises as follows: The existing uncertainties are included by multiplying the transfer payment function hi ( s i ) with the density function P ( s i ) and integrating on the interval, with the interval limits depending on the effort of the supplier. The expected transfer payment function t ih,iin results as follows: s max
Figure 8: Incentive contract.
1 P ( si ) d ( si ) 1 exp( k sˆi ) exp( k s i )
In incentive contracts not a certain design specification is set by the OEM, but an interval on design specifications. For every specification within the interval the suppliers get an agreed transfer payment hi ( si ) (e.g., the transfer payment function increases linear with respect to the achieved specification). Additionally, the suppliers are able to select a component specification within this interval and thus an effort to achieve this specification, which fits best to their structure of development costs and hence to their uncertainties. Figure 8 outlines a simplified incentive contract.
i t ih,iin ( wi ) s min hi ( si ) P ( si ) d ( si )
si
Since the realized specifications and thus the resulting transfer payment depend on the suppliers’ effort, the expected transfer payment functions arise as follows: To model the binary-character of the transfer payment in a continuously and differentiable manner, it is approximated by a Sigmoid-function, which is normalized to [0,1] . The turning point of the Sigmoid-function is described by the required component specification sˆi . The existing uncertainties are included by multiplying the Sigmoid-function with the density function P ( s i ) and integrating on the interval, with the interval limits depending on the effort of the supplier. The expected transfer ˆ payment function t is,i fp , with k specifying the slope in the turning point, k , results as follows: i t is,i fp ( wi ) s min
IMPROVING DECENTRALIZED DESIGN PROCESSES
In this section we present the conceptual design of an incentive contract to make decentralized design processes in the automotive industry more flexible and thus less inefficient.
Figure 6: Decision situation in the decentralized setting.
ˆ
Hence, new types of contracts are necessary to improve the cooperation between OEM and suppliers in distributed design processes. For this purpose, we present the conceptual design of an incentive contract in the next section. 3
Currently, in the automotive industry fixed-price contracts are most commonly used. In these contracts the OEM determines certain recycling-relevant specifications sˆi for the components that have to be fulfilled by the suppliers. Based on these parameters, the OEM then specifies the transfer payment function determining the amount to be paid by him to the suppliers. In this fixed-price contract, a component specification equal or higher than the required specification leads to a payment equal or higher compared to the development costs of the suppliers, while a specification lower than the required specification leads to a payment corresponding to the development costs reduced by the penalty costs. Figure 7 outlines a simplified fixed-price contract.
tis,i fp
specifications sˆi and thus get the agreed transfer payment with certainty. Consequently, inefficiencies in the design process occur and increased development costs and overdesigned components are resulting.
The analysis shows, that the optimal efforts wis, ifp and thus the ˆ expected component specifications sis,ifp are higher than in the centralized setting. Due to the fixed-price contract the suppliers choose a higher effort than necessary to achieve at least the design
(7)
To analyze the decentralized setting with incentive contract ( in ) the decision situation of the suppliers is considered. The profit functions i,hiin of the suppliers are based on the transfer payment functions minus their development costs. The suppliers then determine their effort wi by maximizing their profit function:
ih,iin ( wi ) t ih,iin ( wi ) ci ( wi )
(8)
The analysis shows, that the optimal efforts wih,ini and the resulting expected component specifications sih,ini are higher than in the centralized setting, but lower than in the decentralized setting with fixed-price contract. Thus, using incentive contracts enables to reduce the overdesign of the components and to make a contribution to the coordination of decentralized design processes. The reason for the reduced overdesign of the suppliers is their higher flexibility and the missing risk to get no transfer payment. Due to the higher flexibility for the suppliers the risk of the OEM increases, since the chosen efforts of the suppliers can lead to the non-compliance with the statutory recycling rates. Therefore it is necessary for him to set the contract parameters hi ( si ) advisedly. The application of the model will be illustrated in the next section. 4
ILLUSTRATIVE EXAMPLE
To show the applicability of the previously presented model to analyze different kinds of contracts towards their coordination ability, we employ it in an illustrative example of a decentralized design process in the automotive industry in this section. In the first step, the setting of this example is specified. In the second step we concentrate on the analysis of this illustrative example.
Automotive Life Cycle Engineering - Recycling 4.1
Setting of the illustrative example
In the considered setting, two independent suppliers each develop one component for a new vehicle of an OEM: the underbody protection and the instrument panel. To grant the type-approval of the new vehicle at the end of the design process, the OEM has to demonstrate that the vehicle is able to achieve the statutory recycling rates. For the sake of simplicity it is assumed that the recycling rates of all components, except the underbody protection and the instrument panel, cannot be improved. Thus, the resulting recycling rate of the vehicle only depends on the recycling rates of the two components developed by the suppliers. To meet the statutory recycling rates for the vehicle, the combined recycling rate of the two components needs to be 60 %. To achieve this goal, specific recycling-relevant specifications s i of the components are required. In this illustrative example, the recycling-relevant specifications are equal to the recycling rates of the components: s i Rirec . Until now a coating with mineral-filled PVC has been provided by supplier S1 to protect the underbody. Since such a protective layer cannot be removed from steel with a tolerable outlay, only the steel could be recycled. Thus, nearly 100 % of the mineral/plastic mixture of the underbody protection is going to landfill. To improve the recyclability and thus the recycling rate of the underbody protection, the supplier identified two options. On the one hand a suitable and partially distinguishable layer thickness and alternative materials can lead to the reduction of the proportion of nonrecycable materials. The implementation of this option leads to a recycling rate of s1min for the underbody protection. However, the basic problem of the nondetachable connection between the underbody and the underbody protection remains. Thus, on the other hand the protective layer applied to the underbody can be substituted by underbody cladding made of glass-fiber reinforced polypropylene. Furthermore, the cladding is fixed to the underbody by means of screws and thus in a detachable way. The implementation of this option leads to a recycling rate of s1max for the underbody protection. In addition to these two options combinations of these two options are possible. As a result the following interval on the recycling rate s1 results for supplier S1 : s1 [ s1min , s1max ] . [18] Currently, the instrument panel of supplier S 2 consists of plastic materials, which lead to a recycling rate of s 2min . To improve the recyclability and thus the recycling rate of the instrument panel, those materials can be provided which can be used again as ground material in the production of equivalent parts or can be supplied to a production residue recycling system. In case of the instrument panel for example materials like thermo-plastic SMA-GF 10 recyclate meet these demands. The implementation of these materials leads to a recycling rate of s 2max for the instrument panel. Depending on the extent to which these materials can be used for the instrument panel, the recycling rate s 2 resulting for supplier S 2 lies in the following interval: s 2 [ s 2min , s 2max ] . [18] The achievable recycling rates s i as well as the interval limits depend on the design efforts wi of the supplier. Although the technologies to improve the recycling rates of the two components are known by the suppliers, uncertainties exist with regard to the recycling rates s i achievable with a specific design effort wi . We assume, that the probability f ( si ) of achieving a certain recycling rate si is uniformly distributed in the interval [ s imin , simax ] , with the interval limits depending on the effort wi : s imin ( wi ) and s imax ( wi ) . Since the design effort wi determines the development costs c iex ( wi ) of the suppliers, it is assumed that the development costs of supplier S1 and supplier S 2 can be approximated by linear functions: c1ex ( w1 ) 0.013 w1 and c 2ex ( w2 ) 0.016 w2 .
77 The combined recycling rate of 60 % of the two components, the OEM aims at, can be achieved by a variety of combinations of the recycling rates of the two components. Under the assumption that the masses mi of the underbody protection and the instrument panel are 10 kg and 50 kg respectively these combinations can be described by the following functional relationship:
0 .6
m1 s1 m 2 s 2
0 .6
m1 m 2
10 s1 50 s 2 s 2 0.72 0.2 s1 (9) 10 50
Thus, the expected utility function n ex ( w1 , w2 ) of the OEM in this illustrative example results as follows: n ex ( w1 , w2 )
s 2max
4.2
s1max s 2max
1
s1min s2min 1 exp( d (0.72 0.2 s
1 1 s 2min s1max s1min
1
)) exp( d s 2 )
d ( s 2 ) d ( s1 )
(10)
Analysis of the illustrative example
In this section the centralized setting is analyzed first to determine a benchmark. Afterwards the coordination ability of a fixed-price and an incentive contract is analyzed in the decentralized setting. Centralized setting In the centralized setting ( c ) the OEM develops the underbody protection as well as the instrument panel and integrates both components into the new vehicle. His aim is to meet a recycling rate of 60 %, which maximizes his utility, with minimal effort and thus with minimal costs. His profit function results as follows:
ex
( w1 , w2 ) n ex ( w1 , w2 ) c1ex ( w1 ) c 2ex ( w2 )
(11)
The following optimal efforts w1ex,c 6.45 , w2ex,c 5.53 and thus the following expected recycling rates s1ex,c 83 % , s 2ex,c 79 % of the suppliers components are resulting. They provide as a benchmark for the analysis in the decentralized setting. Decentralized setting with fixed-price contract ( fp ) In this decentralized setting the OEM determines the transfer payment function for the fixed-price contracts concluded with the suppliers. Since the OEM has full information, he chooses the required recycling rates sˆiex for the components developed by the suppliers as well as the maximal transfer payment in accordance to the optimal expected specification in the centralized setting. For the sake of simplicity it is assumed, that the suppliers accept the contract in any case. ˆ
The expected transfer payment functions t is,i fp,ex result as follows: ˆ
t1s,1fp,ex ( w1 )
max
s max
ˆ
1
1
s1 s1min 1 exp( k 0.83) exp( k s ) s max s min d ( s1 ) 1 1 1
2 t 2s2, fp,ex ( w2 ) s min 2
1 1 d (s2 ) 1 exp( k 0.79 ) exp( k s 2 ) s 2max s 2min
(12)
(13)
To analyze the decision situation of the suppliers the profit function ˆ is,i fp,ex of the suppliers is considered as follows: ˆ
ˆ
is,i fp,ex ( wi ) t is,i fp,ex ( wi ) ciex ( wi )
(14) ˆ
ˆ
The following optimal efforts w1s,1fp,ex 13.94 , w2s,2fp,ex 9.53 and thus ˆ ˆ the following expected recycling rates s1s,1fp, ex 100 % , s 2s,2fp,ex 100 % of the suppliers components are resulting. Compared to the results of the centralized setting fixed-price contracts lead to an increase in effort of about 116.12 % and 72.33 %, respectively. The suppliers choose such a high effort to maximize the probability to achieve the
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Automotive Life Cycle Engineering - Recycling
required recycling rate sˆiex and thus to get the agreed transfer payment, taking into account the development costs. Consequently, increased development costs and overdesigned components are resulting. Decentralized setting with incentive contract ( in ) In this decentralized setting the OEM determines the transfer payment function hiex ( s i ) as well as the interval limits for the inventive contracts concluded with the suppliers in dependence to the structure of their development costs: h1ex ( s1 ) 0.188 s1
for s1 [57 %, 100 %]
(15)
h 2ex ( s 2 ) 0.272 s 2
for s 2 [60 %, 98 %]
(16)
The expected transfer payment functions t ih,iin,ex result as follows: s max
1 t1h,1in,ex ( w1 ) s min h1ex ( s1 ) 1
s max
1 d ( s1 ) s1max s1min
2 t 2h,2in,ex ( w2 ) s min h2ex ( s2 ) 2
1 d ( s2 ) s2max s2min
(17)
(18)
To analyze the decision situation of the suppliers the profit function ih,iin,ex of the suppliers is considered as follows:
ih,iin,ex ( wi ) t ih,iin,ex ( wi ) ciex ( wi )
(19)
The following optimal efforts w1h,in1 , ex 7.2 , w2h,2in,ex 6.31 and the following expected recycling rates s1h,1in,ex 89 % , s 2h,2in, ex 86 % of the suppliers components are resulting. Compared to the results of the centralized setting incentive contracts lead to an increase in effort of
6
REFERENCES
about 11.63 % and 14.11 %, respectively. Compared to the results of the decentralized setting incentive contracts lead to a decrease in effort. Reasons for the reduced overdesign of the suppliers can be found in their higher flexibility and their missing risk to get no transfer payment. Consequently, incentive contracts contribute towards a reduced overdesign of the components and thus contribute to the coordination of decentralized design processes. 5
CONCLUSION AND OUTLOOK
In this paper we presented a mathematical model for the analysis of decentralized design processes in the automotive industry and applied it to the case of the efficient compliance with recycling rates. We analyzed a centralized setting and compared the results to a decentralized setting with a fixed-price contract. The analysis showed that fixed-price contracts lead to inefficiencies. We then introduced the conceptual design of incentive contracts to make decentralized design processes in the automotive industry more flexible. Inefficiencies could be decreased by dividing the development risk between the OEM and his suppliers. Finally, we demonstrated the use of this concept based upon an illustrative example to show its applicability. Although the scope of the analysis lies on rather general characteristics, it captures the key elements of design processes in the automotive industry in both structural as well as logical respects. Our future research will concentrate on the mathematical analysis and evaluation of further contract types. This lays the basis for empirical testing using case study methodology. The vision is to support automotive companies in the design of efficient development contracts.
[1]
Andersen, F.M.; Larsen, H.V.; Skovgaard, M. (2008): Projection of end-of-life vehicles: Development of a projection model and estimates of ELVs for 2005-2030, in: ETC/RWM working paper 2008/2.
[10] Kruse, J.; Thomsen, C.; Ernst, R.; Volling, T.; Spengler T.S. (2005): Introducing flexible quantity contracts into distributed SoC and embedded system design processes, in: Proceedings of the conference on Design, Automation and Test in Europe, pp. 938-943.
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Fergusson, M. (2007): End of Life Vehicles (ELV) Directive: An assessment of the current state of implementation by Member States, Study requested by the European Parliament's Committee on the Environment, Public Health and Food Safety (IP/A/ENVI/FWC/2006-172/Lot 1/C1/SC2).
[11] Kruse, J.; Thomsen, C.; Ernst, R.; Volling, T.; Spengler T.S. (2005): Towards flexible systems engineering by using flexible quantity contracts, in: Proceedings of the conference on Automation, Assistance and Embedded Real Time Platforms for Transportation, pp. 480-488.
[3]
Directive 2000/53/EC of the European Parliament and of the Council of 18 September 2000 on end-of life vehicles.
[4]
Directive 2005/64/EC of the European Parliament and of the Council of 26 October 2005 on the type-approval of motor vehicles with regard to their reusability, recyclability and recoverability.
[12] Rox, J.; Schmidt, K.; Winter, A.; Spengler, T.S.; Ernst, R. (2010): Estimating and mitigating design risk in a flexible distributed design process, in: IEEE Embedded Systems Letters, Vol. 2, No. 2, pp. 35-38.
[5]
Roland Berger & Partner (2000).
[6]
Bolton, P.; Dewatripont, M. (2005): Contract theory, The MIT Press, Cambridge.
[7]
Tsay, A.A.; Nahmias, S.; Agrawal, N. (1999): Modeling supply chain contracts: A review, in: Quantitative models for supply chain management, Kluwer Academic Publishers, Norwell, pp. 299-336.
[8]
[9]
Anupindi, R.; Bassok, Y. (1999): Supply contracts with quantity commitments and stochastic demand, in: Quantitative models for supply chain management, Kluwer Academic Publishers, Norwell, pp. 197-232. Tsay, A.A. (1999): The quantity flexibility contract and supplier-customer incentives, in: Management Science, Vol. 45, No. 10, pp. 1339-1358.
[13] Böhme, M. (2004): Ein methodischer Ansatz zur parametrischen Produktmodellierung in der Fahrzeugentwicklung, VDI Verlag, Düsseldorf. [14] Frad, A. (2009): Umwelt- und Recyclingbewertung als Bestandteil des Automotive Product Lifecycle Management, Vulkan-Verlag GmbH, Essen Ruhr. [15] Hab, G.; Wagner, R. (2006): Projektmanagement in der Automobilindustrie, GWV Fachverlage GmbH, Wiesbaden. [16] DIN ISO 22628 (2002): Straßenfahrzeuge - Recyclingfähigkeit und Verwertbarkeit - Berechnungsmethode, Beuth Verlag GmbH, Berlin. [17] Umweltbundesamt (2010): Altfahrzeugaufkommen und -verwertung. [18] VDI Richtlinie 2243 (2002): Recyclingorientierte Produktentwicklung, Verein Deutscher Ingenieure, Beuth Verlag GmbH, Düsseldorf.
A Strategic Framework for the Design of Recycling Networks for Lithium-Ion Batteries from Electric Vehicles 1
1
1
Claas Hoyer , Karsten Kieckhäfer , Thomas S. Spengler 1
Institute of Automotive Management and Industrial Production, Technische Universität Braunschweig, Braunschweig, Germany
Abstract In this paper, we develop a strategic framework for the design of recycling networks for spent lithium-ion batteries from electric vehicles. The framework provides an overview of possible configuration alternatives and an integrated approach for network planning and process configuration tasks. It describes requirements on a network as well as on a process level. For that purpose, we analyse general framework conditions concerning battery return, materials, and recycling processes. On that basis, it is possible to develop a mathematical optimisation model which enables decision support concerning the optimal evolvement of recycling sites, capacities, and processes over time. Keywords: Strategic Network Planning; Recycling; Spent Lithium-Ion Batteries
1
INTRODUCTION
Efficient and reliable lithium-ion batteries are a key technology for electric mobility due to their high specific power, energy density, and lifetime [1]. For the production of lithium-ion batteries, nonferrous metals like copper, cobalt, nickel, and lithium are required, which leads to two main problems. On the one hand, the extraction or production of these metals is partly associated with high environmental impacts. On the other hand, the mining deposits are geographically concentrated and partially scarce. In 2008, seven countries accounted for 85 % of the world’s production of mined cobalt, nearly the same holds for lithium [2]. Particularly for the EU this may lead to a shift of dependency from oil-producing countries to countries that are producing cobalt, lithium, or their alloys. Recycling of the batteries has two main benefits. First, recycling may extend the reserves-to-production ratio of scarce resources, reduce energy-intensive primary production and therefore defer the opening or extension of mines. Natural resource savings of more than 50 % are possible [3]. Second, from a national point of view, recycling may have a stabilising, damping or even reducing effect on prices of and dependency on primary raw materials by providing a secondary feedstock. Beyond that, landfill and incineration of traction batteries are forbidden, and all batteries must be orderly recycled. For Germany, a recycling efficiency of 50 % by weight has to be achieved as from September 2011 [4; 5]. Up to now, neither recycling processes nor the necessary infrastructure exist that would allow for an industrial recycling of these batteries. Thus, a powerful recycling network must be established. Decisions about the evolvement of recycling sites, capacities, processes, and transportation links over time must be made. This is complicated due to heavy uncertainties about the development of the electric vehicle market and battery technology, as well as immature recycling processes. Against this background, the objective of this paper is to deliver a framework for the design of a recycling network for spent lithium-ion batteries from electric vehicles. To build up the framework, in a first step we analyse specific problem characteristics. Based on this
analysis, we develop a framework including the description of actors and requirements that need to be considered to design recycling networks for spent lithium-ion batteries. A planning approach will be presented. Different network constellations in dependence of identified decoupling points and two extreme market scenarios are examined. The results are discussed in a last step. 2 2.1
TERMINOLOGY AND BATTERY CONSTRUCTION Electric Vehicles
In this contribution, the term electric vehicles includes any electric vehicle with a lithium-ion battery used for traction. Electric vehicles may be classified into hybrid-electric vehicles (HEV), plug-in hybrid electric vehicles (PHEV) and battery electric vehicles (BEV). The most important differences between the vehicle types are specifications like mass and materials, costs, and the lifetime of the required battery. 2.2
Lithium-ion batteries
Lithium-ion batteries are rechargeable batteries with promising properties for electric mobility, which is mainly due to their high power density and specific energy [1]. In this paper, the term lithium-ion battery embraces all rechargeable secondary batteries based on the exchange of lithium ions to produce electrical energy. A lithium-ion battery used for traction is constructed on three fabrication levels: cell, module, and system level. Multiple cells are connected in series to raise voltage, resulting in modules. Modules are connected in series and in parallel to raise voltage and capacity. The resulting battery system additionally includes thermal management, monitoring, protection, charge/discharge balancing, and car integration components [1]. Lithium-ion battery cells consist of a cathode, an anode, a separator, an electrolyte, and a casing. Cathode and anode conductors are usually made of aluminium and copper foil, respectively. The cathode is coated with either a mix of lithium and other metals (LiMeO2) or lithium-iron-phosphate (LiFePO4). Metals (Me) used in the former are cobalt, nickel, manganese, and
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_14, © Springer-Verlag Berlin Heidelberg 2011
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Automotive Life Cycle Engineering - Recycling
aluminium. The anode is usually coated with graphitic or amorphous carbon, or lithium-alloying metals [6]. Anode and cathode material can be combined almost freely. All of them have both specific advantages and disadvantages related to power, lifetime, safety, and costs of the cells. Lithium-ion cells can further be classified by their type of electrolyte: liquid-type, gel-type and solid-type cells. The latter two are often referred to as lithiumpolymer batteries. The type of electrolyte used determines the type of separator and vice versa.
essentially influence decisions about total capacity, sites, and required equipment of the recycling network. Sink-related uncertainties can be classified into the three categories product reuse, component reuse, and material reuse opportunities. To design the network infrastructure connecting sources and sinks, the transforming processes and the resulting material flows have to be considered. Process-related uncertainties exist with respect to the optimal combination and configuration of processes.
Since hybrid electric vehicles and battery electric vehicles substantially differ in their requirements regarding power and energy, lithium-ion cells can be divided into high-power cells for HEVs and high-energy cells for BEVs. Table 1 summarises typical materials and mass portions for lithium-ion batteries. For highenergy batteries, the active materials of the anode and cathode make out the largest portion of the battery mass, followed by the electrolyte. The same holds for the costs of the materials [1]. Part
Material(s) [1; 6; 7]
Mass portions [1] HighEnergy
HighPower
Cell level Cathode conductor
Aluminium foil
Cathode active material
Lithium and cobalt, nickel, manganese, or aluminium, or a mix of them, or Lithium-IronPhosphate; Carbon black; Binder (PVDF)
Anode conductor
Copper foil
2%
5%
41 %
19 %
4%
10 %
Anode active Graphitic carbon, amorphous material carbon, or lithium-titanate; Binder (PVDF)
17 %
4%
Electrolyte
Lithium salts (e. g. LiPF6) dissolved in organic solvents
16 %
11 %
Separator
Polyolefins or Polyvynilidenfluorid
2%
4%
9%
28 %
9%
19 %
Cell package Aluminium, stainless and others and/or plastics
steel,
Module and system level Electric conductors
Copper cables
Electronics
Printed circuit assemblies including semiconductors
Module packaging
Polypropylene
System packaging
Aluminium, stainless and/or plastics
Total
3.1
Uniqueness of the Planning Situation
Different recycling networks exist which could serve as a basis for a recycling network for lithium-ion batteries, including legally independent networks for vehicles, lead-acid starter batteries, portable batteries, and electronic waste. However, these networks are not fully qualified for an efficient and environmental recycling of lithium-ion batteries. On the one hand, a high amount of spent batteries would overstrain the capacities of the mostly smallest businesses. Lithium-ion batteries are more complex, heavier, and more hazardous, hence, more difficult to treat than the mentioned products. The dangerous-good property of the batteries complicates the usage of existing transport links. On the other hand, novel and unexplored processes with high economies of scale are required which contravenes the mostly decentralised organisational form of the networks. New actors like battery material producers come into play. These circumstances would demand a restructuring of existing networks anyway. Moreover, a particular challenge to the planning of the recycling network is the directive 2006/66/EC of the European Union. It forces battery and electric vehicle producers to conceive and establish an effective collection and recycling solution before market penetration. Because neither the electric vehicle market nor the battery technology are evolved to a steady state, this planning and investment situation is heavily uncertain. 3.2
steel,
Source-related uncertainties
Quantity of battery returns 100 %
100 %
Table 1: Typical materials for lithium-ion batteries. 3
Figure 1: Generic network structure for lithium-ion battery recycling.
GENERAL CONDITIONS
Figure 1 shows the generic structure of a recycling network connecting material flows between sources and sinks with different transforming processes. Sources, sinks, and processes of the recycling network are fraught with uncertainties. Source-related uncertainties can be classified into quantity, spatial distribution, and specifications of the battery returns. These parameters
Lithium-ion batteries from electric vehicles become available for recycling at the end of life of the electric vehicle or the end of life of the battery itself. Given that, the quantity of spent battery returns is particularly driven by the development of the electric vehicle market over time and the battery lifetime. The forecast of the electric vehicle market is currently subject to many market studies. These most often use scenario technique to determine sales and stocks for specific points in time on an aggregate level [8]. For Germany, the government aims at a market penetration of 1,000,000 PHEVs and BEVs in 2020 [9]. The forecasts are significantly depending on assumptions about oil
Automotive Life Cycle Engineering - Recycling
81
prices, energy supply, public interest in climate change, and more stringent greenhouse gas legislation [8].
3.4
The battery lifetime is expected to be highly dependent on the used active materials and the type of vehicle the battery is used in [10]. It is estimated to be around five years for BEVs, ten years for PHEVs, and up to fifteen years for HEVs [10; 11]. However, these numbers are based on extrapolation, not experience, and it remains unclear how the lifetime will be distributed.
Basically, recycling processes for lithium-ion batteries can be classified into disassembly, mechanical conditioning, hydrometallurgical conditioning, and pyrometallurgical conditioning. In disassembly, battery systems are broken down subsequently to module and cell level which is the prerequisite for a remanufacturing of batteries. Reusable components and large material fractions, for example the battery casing and electronics, are separated before further conditioning processes are applied to the cell materials. In mechanical conditioning, materials of the cells are separated by different comminution, sizing, and concentration processes. Examples are crushing, screening, and magnetic separation. Pyrometallurgical conditioning involves the thermal treatment of the materials at high temperatures. It can be combined with the production of steel, and of ferromanganese or other alloys. Hydrometallurgical conditioning typically combines leaching, solution concentration and purification, and metal recovery methods, including electrometallurgy. With hydrometallurgical processes, pure non-ferrous metals can be recovered from active materials after mechanical conditioning or from the slag of pyrometallurgical processes.
The spatial distribution of battery returns determines transport distances and therefore costs. Decisions about sites for the collection of spent batteries and for the recycling facilities are influenced by that, given that cost minimisation or profit maximisation is preferred and all batteries must be collected. It is not clear yet whether electric vehicles with low cruising ranges will be sold rather in urbanised than in rural areas. Based on that, not only the amount but also the spatial distribution of battery returns can be considered as highly uncertain. Composition of battery return Mass, size, construction, and materials of lithium-ion batteries vary dependent on their application. To give an example, a battery for a mid-sized all electric vehicles with a range of about 200 kilometres would weigh about 300 kilograms [12]. A battery for a plug-in hybrid vehicle with an electric range of 50 kilometres would weigh just about 100 kilograms [13]. This diversity and the highly uncertain technology development demands either highly flexible equipment or manual work, which tends to result in high investments or costs, respectively. 3.3
Sink-related uncertainties
Product reuse opportunities Spent batteries from electric cars which may not be suitable for powering vehicles anymore are expected to be qualified for further stationary use, e. g. storage of energy from fluctuant power generation like wind or solar power [1]. Direct product reuse then would decelerate the return of spent batteries. However, it is not expected to happen in a large scale while the batteries are not further standardised so that they can be easily stocked, connected, and monitored outside of vehicles. Component reuse opportunities
Process combinations As listed in chapter 2.2, lithium-ion batteries can consist of a variety of different materials. Single materials can be recovered selectively with certain processes. Therefore, a combination of different processes is necessary to recover all relevant materials [14]. Figure 2 depicts a comprehensive overview of different process combination possibilities and the recovered materials. It includes potential decoupling points that by way of example occur if a disassembly or mechanical conditioning is intended or necessary. The prerequisite for spatial decoupling is the transportability of the intermediate products, which appears to apply mainly for cells as well as for the coating powder, and, to a less extent, for the intermediate products from pyro- and hydrometallurgical processes. Batteries Disassembly
Mechanical Conditioning
Pyrometallurgy
Hydrometallurgy
Disassembly
Mechanical Conditioning
Hydrometallurgy
Hydrometallurgy
Pyrometallurgy
Hydrometallurgy
Pyrometallurgy
Hydrometallurgy
Disassembly
Material reuse opportunities The high cobalt-price of approximately 40 USD per kilogramme was the driver for the recycling of smaller lithium-ion batteries, but also one important reason to replace parts of the cobalt with other metals [1] or entirely move away from cobalt-containing cathode active materials. By that, the economical incentive of recycling may diminish. Especially lithium-iron-phosphate does not contain any cobalt or nickel (which is priced at about 20 USD per kilogramme). Lithium, in the form of lithium carbonate, is much cheaper at about 6 USD per kilogramme and the mass fraction of it on cell level is just about 3 %. From today’s point of view, the costs of conditioning may excess the revenues. However, lithium prices are expected to rise as the demand is growing dramatically.
Recycling processes
← Decoupling Point
Standardisation is also important to enable component reuse. With standardised battery constructions, electronic interfaces, and cell types, spent batteries could be remanufactured easily by replacing worn components with new ones, resulting in as-good-as-new batteries. Additionally, cells with a similar state from different spent batteries could be assembled to lower-quality batteries, and electronic components could be used as spare parts.
Recycling processes
Casing, Electronics
Recovered materials
Spatial distribution of battery returns
Process-related uncertainties
Copper, Aluminium
Pyrometallurgy
Cobalt, Nickel
Lithium
Figure 2: Combination possibilities of conditioning processes and recovered materials, based on [15]. Exemplary process combinations A promising process to recover all valuable materials is currently subject of investigation at LithoRec. The analysed process begins with demounting the spent battery from the car. After transportation to a disassembly line, the battery will be disassembled to cell level. Cells will be dismantled and the volatile liquids (electrolyte with conductive salts) are absorbed for further conditioning. The coated electrodes will be separated with mechanical processes, resulting in aluminium and copper fractions and coating powder. The coating
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powder, which contains the active material, will be treated in chemical conditioning processes to recover lithium, nickel, cobalt, and manganese, or lithium-iron-phosphate, which will then be available for production again. See Figure 3 for an illustration. The LithoRec process promises to be suitable for large-scale recycling, since a large amount of materials is recovered, including battery components, all metals, and the valuable electrolyte. However, the process is still under investigation.
treatment of the cells could then be realised centrally, exploiting high economies of scale. In summary, the processes chosen and the battery types processed determine the remanufacturing option, recoverable materials and their degree of purity as well as the spatial decoupling potential. While some combinations of processes would be able to recover new active material like LiCoO2, some would result in pure cobalt and lithium-carbonate, among copper and aluminium. Thus, different sinks must be considered in each case. By this means, the decision about processes considerably influences the network structure and must be integrated into strategic network planning. 4 4.1
FRAMEWORK Scope and corporative actors
The framework for the design of a network for the recycling of spent lithium-ion batteries involves different kinds of actors of the economy, namely the battery material producers, battery producers, vehicle producers, vehicle dealers, and vehicle treatment operators. The battery material producer is predestined to recover the materials, since this would be near to his core competence. By this means he can influence the characterisation of the raw materials directly and, concurrently, prevent potential competitors.
Figure 3: Combined mechanical and hydrometallurgical processes. [3] describe a combined pyrometallurgical and hydrometallurgical recycling process for lithium-ion batteries, amongst others, which is already applied at Umicore in Sweden and Belgium. Here, a mix of different consumer lithium-ion batteries is treated in a pyrometallurgical smelting process located in Sweden, ending in a slag containing aluminium, silicium, calcium, and lithium, and an alloy of cobalt, nickel, copper, and iron. The alloy is then transported to Belgium and treated with hydrometallurgical processes to separate the materials, while the materials in the slag may be consumed in lower value production processes. Although this process is already applied, it appears to be less suitable for recycling vehicle batteries in large scale, because valuable materials like aluminium and lithium are lost in the slag. However, in combination with subsequent hydrometallurgical processes, the recovering of these materials should be possible. 3.5
Network design and process configuration
As shown, the quantity and material specifications of the batteries which are to be used as educts fundamentally influence the eligible processes and their products (recovered materials, components, etc.). The configuration of processes, in turn, determines the spatial decoupling potential. In centralised concepts, all batteries would be treated in a single facility to realise economies of scale, resulting in low average costs due to high capacity utilisation. In contrast, a decentralised concept with many small facilities would enable short transportation links and thus low transportation costs. Processes with spatial decoupling points can provide both advantages. Decentralised pre-treatment with low economies of scale, e. g. manual disassembly, would allow for a concentration of the material flow to reduce transportation costs while reducing transport mass and volume by the separation of casings. The subsequent
Although the production of batteries may not be the core competence of vehicle producers today, it will most probably be in the future. Both, battery producers and vehicle producers, can be in charge for the correct treatment of the batteries by law, and both rely on the continuous supply with raw battery materials. So, a clear distinction is not necessary. From the producer’s point of view, the recycling must be carried out in a cost-minimising or profitmaximising manner. At the same time, he is bound to collect and recycle every battery that is given to him by the end-users. Thus, he cannot minimise costs by cherry-picking, but he can share his fixed costs and use economies of scale by the joint operation of a crossproducer network. All mentioned actors of the production chain are reliant on a continuous supply of raw materials. As mentioned in chapter 1, this may become a problem in the light of scarce and geographically concentrated resources. Given that, all producers will be interested in the supply from a secondary feedstock, even though the recovered materials may be costlier or a little less capable than primary materials. The vehicle dealer is involved since he will be the first address for users of electric vehicles if their battery is spent. His task will be the replacement of the spent battery with a new one. Thus, he is interested in an easy and free-of-charge solution for the redistribution of the received spent battery, without wanting to relevantly participate in the network. Nevertheless, his participation will be needed to gather information about the battery conditions. Exactly the same holds for the treatment operator who is engaged with the treatment of end-of-life vehicles. He, however, will try to charge the vehicle manufacturer with additional fees for the demounting of the battery, or alternatively sell it to others. These deliberations show that all actors should be interested in an optimised collection and recycling of spent batteries because the requirements of the actors mostly converge. This can only be achieved by planning and operating the network collaboratively. For the actors, economic interests are prior. Beyond that, they have to respect legal conditions and customer requirements. Therefore, environmental aspects have to be considered as well. For this reason, this framework addresses the design of a sustainable cooperative recycling network with focus on the producers who are legally responsible for its provision.
Automotive Life Cycle Engineering - Recycling 4.2
Requirements
As shown in chapter 3, a recycling network for lithium-ion batteries is subject to many uncertain factors that change over time. This requires a long-term planning horizon, in which uncertainties must be represented adequately. Decisions with respect to the design of the network include the selection of facility locations, processes, capacities, and the point in time of their implementation. Due to the described interdependencies between optimal network structure and recycling processes, these decisions must be done simultaneously. Possible synergy effects by the inclusion of existing sites and processes of recycling networks for vehicles, starter batteries, portable batteries, and electronic waste as well as their actors have to be examined. Sites of treatment operators and vehicle dealers are sources of the networks and could be used for regional collection and even for the disassembly of spent batteries. The processes for the recycling of the batteries, including mechanical, chemical, and thermal treatment, differ in many aspects. They must be represented in an adequately aggregated manner. Their eligibility must be assessed with respect to their differences in:
material flows, including educts, raw materials and supplies, products, and their quality;
energy consumption;
environmental impacts in terms of pollutant emissions;
83 their prices, sources, production and mining methods, reuse potentials, and their scarcity will be analysed. Next, known and promising conditioning processes for these materials and the possible combinations will be screened. A preselection is necessary to narrow down the choice. This will be done by a comparison of the processes with respect to multiple criteria, namely monetary, ecological, social, flexibility, and safety criteria. For that purpose, aggregated material and energy flow sheet models of the processes will be used. For the equipment of the preselected processes, investment needs and costs depending on different capacities will be estimated in a pre-calculation, unless they are known. Potential decoupling points of the processes will be identified to determine the possibility of a decentralised recycling network. Transportation costs regarding the batteries and the different intermediate products will be estimated. Sources and sinks of the network will be identified. Subsequently, location factors for the collection and recycling sites will be established. Appropriate sites for recycling facilities will be classified regarding the different requirements of the various recycling processes. Again, a pre-selection of sites will be necessary. The inclusion of site-specific investments and costs will be investigated to couple interdependencies between expenses for sites, buildings, and equipment. To give decision support, a multi-staged dynamic facility location problem with the specific requirements of reverse logistics problems will be developed and then used in a mathematical optimisation. Input data are:
investments and costs, influenced by economies of scale;
subsequent expandability;
degree of flexibility regarding the processable materials and volumes;
long-term battery return estimations;
costs for transportation and storage of the batteries;
potential spatial decoupling of single process steps;
locations of existing sources and sinks;
hazard potential, particularly for the employees.
distances between these locations and potential collection and recycling sites;
costs and investment needs for facilities and processes;
ecological factors, e. g. CO2-emissions.
To represent centralised and decentralised installation concepts, a multi-stage recycling network must be considered. Processdependent material flows between sources and sinks have to be included. The structure of the network should be flexible to enable subsequent changes with respect to the combination of processes, their spatial decoupling, and capacity expansions. Furthermore, an option to store batteries to delay expenses, balance capacity utilisation, and use economies of scale has to be assessed. 4.3
Approach
The objective of our approach is to give decision support to investors regarding the provision of an optimised sustainable recycling network for lithium-ion batteries, including the selection of facility locations, processes, capacities, and the point in time of their implementation. Thus, this planning task can be classified into strategic network planning within the APS matrix. Strategic network planning approaches have been discussed in the literature for related recycling networks with similar characteristics, for instance, vehicle recycling [16], portable battery recycling [17], electrical equipment recycling [18], and large household appliances recycling [19]. Further similarities can be found in the strategic planning of a biofuel production network presented in [20]. These approaches mainly include location, transport, and capacity decisions; the latter is explicitly regarding uncertainties and process decisions. They provide the basis for the following approach, whereas some of the requirements listed in chapter 4.2 are not satisfied. E. g. none of the approaches considers a multiple objective optimisation. In the first step of our approach the materials which have to be recovered from the batteries due to economical, ecological, or strategic reasons will be identified. For that, the development of
The optimisation will be done with respect to different objective functions. Prevailing uncertainties will be considered by the application of scenario technique, robust optimisation, and sensitivity analysis. The findings will end in an extensive robust investment plan for the design of a sustainable recycling network for spent lithium-ion batteries. 4.4
Exemplary Network Alternatives
Scenario A: Centralised Network In scenario A, a centralised network with a combined pyrometallurgical and hydrometallurgical recycling process is established. Based on an unimportant electric vehicle market, the battery technology is not further developed or standardised. Because of the low amount of lithium-ion vehicle batteries produced, a shortage of resources does not occur, and cobaltbased active materials are prevalent. Only few batteries are available for recycling; consequently, a dedicated recycling network is not economical. In the network, batteries are collected from few specialised garages and brought to a centralised recycling facility that recovers materials from any kind of batteries containing cobalt or nickel to realise high capacity utilisation. Lithium, aluminium, and manganese are not recovered. The more valuable materials cobalt, nickel, and copper are recovered in high purities in a subsequent hydrometallurgical process at the same location and are sold to any kind of markets. Scenario B: Decentralised Network In scenario B, a decentralised network with a combined disassembly, mechanical and hydrometallurgical recycling process
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is established. A prospering electric vehicle market leads to strong technology advances regarding the lithium-ion batteries. These are produced at large scale, which leads to temporary shortages of raw materials. While miscellaneous compositions are used for the production of the cells, lithium-iron-phosphate emerges as the prevailing active material. The spent batteries are collected from dealers all over the country and are brought to decentralised disassembly facilities. Here, they are first used for energy storage until depletion. Afterwards, the batteries are disassembled to cell level. All materials except for the cells are brought to regional recycling facilities. The cells are transported to a centralised recycling facility where they are subject to mechanical and hydrometallurgical recycling. New active material is produced from the recovered lithium, nickel, manganese, and cobalt, and is sold to battery producers, as well as the recovered lithium salts and the electrolyte. 5
CONCLUSION AND OUTLOOK
In this paper, we presented a strategic framework for the design of recycling networks for lithium-ion batteries from electric vehicles. We analysed prevalent uncertainties related to potential sources, sinks, and processes for the recycling. The analysis showed that in particular the quantity of the return of batteries can be considered as uncertain, due to the early development stage of the electric vehicle market and missing experience regarding the lifetime of batteries. This complicates decisions about capacities in the network. Additionally, the uncertainties in the battery material composition and the price development for recoverable materials complicate process decisions. It is not clear which processes will emerge as eligible options for the sustainable recycling of batteries. We highlighted that the decision about processes considerably influences the network structure and must be integrated into the planning of the network. Based on that, we derived a framework, including an analysis of potential actors and requirements that must be considered designing a recycling network for lithium-ion batteries, and an integrated planning approach. Our future research will concentrate on the analysis of eligible processes and the implementation of the approach in a mathematical multi-objective optimisation model. 6
ACKNOWLEDGEMENT
The presented research work is part of the project “LithoRec – Recycling von Lithium-Ionen-Batterien”. LithoRec aims at developing and assessing sustainable processes and full life-cycle concepts for the recycling of spent lithium-ion batteries in Germany. We would like to acknowledge the support of the German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety for funding the research cooperation under the reference 16EM0023. 7
REFERENCES
[1]
Gaines, L., Cuenca, R. (2000): Costs of Lithium-Ion Batteries for Vehicles. Operated by The University of Chicago, under Contract W-31-109-Eng-38, for the United States Department of Energy, Argonne, Illinois. http://www.doe.gov/bridge.
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U.S. Geological Survey (2010): Mineral commodity summaries 2010, Washington. http://minerals.usgs.gov/ minerals/pubs/mcs/2010/mcs2010.pdf (accessed October 26, 2010).
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Dewulf, J., van der Vorst, G., Denturck, K., van Langenhove, H., Ghyoot, W., Tytgat, J., Vandeputte, K. (2010): Recycling rechargeable lithium ion batteries: Critical analysis of natural
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Bundesministerium für Umwelt (2009): Programm Marktaktivierung für Elektrofahrzeuge, Berlin.
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[10] Sarre, G., Blanchard, P., Brouselly, M. (2004): Aging of lithium-ion batteries, in: Journal of Power Sources, Vol. 127, pp. 65–71. [11] Marano, V., Onori, S., Guezennec, Y., Rizzoni, G., Madella, N. (2009): Lithium-ion Batteries Life Estimation for Plug-in Hybrid Electric Vehicles, in: 5th IEEE Vehicle Power and Propulsion Conference, VPPC '09, pp. 536–543. [12] Notter, D. A., Gauch, M., Widmer, R., Wäger, P., Stamp, A., Zah, R., Althaus, H.-J. (2010): Contribution of Li-Ion Batteries to the Environmental Impact of Electric Vehicles, in: Environmental Science & Technology, Vol. 44, pp. 6550– 6556. [13] Zackrisson, M., Avellán, L., Orlenius, J. (2010): Life cycle assessment of lithium-ion batteries for plug-in hybrid electric vehicles - Critical issues, in: Journal of Cleaner Production, Vol. 18, pp. 1517–1527. [14] Xu, J., Thomas, H. R. F. R. W., Lum, K. R., Wang, J., Liang, B. (2008): A review of processes and technologies for the recycling of lithium-ion secondary batteries, in: Journal of Power Sources, Vol. 177, pp. 512–527. [15] Kwade, A. (2010): LithoRec - auf dem Weg zum "intelligenten" Recycling von Traktionsbatterien, in: 7. Braunschweiger Symposium Hybrid-, Elektrofahrzeuge und Energiemanagement, Braunschweig. [16] Püchert, H. (1996): Ein Ansatz zur strategischen Planung von Kreislaufwirtschaftssystemen: dargestellt für das Altautorecycling und die Eisen- und Stahlindustrie. Mit einem Geleitw. von Otto Rentz, Dt. Univ.-Vlg., Wiesbaden. [17] Schultmann, F., Engels, B., Rentz, O. (2003): Closed-Loop Supply Chains for Spent Batteries, in: Interfaces, Vol. 33, No. 6, pp. 57–71. [18] Walther, G., Spengler, T. S. (2005): Impact of WEEE-directive on reverse logistics in Germany, in: International Journal of Physical Distribution & Logistics Management, Vol. 35, No. 5, pp. 337–361. [19] Walther, G., Spengler, T. S., Queiruga Dios, D. A. (2008): Facility location planning for treatment of large household appliances in Spain, in: International Journal of Environmental Technology and Management, Vol. 8, No. 4, pp. 405–425. [20] Walther, G., Schatka, A., Spengler, T. S. (2007): Gestaltung von Netzwerken zur Produktion von synthetischen Biokraftstoffen der zweiten Generation, in: UWF Umweltwirtschaftsforum, Vol. 18, No. 1, pp. 61–69.
Recovery of Active Materials from Spent Lithium-Ion Electrodes and Electrode Production Rejects 1
1
Christian Hanisch , Wolfgang Haselrieder , Arno Kwade 1
1
Institute for Particle Technology, Technische Universität Braunschweig, Braunschweig, Germany
Abstract This article describes two ways to recover valuable and ecologically critical active materials from spent lithium-ion electrodes and electrode production rejects, using the example of a system containing LiNi0.33Co0.33Mn0.33O2 (NMC) active material and a polyvinylidene fluoride (PVdF) binder. First, a physical process using thermal treatment and mechanical stressing to separate the coating from the aluminum foil is discussed. Furthermore, a wet chemical processing using the solvent n-methyl-2-pyrrolidone (NMP) is presented. Recovered coating materials from both processes were characterized by laser diffraction spectroscopy and atomic absorption spectroscopy. Additionally, recycling electrodes were produced and successfully tested in battery test cells. Keywords: Electrode Recycling; Lithium-Ion-Batteries; Production Rejects
1
INTRODUCTION
The increasing demand of lithium-ion traction batteries implies the need for recovering active materials from spent lithium-ion-batteries (LIB) as well as from electrode production rejects to prevent a future shortage and to minimize the dependence on imported key raw materials for lithium battery production. Lithium-ion-battery cells consist of several layers of cathodes and anodes, which are electrically isolated by an ion permeable separator to form several compartments where the electrochemical reactions can take place. These electrode-separator assemblies are embedded in a housing filled with an organic electrolyte containing a lithium rich salt. Wetting the electrodes and separators with electrolyte allows the transfer of lithium-ions between the electrodes. During the discharging process lithium-ions are transferred from the anode to the cathode, where these are inserted in a metal containing lattice structure in order to gain electrons. The electrons are transferred through the composite particle coating of the electrode towards to the current collector (anode-copper, cathode-aluminum), which itself is connected to the external electrical circuit of the battery. The described charging and discharging processes cause electrochemically, chemically and mechanically induced aging effects with a major influence on the battery performance. Since traction battery applications require extended life times of 12 – 15 years or of 4000 up to 5000 charge/discharge cycles these effects are of high significance. Active materials, predominately used on the anode side, are porous graphite hosts. While lithium mixed metal oxides (e.g. LiNi0.33Co0.33Mn0.33O2, NMC) are state of the art for cathodes in the first generation of lithium-ion traction batteries. Therefore, they will be the first materials to be recycled. This research concentrates on the recycling of cathode materials since beside lithium valuable metals such as cobalt, nickel and manganese are contained. To lower the battery material investment costs material researchers try to replace these metals. A good example is lithium-iron-phosphate, which already has been established in industrial electrode production during the last two years. One has to take into account that without valuable metals lithium is the only economical concern
to develop recovering processes. Therefore a recycling process, including a hydrometallurgical processing of the coating powder, is the only pathway to recover of lithium rich products such as LiOH and Li2CO3. These are educts for the synthesis of active materials or can be reused in secondary markets. In pure pyro-metallurgical processes lithium is lost. Selective recycling of the electrodes is of high economic interest, taken into account that
the proportion of active materials is significantly higher in traction batteries compared to smaller consumer batteries,
the current collectors and especially the electrochemically active materials are the components of greatest economic value in the cell,
approximately 75% of the costs of a electric vehicle battery system are determined by the battery cell investment.
Therefore, systematic disassembling of battery systems and the following separation of the coating materials from current collector foil as well as the hydrometallurgically reprocessing of the re-won coating powder may be economically and ecologically in advance to classical pyro-metallurgical processes. This is the approach of the research project LithoRec in whose framework this research was done. There are lots of research and development activities concerning the recycling of lithium-ion-batteries. Recent studies [1],[2],[3], and [4] give a good review about the state of the art. These processes are designed to recycle aged lithium ion secondary batteries in order to recover the valuable metals and partly to re-synthesize the active materials. In contrast to the approach of this research they are intended to recycle smaller consumer batteries. In these processes complete battery systems are destroyed mechanically or thermally to recover valuable metals subsequently by combining pyro- and hydrometallurgical processes. The purpose is to regain materials with a minimal effort and highest quality requirements, which also allow the reuse in the field of LIB, especially directly as recovered cathode material. On the other
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_15, © Springer-Verlag Berlin Heidelberg 2011
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hand, the developed processes can result in highly concentrated suspensions, which can be used as educts for hydrometallurgical processes to recover lithium, cobalt and other purified products. Therefore, the aim was to recover the coating powder from electrode production rejects and cycled NMC electrodes. A direct recoating of the active material applying a merely mechanical process, an alternative wet chemical recovering process or a combination of both is the main focus of this research. Basic process engineering principles were applied to separate the current collector mechanically from the particle coating. By varying the process parameters and the pretreatment of the cathodes, the coating should be separated with the highest possible yield. Additionally, the quality criteria for a use in a direct recoating process had to be achieved. At first, the particle size distribution of the re-won powder should be similar to the one of the original powder. Bigger agglomerates would result in irregularities in a recoated electrode or would have a smaller specific surface for a following hydrometallurgical process. Whereas smaller particles would imply, that the NMC particles had been broken during the deagglomeration process. The aluminum contamination should be minimal, because aluminum can react with hydrochloric acid during the leaching step and detonating gas could degas. Furthermore, the later removal of aluminum reduces the efficiency of the hydrometallurgical step. 2
EXPERIMENTAL
At the LithoRec project two process paths were developed to separate the current collector from the coating in order to gain both fractions in the best purity. Both processes were developed using NMC cathode production rejects, which had not been cycled electrochemically before. The production rejects consist of a 20 µm aluminum foil and a coating composed of NMC, a PVDF based binder and conducting agents. Later, these processes were validated by using electrodes from spent battery cells. These electrodes had been pre-dried for 10 minutes at 120 °C to evaporate the volatile fraction of the electrolyte. 2.1
Figure 1: Physical separation process. 2.2
Wet chemical separation process
The wet chemical process used for this research consists of the steps presented in Figure 2. A pre-cutting step, either manual or using a cutting mill, is necessary to allow stirring of the foil-solvent mixture (step 2). In this research the pre-cut electrode rejects were stirred batch-wise in n-methyl-2-pyrrolidone (NMP) at a temperature of 90 °C for 10-20 minutes. An amount of 200 g NMP had to be used to process 100 g shredded electrode production rejects. The solving procedure was repeated six times with the same foil fraction. Afterwards, the pieces of foil were screened at 50 µm and the fine fraction was centrifuged. The centrifugal sediment was dried at 150 °C and pulverized by an ultra centrifugal mill. Finally, the product was a powder which can be used in a direct recoating process on the one hand or processed hydro-metallurgically on the other hand.
Physical separation process
The developed physical separation process (Figure 1) can be subdivided into 4 parts: pretreatment, mechanical stress, separation, and deagglomeration. Drying for 12 hours at 150 °C or calcination at 500 °C for 15 minutes in a muffle oven turned out to be effective pretreatment steps. During the second step the pretreated production rejects were stressed in a cutting mill with a rotor peripheral speed of up to 10 m/s using a 2 mm trapezoid sieve affecting the product grit size. Afterwards the product was sieved at a mesh size of 200 µm and the resulting fine fraction was deagglomerated in an ultra centrifugal mill with a rotor peripheral speed of up to 60 m/s. Thus, the specific surface of the resulting particles was enlarged to accelerate the following leaching process if a further hydrometallurgical treatment is carried out. Moreover, the pulverization of agglomerates results in a better coating quality regarding a direct recoating process since micron sized agglomerates bound by the PVdF binder are deagglomerated. Without this treatment agglomerates of approximately 200 µm up to 300 µm were observed as irregularities in the recycled coating layer. Being considered the main recycling product only the active material was balanced to quantify the separation results. Thus the fine fraction below 200 µm was accounted as separation product.
Figure 2: Wet chemical separation process.
Automotive Life Cycle Engineering - Recycling 2.3
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Analysis
Electrode material suspensions and recycling electrodes had been prepared by dissolving the recovered materials with and without small amounts of additional binder and finally applying a standardized electrode blade coating process using NMP as solvent. The adhesive strength and the structure of the recycled electrodes were analyzed in addition to an electrochemical characterization. Furthermore, laser diffraction spectroscopy and atomic absorption spectroscopy had been applied to detect the particle size distribution of the recovered active materials and the aluminum contamination. 3 3.1
RESULTS AND DISCUSSION
Spent NMC Electrodes Cutting Mill: RCS = 10 m/s, Six Disc Rotor, 2mm Trapezoid Sieve
-1
< 200 µm
Yield of Recycling YR / kg*kg
The aluminum contamination of the fine fraction was measured by atomic absorption spectrometry. It was determined to 0,7-1% by weight. Since the contamination did not increase with higher rotational speed even higher stress intensities could be tested in further studies. After the deagglomeration step in the ultra centrifugal mill the remaining aluminum foil pieces and agglomerates below 200 µm are pulverized from a medium particle size of 90 µm to 13 µm. Thus, the resulting powder had a particle size distribution (PSD) similar to the one of the industrial standard. Here the PSD was measured by dry dispersed laser diffraction spectrometry.
Physical process
1,0
caused by higher specific stress intensities with increasing rotational speed. Rotor/electrode and stator/electrode impacts become more intensive and higher fracture energy is provided. Thus, the coating layer breaks and flakes off the foil. At lower rotational speeds the stress mechanisms tend to shear and cut with lower specific stress intensities. Whereas a bottom sieve determines the maximum product size so that the foil pieces are stressed until they reach the separation size in any case.
Following, electrodes were made from the recovered coating material by using a standardized NMP based electrode coating process. No supplementary binder was necessary while coating non-calcinated active materials from electrode production rejects. Whereas, for spent electrode materials an amount of 1 wt% PVdF binder had to be added in order to get a mechanical stable coating. This process yet has to be validated for calcinated active materials.
0,8
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However, adhesion tests of these recycled electrodes indicated that the binder had not lost its functionality by processing.
0,2
0,0
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Calcination
Pretreatment Figure 3: Influence of thermal pretreatment strategies on the Yield of Recycling of spent NMC electrodes. The influence of different thermal strategies on the separation grade of spent NMC electrodes is shown in Figure 3. The Yield of Recycling YR should be defined as the mass of rewon active material, in this research study NMC, in relation to the mass of inserted active material. Processing spent electrodes only pre-dried for 10 minutes at 120 °C results in a separation yield lower than 30% of the inserted active material. Whereas drying for 12 hours at 150 °C with a loss of mass of only 0,4wt% already increases the yield to over 80wt%. This can be explained by embrittlement of the PVdF binder by some chemical reactions with the electrolyte. A calcination of the spent electrodes at 500 °C for 15 minutes before stressing them in the cutting mill leads to a yield of over 99.5wt%. According to thermogravimetric analysis the PVdF binder volatizes at 500 °C. This lowers the adhesion between coating and foil to a minimum and thus coating material and aluminum foil can be separated easily. The parameterization of the milling step, especially concerning the rotor circumferential speed, was carried out with production rejects not pretreated and coated on one side only. The rotor’s circumferential speed (RCS) was varied from 1.2 to 10 m/s and the Yield of Recycling was determined by sieve analysis as shown in Figure 4. The fine fractions increase with the rotor’s rising circumferential speed, e.g. the fraction below 200 µm shows an increase from 51% at 1.2 m/s to 78% by weight at 10 m/s. This is
These cathodes were electrochemically cycled in stacked pouch cells cells containing a recycling cathode of 25 cm², a ceramic separator, a graphite anode and LiPF6 as electrolyte. The specific capacity of the recovered NMC from production rejects measured 150 mAh/g at discharge rates of 0.5C and 125 mAh/g at discharge rates of 3C. Due to the variance in electrode preparation and in the cell assembly process these results have to be considered in the same order of magnitude like the capacity of the commercial reference material. Furthermore, the recycling cells had 87.5 % of their initial capacity after 500 cycles at charge/discharge rates of 3C/3C. In comparison, the reference electrode still had 95% of its initial capacity after 500 cycles, as shown in Figure 6. The active material, removed from spent electrodes, had a capacity of 82 mAh/g at a cycling rate of 0.5 C and a capacity of 42 mAh/g at cycling rates of 3C. The cycle stability tests showed no further capacity reduction during the next 500 cycles. And Atomic absorption spectroscopy showed a loss of lithium of about 6 %. Additionally the capacity of 0.5 C is twice as high as the one at 3 C. Due to these facts the reason for the lower capacity of the recycled electrode is seen in structural deformation of the active material and reduced electrical conductivity of the electrode coating layer. The latter is caused by the insufficient use of the conducting agents (acetylene black and conducting graphite). Calcination of these materials and replacing them by new ones will validate this theory in further studies. Another explanation for the reduced capacity is an immobilization of lithium during the formation of the solid electrolyte interface (SEI). The SEI formation occurs during the first cycles on the anode side, where lithium is lost by the SEI film formation on the boundary layer between the electrolyte and the solid particles of the electrode Therefore, it could be that the second delithation of the NMC material leads to a critical amount of lithium in the lattice structure which contributes to the partial destruction of the layered structure of the metal oxide.
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.
advantage for a leaching process if a hydrometallurgical processing is performed later. Sieve Fractions < 200 µm < 160 µm < 100 µm < 75 µm < 50 µm < 20 µm
100 90
Yield of Recycling YR / kg*kg
-1
80 70
NMC - Electrode Production Rejects Oneside Coated, Not Pretreated Cutting Mill: Six Disc Rotor, 2mm Trapezoid Perforated Sieve
As already described for the physio-mechanical process electrodes were made from the recovered coating material using a standardized electrode coating process. To coat the chemically separated powder PVdF binder was added this time in the dose of the original formula to replace the binder lost in the dilution during the recycling process. These cathodes were built into pouch cells containing one recycling cathode, a ceramic separator, a graphite anode and LiPF6 as electrolyte. Electrochemically tested, these cells still had 92.9 % of their initial capacity after 500 cycles at charge/discharge rates of 3C/3C.
60 50 40 30 20
140
10 120
2
4
6
8
10 -1
Rotor's Circumferential Speed (RCS) / m*s
Figure 4: Influence of the Rotor Circumferential Speed on the Yield of Recycling. Wet chemical separation process
1,0
NMC Electrode Production Rejects Wet Chemical Separation Process
Specific Capacity / mAh*g
0
-1
0
3.2
Electrochemical Testing Charge / Discharge Rate 3C/3C 25 cm² Pouch Cell, C = 44 mAh, T = 21 °C
100
Material / Separation method Reference rejects / mechanical rejects / chemical cycled / mechanical
80
60
40
20
Solvent NMP, 90 °C, Stirred 15 min
R
-1 Yield of Recycling Y / kg*kg
0 0
0,9
100
200
300
400
500
Cycle / -
0,8
Figure 6: Electrochemical test results of re-coated NMC electrodes.
0,7
4
0,6
0,5 1
2
3
4
5
6
Number of treatment steps [-]
Figure 5 : Recycling yield of batchwise wet chemical separation. As shown in Figure 5 the absolute yield of active material recycling increases with the number of treatments the foil receives. And it reaches 97wt% after the sixth treatment. The main problem of this segmented process is represented by the lack of solvent in the final phase of separation. The already detached active material partly adheres to the remaining foil when the last solvent has drained through the sieve. Thus, a process with a circulating solvent should be used to wash the remaining active material off the foil in the future. The re-won material exhibits a PSD being similar to industrial standard after pulverization by ultra centrifugal milling. The aluminum contamination measured only 0.06 wt%, which is ten times less than in the mechanical process. This represents an
SUMMARY AND CONCLUSION
In this study two processes to recover active materials from spent lithium ion battery electrodes and electrode production rejects have been developed in this study. On the one hand the physical process using thermal treatment and mechanical stressing to separate the coating from the aluminum foil seems to be the easier and economically more feasible way to separate active materials from the current collector foil. This is mainly because no expensive, unhealthy and chemically aggressive solvent is needed. Further, the equipment to realize this process is simple and thus less costly. By varying the process parameters and by calcinating the cathodes in a pre-treatment step, the coating could be separated with a yield of 98 percent and higher, achieving the quality criteria for a direct recoating process. On the other hand the main advantage of the wet chemical process is the less aluminium contamination of the product. Recycling electrodes from both processes were successfully tested in pouch cells and the first recycling electrodes made of the powder recycled by the wet chemical process had the better electrochemical cycle stability with 92.9% of the initial capacity after 500 cycles at charge/discharge rates of 3C/3C. Considering, the discussed separation of the coating will most likely become a preparation step for a hydrometallurgical treatment, the physical separation fulfils the requirements better. Only in case of a
Automotive Life Cycle Engineering - Recycling direct reuse of the solvent solution for re-coating the wet chemical separation has an advantage. 5
ACKNOWLEDGMENTS
The presented work was generated as part of the research project LithoRec, which is funded by the German Federal Environment Ministry (BMU). The authors would like to thank the BMU for the financial support. 6
REFERENCES
[1]
Bernardes, A.M.; Espinosa, D.C.R.; Tenório, J.A.S (2004): Recycling of batteries: a review of current processes and technologies, in: Journal of Power Sources, Vol.130, pp. 291– 298.
[2]
Espinosa, D. C. R.; Bernardes , A. M.; Tenório, J. A. S. (2004): An overview on the current processes for the recycling of batteries, in: Journal of Power Sources, Vol.135, pp. 311–319.
[3]
Heegn, H.; Rutz, M.(2008): Rückgewinnung der Rohstoffe aus Li-Ionen-Akkumulatoren, in: Teilvorhaben 3 Batterie- und Schlackenaufbereitung. Freiberg, p. 35 ff.
[4]
Xu, J.; Thomas, H.R.; Francis, R.W.; Lumb, K.R. (2008): A review of processes and technologies for the recycling of lithium-ion secondary batteries, in: Journal of Power Sources, Vol.177, pp. 512–527.
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New Technologies for Remanufacturing of Automotive Systems Communicating via CAN Bus 1
1
1
1
2
Rolf Steinhilper , Stefan Freiberger , Matthias Albrecht , Josef Käufl , Eberhard Binder , Constantin Brückner 1
2
Chair of Manufacturing and Remanufacturing Technology, Bayreuth University, Germany 2
University of Applied Sciences Coburg, Germany
Abstract The present paper summarizes the recent methodologies, technologies, results and opportunities in the field of analyzing and extending the life cycle of automotive systems by remanufacturing that have been developed within the European research project “CAN REMAN”, conducted by Bayreuth University in cooperation with two other universities and eight industrial partners. The aim of this project is to develop appropriate and affordable testing and diagnostics technologies that enable small and medium sized enterprises (SME) to remanufacture mechatronic vehicle components. Keywords: Remanufacturing; Testing Automotive Components; CAN Bus Communication
1
INTRODUCTION
Raising requirements on occupant safety and comfort on the one hand and the introduction of new emission regulations on the other hand, forces the automotive manufacturers to enhance their products continuously. In order to achieve these improvements, electronic systems, based on microcontrollers, have found their way into modern cars and they contributed considerably to many new advantages in terms of safety and comfort such as ESP/ESC (Electronic Stability Program), ABS (Antilock Breaking System), PAS (Parking Assist System), EHPS (Electro Hydraulic Power Steering) or EAS (Electro Assisted Steering). Nevertheless, the new trend of modernization has an immense impact on the remanufacturing business. It can be seen that new branches in electronic remanufacturing arise. In contrast to that, the knowhow of traditional remanufacturing companies has eroded rapidly and even the industrial principle of remanufacturing is at risk. Due to the fact that modern cars incorporate up to 80 of these mechatronic and electronic systems that are communicating with each other e.g. via the vehicle controller area network (CAN), remanufacturing of these automotive systems requires innovative reverse engineering knowhow, methodological innovations and new technologies, especially focussing on the tasks testing and diagnostics of systems and their subassemblies. Since traditional remanufacturing companies do not have much capacity to build up the appropriate knowhow, the Chair of Manufacturing and Remanufacturing Technologies at Bayreuth University assists these companies in reverse engineering, as well as finding new methodologies and technologies for remanufacturing [1] [2]. In the following chapters, recent methodologies and technologies for automotive components will be presented on the example of an electro-hydraulic power steering (EHPS) pump. The results have been obtained within the European research project “CAN REMAN” which is conducted by Bayreuth University, Linköping University (Sweden), the University of Applied Sciences Coburg, Fraunhofer Project Group Process Innovation and eight industrial partners. The target of this project is to enable independent aftermarket (IAM) companies to remanufacture modern automotive mechatronics for
multiple life cycles and electronics with innovative reverse engineering skills as well as to develop appropriate and affordable testing and diagnostics technologies. 2
REMANUFACTURING TODAY
Remanufacturing is defined and known as the industrial manufacturing of “good as new” products from used and returned products. This means, that remanufacturing is the process of restoring a non-functional, discarded or traded-in part to like-new condition, giving the product a further life. The products, also known as “cores”, are brought to a shop floor, where they run through the five steps of remanufacturing [3] [4]. 2.1
The Remanufacturing Market - A hidden Giant
While early remanufacturing was limited to a hand full of expensive capital (so called “investment”) goods, the number and variety of remanufactured products is tremendous nowadays and it is still increasing [5]. Even though the estimations about the current scope of remanufacturing activities vary, two comprehensive studies discovered the impact on the economy in the US. The first study, published in 1996 and updated in 2003 by Lund of Boston University is summarized in Table 1. Lund divided the great variety of currently remanufactured products into six major classes as pointed out by Table 1 [3] [6]. Categories Automotive components Electrical devices Office furniture Machines Tires Toner cartridges Others Summation
Companies [Amount]
Annual Turnover [Mio. $]
Employees [Number]
50.538
36.546
337.571
13.231 720 685 1.390 6.501 250 73.315
4.633 1.663 1.272 4.308 2.475 2.009 52.906
47.280 12.148 10610 27.907 31.872 14.372 481.760
Table 1: Main remanufacturing categories and their magnitude in USA [6].
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_16, © Springer-Verlag Berlin Heidelberg 2011
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Automotive Life Cycle Engineering - Recycling The second and more reliable study, conducted by the OEM Product-Service Institute (OPI), is based on a different methodology to fill in the gaps from the Lund study. The idea was to use the current replacement value (CRV) of actual products as basis for estimating the remanufacturing expenditures. These remanufacturing costs were estimated by experts from each industry as a percentage of the CRV. The benefit of this study, which does not make extensive use of real data, such as in Lund’s study, is that a broader range of industries was addressed [7]. The results of the OPI study can be found in Figure 1.
91 measures are applied in every step [1] [4]. At the end of the process chain, each remanufactured product has to pass a 100 % test which should be seen as an integrated part of the whole process chain rather than as an independent “sixth step” [4]. At the bottom line, all remanufactured products arrive at the customers with the same quality level compared to the factory brand new counterpart, or even better [1]. 3
AUTOMOTIVE MECHATRONICS REMANUFACTURING
CHANGE
TODAY’S
The term “mechatronics” was formulated in 1969 in Japan and it is an artifice that describes a system which combines mechanics, electronics and information technologies. A typical mechatronic system gathers data, processes the information and outputs signals that are for instance converted into forces or movements [8]. 3.1
Figure 1: Remanufacturing expenditures by industry [7]. Although the estimates differ to a certain extent, the conclusion given is clear. Remanufacturing offers tremendous untapped opportunities. One of the most beneficial area for remanufacturing is the automotive sector that covers 70 % of all remanufacturing companies and that has an annual turnover of 36 Billion US-$. The background of these companies highly differs. While in some cases OEMs and OEs remanufacture their own products, the plurality of remanufacturers is of smaller subcontractor or “third party” nature. Those smaller companies need to reverse engineer the products themselves. 2.2
Remanufacturing in a five-step-process
As stated before, remanufacturing, also referred to as product recycling, is organized in an industrial process, in order to have the same advantages and benefits as series production. Among others, the remanufacturing process in five steps (Figure 2) aims to maintain a constant quality level as well as to formulate a rational and reproducible production process. Quality Assurance 1. Complete Disassembly of the Product 2. Through Cleaning of all Parts 3. Inspection and Sorting of all Parts 4. Reconditioning of Parts and/or Replenishment by new Parts 5. Product Reassembly
Final Testing Figure 2: Traditional remanufacturing process chain [4]. In order to reach the same quality level, product reliability, safety and lifetime level, to meet current safety standards and to provide the same warranty like a new product, adequate quality assurance
Technological Change of Vehicles
Automotive parts should not longer be seen as isolated standalone applications with few mechanical and electrical inputs and outputs. Now, they have the capability to communicate to each other and to share the same sensor information. Subsequently, the communication of the different automotive subsystems helps the OEMs to reduce weight and cost by sharing the same sensors and reducing cable doubling (cable length) in modern vehicles. For the driver the network and communication within the car remains invisible and he feels the car behaving like ten years ago despite of some additional comfort functions. But if we take a closer look, modern vehicles resemble more or less a distributed system. Several embedded computers – often referred to as electronic control units (ECUs) – communicate, share information and verify each other over the vehicle network. One of the commonly used communication networks in vehicles is the CAN-Bus (Controller Area Network). Within this network structure, each control unit has at least one unique identifier (ID) on which it broadcasts messages that again incorporate different signals and information [8]. Easily speaking, in case of a missing or faulty participant in the network, all other controllers will notice the participant as they have a lack of information. The lack of information or errors on the CAN bus force the other systems to operate in a “safe mode”. In reverse, a controller not connected to the specific vehicle network stops its operation patterns and falls into “safe mode”. 3.2
Difficulties for Remanufacturers
As stated before, the introduction of electronic networks into modern cars entails enormous problems for remanufacturers. Modern electronic and mechatronic vehicle components cannot be tested as easily as traditional electrical and mechanical ones. While it was usually sufficient to link electrical systems to the power supply (battery), modern mechatronic and electronic systems gather a lot of information from the vehicle environment and driving conditions using plenty sensors and the CAN bus network of the vehicle. As a consequence, connecting all sensors and the power plug to the device under test (DUT) is insufficient unless the device is connected to the network of a real car or an adequate simulation of the communication in the vehicle. Following these statements, the key for successful remanufacturing and testing of a certain automotive system lies in the simulation of the complete network communication in the vehicle. Unfortunately, there are no tools available in the market that allow the remanufacturers to simulate a complete car of a specific type and model easily. In each case, the car matrix (CAN database) of the specific vehicle model is required to build a simulation of the CAN communication in a vehicle. However, the OEMs will not release any information on the communication parameters to non-OEs and
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therefore they will not support the independent remanufacturing business. As a consequence, the independent remanufactures (onto which this paper focuses) have to do a lot of reverse engineering themselves or in cooperation with others in order to design their remanufacturing process chain and to come up with test solutions to ensure the quality of their products. The reverse engineering activities focus on the system, its components, the system behavior in the vehicle and the vehicle CAN bus communication. 3.3
The Remanufacturing Process Chain for Automotive Mechatronics
Following the previous aspects, the state-of-the-art process chain for remanufacturing, presented in chapter 2.2., needs to be reconsidered when it comes to mechatronics. Regarding the process steps, disassembly, cleaning and reassembly, great progress has been made, as it can be found in the literature [10] [11] [12]. Primarily, the diagnostics and testing differs to a certain extend from the traditional (final) testing of mechanics, as it has already been discussed before. In addition to this, it was found that a lot of failures of parts and its subassemblies can only be detected or isolated with a test of the completely assembled mechatronic system [1], e.g. by utilization of the onboard-diagnostics and the fault memory of a mechatronic system. This means that the process chain for remanufacturing of mechatronic systems should be extended by an additional first step. The initial (entrance) diagnostics of the system to be remanufactured imply that the product itself and its communication patterns have been reverse engineered before and the appropriate simulation model of the reference vehicle has been developed so that the tested product “feels” like in its original environment. 4
UNDERSTANDING AN AUTOMOTIVE SYSTEM BY REVERSE ENGINEERING
MECHATRONIC
The term “reverse engineering” has its origin in the mechanical engineering and describes in the original meaning the analysis of hardware by somebody else than the developer of a certain product and without the benefit of the original documentation or drawings. However, reverse engineering was usually applied to enhance the own products or to analyze the competitor’s products [13]. Chikovsky describes reverse engineering in the context with software development and the software life cycle as an analysis process of a system, in order to identify the system (sub-) components, to investigate their interaction and to represent the system at a higher level of abstraction [13]. In this context, he also clarifies the terms “redocumentation” and “design recovery”. “Redocumentation” is the generation or revision of a semantically equivalent description at the same abstraction level. That means that the results are an alternative representation form for an existing system description. However, redocumentation is often used in the context of recovering “lost” information [13]. The term “design recovery” defines a subset of reverse engineering that includes domain knowledge, external information (of third parties) and conclusions additionally to the original observations and analyses in order derive meaningful abstractions of the system at a higher level [13]. Overall, reverse engineering of software in the development focuses on the following six targets [13]:
Coping with the system complexity
Generation of alternative views
Recovery of lost information
Detection of side effects
software
Synthesis of higher abstractions
Facilitation of reuse
These targets, that have originally been defined for software reverse engineering, can also be transferred to a certain extend to the reverse engineering of automotive mechatronic systems and hence to the remanufacturing of these systems. First, remanufacturers face the same problem that they usually have to cope with complex mechatronic systems as stated before. “Cope” means in this context, that it must be possible to operate an automotive mechatronic system independently from its original environment (the vehicle). Second, it is possible to detect universal taxonomies which can then be abstracted and used in order to transfer the gained knowledge to similar mechatronic systems or to other variants of the system. Especially the high degree of variation of similarly looking mechatronic systems and control units makes it difficult for the remanufacturers to cope with the complexity of automotive components that usually differ by a slight detail [14]. Third, recovery of missing information rather than lost is one of the most important aspects for the remanufacturing. The following chapter demonstrates how a reverse engineering analysis can be conducted for an automotive mechatronic system. 5
ANALYZING AN AUTOMOTIVE SYSTEM IN FIVE STEPS
After a reference system for the analysis has been chosen it is necessary to procure at least one, ideally brand-new, system to grant correct functionality, for all following investigations. In order to analyze the system in its normal working environment, the original vehicle, in which the reference system commonly is built in, should be procured as well. This investment might be unavoidable, because a mechatronic system communicating via CAN, detached from all other vehicle communication will not work anyway, as essential input information, transmitted via CAN, is missing otherwise (refer to chapter 3). In this case it is very difficult to understand the ECU communication and put up the system into operation isolated from the vehicle. A cheaper way to investigate the communication between vehicle and reference system is to create a CAN trace using a software tool such as “CANoe” from Vector Informatics. This tool allows easily recording of the complete vehicle communication for instance while doing a test drive with a vehicle that may be available only once. Whatever strategy is chosen, it is essential to figure out which input (CAN data) is required to start, operate and control the system. 5.1
Electrical Wiring
After having obtained a reference system, it is essential to know the pinout of all connectors of the system. Therefore, the very first step is to find out which pin belongs to which wire and signal. First of all, the power connector (ground and positive terminal), including ignition, must be identified. One opportunity to obtain this information is the utilization of wiring diagrams or similar credentials. If such documents are not available, for example a visual inspection of the connectors and wire harness in the vehicle or continuity measurements can be beneficial. Afterwards, it is indispensible to identify the CAN connection pins. These can easily be recognized by inspection of the cable harness. In most cases two twisted wires indicate a CAN bus connection, but single wire CAN connection is possible, too. Finally, all connectors for sensors and actuators (auxiliary power and sensor/ actuator signal) must be known as well to go further in the analysis process.
Automotive Life Cycle Engineering - Recycling 5.2
Vehicle Network Topology
The investigation of the structure of all bus systems in the vehicle is placed in front of the proper CAN bus analysis step. It is necessary to determine how many (CAN) bus networks are established and in which network the references system is located. Additionally, the network speed, the presence of a separate diagnosis network (e.g. K-Line), and all ECUs of the specific networks must be found out. Especially those ECUs that provide essential input as mentioned before. Furthermore, possible gateway ECUs should be identified. A feasible solution to gain this information can be for example a web inquiry, documents from the manufacturer of the vehicle or the system, third party documents or technical journals. 5.3
CAN Bus Communication
Having collected all information about the (bus) network structure of the vehicle, detailed knowledge about the received CAN messages and transmitted CAN data is necessary. In the first step, all ECUs and its associated CAN message IDs must be determined. For this purpose CANoe can be used. First of all, a physical connection to access the CAN bus using CANoe has to be installed in the vehicle, ideally nearby the reference system ECU. With the “trace functionality” of CANoe the bus communication and all CAN messages of all ECUs can be displayed easily. Beside of the CAN IDs, the cycle time and the length of each message can be analyzed. This information is relevant for a later rest bus simulation of all participating ECUs to ensure correct functionality of the reference system. The assignment of CAN ID and the associated ECU is more difficult. One possibility to gather this information is, to locate all ECUs which provide relevant data on the CAN bus and to separate the CAN wires out of the cable harness. Afterwards, a kind of software gateway is installed in between the DUT and the other ECUs using CANoe and a simple CAPL (CAN Access Programming Language) program. Each end of the CAN wires in the vehicle must be connected to a computer via CAN hardware (Figure 3). By this means, it is now possible to detect the messages on the bus as well as the transmit direction – receive or transmit. In addition to this, it is also possible
Figure 3: CANoe as software-gateway. to add some filters to the CAPL program in order to filter out unnecessary messages and hence to reduce data complexity. This step is repeated for each ECU which provides relevant input data for the reference system. After having identified the relevant CAN messages, it is inevitable to examine the message data bytes in detail to determine the physical signals. This can be achieved by generating physical inputs manually (e.g. open the throttle, drive, break …) and observe the particular CAN messages as well as its bytes in parallel. After that, a correlation between a certain CAN message, its CAN data and a physical input value can be established.
93 Having performed the steps above, it is possible to setup the desired restbus simulation for the reference system. 5.4
Sensors
Besides the CAN data, analog inputs of sensors and analog outputs of actuators are important in order to ensure correct functionality of the reference system. Therefore, each sensor and nearly each actuator has to be analyzed and simulated, too. The sensors can be analyzed using an oscilloscope and a multimeter in order to characterize current consumption, supply voltage and signal transmission. Typically, sensor output signals are analog to:
Current / voltage, amplitude
Frequency / cycle time
Pulse width / duty cycle
Or they are discrete in the following forms:
Binary
Multi-staged (different scaled)
Multi-staged (equidistant) digital
For the simulation, the measured values must be interpreted and emulated. For example, the internal resistance of a sensor can be calculated from the sensor current consumption. Afterwards, the presence of the sensor can be simulated by a (simple) resistor. The simulation of the sensor signal can be realized using a waveform generator, an analog circuit, a microcontroller or a combination of them. 5.5
Diagnostics
Finally, to test the reference system completely detached from the vehicle, it is necessary to know how the diagnosis communication works in order to check the fault memory and to read internal sensor information of the ECU (e.g. for temperature). First, the applied protocols for transport and application layer must be identified. Often, standardized communication protocols for ECU diagnostics are used (e.g. ISO TP, KWP2000 or UDS). In some cases OEMs use proprietary self-developed keyword protocols (e.g. KWP1281). Thus, it is more difficult to build up a diagnosis connection to the reference system because the protocol specification is unknown to the remanufacturer. Hence, a detailed analysis of the CAN or K-Line communication during a diagnosis session is essential. Sophisticated reverse engineering capabilities are necessary to analyze, understand and recreate such a diagnosis communication. The message IDs, used for the communication, must be investigated independently by observing the diagnosis communication with CANoe. If the CAN IDs and protocols are known, the diagnose communication can be reproduced for example in CANoe. After a remanufacturing company has accomplished all mentioned steps for the reference system, it is able to operate this system detached from all analog (sensor signals) or digital (CAN) inputs. Finally, a test bench can be developed for entrance and final testing in series production scale. 6
EXAMPLE: REMANUFACTURING OF AN EHPS
The following six steps describe the reverse engineering process on the basis of an electro-hydraulic power steering (EHPS) pump that is used in a VW Polo. 6.1
Physical Analysis and Electrical Wiring of the EHPS
At the beginning, the EHPS has to be perceived as a black box with inputs and outputs. Because of the mechanical design and the general function of a hydraulic power steering, the output can be
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determined as the flow rate of the fluid. The inputs are composed of an information about the internal combustion engine state (running or not running) and direct or indirect information about the necessary oil flow rate.
CAN bus data is also accessible through the vehicle on-board diagnosis (OBD) connector. This data can contain the IDs of the EHPS inside the car and further ECUs. Unfortunately, the CAN bus was not linked to the OBD connection of the test vehicle (Figure 5). Therefore, the CAN wires of the EHPS were disconnected in order to use CANoe as gateway for CAN messages. As a consequence, it was possible to differentiate between received and transmitted messages. This test verified the previous observations on the CAN IDs sent by the EHPS. 6.3
CAN Bus Communication Investigations
This step can always be split into two parts. The first one is the analysis of the communication in order to filter out and understand the relevant messages for the EHPS sent by other ECUs. The second one is the simulation of the necessary CAN communication, which is called “restbus” in the following. Figure 4: Examination of the CAN Reman test vehicle. To get a first overview about the electrical connections of the device, a reference unit was completely disassembled. Large connector pins were good indicators for the general power supply by reason that the power consumption of the electric motor is supposed to be high. The ground pin of this connector was found by searching for a direct linkage between those pins and the ground plate of the circuit board. The color of the connected cable usually indicates the ground connection (GND). The other cable on the connector is the positive power supply (Vcc). At this point, the connection of the steering angle sensor, which is directly mounted on the steering shaft, was disregarded. The third connector contained three cables. Two of them were twisted in the following cable harness. That was a perfect indication for CAN cables. The CAN-high cable rises from 2.5 V to 3.5 V and the CAN-low cable falls from 2.5 V to 1.5 V during active communication. When operating the vehicle, the last cable was on 12 V level and therefore it was assumed to be the signal for “ignition on”. At this point the electrical analysis of the device was completed. 6.2
Vehicle Network Topology
The most important questions in this step are: “How does the EHPS communicate in the vehicle network? Will the device communicate outside the car?”
First, the start signal, transmitted to the EHPS via CAN bus, must be discovered as described in step 2. Therefore, a recording of the in-car CAN communication was made at a stationary test. After that, the recording was replayed to the test device outside the car and it started its operation. Next, CAN messages were successively filtered out until the motor of the test device stopped. Hence, the last filtered message contained some kind of a start signal. After some tests, this signal was identified to be the RPM signal of the internal combustion engine. In order to eliminate or to find other input parameters, the same test was made with a recording of a real-road test. It was found that the vehicle speed is another input parameter for the EHPS. Second, the required input parameters were simulated with CANoe. Using a third party diagnosis garage tester (Bosch KTS 650), it was discovered that the fault memory of the external EHPS can only be erased when at least the presence of the missing messages of the in-car communication is simulated, too. This simulation of messages with and without data content is called restbus. At this point the EHPS can completely be operated outside the car, but with a real steering angle sensor. 6.4
Simulation of Sensors
In order to operate the EHPS in a completely simulated environment, the angular velocity sensor had to be simulated. Analog to step one, Vcc and GND were identified on the sensor terminal using a multimeter. The third cable transferred the information about the angular velocity of the steering wheel. This signal was analyzed with an oscilloscope. A pulse width modulated signal was detected and simulated with a waveform generator. Furthermore, the sensor presence had to be emulated by a simple resistor matching the power consumption of the original sensor. 6.5
Figure 5: CAN bus topology of the test vehicle. To get answers for these questions, the reference EHPS, outside the car, was connected to a power supply and an ignition signal. In this configuration, the electric motor of the EHPS was not started by the control unit. By connecting the EHPS via CAN hardware to a computer running CANoe (version 7.2), the message IDs sent by the EHPS were displayed in the trace-window. Usually, selected
Diagnostic Functions of the Device
Most devices, including the present EHPS, can be diagnosed over CAN bus with an external diagnosis garage tester. This tester can, as mentioned above, directly communicate with ECUs using a transport and a keyword protocol. The protocols are only partially defined and the communication differs from brand to brand tremendously. Therefore, the most efficient way to understand how e.g. the fault memory can be erased, is to erase the fault memory with one of those testers and to try projecting the sequence onto known standards. In the present case, it were the standards KWP1281 and TP1.6. Even though the understanding of the diagnosis communication was very time-consuming, it was possible to erase and read the fault memory, to read the internal sensor data or duty cycles, to parameterize the device for different car models or even to completely reprogram the software. Finally, all functions were implemented in CANoe using CAPL which can be controlled by a graphical user interface (GUI).
Automotive Life Cycle Engineering - Recycling 6.6
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Operation Range
At last, the correlations between input and output values were determined in detail. For this reason, the input parameters angular velocity, vehicle speed, RPM and the outputted oil flow rate were recorded simultaneously. In this case the RPM signal only started the EHPS and was disregarded for the measurement. The vehicle speed was found in a particular message on the CAN bus as figured out in step 3. The angular velocity value is part of the sensor data provided by the EHPS in a diagnosis communication session as mentioned in step 5. The resulting oil flow rate was measured by adding an oil flowmeter to the low pressure side of the EHPS in the test vehicle. This flowmeter generates a frequency modulated signal which was converted to a CAN message by a microcontroller and broadcasted to the local in-car CAN network in a separate CAN message. Finally, all necessary input and output values were recorded from the CAN network time simultaneously using CANoe. Figure 6 depicts the flow rate of the steering oil as a result of vehicle speed and angular velocity, measured in a real-road test.
1
same principle can also be applied with some effort to further automotive systems, so that everyone wins, regardless of perspective. 8
9
Steinhilper, R., Rosemann B., Freiberger, S. (2006): “Product and Process Assessment for Remanufacturing of Computer Controlled Automotive Components”, in: Proceedings of the 13th CIRP International Conference on Life Cycle Engineering (LCE2006), pp. 441-446, Leuven, Belgium.
[2]
Steinhilper, R. (2008): “Automotive Service Engineering and Remanufacturing: New Technologies and Opportunities”, in: Proceedings of the 15th CIRP International Conference on Life Cycle Engineering (LCE2008), pp. 2-8, Sidney, Australia.
[3]
Lund, R. T., Hauser, W. (2003): The Remanufacturing Industry: Anatomy of a Giant, Department of Manufacturing Engineering, Boston University, Boston.
[4]
Steinhilper, R. (1999): Remanufacturing: The ultimate form of recycling, Fraunhofer IRB Verlag Press, Stuttgart, Germany.
[5]
Ferrer, G., Whybark, D.C. (2000): “From Garbage to Goods: Successful Remanufacturing Systems and Skills”,in: Business Horizons, Volume 43, Issue 6, pp. 55-64.
[6]
Lund, R. T. (1996): The Remanufacturing Industry: Hidden Giant, Boston University Study, funded by Argonne National Laboratory, Boston, Massachusetts.
[7]
Giuntini R., Gaudette K. (2003): “Remanufacturing: The next great opportunity for boosting US productivity”, in: Business Horizons, Volume 46, Issue 6, pp. 41-48.
[8]
Roddeck, W. (2006): Einführung in die Mechatronik, 3rd Edition, B.G. Teubner Verlag / GWV Fachverlage GmbH, Wiesbaden, Germany.
[9]
Zimmermann, W., Schmidgall, R. (2008): Bussysteme in der Fahrzeugtechnik, 3rd Edition, Vieweg + Teubner / GWV Fachverlage GmbH, Wiesbaden, Germany.
Flow rate
0,8 0,6 0,5 0,4 0,3 1
0,8
0,6
Vehicle speed
0,4
0,2
0 0
0,2
0,4
0,6
0,8
1
Angular velocity
Figure 6: Flow rate as a function of vehicle speed and angular velocity. 7
CONCLUSION
A still increasing number of mechatronic and electronic systems is built into today’s vehicles. In the future, even more of these systems will be introduced to the cars as a result of increasing demand for comfort, safety and reduced fuel consumption. Remanufacturing of failing mechatronic systems offers a great opportunity for all, the OEMs and OEs which can safe resources; the remanufacturing companies as they can make a growing new business with these systems; and the customers that are benefitting from cheaper, but as good as new, spare parts. Progress is not possible without its challenges, but it is achievable. Therefore, remanufacturing companies have to build up new reverse engineering knowhow, have to find methodological innovations and they need to develop new technologies, especially focusing on the tasks testing and diagnostics of automotive systems and their subassemblies. The paper outlines challenges, possible solutions and technological progress for the reverse engineering process of mechatronic automotive systems that are communicating via CAN bus. In addition to this, the reverse engineering process is demonstrated on the example of an EHPS which is used in a VW Polo. Obviously, it was possible to completely understand the steering system of the VW Polo by reverse engineering. Even though the reverse engineering effort was time consuming, it is now possible to run and test the mechatronic system outside the car as well as to adopt the results for remanufacturing the system in series production scale. The
REFERENCES
[1]
0,9 0,7
ACKNOWLEDGEMENT
The research project “CAN REMAN” and the activities described in this paper have been financed by the German Federal Government Department for Education and Research, support code 16INE014. The responsibility for the content of the paper lies with the authors.
[10] Johnson, M. R., Wang, M. H. (1998): “Economical evaluation of disassembly operations for recycling, remanufacturing and reuse”, in: International Journal of Production Research, Volume 36, Issue 12, pp. 3227-3252. [11] Seliger, G., Grudzien, W., Zaidi, H. (1999): “New Methods of Product Data Provision for a simplified Disassembly” in: Proceedings of the 6th International Seminar on Life Cycle Engineering 1999, pp. 250-259, Kingston, Canada. [12] Westkämper, E., Alting, L., Arndt, G. (2000): “Life Cycle Management and Assessment: Approaches and Visions Towards Sustainable Manufacturing”, in: CIRP Annals Manufacturing Technology, Volume 49, Issue 2, pp. 501-526. [13] Chikofsky, E. J., Cross, J. H. II (1990): “Reverse Engineering and Design Recovery: A Taxonomy”, in: IEEE Software, IEEE Computer Society, pp. 13-17. [14]
Haumann, M., Köhler, D. C. F. (2009): “Coping with complexity in remanufacturing“, Rematec News, Volume 9, Issue 3, pp. 32-33.
LCM applied to Auto Shredder Residue (ASR) 1
1
1
1
Luciano Morselli , Alessandro Santini , Fabrizio Passarini , Ivano Vassura , Luca Ciacci 1
1
University of Bologna, Dept. Industrial Chemistry and Materials, Viale Risorgimento 4, I-40136 Bologna, Italy
Abstract Auto Shredder Residue (ASR) is the waste generated from End of Life Vehicles (ELVs) pre-treatment, dismantling, shredding and metals recovery operations. ASR consists of plastics, rubber, textiles, glass, fines, dirt, etc. and many time is contaminated with heavy metals, hydrocarbons and PCBs. ASR is currently landfilled or incinerated but, due to the coming into force of Directive 2000/53/EC, it must be treated aiming at material and energy recovery to reach recycling targets by 2015. This work aims at a sustainable ASR management by using LCA as a decision tool, improved car design and innovative plastic recycling technologies. Keywords: Auto Shredder Residue (ASR); Life Cycle Assessment (LCA); Pyrolysis
1
INTRODUCTION
Automotive industry is one of the most resource-consuming sectors of the industrial production [1]. This holds that its products have both a high content of precious materials, such as steel and other non-ferrous metals, and an embedded energetic content, especially in plastics and rubbers. Consequently, end-of-life-vehicles (ELVs) are a particularly valuable waste stream, amounting to more than 9 million tons per year in Europe, an extent which needs to be properly managed [2]. A correct and efficient management of this kind of waste is thus of great importance in Europe and several other Countries, from environmental, economical and technological points of view. In Europe, at first fluids and other hazardous components (such as batteries) are mandatory removed. Then, according to the market rules, components may be dismantled and further reused and recycled, if it proves to be profitable. After these operations, hulks are baled and transported to a shredding plant where cars are reduced into pieces. The embodied materials are liberated and then sorted for recycling. Metals account for up to 75% of a vehicle mass and, especially ferrous ones, are very easy and profitable to be
sorted, and thus to be recycled. On the contrary, the non-metallic residue, called “car fluff” or “automobile shredder residue” (ASR), is mostly landfilled in Italy, as it happens in many other European Countries [3]. Automobile shredder residue (ASR), the residual fraction of a vehicle obtained after shredder and metal separation steps (named also ‘‘car fluff”), requires a particular attention. ASR is an agglomerate of plastic (19–31%), rubber (20%), textiles and fibre materials (10–42%) and wood (2–5%), which are contaminated with metals (8%), oils (5%), and other substances, some of which may be hazardous (about 10%), e.g., PCB, cadmium and lead [4]. Its composition may vary strongly depending on the shredding input mix (vehicles, white goods and ferrous waste combination) and on the depollution operation carried out. Since some years, even under the pressure of European Community, through the Directive 2000/53/EC which imposes the achievement of specific targets of recycling and recovery in fixed time periods (at least 80% of recycling and 85% of total recovery, by 2006; at least 85% of recycling and 95% of total recovery, by 2015), different possible ways of ASR valorization have been investigated, both aimed to material recovery (e.g., in cement
Figure 1: ASR composition analysis results [6].
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_17, © Springer-Verlag Berlin Heidelberg 2011
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97
concretes), and to energy recovery such as co-combustion in cement works, pyrolysis and/or gasification [4] [5] [6] [7]. The average weight of a vehicle is about 1 ton and 25% of its mass consists of ASR. This corresponds to about 2.5 million tons yearly generated and almost totally landfilled in Europe-25 (with an estimation of 3.5 million tons within 2015), with economical (due to the expenses related to this type of disposal) and environmental problems (associated to the physical–chemical processes of contamination which can occur in this situation). This work aims at applying LCM to automotive sector, in particular to car fluff recycling. Screening LCA results are followed by laboratory-scale experiments of both plastic sorting and thermochemical conversion, which are the two most promising ASR recycling technologies. Expected results are mainly both to understand technical feasibility and “state of the art” of the most sustainable ASR recycling technologies and both to integrate this know-how into new vehicles design. 2 2.1
ASR CHARACTERIZATION ASR Composition analysis
In order to recycle materials contained into ASR it is necessary to know its composition and recyclables share. Thus, a composition analysis was performed on the fluff samples in order to study ASR for future thermo-chemical and separation trials [5]. As it can be easily observed in figure 1, fines (0–20 mm fraction) represent almost a half of the total sample. For the fine fraction, a thorough composition analysis cannot be performed, because of the very small size of the materials included. Anyway, it is possible to identify glass pieces, plastics and metals, blended together with dust and dirt. The remaining fluff mainly consists of polymers, up to 45%, such as polyurethane (foam rubber), plastics and rubbers. Textiles accounts for about 10% on the total and together with polyurethane foam (PUF) are strictly related to car seats and carpeting. 2.2
Chemical-physical analysis
ASR revealed a rough 30% of ashes, LHV equal to 13.8 kJ/kg and heavy metals such as As, Cr, Mn, Ni, and Pb exceeding Italian RDF
law limits [6]. These parameters will be taken into account during LCA study. 3
LCA AS DECISION TOOL
Life Cycle Assessment was applied in this study as a scientific approach aimed to characterize and quantify environmental damages and impacts resulting from different ASR management methods: 1) landfill, 2) nonFe metals recovery, 3) incineration, 4) plastic sorting&recycling plus residue incineration and 5) gasification. Methodology, assumptions and scenarios are fully reported in reference [7]. The results show that industrial processes aimed at matter recovery are not only a necessary solution to fit European recycling and recovery targets for ELVs, but also the options that can obtain greater environmental benefits compared to present practices. Furthermore: (i) ASR landfilling is the worst scenario due to the direct impacts resulting from the disposal of polluted and hazardous waste as such ASR commonly appears, without any treatment aimed at energy or material recovery; thus, it results in a net loss of material. However, the nonferrous metals fraction recovery carried out commonly by most shredders at present allows a reduction of environmental loads, even if strictly for resources consumption. (ii) ASR co-combustion in incinerator would allow a decrease in damages related to plastics landfilling, and further benefits related to energy recovery processes, like waste volume reduction and organic pollutants destruction. In spite of the advantage resulting from the opportunity to operate in co-combustion with MSW (at the rate of 5%, any significant variations in outputs were not observed), ASR incineration should not be considered as a long-term alternative to landfill since this end-of-life strategy do not allow the achievement of 85% recycling target fixed by the European Community. (iii) In terms of environmental impact, better results characterize post-shredder technologies modeled by scenarios 4 and 5, with a little advantage for “feedstock recycling”. It is
Figure 2: LCA results [7].
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interesting to compare results even on the recycling and recovery rates gained by those scenarios: both allow the attainment of European targets, but scenario 4 reaches a higher recycling score than the other, competing with the other strategy in being considered the best solution. Hence, identifying the best way to treat ASR waste may be quite difficult even as a consequence of the frequent variations that occur in ASR composition. Thermochemical plastics conversion and mechanical plastics recycling revealed to be the best environmental solution due mainly to the production of either chemicals (or polymers) and energy which compensates environmental impacts related to disposal with avoided impact coming from the production of goods. Consequently, we decided to study ASR pretreatment and thermochemical conversion via pyrolysis in order to separate and recovery polymers and chemicals from ASR. 4
HYDRO-MECHANICAL ASR PRETREATMENT
At first, ASR has been sieved by means of a 20 mm sieve, obtaining a fine and a coarse sample. Fine, coarse and raw ASR samples have been then floated with a lab-scale equipment with d=1.4 kg/l aiming at polymers separation from the non-organic residue. ASR floating/sinking ratio is 40/60 for coarse fraction and raw samples while it is 25/75 for fines, revealing fines to contain the heaviest materials: metals, heavy rubber, glass and soil. After that, the floating fraction has been floated again with water in order to separate polyolefin, PP and PE (according to figure 3). Density (g/cm³) 0,9
1,0
1,1
1,2
1,3
1,4
1,5
PVC PET PP PE PS PA
Water
Figure 3: density distribution in selected plastics [8]. Polyolefin fraction amounts to a rough 8% of the total ASR mass while plastics with 1 < d< 1.4 kgl/l represent 11% of the ASR raw sample. Further separated poliolefines mechanical recycling trials are still going on in order to understand if this poliolefines mixture can be successfully palletized and re-extruded (as it was in scenario 4, chapter 3). The main benefits of this practice are plastics landfill avoidance and replacement of crude-derived virgin poliolefines with up to 50% recycled materials. 5
PYROLYSIS TRIAL
Thermochemical plastic conversion into hydrocarbons-rich oils is a very promising process in plastics waste management. Aiming at feedstock recycling, different sorted plastic fraction created in chapter 4 have undergone a pyrolysis process. 5.1
Uncondensed gases were collected in a gas sampling bag and analyzed. Once cooled down, reactor was opened and solid char residues taken out and weighted. Liquids were weighted and characterized as well by GC-MS. 5.2
Pyrolysis results
Results show that raw ASR sample has a total conversion (meaning gas + liquid yield) of 22%. Floating samples with d<1.4 kg/l rise total conversion to almost 60%. Moreover, polyolefin alone reach 90% conversion with low liquid viscosity and optimal refining potential. Pyrolysis oil chain length is influenced by waste input. The goal is to achieve light compound for chemical recycling. Polyolefins oil consist of: 16% molecules with more than 14 carbon atoms, olefins and paraffins represents a rough 25% while aromatics only 5%. Unknown compounds are quite high, 35%, but further GC-MS analysis will be carried out in the next future. If compared to mixed plastics oil, the poliolefines one looks much more suitable for further refining due to reduced viscosity and shorter hydrocarbons chain. 6
FEEDBACK TO VEHICLES ECO-DESIGN
DfR and DfD are tools belonging to the set of techniques named Design for Environment (DfE), aimed at the reduction of impacts deriving from EOL treatment and at the maximum product recycling and recovery. These tools can provide better product recyclability, determining the conditions for an increased added value, in a life cycle perspective. Thus, the design of each component must be performed by choosing recyclable or renewable materials, without toxic or hazardous substances, mono-material or composite parts of a high compatibility with recycling processes, low energy consumption materials, according to a life cycle concept. Furthermore, complex products (as automobiles) must be designed in order to simplify as much as possible assembly and disassembly operations, resulting in significant advantages from easiness and quickness standpoints. It can be reminded that actually disassembly is still far to be considered as a reversal of the assembly, thus next years DfD techniques should carefully consider joining parts, structure priority and the correct product dismantling sequence, to reduce disassembly costs, increasing effectiveness, limiting the time employed. Focusing on the recycling and recovery rates claimed by ELVs Directive for year 2015, it appears fundamental to improve recovery processes for non-metallic fraction. Intuitively, the increase in removal efficiency for these materials from ELVs, combined with a required spread of markets for secondary products, will allow the achievement of EC targets and economical profits for the stakeholders involved. Equally important will be the choice of materials employed in vehicle design, since the use of monomaterials and composites with high separation efficiency from vehicle waste, is a nodal point towards a sustainable ELVs management. In a previous work we applied design for dismantling (DfD) guidelines to a car seat [9]. This allows reducing dismantling time to one third whit respect to the baseline model. Applying now also design for recycling (DfR) guidelines, it means that different families of plastics would be substituted by polyolefins, characterized by higher sorting and recycling easiness, when technically feasible.
Pyrolysis reactor
Floated ASR samples were loaded in the pyrolysis reactor under a constant nitrogen flow. The reactor was then heated and, at cracking temperature, volatiles compound created were carried out of the reactor by the N2 flow and condensed in two coolers.
7
CONCLUSIONS
Life-cycle-oriented products development describes the process of systematic consideration and optimization of a product’s technical, economic and ecological characteristics and effects during the
Automotive Life Cycle Engineering - Recycling entire life cycle, within the frame of the product development process. The goal is to meet the requirements of an Extended Product Responsibility (EPR) by using the scope for decisionmaking during the product development to realize a maximum product benefit for costumers and producers during the life cycle and to minimize its economic, ecologic and social cost risks. During 2009 Italian ELVs recycling rate (Rr) was 80.6% [10]. Thus, ASR represents a rough 20% of a vehicle mass since no further treatments are currently carried out on ASR in Italy. So far, in order to reach 85% Recycling rate in 2015, it is necessary to recycle at least 25% of ASR mass. Plastics floatation and pyrolysis may lead to a rough 30% yield that, by summing up residual 5-6% metals in the heavy sinking residue, may lead to a total 35% ASR recycling, corresponding to 88% final Rr. ASR floatation and pyrolysis are feasible but further research are necessary for improving both separation efficiency and refining pyrolysis oil. 8
REFERENCES
[1]
Jody, B.J., Daniels, E.J., (2006): End of-Life Vehicle Recycling: The State of the Art of Resource Recovery from Shredder Residue, Energy Systems Division, Argonne National Laboratory.
[2]
EC, (2000): Directive 2000/53/EC of the European parliament and of the council of 18 September 2000 on end-of life vehicles––commission statements. Off. J. Eur. Comm. L269, 0034-0043 (Brussels).
[3]
Santini A., Morselli L., Passarini F., Vassura I., Di Carlo S., Bonino F., (2010a): End-of-Life Vehicles management. Italian material and energy recovery efficiency, Waste Manage, in press.
[4]
Srogi, K., (2008): An overview of current processes for the thermochemical treatment of automobile shredder residue. Clean Technol. Environ. Policy 10, 235–244.
[5]
Boughton, B., Horvath, A., (2006): Environmental assessment of shredder residue management. Resour. Conserv. Recycl. 47, 1–25.
[6]
Morselli L, Santini A, Passarini F, Vassura I (2010): Automotive shredder residue (ASR) characterization for a valuable management. Waste Management, in press.
[7]
Ciacci L., Morselli L., Passarini F., Santini A., Vassura I. (2010): A comparison among different Automotive Shredder Residue treatment processes, Int J LCA.
[8]
La Mantia, F. (2002): Handbook of plastics recycling, Rapra Technology, Shrewsbury UK.
[9]
Santini A., Morselli L., Passarini F., Vassura I., Herrmann, C., Luger, T. (2010): Assessment of Ecodesign potential in reaching new recycling targets, Resources, Conservation and Recycling, 54, 1128-1134.
[10]
GHK/Bios (2006): A study to examine the benefits of the End of Life Vehicles Directive and the cost and benefits of a revision of the 2015 targets for recycling, re-use and recovery under the ELV Directive. Final Report to DG Environment, Birmingham.
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Eco-Innovation by Integrating Biomimetic with TRIZ Ideality and Evolution Rules 1
Jahau Lewis Chen , Yung-Chiuan Yang 1
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Department of Mechanical Engineering, National Cheng Kung University, Tainan, Taiwan
Abstract Scientists believe that products can get innovated through inspiration by nature with the eco-friendly concepts. The major objective is that the designer can really get ideas from biological phenomena and apply them to engineering problems or products. This paper presents an eco-innovative method by integrating biological cases with the TRIZ ideality method, the trends of evolution and the FAST (Function Analysis System Technique) method. This method can systemically help designers to solve problems or execute product innovation by mimicking nature’s wisdom in a smart way. An example is illustrated to demonstrate the capability of the proposed method. Keywords: Eco-innovation; Biomimetics; TRIZ
1
INTRODUCTION
The development of technology plays a crucial role in modern economic growth but it is also the key factor of environmental crisis. It is usually emphasizing the novelty and economic usefulness of an innovated product but neglects its environmental impacts. Currently, many eco-design methods have been developed to support the designer in reducing the environmental impact of the product throughout its life cycle. However, those methods are focused on the redesign or optimization of existing products. Therefore, there is a need to develop a product eco-innovative design method for this situation. This paper describes a new method of applying biomimetics into product eco-innovation. The Biomimicry Institute [1] proposed “The Design Spiral” concept to find solutions for the design from nature. Hacco and Shu [2] used key words to find corresponding biologic features to generate bionic ideas and applied them to the design at remanufacture tasks. Vincent and his co-authors [3-7] collected a biological effects database and integrated it with the TRIZ method. Mann [8] tried to develop the biological TRIZ contradiction matrix. Chiu and Shu [911] developed a biomimetic design method through natural language analysis to bridge engineering and biology terminology. Chen and Huang [12] proposed a biomimetic design process for eco-product design by adding the biomimetic design cases into the “Innovative Principles for Single Engineering Parameter” TRIZ approach as a “Biomimetic Principle” to offer biomimetic solutions for designers. Chen and Jian [13] presented an eco-innovation process for eco-product design by linking engineering terminology with biological terminology and using the method of keyword searching to find suitable bionic cases for eco-innovation problems. A table of keywords compiled from inventive principles and related bionic cases are proposed to offer the designer a quick searching tool. This paper presents an eco-innovative method by integrating biological cases with the TRIZ ideality method, the trends of evolution and the FAST (Function Analysis System Technique) method. An example is illustrated to demonstrate its capability.
2
BIOMIMETICS
Biomimetics [14-15] is a subject to mimic the organic characteristics and to adopt them in the product design. From ancient times to the present, a lot of successful bionic designs have been made. These design concepts from nature have recently become more and more important. Furthermore, more and more designers try to find the design solution for their problems in nature. 3
TRIZ IDEALITY AND EVOLUTION RULES
The TRIZ method [16-18] is a tool for designers and engineers to handle conflicts. The method was developed in the former Soviet Union by Altshuller, who has analyzed over 400,000 patents. The most fascinating and amazing part of the TRIZ method is the contradiction matrix constructed through investigating and classifying the patents. The contradiction matrix is composed of 39 engineering parameters and 40 inventive principles. When people face design contradictions, they can search for the appropriate parameters, and then locate 1-4 suitable design principles for resolving the particular problem. 3.1
Ideality
The ideal system is a non-existent system with all of its functions still being executed. In other words, function is ideally carried out by already existing resources. Nevertheless, actual systems approach the ideal by increasing their beneficial functions and eliminating harmful factors. In the field of eco-design, the harmful factors are the materials, components or processes during the life cycle of the products that produce severe environmental impacts. The ideal final result (IFR) of eco-products is to perform desired functions without any environmental impacts. Therefore, looking at eco-innovative design problems of products from an ideal final result perspective is usually a very good first step towards success. The green evolution rules can provide assistance in formulating the ideal final result. Six methods for increasing the ideality of a system [18] are as follows: 1. Exclude auxiliary functions. 2. Exclude elements.
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_18, © Springer-Verlag Berlin Heidelberg 2011
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3. Identify self-service.
4
INTEGRATING BIOMIMETICS EVOLUTION RULES
4. Replace elements, parts or total systems. 6. Utilize resources. Evolution rules
Identify the current position of today’s design within an evolutionary pattern and the designer can predict future designs along this pattern. It is obvious that there exist some patterns of evolution rules for eco-products. These green evolution rules provide the designer a useful tool for filtering and selecting solutions of ecoinnovative design problems. These technical evolution trends determine the direction and character of the technical revolution in the next generation. Hence, it can help the designer forecast the future of the technique and concentrate research and development in the most promising directions. Some patterns of the green evolution rules for different green products have been observed and identified [19]. Mann [20] collected 31 different types of trends of evolution. In this paper, 25 of them have been chosen as the useful evolution rules to link with biological cases, as shown in the left side of Table 1. Trends of Evolution
TRIZ Inventive Principles
Smart Materials
31, 40
Space Segmentation
2, 31
Surface Segmentation
1, 31
Object Segmentation
1, 2
Evolution Macro to Nano Scale
1
Webs and Fibers
31
Decreasing Density
8, 35
Increasing Asymmetry
4, 5
Boundary Breakdown
5
Geometric Evolution (Linear)
14, 17
4.1
Trends of Evolution and Biomimetics
Evolution Line: Point 1D Line 2D Plane 3D Surface Inventive Principles 14, 17
17
Trend of Evolution
15, 28
Action Co-ordination
19, 20
Geometric Evolution (Linear)
to
19, 20
External)
Non-
Mono-Bi-Poly Mono-Bi-Poly Difference)
15
Point 1D Line 2D Plane
5, 6 (Increasing
Reduced Damping
Vocabulary Spherical, ball, line, horizontal, plane, arch
Dynamization
(Matching Linearities
AND
It is difficult for designer to find suitable bionic design cases for his eco-innovation problem. However, key vocabulary can help searching related cases which are associated with each vocabulary [21]. Therefore, this paper used an eco-innovative process for ecoproduct design by linking engineering terminology with biological terminology and using methods of searching keywords to find suitable bionic cases for eco-innovative problems [13]. A table of inventive principles, corresponding keywords and related bionic cases is used in this paper to offer the designer a quick searching tool. The first step is to analyze the TRIZ 40 inventive principles and its associated descriptions to find a “verb” with similar meaning as the key vocabulary of this inventive principle. Then, one can use the “WordNet3.0” web [22] to find synonyms for the key vocabulary. The “Biology-Online” web [23] can be used as a tool to check the “biologically significance” of each vocabulary by searching the number of data in this web. Finally, related biological cases can be obtained by searching key vocabulary in biology books, such as Life [24], Biology Demystified [25], and Biology [26]. Table 1 shows the associated TRIZ inventive principles related to 25 trends of evolution. Table 2 shows the key and corresponding vocabulary and associated biological cases related to the TRIZ inventive principles 14 and 17 which are associated with the “Geometric Evolution (Linear)” trend of the evolution rule. A detailed list of all 25 trends of the evolution rule related to the biological cases can be found in Reference 27.
Geometric Evolution (Volumetric)
Rhythm Co-ordination
IDEALITY
This section proposes the techniques to integrate the ideality, the trends of evolution, the inventive principles and the biological cases to provide the designer with some useful tables for the use of the biomimetics concept for eco-innovation.
5. Change the principle of operation. 3.2
WITH
5, 6, 7 11, 29
Biological Cases In blood's red blood cell, white blood cell, blood platelet. …. …. As blood flows from the heart, it will pass a big bend, the aortic arch, and is redistributed when going out. The 180 degrees turn in the artery works analog to a voltage transformation substation which raises the voltage before delivering the current. …. The DNA double helix structure is composed of a 3D structure. ….
Increasing Use of Senses
23
Increasing Use of Color
32
Increasing Transparency
32
Degrees of Freedom
15, 17
Trimming
2, 6, 22
4.2
Controllability
22, 23, 24
Reducing Number of Energy Conversions (Trending to Zero)
35
For helping the designer select the suitable trend of evolution, a method of thinking from ideality is proposed in this paper. The relationship between the six methods for increasing ideality of a system and trends of evolution is investigated and identified. The trends of evolution related to one of the six methods for increasing ideality of a system (5. Change the principle of operation) are identified as shown in Table 3. Detail lists of all 25 trends of
Table 1: Inventive principles related to trends of evolution.
3D Surface
Table 2: Partial list of biological cases related to “Geometric Evolution (Linear)” trend of evolution rule. Ideality and Trends of Evolution
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evolution rule related to six methods for increasing ideality of a system can be found in Reference 27. 5. Change the principle of operation
Reasons Evolution
for
Trends Evolution
of
Suggestion of Evolution Stage
….
….
….
Improve drainage
Surface Segmentatio n
Smooth Surface to Surface with Rib Protrusions
Combining the two pieces to improve the position and integrity
Geometric Evolution (Linear)
Point to Plane
Increase the aerodynamic control
Surface Segmentatio n
Surface with Rib Protrusions to 3D Roughened Surface
….
….
….
5
FAST-Function Analysis System Technique
The FAST (function analysis system technique) diagram [28] is a systematic function analysis method as shown in Figure 1. Each rectangular block in Figure 1 represents a function. The objective function (higher order function) is placed at the left hand side of the FAST diagram. Through answering the question “What is the basic function to achieve the objective function (How)?” one can identify the basic function and place it at the right hand side of the objective function. Then the same technique is used for the secondary
THE PROCEDURES OF ECO-INNOVATION
Figure 2 illustrates the design processes of including biomimetic concepts with the TRIZ method through key vocabulary in ecoinnovation. The TRIZ tools used in this process include the ideality and the evolution rules. The procedures can be explained by five steps as follows: 5.1
Table 3: Partial list of trends of evolution related to the six methods for increasing ideality of a system (5. Change the principle of operation). 4.3
function till it reaches the lowest order function (at the right hand side of the FAST diagram). One can check the FAST diagram by answering the question “Why does it need to achieve a higher order function?” from right to left in the FAST diagram. This method can help the designer to identify the function.
Step 1: ideal final result (IFR) problem definition
First, the designer can identify the ideal final result (IFR) of each product design to assess a function which requires improvement. 5.2. Step 2: establish FAST diagram Next, if the designer cannot find a function in step 1, then one can identify the “significant” function by establishing the FAST diagram. 5.3. Step 3: find suitable evolution line Moreover, if the evolution line is not easy to identify, then the designer can use the ideality concept through the trends of evolution related to six methods for increasing ideality of a system table, such as Table 3, to find a suitable evolution line. After finding the suitable evolution rule, one can analyze the problem’s evolution block to identify the right block. All evolution blocks in the following right hand side of this evolution line can be used for innovation. 5.4. Step 4: search biological cases After obtaining the suitable evolution line, the designer can search related biological cases through the biological cases related to the evolution line table, such as Table 2.
Figure 1: An example of FAST diagram [29].
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5.5. Step 5: eco-innovation Finally, after obtaining the suitable biological case, the designer can use it to perform eco-innovation. The designer can use any
qualitative or quantitative environmental evaluation tool to check the environmental improvement of new concepts.
Figure 2: The design processes including biomimetic concepts with TRIZ method.
Life Cycle Design - Methods and Tools 6 6.1
105
EXAMPLE Problem description
The design example tries to create an innovative, environmentally friendly electrical fan. The electrical fan consumes less energy than an air-conditioner. Noise, efficiency and safety are the problems of current electrical fans of which the designer has the opportunity to improve. 6.2
Ideality and function analysis
The ideal role model of an electrical fan is the natural wind. The natural wind has the same effect as the electrical fan, just without any energy consumption. However, the natural wind is nonpredictable and cannot be used at any time. Therefore, one can take one step back to the intermediate solution of ideality, “an electrical fan can have wind without fan blades”. Next, the FAST diagram of the electrical fan is analyzed as shown in Figure 3. The objective function (higher order function) and the lowest order function are “produce wind” and “transport current”, respectively, as illustrated in the output and input of Figure 3. In Figure 3, the rotation of fan will be “cut air flow” and produce the harmful effect of “distribution of wind” and “noise”. One can select “cut air flow” as the key function for design improvement. 6.3
designer can use the trend of evolution rule (Geometric Evolution (Linear)) related to the biological cases table to search related biological case for this design problem, such as shown in Table 2. As illustrated by Table 2, the suitable biological case is “As blood flows from the heart, it will pass a big bend, the aortic arch, and is redistributed when going out. The 180 degrees turn in the artery works analog to a voltage transformation substation which raises the voltage before delivering the current”. 6.4
Eco-innovation by biomimetics design
The concept of “pressure difference” in the biological case of blood transportation of the heart provides the innovative idea that air can flow from high pressure to low pressure to generate wind from “pressure difference”. The eco-innovative idea is to put the motor and fan blades into the base of the electrical fan so that the fan blades can rotate inside the base to reduce “cut air flow”. Furthermore, a design in the upper part of the electrical fan to change the wind direction and the surrounding pressure difference of air can achieve our design goal. The electrical fan generates wind without visible fan blades [30], as shown in Figure 4, and is reducing the area of ring outlet so that the inner air velocity is higher than the outer air velocity. Therefore, the air behind the fan will automatically be sucked in and steady wind blowing at high speed.
Find suitable evolution line and biological cases
After selecting the key function “cut air flow”, the designer can use the ideality concept through the trends of evolution related to six methods for increasing ideality of a system table, such as Table 3, to find the suitable evolution line. In this example, number 5 of six methods for increasing ideality of a system, “Change the principle of operation”, is selected. As shown in Table 3, reasons for evolution, “Combining the two pieces to improve the position and integrity” is chosen to find the suitable evolution line, “Geometric Evolution (Linear)”. The suggestion of evolution stage is “Point to Plane”.
Evolution Line: Point 1D Line 2D Plane 3D Surface
Figure 4: The electrical fan generates wind without fan blades [30].
From the above evolution line, one can identify the electrical fan problem’s evolution block is the “2D Plane” block. Next, the
Figure 3: FAST diagram of the electrical fan design problem.
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This new design can be evaluated by any qualitative or quantitative environmental evaluation tools, such as Matrix LCA or full LCA, to check whether it is environmentally friendly and has lower environmental impacts. 7
[1]
Chen, J. L., Huang, L.-C. (2007): Eco-Innovation of Products by Biomimetic Concepts, In: proceedings of the Fifth International Symposium on Environmentally Conscious Design and Inverse Manufacturing, Ecodesign07, Tokyo, Japan, December 10-12.
[13]
Chen, J. L., Jian, Y.-H. (2009): Eco-Innovation by Biological Terminology, Biomimetic Concepts and TRIZ Techniques, In: Proceedings of the Sixth International Symposium on Environmentally Conscious Design and Inverse Manufacturing, Ecodesign09, Sapporo, Japan, December 79.
[14]
Benyus, J. M. (1997): Biomimicry: Innovation Inspired by Nature, William Morrow & Co., New York
[15]
Bar-Cohen, Y. (2006): Biomimetics-Using Nature to Inspire Human Innovation, In: Bioinspiration & Biomimetics, Vol. 1, pp. 1-12, Institute of Physics Publishing
[16]
Altshuller, G. (1998): 40 Principles–TRIZ Key to Technical Innovation, Worcester, MA: Technical Innovation Center, Inc.,8.
[17]
Savransky, S. D. (2000): Engineering of Creativity– Introduction to TRIZ Methodology of Inventive Problem Solving, CRC Press LLC.
[18]
Terninko, J., Zusman, A., Zlotin, B, (1998): Systematic Innovation-An Introduction to TRIZ, St. Lucie Press.
[19]
Chen, J.L. (2002): Green Evolution Rules and Ideality Laws for Green Innovative Design of Products, In: Proceedings of Going Green - Care Innovation 2002, 4th International Symposium, Vienna, November 25-28.
ACKNOWLEDGMENTS
This study is supported by The National Science Council of Taiwan, Republic of China, grant number: NSC-97-2221-E-006-055. 9
[12]
CONCLUSIONS
This paper presented an eco-innovative product improvement method by integrating the biological cases with the TRIZ ideality method, the trends of evolution and the FAST (Function Analysis System Technique) method. This method can systemically help designers solve problems or execute product innovation by mimicking nature’s wisdom in a smart way. An example was illustrated to demonstrate the capability of the proposed method. It is required that further research work is carried out on the development of a suitable environmental evaluation tool for the identification of eco-improvements of new design concepts. 8
Retrival, In: Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 21, pp. 45-59.
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Hacco E., Shu, L. H. (2002): Biomimetic Concept Generation Applied to Design for remanufacture, In: Proceedings of DETC’02, ASME 2002 Design Engineering Technical Conferences, Montreal, Canada, September 29- October 2.
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Vincent, J. F. V. , Bogatyreva, O. A., Pahl, A.-K., Bogatyrev N. R., Bowyer, A. (2006): Putting Biology into TRIZ: A Database of Biological Effects, In: Creativity and Innovation Management, Vol. 14, Issue 1, March, pp. 66.
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Bogatyreva, O., Phal, A.-K., Bowter, A ., Vincent, J. (2003): Data Gathering for Putting Biology in TRIZ, in: The 5th Annual Conference of the Altshuller Institute for TRIZ Studies.
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Bogatyrev N., Bogatyreva, O. (2003): TRIZ and Biology: Rules and Restrictions, In: Proceedings of International TRIZ Conference, Philadelphia, USA, 16-18 March, p. 19/1 -19/4.
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Chiu, I., Shu, L. H. (2007): Usingl Language as related Stimuli for Concept Generation, In: Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 21, pp. 103-121.
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Vincent, J. F. V., Mann, D. L. ( 2002): Systematic Technology Transfer from Biology to Engineering, In: The Royal Society.
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Vincent, J. F. V., Bogatyreva, O., Bogatyrev, N., Bowyer, A. , Pahl, A.-K. (2006): Biomimetics – its practice and theory, In: Journal of the Royal Society Interface, 3, pp. 471-482.
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Dale, L. (2003): Biology Demystified, Mc Graw Hill.
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Peter, R., George, J. (2002): Biology, Mc Graw Hill.
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Mann, D. L. (2006): National World Contradiction Matrix: How Biological Systems Resolve Trade-offs and Compromised, In: Proceedings of the ETRIA TRIZ Future Conference 2006, Vil. 1, kortrijk, Belgium, 9-11 November,, pp. 99-108.
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Yang, Y.-C. (2010): Eco-Innovative Design Method by Integrating Bionics with TRIZ Ideality and Evolution Rules, Master Thesis, Department of Mechanical Engineering, National Cheng Kung University, June (in Chinese).
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Chiu, I., Shu, L. H. (2004): Natural Language Analysis for Biomimetic Design, In: proceedings of the ASME 2004 Design Engineering Technical Conferences and Computer and Information in Engineering Conference, Sep. 28-Oct. 2.
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Otto, K., Wood, K. (2001): Product Design-Techniques in Reverse Engineering and New Product Development, London, Prentice-Hall.
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Chiu, I., Shu, L. H. (2005): Bridging Cross-Domain Terminology for Biomimetic Design, In: Proceedings of the ASME 2005 Design Engineering Technical Conferences and Computer and Information in Engineering Conference, Sep. 24-28.
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Dyson Air Multiplier, (2009): http://www.dyson.com/fans/
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Chiu, I., Shu, L. H. (2007): Biomimetic Design through Natural Language Analysis to Facilitate Cross-Domain Information
Reasoning New Eco-Products by Integrating TRIZ with CBR and Simple LCA Methods 1
Cheng Jung Yang , Jahau Lewis Chen 1
1
Department of Mechanical Engineering, Nation Cheng Kung University, Tainan, Taiwan
Abstract The concept of eco-design in product design has been developed for several years. In this study, we proposed a new reasoning model to obtain innovative ideas more easily for designing eco products, which was followed by evaluating whether the proposed design here was more effective than currently available ones. The innovative solution to solving design problems was based on TRIZ evolution patterns. The index system of case-based reasoning then connected the innovative idea to cases located in a database, realizing the promising idea. Finally, simple life cycle assessment was utilized to determine the feasibility of this solution in comparison with the current ones. Meanwhile, an eco-innovative example was also applied to demonstrate the utility of the proposed model. Keywords: TRIZ Evolution Patterns; CBR; Simple LCA
1
INTRODUCTION
Many things change in our life because of a certain demand. For instance, biological organisms adapt them to their surrounding environments. This phenomenon is also similar to the product development. Much effort is being exerted in the quest for products with high performances, multi-functions, low cost and fashion trends. Therefore, engineers should concern about the successful experiences to increase their design feelings. Additionally, the introduction of the systematic methods into the evolution trend can speed up a new design. TRIZ evolution patterns [1-3] are an effective means of forecasting future trends and developments. Cavallucci [4] introduced these patterns to propose an operative application framework in order to analyze their impact in the design process. Petrov [5] reviewed many papers about the laws of technical system evolution and presented a system of laws to makes it possible to perform forecasts of technology evolution more thoroughly. Zlotin and Zusman [6] used knowledge of the evolution patterns to correlate with analytical methods. In the case of eco-design, Low et al. [7] used TRIZ evolution patterns to explain the relationship between product function and service. Mann and Jones [8] showed a successful case that improved the product service, use-service and resulting service concepts in the energy system. Chen [9] proposed an innovative eco design method by introducing green evolution rules and ideality laws to invent “novelty, usefulness, and no environmental burden” new products. Justel et al. [10] utilized TRIZ evolution rules to reason the evolution of the joint parameters for disassembly. Chang [11] integrated the idea of biological evolution with TRIZ evolution rules and inventive principles to develop future eco-products. Although TRIZ evolution patterns can facilitate the production of innovative ideas, forming such ideas still relies strongly on the user experiences. A method called case-based reasoning, CBR, which is beneficial for utilizing prior experiences to solve new problems, was introduced to this approach. CBR originated from the work of Schank and Abelson [12] on dynamic memory and the central role
that they play in recalling earlier situations and situation patterns during problem solving and learning. Some papers have integrated TRIZ with CBR to solve engineering problems in recent years. Estevez et al. [13] increased the level of abstraction of the CBR to extend CBR from routine to inventive design. They used TRIZ problem solving reasoning process to apply the pairing of generic resolution rules. Lee [14] utilized TRIZ method as an assist to increase the performance of CBR in the product design. They made a software about wall lighting design to assist designers in retrieving useful cases efficiently and to give designers systematically creative ideas for a new design. Gao et al. [15] developed a prototype of computer-aided design system to interpret the usefulness and meaning of CBR for innovative design automation based on TRIZ tools (Contradiction matrix, separation principles and the 76 standard solutions) and Generalized Location Pattern. Robles et al. [16] presented an approach based on the synergy between CBR and TRIZ to accelerate inventive preliminary design for chemical engineering. Their presented model offers a way (by CBR) to transfer the solution from an identified analogous problem to a new problem, and to combine the TRIZ ability to get inventive problem solving strategies applicable across domains. Chen et al. [17] proposed a method that first used CBR to obtain a prototype, and then redesigned part of it by using TRIZ to achieve eco-innovation. Yang and Chen [18] proposed a new innovative design method that integrated examples of effective energy-efficient practices in the industry, CBR method, and TRIZ tools to achieve the eco-innovation objective. The impact of products on the environment can be calculated by softwares such as SimaPro and GaBi. Preliminary work involved collecting information with respect to factors such as material properties, weights, and manufacturing processes. However, the necessary quantitative data is hard to get in the stage of concept design. In order to overcome this problem, some kinds of simple life cycle assessment methods have been proposed, such as screening life cycle assessment [19], streamlined life cycle assessment [20], screening and streamlined life cycle assessment [21], iteration screening life cycle assessment [22] and streamlined life cycle
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_19, © Springer-Verlag Berlin Heidelberg 2011
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assessment matrix approaches [21,22]. In this study, we used one qualitative matrix LCA method to evaluate the product environment impacts in the concept design. The paper was organized as following: Chapter 2 illustrated the concept of TRIZ evolution patterns. Chapter 3 presented the framework of CBR including the definition of case retrieve, reuse, revise and retain. Chapter 4 explained the simple LCA methods used in this approach. Chapter 5 proposed a flowchart that combined TRIZ, CBR and simple LCA method for the eco-product design. Chapter 6 showed an eco-innovative example to demonstrate the utility of the proposed model. Finally, some conclusions and future research directions were provided in Chapter 7. 2
PATTERNS OF EVOLUTION – TRIZ TOOL
A technology may evolve into a mature level gradually. TRIZ experts observed the evolution of technology from a wild of patents and formulated several patterns of evolution. These patterns could aid designers to predict the future evolution of products and to solve the inventive problems. This approach used five patterns of evolution for solving eco-innovation design problems [23]. They are shown in Table 1 to Table 5. Patterns of Evolution
Evolution toward increase dynamism and controllability
Exposition
Increasing system dynamism allows functions to be performed with greater flexibility or variety. Increasing system dynamism requires increasing controllability.
Table 1: The exposition of evolution pattern -1. Patterns of Evolution
Exposition
Increase complexity followed by simplification Technological systems tends to develop first toward increased complexity (i.e., increased quality and quantity of system functions) and then toward simplification (where the same or better performance is provided by a less complex system).
Table 2: The exposition of evolution pattern -2. Patterns of Evolution
Evolution with matching and mismatching elements
Exposition
As a system evolves, system elements are matched or mismatched to improve performance or to compensate for undesired effects.
Table 3: The exposition of evolution pattern -3. Patterns of Evolution
Evolution toward micro-levels and increased use of field
Exposition
Technological systems tend to transition from macro-system to micro-system. During this transition, different types of energy fields are used to achieve better performance or control.
Table 4: The exposition of evolution pattern -4.
Patterns of Evolution
Evolution toward involvement
decreased
human
Exposition
Systems develop to perform tedious functions that free people to do more intellectual work. It means to substitute the human but maintain the action on control level or decision-making level of human’s method.
Table 5: The exposition of evolution pattern -5. 3
CASE-BASED REASONING
CBR is a problem solving method. The method utilizes prior cases stored in the database to solve new problems. The main process of CBR to solve problems is illustrated as following: a. Retrieval: Users get the most similar prior case from the database after selecting some certain parameters. The retrieved case is evaluated by calculating the similarity function. The information of the most similar case is the basis to solve the new problem. b. Reuse: In many cases, the retrieved case can likely match the problem to a certain extent, implying that the related solution is a sub-optimal one. There is a gap between them and lead into some revise ways for help. c. Revise: Revision adjusts the parameters of the retrieved solution to conform to those of the current problem. The techniques may generate certain rules, heuristics or domain knowledge. d. Retain: When the new problem is resolved, the related information would be saved as a new case in the database to increase the ability of solving new problems. 4
SIMPLE LCA
Under ISO 14040 [24] and 14044 [25] standards, a full LCA evaluation includes four stages called (1) Goal and scope, (2) Life cycle inventory, (3) Life cycle impact assessment and (4) Interpretation. Using a full LCA method to evaluate a product can clearly know the information of raw materials regarding production, manufacturing, distribution, use and disposal, while including all intervening transportation steps. Although results using the conventional LCA are accurate and acceptable, the conceptual design stage has incomplete information, thus making this method infeasible for use. Therefore, with the help of a method called the AT&T abridged life-cycle assessment [22], this study evaluated how the environment impacts a product at the concept design stage, as described in the following. This method used a 5x5 matrix to evaluate the environment impacts of a product (Table 6). The vertical parameters in the matrix comprise the five life cycle stages, i.e., pre-manufacture, product manufacture, product delivery, product use, and disposal. The horizontal parameters in the matrix address the environmental concerns, i.e., materials chosen, energy used, solid residues, liquid residues, and gaseous residues. Each cell in the matrix is represented by the lowest impact (value 4) to the highest impact (value 0). Refer to the evaluation standard in the textbook [21], designers can assess the value of each cell themselves.
Life Cycle Design - Methods and Tools
109 5.1
Environmental Concerns Life stages Premanufacture Product manufacture Product delivery Product use
Materials
Energy
Solid
Liquid
Gaseous
chosen
use
residues
residues
residues
1,1
1,2
1,3
1,4
1,5
2,1
2,2
2,3
2,4
2,5
3,1
3,2
3,3
3,4
3,5
4,1
4,2
4,3
4,4
4,5
5,1
5,2
5,3
5,4
5,5
Refurbishment, Recycling, Disposal
Table 6: 5x5 assessment matrix proposed by AT&T [21]. 5
THE MODEL FOR REASONING NEW ECO-PRODUCTS BY INTEGRATING TRIZ WITH CBR AND SIMPLE LCA METHODS
In the beginning, this approach introduced TRIZ patterns of evolution to help engineers to select the most promising directions for an innovative design. Next, CBR framework was used to quickly realize a novel concept. This study adopted an index system to link TRIZ evolution patterns with a database to enable engineers to know clearly and comprehensively how similar design problems were solved. Finally, matrix LCA was introduced to check if the new design is better than the current one. The following procedure is displayed in the design flowchart (see Figure 1) and the main stages are illustrated in the following sections.
Make the eco-design objective and a redesign part of the product
Get innovation idea from patterns of evolutions (TRIZ)
Eco-design is responsible for reducing the environmental impact of a product during its whole life cycle. Most of the available approaches to achieving this goal share the three product features─ materials, energy and toxicity. Designers should thus focus initially on improving a product feature. Product information is then analyzed, followed by determining how to redesign a certain portion of the product. This approach emphasizes how to improve two of the product features in order to achieve eco-design, i.e., materials and energy. Additionally, the prior cases stored in the database of CBR framework are also from these two fields. The case information will be described in next section. 5.2
New design
Evaluate by the simple LCA method
No
Satisfy? Yes New Eco Product
Figure 1: The design flowchart of making new eco-products.
Connect CBR framework to quickly realize the idea
The basic description of evolution patterns has been already illustrated in Chapter 2. First of all, the designers select the desired evolution pattern after they analyze information related to what they want to redesign. After selecting a certain TRIZ evolution pattern, the CBR framework will be introduced to accelerate the implementation process. The major works for establishing CBR framework build up a database, in turn setting up an index system and making the revised rules for modifying retrieve cases. For the first task, this approach collects 75 products which vary with the fields of technology innovation in material and energy. These cases are also used as the foundation for implementing the TRIZ evolution patterns. The second task, setting up an index system, is the key role for guiding the design problem to search useful prior cases for help. This study classifies five characters to describe a case and to make a typical formulation for calculating case similarity. The characters are TRIZ evolution patterns (index 1) featuring the function about the design problem (index 2 to index 4) and the improvement function of the product for new design (index 5). The detail description for each index is shown in Table7. The similarity formula in the CBR method generally refers to the similarity between a current problem and a previous one in problem space. This paper makes a formulation (1) to calculate the case similarity. n
w sim ( D , C ) i 1
Connect CBR framework to quickly realize the idea
Make the eco-design objective and a redesign part of the product
i
i
i
i
(1)
Where n is the total numbers of character in an index system and wi is the weighting value of character i. Each weighting value is from 0 to 1 and the sum of weighting values is equal to 1. Di means the selection of Arabic numeral for a new case and Ci is the Arabic numeral for the prior case; simi (Di, Ci) means the individual similarity between Di and Ci; simi (Di, Ci) refers to on of two values (0 or 1). If Di is equal Ci , then simi (Di, Ci) is 1, else simi (Di, Ci) is 0. The total similarity is to sum the multiplication value of each weighting value with its individual similarity. If the retrieve case could not help designers to practice the innovative idea, this study supports three rules for revising. The first one is to refer to the cases that have second and third high similarity under the same TRIZ evolution pattern. The second one will be utilized if the first one is not helpful. The second one is considering the highest similarity of case resulting from different TRIZ evolution patterns firstly. The cases having the second and third high similarity under different TRIZ evolution pattern would use later if deemed necessary. The last one is to use the basic definition of TRIZ evolution pattern to derive a possible solution when both of the first and second rules are not useful.
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Life Cycle Design - Methods and Tools
Index
Illustration
Selection 1.Evolution toward increase dynamism and controllability
1
The TRIZ evolution patterns for changing a product function
2.Increase complexity followed by simplification 3.Evolution with matching and mismatching elements 4.Evolution toward micro-levels and increased use of field 5.Evolution toward decreased human involvement
2
3
The element is determine by the source of energy that is applied to the part of index 4
The field that interacts or acts upon on the substances (index 2 and 4)
1. Material 2. Tool 3. Part 5. Environment
Index 1
(4) Evolution toward micro-levels and increased use of field
1. Mechanical
Index 2
(5) Environment (Thermal)
2. Thermal
Index 3
(8) Thermal-Electrical
3. Chemical
Index 4
(5) Environment (Electricity)
4. Electrical
Index 5
(3) Harmfulness (Side effect)
5. Magnetic 6. Light-Electrical 7. Fluid dynamics 9.Gas field
4
Index description
4. Person
8. Thermal-Electrical
The part which is acted on or changed index 2
researchers have proposed to apply renewable energy like solar energy, full cell and human power to supply electricity to these handsets. Therefore, this study aims to demonstrate the feasibility to use the recyclable resource to produce extra electricity. The proposed TRIZ evolution patterns “Evolution toward micro-levels and increased use of field” is relevant to our idea. Additionally, based on the design objective, the new functional performance is to utilize wasted heat which is recyclable to generate electricity by using a thermal-electricity device. The limitation of present mobile phone is the side effect. Converting the information to the index system, index 1 could be the “Evolution toward micro-levels and increased use of field”, index 2 could be the “Environment (Thermal)”, index 3 could be the “Thermal-Electrical”, index 4 could be the “Environment (electricity)” and index 5 could be the “Harmfulness”. Table 8 is the index selection of the new design.
1. Material 2. Tool 3. Part 4. Person
Table 8: The index selection of the new design. In this example, the importance of each weighting is set at one of the levels: the most important, moderately important, and the least important, by assigning values of 3, 2, and 1, respectively. The importance levels of all weights in this work are w1＞w5＞w2＝w3＝ w4. Therefore, the weighting value of w1 is 0.375(3/8), w5 is 0.25(2/8) and w2 to w4 are both 0.125(1/8). Table 9 is the highest similarity case found from database. Case description
5. Environment
(4) Evolution toward micro-levels and increased use of field
1. Geometric
5
The limitations of the product’s function.
2. Resource 3. harmfulness
Index description
4. Physics
Evaluate by the simple LCA method
The presented new solution and the current product would be both evaluated by the simple LCA method which has been proposed by AT&T for concept design. Designers can know which one is better for the environment by calculating total values from the matrix table. Moreover, they can also discuss the advantages and disadvantages of a new design in each life cycle stage. The new design has significant contribution to the environment if the total values are much higher than the current one. On the other hand, if the value gap is not sufficiently high or even the new design has lower values, designers could use the matrix table to discuss the improvement direction and then to propose a better solution. 6
EXAMPLE ILLUSTRATION
Mobile phone has become a living necessity in our daily life. From the eco-design viewpoint, there is a trend to minimize some product features (volume, component and weight). For energy saving, some
(8) Thermal-Electrical (3) Harmfulness (Side effect)
6. Control
5.3
(5) Environment (Thermal) (5) Environment (Electricity)
5. Capability
Table 7: The description of classified index.
Drive the mouse from the waste heat of a notebook (Case num: 59)
Similarity value
1*0.375+1*0.125+1*0.125+1*0.125+1* 0.25=1
The limitation of the product function.
Waste heat operation.
The knowledge for technology innovation
Utilize a device to collect waste heat to produce electricity.
is
produced
during
Table 9: The highest similarity case found from database. Case 59 introduces a heat-electricity transfer device to collect waste heat to produce electricity for a mouse. This innovation idea can be used to another product which also emits useless heat to air. The advantage of adding this device to current mobile phone not only extends the working time of battery but also reduce the discomfort from high temperature. It is a good design idea to new mobile phone. However, there is no reported example that a remarkable quantity of heat is produced in using and how the energy can be retrieved to recharge the battery. Therefore, this new design and current one should both be evaluated by simple LCA to clarify if this idea can be used to make a prototype or not. The meaning for the environmental impact is analyzed via the matrix
Life Cycle Design - Methods and Tools
111
and the guidelines proposed in [21]. For instance, both designs get rating of “3” in product delivery process because of using the same metals. Table 10 shows the result of all evaluation values for both designs. Table 11 calculates the total simple LCA value for both designs.
Premanufacture Product manufacture Product delivery Product use
Mann, D.L. (2002): Hands on Systematic Innovation, CREAX Press, Belgium.
[2]
Clausing D., Fey V. (2004): Effective Innovation, ASME Press.
[3]
Fey V., Rivin E. (2005): Innovation on Demand, Cambridge University Press.
[4]
Cavallucci, D. (2001): Integrating Altshuller’s Development Laws for Technical Systems into the Design Process, in Annals of The CIRP, Vol. 50, pp. 115-120.
Materials
Energy
Solid
Liquid
Gaseous
chosen
use
residues
residues
residues
3 / 2.9
3 / 2.9
3 / 2.9
3 / 2.9
3 / 2.9
2 / 1.9
2 / 1.9
2 / 1.9
2 / 1.9
2 / 1.9
3/3
3/3
3/3
3/3
3/3
4/4
2 / 2.5
4/4
4/4
4/4
[5]
Petrov, V.M. (2002): The Laws of System Evolution, in The TRIZ Journal, March. http://www.trizjournal.com.
3/3
3/3
3/3
3/3
3/3
[6]
Zlotin B., Zusman, A. (2006): Patterns of Evolution: Recent Findings on Structure and Origin, TRIZCON2006, Milwaukee, WI, USA.
[7]
Low, M.K., Lamvik, T., Walsh, K., Myklebust, O. (2000): Product to Service Ecoinnovation: The TRIZ Model of Creativity Explored, in: Proceedings of International Symposium on Electronics and the Environment, IEEE, pp. 209-214, San Francisco, California.
[8]
Mann. D., Jones E. (2002): Sustainable Services & Systems (3s) through Systematic Innovation Methods, The Journal of Sustainable Product Design Vol.2, No. 3-4, pp.131–139.
[9]
Chen, J. L. (2002): Green Evolution Rules and Ideality Laws for Green Innovative Design of Products, In: Proceedings of Going Green-Care Innovation 2002, Fourth International Symposium, Vienna, Austria.
[10]
Justel, D., Vidal, R., Chiner, M. (2005): TRIZ Applied to Innovate in Design for Disassembly, 1st IFIP TC-5 Working Conference on CAI, IFIP-TC5 ULM, Germany.
[11]
Chang, H. T. (2006): A Study of Develop and Predict Ecoproducts from Evolution, in: Proceedings of the Symposium on Sustainable Products and Industrial Management. (in Chinese)
[12]
Schank, R., Abelson, R. (1977): Scripts, Plans, Goals and Understanding. Hillsdale, N.J.: L. Erlbaum Associates.
[13]
Estevez, I., Dubois, S., Gartiser, N., Renaud, J., Caillaud, E. (2006): Le raisonnement `a partir de cas est il utilisable pour l’aide `a la conception inventive, in: Proceedings of 14 Atelier de Raisonnement `a Partir de Cas Besancon, pp. 123–129.
[14]
Lee, Y. C. (2007): Integrating the TRIZ Method within a Casebased System for Product Design Problem Approach, Master Thesis, Department of Computational design, National Yunlin University.
[15]
Gao, C., Huang, K., Chen, H., Wang, W. (2006): Case-Based Reasoning Technology Based on TRIZ and Generalized Location Pattern. Journal of TRIZ in Engineering Design, Vol.2, No.1, pp.40-58.
[16]
Robles, G. C., Negny, S., Lann, J. M. L. (2009): Case-based Reasoning and TRIZ: a Coupling for Innovative Conception in Chemical Engineering. Chemical Engineering and Processing Vol.48, No.1, pp.239-249.
[17]
Chen, J. L., Huang, L. C., Yang, C. J. (2008): Two TRIZ based Eco-innovation Methods by Biomimetic Concepts and
Disposal Current product / New product
Table 10: Environmental assessments for current and new design of the mobile phone. simple LCA total value
Current design
New design
73
72.5
Table 11: Total simple LCA value between current and new design of the mobile phone. From table 10, since new design requires adding a heat-electricity transfer device, the rating of new product in all environmental concerns are lower than the current one in pre-manufacture and manufacture process. On the other hand, the heat-electricity transfer device reduces the electricity demand in product use stage. The overall rating for the new design is 72.5, lower than the current design. Therefore, this new design needs to be revised for worth making a prototype for further study. The ways to improve the new design for better environment assessment can be performed from two directions. One is reducing the amounts of material usage and the complexity of composition. The other is to find another device which reduces energy use in product usage stage. 7
REFERENCES
[1]
Refurbishment, Recycling,
ACKNOWLEDGMENTS
This work is supported by the National Science Council, Taiwan, under grant numbers: NSC96-2621-Z-006-001-MY3. 9
Environmental Concerns Life stages
8
CONCLUSIONS AND FUTURE RESEARCH DIRECTIONS
From the demonstrated example, integrating TRIZ evolution patterns, CBR and simple LCA methods to make new eco-products is practicable. TRIZ evolution patterns make engineers easily decide the way for product innovation. Prior cases from the CBR framework help designers quickly to realize the product ecoinnovation in material and energy field. Matrix table of simple LCA method supports sufficient confidence for designers to make a prototype. For the future research, some continuing works should be undertaken. Increasing the number of evolution patterns is the first priority, which will support engineers more innovation ideas for solving design problems. Continuous modification of the character definition of index system is the second future work. It will increase the ability of CBR framework to support more appropriate cases. Adding new cases from a variety of domains is the third future work. It can increase the ability of solving different types of domain problems. In addition to the material and energy problems, there is still an environment problem from products’ toxicity. The last future work is to include the topic to this approach.
112
Life Cycle Design - Methods and Tools Case-based Reasoning, in: Proceedings of Electronics Goes Green 2008+, pp.707-712.
[18]
Yang, C. J., Chen, J. L. (2009): An Eco-innovation Method for Product Design based on TRIZ and Case-based Reasoning, in: Proceedings of the 16th CIRP International Conference on Life Cycle Engineering (LCE2009), pp.65-70.
[19]
Svensson, G., Ekvall, T. (1995): LCA – A Fair and Cost Effective Way to Compare Two Products?, SAE Technical Paper 951827, Society of Automotive Engineering.
[20]
Christiansen, K., (1997): Simplifying LCA: Just A Cut, Final Report from the SETAC - Europe LCA screening and streaming working group, SETAC-Europe, Brussels, Belgium.
[21]
Graedel, T. E. (1998): Streamlined Life-Cycle Assessment, Prentice Hall, New Jersey.
[22]
Graedel, T. E., Alleyby, B. R., Comrie, P. R. (1995): Matrix Approaches to Abridged Life Cycle Assessment, Environmental Science & Technology, Vol.29, No.3, pp.134A139A.
[23]
Ideation International Inc., (1999): Tools of Classical TRIZ, Ideation International Inc., Southfield, MI.
[24]
ISO 14040, (2006): Environmental Management - Life Cycle Assessment - Principles and Framework, International Organisation for Standardisation (ISO), Geneve.
[25]
ISO 14044, (2006): Environmental Management - Life Cycle Assessment - Requirements and Guidelines, International Organisation for Standardisation (ISO), Geneve.
Proposal of an Integrated Eco-Design Framework of Products and Processes 1
Shinsuke Kondoh , Nozomu Mishima 1
1
National Institute of Advanced Industrial Science and Technology (AIST), 1-2-1, Namiki, Tsukuba, Ibaraki, Japan
Abstract The objective of this study is to propose an integrated eco-design framework of products and processes aiming to support decision making and communication among multiple divisions in manufacturing firms to improve sustainability of their products. Especially, this paper focuses on the integration of three different methods calculating environmental load, cost, and customer utility value, so that manufacturers can simultaneously improve environmental and economic performance of their final products. To do so, Total Performance Indicator (TPI), which has been developed by the authors' group, is introduced and utilized as a glue for connecting these methods by interrelating their inputs and outputs. Keywords: Integrated Eco-Design Framework; Total Performance Indicator; User Value
1
INTRODUCTION
Due to growing concern about environmental problems, manufacturing industry is confronting increasing pressure to improve its environmental performance with increasing (or maintaining) economic value of its final products. Especially for reducing CO2 emission to prevent global warming, improving energy efficiency in production processes as well as product usage stage is quite important. In order to help decision making in product and process design aiming at improving sustainability of manufacturing businesses, many tools and concepts have been proposed recent years. Life cycle assessment (LCA) and eco-efficiency assessment methods (e.g., MIPS [1], Factor X [2]) are popular means for evaluating environmental performance of products and their life cycle (including production processes) and identifying the bottlenecks to be improved from the environmental viewpoint. Industrial engineering (IE) tools [3] and value analysis (VA) [4] method, which help to screen out the bottlenecks from the economical viewpoints, are also popular among production engineers. Although these tools are effective for their own purposes, it is not sufficient to utilize each individual tool alone. LCA is quite useful for evaluating the environmental load of products and services throughout whole life cycle. However, LCA only deals with the environmental aspect of products and services, although the economic aspects are as important as the environmental aspects from the viewpoint of manufacturing businesses. The improvement targets and their potential benefit suggested by LCA should be considered together those from the other aspects. In order to overcome the limitation of each individual method, adequate and integrated utilization of multiple tools for different purposes is quite promising. The objective of this study is to propose an integrated eco-design framework of products and processes aiming to support decision making and communication among multiple divisions in manufacturing firms to improve sustainability of their products. Especially, this paper focuses on the integration of three different methods calculating environmental
load, cost, and customer utility value, so that manufacturing firms can simultaneously improve environmental and economic performance of their final products and services. To do so, Total Performance Indicator (TPI), which has been developed by the author's group [5], is utilized as a glue for connecting these tools by interrelating their inputs and outputs. The paper is organized as follows. Section 2 discusses what kinds of requirements should be satisfied by integration of multiple tools and clarifies the focus of this study. Section 3 introduces TPI to connect three different evaluation tools for environmental load, life cycle cost, and customer utility value of a product, and illustrates how to use these tools together to meet the requirements described in section 2. Section 4 presents a simplified case study of a digital camera to illustrate our method. Section 5 summarizes our methods and discusses future challenges and expansions of our framework. 2
REQUIREMENTS FRAMEWORK
FOR
INTEGRATED
ECO-DESIGN
The requirements for integration of multiple tools are summarized as follows. 2.1
Simultaneous thinking of environmental and economic factors throughout whole product life cycle
Since all businesses have to make profit, both of environmental and economic factors should be considered simultaneously. In addition to that, environmental load and cost should be evaluated from the viewpoint of entire product life cycle, even when the manufacturing firms are not directly responsible for those in after-sale products. 2.2
Representation and consideration of interdependency
There generally exists complicated interdependency among multiple decision variables in manufacturing businesses. Some decisions in product design may cause significant increase (or decrease) in the cost and environmental load at production processes and others in production may cause the improved (or deteriorated) quality (or physical life time) of a target product. For example, application of a new manufacturing technology to improve
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_20, © Springer-Verlag Berlin Heidelberg 2011
113
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the smoothness of the sliding surfaces in mechanical equipment may extend its physical lifetime with slight increase in the manufacturing cost. In this case, investment for this technology can be justified only when management and marketing divisions understand its contribution to product lifetime and value for the customers. Therefore, explicit representation and consideration of interdependency among various decision variables in different divisions with different interests is quite important. 2.3
Communicability and understandability
Communication among different divisions with different interests (e.g., environmental performance, production performance, financial performance etc.) is also important. Since the different divisions use the different tools, performance measures, and terminologies, a common and easily understandable performance measure should be established. 3
PROPOSAL OF FRAMEWORK
3.1
AN
INTEGRATED
ECO-DESIGN
Introduction of TPI as a single integrated performance measure
In order to support simultaneous thinking of environmental and economic aspects of products and processes, we introduce a single performance indicator, TPI, in this study. TPI is a measure that represents the combined efficiency of utility value production from environmental and economic viewpoints. TPI is defined as the balance of customer utility value (UV) and its resulting environmental load and cost of the products as follows: TPI
UV LCE LCC
(1)
where LCE and LCC denote environmental load and cost throughout the entire life cycle, respectively. Using TPI as an objective function (and a common performance measure) of products and process design, both of environmental and economical aspects are simultaneously taken into account. 3.2
Tools for calculating UV, LCE, and LCC
In order to calculate the value for UV, LCE and LCC in equation 1, we employ three different methods in this study. For calculation of LCE and LCC, conventional LCA and Life Cycle Costing (LCC) methods are employed, respectively. For calculating UV, we have employed multi-attribute utility theory (MAUT) [6] and extended it to represent the deterioration and obsolescence of a product value along time [5]. 3.3
Representation and consideration of interdependency by using QFD
As discussed in section 2, there generally exists interdependency among multiple decision variables in different divisions. Therefore, in order to support adequate design considering this interdependency, causality among these parameters should be identified and represented explicitly. To do that, the Quality Function Deployment (QFD) [7] technique is employed. QFD is a systematic method widely used for translating true customer needs of product/service into technical requirements for design, development, and delivery of a product, by correlating these items with each other 3.4
Design flow and integration of multiple tools
Figure 1 summarizes a design flow of the proposed method. Detail of each step and applicable methods and tools other than LCA, LCC, and UV calculation tools are described as follows;
(i) Definition of a product and its life cycle First, manufacturing firms should define a target product and its life cycle scenario which describes the every flows and life cycle options (e.g., reuse, recycling, landfill etc.) for its constituting components based on their current business model. (ii) Estimation of LCE, LCC and UV Next step is calculation of LCE, LCC and UV of a product. To estimate LCE and LCC, conventional LCA and LCC tools are utilized. In order to reduce the time for conducting LCA, it is effective to simplify life cycle process model of a target products by referring the conventional life cycle inventory database. UV is calculated based on MAUT, which represents the customer utility value of a product as a weighted sum of its dominant functional requirements (FRs). The weighting factors for each FR are generally calculated by conjoint analysis method [8]. Regression analysis of product functional performance and its market price is also usable for this calculation. (iii) Identification of causality/interdependency among various decision variables In addition to the values for LCE, LCC and UV, interdependency of various decision variables are identified and represented by QFD. Interdependency among parameters, which construct UV, LCE, and LCC, is represented by using relational matrix in QFD. First, UV of a product is allocated to its dominant FRs. LCE and LCC of a product are also allocated to its constituent components. Then, relations between (a) FRs and components and (b) components and production processes are identified and represented as FRscomponents relation matrix and components-processes relation matrix, respectively. By using these matrix, a possible increase (or decrease) in environmental load and cost at each production process caused by changes in product design (FRs performance) is calculated. A possible increase (or decrease) in performance of each FR caused by the changes in production processes is also calculated. In this way, interdependency among decision variables for product designers and production engineers are considered. (iv) Identification of bottlenecks After calculation of environmental load and cost for each life cycle stage, bottleneck processes in the product life cycle (including production processes) can be identified from the environmental and economic viewpoints, respectively. Using UV values and calculating TPI of each component, FR, and production process, improvement targets from both of environmental and economic viewpoints are also screened out. Since components or processes with low TPI produce relatively low utility value comparing to their corresponding environmental load and cost, these components and processes should be focused on. In addition to that, it is quite promising to enhance the performance of FRs with high TPI, because these FRs provide high utility value to the customer with relatively small environmental load and cost. (v) Generation of redesign solution and its evaluation After screening out improvement targets, redesign solutions should be generated in terms of EOL options, production process design, and product design. Table 1 summarizes the design guideline for screened out targets in previous step. Although the design guideline gives useful suggestions to the designer, it doesn’t give him/her any practical design of a target product. Adequate utilization of multiple DfX methods (e.g., design for reuse, design for recycling, design for maintenance, design for disassembly etc) in combination with the guideline will be quite effective.
Life Cycle Design - Methods and Tools
115 (i) Definition of a product and its life cycle
(ii) Estimation of environmental load (LCE), cost (LCC), and utility value (UV) Multi Attribute Utility Theory (MAUT)
LCA
(iii) Identification of causality / relationships/ interdependency among UV, functional performance, product design, processes, environmental load, and cost.
LCC QFD Calculation of TPI
(iv) Identification / screening out of target FRs, components, and processes from both of environmental and economic viewpoints
Sustainable business case base (v) Generate redesign solution and its evaluation
Yes
DfX
(vi) More detailed Life cycle model creation Montecarlo simulation
No
Uncertainty / time variation etc. have great effect on the result?
Life cycle simulation (LCS)
Figure 1: Design flow and integration of multiple tools. In addition to that, consideration of different business models (e.g., function selling etc.) from the current one is also promising in some cases. Provision of the design guidelines of sustainable businesses with existing sustainable business cases is also quite helpful for the designer. (vi) Detailed life cycle model creation (if necessary) Sometimes, there exist significant uncertainties in calculation of LCE, LCC and UV. The exact values for some parameters might not be known and stochastic distribution of other parameters might also cause the significant variations in LCE, LCC, and UV values. If the uncertainties of the parameters have a significant impact on the result of calculation, detailed life cycle process model handling these uncertainties should be developed. For this purpose, discrete event simulation method is quite suitable. Examples of this approach include Life Cycle Simulation method [9]. To finding out adequate design solution under significant uncertainty, montecarlo simulation method and robust design method can be also effectively utilized. Reduction of cost and E.L.. Components with low TPI Design options
Reuse Recycling Reduce
Components Components with low CP with low PP
Enhancement of value FRs with high TPI
FRs with high obsolescence rate
X X X
Prolongment of physical life
X
Upgrading Enhancement of functionality
FRs with high deterioration rate
X X
Table 1: Design guidelines based on TPI of each component, process, and FR.
4
CASE STUDY: A DIGITAL CAMERA
In order to illustrate how to use each method and integrate them together, a simplified case study of a digital camera is conducted. The target product is an imaginary compact digital camera with rechargeable battery. LCE and LCC of the product are dominated by those in production stage, which are further divided into production of each component as shown in Figure 2. From the result of LCA and LCC, production processes of main board and lens unit should be improved, respectively. TPI of each component and FR is calculated by using the result of LCA and LCC, considering the causal relationships between environmental load and cost of components and UV of a product. Figure 3 shows the TPI of each FR and component. Considering the both of environmental and economic aspects, production processes of LCD unit and body should be focused on due to their low TPI values. In addition to that, this result suggest that enhancement of functional performance of "FR: Clear picture" is quite promising to improve its customer utility value. Improvement potentials in TPI are also calculated based on two redesign solutions, namely, (i) enhancement of "FR: clear picture" and (ii) reuse of LCD unit and body. Estimated improvement in TPI of these solutions is 26% and 19%, respectively. LCA and LCC results suggested a different improvement targets in this example. In order to prioritize improvement targets, environmental and economic aspects should be considered simultaneously. This problem is solved by using TPI. TPI results suggested most promising FRs to improve environmental and economic performance of a target product in addition to bottleneck production processes, while LCA and LCC only suggested bottleneck production processes. Integrating LCE, LCC and UV into one single indicator and interpreting TPI calculation results with design guidelines proposed in section 3 are quite effective. Estimating improvement potential of each redesign solution is also effective for prioritizing them. Since decision making in product
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Life Cycle Design - Methods and Tools
E.L. at Production [kgCO2]
LCE [kgCO2]
Lense unit CCD unit Production Distribution
Unti-blurring mechanism
Use
Main board
LCD unit
Collection
Flash unit
EOL
Production process of a main board should be improved.
Body Battery
Production cost [kJPY]
LCC [kJPY]
Lense unit Production
CCD unit
Distribution
Unti-blurring mechanism
Use
LCD unit
Collection
Main board
EOL
Production process of Lens unit should be improved.
Flash unit Body Battery
Figure 2: LCA and LCC results and suggested improvement targets.
TPI of FRs
400.00
FR with high TPI
300.00 200.00 100.00 0.00 Clear
Applicability
picture (high to multiple resolution) 350.00 300.00 250.00 200.00 150.00 100.00
Less
Beautiful
Overall
blurring
portrait
product
•Production processes of LCD unit and body should be improved. •Reuse or recycling of LCD unit and body is promising. •Performance of FR: “Clear picture“ should be enhanced.
situations
TPI of components
50.00 0.00
Components with low TPI Figure 3: TPI of FRs and components and suggested improvement targets. design and production (or service) process design are executed by different people and there sometimes exists trade-off among these decision variables, prioritizing design solutions proposed by different divisions based on an easily understandable single indicator is very promising. 5
CONCLUSION
This paper proposed an integrated eco-design framework using a TPI as a glue for connecting different tools focusing on
environmental load, cost and utility value calculation, so as to support decision making and communication in manufacturing firms to improve the environmental and economical performance of their products and services. This paper also illustrated how to combine these tools with a simplified example of a digital camera, and compared redesign solutions derived by each LCA, LCC, and TPI method to clarify advantages of our method. As shown in the case study, TPI was successfully utilized to find out bottlenecks in product and process design simultaneously. Proposed integrated
Life Cycle Design - Methods and Tools framework for utilizing multiple tools for sustainable product and process design is concluded to be effective. In addition to LCA, LCC, MAUT, and QFD, integrated utilization of other design methods is also quite promising. For example, in order to handle significant uncertainties in product life cycle (e.g., collection rate for recycling, product usage condition and duration etc.), robust design method that derives adequate design solutions considering the variations in given parameters can be effectively utilized [10]. In order to design upgradable products, modular design method is quite useful [11]. Existing various eco-design guidelines [12] and their case base are also useful to generate improvement ideas of product, process, and eco-business design, when their applicable conditions are interrelated to the TPI calculation results [13]. Expansion of our methods and integration of these tools will be executed as our future work. Future work also includes implementation of the method and framework and their application to real industrial cases. 6
117 operations, Kingston, R. S. Means Company, Inc. [5]
Kondoh, S.; Masui, K.; Hattori, M.; Mishima, N. (2008): Total performance analysis of product life cycle considering the deterioration and obsolescence of product value, in: Int. J. Product Development, Vol. 6, Nos. 3/4, pp.334-352.
[6]
Winterfeld, D. V.; Edwards, W.; (1986): Decision Analysis and Behavioral Research, Cambridge University Press, Cambridge, England.
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Akao K. (1990): Quality function deployment, Productivity Process, Cambridge, M.A.
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[9]
Komoto, H.; Tomiyama, T. (2009): Integration of a service CAD and life cycle simulator, in: Annals of the CIRP 57(1), pp.9-12.
[10]
Kondoh, S.; Mishima, N.; Masui, K.; Matsumoto, M. (2009) Total performance design of product life cycle considering future uncertainties, in: Int. J. Design Engineering, Vol. 2, No. 3, pp.278-298.
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UNEP (1997): ECODESIGN: a promising approach to sustainable product and consumption, UNITED NATIONS PUBLICATION.
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Kondoh, S.; Mishima, N. (2010): Strategic decision-making method for eco-business planning. In: CIRP AnnalsManufacturing Technology, 59(1), pp.41-44.
[4]
Dell'Isola, A. (1997): Value Engineering: Practical applications, for design, construction, maintenance &
Development of CAD System for Life Cycle Scenario-Based Product Design 1
1
Eisuke Kunii , Shinichi Fukushige , Yasushi Umeda 1
1
Department of Mechanical Engineering, Graduate School of Engineering, Osaka University, Japan
Abstract Life Cycle Management (LCM) has established as research area to foster sustainability in all fields of action of organizations. To provide an understanding for this broad research area suitable teaching methods are required. This paper presents the concept for a business game based on the Framework for Total Life Cycle Management (TLCM). It enables participators to understand the interdependencies of four different management disciplines: product, production, after sales and end-of-life management. The educational objective is to continuously develop a holistic life cycle strategy. The game requires communication of all actors and reasonable activities towards sustainability. By that prospective managers can understand relationships and interdependencies of TLCM. Keywords: Life Cycle Scenario; Product Design; CAD
1
INTRODUCTION
In order to increase value and reduce environmental loads of a product life cycle, it is important to design the product considering its life cycle. We refer to this approach as life cycle design. In such design, it is necessary to plan a life cycle strategy based on the characteristics of the product and its market and to design the product so as to realize this strategy. For instance, to reduce the consumption of materials in a product, the implementation of lightweight design may be efficient. However, if a product has a long life, a design that resists abrasion and degradation and offers a longer life is a more reasonable strategy. We therefore believe that designing product based on the life cycle strategy results in successful design solutions both environmentally and economically. This paper proposes an integrated design system to support both life cycle strategy planning and product design based on the life cycle strategy. As the first step for this purpose, this paper proposes as integrated representational scheme of a product and its life cycle strategy. This paper is organized as follows. Section 2 briefly summarizes literature review and then shows the concept of an integrated life cycle design system. Section 3 proposes the integrated representational scheme of a product and its life cycle strategy. Section 4 proposes an integrated design method to support life cycle scenario-based product design. Section 5 outlines a life cycle scenariobased product design support system. Section 6 concludes this paper. 2 2.1
TOTAL LIFE CYCLE MANAGEMENT Literature Review on Life Cycle Design
Life cycle design has been proposed as a new approach in which a product is designed with considering its entire life cycle from the stage of product conception to final reuse/recycling or disposal [1]. In the life cycle design, a life cycle of a product should be designed as well as the product itself.
We previously proposed the procedure for the life cycle design shown in Figure 1 [2]. First, a designer analyzes the present condition of society, markets, etc. Second, the designer determines business strategies, environmental targets and product concepts based on the results of this data analysis. Third, the designer formulates a life cycle strategy according to these business strategies, environmental targets and product concepts. Then, the designer designs the product and its various life cycle processes (including, manufacturing process, sales process, and recycling process) so as to realize the life cycle strategy. Finally, the designer evaluates the life cycle of the product in order to check that the life cycle strategy can be achieved. In short, the life cycle strategy is planned at an early stage of life cycle design, and the product is designed so as to realize this strategy. With the life cycle design, the designer can propose a product optimizing environmental loads and costs throughout its life cycle. Many studies focus on the support of life cycle design. Kobayashi [3] proposed the Life Cycle Planning, which supports the choice of life cycle options of parts through the linkage of QFD and LCA data. Kwak [4] proposed a framework for evaluation of the relationship between product design and resource circulation flow with Transition Matrix. Phang et al. [5] proposed a life cycle design method Present data analysis
Business strategy
Environmental target
Product concept
Life cycle strategy planning
Product design
Process design
Life cycle evaluation
Figure 1: Life cycle design procedure [2].
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_21, © Springer-Verlag Berlin Heidelberg 2011
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using Distributed Oriented Modeling and Environment (DOME). A number of studies have also addressed environmentally conscious design technologies including design for recycling, and such technologies have been practically adapted to product design [6]. However, the relationship between life cycle strategy and product design has not been clarified, and the integrated support environment for both life cycle strategy planning and product design remains insufficient.
2.2
In this paper, we propose an integrated representation scheme of a product and its life cycle for supporting product design based on life cycle strategy.
Fundamental Concepts of Life Cycle Scenario-Based Product Design
As mentioned in Section 2.1, a life cycle strategy should be planned at an early stage of the product design. The life cycle strategy should be determined depending on the characteristics and the structure of the product. For example, it is inappropriate to decide the maintenance strategy for the product which has a short value life. An integrated computational design environment with managing the interaction between the life cycle strategy and the product is required. In this paper, we employ the life cycle scenario as a representational scheme of a life cycle strategy to support life cycle strategy planning. As shown in Figure 1, life cycle strategy is planned prior to product design, and a product is designed based on the life cycle scenario. Here, we call a series of the design from life cycle strategy planning to product design “life cycle scenario-based product design”. In order to support such design, this paper provides a workspace for a designer to plan the life cycle strategy and to design a product based on this strategy in an integrated manner. Therefore, we identified four requirements for the support method of the life cycle design as follows:
The representational scheme of a product and its life cycle strategy A product and its life cycle strategy should be modeled in an integrated manner. In this paper, we propose an integrated representational scheme in Section 3.
To maintain the relationship between a product and its life cycle strategy Life cycle design should be support by managing the consistency between them and evaluating a life cycle. In this paper, we propose the method of linkage between two topological models: the product model and the life cycle scenario.
The product should be designed so as to realize the life cycle strategy. We propose an integrated product design method based on the life cycle scenario. 3
3.1
Structure model
:
Module node, Part node
:
Hierarchical structure
:
Connection structure
MODELING OF A PRODUCT AND ITS LIFE CYCLE STRATEGY
Product Model
In this method, a product model consists of two sub-models; namely, a structure model and a geometrie model (see Figure 2). The structure model represents the hierarchical relationship and the structure connection between modules and parts. In the structure model, we define three kinds of constituent nodes: product node, module node and part node. A product node represents the whole product at the top of the hierarchical structure, a part node at the bottom, and a module node represents an assembly of parts or other modules in the middle of the hierarchy. Here, the module node is related to the activities of assembling or disassembling in the product life cycle. Each node has attributes such as constituents, lifetime, weight, etc. The connection structure is represented in the form of topology between modules or parts in the same level of hierarchy. The geometric model is drawn with 3D CAD, and each part has links with correspondent nodes of the structure model. The link enables to construct a product model with correspondence between structural and geometrical information. 3.2
Life Cycle Scenario
Describing a life cycle scenario is an effective method for planning the life cycle strategy. Here, a life cycle scenario is defined as a description of the anticipated product life cycle. In other words, by describing a life cycle scenario, a designer can easily identify appropriate life cycle options and requirements for product design. In the method for life cycle scenario description [2], a life cycle scenario consists of the following four items. In this approach, we employ this representation for describing the life cycle strategy.
Objective of the life cycle The objective of the life cycle highlights objectives for life cycle strategy planning. We represent the objectives by using sentences and parameter values that quantify the sentences.
Planning method of a life cycle strategy In this paper, the life cycle strategy is defined as a combination of life cycle options for a product and its components (e.g., upgrading, spare parts reuse, material recycling), which should be logically planned based on the business strategies, environmental targets and the product concept (as shown in Figure 1). We employ the method for life cycle scenario description [2].
The product design method based on the life cycle strategy
Life cycle scenario concept The life cycle scenario concept indicates the basic direction for constructing a life cycle scenario by using brief sentences (e.g., rapid circulation scenario). This item plays an important role as a medium between the objectives and the life cycle option.
Geometry model
Life Cycle Flow
Situation
Product
Manufacturing
Distribution
Maintenance
Use
Discarded products are collected from users by a secondhand goods dealer at a second hand dealer shop.
Collection
Attribute values e.g.,Constituent, Lifetime, Weight, etc.
Reuse
Disassembly
Landfill
Recycling
Figure 2: Example of a product model.
Figure 3: Example of a life cycle flow.
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Life cycle option The life cycle option is an alternative of a life cycle for the product and its components, and determines the basic structure of the life cycle scenario. The most critical target of the life cycle scenario description is to select the life cycle options that are most appropriate for the product.
A designer should describe the product model, the life cycle scenario and the linkage between them without physical inconsistencies. An example of the inconsistencies is the wrong connection between the life cycle flow model and the structure model shown in Figure 5; a certain module node of the structure model cannot be linked to the next link of the process which disassembles this module.
Life cycle flow model The life cycle flow model is the central model of a life cycle scenario and represents the flows of products, components and materials as a network of life cycle processes (see Figure 3). Each life cycle process has a situation formulated using the 4W1H (When, Where, Who, What and How) approach and a description of the income and expenditure of the operator of the process for use in evaluating the economic aspect of the life cycle.
In order to embody the life cycle strategy in a product in the later stages of life cycle design, it is essential to clarify requirements for product design. We therefore describe such requirements in the situation (e.g., the lifetime of a module for reuse must be at least twice that of the product). 3.3
Linkage between the Product Model and the Life Cycle Scenario
To support integrated management of a product and its life cycle toward the fulfillment of such requirements, we link two topological models: the product model (outlined in Section 3.1) and the life cycle scenario (outlined in Section 3.2). The linkage between the two models is shown in Figure 4. Here, each link of the life cycle flow model in the life cycle scenario links to the corresponding node of a structure model for mutual information reference. Through such linkages, a designer can identify which node of the structure model passes through which process of the life cycle flow model. Through this linkage, a designer can easily check the following two elements, which is important for the life cycle scenario-based product design.
In the life cycle scenario-based product design, the product model must fulfill the requirements of the life cycle strategy in order to enhance the feasibility of a life cycle scenario. For instance, when a certain module will be reused, its lifetime should be designed to fulfill the lifetime set in the scenario. 4 4.1
LIFE CYCLE SCENARIO-BASED SUPPORT METHOD
PRODUCT
DESIGN
Outline
In this paper, we propose an integrated design method to support life cycle design by using the model outlined in Section 3. The integrated design method consists of two phases: life cycle strategy planning phase and product design phase. In the first phase, we describe the life cycle strategy by using the life cycle scenario as outlined in Section 3.2 and the method supports the description of a life cycle scenario. In this phase, it is important to describe a life cycle scenario so as to enhance its effectiveness, which is defined as the evaluation results of the life cycle scenario. In the second phase, the method supports product design based on the life cycle scenario determined in the first phase while maintaining consistency between the product model and the life cycle scenario. In this phase, product design should be performed in order to enhance the feasibility of the life cycle scenario.
4.2
Life Cycle Flow model
Structure model
Figure 4: Correspondence between structure model and life cycle flow.
Transportation
Recycling
Fulfillment of life cycle strategy requirements:
The main aim of the proposed method is to manage the interaction between these two phases; in other words, the method enables a designer to design and modify the product and/or the life cycle scenario interactively according to the evaluation results.
Landfill
Recycling
Disassembly
In these phases, the method enables the life cycle evaluation of environmental loads and costs by providing the evaluator with the information of the product model and the life cycle scenario.
Transportation Disassembly
Maintenance of consistency on a topological level:
Landfill
Since most product characteristics (e.g., recyclability) are mainly decided in the design phase, the life cycle strategy should be planned prior to product design. As outlined in Section 3.2, a designer describes the life cycle strategy in the form of the life cycle scenario. In the proposed method, we assume that business strategies, environmental targets and the product concept are given (which should be determined prior to planning the life cycle strategy as described in Figure 1). Here, the product concept includes the main modules of the product; that is to say, the main modules are determined prior to the life cycle scenario description phase. To support the life cycle strategy planning phase, we developed a life cycle scenario description methodology in a previous work [2]. In this methodology, a designer describes the life cycle scenario in a trial and error manner using a top-down approach as follows:
Inconsistency!!
I.
Life Cycle Flow model
Life Cycle Strategy Planning Phase
Structure model
Figure 5: Example of an inconsistency.
The designer sets the objectives of the life cycle to be planned, based on various kinds of information such as the target product and market in order to identify the target of the life cycle scenario.
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II. The designer clarifies one or more life cycle concepts based on these objectives in order to determine an outline of the life cycle scenario.
III. The designer selects life cycle options for each main module based on the life cycle concept by using the external support resources such as Life Cycle Option Selection Support Tools [7].
When the designer makes design changes, the consistency on the topological level must be checked in order to support the design changes without inconsistency on a topological level. If the fulfillment of the requirements cannot be expected even with design changes, it is necessary to return to the life cycle strategy planning phase and revise the life cycle scenario with the reason for this revision as a new design rationale.
IV. The designer draws up a life cycle flow model of the main modules and specifies the situation of each process. In this phase, it is necessary to clarify the requirements for product design in order to embody the life cycle strategy in the product design phase. We describe the requirements as the situations of the life cycle flow model in step IV above. 4.3
Product Design Phase
In this phase, the designer designs the product based on the life cycle scenario determined in the life cycle strategy planning phase. According to the progress of product design, the designer improves the life cycle scenario (especially the life cycle flow model) as needed. The main purpose of this phase is to design a product structure so as to enhance the feasibility of the life cycle scenario. In the proposed method, the designer designs the product as follows (see Figure 7). I.
The designer determines attributive values of a certain module or part, and creates its geometric model, and then makes the module or part node of a structure model.
Addition to the life cycle flow model of new processes that the created module or part passes independently of the node selected in the second step (see Figure 6)
Whenever a new module or part is designed, the designer follows steps I-V. Here, the design of the geometric model and the attributive values (performed in step I) is performed arbitrarily according to the various requirements to be considered in product design (e.g., function, design, etc.). That is to say, the proposed method does not interfere with the adaptation of existing design method. 4.4
II. The designer selects the node of the structure model that contains the module or part as its component created in the first step. And then, the node corresponding to the created module or part is automatically added to the structure model with hierarchical connection between the parent node and the new child node added in this step. III. The designer determines the connection structure between the added node and the other existent nodes in the structure model as needed. IV. The designer checks whether the designed module or part fulfill the requirements of the life cycle scenario described in the life cycle strategy planning phase comparing the product model (geometric model and structure model) with the life cycle scenario.
Evaluation of a Life Cycle
The overall environmental loads throughout the product life cycle are the most critical indicator in estimating the product life cycle. On the other hand, the feasibility of the life cycle design depends heavily on the related costs. Accordingly, both the environmental loads and the costs should be evaluated in life cycle evaluation. To this end, we employ Life Cycle Simulator (LCS) [8], which can evaluate environmental loads and costs throughout the entire product life cycle using a discrete event simulation technique and LCA data Product
Landfill
Disassembly Recycling
Design change This node cannot be applied to recycling by a certain reason!
Can the life cycle flow of the parent node apply to the child node?
IV. Check requirements
Use Collection
Reuse
Improvement of the attributive values or the geometric model of the designed module or part Product
Distribution
Maintenance
Product
A designer decide to separate and landfill the offending node.
Distribution
Manufacturing Maintenance
If the designer decides that improvements are needed because the requirements remain unsatisfied, he or she makes one or more of the following design changes. Improvement of the hierarchical structure; in other words, selection of another node of the structure model, containing the module or part designed in the first step
Manufacturing
Use Collection Disassembly
Reuse
Landfill
Disassembly Recycling
Added process and links
Figure 6: Example of an addition of new processes and links.
Manufacturing Maintenance
Distribution Use
Parent node Collection
II. Add hierarchical connection I. Create new node III. Add connection structure
Reuse
New child node
Figure 7: Flow of a product design based on a life cycle scenario.
Disassembly Recycling
Landfill
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such as intensities for LCA. For this simulation, the life cycle flow model of a life cycle scenario is used. Entering information on a product model and a life cycle scenario enables interactive evaluation of the designed life cycle. 5 5.1
Management of the linkage between the product model and the life cycle scenario: In the life cycle scenario-based product design, it is important to link the product model with the life cycle scenario and to manage such linkage. The system provides integrated management of this linkage to allow mutual information reference.
Consistency Manager:
Evaluation of life cycle: In the life cycle scenario-based product design, life cycle evaluation should be performed at all stages of the process for design with verification of life cycle scenario effectiveness and feasibility with LCS.
Structure Model Editor and 3D Solid Modeler: Allow the designer to draw and modify structure models and geometric models.
Checking of consistency on a topological level: In order to prevent the designer from creating inconsistencies on a topological level, the system automatically checks consistency. If the system finds out inconsistencies created by the designer, the system issues a warning and highlights the offending part.
System Architecture
Manages the consistency on a topological level between the product model and the life cycle scenario.
Main Function of the System
To support the life cycle design cycle, the proposed system includes the following three functions:
LIFE CYCLE SCENARIO-BASED PRODUCT DESIGN SUPPORT SYSTEM
We propose an integrated design support system for both life cycle strategy planning and product design based on life cycle strategy. The architecture of the proposed system is shown in Figure 8. The system consists of five sub-modules: Consistency Manager, Structure Model Editor, 3D Solid Modeler, Life Cycle Scenario Editor and Life Cycle Simulator.
5.2
Life Cycle Scenario Editor: Helps the designer to describe and revise life cycle scenarios.
6
Life Cycle Simulator:
With the aim of supporting life cycle scenario-based product design, this paper proposed a procedure of describing life cycle scenarios and designing products, and outlined a related design support system. A product model and life cycle scenario are defined to describe the product and life cycle strategy, and the product model is linked to the life cycle scenario. In future work, we plan to propose a framework that allows the application of suitable environmentally conscious design technologies (such as design for recycling) using the proposed description and linkage method for products and their life cycles.
Evaluate environmental loads and costs throughout the entire product life cycle. In this paper, we employ LCS as mentioned in Section 4.4. There are also three databases: the material database, the process database, and the LCA database. The material database stores information on various materials data such as specific gravity, market price and recycling costs. The process database stores various types of process model such as those for collection, land transportation, and recycling. And the LCA database stores information on intensities for LCA such as CO2 emission in electricity generation (kgCO2/kWh). The system supports life cycle design systematically and effectively by accelerating the cycle of life cycle strategy planning, product design, and life cycle evaluation through these components.
CONCLUSION
Designer designs a product while comparing three models (3D Solid Model, Structure Model and Life Cycle Flow) visually!!
Designer
Consistency Manager
Product
Manufacturing Maintenance
Distribution Use Collection
Link
Link
Reuse
Disassembly
Landfill
Recycling
3D Solid Modeler
Structure Model Editor
Life Cycle Scenario Editor
Product Model
Database
Material DB
Process DB
LCA DB
Figure 8: System architecture.
Life Cycle Simulator
Life Cycle Design - Methods and Tools 7
REFERENCES
[1]
Kimura, F., (1999): Life cycle design for inverse manufacturing, The Institute of Electrical and Electronics Engineers, Tokyo, in: Proceedings of Eco Design 1999, pp. 995-999.
[2]
Suesada, R., Itamochi, Y., Kondoh, S., Fukushige, S., and Umeda, Y., (2007): Development of Description Support System for Life Cycle Scenario, in: Proceedings of CIRP LCE 2007, Springer, pp. 29-34.
[3]
Kobayashi, H., (2000): A Method of Life Cycle Planning for Product Eco-improvement, in: International Journal of Environmentally Conscious Design & Manufacturing, No.8, pp. 2737.
[4]
Minjung Kwak, Harrison M. Kim, (2010): Evaluating End-ofLife Recovery Profit by a Simultaneous Consideration of Product Design and Recovery Network Design, in: Journal of Mechanical Design, ASME, Vol.132, No.7, 071001.
[5]
K. F. Phang, N. Senin, and D. R. Wallace, (1998): Distributed modeling and evaluation of product design problems, in: Computer-Aided Design, Vol.30, No.6, pp. 411-423.
[6]
Keijiro M., (2009): Current Status of Environmentally Conscious Design among Japanese Manufacturers, in: International Journal of Automation Technology, Vol.3, No.1.
[7]
Umeda, Y., Life Cycle Design Committee, (2001): Toward a Life Cycle Design Guideline for Inverse Manufacturing, in: Proceedings of Eco Design 2001, pp. 143-148.
[8]
Umeda, Y. et al., (2000): Study on Life-cycle Design for the Post Mass Production Paradigm, in: Artificial Intelligence for Engineering Design, Analysis and Manufacturing, Vol.14, No.2, pp. 149-161.
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Environmental Impact Assessment Model for Wireless Sensor Networks 1
1
1
Jérémy Bonvoisin , Alan Lelah , Fabrice Mathieux , Daniel Brissaud 1
1
G-SCOP Laboratory, Grenoble University, Grenoble, France
Abstract The aim of our research is to understand what design parameters are responsible for the environmental impacts of deployment and operation of wireless sensor networks (WSN), with the underlying aim to support their ecodesign. In this paper, we propose an environmental impact assessment model for WSN based on lifecycle assessment (LCA) and network energy analysis. It is based on a two-level approach, given that environmental impacts of WSN are generated by both the network elements and their synergetic effects. This model will help analyse the sensibility of the environmental impacts to parameter changes, and compare different solutions. Keywords: Lifecycle Assessment (LCA); Machine-to-Machine (M2M); Energy
1 1.1
INTRODUCTION Wireless Sensors Networks (WSN)
Information and Communication technology (ICT) is changing the world we are living in. Electronic devices surround us, processing information which is influencing the way we carry our every day businesses. Their spread has led to the idea of an “information society” which is seen as very promising for a rational management of human organisations, based on the idea that information about a process allows for better control [1]. Wireless Sensor Networks (WSN) is part of these promising technologies. WSN consists of communicating autonomous sensors that cooperate to monitor physical phenomenon, e.g. temperature or noise. Recently, linked to WSN, concepts such as “Ambient Intelligence”, “Smart Environments” [2] or “Ubiquitous society” [3] have grown. WSN could potentially be applied to a broad range of applications in several domains such as military, home, health and environment [4]. WSN could be embedded in buildings, automotive, clothes and even in vivo [2]. At a city scale, it could be used to enhance efficiency of urban services such as energy and water supply or transport management [5]. According to outlooks [2, 4], WSN will tend to be more and more used, as their production costs go below the economic benefits they are supposed to generate. 1.2
WSN and the environment
Many commentators tend to be optimistic on the ability of ICT to address optimization problems, particularly in the field of environmental protection [6]. Many industrial publications try to show economic and environmental benefits of ICT [3, 7]. However, as Erkman, Berkhout, Yi and Loerincik [8-11] temper, the environmental impacts and benefits of ICT are not yet well understood. There is overconfidence on the belief that ICT will help us to deal with complexity and help us in our way to sustainability [12]. It is a promising technology, but it is not neutral, as electronic production generates important environmental impacts [13]. We can not nowadays predict the effects of ICT, as they will depend on our ability to use them in a sustainable way [11, 14]. The question just goes as well for WSN as an ICT amongst others.
Wireless sensors are small objects, and their individual impact seems very low. However, the deployment of large scale WSN will lead to the spreading of thousand (or even millions) of devices [4], merely in a “deploy and forget” way [2]: which means that they may not be retrieved at the end of their service life. It seems therefore relevant to study their environmental impacts. Some examples show interesting benefits [5, 15], but studies tend to be optimistic, presenting benefits and forgetting the drawbacks. Some others, trying to draw the whole picture, conclude in the same way [11, 16], while other draw a more nuanced picture [17, 18]. 1.3
Research objective
The aim of our research is to understand the environmental impacts of WSN, which have been until now less often considered as their potential benefits. We want to identify the most influent design parameters regarding the environmental impacts of WSN, in order to support their eco-design. We adopt a two-level approach, at product level (WSN node) and at system level (network) and we set up a calculation model based on lifecycle assessment (LCA) and energy modelling. Based on the exploratory research done by Hoang [17], we go into further details in the definition of an impact assessment method for WSN. The aim of this paper is to present this method, and to propose a calculation model for environmental impacts of WSN. We will first explore the WSN infrastructure and its lifecycle. Then we will present our WSN environmental impact assessment model in details. Finally, we will apply the model on an example to show its possible results. 2 2.1
WSN AND THEIR LIFECYCLES Infrastructures of WSN services
Services based on WSN rely on cooperation of different product types, as follows:
Sensor/actuator nodes: small electronic devices embedded in the phenomenon that needs to be monitored. They can gather physical information due to one or more sensors, and can
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_22, © Springer-Verlag Berlin Heidelberg 2011
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Repeaters: small electronic devices that transmit information across the network. They help concentrate information from the sensors towards the gateways. They are simply composed of a communication module and a power supply unit. As for sensors, the power supply is most of the time an irreplaceable battery.
Gateways: electronic devices that gather the information generated by the sensors. These are more complex devices that link the WSN to the service platform. They manage communication with two different communication protocols, one to gather information from the WSN (IEEE 802.15.4), the other to reach the service platform (IP, GPRS). Each gateway may thus consume more energy than the sensors and repeaters. The power supply unit can be of various technologies (battery, photovoltaic, connected to the mains, etc.).
Telecommunication network: once gathered by the gateway, information is concentrated and may be too heavy to be handled by WSN protocols that have limited capacities. After the gateway, conventional telecommunication networks (IP, GPRS), which can transmit information at higher rates, relays the WSN protocols.
WSN Lifecycle
Whatever the energy supply unit is, a device consumes energy, and this has an influence on its environmental impact. It is quite obvious for plugged devices: they take energy from the mains, which has environmental impacts due to the electricity production. It is more indirect for battery-driven devices: the energy consumption, along with the battery capacity, highly conditions the lifespan of products. The more battery-driven nodes consume energy, the shorter their lifespan, the more often they need to be replaced, and the greater the environmental impacts of the WSN become. Energy is known to be the major technical limiting factor of WSN in terms of computing power and lifespan, as “sensor nodes carry limited, generally irreplaceable power sources” [4]. Research is therefore active to look for techniques to enhance the nodes’ autonomies. Several directions are explored, such as efficient network protocols [19], or energy scavenging technologies [2]. We adopt a new point of view, identifying the system design parameters that decrease nodes’ energy consumption, and thus have an effect on the overall environmental impact of the WSN. The network lifecycle
In order to calculate the environmental impact of the WSN, we have to take into account the environmental impacts of the lifecycles of its components (nodes), and the synergetic effects. Thus, as shown
Deployment
Exploitation
Manufacturing
Transport
Installation
End-of-life
Usage
Removal
End-of-life
Table 1: The two interconnected lifecycles of the WSN and of its composing nodes. According to these lifecycle considerations, the environmental impact of the overall WSN can be represented by the equation (1):
I
A System Level Approach
The environmental impact of a WSN is more than just the sum of the individual environmental impacts of its components. The networking of these communicating objects creates synergetic effects: the more a sensor stimulates its neighbours, the more they consume energy. Impacts of WSN can not be assessed by a simple product-based approach: a system-wide approach is needed to take synergetic effects into account.
2.3
In the first stage of its lifecycle, the network is set up. To do so, WSN nodes need to be manufactured, transported and installed on site. In the following stage, the network is operated. In that phase, the nodes consume energy, and the battery-driven ones may be replaced by new ones once their battery discharged. Therefore, new nodes need to be manufactured, transported and installed, while the old ones need to be removed and processed for their end of life. Note that no impact is counted for the usage phase of battery driven nodes, as energy is used to produce the battery during production phase and no extra energy is needed during usage. In the case of a mains-driven node, usage impact consists in supply grid energy consumption. At the network end of life, it is dismantled, and each node is removed to undergo end of life processing.
Service platform: servers that concentrate and transform information in order to provide a service to users.
In this paper, we focus only on the environmental impact of the WSN, meaning that we do not consider the environmental impact of the service platform and telecommunication network. This will be the object of future publications. 2.2
in the Table 1, we have to consider two interconnected lifecycles: those of the components (WSN nodes lifecycles), and those of the infrastructure itself (WSN lifecycle)
Nodes lifecycles
eventually respond with actuating capability. It is in the least composed of a sensing unit, a processing unit, a transmitting unit, a power supply unit, and eventually other modules [4]. The power supply is usually a battery, which is often difficult, if not impossible, to replace.
125
DEP
node e
e
REPe
P CON e DIS e Le
(1)
Where:
I is the total environmental impact of the WSN lifecycle, expressed as the sum of the environmental impacts of the nodes (including synergetic effects) which compose the WSN;
DEPe is the environmental impact of the node e during the deployment stage of the WSN, which corresponds to manufacturing, transportation and installation of the node e;
REPe is the environmental impact of the replacement of the node e during the exploitation stage of the WSN, which corresponds to the whole lifecycle of the new node replacing the node e;
CONe is the environmental impact of the energy consumption of the node e during exploitation;
DISe is the environmental impact of the node e during the dismantling stage of the WSN, which corresponds to the removal and the end of life of the node e;
Le is the lifespan of node e;
P is the WSN exploitation period considered.
3
PROPOSED MODEL
ENVIRONMENTAL
IMPACT
ASSESSMENT
We base the calculation of environmental impacts on equation (1). In this equation, DEPe, REPe and DISe can be quantified by performing a classic lifecycle assessment (LCA). However, in order to determine Le and CONe, further calculi on energy are necessary. We therefore set up a calculation model based on three phases: 1.
perform LCA for each product composing the WSN;
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2.
perform energy analysis and determine Le and CONe;
3.
aggregate environmental impacts to get a final figure.
PRX PTX G RX GTX L
The first phase is well defined by the scientific community [20], so it won’t be detailed in this paper. The third is represented by equation (1). The purpose of the following section is to detail the second step: determination of Le and CONe. This will be detailed in three sub-steps: 1.
determine the electromagnetic neighborhood of each node;
2.
calculate the energy consumption of each node;
3.
derive corresponding Le and CONe.
3.1
Determine the neighborhood of each node
Where:
PTX and PRX are respectively the logarithmic expressions of the emitted and the received powers;
GTX and GRX are respectively the logarithmic expressions of the emitting and receiving antennas gains;
L is the logarithmic expression of the losses due to propagation of radio waves. This can be calculated using the Okumura-Hata model.
3.2
The energy consumption of a node is dependent on network events, which are determined by the node’s surroundings. One particularity of wireless networks is that the logical network does not exactly match to the physical network: data are not sent though a wire, but via radio waves, which are not directional, are evanescent, and may encounter obstacles. This creates two problems:
communication between nodes may fail due to losses in wave propagation;
nodes may receive information not intended for them. This phenomenon is called overhearing.
N P i 1 P i 0
Where:
N is the mean number of tries that are needed to be realized to transmit a data without fail;
P is the probability to transmit the data successfully in one try.
This last term (the probability to transmit a data successfully in one try) is dependant on the physical position of both concerned nodes. It depends on their distance and on the surrounding obstacles. It can be estimated using the empirical Okumura-Hata model, which is the most widely used model for predicting radio waves propagation in urban areas.
This is taken into account in the environmental impact assessment model by listing for each node the neighbors it can hear. We consider that a node can hear a message if the signal has not been attenuated to a level below the reception sensibility of the node. The received power is calculated with a standard link budget:
i
ni
(4)
activity i
Where:
E is the energy consumed by the device during a considered time period;
ei is the energy consumed by the device to perform the elementary task i, it is a physical property of the node;
ni is the number of times the task i is repeated during the considered period. It is both determined by the node, its environment and the network.
More precisely, we can consider three types of tasks:
Environment-driven tasks, which are provoked by the environment, the phenomenon monitored by the sensors. They are by nature probabilistic, as deterministic phenomenon does not need to be monitored.
Self-driven tasks, which are provoked by the node itself. They are by nature periodic.
Network-driven tasks, which are provoked by the radio signals sent by the neighborhood. They are probabilistic, as they are the combination of deterministic and probabilistic phenomenon (e.g. the number of measures sent by a sensor may be deterministic whereas the number of tries to send them is probabilistic).
3.3
Determine Le and CONe
The energy consumptions calculated will allow us to determine the missing terms of equation (1):
CONe, for mains-driven devices: the environmental impact of energy consumption is given directly by multiplying the impacts per kWh by the quantity of energy taken from the mains.
Le, for battery-driven devices: the environmental impact is given by their lifespan. It is defined as the minimum of the lifespan of its components, including the battery, whose lifespan is calculated by the following differential equation :
Overhearing As waves do not propagate in just one direction, nodes can hear radio waves they are not intended to. Each time a message is sent between two nodes, all the nodes in the neighborhood of the sending node are awakened by the message, they have to understand they are not concerned, and then stop listening. This means that, each time a communication occurs between two nodes, some others consume a little bit of energy.
e
E
i 1
(2)
Calculate energy consumption
Wireless network nodes are simple electronic devices. They can generally perform only one task at a time. Therefore, we can consider that their energy consumption is the sum of those of the tasks they perform. Moreover, they perform only a small number of basic repetitive tasks. Their energy consumption can be considered as the sum of the consumption of the elementary tasks multiplied by the number of times they are repeated:
Communication quality Communication between two nodes may fail due to signal dispersion. Links are probabilistic. A communication must then be seen as a probabilistic series of tries. The mean number of tries for successful communication is defined as the probability to succeed at the first try, plus two times the probability to fail the first try and to succeed the second try, plus three times the probability to fail the two firsts tries and to succeed the third try, and so on. This can be expressed by equation (2), which represents the number of tries to realize in order to transmit a data without fail:
(3)
dxt xt dt Where:
x(t) is the remaining energy in the battery at time t;
α is the mean consumption;
β is the battery self-discharge factor.
(5)
Life Cycle Design - Methods and Tools 4 4.1
APPLICATION IN THE EXAMPLE OF WASTE COLLECTION Waste collection service
In many cities, the glass waste collection has inefficiencies due to uncertainty on the glass levels in the containers: collector trucks have to visit each container in order to know how full it is. When trucks have to visit empty containers, time and kilometres are spent unnecessarily. This is an example in which having the information can help to reduce energy consumption.
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c) the reply is given to the intermediary node;
d) the reply is transmitted to the requesting node.
Each of these transmissions is realized by two-way communication between nodes. Each time a message is sent, the receiving node sends an acknowledgment. In case of transmission error, no acknowledgment is received by the requesting node, and the transmission/acknowledgment is repeated.
To do so, WSN-based service can be set up: glass containers at bring-in points in a city are provided with sensors that monitor the glass levels. The sensors are included in a wireless networks that concentrates the information on individual containers to a service platform. The collected data is used to calculate the optimal way to collect glass in the city. The simulated example
In order to show the possible results of our environmental impact assessment model, we applied it to the example of optimization for of glass waste collection. We made a simulation based on the real locations of the glass waste bring-in points in the city of Grenoble, France. We applied our model on an hypothetic WSN based on equipment provided by three industrial partners of the SensCity project [21]: BH Environment (who offers WSN urban services and produces the container’ sensors), Coronis (who designs and produces equipment for the Wavenis wireless communication protocol, particularly repeaters) and Webdyn (who designs and produces gateways for Wavenis networks). Calculations are done based on the construction of a simplified hypothetic network and LCA we performed on sensors, repeaters and gateways. We used the EIME LCA software and its database v.10 (www.codde.fr). We chose to compare two different network topologies that can be applied to the glass collection example (Figure 1):
a four-level tree network composed of 287 sensors, 60 first level repeaters, 7 second level repeaters and one gateway;
a three-level tree network composed of 287 sensors, 60 repeaters and 7 gateways.
Sensors and repeaters are battery-driven, while gateways are plugged to the mains. The calculation is done for a 10 years exploitation period of the WSN.
Figure 2: Simplified communication protocol synopsis. 4.3
Results
Results are only presented here in terms of global warming potential. Our model provides results for other environmental indicators usually used in LCA, such as raw material depletion, energy depletion, hazardous waste production, etc, but for better clarity, these results are not shown in this paper. Moreover, in this example, results are almost equivalent for all the indicators. Figure 3 shows a comparison of the results for the two scenarios. The impacts of the deployment and dismantling stages put together are about equal for both scenarios. We call them “static impacts”, as they are frozen at the beginning of deployment of the network, and cover the entire product lifecycle, excluding use (see Table 1). The difference between the two scenarios comes from the exploitation phase, whose impacts are much higher in the case of the four-level network. The synergetic effects created by communication between nodes lead to different results between the two topologies. 8 7 6 t eq. CO2
4.2
5
Exploitation
4 Deployment Dismantling
3 2 1 0 3 Levels
4 Levels
Figure 3: Comparative results for both computed scenarios. Static impacts
Figure 1: Two computed network scenarios (a) 4-level scenario (b) 3-level scenario. Each sensor is required to make a measure every hour and to send the data through the network. We modeled a simplified communication protocol based on request/response mechanism. Figure 2 illustrates this for a three-level network:
a) a request is sent by the top-level node to the intermediary node;
b) the request is relayed to the leaf node;
Figure 4 shows detailed results for both the deployment and dismantling stages of the network lifecycle put together. The results are roughly equal as the two deployments show only small differences: in a total number of 355 nodes, only 8 differ between the two scenarios. The most noticeable result is that sensors, due to their greater number (80% of the deployed nodes in this example), represent a large part of the environmental impact. Impacts of synergetic effects Figure 5 shows the exploitation phase impacts’ breakdown for both scenarios. It can be seen that the difference earlier noticed in exploitation phase impacts is mainly due to replacement of sensors and second level repeaters.
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Table 2 shows the details of these differences. In the three-level network, only eight sensors are replaced, while all the sensors are replaced once in the four-level network. This is due to a dramatic increase of overhearing in this last scenario. Sensors are awakened five times more often by signals they are not intended for than in the three-level scenario. Energy spent in communications remains the same in the two scenarios, but energy spent in overhearing in the four-level scenario shortens the average lifespan of sensors from 9.7 years to 6.3 years.
5
In the two previous sections, we have presented both the proposed environmental impact assessment model and its results on an application example. This model has several limits that can introduce bias in the results:
It does not take into account what are called “collisions”: when two nodes send a message at the same time, radio signals merge together and are not decodable. Depending on the communication protocol, this may generate information losses or additional communication due to message repetition. Taking this into account would require more than an analytic model, but a real simulation model, which is out of the scope of our project. The influence of this omission is not of great importance when dealing with non flooded radio frequencies (less than ten percent of time occupation of the bandwidth), but may become critical for higher utilization rates.
It is limited to the WSN itself, and does not take into account the impacts of other equipment beyond the gateway, namely the telecommunication infrastructure, the application servers and the end-user equipments. This is a quite different research topic which will be the purpose of future publications.
6
CONCLUSION AND PERSPECTIVES
This difference is less significant for first level repeaters: they spend the most of their energy in communication, and overhearing only represents a small part of their consumption. Their lifespan is thus less affected by an increase in overhearing. While the average lifespan of sensors is shortened by more than a third, the average lifespan of the repeaters is only reduced by a tenth. This increase in the number of overhearing events is due to a higher number of communications between nodes: in the four-level network, information has to be relayed two times, and thus more radio messages are sent across the area, and nodes are awakened more often. The supplementary level in the four-level scenario is moreover at a high level, which means that it concentrates and transmits a lot of information, which overloads the network. Table 2 also shows that the second level repeaters are overloaded in terms of energy: they transmit too much information for their battery capacity, and their average lifespan drops under a year.We can derive from these results that second level repeaters may not be relevant regarding energy and environmental impacts.
LIMITATIONS
In this paper, we have presented a calculation model for assessing the environmental impacts of wireless sensor networks. This model, 5 4,5 4 t eq. CO2
3,5 Gateways Repeaters 2 Repeaters 1 Sensors
3 2,5 2 1,5 1 0,5 0 3 Levels
Figure 4: Impacts of the deployment and dismantling phases (static impacts). Replacements unit
-
Lifespan years
4 Levels
Figure 5: Detailed impacts of the exploitation phase for both scenarios (synergetic effects).
Communication
Overhearing
Energy spent in
Energy spent in
frequency
frequency
communicating
overhearing
-/day
-/day
mA.h
(%)
mA.h
(%)
3 Levels Network Sensors
180
9,7
24
1600
500
(15)
1000
(30)
Repeaters
130
4
116
2400
32100
(90)
1500
(4)
4 Levels Network Sensors
310
6,3
24
4900
500
(9)
3200
(56)
Repeaters L1
150
3,7
116
7100
32100
(84)
4500
(12)
Repeaters L2
100
0,7
1142
16000
184000
(94)
10000
(5)
Table 2: Energy consumption and lifespan of nodes from both scenarios (mean values).
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applied on an example, allowed us to compare two different network topologies, and conclude on their respective relevance.
speculations and evidence. Report to OECD, University of Sussex, SPRU - Science and Technology Policy Research.
Deployment of wireless sensor networks results from the choices on a high number of design parameters of different levels, and done by distributed actors. The environmental impacts can be reduced by working on both product and system levels. The aim of this model is first to help us understanding what product and system parameters have an influence on the impact of wireless sensor networks, in order to be able to provide recommendations for eco-design.
[10] Yi, L., Thomas, H.R. (2007): A review of research on the environmental impact of e-business and ICT, Environment International, Vol. 33, No. 6, pp. 841-849.
Based on this model, future research will be led in order to determine, with the help of the industrial partners of the SensCity project, design alternatives that may lead to decreased environmental impacts. This model will thus provide a basis for defining design guidelines for product and network designers. Further, this model can also be used as a test bench for networks, and be used as a design tool.
[12] Mulvihill, P.R., Milan, M.J. (2007): Subtle world: Beyond sustainability, beyond information, Futures, Vol. 39, No. 6, pp. 657-668.
Finally, further research have to be addressed in order to broaden the scope of the environmental assessment of WSN beyond the gateway, in order to include telecommunication, servers, and enduse of the services created due to WSN. 7
ACKNOWLEDGMENTS
The project SensCity is co-funded by the French Ministry of Economy. The authors want to thank the participants of the project, with the help of whom this research is led, with a special mention to Michel Courtitarat and François-Régis Meugniot (BH Environnement), Lydie Desperben (Coronis), Vincent Gimeno and Madhusudan Giyyarpuram (Orange Labs). We also would like to thank Marwane Bouznif, Susan Shrenk and Julien Darlay to have offered their technical skills to help the authors. 8
REFERENCES
[1]
Tsoukas, H. (1997): The tyranny of light: The temptations and the paradoxes of the information society, Futures, Vol. 29, No. 9, pp. 827-843.
[2]
Mathúna, C.Ó. (2008): Energy scavenging for long-term deployable wireless sensor networks, Talanta, Vol. 75, No. 3, pp. 613-623.
[3]
Nakamura, J. (2006): Analysis of the Potential Contribution of ICT Services to a Sustainable Society, in: Proceedings of the 2006 IEEE International Symposium on Electronics and the Environment, Scottsdale, AZ USA.
[4]
Akyildiz, I.F. (2002): Wireless sensor networks: a survey, Computer Networks, Vol. 38, No. 4: pp. 393-422.
[5]
Watson, B.J. (2009): Creating a sustainable IT ecosystem: Enabling next-generation urban infrastructures, IEEE International Symposium on Sustainable Systems and Technology, Tempe, AZ, USA.
[6]
Abukhader, S.M. (2008): Eco-efficiency in the era of electronic commerce - should `Eco-Effectiveness' approach be adopted, Journal of Cleaner Production, Vol. 16, No. 7, pp. 801-808.
[7]
The Climate Group - Global e-Sustainability Initiative (2008): SMART 2020: Enabling the low carbon economy in the information age.
[8]
Erkman, S. (2004) : Vers une écologie industrielle, Mayer Charles Leopold Eds.
[9]
Berkhout, F., Hertin, J. (2001): Impacts of information and communication technologies on environmental sustainability -
[11] Loerincik, Y. (2006): Environmental impacts and benefits of information and communication technology infrastructure and services, using process and input-output life cycle assessment, École polytechnique fédérale de Lausanne.
[13] Williams, E. (2002): The 1.7 kilogram microchip: Energy and material use in the production of semiconductor devices, Environmental science & technology, Vol. 36, No. 24, pp. 5504-5510. [14] Köhler, A., Erdmann, L. (2004) Expected Environmental Impacts of Pervasive Computing, Human & Ecological Risk Assessment, Vol. 10, No. 5, pp. 831-852. [15] Beucker, S., Clausen, J., Schischke K. (2008): Wireless Sensor Networks for Agriculture and Automation: Challenges and Chances for Sustainability, Electronics Goes Green 2008+, Fraunhofer IZM, Berlin, Germany. [16] Dubberley, M., Agogino, A.M., Horvath, A. (2004): Life-cycle assessment of an intelligent lighting system using a distributed wireless mote network, in: Conference record of the 2004 IEEE International Symposium on Electronics and the Environment, Scottsdale, AZ, USA. [17] Hoang, T. (2010): Environmental evaluation of machine-tomachine services: the case of kerbside collection of glass waste, CIRP IPS2 Conference, Linköping, Sweden. [18] Bouzin, E. (2008) : Analyse de Cycle de Vie comparative: relève manuelle et télérelève de compteurs d'eau. CityPulse Project, Agro Paris Tech, Orange Labs. [19] Suri, A., Iyengar, S.S., Cho, E. (2006): Ecoinformatics using wireless sensor networks: An overview, Ecological Informatics, Vol. 1, No. 3, pp. 287-293. [20] Rebitzer, G. (2004): Life cycle assessment: Part 1: Framework, goal and scope definition, inventory analysis, and applications, Environment International, Vol. 30, No. 5, pp. 701-720. [21] Lelah, A. (2010): SensCity: a new project opening the way for sustainable services in the city based on a mutualised M2M infrastructure, CIRP IPS2 Conference, Linköping, Sweden.
9
CONTACT
Jérémy Bonvoisin G-SCOP Laboratory
[email protected]
Considering the Social Dimension in Environmental Design 1
Berenice Dreux-Gerphagnon , Nizar Haoues 1
2
Université Lyon 3 Faculté de Philosophie, Ecole Nationale Supérieure des Mines de Saint-Etienne, France ² Eco-design Center and Life Cycle Management, France
Abstract Designers are facing a new challenge: the integration of all the sustainable development dimensions in the product design. We can find tools and methods taking into account the environmental, economic and technical aspects in product design, but the social dimension is not yet considered enough. However, the integration of social aspects is more and more seen, by institutional and industrials actors, as the future of a responsible design. In this paper we propose a new method so as to associate the social dimension and the rational framework it implies to the technical rationality which prevails in tools for EcoDesign and Life Cycle Assessment. Keywords: Product Design; Sustainable Development Dimensions; Product Life Cycle
1
INTRODUCTION
The recognition of the environment in product design is a first step towards a responsible design. However, this first march, though necessary, is not sufficient to make possible a product design which complies with the different standards of sustainable development. Traditionally the organisation considers, in the specifications of a product, technical and economical measures. For about ten years tools serving for an approach of EcoDesign have been developed and improved. Although, how about the social dimension? How can we assess the impacts of an organisation on its different stakeholders, whether it be the employees, the consumers or the supply chain actors? Whereas the ecological dimension can be estimated with quantitative measurement that can be sometimes used regardless of the context, which corresponds to an analytic rationality, the relation of the organisation with its various stakeholders, especially with the different actors of the life cycle of a product, can not be assessed every time with the same measurement. Rather than imposing international social standards, isn’t it necessary to create the conditions of a dialogue between international texts universally oriented and the various social and cultural environments of the life cycle actors of a product? The challenge of a methodology of eco-socio-design is thus neither to avoid reducing the social dimension to a strictly analytic approach nor to deal with this dimension as a horizon which can not be assessed in a scientific way. We could, on the contrary, keep at the same time an analytic and a more comprehensive or subjective approach. And we make the hypothesis that it is by relying on a complex rationality or rationality of the activities [1] which transcends the division of the two approaches that we will manage to connect the different dimensions of sustainability in a methodology for product design. 2
BACKGROUND
In order to meet sustainable development demands as they are formulated in the Brundtland report it is necessary to consider, as
well as environmental and economic aspects, the social dimension. The Brundtland report defines sustainable development as ”a development that meets the needs of the present without compromising the ability of future generations to meet their own needs” [2]. But how can we adapt such a concept to a product design methodology? Indeed as Labuschagne et al. write it: “although the concept is understood intuitively it remains difficult to express in concrete operational terms” [3]. Over the past decades the environmental aspects has been more emphasized, considering the whole life cycle, than the social ones. Following on from the Club of Rome concerns focus has mainly been on the quantitative assessment of the environmental impacts: emissions from the product system, consumption of energy and other resources [4]. A Life Cycle Assessment methodology has been developed in accordance with the ISO 14040 and ISO 14044 standards. This methodology aims to translate the type of consumption and the emission into their potential impacts on the environment [4]. But it is with the tools and methods for EcoDesign that industries can get the opportunities not only to assess the impacts of their products but also to make the right choices in order to innovate by integrating the environmental aspects, as well as the technical and economic ones. Considering these different aspects allows the industries to increase their eco-efficiency. As we can understand it, through the Figure 1, eco-efficiency is located at the intersection of the environmental and the economic dimension. Fair trade products are, on their side, designed with an attention given to the economic and social aspects at the level of the production stage. However, in order to contribute to the improvement of the living conditions at local scale and worldwide, in the present and for the future generations, it is necessary to consider, in product assessment and product design, environmental and social standards simultaneously all along the life cycle of the product. As such the objectives are, on the one hand to develop methodologies that would offer the opportunities to assess the
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_23, © Springer-Verlag Berlin Heidelberg 2011
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three dimensions of sustainability, on the other hand to create tools that would help to take into account these three dimensions in a changing competitive context.
Eco-efficiency Environment
The Table 1 below indicates for each phase identified for conducting a LCA the general description and the social specificities. The conditions required for Social LCA (S-LCA) are mentioned by the Guidelines [6] but we can add, in the goal and scope phase, a questioning about the ethical acceptability of the product. This deals with the question of knowing whether a product should be produced or not. 3.2
Main issues of the integration of the social dimension
This second chapter of the third part deals with the description of the social dimension specificities related to the ISO standards. Lake of methods and tools
Goal and scope
Economic Fair trade
Social
It is necessary, at this level, to identify the various stakeholders of the organization which wants to assess one of its products. These stakeholders can be verticals: the actors of the life cycle and those who are particularly affected or affecting the functioning of these actors. They also can be horizontals. The horizontal stakeholders include for example the employees of the organization, the local communities, the shareholders or the authorities. The identification of the stakeholders will help to determine the boundaries of the study. See the Figure 2 next to the text.
Figure 1: The need to connect environment and social in product assessment and design. 3 3.1
Vertical stakeholders
METHODOLOGY
Actors of the life cycle
A Social Life Cycle Assessment
The thinking about the integration of the social dimension in Life Cycle Assessment (LCA) has begun with the creation of a task group inside the Life Cycle Initiative (LCI), an initiative from the United Nations Environment Program (UNEP) and the Society of Environmental Toxicology and Chemistry (SETAC). Since 2004 ten meetings of this task group have taken place and a feasibility study about the integration of social aspects into LCA have been conducted [5]. This has led to the publication of the Guidelines for Social Life Cycle Assessment of Products [6] in June 2009. By referring to this document we get the opportunity to consider the social dimension through the four phases identified by the ISO 14040 and ISO 14044. This will help us to define the specificities of the assessment of the social aspects.
Horizontal stakeholders
Entrepriser
Actors of the life cycle Figure 2: The different types of stakeholders.
PHASES FOR LCA
GENERAL DESCRIPTION
SOCIAL SPECIFICITIES
Goal and scope
Boundaries of the product system
Ethical Acceptability
Functional unit
Function (not necessarily functional unit) Identification of the stakeholders
Life Cycle Inventory
Inventory indicators Data collection
Identification of social hotspots Global issues
Life Cycle Impact Assessment
Selection of impacts categories Characterization models Normalization
Interpretation
Horizontal stakeholders
Subcategories Characterization: scoring system based on performance reference points
Valuation
The knowledge of the stakeholder is helpful
Identification of the significant issues
Level of engagement with stakeholders
Recommendations Table 1: Integration of the social dimension in LCA.
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If the data collected cannot be expressed in terms of quantity there is no way to define a functional unit. However we need to determine the function of the product, its social role. This rule can be socially beneficial or harmful, or it can be both of them, depending on the use. Let us take the example of a technical object: the television. On the one hand it can be a means of being informed about what happens in the world, on the other hand one can be exposed to isolation and manipulation with this object.
for and of the treatment of the people resettled. Privileging in most of cases qualitative indicators, combining qualitative and quantitative indicators does not allow realizing a characterization model in S-LCA similar to the type of characterization model which prevails in LCA. S-LCA characterization models are not an objective multiplication between data collected and a characterization factor. They above all consist in scoring system based on performance reference points.
Life Cycle Inventory
Characterization models need to be established depending on the sectors and contexts. The normalization is not possible yet because no software or tools are available to organize the information according to data basis and characterization models which would already exist.
Identifying the stakeholders leads to take into account the various contexts where they are located. Indeed, when considering the social dimension, information about economy, culture, regulations, level of technological development can be required. One could say that Social Life Cycle Assessment has an interest to the environment, not from a physical point of view but as long as human being has a cultural, technological and economical relationship to this environment. This relationship is called “ecoumene” by Augustin Berque [7]. Considering the social dimension in environmental assessment leads to a more comprehensive understanding of the environment. But to obtain a social assessment in which the uncertainty will be reduced as far as possible one needs to consider, as well as the local contexts of the stakeholders, the global issues or the main issues of the part of the world where some of the actors of the life cycle are located. To illustrate this point we can take the example of some European merchants who call on producers of handmade carpets in Afghanistan. Most of these producers are employing children. A NGO which is settled in this country has negotiated with the producers so that they make work the children only in the morning. On the afternoon some lessons are organized for them. Considering the global context leads to questions like this: is it socially and environmentally desirable that some carpets produced in Afghanistan be sailed in Europe? Does the wool come from Afghanistan? Is it a local know-how? Does the handmade carpet contributes to the economical and social development of the country or does it represent a marginal activity? If it doesn’t, a socially beneficial choice could be to give training for the children in the sector which represents the future of the country. Life Cycle Impact Assessment Depending on the sector studied and on the contexts where the life cycle actors are located the stakes will be different. Identifying the main stakeholder, verticals and horizontals, of the organization and the social hotspots is necessary to determine the stakes. The social hotspots are considered as problems, risks or opportunities in relation to social themes of interest such as human rights, work conditions, poverty and so on [6]. These social themes are drawn on in the subcategories. Those are classified according to stakeholders and impacts categories. The Life Cycle Initiative proposes a methodological sheet for each one of the thirty-one subcategories. A methodological sheet contains a definition of the subcategory and it presents a list of generics and specifics indicators. The generics indicators are relying on data that are indicated on national or international organisms websites whereas the specific indicators require interviews carried out among stakeholders. We need though to identify the subcategories that will be relevant to sector studied and to chose some indicators related to the subcategories. Most of the indicators are qualitative or semi-quantitative. When assessing the social impacts one could not try to replace a qualitative indicator by a quantitative or more objective one. This would introduce more uncertainty in the assessment. For example, if we take the subcategory “Delocalization and Migration” we cannot satisfy ourselves with a quantitative specific indicator as the number of people who resettle that can be attributed to the organization. We mostly need to know the description of the causes
The valuation step is regarded as a step which introduces a great amount of uncertainty in the study because the different points of view of the stakeholders are taken into account. These points of view are favoring some parts of the study in a subjective way. But, precisely, the fact of identifying the stakeholders and their points of view from the beginning of the assessment isn’t it a good means to reduce uncertainty? Interpretation Considering the level of engagement with stakeholders allows entering in an iterative process. The responsibility of the organization towards its stakeholders is better identified at the end of the study. 4
PROPOSITION OF A METHOD AND A TOOL
We have indicated, in the precedent part, the specificities of the assessment of the social dimension. This dimension introduces more subjectivity in the assessment but it helps to understand the complexity of reality. Integrating the social aspects to the LCA does not lead to confusion or to a less scientific approach. On the opposite it allows to associate, to an analytic rationality, a more comprehensive one. This is what Paul Ricœur calls the dialectic of the interpretation [8]; it offers a better understanding of the reality. The local and global contexts are taken into account in order to identify the stakes of the sector. The point of view of the stakeholders, once identified, are some more manageable parameters. Hence one has the opportunity to get information on which relying to make a right choice. It is precisely about not only the assessment but also the product design considering the environmental and social dimensions in the whole life cycle that it can be interesting to develop a method et a tool: this is what we can call Eco Socio Design. 4.1
The existing tools
In order to develop a method for Eco Socio Design we need to lean, as it has been explained before, upon the Guidelines for Social Life Cycle Assessment of Products. But the different elements of assessment proposed in the Guidelines are not sufficient to integrate the social dimension to the Eco Design tools and methods. Furthermore, as we have already mentioned it, they do not discuss of the ethical acceptance of a product. But they propose a good basis for the creation of social indicators through the subcategories list. Each subcategory can be assessed thanks to generic and specific indicators presented by the Life Cycle Initiative. The list of indicators hence obtained can be enhanced by the fields of actions of the ISO 26000 standard on social responsibility [9]. This standard has a site specific approach but it is interesting to cross its fields of actions with the indicators of the Life Cycle Initiative and so to organize them according to a life cycle perspective. We will finally rely on the SD 21000 guide for sustainable development and social responsibility [10] which is required so as to identify and to prioritize the stakes of the organization and, as far as
Life Cycle Design - Methods and Tools
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Eco Socio Design is concerned, of the whole life cycle. The consideration of the stakes hence identified and prioritized can be assessed in a qualitative way and recommendations can be made in order to improve this consideration, given the goals of sustainable development. 4.2
The approach
We can imitate the different step of the method we want to propose.
Stakeholders identification Environmental stakes
level, the extraction of one kilogram of aluminum can be estimated in CO2 equivalent according to an energetic mix regardless of the context, which is peculiar to technical rationality, this operation requires various different indicators so as its social impact could be correctly assessed. The practical rationality implies that one knows the economic, cultural and technical context of its interlocutor. Understanding the realities which are under the words allows them to get a language shared in an intersubjective way which differs from the abstract language of the technical rationality [11]. Hence the rational framework of the environmental approach finds itself enhanced by the rational framework to the social approach. Realizing a checklist
Dialogue with stakeholders
Social stakes
Representing the impacts and the positive externalities
Reduction of the impacts Checklist Ex: toxicity, health at work…
Production of externalities
The main subcategories and their indicators once chosen, the next step consists in establishing a checklist which will contain two parts. One of these parts will bear upon the environmental aspects and the other upon the social ones. This checklist could gather the social indicators and some environmental indicators so as to present the trouble spots and the potential segment of growth.
positive
Figure 3: The Eco Socio Design steps. Crossing the life cycle and stakeholder approach In a first time, we need to describe the various different phases of the life cycle of a product. For each one of these phases we could identify the actors and their main stakeholders. We hence obtain a figure representing the vertical stakeholders of the life cycle of the product and the horizontal stakeholders who are the stakeholders of each actors of the life cycle. Identifying the main stakes In order to determine these stakes one needs to get information about the local contexts of the actors of the life cycle and about the main issues of the part of the world where they are located. Knowing the sector and having identified the stakeholders help to determine the social hotspots. This knowledge will give a first basis to prioritize some subcategories. Each subcategory can be assessed with at least seven indicators. By having crossed the fields of action of the ISO 26000 standards and the indicators proposed by the Life Cycle Initiative we have obtained a list of three hundred and forty-nine indicators. It is then essential to prioritize some subcategories. This will allow us to concentrate on a reduced number of indicators which will be more adapted to the challenges of the sector. Dialoguing with the stakeholders So as to take into account the social dimension and to reduce the uncertainty of the results it seems essential to establish the conditions of a dialogue with the stakeholders through a collaborative platform. This will allows the actors of the life cycle of the product and, as far as possible, their stakeholders to discuss about the choice of the subcategories and about the way they would answer to the questions asked by the qualitative indicators. The dialogue aspect is fundamental in order to understand the social dimension. This dimension belongs to what Habermas could call the practical rationality [11]. Whereas, at the environmental
The results of the checklist can be presented in different types of figures but what is really important is that the figure could show not only the impact or reduced impacts but also the positive externalities. There is positive externality when the activity of an organization has positive effects on groups or individuals who are not implied by this activity, without these groups or individuals need to pay for the benefices received. For example if a firm is settling in an economically disaster town that can contributes to the development and to the reinforcement of the town’s economy, that can helps to the immigration of new workers and to the improvement of the local employment. As the town will have a less strong emigration rate perhaps the town council will decide to renovate schools and to enhance the transport infrastructures. These two actions can be seen as positive externalities. EcoDesign is supposed to offer a help to improve the use value of the product in terms of length, diversity of functions and efficacity while giving recommendations to reduce the impacts on the environment. U Use value of a product
Improvement of the use value
Positive externalities
Impacts on the stakeholders I
Reduction of the impact on the stakeholders
Figure 4: The production of positive externalities. Eco Socio Design, as the Figure 4 above shows it, is also supposed to allow an improvement of the use value of the product not only while reducing the impacts on the stakeholders, but also while producing positive externalities. Not having impact is the goal of the environmental approach but environmental positive externalities can exist as well, like the CO2 uptake in the growth of plants [6].
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Life Cycle Design - Methods and Tools CASE STUDY
After having proposed a method we would like to apply it to a product: a snack pocket for children which can be used to contain the packaging wastes. We have identified, in a first time, the different actors of the life cycle of the snack pocket. See the Figure 5 below. From the cultivation to the distribution we need to consider the employees and the working conditions, the local communities, the authorities and the economic partners. As many chosen indicators will be qualitative, especially at the level of the use, it seems difficult to find a functional unit. We can only define one or more functions for the product. Indeed the children will appropriate the pocket. Some of them will put inside the snack prepared by their parents, other will tide their industrial snack in it and use the little bin pocket to put the packaging wastes. The function of this product is to contain the snack and to collect the packaging wastes of the snack. The social role, especially if the pocket is distributed to children by the local authority through the intermediary of schools, is to increase children and parents awareness of some issues. These issues are the fact of throwing wastes in a bin, even a portable bin in this case, the waste sorting, the fact of preparing a snack to the child instead of giving him an industrial snack and the possibility of keeping the object which contains the snack and to reuse it.
Raw materials Cultivation and spinning Transport
Cotton Certified Ecocert
Hemp
+
Certified Bio IMO control China (Mongolie)
Benin
scop Boat
Boat
Weaving
public awareness of the waste sorting or even the type of substances used for the printing of the pockets. The raised points can be related to social or environmental concerns or to both of them. Concerning the social aspect we can then prioritize subcategories such as the local employment, the contribution to economic development or even the end of life responsibility related to the consumer or user stakeholders. This third subcategory is at the junction of social and environmental concerns. We will then dispose of a few questions concerning the social and the environmental aspects, questions which will include quantitative, qualitative, generic and specific indicators. For example: is the part of the product made in France contributes to the local economic development? We need generic indicators like the economical situation of the sector or the interest of the sector for the (local) economy and some specific indicators such as the contribution of the product to the economical development of the French organizations considered and the support of these organizations to the emergence of local enterprisers. We can also wonder if one of the steps of the life cycle of the product executed in France such as silkscreen uses toxic substances for the environment or organic substances and less energy-greedy processes. From the chosen questions the organization will be able to elaborate a strategy in order to communicate on its actions or to improve these mains fields of action which are raised by the analysis of the life cycle steps, by the identification of the stakeholders and by a phase of dialogue. The following figure (Figure 6) helps to understand that the product’s role is to contribute to the reduction of the impacts of the waste packaging. The impacts can be reduced at the level of the other steps of the life cycle of the product with regard, notably, to the substances used. The value of use of this packaging made of cotton and hemp is improved because this object can have at least two functions: carrying the snack and collecting the snack wastes. It contributes as well to the production of positive externalities at the level of the textile sector in the Loire department.
France (Roanne) scop
Fashioning
France (Roanne) Asset stripping
Silkscreen
U Use value of a product
France (St Etienne)
Improvement of the use value
Artist from a scop Sale and monitoring Distributors
France (St Etienne)
Artist Designer Shops
Local authorities
Impacts I
Users End of life
Improvement of the economic situation of the textile sector in Loire department.
Children Rag
Figure 5: The life cycle and the different stakeholders of the pocket. So as to improve transparency between the actors of the life cycle of the product and the stakeholders it is important to organize a task group on a collaborative platform. This will help to choose the most appropriate indicators. The main stakes brought to light by such a task group can eventually be the questions of the working conditions in China and in Benin, the economic development of a disaster sector in a region of France, the increase of the
Reduction of the waste packaging impacts
Figure 6: Example of the representation of a positive externality in the life cycle of the pocket. An Eco Socio Design method allows the organization to invent a strategy which aims to improve the social role and the functions of the product by reducing the environmental and social impacts but also by creating positive externalities all along the life cycle of the product. With such a method the organization gets the portray of the steps, actors and stakeholders of the life cycle of its product and it can chose to communicate on one of these points or to improve some of them.
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CONCLUSION
If innovation can be defined as “establishing new combinations” [12] we can conclude, with Seliger, G. [13] that the fact of associating different fields can enhance innovation potentials in cross disciplinary cooperation. Associating environmental, social and economic concerns opens more potentialities for innovation. We could then design a last figure (Figure 7), not only for Eco Socio Design but also for the Eco Socio Innovation.
Perspectives, Technology.
-Technological performance
-Reduction of
Innovation
the emissions
- Marketing
the social role
Figure 7: The Eco Socio Innovation. Indeed, the integration of the social dimension brings a strong potential of creativity and it helps to establish a link between engineer sciences and social sciences. If we really want to take into account the social dimension and to integrate it into the EcoDesign tools we need to localize ones approaches in a the framework of a complex rationality. According to Edgar Morin complex rationality implies a dialogic principle [14]. This principle designates the interaction between different logics and concerns as the environmental, the social, the technological and the economic ones. Furthermore the knowledge of the contexts, of the sectors and of the main issues requires a more practical rationality and a more comprehensive perspective which can help to identify the potential segment of change and improvement. 7
ACKNOWLEDGMENTS
We extend our sincere thanks to the members of the Eco-Design Center, of the CIRIDD, of the Université Lyon 3 and of the Ecole des Mines de Saint-Etienne who are helping us in the development of our reflections and methods. 8
REFERENCES
[1]
Pierron, J.-P., (2009): Penser le développement durable, ellipses, Paris.
[2]
Brundtland, G., (1987): Our common future: the world commission on environment and development, Oxford University Press, Oxford.
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Labuschagne, C.; Brent, A. C. (2008): An industry perspective of the completeness and relevance of a social assessment framework for project and technology management in the manufacturing sector, in: Journal of the Cleaner Production 16, pp. 253-262.
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Hauschild, M.; Jeswiet, J.; Alting, L. (2005): From Life Cycle Assessment to Sustainable Production: Status and
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PNUE; SETAC; LCI (2009): Guidelines for Social Life Cycle Assessment of Products.
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Berque, A. (1987): Ecoumène, Introduction à l’étude des milieux humains, Belin.
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Ricœur, P. (1986) : Du texte à l’action, d’herméneutique, II, Editions du Seuil, Paris.
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[9]
ISO (2010) : ISO 26000, Guidance on social responsibility.
[10]
AFNOR (2003) : Guide SD 21000, Développement durable – Responsabilité sociétale des entreprises – Guide pour la prise en compte des enjeux du développement durable dans la stratégie et le management de l’entreprise, FDX30-021.
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Habermas, J. (1968), La technique et la science comme « idéologie », Editions Gallimard, 1973, translated from German to French by Jean-René Ladmiral.
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Schumpeter, J., A. (1912), La théorie de l’évolution économique, translated from German to French by J. J. Ansttet.
[13]
Seliger, G. (2001), Product Innovation – Industrial Approach, in: Annals of the CIRP, Manufacturing Technology.
[14]
Morin, E. (2005), Introduction à la pensée complexe, Editions du Seuil.
approach
-Questioning on
the
[6]
the energy
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Rainer, G.; Benoît, C.; Dreyer, L. C.; Flysiö A.; Manhart A.; Mazjin B.; Méthot A.-L.; Weidema B. (2006): Feasability Study: Integration of social aspects into LCA, Öko-Institut, Freiburg.
-Better use of Social:
Annals
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Environmental:
Economic:
in:
Proposal of an Ecodesign Maturity Model: supporting Companies to improve Environmental Sustainability 1
Daniela C. A. Pigosso , Henrique Rozenfeld
1
Department of Production Engineering, University of São Paulo, São Carlos, Brazil
Abstract Ecodesign is a strategic design activity for conceiving and developing sustainable solutions. The implementation of ecodesign in product lifecycle management (PLM) is still a challenge today because companies find it very difficult to determine which practices are the most suitable for their reality, drivers and current maturity level. This paper presents an Ecodesign Maturity Model that aims to support the application of ecodesign practices in PLM, improving the environmental performance of products. The model comprises three elements: Ecodesign Maturity Levels, Ecodesign Practices, and Application Method. Keywords: Ecodesign Maturity Model; Ecodesign Practices; Product Development Process
1
INTRODUCTION
Ecodesign can be defined as a proactive approach to environmental management which involves the inclusion of environmental issues in product life cycle management (PLM) in order to minimize environmental impacts during the product’s life cycle (from material extraction to end-of-life), without compromising other essential criteria such as performance, functionality, esthetics, quality and cost [1] [2]. The application of Ecodesign practices is essential to companies that recognize the importance of environmental responsibility for long term success, since it enhances their reputation, reduces their cost and risks, leads to product innovation and attracts new consumers [3]. However, companies are still unsure about the strategies, guidelines and methods/tools they should adopt and about how to manage the process of integration in the product development process and of continuous improvement. In this sense, it can be argued that the application of ecodesign has not reached companies worldwide over the last decade mainly due to the following factors:
companies effectively implement ecodesign in their product development processes. Such a model should be based on the systematization of existing ecodesign practices, linking them to the company’s development process and strategy and providing guidelines on how to implement them according to the company’s maturity level. In this context, the main goal of this paper is to present the development of an Ecodesign Maturity Model, which aims to guide companies in the effective implementation of ecodesign practices in their product life cycle management in line with their strategic objectives and drivers. The next section presents the methodology used in the development of the Ecodesign Maturity Model. The model is detailed in Section 3, which describes the goal and the elements of the Ecodesign Maturity Model, i.e., Ecodesign Practices, Ecodesign Maturity Levels and Application Method. The Summary and Outlook, Acknowledgments and References are presented in sections 4, 5 and 6, respectively.
(1) Existing ecodesign practices are not systematized;
2
(2) There is intense development of new ecodesign methods and tools in detriment to the study and improvement of existing ones [4];
The Ecodesign Maturity Model was developed based on the following activities:
(3) There is a lack of integration between ecodesign and the broad context of the product development process and product life cycle management [5][6][7][8];
The ecodesign practices were obtained and classified based on a systematic review of the literature, enabling the researcher to map the existing knowledge and initiatives developed in a specific area of research. In addition to the analysis of previous discovery, techniques, ideas and ways to explore topics, the systematic review also allows for an evaluation of the relevance of the information to the issue, its synthesis and summarization [9] [10].
(4) Ecodesign is poorly integrated into corporate strategy and management [4]; (5) No roadmap for continuous improvement is available for companies that want to implement ecodesign; (6) Companies usually fail to adopt ecodesign because the selection of practices is not in line with their current maturity level. Consequently, there is a need to propose models that help
2.1
METHODOLOGY
Systematization of Ecodesign Practices
The ecodesign practices identified in the systematic literature review were divided into three groups: Ecodesign Management Practices, Ecodesign Operational Practices and Ecodesign Methods and Tools. The description of these practices is presented in section 3.2: Ecodesign Practices.
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_24, © Springer-Verlag Berlin Heidelberg 2011
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Establishment of Ecodesign Maturity Levels
Structured collection of practices classified according to the Maturity Levels
To ascertain maturity levels in ecodesign, a review was made to understand the evolution of companies in adopting Ecodesign and the dimensions that influence this evolution. The most appropriate practices for each maturity level were analyzed and the correlation and dependencies between practices were also determined. The maturity levels defined in the Ecodesign Maturity Model are presented in section 3.3. 2.3
Ecodesign Practices
Development of the Application Method
An Application Method was developed to determine how a company can apply the Ecodesign Maturity Model, based on the literature about business process management (BPM), which provides guidelines for process improvement. This Application Method is presented in section 3.1. 3
Sequence for developing maturity in Ecodesign
Application Method
ECODESIGN MATURITY MODEL
The Ecodesign Maturity Model is a conceptual framework composed of a structured collection of ecodesign practices that can be used to develop and improve an organization's product life cycle management (PLM) in terms of environmental issues. The model provides:
a benchmark in ecodesign best practices;
an assessment of strengths and weaknesses in the application of ecodesign practices;
a common language and a shared vision across the organization;
a guide for integrating ecodesign practices into PLM; and
a roadmap for PLM improvement towards environmental sustainability.
The goal of the Ecodesign Maturity Model (EcoM2) is to guide companies in the effective implementation of ecodesign practices in product life cycle management (PLM) and the related process in accordance with their strategic objectives and drivers. The Ecodesign Maturity Model is composed of three main elements (Figure 1):
Application method: explains how companies can use the model for process improvement. It contains a scheme for continuous improvement (such as PDCA – plan, cycle) based on the Business Process Management (BPM) approach.
Ecodesign practices: these are the best practices currently adopted by companies and developed by universities for the integration of environmental concerns into the product development process. These practices comprise management practices (involving the management of ecodesign), operational practices (related to technical design issues) and ecodesign methods and tools which support the application of management and operational practices.
Maturity levels: indicate the company’s level of evolution in including environmental issues in their business processes.
The elements of the Ecodesign Maturity Model are detailed in sections 3.1, 3.2 and 3.3. 3.1
Ecodesign Application Method
The Ecodesign Maturity Model Application Method (Figure 2) was developed based on the BPM (Business Process Management) approach for process improvement. Its purpose is to guide the application of the Ecodesign Maturity Model. This method comprises six steps.
Maturity Levels
How to use the model, evaluate the maturity, select and apply the improvement projects
Figure 1: Elements of the Ecodesign Maturity Model.
Diagnosis of the current Maturity Level: the first step is to assess the current situation of an organization in terms of its attention to environmental issues. This assessment involves a diagnosis of the company’s current maturity level based on the Ecodesign Management Practices it applies and at which capability level (see section 3.3).
Proposition of Ecodesign Management Practices to be implemented: once the current Maturity Level is determined, the most suitable practices for the company to adopt are proposed according to its maturity level (first filter of the practices), as well as the strategic drivers for the adoption of ecodesign (which include environmental laws and regulations, cost reductions, consumer environmental awareness, new business opportunities, value creation and opportunities for innovation).
Selection of practices to be implemented: the company then selects Ecodesign Management Practices following the weighting method for ecodesign practices developed by Charter and Tischner [13], which classifies these practices into the following groups:
Rejected 0: the option is rejected; moreover, it holds no future interest for the company.
Of interest 1: the option is studied in greater depth but its implementation is still rejected; 2: the option has not yet been studied, but the company’s future interest in it is ensured. 3: the option is under study and its implementation is still uncertain.
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Continuous Improvement
Performance Measurement
People Change Management
Diagnosis of the current maturity level
Assessment of the results
Proposition of practices to be implemented
Selection of practices to be implemented
Implementation of improvement projects
Planning of improvement projects: scope, resources, schedule, responsibilities, etc.
Assessment of the application of ecodesign practices
Definition of improvement projects
Maturity Level Drivers
Management, operational and methods/tools
Portfolio management of improvement projects
Figure 2: Ecodesign Maturity Model Application Method.
Prioritized
3.2
4: the option has been implemented; its realization is expected within three years;
Ecodesign practice is the generic name for ecodesign activities, strategies, methods and tools, etc. aimed at integrating environmental issues into the product development process. The Ecodesign Maturity Model divides ecodesign practices into the following three groups:
5: the option has been implemented; its realization is expected within one year; 6: the option has been implemented or will be implemented very soon (the option is effectuated).
Ecodesign Management Practices: related to the management activities and tasks of the product development process that address environmental issues. These practices serve to evaluate the maturity levels of companies in applying ecodesign. The 75 Ecodesign Management Practices identified are classified according to the maturity levels (see section 3.3) and to the phases of the reference model for product development [11] (strategic product planning, project planning, informational design, conceptual design, detailed design, production preparation, product launch, product monitoring and product takeback). The application of ecodesign management practices can be supported by Operational Practices and/or by Ecodesign Methods and Tools.
Ecodesign Operational Practices: This is a compendium of ecodesign guidelines for product design containing more than 480 identified and systematized practices. This compilation is divided into three levels of detail: ecodesign strategies (minimize energy consumption, minimize material consumption, extend material lifetime, optimize product lifetime, select low impact resources and processes, and facilitate disassembly – adapted from [12]), ecodesign guidelines (which provide detailed ecodesign strategies), and design options (to support the creation of solutions to meet the guidelines). Ecodesign Operational Practices involve the technical issues of product design and are linked to Ecodesign Management Practices. The guidelines can also be applied by using the Ecodesign Methods and Tools.
Ecodesign Methods and Tools: a systematic means to apply ecodesign that can support the application of both Ecodesign Management and Operational Practices. Based on a systematic review of the literature, more than 100 ecodesign methods and tools were identified and classified into six criteria to support the selection of the most suitable ones for companies according to their needs and current maturity level: (1) Nature of the main goal of the method/tool (Prescriptive/ Comparative/Analytical); (2) Type of the tool used (Checklist/ Guideline/Matrix/Software);
Not considered 7: the option has not received special attention because it is considered a bottom-line matter in product development and is therefore of ongoing interest to the company; 8: the option has not been studied because it is considered irrelevant.
The information about the classification of the company’s practices will be applied in the subsequent process improvement cycles. For example, if a practice is rejected in a given cycle, it will not be proposed for application in the subsequent cycles.
Definition of improvement projects: after defining the prioritized Ecodesign Management Practices to be applied by the company, an analysis is made of the correlation between the selected Ecodesign Management Practices within the Ecodesign Operational Practices and the Ecodesign Methods and Tools (see section 3.2). The company’s improvement projects should include a set of Ecodesign Management Practices, Operational Practices, and Methods and Tools to support the application of Ecodesign Management Practices. A portfolio management of these improvement projects should be set up, as well as project planning to define schedules, responsibilities, resources, etc.
Implementation of improvement projects: during the implementation of the ecodesign practices selected for improvement projects, special care should focus on People Change Management, since people are crucial to the process.
Assessment of the results: performance indicators for the practices must be determined in order to evaluate the results of the improvement projects.
The improvement cycle can be repeated as many times as necessary for the company to maintain its continuous improvement towards higher maturity levels in Ecodesign.
Ecodesign Practices
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(3) Nature of input and output data (Qualitative/Quantitative); (4) Considered Product Life Cycle Phases (Raw material extraction, manufacturing, use, end-of-life), (5) Evolution of the application (Theoretical/Experimental/ Consolidated); (6) Evaluation of Environmental Impacts (Yes/No). The classification criteria used in the Ecodesign Maturity Model are presented in detail in this paper [13]. The Ecodesign methods and tools can support the application of ecodesign management and operational practices. The practices are organized on a spreadsheet containing all the information displayed together in an interrelated way. For a broader view of the interrelationships among Ecodesign Management Practices, Ecodesign Operational Practices and Ecodesign Methods and Tools, an example is presented in Figure 3. One of the Ecodesign Management Practices is “20001: Assess the environmental impact of products.” Numerous ecodesign methods and tools can be used to support the application of this management practice, e.g., Life Cycle Assessment (LCA) [14][15][16][17], MET (Material, Energy and Toxicity) matrix [18][19], MECO (Material, Energy, Chemicals and Others) matrix [20][21], etc. The most suitable ecodesign method/tool can be determined based on a selection according to the company’s needs, current maturity level and the method classification criteria [11]. The 20001 practice is also a prerequisite for the application of the “80001 practice: Analyze and select the most suitable ecodesign strategies according to the environmental goals and phases/environmental aspects of the product life cycle that offer the greatest opportunities for environmental improvement,” since these opportunities underpin the identification of the environmental hot spots of the product under analysis. Ecodesign operational practices must be analyzed in order to select the most suitable ecodesign strategies. For example, if the hot spot occurs at the product’s end-of-life, thus involving its disposal, the “Facilitating Disassembly” and “Product Lifetime Optimization” strategies are probably the most suitable. Based on “Product Lifetime Optimization” and an evaluation of its guidelines, it can be concluded that “Facilitating Remanufacturing” is of higher importance. The application of this guideline is also supported by a set of ecodesign methods and tools such as EDIT (Environmental Industrial Template) [22], ELDA (End-of-Life Advisor) [23] and LCP (Life Cycle Planning) [24]. 3.3
Ecodesign Maturity Levels
The Ecodesign Maturity Model is composed of five maturity levels that represent the evolution from an absence of awareness of and
consideration for environmental issues to the integration of environmental concern into the company’s strategic planning and decision-making process. The Ecodesign Maturity Model indicates the steps a company should take to improve the environmental performance of its processes and products. The maturity levels were developed by combining two dimensions: level of knowledge about ecodesign (adapted from [25] [26]) and level of innovation in ecodesign (adapted from [12][26][27][28]). The ecodesign maturity levels defined in the Ecodesign Maturity Model are:
Level 1: The company has no experience in ecodesign and does not yet apply ecodesign practices to improve the environmental performance of its products. The environmental issues of its products and the benefits of adopting ecodesign are so far not exploited. At this level, the company must understand the concept of ecodesign, define its internal and external drivers for the adoption of ecodesign, carry out a benchmark test to understand what competitors are doing in this area, and make a compilation of all the legal issues and standards related to the environmental performance of its products.
Level 2: The company has taken the first steps in the application of Ecodesign and is familiar with some of its practices and potential benefits. Pilot and punctual projects are implemented focusing on the incremental improvement of the environmental performance of existing products, with emphasis on specific phases of the products’ life cycle. The company uses punctual and non-consolidated approaches to ecodesign practices that emphasize the application of the practices involved in product design (operational practices). At this level, company endeavors to generate awareness of and motivation for ecodesign and begins a formal Ecodesign Program.
Level 3: At this level, the company recognizes the importance and benefits of Ecodesign based on the results if its application. Projects are implemented to improve the environmental performance of its products (e.g., materials and energy intensity), considering all the phases of the life cycle from the extraction of raw material to the product’s end-of-life. Ecodesign is integrated technically into the Detailed Design phase, ecodesign practices begin to be inserted into the processes, and the first steps are taken to structure the environmental approach and common patterns. At this level, ecodesign projects begin to focus on redesigning existing products
Figure 3: Inter-relationship among the ecodesign practices.
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Life Cycle Design - Methods and Tools Level 4: Ecodesign practices are incorporated systematically into the product development process starting from the initial phases (e.g., idea generation and portfolio management). Functionality analysis is now applied to conduct ecodesign. New concepts (products, services or PSS) are developed to satisfy consumer needs with better environmental performance. Level 5: Environmental issues are fully incorporated into the company's corporate, business and product strategies. Environmental issues are considered jointly with technical and economic issues to underpin the decision-making process. The company aims at system innovation through the development of new products and services that require changes in its productrelated infrastructure.
In addition to the Maturity Levels, the Ecodesign Maturity Model also considers the Capability Level in applying a specific management practice. The capability levels (adapted from [29]) are:
1: do not apply;
2: apply ad hoc;
3: apply in a formal way;
4: apply in a controlled way;
5: ongoing improvement of the application.
4001540016 40014 5 40003 110005 110004 110003 110002
SUMMARY AND OUTLOOK
This paper presented the general concept of the Ecodesign Maturity Model and its constituent elements: Application Method, Ecodesign Practices and Ecodesign Maturity Levels. The Ecodesign Maturity Model is innovative and aims to guide companies in the continuous improvement of their PLM process with the incorporation of the environmental dimension aimed at achieving sustainability in their business. The model considers the individual needs, drivers, characteristics, maturity levels and strategies of companies to support their decision-making process. In addition to its contribution to companies, the Ecodesign Maturity Model should also contribute to the organization of knowledge in the areas of ecodesign and PLM research, structuring existing practices and establishing interrelationships among them. The theoretical concept of the Ecodesign Maturity Model was validated by 15 experts on Ecodesign from Europe, the United States and Brazil between March and August 2010. The concept is currently being applied in companies with different levels of maturity. The results of its validation and application are slated for presentation in future papers. 5
Figure 4 presents a graphical representation of maturity levels considering the Ecodesign Management Practices that are applied (represented by codes in the figure) and the Capability Level of the application of each practice. For example, to be classified as Maturity Level 1, the company must apply the Ecodesign Management Practices of Level 1 with Capability Level 3 (applied formally). A company is classified as Maturity Level 2 if it applies the Ecodesign Management Practices of Level 1 with Capability Level 4 and the practices of Level 2 with Capability Level 3. The subsequent maturity levels follow the same logic. A company is classified at Maturity Level 5 if all the Ecodesign Management Practices are applied with Capability Level 5. Level 1 Level 2 Level 3 Level 4 Level 5
4
ACKNOWLEDGMENTS
We extend our sincere thanks to the following experts on Ecodesign who agreed to participate in the validation of the Ecodesign Maturity Model: Ab Stevels, Aguinaldo Silva, Anna Hedlund-Åström, Casper Boks, Conrad Luttropp, Dorothy Maxwell, Erik Sundin, Fabrice Mathieux, Han Brezet, Mark Goedkoop, Martin Charter, Mattias Finkbeiner, Mattias Lindahl, Tom Swarr, Tracy Bhamra and Vicky Lofthouse. We also gratefully acknowledge FAPESP (Fundação de Amparo à Pesquisa do Estado de São Paulo) and CAPES (Coordenação de Aperfeiçoamento do Pessoal de Nível Superior) for their financial support of this work.
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Figure 4: Graphical Representation of Ecodesign Maturity Levels.
Life Cycle Design - Methods and Tools 6
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Environmental and Operational Analysis of Ecodesign Methods Based on QFD and FMEA 1
Fabio Neves Puglieri , Aldo Roberto Ometto 1
1
Universidade de São Paulo, Departamento de Engenharia de Produção, São Carlos, Brazil
Abstract In 1990s began a scattering of methods and tools aimed at the environmental suitability of products whose application is performed at certain stages of development. However, many of these published methods do not incorporate important environmental issues, often resulting in inefficient product development from the standpoint of ecodesign. This study aims to propose environmental and operational criteria for ecodesign methods, especially that based on QFD and FMEA (which are numerous), and analyze these methods. In order to achieve this goal, it was carried out a questionnaire application with experts that allowed us to identify characteristics that these methods must meet from the environmental and operational point of view. Keywords: Ecodesign; Criteria; QFD
1
INTRODUCTION
With the increasing of population and demand for manufactured goods, designers began to consider as an alternative to creating competitive advantage for their products not only reduce costs and improve quality, but also environmental issues, which became known as design for environment (DfE) or eco-design. Eco-design has received much attention from companies due to increased demand for environmental goods [1]. According to [2] the reasons that companies are taking interest in development of socalled "green" products are: cost reduction by optimizing the use of resources and processes, customer loyalty, improve image organization and reduction of violations through the reduction of environmental impacts. Some authors [3] present as an early contribution to the emergence of eco-design the text "Design for the Real World: Human Ecology and Social Change" by Papanek in 1971, where the author criticizes the focus given only for the aesthetic and stylistic development product rather than considering other issues such as environmental and social aspects, functionality and repair ability. Only since the 1990s that eco-design became more studied [4] mainly through the Life Cycle Assessment (LCA). It was possible to observe between the years 1990’s and 2000’s the publication of a large amount of eco-design methods, some based on software, checklists, guidelines and even adaptations of other methodologies already applied in product development, such as FMEA (Failure Mode and Effect Analysis) and QFD (Quality Function Deployment). However, faced with an excess of a hundred methods / tools for eco-design published in this period [5], some questions arise: These methods really allow the minimization of environmental impacts and natural resource consumption in various stages of the product’s life cycle? Can eco-design methods be easily adopted and applied by business? These issues have become a motivator for writing this work, whose main objective is to determine a set of criteria for analysis of eco-design methods. These criteria are divided into two groups: environmental criteria, i.e., based on those
requirements that the methods should consider to reduce environmental impacts at all stages of the life-cycle, and criteria related to the applicability of these methods, here defined as operational in order to identify which characteristics must meet i to facilitate adoption and implementation. The methodology employed is a qualitative approach with exploration goals, made through a literature search to identify the main eco-design requirements and eco-design methods based on QFD and FMEA, methods which were chosen to be analyzed in this paper. 2
ECODESIGN AND CRITERIA
According to [6] eco-design can be defined as "the development of products through the application of environmental criteria aimed at reducing environmental impacts throughout all stages of the life cycle of the product." Other concept for eco-design is "designing products considering environmental issues, minimizing its environmental impacts at every opportunity possible" [3]. Other authors [4] state that the eco-design refers to "actions taken in developing products aimed at minimizing the environmental impacts of products throughout their whole life cycle, without compromising other essential characteristics such as performance and cost." Based on these statements, we can say briefly that eco-design is an approach to product development concerned with the reduction of environmental impacts at all stages of the life cycle. However, often using eco-design does not guarantee the development of better products through environmental point of view [7]. Despite of the apparent benefits brought by eco-design methods, we cannot say that they are applied and do not even have any effect on product development [8]. These statements suggest that many eco-design methods fail to consider important criteria, whether related to the environmental suitability of the products (environmental standards) or even to respect the applicability of the method (operational criteria). Based on the definition of criteria, we can think of it as being requirements for deciding if something meets a specific function /
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_25, © Springer-Verlag Berlin Heidelberg 2011
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Life Cycle Design - Methods and Tools specification. Thus it is understood that eco-design criteria should consider a set of requirements that methods and tools for environmental product development needs in order to minimize environmental impacts at all stages of product life cycle, and to facilitate its use in the Product Development Process (PDP), called here operational criteria. Tables 1 and 2 present these requirements as some authors suggest. 3
CRITERIA DEFINITION
From the requirements and criteria presented on tables 1 and 2, proposals were made for these criteria and requirements already identified in order to allow the analysis of eco-design methods based on QFD and FMEA. Considering the particularities of each method, as its structure, characteristics and application, it was proposed the following criteria for QFD and FMEA eco-design methods, divided into environmental criteria and operational criteria. 3.1
Environmental Criteria
143 Regulations QFD: the environmental legislation and environmental standards are considered as a product requirement? FMEA: the method considers environmental legislation as a failure mode? Environmental Impacts QFD: the environmental impacts are considered as technical requirements (quality characteristic) that allow to be correlated with the quality required by the client? FMEA: are environmental impacts associated with potential failure modes and are they evaluated quantitatively and objectively? 3.2
Operational Criteria
Easy to use QFD and FMEA: the method has its stages of implementation detailed and does not use complex mathematical language (not known by the designers)? Cost
Life-cycle phases QFD: environmental requirements are considered in whole product’s life cycle (not only the use phase)?
QFD and FMEA: the method requires the purchase of software, hiring of experts and / or training?
FMEA: are life cycle phases related to each failure mode? Requirement/criterion Life-cycle phases
Description The method should consider all phases of the product’s life cycle to meet this criterion, i.e., the extraction of raw materials, product manufacturing, use and disposal. It should also include strategies for end-of-life product, such as recycling, remanufacturing and reuse, and its requirements.
References [5]; [7]; [9]; [10]; [11]; [12]; [13]; [14]
Regulations
The method should consider the product of environmental legislation for the [8]; [9] countries / regions where they are produced, used and disposed.
Environmental impacts
The method should identify and assess all environmental impacts during the [5]; [9]; [10]; [11]; whole life-cycle of the product such as natural resources and energy [12]; [14]; [15] conservation, human toxicity, effects and emissions in the air, water and soil. Table 1: Environmental requirements and criteria for eco-design methods.
Requirement/criterion Easy to use
Description References The method is considered easy to use when the procedures for application [7]; [8]; [16]; [17]; are presented very detailed, using pictures / images, describing each activity [18]; [19]; [20] and avoiding mathematical models and complex calculations involving the use of scientific language.
Cost
A method is considered expensive when there is need for training, experts, [5]; [8]; [16] consulting or software purchase.
Validated
Validation will be proven when there is a case study in the literature about its [8]; [16] application in business.
Time
The method can be considered fast when its application time is less when [5]; [18]; [19] compared to quantitative methods, such as those based on LCA and does not require a large amount of data.
Benefits of visualization
When the method shows which are the main benefits and results of its [18] application.
Trade-offs analysis
When there is analysis of trade-offs between environmental requirements, [15] between product’s requirements and between product and environmental requirements of product. Table 2: Environmental requirements and criteria for eco-design methods.
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Time QFD and FMEA: the method has a higher number of steps that its traditional version? Validated QFD and FMEA: the method was applied to businesses? Benefits of visualization QFD and FMEA: does the method presents the main results and benefits of its application? Trade-offs analysis
QFD: can the method identify relationships / conflicts between environmental requirements and product quality required and the quality characteristics? FMEA: can the method identify relationships / conflicts between the proposed recommendations? These three environmental criteria and six operational criteria were sent and answered by eight experts using questionnaires in order to identify which are more important. Experts were chosen from areas such as product development, eco-design and QFD/FMEA methods. Figures 1 and 2 shows the importance level obtained for each criterion.
Figure1: Answers of questionnaire application with specialists for environmental criteria.
Figure 2: Answers of questionnaire application with specialists for operational criteria.
Life Cycle Design - Methods and Tools
145
According to Figure 1 and Figure 2, it was defined weights for each environmental and operational criterion. Environmental criterion which had higher priority (related to environmental impacts) received weight 3, the second more important received weight 2 and the less important weight 1. The same idea was also used for operational criteria considering the number of criteria, so “Validated” criterion which had higher priority by specialists, received weight 6, “Time” received weight 5 and successively. The correlation between the criteria and eco-design methods based on QFD and FMEA were defined using the following table: Likert Scale Criterion is totally considered
3
Criterion is parcially considered
2
Criterion is not considered
1
(IGQFD) [30], Integration between QFDE/LCA/TRIZ [31], Integration QFDE/LCA [32], QFD-DfE [33], QFD by Wolniak and Sędek [34], QFD based on RSP [35], Eco-QFD by Kuo [36], and Eco-QFD by Utne [37]. The only two eco-design methods based on FMEA identified were Environmental Effect Analysis (or E-FMEA) [1] and integration FMEA-TRIZ [38]. As Figure 3 shows, among the environmental criteria, regulations was the less considered by all nineteen methods, i.e., it was not considered by 74% of the methods. This criterion is followed by environmental impacts (not considered by 42% of the methods). The criterion about life-cycle phases was identified in 63% of the methods. Among the operational criteria, the majority of the methods analyzed were considered easy to use and low cost. 21% of the methods were not considered fast to use, 63% did not consider trade-off analysis situation and 53% were not applied in companies.
Table 3: Likert scale for methods analysis. It was used a correlation matrix based on the same example of Quality House, as follows on the Figure 3. Methods analyzed based on QFD were: Quality Function Deployment for Environment (QFDE) [21], Environmental QFD [22], QFD by Hochman and O’Connell [23], Eco-QFD by Ernzer and Birkhofer [24], Green Quality Function Deployment (GQFD) [25], 3D-QFDE [26], Eco-VoC [27], EI2QFD [28], Green QFD-II [29], Integrated Green QFD
Another result of this analysis is related to environmental/operational situation, as Figure 4 presents. This situation reveals that the better possibility of the method to bring environmental benefits in the product development, many times it reflects in problems related to application, i.e., cost, time and hard to use. Figure 4 presents in % the maximum score which each method achieved for both environmental and operational criteria.
Ecodesign methods based on FMEA
Weight
QFDE
Environmental QFD
GQFD
3D-QFDE
Eco-VOC
EI2QFD
Green QFD-II
IGQFD
QFDE/LCA/TRIZ
QFDE/LCA
QFD-DfE
QFD by Wolniak and Sędek
QFD based on RSP
Eco QFD by Kuo et al.
Eco QFD by Utne
E-FMEA
FMEA TRIZ
Life-cycle phases
1
3
3
1
3
2
3
3
3
3
2
3
3
3
1
1
2
1
3
3
Regulations
2
1
2
1
1
2
1
1
1
1
1
1
1
1
2
1
1
1
2
2
Environmental impacts
3
2
1
1
1
1
2
1
2
2
2
3
3
1
2
1
1
2
2
2
Easy to use
4
3
2
3
3
2
2
3
2
2
2
2
2
3
3
3
1
3
3
3
Cost
3
3
1
3
3
3
3
3
2
1
3
1
1
3
3
3
2
3
3
3
Time
5
2
2
2
3
3
2
2
1
1
2
1
1
3
3
2
2
2
2
2
Validated
6
3
3
1
2
1
1
1
1
1
3
3
1
1
2
3
3
1
3
1
Benefits visualization
1
3
3
1
1
1
3
3
3
3
3
3
3
3
1
3
3
3
3
3
Trade-off analysis
2
1
1
2
1
2
1
1
1
3
2
1
1
3
2
1
1
2
1
1
Environmental Criteria Score
11
10
6
8
9
11
8
11
11
10
14
14
8
11
6
7
9
13
13
Operational Criteria Score
54
44
42
51
43
38
42
30
31
52
39
27
51
53
54
43
44
54
42
Environmental
Criteria
Operational
QFD by Hochman and O'Connell Eco-QFD by Ernzer and Birkhofer
Ecodesign methods based on QFD
Figure 3: Analysis of ecodesign methods based on QFD and FMEA.
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Life Cycle Design - Methods and Tools
0,90
0,80
0,70
0,60
0,50
0,40
0,30
0,20
0,10
FMEA TRIZ
E-FMEA
Eco QFD by Utne
Eco QFD by Kuo et al.
QFD by Wolniak and Sędek
QFD-DfE
QFDE/LCA
Operational criteria
QFD based on RSP
Environmental criteria
QFDE/LCA/TRIZ
IGQFD
Green QFD-II
EI2QFD
Eco-VOC
3D-QFDE
GQFD
Eco-QFD by Ernzer and Birkhofer
QFD by Hochman and O'Connell
Environmental QFD
QFDE
0,00
Figure 4: Comparison between QFD and FMEA methods considering environmental and operational criteria.
4
CONCLUSION
It was noted normally the operational requirements such as time and cost are related to ease of use, since one affects the other. In other words, the more difficult is the application of a method, longer and more costly it will be. Another conclusion is that the greater its ability to objectively assess environmental impacts in the product’s life cycle, the greater the difficulties in its implementation because of training costs, software purchase, specialists hiring, application time due to the increased number of steps and activities, mathematical language, and other information not known by designers. The identification of many methods that are not validated shows that there is no concern of testing methods in real cases with product development professionals. When considering the main concepts of what eco-design means, in general the authors define the term as a way to develop products in order to reduce environmental impacts throughout the product’s life cycle. So, from this definition we conclude that at least 37% of the eco-design methods based on QFD and FMEA could not be considered an eco-design method, since they do not consider the whole lifecycle of the product. This percentage may even be greater since only two of the nineteen methods allow to identify and to assess the environmental impacts generated by the products, thus
enabling to reduce environmental impacts more effectively. Thus, we conclude that the methods of eco-design need to consider a set of factors, requirements and criteria during its conceptualization and development, so they can be more easily inserted in the process of product development and allow environmental gains, contributing to the reduction of environmental impacts in the whole product’s life cycle. 5
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1
Lucie Domingo , Damien Evrard , Fabrice Mathieux , Guillaume Moenne-Loccoz 1
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Laboratory G-SCOP, University of Grenoble, Grenoble, France 2
Neopost Technologies, Bagneux, France
Abstract One strategy to face the challenge of energy consumption has been the promotion of more efficient products and infrastructures. For the electr(on)ic industry, in-use energy consumption is a significant contributor to environmental impacts of a product lifecycle. Synergico is a contribution to the Design for Energy Efficiency of electr(on)ic equipments focusing on the use phase. This paper presents one of the Synergico tools that verifies that a higher in-use energy efficiency indeed decreases the environmental impacts of the product lifecycle and our method to be articulated with corporate design processes. Both are illustrated with a case study from the industry. Keywords: Ecodesign; Use Phase; Energy Related Product
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INTRODUCTION
When assessing the environmental impacts of an electr(on)ic product through a lifecycle assessment, the energy consumption in use phase is often the main contributor to a majority of impact categories ([1-3]). Energy consumption has also been a great challenge for modern society for the last two decades. The scarcity of fuel resources and polluting means of production are serious issues that have to be dealt with. Policies and regulations both are important means to handle these problems. The ErP – Energy-related Product – directive [4] is a response to those two challenges which aims at enabling citizens to access more environmentally friendly electric and electronic appliances and to decrease the energetic demand at the European level. Design for Energy Efficiency – DfEE – is a way to decrease the overall energy demand of systems, such as mobile devices [5], or manufacturing processes [6]. The method we propose in this paper is a DfEE in use phase meant to contribute to the design for environment of electr(on)ic product. This method and its tools have been developed in the framework of the Synergico (Synergy Energy Design) project. This joint project has been funded by the French Environmental Protection Agency – ADEME – and is based on a partnership between two research laboratories and two industrial partners from the electr(on)ic industry: Neopost , and Sagemcom. From this collaboration, three tools and one method have been developed:
An indicator of energy consumption in use phase – IUE – based on the functions and components of the product. The unit used is the watt per hour over the product time of use; the tool has already been presented in [7],
A guideline-based tool, to sort out the strategies to meet energy efficiency target of the project by criteria; the tool has already been presented in [8],
A simplified lifecycle assessment – LCA – tool, to check that the choices made to improve the energy efficiency in use phase do
not generate environmental transfers on other phases, or on other impacts.
A method to gather the relevant information, detailing the Who?, What?, Where?, When?, Why?, How? of the design for energy efficiency in use phase process.
This paper introduces the lifecycle verification tool, illustrated by the case study of a microcontroller on a postage meter. It then presents the global Synergico design method. This section is an illustration with a proposition of integration of the Synergico method into the design procedures of Neopost, one of the leading companies in the design and manufacturing of mailing solutions (postage meters, folding inserting systems, addressing systems, etc.). Conclusions are finally presented. 2
LIFECYCLE CHECK TOOL
A Lifecycle Assessment, as defined in the ISO 14040 [9], is useful to assess the environmental impacts of a product during its lifecycle. Since several impact categories and lifecycle phases are considered, it is possible to compare two products with similar functions and to identify impact tradeoffs. The central specification for our simplified LCA tool was to help verify that the design of implemented solutions for energy efficiency in the product actually implies a better environmental performance. It is meant, as required for example by the ISO 14062 [10], to confirm that the modifications made to improve the energy efficiency do not generate unexpected additional environmental impacts on any other lifecycle stages or on any other environmental impacts. 2.1
Environmental Impact of solutions for Energy Efficiency
In order to have an overview of the best available and non-available technologies for energy efficiency, we based our list of solutions on the analysis of the preparatory studies for the development of specific ecodesign requirements for the ErP directive [4]. The sixth task of those reports is dedicated to viable technologies for the improvements of energy efficiency of the product category under
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_26, © Springer-Verlag Berlin Heidelberg 2011
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Life Cycle Design - Methods and Tools study. The review was based on the following product categories: Boilers and Combi-boilers (Lot 1), Personal Computers and Computer monitors (Lot 3), Imaging equipment (Lot 4), Televisions (Lot 5), Stand-by and off modes losses of EuPs (Lot 6), Battery chargers and external power supplies (Lot 7), Tertiary Lighting (Lots 8-9), Electric Motors (Lot 11), Commercial and Domestic refrigerators and freezers (lots 12-13), Domestic dishwashers (Lot 14) and Complex Set top boxes (lot 18). We then classified the solutions into three categories according to the following hypotheses in order to simplify the tool:
Impact transfers are caused by physical changes in the components, hence software modifications are not supposed to imply any impact transfers;
New technologies will not be assessed by the tool due to the confidentiality or the lack of available information;
The suppression of a component leads to a reduction of the product impact.
In this classification, we sorted the solutions in two categories whether it is a solution with a:
“Need to be assessed for impact transfers”: the solution for energy efficiency seems to generate an impact transfer toward another impact category, another lifecycle phase or another part of the product “No need to be assessed for impact transfers”: the solution for energy efficiency does not seem to generate an additional environmental impact on another impact category, another lifecycle phase or another part of the product.
Need to be assessed for impact transfers:
149 representative of usual impact transfers for the categories 1 and 2 defined above. To select the right indicators, we explored the subject of the environmental impact of electronic products while resorting to three different approach angles. The first one was based on a general assessment of products in Europe [12]. It showed that the influence of electr(on)ic goods was significant on two indicators: on non-renewable resources depletion and energy consumption (as well as a correlated impact: global warming). The second one focused on literature about the environmental assessment of electr(on)ic products, to reveal a pattern in the types of indicators and was based on the result of joint initiatives between governmental bodies and industrials from the electr(on)ic sector. Two projects, in Denmark [13] and in France [14], were of particular interest. Even if their goals were different, their conclusions on the most interesting indicators to follow were the same. According to these studies, the most appropriate indicators to assess [13] and to communicate [14] on environmental impacts are related to two environmental issues: energy consumption and non-renewable resources depletion over the entire lifecycle. The third one was based on the Synergico assessment of a solution implemented for energy efficiency by our industrial partner: a microcontroller, which is an electronic component. The impact assessment of this component was done using SimaPro V7.1 with EcoInvent Lifecycle Inventory database and evaluated according to the commonly used methods: Eco-indicator 99 E (see Figure 1) and the CML 2 baseline 2000 (see Figure 2). This modelling helped highlight the most relevant impact categories for this component, therefore we considered it as representative for other electronic components.
1. Changes in material, coating or manufacturing shaping: mostly for insulation, heat dissipation material, coating or shaping can be modified. 2. Component inclusion: the addition of electronic components which enable a better, finest control of product energy consumption. No need to be assessed for impact transfers: 3. Software insertion: the addition of software code to manage the energy or to communicate information to the user is a common practice for energy efficiency. We stand that this kind of improvement will generate few or no impact transfers. 4. New technology: more efficient technologies are developed continually. Nevertheless, modelling their environmental impact is difficult due to the confidentiality or the instability of the techniques [11]. For that purpose, if a new technology is used to enhance the energy efficiency, its environmental impact calculation will not be supported by our tool. 5. Component suppression: unnecessary components might be implemented on some products. Their suppression will not generate additional environmental impacts.
Table 1: Classification of the solutions for the lifecycle check tool. A classic problem in integrated design is to handle tradeoffs between different design objectives: energy efficiency often needs for example to be balanced with manufacturing costs or user safety. A similar issue applies in lifecycle assessment. Choices are difficult to make when there are many indicators to weigh the pros and cons. To overcome this problem, we had to limit the number of indicators in our tool. Nevertheless, those few indicators had to be
Figure 1: Environmental impacts of the microcontroller lifecycle with Eco Indicator 99 E. Figure 1 shows that the main impacts of the microcontroller are on fossil fuels, respiratory inorganics, radiation, climate change, and minerals. The last one of this list can be described by the category “raw material depletion”. The contribution to fossil fuels, climate change and radiation comes from the energy production.
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A comparison with a base case product, in order to plot a result based on understandable references
A raw modelling of the component architecture to be implemented.
For each component, we predefined scenarios for the phases which are difficult to assess during design, namely distribution and end-oflife [17]. For the use phase, we modelled it as the energetic gain generated by the implemented architecture. This last value can be obtained in the IUE tool (Indicator for in-Use Energy Consumption) [7]. The recycling phase includes the recovery of materials such as precious metals which enables the saving of primary raw materials. In order to simplify the model, each electronic component has been associated with a soldering process and a piece of electronic board and each material is linked to a shaping process. The following section shows the input information needed to assess the environmental transfers generated by the implementation of a microcontroller on a postage meter. Figure 2: Environmental impacts of the microcontroller lifecycle with CML 2 baseline 2000. In Figure 2 the main impacts are marine aquatic ecotoxicity, Abiotic depletion, fresh water aquatic ecotoxicity, global warming, and acidification. The lifecycle of the microcontroller highlighted the influence of the following impacts:
Toxicity (Eco- and Human)
Acidification
Abiotic resources depletion / Raw material depletion
Global warming / Climate change
After a concertation with our industrial partners, we decided not to consider the toxicity impacts because they are already taken into account in both the RoHS and the REACH directives. As for the acidification impact for which transportation is the main contributor and is very low if reported to the functional unit considered in the study of the whole product.
2.3
Application to the case study of the microcontroller on a postage meter
The available data for the modelling of the microcontroller were: the architecture of such a component and the potential savings of this solution. In this case study, the microcontroller was used to influence the energy consumption in standby mode, hence a gain of 7.5 W with the same level of performance for the product by adding a card of 2.25 cm². First, we chose the product architecture that represented the best the actual product design. Secondly, the modelling of the components of the microcontroller was done. Thirdly, the amount of energy saved over the product use-time by this solution was documented (in kWh). After modelling the data, the assessment was carried out. Figure 3 shows the comparison between the environmental impacts of the base case product and the under-design product.
Finally, to keep a simplified tool, it appeared that two impact categories were actually significant for our purpose. All those approaches pointed out two environmental issues that are significantly influenced by the addition of solutions for energy efficiency such as the addition of electr(on)ic: raw material depletion and energy depletion (which reflects global warming). 2.2
Simplified LCA-based tool to be used during design for supporting design activities
This tool is meant to assess a product or a module in its earliest stages of design when the information on the product lifecycle is still incomplete. Simplified LCA approaches are based on different assumptions: [15] proposed to group a product and its components according to their material and energy efficiency in order to support environmental decision making in the early stages of the design process. Other simplified LCA propose excluding phases with nonsignificant contribution to the entire lifecycle [16]. Our approach is based on a combination of these two approaches: finding the most relevant information on environmental impact for designers (see paragraph 2.1) and restricting the number of information to be used in the model. The most reliable available information is about the physical architecture of components and previous or concurrent products with a similar lifecycle. This is why we decided to quantify environmental impact transfers based on:
Figure 3: Result of the assessment of the impact transfers for the microcontroller implemented on a postage meter. For the manufacturing and the end-of-life phases, the new product has slightly higher impacts of about 1%, for both raw material depletion and energy depletion. The contribution of the microcontroller on raw material depletion during recycling is negative because of the increase of potentiality for recycling due to the addition of the microcontroller, which means that the new design has a better performance than the base case study. It is all the more visible during the use phase with both
Life Cycle Design - Methods and Tools impact categories around -60%, which overcomes the loss of performance during the manufacturing and the end of life. The largest contribution is generated by the saved energy thanks to the microcontroller. The suggested lifecycle check tool is not meant to replace an LCA but to support designers in a rapid way for analyzing the environmental impacts of the product architecture. Therefore, it can be viewed as an alternative to a full lifecycle assessment when a comparison between energy savings and the addition of components is needed during design.
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SYNERGICO METHOD: INTEGRATING THE TOOLS INTO THE PRODUCT DESIGN PROCESS
In order to drive the tool described above and the other two tools developed for this project [7], [8], the Synergico method has been written as a means to support the design management in the implementation of the tools in the design process by specifying the Who?, What?, Where?, When?, Why?, How? of the tools. 3.1
Design process of electr(on)ic products
A preliminary condition to define a design method is to observe and to identify the form of the actual design process for the development of electr(on)ic products. Various design processes exist and many companies have their own procedure. One of our objectives was to ensure the compatibility of the Synergico method with the corporate design processes. Our choice had been to base our work on existing generic models for product development like [10], [18]. We then assessed their compatibility with the design process used by our industrial partners and [10]. Eventually, we chose the most appropriate form of process with the following steps: Planning, Preliminary design, Detailed design, Test, Industrialization. Those design steps define the milestones of the method and will be the five values that the When? can take. The chosen design process is linear (see Figure 5) with no possibility to go back to previous steps, since we considered in the method, that the design steps are milestones and Synergico helps validate a step before entering to the following one. To define Who?, our approach has mostly been based on the departments implied in the design activities of our partners. Seven different competences were identified in the following departments:
Electronic design: in charge of the design of the boards, their drivers, power supplies and all other electronic components,
Mechanical design: in charge of the design of the kinematic sets (engines, actuators, sensors…) and the structure of the product (shell…)
Software design: in charge of the software coding and its implementation on the hardware structure
Project management: managing the different actors of the design project, fulfilling the objectives and coordinating the relations between the different departments
Marketing: in charge of the interface between client and design team by means of specification and product requirements.
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Purchase: in charge of finding the adequate suppliers for components, materials, machinery, etc. according to project management specifications
Ecodesign expertise: in order to go deeper in the DfE, such an expert may be useful.
In order to simplify Figure 5, we used in this paper only two actors, namely the “designers” (electronic, mechanical, and software design) and the “transverse functions” (project management, marketing, purchase, and ecodesign expertise). To define Where?, a list of the key “places” in the Synergico toolbox was defined: Lifecycle Check Tool, Guidelines, Indicator of in-use energy consumption, Specification, Functional splitting, Physical splitting, Use scenario, Power. We named those “places” sub-tools. 3.2
Integration of the tools in the design process
The What?, Why?, How? are highly dependent on the tool we consider. In Synergico, What? refers to the input data to be processed at this stage, Why? refers to the expected results, i.e. the output and How? refers to the way the input data are processed to obtain output data. The structure of the method is defined in two diagrams with the same axes: the different sub-tools (Where?) and the steps of the design process (When?). The first one presents the actors (Who?) (see Figure 5) and in the second one, which is not illustrated in this article, the data flows (What? and Why?). The association of a design process step with a sub-tool defines what we call a “block”. For each block, the method specifies Who?, What?, Where?, When?, Why?, How?, which correspond to the input / output data, the actors involved at this step whose skills are needed to fill in the tools. The Synergico method is composed of 23 blocks indexed according to the design stage and the sub-tool considered. For example, the block 3.C (see Figure 4) represents the sub-tool “power” during detailed design. In this case, the input data are the IUE results from the power subtool and the datasheets of the components providing information about the power they require. The electronic, software and mechanical departments are the ones involved in this step. They can use the help of the Guidelines tool to drive the design of the prototype. The outputs are new specifications for a prototype and new IUE results.
Figure 4: Block 3.C Power during detailed design.
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Figure 5: Articulation between Synergico, the design process and the actors of the project. 3.3
Implementation at Neopost
Once we had defined a generic method, some adjustments had to be done to fit in the design process of our project partners. This section presents the integration of Synergico in the design process of Neopost. This part of the project involved the corporate Quality,Health, Safety & Environment manager and the Ecodesign Expert. The first step was to adjust the general method to the actual practices of Neopost and especially, an adaptation of the vocabulary used in the method and tools has been necessary. Thus, the generic terminology was adapted to the one currently used in the company concerning the departments and the design phases. The second step consisted in specifying the interaction of the Synergico tools with other Neopost’s design practices, tools and methods in terms of:
Where to find the external input information?
Where to use the external output information?
We defined, as external input, data such as the information contained in the customers’ requirements and the capitalization
data. During the implementation process we defined who, in a design project team, was the most adequate department or person to provide such information and to mediate its use in the design process. We defined, as external output information, the results of the InUse-Energy consumption indicator, for example. The aim of the implementation was to define how this data can be used outside design to support communication and marketing experts. After adapting and connecting Synergico with other design procedures of Neopost, Design for Energy Efficiency was integrated in Neopost strategy as part of the tool kit for integrated design of postage meters. 4
CONCLUSIONS
This paper presents a tool whose purpose is to validate, from a lifecycle perspective and considering two impacts, the modifications of an electr(on)ic product during its design or redesign in order to increase its energy efficiency. It also introduces the method developed to coordinate the tools indicator for In Use energy consumption [7], Guidelines [8], and Impact transfers.
Life Cycle Design - Methods and Tools As stated earlier, energy efficiency in use is a central issue for electr(on)ic products and their environmental performances can be highly modified by decreasing their energy consumption. It is the reason why considering this aspect as early as possible in the design process is essential. Synergico method is aimed at driving design toward a more efficient product in use phase by providing a step-by-step methodology based on three complementary specifically tailored for the design process of electr(on)ic equipments. The proposed case study happened during the redesign of a product and, in order to validate the Synergico method, the implementation of the method and tools. The collaboration with both industrial partners, Neopost and Sagemcom, a manufacturer of telecommunication equipments, has been essential to insure the practicability and relevance of the tools and method for the electr(on)ic industry. Further developments are ongoing to improve the reliability of the tools and to make them robust enough to be used by the whole sector of electr(on)ic equipments. In order to verify the universality of the method, other applications with other industrial partners will be carried out. A new manufacturer has already been identified to test the Synergico method from the early stages of the design of a new electr(on)ic product to its final market launch. 5
[7]
Domingo L., Mathieux F., Bonvoisin J., Brissaud D. (2010): Indicator for in Use Energy Consumption (IUE): a tool enhancing Design for Energy Efficiency of products, In: IDMME - Virtual Concept 2010.
[8]
Bonvoisin J., Mathieux F., Domingo L., Brissaud D. (2010): Design for energy efficiency: proposition of a guidelinesbased tool, In International Design Conference- Design 2010.
[9]
International Standard Association (2006): ISO 14040: Life cycle assessment, principle and framework.
[10]
International Standard Association (2003.): XP ISO/TR 14062: Environmental management -Integrating environmental aspects into product design and development.
[11]
European Commission (2006): Proceedings of the Workshop on Nanotechnology and Life Cycle Assessment.
[12]
Tukker A. , Jansen B. (2006): Environmental Impacts of Products: A Detailed Review of Studies, In: Journal of Industrial Ecology, vol. 10, n°. 3, p. 159-182.
[13]
Institute for Product Development DTU Denmark, Danish Toxicology Center (2005): Eco-conscious design of electrical and electronic product.
[14]
ADEME, AFNOR (2010): Environmental label of consumer market's product.
[15]
Kaebernick H., Sun M., Kara S. (2003): Simplified Lifecycle Assessment for the Early Design Stages of Industrial Products, In: CIRP Annals - Manufacturing Technology, vol. 52, n°. 1, p. 25-28.
[16]
Hur T., Lee J., Ryu J., Kwon E. (2005): Simplified LCA and matrix methods in identifying the environmental aspects of a product system, In: Journal of Environmental Management, vol. 75, n°. 3, p. 229-237.
[17]
Nielsen P. H. , Wenzel H. (2002): Integration of environmental aspects in product development: a stepwise procedure based on quantitative life cycle assessment, In: Journal of Cleaner Production, vol. 10, n°. 3, p. 247-257.
[18]
Pahl G., Beitz W., Wallace K., Feldhusen J., Blessing L.( 2007): Engineering design, In: Springer.
ACKNOWLEDGMENTS
We acknowledge the financial support of the French EPA ADEME (Contract #0877C0032) in the framework of the 2008 call for proposal on “Research,development and innovation in Ecodesign.” We also want to thank our industrial partners and especially, the entire design team at Neopost Technologies that collaborated in the construction of the Synergico method and tools. 6
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REFERENCES
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Junnila S. (2008): Life cycle management of energyconsuming products in companies using IO-LCA, in: The International Journal of Life Cycle Assessment, vol. 13, n°. 5, p. 432-439.
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Kim S., Hwang T., Overcash M. (2001): Life cycle assessment study of color computer monitor, In: The International Journal of Life Cycle Assessment, vol. 6, n°. 1, p. 35-43.
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Muñoz I., Gazulla C., Bala A., Puig R., Fullana P. (2008): LCA and ecodesign in the toy industry: case study of a teddy bear incorporating electric and electronic components, In: The International Journal of Life Cycle Assessment, vol. 14, n°. 1, p. 64-72.
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European Commission (2009): DIRECTIVE 2009/125/EC: establishing a framework for the setting of ecodesign requirements for energy-related products.
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Heath T., Pinheiro E., Hom J., Kremer U., Bianchini R. (2004): Code transformations for energy-efficient device management, In: Computers, IEEE Transactions on, vol. 53, n°. 8, p. 974-987.
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Rahimifard S., Seow Y., Childs T. (2010): Minimising Embodied Product Energy to support energy efficient manufacturing, In: CIRP Annals - Manufacturing Technology, vol. 59, n°. 1, p. 25-28.
Improving Product Design based on Energy Considerations Yingying Seow, Shahin Rahimifard Centre for Sustainable Manufacturing and Reuse/Recycling Technologies, Loughborough University, UK
Abstract The industrial sector consumes a significant amount of the world’s energy supply; the rationalisation of energy consumption would provide the most effective method of reducing greenhouse gas emissions attributed to manufacturing and use of products. Energy consumed across the various stages of a product’s lifecycle varies significantly depending on the product design and its application. In non-energy using products such as furniture, food, and clothing, the material preparation and production phases represent a significant proportion of energy consumption over the product lifecycle. This paper proposes a new design methodology targeted at these products to minimise energy consumption during ‘production’ phase. Keywords: Energy Efficiency; Design for the Environment; Low Carbon Manufacturing
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INTRODUCTION
Increasingly, energy consumption of products has become the focus of environmental concerns due to carbon emissions from the combustion of fossil fuels for energy generation. As energy demand continues to grow and fossil fuels remain the main source for power generation in the foreseeable future [1] the most effective method of CO2 reduction is still through the rationalisation of energy consumption. This has led governing bodies to introduce a number of energy auditing and accreditation standards, such as European directives on the ‘Eco-Design of Energy using Products (EU Directive 2005/32/EC)’ and ‘Energy End-Use Efficiency and Energy Services (EU Directive 2006/32/EC)’.
Cumulative lock-in of environmental impact
According to Otto and Wood [2], 80% of the environmental damage of a product is established after 20% of the design activity is completed. Decisions made early in the conceptual design phase can influence the outcome of a design exercise more significantly than any optimisation step later on in the design process [3]. Therefore environmental considerations should be integrated early in the design phase during the product development process, see Figure 1 [4]. The most commonly adopted method is ‘Design for Environment’ (DfE) which is concerned with the impact of design throughout the lifecycle, from material preparation and manufacture to use and end-of-life management of a product [5].
STRATEGIC PRODUCT AND MARKET RESEARCH
Cumulative ‘lock-in’ of environmental impact arising from decisions made along the product development cycle
DfE considers a range of environmental issues associated with a product including resource consumption, end-of-life disposal, waste management, recyclability reusability and use of toxic and hazardous materials. Energy is clearly consumed across the various stages of a product lifecycle; furthermore the level of energy consumed in each lifecycle phase significantly varies depending on the product. For example in the case of electrical products, the greatest contributor to environmental impact is often due to the consumption of electricity during the ‘Use’ phase, thus the reduction of this energy use during this stage has been the focus of most design tools and guidelines. However in the majority of manufacturing applications, the production phase still represents a significant proportion of energy consumed over a product’s lifecycle, in particular for non-energy consuming products. This highlights the need to investigate the opportunities for optimisation of energy consumption during the production through design improvements. This paper proposes a new design methodology which aims to minimise the energy consumption during the manufacturing phase of a product. This is achieved by providing a detailed breakdown of energy flows attributed to the production of a product and utilise this energy data to improve the design process. The initial section of the paper provides an overview of ‘Design for X’ approaches, together with an overview of the established design methodologies used in most applications. The latter part of this paper describes the Design for Energy Minimisation (DfEM), during manufacture and outlines its application in the design of a chair. 2
Environmental improvement strategies DESIGN FOR ENVIRONMENT LIFE-CYCLE ASSESSMENT
CLEANER PRODUCTION
CONSUMER EDUCATION
WASTE MAMAGEMENT AND RECYCLING
Figure 1: Conceptual representation of environmental ‘lock-in’ over a product’s development cycle [4].
2.1
PRODUCT DESIGN AND THE APPLICATION OF DFX TOOLS Product Design Process
A common design model as proposed by Pugh [6] consists of four generic stages: 1) Specification, 2) Conceptual Design, 3) Detail Design and 4) Manufacture. As illustrated in Figure 2. The first stage involves planning of the design task by collecting
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_27, © Springer-Verlag Berlin Heidelberg 2011
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information about the customer requirements and creating a product design specification. The next step is to generate ideas by searching for essential problems and combining working principles and selecting a suitable concept. The third stage is detail design which develops the concept chosen at the previous stage into a more concrete proposal with specifications of geometry, materials and tolerances of all parts in the product. Production costs and robust performance are the main concern at this stage. Finally the last stage is manufacturing and typically at this stage the main design aim is to minimize the component and assembly cost. Various design for ‘X’ (DfX) tools can be applied to each design stage. DfX is a term that is used to represent a variety of considerations that must be made whilst designing a product and stems from the fact that designers cannot be subject experts on every factor that arises during the design process. It can be used in the early stages of concept design as a benchmarking tool as well as helping to simplify new un-built concepts [2]. For example Design for Life Cycle which considers the environmental impact of a product from cradle to grave, may mean a radical design change to a vehicle such as powering a car from renewable energy to minimize the impacts from the use of fossil fuels. Other DfX tools like Design for Manufacture and Assembly might be considered to minimize the number of parts thereby reducing manufacturing and assembly costs and time. Figure 3 shows an example of how the various DFX tools alongside others, can support the different phases in the product development process. More recently with the increasing concern about climate change and the environmental impact of products, a new generation of DfX tools have been developed to help designers reduce these impacts through their design. These tools aim to integrate environmental considerations in the design of new products to reduce the overall environmental impact of a product [8,9] as most of the environmental impact in a product’s lifecycle is ‘locked in’ into the product at the design stage when materials and production process are selected, and product performance is largely determined.
Figure 3: Application of various design tools in problem solving, product synthesis and in product development [7]. As there is no single measure for environmental impact, various tools and techniques have been developed under the DfE strategy some of which are: Design for recyclability [10], Design for disassembly [11], Design for Lifecycle [12], Lifecycle Costing [13], Sustainable design [14] and Design for End-of-life [15]. This research paper is proposing a new framework for improving product design through energy considerations. To demonstrate how a Design for Energy Minimisation approach can be adopted across the various design stages, 3 generic phases of Concept design (CD), Detail design (DD) and Manufacture (M) in line with Pugh’s [6] design methods have been used. 2.2
Design for Energy Minimisation Approach
DfEM is a wide ranging consideration that not only includes energy consumed during the Use phase, but also considers the other stages in the lifecycle. This is currently supported by Life Cycle Assessment (LCA) which is used to compile and evaluate the environmental burdens of the product from the material production to part manufacture, product assembly, operation, servicing, maintenance and end-of-life disposition [16]. However there are two main shortcomings with conducting a LCA: 1) The inventory analysis required in a LCA is time consuming due to the complexity and data intensive nature of the process. In various studies conducted [17,18,19] the industry found this process to be too complex and requiring a great deal of effort for existing products. The data intensive nature of a LCA coupled with the lack of accurate data related to the energy consumption across the entire lifecycle of a product often results in significant assumptions and simplifications [19]. This is perhaps even greater for a product that does not yet exist as it is unrealistic for a designer to have access to all the specific information about the materials and processes required for a comprehensive LCA at the early stages of product design i.e. concept design phase.
Figure 2: Pugh’s product design model showing the 4 central stages of the product design process [6].
2) On a more specific level, the energy data available in existing Life cycle inventory (LCI) databases such as the Ecoinvent [21] database is based mainly on industry sectors located in Switzerland and Western Europe and is therefore not always relevant for products that are manufactured in a different location. In addition, much of the process energy data is indicated as kJ per unit weight of material processed which does not provide any indication of how much energy was used by the manufacturing facility, how much
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was for the actual processing and how much was from the supporting auxiliary processes. The total energy consideration established through the LCI can only provide a generic ballpark energy consumption value which may not be reliable for processes which are executed in a different way in other manufacturing environments. Hence there is a need to consider the energy flow modelling during the ‘manufacturing’ phase in more detail. For these reasons, there have been a number of recent developments in the following two areas: a. In order to minimize the complexity and time taken to conduct a full LCA, simplified models and additional assumptions have been used to reduce the evaluation effort in traditional LCA. These condensed LCA are known as streamlined LCA (S-LCA) which encompasses a group of approaches designed to simplify and reduce the time, cost and effect involved in conducting a full LCA while still facilitating accurate and effective decisions. For example, Granta Design [22] has developed a simplified LCA tool called the Eco Audit tool (part of the Cambridge Engineering Selector (CES) suite of software) which uses information about product composition, processing, usage, transportation, and disposal. The tool then combines this with eco property data on the materials and processes used in the design to calculate the energy usage and CO2 output resulting from each stage in the product lifecycle, see Figure 4. This high level overview is particularly useful during the first stage of product design (i.e. concept design) which can guide the design strategy by the identifying the lifecycle phase which has highest environmental impact.
In this context, the authors argue that a holistic DfEM approach should first provide support across the design process from concept design to manufacture and should secondly consider the energy consumption throughout the entire product life cycle. S-LCA tools such as CES mostly provides support during the concept design stage by providing high level energy information of each phase of a product lifecycle whilst AEMS provides support at the manufacture stage through the monitoring and tracking of energy consumption within the manufacturing facility. This highlights the need for a tool at the detail design phase that can provide energy data that is sufficiently detailed to correlate production processes and operation parameters to energy consumption. It is therefore proposed that an Energy Simulation Model (ESM) can be used at the detail design phase to bridge the gap between high level simplified LCA tools used at conceptual design and those used to monitor energy consumption as part of the manufacturing stage, as illustrated in Figure 5. This is achieved through a framework to model energy flows within the manufacturing phase of a product lifecycle and to support the detail design activities within the product design process which is described in the next section. 3
ENERGY SIMULATION MODELLING FRAMEWORK
b. In order to gain an accurate picture of the energy consumption in manufacturing, energy management systems are now used to track and measure the energy used in a production facility, providing a breakdown of energy consumption by various elements in a production system including both the buildings and production facilities.
Much of the current work on energy consumption within production or manufacturing can be broadly viewed under two different perspectives of ‘plant’ and ‘process’. The work directed at the ‘plant’ level has focused on the energy consumed by infrastructure and other high level services that are responsible for maintaining the required production conditions/environment [24, 25]. Examples of such energy consuming activities would be heating and lighting, transportation equipment and ventilation systems [26 ]. On the other hand, research concentrating on the ‘Process’ levels have targeted energy consumption of the individual equipment, machinery and workstation within a production system [27, 28].
An example of energy management software is Optima developed by Optima Energy Management [23]. It can track and monitor real time energy consumption, buys energy at best available prices and allows budgets and targets to be set for cost savings. Whilst AEMS provides correlation with external factors affecting energy use such as weather and building occupancy, much of the data is related to building energy consumption and only provides a high level breakdown of a plant’s energy consumption by generic areas.
This research proposes a third perspective which considers the energy consumed by a product as it is being manufactured and attributes the energy used on the plant and process levels to single unit of product made. This also includes energy that is required for pre-production (i.e. material preparation), production (i.e machining) and post production (i.e. packaging). This ‘product’ perspective along with the other two ‘plant’ and ‘process’ viewpoints on energy modelling are depicted in Figure 6. Currently most energy analysis of a product is conducted through a LCA methodology which typically uses the weight of the material
Figure 4: Inputs and outputs for the Eco Audit Tool [21].
Figure 5: DfEM should support the design process as well as the product’s lifecycle.
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being processed as the basis for the calculation. This paper proposes a different approach to energy modelling which differs from traditional LCA calculations by taking into account the amount of material being processed as well as the processing time required to convert these materials to finished products. For example in the case of machining processes, it is not only the weight of the material removed but also the complexity of the required operations (e.g. number of holes or slots), hence the total processing time, can greatly influence the energy consumption.
The energy simulation model consists of a simulation engine, an energy database, and a decision support tool, see Figure 7. The simulation engine is based on the framework and has been developed to allow a number of ‘what-if’ scenarios for the analysis and evaluation of energy consumption during the manufacturing phase of a product life-cycle. The simulation engine which has been TM developed using Arena , a discrete event simulation and automation software from Rockwell Automation is capable of modelling various manufacturing process flows for different products and can be expanded to include product or process variations. Additional production variations such as batch sizing, lead times and queue times can also be included. The energy database also provides the simulation engine with the primary energy information such as energy values associated with the manufacturing processes and auxiliary activities. Initial data can be determined either theoretically or empirically and statistical relationships can eventually be established to train the simulation engine to predict the amount of energy consumed by the processes and activities for different production parameters such as batching, queue times, process routing and process set ups.
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Figure 7: Energy simulation model consists of the decision support tool, the energy model data base and the modelling engine. As the energy model becomes more robust, the data output from the predictive models can in turn be added into the energy database to build up a comprehensive understanding of the energy requirements of processes and production systems. It should be noted that the data related to energy consumption within logistics and reverse logistics activities can also be included. The final aspect of the energy simulation model is the decision support tool which correlates various design and processing parameters with energy consumption data derived from the energy database. Using a correlation matrix, the energy intensity and efficiency of various manufacturing parameters can be evaluated against the functional requirements of the product to derive at a design that has minimal energy consumption during manufacturing. This energy simulation model not only supports operational decisions but also provides energy transparency right back to the design process, enabling designers to select the most energy efficient materials and processes whilst fulfilling the requirements in the product specification. Such an approach will potentially enable businesses to go beyond the incremental improvements achievable via existing energy management systems to consider energy efficiency and utilisation across both the design and manufacturing phases of a product life cycle. 4 4.1
Figure 6: Different Perspectives of Energy Modelling.
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In this framework, the energy consumed within a manufacturing facility is categorised into Direct Energy and Indirect Energy. The Direct Energy (DE) represents the energy utilised by the manufacturing processes used to produce the product. This includes pre-production, production and post-production processes (e.g. casting, machining, spray painting, inspection, etc). The Indirect Energy (IE) is the energy consumed by activities required to maintain the ‘environment’ in which the production processes are carried out within a manufacturing plant (e.g. lighting, heating, ventilation, etc.). Further details of this framework can be found in Rahimifard and Seow [29]. In this approach, the EPE model is not only able to detail the energy consumption for the various processes, but also highlights the energy hotspots within a manufacturing facility to support energy efficient manufacturing [30]. Energy intensive or energy inefficient processes can be identified for replacement or improvement.
Energy Simulation Model
APPLYING DFEM TO PRODUCT DESIGN Tools to aid DfEM
As mentioned before, the adoption of DfEM methodology involves a series of tools that can be applied at the different stages in the product design process. For example at the conceptual design phase where high level decisions are required for concept selection a simplified LCA package like CES Eco Selector could be adopted to provide an overview of the energy requirements for a new design within a product lifecycle. CES Selector can provide support to determine the appropriate materials based on specific product specification [31] and relate the energy consumption to the selected materials. It can also provide designers with a rough estimate of the energy requirements of processes which can aid decision making when short listing suitable processes.
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Figure 8: Application of various tools in a product design process to aid energy minimisation. In the second phase- detail design, where product structure, assemblies and components are established, there is a need for more detailed considerations of the relationships between product attributes and production processes required for manufacturing. At this stage the decision support tool can be used to identify tradeoffs between energy intensity of processes and functional requirements. After a process has been chosen, the energy simulation model can be used to evaluate the energy consumption during the manufacture, and hence provide appropriate information during the selection of least energy intensive processes or setting process parameters so that they consumed the least energy. In a similar manner during the final phase (i.e. manufacturing stage), production machinery and facilities need to be monitored to improve energy flows and efficiency. Advance energy management systems can be used to measure, record and improve the energy consumption within a production system and track energy demand of the manufacturing site. 4.2
Application of DfEM to a Product
DfEM was applied to the design and manufacture of a plastic chair to evaluate the areas where energy consumption could be minimised. In the case of a simple product like a plastic chair, various energy considerations and goals can be defined for the product at the start of the development phase and while creating a Product Design Specification (PDS). In this case, the CES Eco Selector can be used to assess the energy requirements for extraction, preparation and processing of various plastics to further narrow down the list of materials that meet the functional requirements of this product. This evaluation may show that of the Acrylonitrile-ButadieneStyrene (ABS) and reinforced Polypropylene (PP) can both fulfil the product specification, but PP is the least energy intensive to extract and prepare. After selection of the material, the energy simulation model can then be used to evaluate the various production processes that can be used to manufacture the chair using the PP, and provide an indication of the least energy intensive processes. In this case due to specific product geometry, the feasible processes that can be adopted are high impact injection moulding and gas
assisted injection moulding. The evaluation of these two processes indicates that the gas assisted injection moulding will potentially consume more energy due to the high energy requirements for compressed air. The energy model is therefore able to determine energy hotspots at the same time provide a breakdown of the energy consumed by the direct processes, auxiliary processes as well as the indirect processes from the facility. This information could aid the decision making when deciding on the best method of manufacturing of the product and provide designers with a greater insight into energy consumption. Finally during the actual production of the chair, AEMS can be used to monitor the real time energy consumption by injection moulding, process cooling, drying ovens, heating and ventilation systems as well as lighting to improve the efficiency of the production facility. Although these tools can be used independently within each phase of the design process, clearly greater benefits could be achieved through integration of these tools, as the data/knowledge generated by each can support the decision made in other phases. For example, the data collected from the AEMS can help to improve and expand the database used by the ESM, providing more accurate energy consumption values for the actual production facility rather than a generic plant. Likewise, ESM can help CES by providing more customised data for the processes carried out at the facility so that an accurate streamlined LCA can be carried out for subsequent products designed and manufactured at the plant. 5
CONCLUSIONS
Design is an integral part of any product development process and much of the decisions taken at this stage accounts for majority of the financial and environmental cost of a product. Therefore to reduce the energy consumption of a product during the manufacturing stage, energy considerations need to be included at the design stage. By identifying where the energy is used during production and how productively it is used, the designer gains an insight into the energy effectiveness of the process in relation to a product. This knowledge can empower the designer to intelligently
Life Cycle Design - Methods and Tools explore the suitability of a product feature, a material and consequently the chosen manufacturing process with energy minimisation in mind. The DfEM methodology presented in this paper together with the simulation tool would enable designers to do ‘what if’ scenarios to identify the most practical and economically feasible design improvements that could reduce the need for energy consumption during manufacture. As with most DfX tools which improves design from just one perspective, DfEM only provides a singular view focusing on energy consumption during production. The reduction of energy consumption in the manufacture phase may have an adverse effect on the other stages of the life cycle. Clearly the scope of this approach has to be extended to consider the energy considerations related to wider issues within a product life cycle such as the energy requirements during the use phase, logistics and reverse logistics and end-of-life. As such this approach should be used in conjunction with other LCM tools to evaluate the overall life cycle impact of the product to ensure that the absolute environmental impact is reduced and not increased. The matter of minimising energy consumption of a production system must be addressed as part of a multi objective optimization problem. 6 [1]
World Resource Institute (2005): Greenhouse Gas Emissions: 2005, Available at: http://www.wri.org/chart/worldgreenhouse-gas-emissions-2005, [Accessed 10 August 2009]. Otto, K. N. and Wood, K. L. (2001): Product Design Techniques in Reverse Engineering and New Product Development, Prentice Hall, New Jersey.
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Duflou, J. R. and Dewulf, J. (2004): Eco-Impact anticipation by Parametric Screening of Machine System Components, In Product Engineering (Eds, Talaba, D. and Roche, T.) Springer, Netherlands, pp. 17-30.
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Guinée, J. B., Gorree, M., Heijungs, R., Huppes, G., Kleijn, R., van Oers, L. et al., (2002): Handbook on life cycle assessment-operational guide to the ISO standard-, Kluwer Academic Publishing., Dordrecht, The Netherlands
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McAloone, T. C. (2000): Industrial Application of Environmentally Concious Design, Professional Engineering Publishing, London.
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Fitzgerald, D. P., Herrmann, J. W., Sandborn, P. A., Schimdt, L. C. and Gogoll, T. H. (2007): Design for Environment (DfE): Strategies, practices, guidelines, methods and tools, In Environmentally Conscious Mechanical Design(Ed, Kutz, M.) John Wiley & Sons, New Jersey.
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Knight, P. and Jenkins, J. O. (2009): Adopting and applying eco-design techniques: a practitioners perspective, Journal of Cleaner Production, 17, (5) 549-558.
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Hauschild, M., Jeswiet, J. and Alting, L. (2005): From Life Cycle Assessment to Sustainable Production: Status and Perspectives, CIRP Annals - Manufacturing Technology, 54, (2) 1-21.
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Swiss Centre for Life Cycle Inventories, Ecoinvent, Available at: http://www.ecoinvent.ch/ [Accessed 8 June 2010].
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Granta Design Ltd. (2010), Available http://www.grantadesign.com/ [Accessed 1 July 2010].
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[23] Optima Energy Management, Available at: http://www.optimaenergy.net/ [Accessed 20 April 2010]. [24] Boyd, G., Dutrow, E. and Tunnessen, W., (2008): The evolution of the ENERGY STAR® energy performance indicator for benchmarking industrial plant manufacturing use, Journal of Cleaner Production, 16, 709-715 [25] Kissock, K. J. and Eger, C. (2008): Measuring Industrial Energy Savings, Applied Energy, 85, (5) 347-361. [26] Harvey, L. D. D. (2009): Reducing energy use in the buildings sector: measures, costs, and examples, Energy Efficiency, 2, (2) 139-163. [27] Gutowski, T., J. Dahmus, and Thiriez, A., (2006): Electrical Energy Requirements for a Manufacturing Process. Proceedings of 13th CIRP International Conference on Life Cycle Engineering, Leuven, Belgium, 623-627. [28] Herrmann, C. and Thiede, S. (2009): Process chain simulation to foster energy efficiency in manufacturing, CIRP Journal of Manufacturing Science and Technology Life Cycle Engineering, 1, (4) 221-229. [29] Seow, Y. and Rahimifard, S., (2010): A Framework for Modelling Energy Consumption within Manufacturing Systems, Proceedings of 43rd CIRP International Conference on Manufacturing Systems, Vienna, Austria. pp. 222-227. [30]
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Eco-Design Tool to support the Use of Renewable Polymers within Packaging Applications James Colwill, Shahin Rahimifard, Allen Clegg Centre for Sustainable Manufacturing and Reuse/Recycling Technologies (SMART), Wolfson School of Mechanical and Manufacturing Engineering, Loughborough University, UK
Abstract Bioplastics derived from renewable polymers such as sugars, starches and cellulose, have attracted significant interest from companies looking to reduce their environmental footprint. New production capacity and improved materials have resulted in their increasing adoption for mainstream consumer products packaging. However questions remain regarding their overall environmental benefits and how the maximum environmental gain can be achieved. These uncertainties highlight the need for a decision support tool to aid the packaging design process. This paper examines the issues surrounding bio-derived polymer use and discusses the development of an eco-design tool to assist in their rapid and efficient adoption. Keywords: Eco-design; Renewable Materials; Biopolymer Packaging
INTRODUCTION
Unfortunately, the current level of scientific understanding of the environmental benefits achievable from these materials, particularly post gate (use and end of life stages), is inadequate or simply nonexistent [4]. This is supported by the findings of a review of 25 published LCA reports from the academic and commercial literature, spanning the period between 1997 and 2009, Figure1 [2]. Specific questions, regarding the impact on food production, genetic modification, consistency of supply, technical performance, contamination of conventional polymer waste streams and biodegradability, remain unanswered. Whilst government support for renewable materials is desirable if not essential, caution should be taken to avoid the premature or inappropriate adoption of a particular BDP or technology, which in turn could hinder future development, particularly if the environmental claims are later proven to be false or vacuous. This paper begins with an overview of the main BDPs used as packaging, their key applications and potential market growth. It then considers the various issues that surround the use of BDPs and identifies the key barriers and drivers to wider and greater adoption. In light of the growing need for sustainable manufacturing,
of Studies
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The annual global production capacity of bio-derived polymers (BDPs) has been forecast to grow annually by 37 percent, reaching 2.33 Million tonnes by 2013 [1]. This rapid growth has been sustained as BDP packaging markets expand from the early adopters producing niche and synergetic items such as organic drinks and whole foods, to global mainstream products and brands such as cola, crisps and chocolate [2]. A key driver of this success has been the desire for environmentally friendly, sustainable packaging and the belief that BDPs meet this requirement. To a large degree this view has been fostered both from the claims made by manufacturers, and the obvious emotional attraction towards a material with a natural, renewable pedigree. More recently this market demand has been further encouraged by various government initiatives which promote and support the procurement of ‘bio-based’ and ‘sustainable’ products [3].
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Figure 1: Findings of LCA study against review criteria (2009) [2]. we then consider the range of eco design and decision support tools that are available to industry to assist in the identification, selection, application and assessment of BDP packaging. This study, through an assessment of the key strengths and weaknesses of each tool, aims to identify the key unfulfilled needs in this area and thus establish both the need and the framework for the new eco-design tool. The paper concludes with an overview of this new tool, its proposed structure, and how this will meet the unfulfilled needs of industry. 2 2.1
BIO DERIVED POLYMERS IN PACKAGING Key BDPs: Their Origins and Evolution
Whilst a small number of BDPs, such as cellulose film, have maintained a commercial presence in the packaging market, the resurgence in interest of BDPs as a viable alternative to conventional polymers began during the 1990’s in response to increasing pressure from both consumers and government to reduce the environmental impact of packaging culminating in the EU directive 94/62/EC on Packaging and Packaging Waste [5]. Whilst the direc-
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_28, © Springer-Verlag Berlin Heidelberg 2011
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tive and subsequent legislation does not promote the use of bio derived materials over conventional ones, it obligates companies to formally consider the environmental aspects of their packaging designs in addition to the commercial and technical ones.
Polymers extracted from biomass
BIO-DERIVED POLYMERS
Starch Lignocellulose
It is an expected and an accepted limitation of this review that as a material becomes established, i.e. first generation bio-polymers such as cellulose film and foamed starch chips, it will probably become less noteworthy of comment and so its frequency will decline even if use actually increases. Also, the results recorded launch activity, not ongoing use, and so should not be viewed accumulatively. When these new introductions are plotted against their launch dates, a picture emerges of a gradual annual growth in use, see Figure 3 lower line. However, this only shows the frequency of product launches and does not consider the individual significance of each new introduction in terms of the BDP used. As it is not possible from these announcements alone to ascertain accurate data with regard to the volume of sales, material use, specific barrier properties, transmission rates etc, a simple weighting factor was applied instead. The factor used was allocated based on five easily assessable key criteria: Brand awareness, Company size, Launch market size, Potential market size and Application complexity. A weighting factor was applied for the first four criteria of 1x for local, 3x for national or 5x for global. For the fifth criteria, application complexity, a weighting of 1x for low complexity, 3x for medium (thermoformed/ laminated), 5x for high complexity (injection molded, blown, high barrier). Once applied the sum total was divided by five to a final value of between 1 and 5 for each application. When this data is re-plotted with the weighting factor it shows a much sharper growth curve (figure 3, upper line) particularly during the last two years, which might indicate that BDPs are entering a new accelerated growth phase. This would lead to higher growth than other data has previously suggested, such as BDP production capacity investments [1], which forecast growth by 2020 to reach 3.5 Mt capacity and earlier projections which forecast volumes of between 2.5Mt and 4.17Mt by 2020 [6]. In addition, when the two graphs are compared it suggests that in addition to a general increase in use, these new BDPs are gaining wider market acceptance, moving from niche, synergetic applications such as organic, fair-trade and health food products to mainstream, high profile brands.
Modified starch-based Modified cellulose-based Modified lignin-based
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Packaging Applications Study
To understand how the application of BDPs for packaging has evolved, an online review of published announcements for new product launches in BDP packaging was undertaken. This included searching the websites and press archives of all the main BDP manufacturers, associated trade press and the key industry bodies, associations and institutes for; the environment, packaging and plastics industries, dating back to 2004.
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The first generation of BDPs were limited to low technical performance applications, in the past decade a new generation of materials have been developed, capable of being used for processed, long shelf life products such as crisps, cereals, chocolate and beverages. Figure 2 identifies the key BDPs used in packaging and the main source/route to production [5]. As their availability and costs have improved, so their uptake has increased. The most commercially successful of these to date are PolyLactic Acid (PLA) and Bioethylene based PE and PET. Both these materials have been used in full or in part across a wide range of pack formats and processes such as; stretch blow molded bottles, injection molded components, thermoformed trays and flexible films (including high barrier laminated films for coffee and crisps).
Bacterial compounds Polyhydroxyalkanoates (PHAs)
Figure 2: Overview of principal bio-derived polymers (adapted from [6]). Flows in bold indicate routes to the principal BDPs. New Introductions of BDP Packaged Products 70 60 50 40 30 20 10 0
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Capacity and New Investments
In anticipation of the future demand, a number of companies have invested in plant for the production of BDP’s The annual global production capacity of BDPs, based on company announcements, is now forecast to grow from 0.36 Mt (million metric tonnes) in 2007 to 2.33 Mt in 2013, an annual increase of 37 percent [1]. Figure 4 shows the projected growth in the production capacity of Class A and Class B BDPs. Class A BDPs include PLA, PHA. TPS and cellulose, whilst class B BDP’s are those which are identical to conventional polymers apart from the original monomer source, such as PE and PET derived from bio-ethylene. 3 3.1
THE KEY ISSUES TO USING BDPS IN PACKAGING Drivers and Barriers
There are a number of factors which to a greater or lesser degree have had or will continue to have an influence on the development, uptake and growth of bio-derived polymers within the packaging
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Factors influencing BDP adoption
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The limited availability and increasing cost of fossil resources (oil and gas) and the need to secure National energy supplies.
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BDP Production capacity Increases Figure 4: Global production capacity of bio-derived polymers based on company announcements up to May 2009 [2]. sector. The most significant of these are listed in Table 1, however whilst many of these have a foreseeable resolution as technology or commercial advances are made, there are two key issues that in our view will require a much more substantial and collaborative effort to resolve, these are:
Development of alternative feedstocks to avoid direct competition with food production (materials and land use) in order to provide a sustainable and scalable polymer source. Development of new technologies and infrastructure to enable the conservation of this resource and to avoid contamination and disruption of existing conventional polymer recycling.
In terms of positive influences, policy and government initiatives such as the EU’s “Lead Market Initiative”, the ADEME’s “Bioproducts Guidebook for Greener Procurements” and the USA’s “Federal Bio-based Products Preferred Procurement Program” have the potential to be a major influence on BDP growth and uptake. The other major driver will be cost and performance parity as the gap between BDPs and conventional plastics narrows. 3.2
Packaging Design and Development
The varied and cross departmental responsibilities for packaging functions within a business add yet further complexity to the packaging development process, (Figure 5). Whilst the majority of functions are clearly aligned to a particular hierarchical structure, e.g. Finance and Accounting, Sales and Marketing, Engineering and Production, packaging impacts on almost all aspects of the business and often the control hierarchy will change on a regular basis as a means to adjusting an imbalance caused by that particular departmental bias, (finance, marketing, operations etc). This has often resulted in the packaging function ‘ownership’ being rotated through different business functions on an almost cyclical basis, Manufacturing, Marketing, Finance/Purchasing etc. One approach some companies have taken is to break the packaging functions into three separate groups as shown in Figure 5. This allows each function to be more closely aligned with the most appropriate business functions. However this then creates the problem of ensuring that communication and cooperation between the groups maintains the skills and potential of the whole, particularly important in the development of new packaging.
Consumer demand driven by the growing awareness of the need for sustainable management of natural resources. Other factors include: Organic and green brands, Retailer pressure, anti litter action and increasing environmental problems and severe climate changes. Higher costs and more complex supply chains, including capacity limitations and a restricted supplier base. Technical performance limitations compared to conventional polymers. Lack of clarity and quality of data on the overall environmental impacts. Other factors include: Greater recycling of conventional polymers and problems of waste stream contamination by BDPs, Land availability and food production. Bio-Fuel developments compete for limited feedstock resources but also provide volume, a secure market, and commercial scale.
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Policy and legislation, particularly within the area of climate change, sustainability and economics.
Pressure groups can influence public opinion and government policy. However, views are polarised for and against at present. New technologies such as genetic modification and nano engineering bring huge potential benefits but also huge potential risks. Tend to polarise opinion particularly within an already sensitized and sceptical public.
Table 1: Barriers and Drivers to increased BDP adoption. It is clear that the decision to adopt BDPs for packaging within an organization will not be restricted to any one group, function or skill set. For the tool to be fully inclusive it needs to engage actors at all levels and stages depending on their abilities and needs. This is true not just within the company but also throughout the wider supply chain and where possible engaging the consumer. 4
AN OVERVIEW OF CURRENT ECO DESIGN TOOLS
A study of academic papers and industrial reports was carried out across a range of eco-design tools. This included individual [7, 8] as well as multiple [9,10] tool reviews. The main focus was on packaging but general eco-design tools that could be used for packaging design were also considered. The review focused on a number of criteria, four of which have been selected for comparison in Table 2 and Figure 6. These are: Sustainability Considerations (Which of the three key pillars of Sustainability, Environmental, Economic and Social, were considered by the tool), Life Cycle Approach (What life cycle stages were considered), User Guidance (Which of the 5 guidance criteria listed were output to the user) and User Inclusiveness (of the user groups listed, how many would the tool be useful and accessible to). In all, 40 tools were assessed using a combination of previous design tool studies and individual tool reviews. The main criteria and sub divisions are listed below in Table 2. It is clear that significant interest exists, within a range of industries operating at various stages along the supply chain, in the development of tools for the purpose of improving the environmental design of packaging as well as using renewable materials.
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163
High
Business Functions Low
Support
P otential Business Impact
C ost of Change
Packaging Management Short term focus – Current
Tactical Packaging Development Mid term focus – 1-2 year
Strategic Low
Packaging Research Long term focus – 3+ years
Engineering
Operations
Installation, Maintenance, Repair, Improvement
Efficiency, quality, production cost, output, Capacit
Quality Control
Logistics
Efficiency, Damage, Supply, Storage.
Control, Testing, Compliance
Purchasing
Supply, Costs, Quotes, Availability, Control.
NPD Formulate, Specification, Prototyping.
Finance
Costings, Justification, Capex, Approval.
Marketing / Sales Product, markets, consumer, formats.
Legal / CSR
Local M anagement and Control
Packaging Functions
Legislation, compliance IP protection, enforcement
High
Central
Figure 5: Key functions of a packaging dept and their relation to other key business areas. 5
FRAMEWORK FOR THE TOOL
5.1
Feature
Introduction to the tool
The development of the proposed tool arose from the recognition of the necessity to ensure that the limited capacity of bio-polymers needs to be directed towards applications where the greatest overall environmental benefit can be achieved. It was envisaged that a tool which could help achieve this through the appropriate selection and application of materials within the pack design and development process, would be widely welcomed by industry. [11]. It is also clear that a direct comparison of BDPs with their conventional counterparts would be misleading as to the future potential that could be achieved once the BDP industry and markets mature. The ability of the tool to evaluate the pack based on future potential, as well as current performance, is essential if it is to play a strategic role [12]. 5.2
Key requirements of the tool
The requirements for the eco-design tool were identified from both a literature review and through industry consultation. Six key requirements are listed in Table 3. The features highlighted in bold are those which are considered to be absent or inadequately provided for in existing tools. These are supported by similar findings in a recent Canadian Government report [10].
Assessment Criteria Sustainability Considerations: Environmental (En), Economic (Ec), Social (So) Life Cycle Approach: Full (C2C), Cradle to Gate (C2G), Gate to Cradle (G2C), None User Guidance: Descriptive, Selective, Prescriptive, Assessment, Comparative
Should consider performance across the whole life cycle, cradle to cradle.
Sustainable Focus
The tool should consider all three pillars of sustainability: Social as well as Environmental and Economic.
Strategic and Tactical
The tool should support strategic decision making looking at future performance as well as current properties and performance.
Holistic and Inclusive
Should be usable and provide guidance across the whole supply chain, including consumers.
Total Stage Support
Should provide support at each stage of the design / development process through a series of individually targeted but connected tools.
Feedback
Tool should provide feedback which allows progress to be measured and improved.
Table 3: Key features, requirements and intended users of the tool. 5.3
Proposed Structure for the Packaging Eco-Design Tool
The tool aims to support the decision process at three different levels depending on the expertise of the user, availability of input data and required detail of output data as shown in Figure 6. This will include; type of application or product to be packaged, selection and use of the BDP material, pack construction, manufacturing process, distribution and retail methods, consumer use and ‘end of life’ management.
En 20 50.0%
En & Ec 13 32.5%
None 16 40.0%
C2G 6 15.0%
G2C 11 27.5%
C2C 7 17.5%
The three separate but interlinked tools, which can be used independently or in combination, are as follows:
1 21 52.5%
2 18 45.0%
3 or 4 1 2.5%
all 5 0 0.0%
Assesses the potential for including BDP packaging as part of the company’s overall packaging / corporate sustainability strategy:
SC 5 12.5%
SC&C 0 0.0%
User Inclusiveness: Specialist Business 24 11 Specialist, Bussiness, Supply Chain (SC), 60.0% 27.5% Supply Chain & Consumer (SC&C)
En & So En, Ec & So 6 1 15.0% 2.5%
Requirements
Full Life Cycle Perspective
Table 2: Results of Ecodesign Tool Study against review criteria.
EcoD2 Part 1 - Justification Level
Method: A series of questions, in the form of a decision tree, are asked which highlight the key threats and opportunities, strengths and weakness for the adoption of BDPs by the company, both short and long term. Result: The results from the questions will give a top level guidance on how the company should proceed. This might include statements such as:
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BDPs are not compatible with your current business practice and strategy.
BDPs will provide significant benefits but not within current cost limits.
BDPs are a viable option for your company, proceed to next level.
EcoD2 Part 2 - Specification Level Identify specifically which BDPs will meet the essential and desirable requirements of the specific application regarding technical, commercial and operational feasibility: Method: A technical relational database of all BDPs commercially available will allow specific requirements to be searched and the suitable polymers to be identified. Each of the key known factors can be entered via a series of blank forms or lists, e.g. Barrier, Strength, Elasticity, Compression, Melt temperature, Process etc.
logical therefore that the packaging should reflect those product values. The category manager of the retailer and the marketing director of the manufacturer/supplier arranged a working meeting to discuss and agree a way forward to achieving this goal. During the meeting the Eco-design Tool (EcoD2) Part 1 was used to investigate whether BDPs might provide a viable packaging solution. EcoD2 Part 1 - Justification for Using BDP Packaging. With only a limited time available a quick answer was required to be derived from information that was readily available to the two ‘high level’ experienced but not technical business people. Method: The company’s Marketing director accessed the tool online to assess the suitability of BDPs as a means to package their product in a ‘carbon neutral’ way. Following a decision tree based question and answer process, he input top line information about the company, its product and overall aims and objectives, a process that took approximately 10-15 minutes. Result: The tool provided guidance as to the suitability of BDPs, the main implications of its use and recommended next steps on how the company should proceed:
Based on the product’s brand values, market positioning, premium price, technical/performance requirements and potential end of life disposal options, there is a strong possibility that BDPs could provide a suitable packaging medium for this product
The BDPs which meet the product requirements and are within a viable geographic range would be Starch, Cellulose or PLA based. Option buttons would be provided which would allow the company to produce a chart comparing specific properties of these ‘base’ materials on factors such as cost, bio-degradability and technical properties. A list of suppliers could also be generated within a given geographic range.
The suggested next steps, assuming that the commercial and technical requirements fell within the given range, would be to select and contact the suppliers of these materials initially with a specification / brief to be prepared from the information added to the system so far and to be further populated by the technical and operational staff within the two organizations.
The specification is sent to the supplier and linked to the tool. The supplier’s response is entered into the tool online. This allows comparisons between the different supplier/material options to be compared.
Figure 6: The relationship between User time and skill levels with the three separate Tools parts 1 - 3. Result: The results from this stage will be in the form of single datasheets and comparative performance graphs to include:
Data sheet for each BDP that meets or exceeds entered criteria.
Multiple BDPs can be plotted against single or multiple criteria.
Potential future scenarios can be used to give a predicted performance potential.
EcoD2 Part 3 – Comparison Level Compares different pack concepts across a range of criteria and supports the final selection process as part of existing new pack development procedures. Method: Each concept is measured in terms of its material content, material type, performance, size, dimensions, weight and key features. These are input into a program via a menu system which performs the necessary calculations. Result: The final concepts will be measured in terms of their individual material components, total pack performance, construction costs, cube, environmental footprint etc. The results from this stage will be in the form of single page report that summarises the key benefits, costs and performance of each concept. 6
CASE STUDY – A BAG FOR ORGANIC SALAD
The following example illustrates how this proposed eco-design tool might have been used during the decision, design and development process for a possible packaging development project. We created the following scenario as the basis for the case study: A company (UKCM) supplies a leading UK supermarket with pre-washed mixed organic salad. Both the manufacturer and retailer had been meticulous in ensuring that the product meets the highest standards of purity, quality and environmental performance. It was desirable and
EcoD2 Part 2 - Specification Level In order to complete the specification, the technical/packaging manager/technologist identifies specifically which commercially available grades of BDPs from which suppliers meet the technical and performance product requirements. The materials that fulfill these needs are added to the specification. Method: A technical relational database of all BDPs commercially available allows for specific requirements to be searched and the suitable polymers to be identified. Each of the key known factors can be entered via a series of blank forms or lists. e.g. Barrier, Strength, Elasticity, Compression, Melt temperature, Process etc. Result: The results from this stage will be in the form of datasheets and comparative performance graphs. In addition the qualifying materials and supplier information can be transferred from the database to the specification sheet for transmission to the supplier. This can also be used to automatically request quotes, technical data and trial sample materials. EcoD2 Part 3 – Comparison Level Following initial trials of the different materials, the comparison tool is used by the designer to compare the different pack concepts
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165
across a range of criteria and to use this data to support the final selection process as part of in-house new pack development procedures. The outputs of this information can be stored and made available to consumers via the tool or other medium such as the retailer’s website.
ing for packaging will be a significant step towards achieving these goals.
Method: Each concept is measured in terms of its material content, material type, performance, size, dimensions, weight and key features. These are input into a program via a menu system which performs the necessary calculations.
[1]
Shen, L., Haufe, J., Patel, M. (2009): Product overview and market projection of emerging bio-based plastics, PRO-BIP 2009, Group Science, Technology and Society (STS), Copernicus Institute for Sustainable Development and Innovation Utrecht University.
[2]
Colwill, J et al (2009): Opportunities for bio-polymer resource conservation through closed loop recycling, in: Proceedings of GPEC 2010 on Sustainability & Recycling: Raising the Bar in Today’s Economy, Orlando, Florida USA.
[3]
Skibar W., Grogan G., Pitts M., Higson A., (2009): Analysis of the UK Capabilities in Industrial Biotechnology in Relation to the Rest of the World, a sector assessment for the (IB-IGT) prepared by Bioscience for Business KTN, Chemistry Innovation KTN and the NNFCC.
Whilst the growth and development of bio-derived polymers has continued to gain momentum over the past few years, there is a clear danger that this could stall if confusion regarding their overall environmental impact is not removed. A number of methods for categorizing BDPs have been suggested, such as by feedstock type or production method, however in terms of application and end of life management there are two main divisions: Class A, unconventional polymers extracted or synthesized from renewable feedstock but not compatible with conventional plastics and Class B, conventional polymers synthesized from bio-ethylene e.g. polyethylene and PET. It is these former class A bio-polymers, such as PLA, Cellulose, PHA and TPS, that require further investigation in this area in order for them to achieve their environmental potential.
[4]
Song, B., Lee, R.J., Lu, W.F. (2010): A Study on the EcoPerformance of Plastics in the Injection Molding Process, Proceedings of 17th CIRP International Conference on Life Cycle Engineering, Hefei, China, 180-185.
[5]
European Parliament and Council Directive 94/62/EC of 20 December 1994 on packaging and packaging waste, Accessed online at http://eurlex.europa.eu/LexUriServ/LexUriServ.do?uri=CONSLEG:1 994L0062:20090420:EN:PDF
[6]
Quieroz A.U.B. and Collares-Quieroz F.P. (2009): Innovation and Industrial Trends in Bioplastics, Journal of Macromolecular Science, Part C: Polymer Reviews 49 65-78.
In parallel with the growth of BDPs, there has been the pressure on companies to reduce their manufacturing environmental footprint particularly that associated with their packaging. To-date this has focused primarily on waste reduction and recycling and in some instances materials substitution, such as replacement of PVC with PET. As a result, a number of guides and tools have been developed to assist companies in achieving these goals; including Life Cycle Assessment, Retailer Scorecards and Green Design Guides. However these guides tend to be limited in the guidance that they give, strategic and early design stage use, the range of impacts measured, the cost and complexity of use and/or the over simplification of the results. In particular for BDPs, it is important to consider the likely future impacts as technologies, costs and methods advance.
[7]
Crank, M., Patel, M.K., Marscheider-Weidemann, F., Schleich, J., Hüsing, B., Angerer,G. (2005): Techno-economic feasibility of large-scale production of bio-based polymers in Europe. Report No. EUR No: 22103 EN, Catalogue (OPOCE): LFNA-22103-EN-C.
[8]
Walmart (2009), Sustainable Product Index: Fact Sheet, accessed online at www.walmartstores.com/Sustainability/9292.aspx, 5th October 2009.
[9]
Huo, L., Saito, K. (2007): Concept Identification and Implementation of Sustainable Packaging Systems, Journal of Packaging Science Technology, Vol. 16 No. 4 P269-281.
[10]
Five Winds International (2008): Inventory of Sustainable Packaging Initiatives and Proposed Approach to Develop Sustainable Packaging Guidelines, prepared for the extended Producer Responsibility Task Group CCME (Canadian Council of Ministers of the Environment).
[11]
Novkov, S. (2008): Sustainability Management of Industrial Enterprises Advanced Concepts, Techniques and Tools, presented at the 5th International Scientific Conference ‘Business and Management’ Vilnius, Lithuania, 16-17 May 2008.
[12]
Sonneveld, K., James, K., Fitzpatrick, L., Lewis, H., (2005): Sustainable Packaging: How do we Define and Measure It?, presented at the 22nd IAPRI, Symposium, Campinas, Brazil, 22-24 May 2005.
Result: The final concepts will be measured in terms of their individual material components, total pack performance, construction costs, cube, environmental footprint etc. The results from this stage will be in the form of single page report that summarises the key benefits: environmental, commercial, social and physical performance for each concept. In addition, comparative charts and graphs can be produced for each of these key criteria. 7
8
SUMMARY AND CONCLUDING DISCUSSIONS
As packaging is a multi disciplinary function that extends across the majority of traditional business departmental boundaries, it is essential that this tool provides a mechanism for a wide range of users with different skills and requirements to input into and benefit from its use. Furthermore, the use of the tool should extend beyond the traditional business operations and be available to the whole supply chain. In particular the information should be available to the consumer to enable them to make informed choices about the products they buy which in turn will drive further environmental investment and development by industry. It is clear therefore that a holistic approach is needed to eco packaging design if the future challenges of sustainability are to be achieved. It is also clear that better guidance at both the strategic and tactical level on the selection and use of bio-derived polymers in packaging applications is required by industry to avoid ‘green wash’ and ensure the greatest environmental, sustainable and ecological return is achieved from this renewable but ultimately finite resource. The eco-design decision tool which we are develop-
REFERENCES
State-of-the-art Ecodesign on the Electronics Shop Shelves? A Quantitative Analysis of Developments in Ecodesign of TV Sets 1
2
Casper Boks , Renee Wever , Ab Stevels 1
2
Department of Product Design, Norwegian University of Science and Technology (NTNU), Norway 2
Faculty of Industrial Design Engineering, Delft University of Technology, The Netherlands
Abstract From 2005 to 2010, environmentally relevant data was collected on mainstream television sets to select the yearly EISA Green Award winner. As the sets represent state-of-the-art developments in application of ecodesign principles, the data is used to sketch progress in ecodesign application in mainstream development of television sets. It is concluded that improvements in energy consumption were substantial, fuelled by application of new physical principles, from CRT to LCD and plasma, to LED. This has also enabled thinner screens, lower weight, and more efficient product architecture and packaging. For chemical substances and recycling implications, progress is less evident. Keywords: Progress in Ecodesign; Environmental Assessment; Environmental Benchmarking; Energy Consumption
1
INTRODUCTION
The European Imaging and Sound Association (EISA) is an association of 50 special interest magazines from 19 European countries. EISA started in 1982 when the editors-in-chief from five European photo magazines came together to select “The Camera of the Year” for the first time [1]. Since then, EISA has evolved to the organization as it is known today, perhaps best for the European EISA Awards. In addition to many other technological awards, each year awarded during the IFA fair in Berlin [2], the main electronics fair in Europe, EISA gives, since 2005, the EISA Green Award to a television set which is environmentally superior to competing products. These products are selected and entered into the competition by electronics manufacturers themselves and can therefore be assumed to be the state-of-the-art in ecodesign at the time of entering the competition; since each product needs to be available on the European market in at least 10 countries, the products can also be assumed to be commercially viable. EISA’s first Green Award went to a conventional, bulky analogue television, but later, flat TVs became common and although a few years back, there was a serious attempt to shelter both Plasma and LCD under the same umbrella; the winner ultimately was always LCD-based. In 2009, the winning design was based on LED technology for the first time. Clearly, in only 6 years, the state of the art in ecodesign of TV sets has progressed enormously. According to the EISA Green Award Jury, the most recent winner of the Green Award is, for the first time, a truly ecodesign product (Figure 1). 2
they have designed, this data provides a unique source for charting developments in ‘commercially viable ecodesign’ of television sets in this period. The paper has three aims:
To quantitatively illustrate the development of ‘commercially viable ecodesign’ since 2005. Based on this historical data, this paper points out the main developments over time by illustrating how and to what extent progression has been made on aspects of energy consumption, material application, packaging, product chemical substances, and end-of-life related characteristics such as disassemblability.
To discuss the challenges in the relative assessment and weighting of different aspects of environmental performance of products, especially within a limited time frame.
To reflect on the relevance of the findings for ecodesign and green supply management in general,
The paper is structured as follows. Section 3 addresses background information on the measurements. Section 4 presents, for each of the main criteria, the developments of the various product characteristics over time. In section 5 these are further discussed and section 6 concludes with observations about progress in ecodesign of television sets in the period 2005-2010.
GOAL OF THE PAPER
This paper, written by two EISA Green Award jury members, is based on the collected data from 2005-2010, on basis of which the EISA Green Awards have been awarded. This data consists of product data relevant for the determination of the products’ environmental performance. For each year, data from 3 to 6 television sets are available with a total of 27 sets over 6 years. Based on the supposition that OEMs indeed enter into the competition the most environmentally friendly, commercial product
Figure 1: EISA Green Award winner 2010.
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_29, © Springer-Verlag Berlin Heidelberg 2011
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Life Cycle Design - Selected Applications ENVIRONMENTAL ASSESSMENT
For the EISA Green Award jury, the main challenge has been to develop a scorecard that does justice to the environmental performance of the candidate products, given the limited time available; jury membership is unpaid resulting in a maximum of a few days available for the job. In 2005 it was decided that the environmental performance of the products would be most fairly represented by scoring scheme addressing a number of categories representing environmental focus areas such as energy consumption, material application, recyclability, chemical substances, and packaging. Each year an additional category, the so-called bonus category, was added to allow the jury to award bonus points for smart design solutions reflecting an innovative ecodesign spirit. It was well realized that the environmental performance as determined by the jury would never 100% reflect a true scientific environmental assessment based on Life Cycle Assessment (LCA). The rationale for this decision was on the one hand a lack of time, data and resources to perform such an assessment, and on the other hand the opinion that superior environmental performance cannot always be covered 100% by LCA, and is for example also partly determined by a positive, innovative environmental attitude. Such an attitude might at first not result in the lowest environmental performance for a certain criteria, but might for example have positive rebound effects over time, or positively influence user attitudes and behavior. This way, ‘green’ was to reflect a fair balance between different understandings of what ‘green’ is. The presence of the EISA Green award can be regarded as recognition for environmental excellence in product design, the importance thereof, and may even contribute to stimulating environmental innovation in design of consumer electronics. Of course, having an environmental award for a large, energy consuming piece of equipment mainly offering ‘mere entertainment’ may by some be considered as a contradiction in terms. It can be argued that the design and sale of such products are contrary to basic ecodesign principles, in particular dematerialization (“the best TV is no TV at all”). However, the presence of these products on today’s market is a fact of life, and it is not within the scope of this paper to discuss today’s negative effects of ‘consumerism’ Each year, only television sets of a certain screen size are eligible for the EISA Green Award; sets with this screen size reflected what were at that time considered mainstream products aimed at a large share of the market (not high- or low-end products), and not specifically aimed at environmentally very conscious users. The Award as such does not aim at awarding very basic products with superior environmental but low functional quality. Future EISA Green Awards may therefore take into account more explicit considerations of eco-value (environmental impact per monetary value) [3]. The subsequent subparagraphs discuss in more detail the data collected for the various scorecard items, and reflect on the process for doing so. 3.1 General comments on data collection. A recurring issue has been the comparison of different sized television sets. Each year, contesting OEMs may enter sets in the most commonly sold size range (Table 1). In order to allow for a fair comparison, in practice data has been normalized using the screen surface area as a normalization constant. Year
2005
2006
2007
2008
2009
2010
Allowed set size range
28”-32”
37”-42”
40”-42”
40”-42”
40”-42”
42”-46”
Table 1: Screen size of sets in the competition per year.
Each year, individual product scores for each scorecard were used to determine a score on an interpolated scale from 1 to 5. For each of the five scorecard items weighting factors were assigned based on EcoIndicator’99 calculations, resulting in a total one-figure score determining the winner. Both the scoring system and the data collection process did change to a certain extent over the years, as certain aspects became more or less relevant as technology, legislation and recycling infrastructure changed. This resulted among other things in changing weighting factors. However, for the paper at hand this is not relevant, as the main focus is not the determination of winners, but to discuss the development of environmental performance of television sets over time. For the preparation of this paper, using the normalization constants, it was possible to derive a coherent set of data allowing for fair comparison over time. 3.2 Energy consumption Over the years, a number of observations were made: energy consumption figures for default settings provided by the brands themselves were in some cases not in line with actual measurements for the same settings, which is why the jury performed energy measurements themselves. In some cases, our measurements were considerably above the provided figures, in other cases, measurements were considerably under the provided figures. It was also found that for the best scoring sets, often the most accurate measurements were provided by the OEMs. Consultation with OEMs learned that energy consumption data as provided in official documentation may be much higher than in reality, due to official measurements being done based on maximum power conditions for reasons of safety margins. For measuring the energy consumption, over the years different usage profiles have been applied for comparing scores for individual sets. These usage profiles generally consisted of four hours per day “on mode” in various settings. These settings included standard out-of-the-box settings, vivid or dynamic settings if present (from 2009), so-called ‘dark measurements’, allowing for including for automatic brightness settings (from 2009), and socalled eco-modes (from 2009), and 20 hours in standby. Because the usage profiles have not been consistently the same for year to year as new settings were included (for example, eco modes have only been observed since 2009), Figure 2 displays the development of energy consumption in W per square dm screen size measured using the standard out-of-the-box setting only. For the determination of energy consumption of TV sets, complex standards exist prescribing the required tests. These standards are intended to ensure comparable results from independently executed measurements by different OEMs themselves. As for the EISA awards all competing products can be measured under the same conditions, and only the relative performance is relevant for the determination of the scoring, a simplified approach was utilized by the jury, based on measuring the total energy dissipation over a period of time while displaying the same content. Figure 2 shows that, after an initial increase in energy consumption due to the change from CRT technology to LCD/plasma technology in 2006, the energy consumption has decreased considerably. In addition, the scores for the different entries per year show less variance over time, which indicates perhaps maturing technology and/or toughening competition. However, variance has slightly increased again in 2010, mainly due to one outstanding entry (the eventual winner for 2010) that was able to reduce energy consumption to an impressive 45 W with standard settings, whereas measurements for the other entries were in the range of 80-90 W.
Life Cycle Design - Selected Applications
169 the 40” sets was on average not smaller than the 42” sets. The decline of the volume is not so much due to improvements in packaging technology, but rather due to the maturing of the technology of the TVs themselves. The newer products are likely less fragile, and most importantly, slimmer. As screen size determines to a large extent the height and width of the product, only the depth presents potential for improvement. Figure 5 shows the decline in depth of all packaging (in dm), clearly indicating a substantial reduction in thickness. Also the introduction of the detachable foot (which none of the products had in 2006, but all in 2010) has allowed for packaging optimization.
2
Figure 2: Energy consumption in Watt per dm over time. 3.3 Material application In determining relative scores for the environmental impact of materials for structural elements (such as housings, trays, brackets) that represent the main contribution to the weight of the television sets, weight of the total product has been considered as a guidelines as a more detailed analysis is not possible. This decision was partly based on years of experience at Delft University of Technology and Philips Consumer Electronics with environmental benchmarking of products [4]; this practice, which included both the collection of physical data (kilograms, watts, seconds) and Life Cycle Assessment suggested that the relative total environmental impact of material compositions in this type of consumer electronics products corresponds well with the weight of the products (without packaging).
In terms of packaging technology used, little improvement has been observed over time, as the vast majority of products was consistently packed in the same way, utilizing expanded foam cushioning.
3
Figure 4: Trend in packaging volume (in dm ) for 40” and 42” sets.
2
Figure 3: Weight in kg per dm over time. Figure 3 shows the development of the minimum, average and maximum weight (per square dm to adjust for the set size differences) of the entered sets over the years. In the three years where the set sizes were stable (2007-2009) a clear decline can be observed. However, the figure suggests that, even when measurements are normalized for set size, an increase in set size may imply no decrease or even an increase in weight per square dm in the first year after the wide-scale implementation of a new technology (2007 and 2010).
Figure 5: Trend in packaging thickness for flat TV sets 2005-2010.
3.4 Packaging and transport
3.5 Chemical substances
Packaging has mainly been judged on volume efficiency, expressed in both volume index (packaging volume/product volume) and container loading (number of products in a standard 40' sea container), adjusted for different screens sizes. Additional considerations included (the variety of) material use and accessories. As regards packaging, it is interesting to observe two parameters. Figure 4 shows the trend line for the volume (in liters) of the packages of 40” and 42” TV sets over time. Here a clear decline over time can be observed. Interestingly, the packaging for
Although a full Life Cycle Assessment would be preferable to determine the environmental performance on this specialist issue, because of time and data restrictions a more pragmatic way had to be found in order to arrive at a fair comparison. As the jury was mainly interested in relative rather than absolute scores, it was decided that scoring on chemical substances would be done based on size and design of printed circuit boards, and the presence and size of various electric components.
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3.5.1 Size and design of printed circuit boards (PCBs) In practice, up until 2010, most television sets include both traditional PCBs (dominated by sizable transformers and capacitors) and modern PCBs (dominated by ICs (integrated circuits) and SMDs (surface mounted devices)), although there appears to be little correlation between them: using smaller traditional PCBs does in many cases not necessarily mean using larger modern PCBs or vice versa. To arrive at relative one figure scores, the surface area of the PCBs has been measured as a strong correlation appears to be present between size and number/weight of (in particular smaller) components. Due to different chemical composition, traditional PCBs have over the years been attributed double weight in comparison to modern PCBs.
3.5.2 The presence and size of electrolytic capacitors, transformers, and other electric components. Although a relation between PCB size and components is clearly present, differences in number and density of large components such as electrolytic capacitors and transformers do exist. This is why over the years the number of such components of different sizes has been calculated. Analysis of the data does not show a clear pattern over time. The amount and size of transformators on PCBs (environmentally relevant because of high copper content in the coils) have been largely constant over the years, with most sets having 5-9 such components present, with few sets showing 3-4, and negative outliers showing as much as 24 transformators (the latter was seen in 2009). The same impression exists for electrolytic capacitors. Throughout the years, the average number of large ones has been 1-3 components, and on average 16-19 mediumsized or smaller ones. Differences between the best scoring sets in each year have been fairly small as well, though some sets choose different solutions, using several smaller components instead of few large ones and vice versa. This lack of clear distinction between sets has also been the reason why, in terms of relative scoring for the chemical substances scorecard item, the main attention has been paid to PCB size and design as discussed in the previous subparagraph. 3.6 General Product architecture The product architecture scores have been based on a general comparative impression of the sophistication used in the design, taking into account presence and location of the main components, including electronics, speakers, supporting structures and cabling.
2
2
Figure 6: Classic PCB surface area (cm ) per cm of screen size over time. 2
Figure 6 shows the classic PCB surface area per cm screen size, where it should be noted that the similar values for 2008 and 2009 are no mistake in the analysis. It is striking that in 2010 for the first time one of the entries did not give evidence of any classic designed PCB as it only contained modern PCBs.
The most striking development in product architecture in flat screen TV sets has been the reduction in thickness of the screens. This is not only a result of improved technology (i.e. less electronics required) but also a result of lowered energy consumption, which has reduced the need of heat dissipation. As of 2010 the screen thickness has reduced to an extent that a design change can be observed in the housing. The back cover of the sets was plastic for many years, but with the very thin LEDbased screens this was changed to metal covers (steel or aluminum), apparently due to the required stiffness. Over the period studied a clear improvement could be observed with regard to the amount of wiring. Early sets seemed to be the result of a design process where different components were designed separately, and then joined together. In early flat-screens, a ‘spaghetti’ of wiring was not uncommon. For a new technology this is also not surprising. However, over the years a vast improvement has been observed in general product architecture. 3.7 End of life
2
2
Figure 7: Modern PCB surface area (cm ) per cm of screen size over time. Figure 7 shows the same data, but for modern PCBs. The reduction 2 in size per cm screen size for classical design PCBs does not seem to have led to an increase in size for modern PCBs. In fact, the only increase observed (for 2009) corresponds with a period where no reduction in classic PCBs was achieved. Since the average set sizes in 2008 and 2009 were similar (40”-42”), a change in set size can be eliminated as reason for this. It should be noted that in 2010, the largest area score (0.28) for 2010 was observed in the set that had reduced classic PCB area to zero.
The development of applied physical principles (from CRT to LCD and plasma, to LED) has made rethinking of the scorecard criteria for recycling and end-of-life necessary. For LCD sets in the period 2006-2009 there was a clear issue with the back mercury containing lights. Mercury is prohibited by the European Directive on Restriction of Hazardous Substances (RoHS), but LCD TVs are exempted. Nevertheless, the Waste of Electrical and Electronic Equipment Directive (WEEE), which has been implemented in all member states of the EU, requires in its Annex II that mercury containing parts be removed in the end-of-life stage. For all sets this is theoretically possible, but practically not. If the TVs are shredded one cannot recover the mercury, hence the only option would be manual disassembly. However, due to the use of many screws (fairly consistent over the years, between 22-25 on average per set) and also different types of screws (3 to 5) in one TV set, and alternating directions (the set would have to be turned several types to unscrew all layers to get to the back lights), this is economically
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not realistic, as it would take more than 20 minutes to perform per set. Also, the return logistics by which the sets arrive at the recycling center would have to be of such carefulness as not to break any of the fragile backlights. Current collection systems do not achieve this standard as economically feasible demercurisation units that can handle large screens including backlight are lacking. However, since all sets show this problem, it has in general not been a major distinctive aspect in the considerations of the jury. As of 2010, all entries have LED-based backlights. Hence this problem no longer occurs. As for ease of disassembly, the jury did therefore not see reason to distinguish the scoring, hence all sets scored equal. However, because there will be a mixed stream of mercurybased LCD TVs and LED-based TVs in future electronics recycling, it would be good if current LED-based TVs themselves would be clearly marked as “mercury free” or “LED-based”. In 2010 this was the case for none of the entries. 3.8 Bonus category and development of environmental features
Figure 8: Last CRT-based design (2005).
In each year, in addition to the more easy to quantify scorecard items, ‘bonus credits’ could be earned if entries gave evidence of smart design solutions, such as related to (but not limited to) inventive approaches regarding product architecture backlights, shielding plates, solutions for smart and safe disassembly, influencing consumer behavior regarding use of various on-modes, et cetera. This was especially relevant in cases where such smart design solutions could not be rewarded through any of the other scorecard items. Credits could also be earned through credible statements about environmental friendly production. Table 2 gives an overview of when new environmentally friendly features became integrated in the contesting sets. Bonus credits were in some cases granted because features were unique for a particular set. Also a number of more general developments have been included. Pictures of insides from the sets have been included to provide a visual impression of efficiency developments over the years. Year
Last year with CRT-only designs (Figure 8) Fully flame retardant cabinet
Use of water based paints Use of vegetable based inks
From CRT to LCD and plasma; emerging issue with mercury containing backlights in LCD sets
relative ease for the user to make power saving adjustment to default settings
2007
Inclusion of automated brightness settings First detachable feet allowing for more efficient packaging
2008
Inclusion of (non-automatic) reduced power mode General improvement in printed circuit board design Reduction in screen thickness
2009
First LED TV, hence slim design (Figure 10) Still insufficient improvement on mercury backlights in LCD sets
2010
Eco settings in the standard out-of-the-box setting Solar powered remote control. Bold rethinking of the total concept. Smart use of a small back panel to cover the electronics is smart (Figure 10).
2005
2006
Environmentally relevant design developments
Double function for the standard and wall-mount.
Table 2: Overview of new environmental features in TVs.
Figure 9: First LED TV (2009).
Figure 10: 2010 LED-based Green Award winner. 4
DISCUSSION AND CONCLUSIONS
In the period 2005-2010, environmentally relevant data was collected on mainstream television sets to select the yearly winner of the EISA Green Award. It is argued that the sets can be considered to give evidence of state-of-the-art developments in application of ecodesign principles – otherwise, the competing OEMs would have entered other, or no sets to the competition.
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The process for collecting the data has been characterised by pragmatism due to the comparative, relative nature of the assessments, and due to lack of time and resources to perform a thorough environmental assessment based on scientific life cycle impact assessment. At the same time, a pragmatic, holistic method has allowed for the inclusion of considerations which are not easily quantified scientifically. One such consideration is progress in product architecture, which reflects a more integrated design approach, which presumably has a positive effect on overall design efficiency leading to environmentally preferred design solutions. Another consideration is the inclusion of bonus credits, which were used to award individual features that give evidence of smart design, but may not show up in the standard scorecard items. To nevertheless improve data collection, from 2011, the jury for the EISA Green Award will cooperate with an established electronics recycler and thus be able to obtain data from material analyses of the shredded printed circuit boards. This process has been used already for the jury evaluation for the EISA Green Award for Mobile Phones, awarded for the first time in 2010, since for this product category environmental impact is to a much larger extent determined by PWB material composition, in particular precious metals and beryllium. The same process for PCBs of television sets will allow for a more precise analysis for the chemical substances scorecard. The data thus collected has been used to sketch an overview of how ecodesign application in mainstream development of television sets has progressed over this period. Summing up the results, it is concluded that:
Improvements in energy consumption have been considerable, since 2007, fuelled by application of new physical principles, from CRT, to LCD and plasma, to LED. The results also show increasingly less variation between brands, suggesting a higher general standard of attention for energy consumption. The successive shifts in technology have also enabled thinner screens, resulting in lower weight, especially from the year after the first implementation. New screen technology has also allowed for more efficient packaging. A clear improvement has been observed related to product architecture. This is partly allowed by using new screen technologies, but it is the impression of the jury that OEMs have become increasingly more successful in applying an integrated design approach, aligning electronic, mechanic and aesthetic requirements rather than addressing these separately. As regards chemical substances and implications for recycling, progress is less evident based on the data available. Whereas printed circuit board design has certainly become more modern, the amount of most relevant components related to the use of chemical substances does not seem to have decreased significantly. Whereas the use of LED technology may have done away with the complicated issue of removal of mercury containing backlights in LCD based sets, there is not yet evidence of a clear brand-wide improvement in attention for disassembly friendliness, give the continuing large number of screws, type of screws, and screw directions still present in most sets.
Reflecting on the relevance of these findings for ecodesign and green supply management in general, a number of issues are relevant. The mentioned improvements are assumed to be partly because of a drive to make television sets more environmentally friendly (whether it be because of legal compliance motives, normal competitiveness, or a more proactive attitude), and partly because of autonomous progress in technology development. What has
been the major factor in general, is unclear. A closer examination of the EISA Green Award winner in 2010 reveals that about half of the items why this TV scores well are related to the smart application of, classical applied ecodesign principles, namely smart design of the electronics, material application, and packaging design. One quarter of the solutions is related to upstream (chemical content) and downstream (recycled materials) green supply chain management, and about one quarter is directly related to applying the latest technology. This last factor has clearly gained importance in the last two years. It may also explain, to some extent, the lack of progress for recyclability and chemical substances as this at least partly due to a design dilemma, where progress in the environmental impact reduction of these focal areas needs to be sacrificed for enabling advances in smart energy and material technology applications. It is also striking that in the latter two areas, differences between brands have become much smaller (possibly indicating a competitive issue) than in the case of recyclability and chemical substances. Over the years, eight brands have participated at least once in the competition, of which five three times or more. The performance of competing individual brands has fluctuated considerably over the years. It took until 2010 for a brand to take its second victory, All winners from the past five years have also been ranked at least once as the worst in another year; in fact, at two occasions the winner scored worst in the year after the victory. As such, television sets clearly have seen an improvement overall, but it is unclear to what extent companies systematically have improved their performance throughout their product portfolio. But it is positive that, for the first time, the 2010 EISA Green Award set showed evidence of an intentional, holistic approach to ecodesign, evident from many details such as the unorthodox product architecture, the solar powered remote control, a double-function stand and wall-mount, and the e-manual. With this approach, the set scored very well on all relevant environmental aspects, and outperforming the competition in particular on energy efficiency. The authors hope that this design will be an inspiration for, and set the standard for future ecodesign of consumer electronics. In any case, EISA itself has indicated that the Green Award has led to increased environmental awareness among its members [5]. 5
ACKNOWLEDGEMENTS
The authors wish to acknowledge the European Imaging and Sound Association (EISA), and in particular EISA Vice-President Han Avôt, for his support for this paper, allowing the use of the data collected during the jury evaluations for the EISA Green Award in the period 2005-2010. It should be noted that the opinions and reflections in the paper are those of the authors only, not necessarily those of EISA. 6
REFERENCES
[1]
http://www.eisa.eu/about.html
[2]
www.ifa-berlin.com
[3]
Pascual, O. and Stevels, A. (2006). Maximizing profitability with ecovalue. Proceedings of Eco Design 2006 Asia Pacific Symposium, Tokyo, NPO EcoDesign Promotion Network.
[4]
Boks, C. and Stevels, A. (2003) “Theory and Practice of Environmental Benchmarking for Consumer Electronics”, Benchmarking - an International Journal, Vol. 10, No. 2.
[5]
Cole, G. (2007) EISA, looking back, moving forward. European Imaging and Sound Association, Lisbao, Portugal.
Simultaneous Application of Design for Sustainable Behavior and Linked Benefit Strategies in Practice 1,2
Johannes Schmalz , Casper Boks 1 2
2
Institute for Engineering Design and Industrial Design (IKTD), University of Stuttgart, Germany
Department of Product Design, Norwegian University of Science and Technology, Trondheim, Norway
Abstract Design for sustainable behavior implies applying design strategies that explicitly take into account human behavior, thereby minimizing user-related environmental losses. In addition, the application of linked-benefit strategies has been suggested to ensure the design of commercially interesting products, linking sustainability benefits to financial, functionality, aesthetical or convenience benefits. However, no case studies have been identified that apply both design strategies in one particular product design. Since a methodology for doing so is lacking, this paper, based on the development of a desk lamp, suggests, discusses, and evaluates possible directions for such an approach. Keywords: Design for Sustainable Behavior; Ecodesign; User-centered Design
1
BACKGROUND
Sustainable design research has until recently mainly focused on the environmental impact of the product itself. End-of-life issues have always played a dominant role, resulting in redesign strategies dominated by material and product-architecture based solutions. Even when the use-phase was concerned, redesign strategies have mainly focused on applying more energy-efficient technologies. Recently it has been realized however that user behavior itself may considerably affect the life-cycle environmental impact of products; For some product categories, around 30 percent of the energy consumption of a product may be due to user-related losses [1]. Consequently, a growing research community is focusing on how to minimize avoidable environmental impacts due to user-related losses [2]. Several design for sustainable behavior strategies have been suggested based on mechanisms such as feedback, persuasion, seduction or automatic control to create desired behavioral change [3-6,8-9]. Commercially available products, and product features using principles of design for sustainable behavior include for example dual-mode flushing buttons, eco-buttons, and television sets with automatic brightness settings. A range of products also exists that remain within the gadget domain. However, a well-researched framework for the development of such products is not yet established, and many design problems are likely to exist for which no obvious design solution exists for altering user behavior. An aspect that may be crucial in developing successful design for sustainable behavior strategies, especially for products where existing habits are not easily broken, could be the consideration of so-called ‘linked benefits’ [10]. This implies searching for design solutions than benefit users and producers in more ways than just providing an environmental benefit; rather, products should offer economical, convenience and emotional (for users) and strategic (for producers) benefits as well.. Designing with that philosophy in mined may increase the chances for products to become commercially successful as a commodity product, beyond the gadget level.
2
GOAL AND APPROACH
Although academic activity in developing design for sustainable behavior methodology is increasing, few practical design projects have been reported on, and none of them explicitly consider linkedbenefit strategies. This paper presents a project where both linkedbenefits and sustainable behavior aspects were to be considered explicitly. This means that "sustainability" was added as a criterion of product development alongside other classical criteria of functionality, profitability, safety, reliability, ergonomics, technical feasibility, and last but not least aesthetics. Although a prototype was developed, the main project goal was not to design a product per se, but to gain experience with this type of design exercise, as a clear cut method for doing so not yet exists. One of the considerations beforehand was whether to use a common or a customized design approach. In order to investigate this further it was decided to take an explorative approach building on existing insights, and to focus and reflect on user-benefits and their possible correlations, and on ways to influence users towards more sustainable ways of use. Accordingly, the paper presents the considerations about the approach that was to be taken parallel to results obtained from following that approach. After careful consideration of various practices to potentially focus on, domestic (desk) lighting was chosen as a theme for this study. Even though lighting on an individual product level may have negligible impact, in broader context it is a huge factor in most energy surveys. At the same time, the environmental impact of domestic lighting heavily depends on user practices in terms of purchasing different types of light bulbs, switching lights on and off, and even ways to discard bulbs. Also, a wide range of design solutions and prices already exist for domestic desk lighting, but no desk lamps were identified featuring multiple design solutions to influence the user towards a more sustainable way of use. Lastly, the emergence of Light Emitting Diode (LED) technology, as an increasingly affordable and environmentally friendly alternative to conventional technologies may open up new opportunities for innovative design.
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_30, © Springer-Verlag Berlin Heidelberg 2011
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174 3 3.1
Life Cycle Design - Selected Applications EXPLORATIVE DEVELOPMENT OF A DESIGN APPROACH
To chart the benefits people may possibly expect from a lamp, interviews were done with 25 subjects in the age between 20 and 65. Issues addressed were awareness of environmental issues caused by lamps, benefits that people expect from a lamp, alternative ways to realize these benefits, anticipated behavior in operating lamps, as well as purchase decision factors. The main findings are summarized below:
Price was found to be a major factor in considering purchase of a lamp or not. Trade-offs were identified between price and material choice, and aesthetics/exclusivity of the lamp.
Safety was mentioned from two perspectives: lamps/light makes people feeling safer, and lamps should be safe themselves. Adjustment of brightness and direction of light for reasons of adjusting to various activities and/or (desired) atmospheres also came out as an important benefit that the subjects were after.
Regarding sustainability were the subjects partly, but not completely aware of the environmental impacts of lighting solutions, suggesting that even a rather simple product like a lamp may reflect a complex issue for most users. The subjects did in fact prefer sustainable solutions, suggesting that relieving people from the responsibility of environmentally responsible use may be a relevant design problem to address. 3.2
The lamp should be a desk lamp for daily use, in the price range of 50 euro (IKEA 2010 oriented)
It can be used for decorative, leisure as well as professional activities at home (meaning that the lamp can be found on any table or furniture of a household)
The lamp should also allow for use by children
User definition and screening
User interviews were chosen as the most appropriate method to gather more insights in what different user groups expect of a lighting product in terms of functionality and benefits, and to define the target audience for the lamp. Once a target audience would have been chosen, both the behavioral patterns to be analysed and the types of functionality benefits most relevant for this audience would become clearer. A semi-structured interview approach was taken, in order to scan the main issues, opportunities and challenges that would need to be dealt with. It was considered that answers from prospective users may not accurately reflect their actions, behavior or even preferences in daily life. In the context of the project, it was considered that people may often have a false estimation about the environmental impact of their actions [11]. Also, it was considered that it may be challenging to make people talk about what could be regarded as ‘bad’ or undesirable behavior in the given (sustainability) context. To tackle such challenges, some special techniques of interviewing exist, such as first asking people about bad behaviors in general using fictive scenario/personas [12].
Defining the target audience
It was decided that the lamp design should target a broad audience, as relatively larger sales numbers would result in a meaningful contribution to environmental impact reduction, even though the impact of a single lamp is negligible in a societal context. Secondly, a large audience would enable mass production, and reduce production costs (and environmental impacts) per product, and therefore sale price. Thirdly, design solutions for a broad audience were considered to potentially increase the robustness and generalizability of any result that would be achieved. The target audience for the desk lamp project was defined to be young families with working parents in the beginning of their 30’s), living in a European country, and averagely interested in design and aesthetics with an open mind towards unconventional design solutions. This was translated into a design brief including the following design requirements:
Additional functional requirements are presented in Table 4. 3.3
Matching benefits and behavior
As literature does not provide much guidance in how to do so, the central and most challenging (and probably most innovative) step of the process would be the decision how to correlate the linkedbenefits perspective with the insights gained from design for sustainable behavior research. It was decided to categorise the different types of behavior into four ways of influencing the user. Based on recent literature on design for sustainable behavior strategies, where different possibilities of categorization of user influencing have been proposed, this was assumed to provide design suggestions. For the categorization, the subdivision proposed by Wever et al. was used [6]:
Functionality matching aims at adapting the function of a product in a way that the existing (bad) behavior of the user is absorbed.
Eco-feedback aims at just informing the user about the impacts of his action, leaving the decision to act upon this to the user
Scripting aims at realizing that sustainable use of a product ‘coincidentally’ is the most simple and comfortable way of using the product fort he user, allowing intuitive sustainable use
Forced functionality aims at either making sure that the product is intelligent and adapts to changing environmental conditions, or at designing the product in a way that non-sustainable use is made impossible.
Several other divisions of dividing the freedom of control over behavior between the product and the user have been proposed discussed in recent literature as well [3,13-15] but as a starting point for demonstrating the principle, it was considered sufficient to cover all these divisions with these four umbrella terms. Extending the analysis with more or more detailed strategies, or with a selection of strategies appropriate to the problem at hand, could lead to additional design suggestions and therefore be subject of study, but would not principally alter the approach discussed here. Once a number of potential benefits of a product would have been derived, these could be linked to the mentioned design strategy categories. An important input for this process would be to investigate to what extent the potential benefits would be correlated. To this end, it was suggested to develop a matrix correlating potential benefits, to assess to what extent they might agree or disagree with each other.. Benefits not principally excluding each other could then be allocated to different types of behaviour to get a feeling about their influence on the user and his environment. In the project, this was very helpful to get a keen understanding of the system, and provided input for deriving possible design solutions, presented in Table 1. The solutions are categorized according to each type of design for sustainable behavior strategy discussed above. The solutions were then used to determine what kind of benefits could be expected from them. For example, whether adapting to the surrounding light could be seen as the benefit of being able to dim the lamp (as the adaption to the light could be combined with a dimmer). With the above considerations in mind, a benefit correlation matrix was derived in the context of the lamp project (see sample in Table
Eco feedback
Scripting and/or Steering Forced functionality
Standardized socket
-
…
+
…
-
…
Timeless design Directional light Standardized socket Etc…
-
3.4
+
-
…
…
…
…
emotional
X X X X …
…
X X X …
X …
X X X X X …
X X …
Setting up requirements
List of requirements and wishes to be included in the prototype Dimming possible Adaption to light in surrounding Not attract flies and mosquitoes Lamp should not get hot Long durability Recyclable materials Directing lighting possible Switch off when not in use Not fall down when someone stumbles over the cable Hinder incorrect and excessive use Modifications not possible
… …
X
It was expected that within the context of this project, setting up a conventional “list of demands and wishes” might be an insufficient approach for reaching an optimal result. It was expected that, towards a first conceptual design, the balancing act of including as many benefits as possible for as many people as possible, and taking into account aspects as well, would be less straightforward. From the designer it would ask for a keen sense in balancing the different benefits given the challenge of covering all the interactions between the benefits. One aspect of this challenge is the uncertainty whether technical implementation will be possible due to interactions between the different technical and economical requirements.
…
-
X
Table 3: Evaluation of benefits matrix sample.
Etc…
Timeless design
-
-
Price
X
For the desk lamp design project, based on the previous analyses, the following functionalities were selected into the list of requirements for the lamp (see Table 4 – although some of them were included as wishes rather than requirements.
Directional light
Dimming
Price
Dimming
Table 1: Possible design solutions sorted after design for sustainable behavior strategy.
X X
physical
Functionality matching
Possible design solution Lamp gives only the amount of light that is needed Lamp is able to adapt to user’s needs Lamp shows user actual impact caused by using the lamp (energy, CO2, etc.) User is shown how the durability of the lamp is changing due to his use Show user how much energy was needed to produce the lamp Design the lamp in a way, that using it eco friendly is fun/easy, most convenient Design the lamp that user can show of with the functionalities of the lamp The lamp burns only when user is around The lamp adapts to the light around The lamp gains energy from surrounding without influence of the user
Dimming Price Timeless design Sustainable materials Directional light Standardized socket Etc…
social
Main strategy
economical
2), where benefits as identified from the user interviews were compared side by side, systematically evaluating how each aspects may affect other aspects; thus revealing where win-win situations may occur, and where design trade-offs may need to be made. For example, the inclusion of a standardized socket may reduce the price for the product, whereas adding a dimming option may increase the price.
functional
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ecological
Life Cycle Design - Selected Applications
…
Table 2: Benefit correlation matrix sample. A next step was included to systematically check each potential benefit according to the effect it may have on economical, ecological, functional/technical, emotional, social or physical performance of the product (see sample in Table 3). This proved to be helpful in balancing the evaluation of including different design features in the final concept. This was mainly done through discussion; for example: including a dimming option may affect price negatively, but may lead to ecological benefits as it may reduce total energy consumption, it would add to the functionality of the lamp, and enable adjusting to the mood a user desires, thus providing emotional benefits. These two matrix exercises provided insights about which dilemmas and challenges should be addressed towards conceptualization, and as such guided the exploration of the system boundaries. They became input for determining the list of requirements (paragraph 3.4) and finally the conceptualization step (paragraph 4).
Easy to control Handling should be fun Safe for children to operate Opening only possible with tools Light adaptable to user’s mood The illumination level should be >500Lx The Unified Glare Rating (UGR) should be < 22 Function of the lamp shown by the design/aesthetics Design/aesthetics should attract broad audiences
Table 4: List of requirements and wishes. 4
CONCEPTUALIZATION AND TECHNICAL REALIZATION OF THE LAMP
In this section is tried to realize all the functions listed in the list of requirements, the challenge being in the shift from the functional description to a technical implementation that fits with all the factors or at least the must factors listed in the list of requirements. Again it seemed to be preferable to do first some “technical” brainstorming (with the factors as system boundaries) and afterwards indexing these ideas. Indexing is a good tool in this case as it does not only give the process a traceable structure but also the possibility to combine completely different approaches to a completely new, innovative approach. One method used for this was a
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morphological box, allowing to quickly combine several possible solutions while rating them with the list of requirements.
the USA, as it was not available with the needed configuration in neither Germany nor Norway.
Especially the economical requirements drew the range of possible solutions pretty narrow. The most important features realized in the final prototype are the following:
Dimmable LED light
Safety release cable
Directional light
Motion detector with several sub features like timer etc.
Light detector
Due to safety reasons is whole system realized in 12V
Less flies and mosquitoes being attracted
90-98% less energy consumption than equivalent lamp
4.1
Description of the prototype
Figure 1: Safety release cable. Directional light:
The system itself can be seen from a functional perspective be divided into five subparts: all the sensors, the electronics to dim the LED, the LED itself, the safety release cable and the part to allow directional light. Each of these subparts is only briefly discussed as the main focus of the project was on discussing the methodology.
The origin of this idea originates from the ability to have directional light as well as non-directional light coming from one single lamp. This was realized with a removable piece of metal in tree shape which avoids the light from going into one direction while the illumination on the other side of the lamp is being increased due to the reflections coming from the tree.
Sensor system:
4.2
Translating the list of requirements, the following functions were wanted from the sensor system:
In a strict form-follows-function approach it was now considered how to embed the functions into the aesthetics. Therefore several different cardboard mock-ups were built to investigate different possibilities of realizing the functions in an aesthetically pleasing and ergonomical system.
Measure light intensity
Check if human in surrounding
Possibility to adapt to different environments
Timer
An interesting fact is the possibility of adapting the module to different environments as well as the customization of the whole responding behaviors (in a defined range). Electronics: A challenge is the fact that is it not possible to dim a LED with a normal dimmer used in lamps with bulbs. The reason is in the physical properties of the LED [16]. It is therefore necessary to use other electronic circuits to dim LED lights. One possibility is the use of the so-called “pulse width modulation” [17]. This device basically modulates the current that it is changing between two values what allows to steer the output signal. This is what is installed in the prototype.
Aesthetics and realization
Within the lamp project several factors were considered are more important than the aesthetics of the lamp. At the same time it relates to several other factors like attractiveness, lifetime, quality properties, the usability etc. As there are some parameters determined due to the bought-in parts there is not as much freedom as designing a completely self produced product. The mock-ups were then tested on their characteristics, especially the functional and ergonomical ones. This was especially important to ensure sufficient illumination. After an iterative process of eliminating different problems one conceptual model was chosen. Based on this there a working prototype was built which was called LOGI, after the god of fire in Norwegian mythology.
The LED: There has been quite some progress in the LED industry in the last years. From glaring, white light that was not comparable to normal bulbs to all kinds of illuminants with the same lighting properties as normal bulbs. Most of these new illuminants are made for standard bulb sockets and 220V. As it was decided that, due to safety reasons, the design should be realized in 12V, finding such as solution was quite time consuming. Closely linked to the LED is the socket. In this case a small standard socket was chosen to ensure maximum design freedom with all the benefits of a standard socket. The chosen LED provides light comparable to a 40W bulb (what is used in most desk lamps on the market). The superior environmental qualities of LED made it a priori clear that LED would be a logical choice of technology to base the prototype on. The safety release cable: The idea of the safety release cable (a cable that releases when being pulled no matter in which direction it is intentionally or unintentionally pulled, instead of making the whole lamp falling) may sound obvious, but such a cable needed to be shipped from
Figure 2: LOGI prototype.
Life Cycle Design - Selected Applications 5 5.1
EVALUATION OF THE LAMP Linked-benefits
In the working prototype, the following linked-benefits were realised:
The electronics (motion detector as well as the timer) render the installation of a switch unnecessary, saving on cost and material use. Convenience for the user is increased as the lamp does not to have to be switched on or off.. However, the switching of the lamp is possible via the dimmer.
Implementing a safety release cable and using timeless aesthetics, the life-time of the lamp may be increased as it became less likely that the lamp will need to be replaced because of being out-of-date or because of accidental damage. An additional benefit is a higher safety level
The adjustable light reduces the energy consumption at the same time as it increases convenience for the user as the light can be adjusted for the purpose it is needed for.
The possibility of direction light to where it is most needed also creates the possibility to create aesthetically pleasing shadows on the wall.
If the light detector was configured in the right way, it could assure that the user always has the right amount of light that is ergonomically needed. This could, especially in professional environments, make sure that there is a perfect balance between energy efficiency and the avoidance of tiredness or even damage to the eye of the user due to insufficient light.
LED light does not only significantly reduce the energy consumption, but seems also to not attract flies and mosquitoes in a way normal lamps do. This offers completely new userbehaviors concerning the use of artificial light during summer time. Another benefit of the LED is the long life expectancy when compared to a normal bulb, which means that the user does not have to change the LED for a long time. The reduced heat-development of the LED increases the safety of the whole system. Furthermore this affects the whole design, for there is less space between LED and surrounding materials needed.
5.2
177 6
REFLECTION ON THE PROCESS
The intention of the project was to explore ways for successfully combining design for sustainable behavior and linked benefit strategies in product design. Reflecting on the process, good use has been made of a variety of established (user screening, context mapping, morphological box) and more hands-on methods (benefitcorrelation matrix, benefit-interaction matrix). Evaluating the approach it was considered that this could be more structured, especially for application in more complicated projects. It is therefore suggested that in future similar projects, linking the two perspectives could be done with a matrix based on cross comparison of potential benefits (abstracted wishes voiced by the customer) and different ways to influence behavior as explicit design parameters; a structure very similar to what is known from Quality Function Deployment (QFD) methodology. Figure 3 proposes such a tool, linking possible solutions for each design strategy with identified benefits from the user screening. A positive correlation would mean that the solution is expected to influence the benefit in a positive way, negative the other way round. To rate the intensity of the interaction figures like 3 for low interaction, 6 for middle interaction, 9 for strong interaction (in the positive as well as the negative direction) could be used. Adding up these correlations would then allow a ranking of possible solutions. It could also be a possibility to rank the benefits beforehand and bringing this into the process by giving the benefits different weights which would then be multiplied with the specific benefit-solution interaction. An extra source of information for the next steps is the comparison of the benefits and the solutions, which happens in the “roof-like” parts of the matrix. Again, plus means that the benefits respectively the solutions correlate in a positive way, negative that the influence each other in a bad way. This allows being aware of possible clashes right from the outset and can help when considering which functions to realize or not. It is also suggested explore the use of TRIZ-like methods to find ways of avoiding possible clashes.
User-related losses
The lamp is an intelligent product able to reduce user-related losses to a minimum. The problem of rebound effects is rather low as only few, if any possibilities exist to by-pass the electronics mechanism but incentives to do so neither exist. This might be the most important and most interesting fact of the whole process and the product. As such, the prototype would be a highly sustainable product, delivering exceptional service, without being invasive, and for a reasonable price. 5.3
Sustainability
The lamp is thought to use 90-95% less energy than a comparable lamp. Most of the savings are realized by the use of the LED:
The amount of light given by the used LED (3W), which is equal to a 40W bulb, is equal to 92,5% savings.
Taking into account the percentages stated by Palmborg [18] or Gram-Hansen [19] considering user-related losses, there might be another 3,25% savings possible.
The lamp is designed in a way that makes the use of laser cutting machines possible. This ensures on one hand fast, cheap and high quality production, but on the other hand could also be used to customize the lamp. As mentioned before are low (production) costs vital for the success or failing of a product. An important aspect could therefore be a production-geared design of the product in the future to ensure a cheap and possibly automated production of the product.
Figure 3: QFD-type matrix for matching behavior and benefits.
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In the process of developing the lamp some aspects were discovered quite late in the process, some even after finishing the prototype (such as the use of the directional light tree to gain nice shadows on the wall). The aim has to be to pull those aspects to the earlier parts of the development process. One could say that control over the benefits from the beginning allows the control over the product and the process from the beginning on. This also allows new benefit combinations to be embedded early on in the design process. 7
CONCLUSIONS
This article has described a design project with the aim of developing a lighting solution that addresses both design for sustainable behavior and linked-benefit considerations as a starting point. The project resulted in a prototype that exhibits a unique set of features for a desk lamp, such as very low energy consumption and very limited electronics, combined with dimming, safety features, directional light, less attraction of flies and mosquitoes, and at relatively low costs. As no explicit methodology such a project existed yet, it was found useful to use established methods in combination with pragmatic approaches developed such as using a benefit-correlation matrix and a benefit-interaction matrix. This eventually led to a proposal for a QFD-type matrix that would allow for a more systematical simultaneous consideration of incorporating linked benefit and design for sustainable behavior strategies. Future research will look into testing this approach, using additional case studies. The 2011 spring semester at NTNU’s Department for Product Design will have ca. 40 students working explicitly with Design for Sustainable Behavior projects, and the desk lamp project is expected to be of inspiration to them. One of the goals of these projects is to provide further input for methodology development for design for sustainable behavior, contributing to ongoing research at the department. 8
ACKNOWLEDGEMENTS
[5]
Lockton, D., D. Harrison, and N. Stanton (2008). “Making the user more efficient: design for sustainable behaviour”, International Journal of Sustainable Engineering, 2008, 1(1), 3–8.
[6]
Wever, R., J. van Kuijk, and C. Boks (2008). ”User-centred design for sustainable behaviour”, International Journal of Sustainable Engineering, 2008, 1(1), 9–20.
[7]
Mazé, R., and J. Redström (2008). ”Switch! Energy Ecologies in Everyday Life”, Int. Journal of Design, 2008, 2(3), 55-70.
[8]
Lilley, D. (2009). “Design for sustainable behaviour: strategies and perceptions”, Design Studies, 2009.
[9]
Scott, K., J. Quist, and C. Bakker (2009). “Co-design, social practices and sustainable innovation: involving users in a living lab exploratory study on bathing”, Joint Actions on Climate Change, Aalborg, Denmark, 8-10 June 2009.
[10]
Stevels, A. (2000). ‘Green marketing of consumer electronics’, Proceedings of the ‘Electronics Goes Green’ Conference, Berlin, Germany, September, pp.539–544.
[11]
Mansouri-Azar I., Newborough M., Probert D. (1996). Energyconsumption in UK domestic households: impact of domestic electrical appliances. In: Applied Energy 54/3, pp. 211–285.
[12]
Blythe, M. and Dearden, A. (2008). Representing older people: Towards meaningful images of the user in design scenarios, Universal Access in the Information Society, Volume 8, number 1, pp. 21-32.
[13]
Lilley, D., Lofthouse, V. and Bhamra, T. (2005). Towards instinctive sustainable product use. Presented at the 2nd International Conference: Sustainability Creating the Culture, 2-4th November 2005, Aberdeen Exhibition & Conference Centre, Aberdeen.
[14]
Lockton, D., Harrison, D.J., Stanton, N.A. (2010). Design with Intent: 101 patterns for influencing behaviour through design (v.1.0), Windsor: Equifine 2010 (ISBN 978-0-9565421-0-6 print; 978-0-9565421-1-3 eBook).
[15]
Elias, E., DeKoninck, E. and Culley, S. (2007). The potential for domestic energy savings through assessing user behaviour and changes in design In: EcoDesign 2007: 5th International Symposium on Environmentally Conscious Design and Inverse Manufacturing, 10-13 December 2007, Tokyo, Japan.
[16]
Dyble M., Narendran N., Bierman A., Klein T. (2005). Impacts of Dimming White LEDs: Chromaticity Shifts Due to Different Dimming Methods. In: Proceedings SPIE 5941, pp.291-299.
[17]
Wallentowitz H., Reif K. (2006). Handbuch Kraftfahrzeugelektronik: Grundlagen, Komponenten, Systeme, Anwendungen. Vieweg Verlag, Wiesbaden.
[18]
Palmborg, C. (1986). Social habits and energy consuming behaviour in single-family houses. Stockholm: Swedish Council for Building Research.
[19]
Gram-Hansen, K. (2003). Domestic electricity consumption— consumers and appliances. Paper, Nordic Conference on Environmental Social Sciences (NESS), June, Turku/Åbo.
The authors are grateful for those that participated in the interviews, They also wish to acknowledge Ida Nilstad Pettersen for sharing the supervision of this project. Øyvind Kurisaki-Sagberg is acknowledged for the photo of the LOGI desk lamp. 9 [1]
[2]
REFERENCES Wood, G., Newborough (2003). Dynamic energy-consumption indicators for domestic appliances: environment, behaviour and design, School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, Scotland, UK. Pettersen, I.N., Boks, C. (2009). The future of design for sustainable behaviour. Ecodesign 2009: Sixth International Symposium on Environmentally Conscious Design and Inverse Manufacturing, December 7-9. 2009, Sapporo, Japan.
[3]
Jelsma, J. (2006). “Designing ‘Moralized’ products: theory and practice”. In: Verbeek, P.-P., and A. Slob, eds., User behavior and technology development, Springer, Berlin, 2006, 221–231.
[4]
Fogg, B.J., (2004). Persuasive technology: using computers to change what we think and do, Morgan Kaufmann Publishers, San Francisco, CA.
Strategic Evaluation of Manufacturing Technologies 1
1
Gunther Reinhart , Sebastian Schindler , Pascal Krebs 1
1
Institute for Machine Tools and Industrial Management, Technische Universitaet Muenchen, Muenchen, Germany
Abstract Strategic technology planning sets the course for the future production of a manufacturer. Therefore, paying attention to market trends and new emerging technologies is indispensable. In order to compare different technologies from a longterm perspective, this paper presents an approach for the strategic selection of manufacturing technologies. Thereby, a multi-criteria evaluation is employed to incorporate the strategy of a producing company and consider quantitative and qualitative influencing factors. Because most factors are uncertain and the result of expert estimations, the approach includes the consideration of intrinsic uncertainties. Keywords: Manufacturing Technology Life Cycle; Maturity Assessment; Strategic Technology Planning
INTRODUCTION
Manufacturers are compelled to use the most effective and efficient production processes and structures [1]. Producing companies located in high-wage countries, in particular, must permanently ensure that their technologies will fit future requirements, such as fast adaptations to market demand or economies of scope [2]. The term “technology” denotes, in this context, all manufacturing processes and techniques which are needed to produce a special product [3]. This also includes all essential value-adding assembly operations. The decision of which technology to use in future production depends on several influencing factors, such as competitive potential or expected production cost. However, the competitive potential or the profitability of a technology is changing continually. Individualized customer requirements or shortened product life cycles intensify this trend [4]. In the scientific literature, this circumstance is called “turbulent production environment” [5]. From a long-term perspective, there exists a dynamic range of potentially available technologies in the production planning process. Each technology passes through an evolutionary development [6]. During this dynamic change, the competitive potential or the maturity of a technology varies as well as profitability compared to alternative technologies. Competitive potential means, in this context, the possibility of gaining advantages vis-à-vis competitors, such as reduced processing times, decreased production costs or new product features. This evolution can be described as a kind of technology life cycle [7]. In order to hold technological leadership and to increase competitive advantages, companies must decide when to change to a new emerging technology. Figure 1 shows the curves of the technology maturity and the competitive potential over time. The technology life cycle model according to Sommerlatte and Deschamps inspires this coherency [7]. The technology maturity increases over time while the competitive potential decreases. Depending on the maturity, the life cycle of a technology can be classified into one of four different strategic states. An innovation technology offers great potential in gaining competitive advantages while managing organizational and technical risks.
Standard technologies are commonly used. Producing companies will not enhance their competitive position by employing standard technologies, however, neglecting them will cause a loss in market share. Displaced technologies have become obsolete and offer few advantages because of inefficiency. New planning methods are needed in order to decide which technology is suitable for future production and when to change to a new alternative [8]. For selecting with the most efficiency and the lowest risk, producing companies must consider the current state of the technology life cycle. Moreover, the different dynamic changing factors which influence the suitability of a technology must also be contemplated. Some of the influencing factors can be described quantitatively, such as increasing product units. Other factors are more qualitative because they are the result of forecasts and expert estimations, for example, the assessment of the technology maturity [9]. However, the future development of both kinds of factors involves an intrinsic uncertainty. This paper presents an approach for the strategic evaluation of manufacturing technologies. The approach considers the uncertainty of the planning data and considers both quantitative as well as qualitative factors. A multi-criteria evaluation, in particular, focuses on the dynamic change of the influencing factors. Technology Maturity and Competitive Potential [%]
1
Innovation Technology
Key Technology
Standard Technology
Competitive Potential
Displaced Technology
Maturity
Time [Years]
Figure 1: Technology Life Cycle (based on [6] and [7]).
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_31, © Springer-Verlag Berlin Heidelberg 2011
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Operational Technology Planning • • • •
Rough Unit Quantities Several Material Types Approximate Dimensions Innovative Technologies
Short-term Planning Horizon Low Uncertainty
Long-term Planning Horizon High Uncertainty
Uncertainty
Defined Unit Quantities Determined Materials Detailed Geometries Established Technologies
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• • • •
Strategic Technology Planning
Time
2.3
Time
Figure 2: Uncertainty due to different planning horizons relating to operational and strategic technology planning (based on [8]). In this approach, the criteria product feasibility, competitive potential, resource efficiency, technology maturity and profitability are valuated and flow into the suitability of a technology, which is the main target value. In this paper, the valuations of profitability and technology maturity are presented in more detail. By studying selected application examples, the valuation of the two criteria can be better understood. This approach results in a technology suitability evaluation as well as a dedicated interpretation of expected risks. A comparison of the evaluation results from several alternatives helps producing companies to choose the most suitable technology for future production requirements. 2 2.1
MANUFACTURING TECHNOLOGY PLANNING Operational Technology Planning
Regarding the planning process of manufacturing technologies, the planning horizon can be roughly divided into short- and long-terms. Operational technology planning takes place in the short-term. The information, available for operational planning tasks, consists of accurate product data, such as defined unit quantities, specified alloy materials or detailed geometric dimensions, and employable production resources and structures, such as the quantity of installed robots or useable machine tools. The main objective of operational technology planning is to concretely determine which product features will be manufactured using what kind of technology installed in which kind of production resource [8]. 2.2
Hence, new external technologies often need a further development. By considering and especially integrating company external technologies in the existing production environment significant research and development efforts arise. Here, the company has to contemplate organizational as well as technical properties and requirements of the technology. However, building up know-how can be very time-consuming and expensive. Therefore, the decision of eliminating an established technology or following up a new one is crucial. In order to select the right technologies in terms of strategic planning, companies need to evaluate the existing alternatives. Within this evaluation, important time-dependent and uncertain criteria must be considered [6].
Strategic Technology Planning
Criteria for Strategic Technology Evaluation
The suitability of a technology to future requirements depends on several influencing factors. Most common methods for technology evaluation focus on production cost calculations, product feasibility studies or a strength and weakness analysis [11]. From a strategic point of view, the technology maturity should also be integrated into the evaluation process [12]. Furthermore, trend-setting technology properties such as competitive potential [8] and savings of resources [14] should also be considered. The following describes the evaluation criteria used in this approach. Product Feasibility This criterion contains relevant data about the product to be fabricated. Herein, rough product properties are contemplated, such as geometric dimensions, alternative alloy materials as well as important tolerance values of functional surfaces. Furthermore, future product quantities are estimated along the product life cycle. Based on the product properties, the feasibility is finally valuated. Competitive Potential Within strategic technology planning, competitive potential contemplates all technological potentials for reaching lower cost per unit, shorter processing times and higher productivity [8]. Furthermore, technology attributes are considered helping to achieve higher product qualities, such as smoother surfaces, or completely new product features, such as brand new processable materials. Resource Efficiency Because raw materials or water are growing scarce, the resource efficiency of a technology must be contemplated within the strategic planning process [13]. This criterion concentrates on product and production resource flows and is based on a process balancing method. In this context, an index estimating the future resource efficiency is used leaning on methods employed within the resource efficiency valuation of established manufacturing processes [14].
Strategic technology planning focuses on the long-term orientation of a producing company [8]. As shown in Figure 2, the uncertainty of the available planning data for this challenge is very high compared to the short-term planning horizon. The accuracy of known information on future production requirements and necessary product features is rather low. For example, only rough unit quantities, approximate product dimensions or potentially useable types of material are available for the planning tasks. In contrast to operational technology planning, the strategic aspect includes the possibility of considering a company’s internal as well as external manufacturing processes for future production.
Technology Maturity
Innovative external technologies, in particular, are often not mature enough to be efficiently used in the production environment [9]. However, these technologies are perceived by producing companies to be important enablers for reaching competitiveness although their implementation often fails to achieve the expected benefits [10].
In order to calculate important future production costs, such as expected costs per unit and investments for new production resources and structures, this criterion takes the economic aspects of a technology into account. From a strategic point of view, future costs can only be estimated [15]. Therefore, the method for the profitability valuation roughly calculates expected production costs per unit with consideration for employee and machine hour rates.
This criterion focuses on the current stage of development of a manufacturing technique or process. The technology maturity correlates with technical and organizational risks [12]. In order to estimate the maturity of a technology, a method is employed using different maturity stages. The technology maturity valuation is based on the concepts of “technology readiness level” (TRL) as used by the National Aeronautics and Space Administration (NASA) and the “technology maturity assessment” (TMA) [9]. Technology’s Profitability
Life Cycle Design - Selected Applications 3 3.1
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UNCERTAIN INFLUENCE FACTORS Qualitative and Quantitative Influence Factors
Quantitative factors are numerically measurable in their nature and level of occurrence [16]. In contrast to the quantitative factors, qualitative factors are often expressed in ordinal scales that allow, for example, the assessment by terms such as “high” or “low” [17]. An assignment of specific numerical values is, however, not possible and they exist often as expert estimates [15]. Both quantitative and qualitative influence factors in the evaluation can be uncertain and pose some risk for producing companies [15]. 3.2
Modelling of Uncertainties
Quantitative uncertainties can be integrated into the evaluation model using probability distributions [18]. By using different distribution types, mean values and variations, multiple uncertainties can be modelled. Qualitative uncertainties cannot be directly modelled as probability distributions. In the course of a technology evaluation, these so-called weak factors exist only based on studies or as statements from experts. Such ambiguous statements are described as linguistic uncertainties [15]. Hence, all qualitative parameters can be interpreted as uncertain. One method to evaluate qualitative, linguistic statements is to use Fuzzy Logic. Fuzzy Logic is especially powerful when linguistic variables are represented in words or sentences whereby each linguistic value can be modelled by a fuzzy set [19]. These fuzzy linguistic variables are extensions of numerical variables because they are able to represent the condition of an attribute at any given interval by taking fuzzy sets as their values [20]. An element in Fuzzy Logic can be partially or totally inside or outside of a specific set. Therefore, each position of the element is described by the membership value μ that has a value of one (μ=1) if the element belongs completely to the set and a value of zero (μ=0) if the element does not belong to the fuzzy set. Any value between zero and one (0<μ<1) is possible when the element belongs partially to the fuzzy set. Although the inputs to the Fuzzy Logic models are always linguistic variables and their fuzzy sets, the outputs can either be linguistic variables described by fuzzy sets or linear functions [21]. In a Fuzzy Logic based model, If-Then rules between the input and output fuzzy sets describe the heuristic knowledge about the behaviour of the system. The process of fuzzification of the inputs, definition and aggregation of the If-Then rules is known as fuzzy inference. For the integration of qualitative uncertainties into a quantitative evaluation method, Fuzzy Logic is a proper approach.
e1 High Middle Low
Fuzzification µ 0,4 e2 µ 0,6 High 0,3 0,2 ei Middle 0,4 High 0,2 Low Middle Low
Defuzzification P
P
x xs P: Probability x: Value xs: Centre of Mass
µ 0,1 0,2 0,4
x
Therefore, Reinhart et al. [23] developed a so-called Fuzzy Evaluation Net which transforms qualitative parameters available in linguistic statements into measurable values as shown in Figure 4. In this evaluation net, a so-called inference calculates the membership values of the output neurons based on weighted connections between the neurons ei and a certain rule base. Thereby, all rules and connections are evaluated simultaneously and the results are combined. At this stage, an aggregate output neuron is derived in an output layer. In order to obtain a precise (i. e. measurable) variable as output data, the output neuron has to be defuzzified using the so-called Centre of Mass method. This method consists of three steps. The first step is the assignment of membership functions to the linguistic terms describing the output neuron. The most common membership function has the graph of a triangle. Hence, for each linguistic term a triangle function has to be defined for this methodology. The x-coordinate of the peak of each triangle is the most likely value described by a linguistic term. In the second step, the membership functions of the output data are cut at the respective straight line of the membership value μ assigned. The third step contains the calculation of the x-coordinate of the area’s Centre of Mass. Afterwards, the transformed qualitative uncertainties can be integrated in the evaluation model. The integration of multiple uncertainties increases the complexity of the calculation model. Hence, a Monte Carlo simulation is a valid technique to calculate and combine uncertainties in order to determine the range of a result. Out of the defined probability distributions for the uncertainties, the simulation executes a random sampling and integrates it into the evaluation model. 4 4.1
STRATEGIC EVALUATION OF TECHNOLOGY Technology Evaluation Methodology
In order to enable producing companies to select the optimal technology for future production requirements, this approach provides a structured methodology for strategic evaluation. The methodology consists of three different steps built on each other and depicted in Figure 4. In the first step, important product properties for technology evaluation have to be defined, such as geometry dimensions or potentially usable alloy materials. Furthermore, potential technological alternatives are considered. Herein, methods for technology screening and trend forecasting can be used as recommended by Bullinger [11]. In the second step, the technology evaluation takes place where five evaluation criteria, in particular, are calculated. Herein, qualitative and quantitative influencing factors are integrated. Some of the factors are quite uncertain and interact with each other. For example, if the maturity of a technology were “high”, loss rates would be “low”. Thereby, factors influencing different evaluation criteria also interfere among themselves. Hence, the valuation of the five criteria cannot be done independently, but rather must be accomplished regarding the interactions and developments of all influencing factors.
Step 1
Step 2
e1 High Middle Low
µ 0,4 0,6 0,2
e2 High Middle Low
µ 0,3 0,4 0,2
ei High Middle Low
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Rule Base ei: Output Neuron µ: Membership Value Figure 3: Integration of qualitative uncertainties using Fuzzy Logic.
Probability
Inference
Step 3
Product Properties
Suitability Alternative Technologies
Multi-Criteria Evaluation
Comparison and Selection
Figure 4: Methodology for Strategic Technology Evaluation Consisting of three Steps.
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P
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Technology Readiness Level Quantifier [-]
P
7. Serial Production
q7 = 0,20 mTRL,7 25
6. Production Environment
q6 = 0,20 mTRL,6 30
5. Production Resource
q5 = 0,20 mTRL,5
4. Technology Demonstration
q4 = 0,15 mTRL,4
3. Technology Development
q3 = 0,15 mTRL,3
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2. Feasibility Studies
q2 = 0,05 mTRL,2
69
1. Basic Technology Research q1 = 0,05 mTRL,1 Maturity of Technology: MT = 47,3%
Technology Suitability P: Probability wi: Weighting Factor Figure 5: Aggregation of the Evaluation Criteria to the Technology Suitability Histogram. The uncertainties and interdependencies are integrated in the valuation using probability distributions and Fuzzy Logic. The results of the several valuations finally flow into the target value described as technology suitability. The aggregation of the five evaluation criteria is pictured in Figure 5. Each valuation result consists of a distributed histogram of the particular target value, such as technology maturity. Depending on the weighting factors wi, the aggregation of the multi-criteria evaluation constitutes a histogram of technology suitability. Finally, the evaluation results of alternative technologies can be compared in the third step of the methodology. Thereby, the selection of the optimal technology depends on the interpretation of the suitability histogram. Following this, the two evaluation criteria “technology maturity” and “profitability” are examined. The procedure for valuation is also contemplated in more detail. Thereby, relevant influencing factors are described. Furthermore, the modelling of the quantitative and qualitative factors influencing the focused valuation is explained. 4.2
Valuation of Technology Maturity
The method for valuating the technology’s maturity is based on the concepts of “technology readiness level” (TRL) according to the NASA [12] and the “technology maturity assessment” (TMA) according to Brousseau et al. [9]. In this approach, these concepts are combined and enlarged by considering the time-dependent change of technology maturity [22]. Furthermore, indicators and limits are introduced in order to permit maturity definition and impartial quantification. The method for maturity valuation employed in this approach is based on seven different TRLs, shown in Figure 6. Beginning with basic technology research activities (TRL 1), the development of a technology passes through several feasibility and demonstration stages (TRL 2-4). After integration into real production resources (TRL 5) and environments (TRL 6), the technology is finally used in a company’s serial production (TRL 7). The further development of a technology does not proceed in one stage alone, but rather in all seven levels. Hence, the maturity of each TRL mTRL,i has to be estimated. The TRL maturities arise out of a questionnaire consisting of TRL specific demands that help to assess the maturity development at each stage. The questions also aim to determine the limits of factors and indicators influencing the technology maturity, such as loss rates or changes in process costs [22]. Because they stem from expert estimations, the questionnaire responses are somewhat vague and are therefore viewed as a kind of uncertainty.
Maturity [%]
55 45
91
Specific Maturity Limit lspec,i
Figure 6: Exemplary Calculation of Technology Maturity depending on the Maturity of each TRL (based on [14]). Depending on the technological strategy of a company, another specific maturity limit lspec,i may be introduced in order to determine the minimum grade of maturity at each stage. A technology is not applicable in a company’s production environment before having passed all specific maturity limits. In order to weight the different TRL, quantifiers qi have to be defined in each TRL. The determination of the seven quantifiers is company specific and can vary by case. Normally, higher TRLs have to be rated higher than lower ones, because these TRLs influence the application of the technology in reality much more. Finally, the technology maturity MT is the result from the calculation of the seven maturities of the different TRLs mTRL,i according to equation (1). Figure 6 shows the principle of an exemplary calculation of the technology maturity. Reinhart and Schindler [22] introduce further information concerning technology maturity valuation employed in this approach. Thereby, the detailed framework of the maturity valuation model and the structured procedure are presented and explained. The result of the technology maturity calculation finally delivers a maturity histogram as is shown in Figure 5. The fluctuation of this histogram depends on technology maturity representing technical as well as organizational risks. This histogram finally flows into the evaluation of the technology suitability.
7 MT 1 1 mTRL ,i q i 100 % i 1 4.3
(1)
Valuation of the Technology’s Profitability
The valuation of the profitability of a technology generally focuses on estimated production cost per unit. As shown in Figure 7, production cost divides into material and manufacturing costs. Material cost consists of material prices and preceding value add, which are not done inside the company such as alloying or forging. Manufacturing cost is composed of forecasted man and machine hour rates. Based on this cost structure, the technology’s profitability is calculated. From a strategic perspective, not only single costs per unit must be contemplated, but also influencing quantitative and qualitative factors to ensure a chargeable valuation. Figure 7 illustrates the way influencing uncertainties are integrated. In the profitability valuation model of this approach, quantitative uncertainties, such as future product quantities or forecasted material prices, are implemented using different kinds of probability distributions. Qualitative Uncertainties such as competitive potentials first are quantified using Fuzzy Logic. After the fuzzification, the qualitative uncertainties are then modelled in the same way as quantitative ones.
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0,05
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0,04
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Probability [-]
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Mean A: 77,8%
Mean B: 95,6% Technology A Technology B
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Quantitative Uncertainties P A P B
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µ 0,4 0,6 0,2
P
P
xsα
xsβ
α
β
Probability Quantitative Uncertainties Qualitative Uncertainties Centre of Mass Output Neuron Membership Value
Figure 7: Composition of Estimated Production Cost influenced by Quantitative and Qualitative Uncertainties. The complexity of valuating the technology’s profitability increases because the different production cost influencing factors interact with each other. The technology maturity represents a qualitative influence for the profitability valuation, for example. If the technology maturity is high, the uncertainty of processing time estimations tends to be low. In the same way, maintenance costs decrease with increasing technology maturity. These existing interactions and resulting impacts between influencing factors as well as entailed uncertainties affect the result of the profitability valuation. Interactions between quantitative factors can be implemented by defining correlations [15]. Interdependencies of several qualitative factors as well as dependencies between qualitative and quantitative factors are modelled by using Fuzzy Logic. Due to integrating qualitative and quantitative uncertainties, the result of technology’s profitability valuation consists in a profitability histogram as it is pictured in Figure 5. Depending on the influencing uncertainties, the range of the profitability target value varies considerably. The histogram of the technology’s profitability finally flows into the technology suitability evaluation. 4.4
50 100 150 Technology Suitability [%]
200
Figure 8: Evaluation Results of Alternative Technologies A and B.
Qualitative Uncertainties
...
... P: A, B: α, β: xs: ei: µ:
Man Hour Rates
...
Value Add Forging
...
Value Add Alloying
Strategic Evaluation Results
In order to select the optimal technology, the technology evaluations of the existing alternatives have to be compared. As shown in Figure 5, the result consists of an aggregation of the outcomes from the weighted criteria valuations. Thereby, the suitability of a single technology represents the target value and is constituted as a histogram. Figure 8 displays the result of the Monte Carlo simulation for two alternative technologies A and B. The diagram plots the probability against the technology suitability histogram based on random sampling.
Because the histograms of technology A and B result from the same simulation and exhibit the same quantity of drawings, the area integral of both is equal. The fluctuation range of one single histogram permits conclusions regarding expected risks. The more a histogram disperses the higher the uncertainty is. Regarding the means of both alternatives, technology B has to be preferred as it has about 17,8% better technology suitability than technology A. However, a look at the fluctuation range of technology B highlights the risks entailed with technology B. As there exist several drawings of technology B being placed worse than technology A, the risk of B is significantly higher. Nevertheless, technology B also offers a great chance to get more suitability, because of drawings up to 150% of technology suitability. Generally, technology A is rated worse than the alternative B, but it shows a comparably low-risk property. Technology B offers a greater potential in reaching higher suitability although it involves higher risks. In order to decide which technology to select, further analyses have to be done. Firstly, sensitivity analyses need to be contemplated in order to find out which uncertainties are responsible for each fluctuation result. 5
SUMMARY AND OUTLOOK
As producing companies located in high-wage countries continually have to ensure that their technologies fit future requirements, new methods for evaluating technologies are needed from a strategic point of view. Therefore, this paper presents an approach for the strategic technological evaluation of uncertainties. First, the individual evaluation criteria are described and it is explained that quantitative or qualitative uncertainties may underlie the evaluation of integrated factors. Quantitative uncertainties can be modelled using probability distributions; qualitative uncertainties with Fuzzy Logic. Subsequently, the evaluation model is described in general and it is clarified that the individual evaluation criteria may also affect one another. Finally, the evaluation of two criteria – maturity and profitability – is explained in more detail and it is described how to interpret the results of the strategic technology evaluation. The approach presented in this paper concentrates on the strategic evaluation and comparison of a single technology alternative at a particularly date. As products are only produced using a combination of several technologies, the strategic evaluation needs to be expanded focusing on a so-called technology chain. However, strategic technology planning has to cover the long-term horizon. Therefore, not only one single point of time needs to be considered but also the whole future period of the technology chain. Further research activities must therefore find out how to integrate this timedependent and long-term contemplation into the presented approach. Latest investigations show that the use of recurrent Fuzzy Systems is a suitable aid to meet this challenge [24].
184 6
Life Cycle Design - Selected Applications ACKNOWLEDGMENTS
The German Research Foundation (DFG) funds this research and development project. We extend our sincere thanks to the DFG for the generous support of the work described in this paper. The research project is part of the Collaborative Research Centre 768 ”Managing cycles in innovation processes – Integrated development of product services systems based on technical products”. The goal of the Collaborative Research Centre 768 is to reduce the knowledge gap regarding cycles and cyclic influences within the innovation process. 7 [1]
[2]
REFERENCES Tolio, T.; Ceglarek, D.; ElMaraghy, H. A.; Fischer, A.; Hu, S. J.; Laperrière, L.; Newman, S. T.; Váncza, J. (2010): SPECIES – Co-evolution of products, processes and production systems. Annals of the CIRP, Vol. 59, No. 2, pp. 672-693. Klocke, F. (2009): Production Technology in High-Wage Countries – From Ideas of Today to Products of Tomorrow. in: Schlick, C. M. (Ed.): Industrial Engineering and Ergonomics – Visions, Concepts, Methods and Tools Festschrift in Honor of Professor Holger Luczak. Springer, Berlin, New York, pp. 1330.
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DIN 8580 (2002) Manufacturing processes – terms and definitions, division. Beuth, Berlin.
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Zaeh, M. F.; Reinhart, G.; Pohl, J.; Schindler, S.; Karl, F.; Rimpau, C. (2009): Modelling, Anticipating and Managing Cyclic Behaviour in Industry. in: Proceedings of the 3rd International Conference on Changeable, Agile, Reconfigurable and Virtual Production (CARV2009), pp. 16-43, Munich, Germany.
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Wiendahl, H. P.; ElMaraghy, H. A.; Nyhuis P.; Zaeh, M. F.; Wiendahl, H. H.; Duffie, N.; Kolakowski M. (2007): Changeable Manufacturing – Classification, Design and Operation. Annals of the CIRP, Vol. 56, No. 2, pp. 1-25. Reinhart, G.; Schindler, S.; Pohl, J.; Rimpau, C. (2009): Cycle-Oriented Manufacturing Technology Chain Planning. in: Proceedings of the 3rd International Conference on Changeable, Agile, Reconfigurable and Virtual Production (CARV 2009), pp. 702-711, Munich, Germany. Sommerlatte, T.; Deschamps, J. P. (1985): Der strategische Einsatz von Technologien. in: Little, A. D. (Ed.): Management im Zeitalter der strategischen Führung. Gabler, Wiesbaden, pp. 39-76.
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Eversheim, W.; Schuh, G. (1996): Betriebshütte – Produktion und Management. Springer, Berlin, Heidelberg, New York, 1996, pp. 10.36-10.72.
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Brousseau, E.; Barton, R.; Dimov, S.; Bigot, S. (2010): Methodology for Evaluating the Technological Maturity Micro and Nano Fabrication Processes. IFIP, Advances Information and Communication Technology, Vol. 315, No. Springer, Boston, pp. 329-336.
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[10] Gouvea Da Costa, S. E.; Platts, K. W.; Fleury, A. (2006): Strategic selection of advanced manufacturing technologies (AMT), based on the manufacturing vision. International Journal of Computer Applications in Technology, Vol. 27, No. 1, pp. 12-23. [11] Bullinger, H. J. (2008): Fokus Technologie – Chancen erkennen – Leistungen entwickeln. Carl Hanser Verlag, Munich, Vienna.
[12] Mankins, J. C. (1995): Technology Readiness Level – A White Paper, Advanced Concepts Office, Office of Space Access and Technology. [13] Herrmann, C.; Thiede, S. (2009): Process chain simulation to foster energy efficiency in manufacturing. CIRP Journal of Manufacturing Science and Technology, Vol. 4, No. 1, pp. 221-229. [14] Reinhardt, S.; Reinhart, G. (2009): Resource Efficiency Valuation of Manufacturing Processes. in: Digiesi, S.; Mossa, G.; Mummolo, G.; Ranieri, L. (Ed.): Quaderni della XIV Summer School 'Fancesco Turco'. pp. IV.84-IV.91, Monopoli, Italy. [15] Krebs, P.; Müller, N.; Schellmann, H.; Reinhardt, S.; Bredow von, M.; Reinhart, G. (2009): Risikobewertung produzierender Unternehmen. ZWF – Zeitschrift für wirtschaftlichen Fabrikbetrieb, Vol. 104, No. 3, pp. 174-180. [16] Rosenkranz, F.; Missler-Behr, M. (2005): Unternehmensrisiken erkennen und managen – Einführung in die quantitative Planung. Berlin: Springer. [17] Brieke, M. (2009): Erweiterte Wirtschaftlichkeitsrechnung in der Fabrikplanung (EWR). Diss. Hannover. Leibniz Universitaet Hannover. [18] Korves, B.; Krebs, P. (2008): Planung und Bewertung von Flexibilität in Fabrikplanungsprojekten bei der Siemens AG. in: Tagungsband Muenchener Kolloquium – Innovationen für die Produktion, pp. 57-67. [19] Zimmermann, H. J. (1991): Fuzzy set theory - and its applications, Boston: Kluwer. [20] Wang, L. X. (1997): A course in fuzzy systems and control, Upper Saddle River N. J.: Prentice Hall. [21] Yen, J.; Langari, R. (1999): Fuzzy logic: Intelligence, control, and information, Upper Saddle River N. J.: Prentice Hall. [22] Reinhart, G; Schindler; S. (2010): A Strategic Evaluation Approach for Defining the Maturity of Manufacturing Technologies. in: Proceedings of the International Conference on Engineering and Technology Management (ICETM2010), Venice, Italy, Vol. 6, No. 71, pp. 920-925. [23] Reinhart, G.; Krebs, P.; Zaeh, M. F. (2009): Fuzzy Logicbased Integration of Qualitative Uncertainties into Monetary Factory Evaluations. in: Proceedings of the IEEE International Conference on Control and Automation (ICCA2009), pp. 385391, Christchurch, New Zealand. [24] Dieplold, K., J.; Lohmann, B. (2010): Transient Probabilistic Recurrent Fuzzy Systems. IEEE International Conference on Systems, Man, and Cybernetics (SMC2010), pp. 3529-3536, Istanbul, Turkey.
Consideration of the Precautionary Principle – the Responsible Development of Nano Technologies Marcel Weil 1
1
Institute for Technology Assessment and Systems Analysis (ITAS), Karlsruhe Institut of Technologie (KIT), Karlsruhe, Germany
Abstract Carbon nanotube papers, referred to as bucky papers, are attracting a growing attention in different disciplines and application fields. The production of bucky papers is still considered to be too elaborate and costly for a broader application. Additional there are nano particle emission during production with unknown effects on environment and human health. For a planed upscale a carbon nanotube paper production is analysed, not only to identify optimisation potentials regarding technical and economic but also environmental and health aspects. For known environmental impacts, a streamlined Life Cycle Assessment (LCA) is conducted. Whereas for the unknown impact of nanotube emissions on environment and human health a qualitative method is used within a optimisation process, to foster the prevention of nano particle emissions, in respect of the precautionary principle. Keywords: Carbon Nanotube Paper; Streamlined LCA; Economic Assessment; Precautionary Principle
1
INTRODUCTION
In the presented prospective approach, system analysis is used to optimise the manufacturing process of carbon nanotube (CNT) paper by the consideration of technical, but also economic and ecological aspects, including nano specifics.
of uncertainty and unknown effects, but on the other hand has the chance to use the high degree of freedom (for development options) to guide the development itself in a more sustainable direction. Life Cycle Assessment (LCA) is an appropriate tool to analyse the environmental impact of a technology or product (cf. Figure 1). Due to uncertainties, inconsistent scientific results and the lack of knowledge about the eco- and human toxicity of nano particle, the potential environmental and health impact of nano particle is not covered by traditional LCA. Therefore the chosen approach includes a qualitative method (ABC-method) to ensure that also potential environmental and health risks will be minimized during an optimisation process by reducing or even preventing the emissions of nano particles to the environment (Figure 1). The minimization of nano particle emission with unknown risks is a requirement of the precautionary principle. In parallel to LCA (cradle to gate) also an economic assessment is carried out to identify overall more sustainable options. The described approach is focused exemplarily on the production of CNT-paper production.
2
3
New materials on a nano scale have the potential to overwhelm existing technical barriers and are one of the most promising key technologies to enable the decoupling of economic growth and resource consumption. Developing these materials for industrial application means facing a complex qualification profile, which includes among others technical, economic, and ecological aspects. The two latter aspects are not sufficiently included in material development, especially from a life cycle point of view [1]. If and when economic/ecological assessment is carried out in material development, it is performed after technical investigations are finished. Quite often, these assessments are postponed until the subsequent phase of product development.
APPROACH
3.1
PRODUCTION OF CNT AND BUCKY PAPERS Production of CNT
Carbon Nano Tubes (CNT) belong to a interesting material group with very promising technological properties, like very low optical reflection, high tensile strength (by low weight), good electric and heat conductivity. They can be produced basically by three different methods:
Figure 1: Different levels of uncertainties regarding new technologies require the selection and combination of different methods and tools. The integration of system analysis in the early phase of technology development has one the one hand to struggle with the high degree
arc discharge
laser ablation
chemical vapour deposition (CVD)
The CVD process shows the greatest promise for a large-scale manufacturing of CNT, in particular fluidized bed CVD techniques [2]. Due to the fact that to date the quality of CNT from fluidized bed CVD has not met all the requirements for bucky paper production, a CVD in the batch mode is considered and used for the production of multi-walled carbon nano tubes (MWNT) for the presented work.
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_32, © Springer-Verlag Berlin Heidelberg 2011
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186 3.2
Life Cycle Design - Selected Applications Production Process of Bucky Papers
The synthesized MWCNT are purified in a liquid medium. After the separation of the liquide medium and the MWCNT by the help of vacuum filtering technique, the MWCNT are dispersed in a solvent, supported by the use of an ultrasonic treatment. Only well dispersed MWCNT are useful for CNT paper production. CNT Synthese
CNT Purification
CNT Dispersion
Filtering 1
Filtering 2
CNT Paper cut to order
CNT Paper
Global warming or greenhouse effect is quantified by using GWP for substances having the same effect as CO2 in reflection of heat radiation. GWP for greenhouse gases are expressed as CO2equivalents. Hence the impact indicator GWP describes the potential greenhouse effect of a product/process by calculating all emitted greenhouse gases (e.g. CO2) per functional unit, in this specific case, the production of bucky paper. In Figure 4 the shares of all process steps and pre-chains (e.g. electric energy) in the GWP are presented. It reveals that the dispersion process of MWNT contributes most to the GWP result. Responsible for the GWP contribution is mostly the use of a solvent. In addition, the transport of the dispersion medium (solvent) has a significant contribution. Other impact categories [3, 4] demonstrate results similar to GWP, beside “photo-oxidant formation”.
Global Warming Potential
Figure 2: Manufacturing process of CNT paper.
dispersion
By using again filtration techniques, MWCNT build up a layer on the top of a filter medium. After drying under room temperature and separation of the filter medium and the MWCNT layer, a free standing paper is produced. The manufacturing process is shown in Figure 2. This paper consists of 100% MWNT, meaning that no supporting fibres are contained.
transport of solvent others purification
4
ANALYSIS OF CNT-PAPER PRODUCTION
4.1
transport acid (purification)
System boundary of economic and life cycle assessment
electric energy
resources system boundary
catalyst
mixing
Figure 4: Shares of all process steps and pre-chains in GWP.
processing
4.3 MWNT
CNT synthesis
purification
filtration I
dispersing
filtration II
drying
bucky paper
emissions (air, water, earth)
Figure 3: System boundary for economic and ecological assessment. The system boundaries for economic and ecological investigations are shown in Figure 3. The results of the assessment are used both to identify the economic and ecological hot spots and to select the most favourable optimisation strategy. The manufacturing process can be divided into three main steps: synthesis of catalyst, synthesis of MWNT, and production of bucky paper, conf. Figure 3. For the investigation all prechains of inputs (e.g. electric energy) or services (e.g. transport) which are necessary for the whole manufacturing process are considered. 4.2
Economic assessment
Figure 5 display the shares in production costs of bucky papers (including supplies). The costs for labour, leasing and depreciation are not included.
LCA
For the understanding of the ecological implications of bucky paper production a streamlined Life Cycle Assessment (LCA) is conducted. The CML method [3, 4] is used for the ecological impact assessment. Human and eco toxicity of nano-materials are not included in this investigation. In the presented contribution, we display only one impact indicator: Global Warming Potential (GWP).
It indicates that the dispersion process of MWNT contributes most to the production costs. Again the use of solvents is the most important cost driver. The filtration process has a noteworthy contribution. All the other processes have only a small share to the production costs.
Material Costs dispersion synthesis MW NT purification catalyst filtration others
Figure 5: Shares in production costs.
Life Cycle Design - Selected Applications 5
187
DEVELOPMENT OF OPTIMISATION STRATEGIES
5.1
Lessons learned
Both the economic and ecological assessment identified the dispersion process, especially the use of solvent, as a major driver. Hence an optimisation strategy has to be developed, which allows the minimization of solvent use, or alternatively which substitutes the use of the solvent. But to focus during the optimization process only on economic and ecological goals can be described as insufficient, because the technical performance could be reduced or nano particle emissions increased. Thus the optimisation considers the following aspects:
Stabilisation of the production process and stabilisation of the product quality (only minimal reduced technical performance is acceptable)
Reduction of the production costs
Reduction of the environmental impact
Reduction (or prevention) of nano particle emission to the environment
Subsequently it was investigated if a continuously recycling of the solvent causes an accumulation of substances, which might have a negative impact on the bucky paper quality. But also a continuous recycling of the solvent over more than 30 cycles doesn’t have any quantifiable negative effects on the produced quality of the buck papers. 6
QUANTITATIVE OPTION
OF
CHOSEN
OPTIMIZATION
90 80 70 60 50 40 30 20 10 0
reference
Comparison of alternative optimization strategies
Together with the industry partners six different optimization strategies were developed (Table 1):
Use of an alternative solvent
Replacement of solvent by water (up to 30 %)
Use of a water tensideX1 mixture
Use of a water tensideX2 mixture
Use of a water tensideX3 mixture
Recycling of solvent
All six alternatives are analyzed and qualitative evaluated regarding the mention aspects mentioned above (Table 1).
100% Solvent (Reference)
RESULT
100
It hast to be stated, that the latter focused on the nano particle emissions to water. Without any doubts, the CNT paper production process also causes nano particles emissions to air, but due to very efficient air cleaning machines, the nano particle concentration in the laboratory is lower than outside (background concentration of nano particle). This is shown by nano particle air measurements (10-1000 nm) of a German labour protection organisation. 5.2
The comparison of the six different optimization options reveals that the last option, recycling of solvent, shows the best overall performance and is in addition the only option, which reaches a comparable technical quality of the gained CNT paper, which is an important requirement.
recycling solvent
reference
Global Warming Potential
recycling solvent
Material Costs
reference
recycling solvent
CNT Emissions to Water (Dispersion)
Figure 6: Quantitative effect of the optimisation process At comparable bucky paper quality, the recycling of solvent represents the best of six investigated optimisation option. Within the dispersion process, up to 70% of the solvent can be recycled with low efforts for reprocessing. The quantitative effects of the optimisation option “recycling solvent” are shown in Figure 6. The Global Warming Potential (GWP) is reduced by 65%, the production costs are decreased by 59% (total costs by 16%) in comparison to the reference case (status quo of CNT paper production) [5]. In addition by a continuously recycling of the solvent the nano particle emission to the water path could be prevented.
Solvent Solvent Solvent Solvent replacement by Solvent replacement by replacement by replacement by 100% replacement up 100% 100% 100% alternative to 30% by water water/tensideX1 water/tensideX2 water/tensideX3 solvent mixture mixture mixture
Recycling of reference solvent
Quality bucky paper
A
A/B
B/C
B
C
C
A
Economic profile
C
A/B
B
A/B
A/B
C
A/B
Ecological profile
C
C
B
A/B
A/B
A/B
A/B
CNT emissions (to water)
C
C
C
C
C
C
A/B
Table 1: Qualitative comparison of optimisation strategies.
188 7
Life Cycle Design - Selected Applications SUMMARY
The use of multi-walled carbon nanotubes for the production of bucky papers has opened up new application fields. But the production of bucky papers still is considered to be too elaborated and costly for a broader application. Furthermore the ecological implications of the bucky paper production are unknown. The economic and ecological assessment of the carbon nanotube paper production on a laboratory scale reveals that the dispersion of the MWNT has a great impact on the ecological profile and the costs. Mainly responsible is the use of solvents for dispersing nanotubes. The economic and ecological results concerning the status quo of bucky paper production is essential for the development of optimisation strategies. Different optimisation strategies are investigated and qualitative evaluated. The selection of the most promising optimisation option is based on technical, economic, and ecological aspects, including nano particle emissions. A substantial decrease of costs and ecological impact with good product quality (of bucky paper) is reached by the optimisation option “recycling of solvent”. The gained information is essential for an upscale of bucky paper production in a continuous mode. Further investigations are needed to compare bucky papers with traditional materials in different application fields over the entire life cycle. The presented work showed one possible approach enabling a more responsible development of a new technology, which might bear some unknown risks. 8
REFERENCES
[1]
Weil, M., Buchwald, A., Dombrowski, K., Jeske, U., Buchgeister, J. - Eds. - (2007): Materials Design and Systems Analysis, Shaker-Verlag, p. 321.
[2]
See, C. H., Harri, A.T. (2007): A Review of Carbon Nanotube Synthesis via Fluidized-Bed Chemical Vapor Deposition. Ind. Eng. Chem. Res., 46 (4), pp 997–1012.
[3]
Guinée, J.B. - Eds - (2002): Life cycle assessment. An operational guide to the ISO standards, CML Leiden, p. 708.
[4]
Heijungs, R., Guinée, J.B., Huppes, G., Lankreijer, R.M., Udo de Haes, H.A., Wegener Sleeswijk, A., Ansems, A.M.M., Eggels, P.G., Duin, R. van, Goede, H.P. de (1992): Environmental Life Cycle Assessment of Products; Guide & Backgrounds, CML Leiden.
[5]
Weil, M., Schebek, L., Forero, S., Crizeli, S. (2009): Bucky papers - a prospective economic and ecological investigation for the upscale of the production process. SETAC Europe 19th Annual Meeting. Göteborg, Sweden.
Proposal of a Design Support Method for Sustainability Scenarios 1st Report: Designing Forecasting Scenarios 1
1
1
1
1
Haruna Wada , Yusuke Kishita , Yuji Mizuno , Maki Hirosaki , Shinichi Fukushige , Yasushi Umeda 1
1
Department of Mechanical Engineering, Graduate School of Engineering, Osaka University, Osaka, Japan
Abstract Although there are many scenario description methods, computational support has not been provided for scenario design. This paper proposes a method for supporting design of forecasting scenarios. This method supports determining storylines, which are key assumptions of sub scenarios, and describing sub scenarios based on the causal network and storyline. As a case study for verifying feasibility of this method, we described a forecasting scenario about changes of automobile industry when electric vehicles are diffused. Results illustrate that the usage of causal networks effectively supports generating storylines. Also, describing scenario texts is supported by detailing causal networks and the storylines. Keywords: Sustainability Scenarios; Scenario Design; Forecasting Scenario
1
INTRODUCTION
The manufacturing industry must be changed so as to contribute to constructing a sustainable society by solving a range of problems, such as resource exhaustion. However, it is difficult to achieve sustainable manufacturing only by piling up individual techniques. Rather, we should clarify future visions of sustainable manufacturing and integrate various key technologies, socioeconomic factors, and policy framework toward them. In Life Cycle Engineering, we thus should clarify the relationship between technological opportunity and visions of sustainable manufacturing. This leads to grasping the direction of Life Cycle Engineering. One of hopeful approaches for drawing such future visions is to describe scenarios. Besides scenario planning, there are several methods for drawing futures including trend analysis, simulation modeling, historical analysis, and visioning [1]. Nevertheless, since scenario planning is more comprehensive than others, this paper chose scenario planning as its approach. A scenario is a story that connects a description of future to present realities in a series of causality [2]. Numerous scenarios have already been described in order to draw visions of future societies, such as IPCC’s emissions scenarios [3] and IEA’s World Energy Outlook [4]. However, scenario description processes have not yet been well formalized and sufficient computational support has not been provided for them. As the first step to solve these problems, this paper proposes the concept of “scenario design,” which means a sequence of activities required to describe scenarios. It should contain not only scenario description, but also analyses and verification of scenarios. Based on this concept, this paper proposes a method for supporting the design of scenarios. For this purpose, we formalize a scenario design process. In general, description processes of sustainability scenarios are categorized into two types; that is, forecasting and backcasting [5]. In forecasting process, scenarios are written by envisioning various futures from the present, while in backcasting process a scenario writer firstly describes a future end-point and then describes pathways from the end-point to the present. This paper focuses on
the design of forecasting scenarios (a scenario designed by the forecasting process), because the backcasting process involves the forecasting and, therefore, the forecasting process is a fundamental element in scenario design. This paper is organized as follows. Section 2 briefly summarizes literature reviews regarding sustainability scenario design and then shows problems of existing scenario description methods. Section 3 proposes a method for designing forecasting scenarios. Section 4 presents a system for supporting the proposed method. Section 5 illustrates a case study. Section 6 discusses advantages and issues of the proposed method based on the case study. Section 7 concludes this paper. 2 2.1
DESIGNING SCENARIOS Requirements for designing forecasting scenarios
Designing forecasting scenarios involves, at least, three tasks; describing forecasting scenarios, reviewing and evaluating scenarios, and feeding the evaluation back to scenario description [6]. There tasks lead to the following four requirements for designing forecasting scenarios: I.
To describe scenario texts by detailing the texts gradually.
II. To describe several sub scenarios, each of which describes a specific future, in order to envision different futures from the current situation [7]. III. To collect abundant information to describe scenarios [8]. IV. To clarify the logical structure of scenarios for enabling us to rationally understand and review scenarios. Requirement IV is related to scenario representations, while the other three requirements concern the design process of scenarios. 2.2
Existing scenario description methods
There are a number of methods for describing forecasting scenarios, such as Story and Simulation Approach [7], Scenario Development and Analysis [9], Dynamic Scenarios [10] and Scenario planning [11]. For example, Story and Simulation
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_33, © Springer-Verlag Berlin Heidelberg 2011
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Life Cycle Design - Selected Applications
Approach [7] is a method for description based on narrative stories and quantitative simulations and Scenario Development and Analysis [9] extends Story and Simulation Approach [7]. In this method, scenarios are described by four steps; (1) clarifying the purpose and structure of the scenario, (2) laying the foundation for the scenarios, (3) developing and testing the actual scenarios and (4) communication and outreach. In Dynamic Scenarios [10], a scenario is expressed as a system dynamics model, which is a kind of causal networks.
Node
Link
In describing scenarios, other general methods are used, such as Probability trees [6], Morphological analysis [6], SWOT analysis [12] and PEST analysis [11]. However, existing scenario description methods do not provide computational support of the scenario design and scenario’s logical structure is not clearly described. 3
element positive(A, B)
Node A causes a positive (+) effect on node B.
negative(A, B)
Node A causes a negative (-) effect on node B.
related(A, B)
Node A causes and effect on node B, where the polarity (+/-) is undetermined.
Table 1: Types of nodes and links in Word Level.
Approach
This study mainly supports a first task mentioned in Section 2.1 and this paper proposes a design method for forecasting scenarios in order to satisfy the four requirements mentioned in Section 2.1 by tackling the following two tasks:
ii.
Definition A constituent of the object world of the scenario.
Type
Definition
deploy(A, B)
Node A (Word level node, i.e., a node of a causal network) is deployed to a description of node B (Expression Level node, i.e., a clause of a sub scenario texts).
DESIGN METHOD FOR FORECASTING SCENARIOS
3.1
i.
Type
To formalize the process of scenario design as four steps, that is, setting a problem, construction of rough causal network, describing storylines, and describing sub scenarios, as shown in Figure 1. With this process, the design team can gradually detail a scenario to be designed in the form of causal networks, storylines, and scenario texts. After setting a problem for recognizing the main theme of the scenario, the team describes the present situation of the theme as two causal networks in different levels of granularity as described in Section 3.3. A storyline represents a set of key assumptions of future direction to describe a sub scenario. A storyline is described by detailing critical elements of the causal network. And scenario texts are composed in order to understand the scenario easily. In order to formalize the format of scenario expression, this paper employs the structural scenario description method [13] [14]. The structural description method clarifies logical structure of scenario texts in a computable manner well as enable us to rationally understand scenarios (see Section 3.2 for details).
Table 2: A type of a link between Expression and Word Level. 3.2
Scenario Level: expresses relationships among sub scenarios, including the hierarchical relation between a parent scenario and its sub scenarios.
Expression Level: expresses logical relations of clauses in a sub scenario.
Word Level: expresses causal networks of the target world.
Data Level: expresses simulations used descriptions and their input and output data.
Problem
I.
Rough causal network
Design process of forecasting scenarios
Setting a problem
As the first step, a scenario design team set a problem of a scenario to be designed. For describing the problem, we define a template as shown in Table 3. This step intends to make sure that the team can organize their ideas and recognize what to describe in the scenario. II. Constructing rough causal network
Storyline
Detailed causal network
scenario
In this method, we model the design process for forecasting scenarios as follows (see Figure 1):
Ⅱ Constructing rough causal network
Ⅲ Describing storylines
for
In this paper, we express sub scenarios and storylines using Expression Level, while we represent causal networks using Word Level (see Table 1). And we define the relation between Expression and Word Level, which are used for describing sub scenarios, as shown in Table 2. 3.3
Ⅰ Setting a problem
Structural scenario description method
We have proposed a structural scenario description method for explicitly representing logical structure of a scenario on a computer. To represent a scenario structure from macroscopic and microscopic viewpoints, this method represents a scenario in the following four levels [13] [14]:
In this step, the design team expresses the target world of the scenario as causal networks.
Sub scenario
Ⅳ Describing sub scenarios Figure 1: Design process for forecasting scenarios.
According to our preliminary study, it is effective to describe the target world by using two causal networks different in granularity; namely, a rough causal network, which is described in this step, and a detailed causal network that is made by detailing the rough causal network in step IV (see Figure 1). On one hand, describing the rough causal network helps the design team to examine different directions of visions. On the other hand, constructing the
Life Cycle Design - Selected Applications Item
Description Title of the scenario
Title
Objective
Background
Time horizon
In this process, we assume that the team selects key drivers based on uncertainty and impact on elements. Then, the team describes a storyline by assuming states of the key drivers.
Objective for describing the scenario
IV. Describing sub scenarios
E.g. To discuss changes of automotive industry. To calculate CO2 emissions when EV is diffused.
The scenario design team describes each sub scenario in the form of structured scenario shown in Section 3.2, by detailing the storyline and the detailed causal network.
Motivation for describing the scenario
In this step, the team constructs a detailed causal network from the rough causal network constructed in step II. The detailed causal network guides the team; the team describes a sub scenario by detailing elements of the causal network and its relation.
E.g. For achieving the low carbon society, EVs will be diffused. Policy makers want to look how this diffusion changes structure of automobile industry. Starting year of the scenario
End year
End year of the scenario
E.g. 2010 E.g. 2030 Targeted region in the scenario E.g. Japan
Main actors
Main stakeholders in the scenario E.g. Automotive industry Stakeholders involved in the scenario, other than the main actors
Actors
elements that affect futures and have high uncertainty in the causal network.
E.g. Electric vehicles (EV) diffused society scenario
Start year
Region
191
E.g. Japanese automotive companies, Japanese electric and electronics manufacturers, automotive companies in developing countries Table 3: Problem description of a scenario.
detailed causal network is helpful for supporting composition of the sub scenario texts in Step IV. III. Describing storylines In this step, the scenario design team describes storylines, which are key assumptions of sub scenarios by extracting key drivers of the rough causal network. These key drivers are the crucial
For example, assuming a part of a detailed causal network as shown in Figure 2, we can describe a sub scenario consisting of node (A) “If the supply of rare earth decrease,” from node (a) and describe node (B) “the Japanese manufacturing industry will be less competitive,” from node (b). Then, nodes (A) and (B) are connected to nodes (a) and (b) by the link ‘deploy,’ respectively. In this case, we put a link of “logical_jump” in Expression Level, because node (B) is derived from node (A) with a leap of logic. This relation between nodes does not fill the necessary and sufficient condition. In this way, we describe sub scenarios with the structural scenario description method. Finally, the scenario design team organizes scenario texts by changing order of sentences and classifying them into their meanings. 4
SCENARIO DESIGN SUPPORT SYSTEM
In this study, we developed Scenario Design Support System for supporting the design of forecasting scenarios based on the method proposed in Section 3. Figure 3 depicts the architecture of this system, which consists of five elements; Scenario Design Manager, Problem Editor, Causal Network Editor, Storyline Editor, and Scenario Structural Description Support System. Scenario Design Manager manages the three sub systems of Problem Editor, Causal Network Editor, and Storyline Editor. The design team describes problems, causal networks, and storylines of scenarios with these sub systems. Scenario Structural Description Support System supports describing sub scenarios from these scenario elements (i.e., storylines and causal networks).
Figure 3: Scenario Design Support System. Figure 2: Example of describing sub scenarios.
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Storyline Item
Urban Centralization Sub Scenario
Compact City Sub Scenario
Form of city
There are some metropolitan areas of 10 million people living there.
Form of many cities is Compact City. Compact cities about 30 km in diameter disperse over Japan.
Transportation
In urban area: Mainly public transportation. In rural area: Community buses and automobiles. Between urban and rural areas: Public transportation and automobiles.
In city: Small EVs at low speed, LRT, and bicycle. Most of automobiles are used on a park-and-ride basis. Between cities: Mainly public transportation.
Table 5: Storylines of two sub scenarios. 5
CASE STUDY
1
We execute a case study for verifying advantages and issues of the proposed method. This case study targets diffusion of Electric Vehicles (EVs) in Japan, because the diffusion of EVs is effective for achieving a low carbon society. We here assume that policy makers design a scenario to discuss changes in the structure of automotive industry when EVs are diffused. 5.1
Scenario design process in the case study
The scenario design process in this case study was carried out as follows: I.
Setting a problem
We set the problem as the example shown in Table 3. We set automotive industry as the main actor and Japanese automotive companies, Japanese electric and electronics manufacturers, automotive companies from developing countries as actors. In steps II, III and IV, we discussed particularly about automotive industry set in this problem setting.
II. Constructing rough causal network According to the problem setting, we picked up elements about automotive industry, such as “Energy supply for vehicles,” “Energy price,” “Users’ needs for mobility,” and “Form of city” by brainstorming and constructed a rough causal network by relating them as shown in Figure 4. Here, we constructed the relations between these elements by “related” links, because we considered that node (a) effects on node (b) and that nodes (b) and (c) effect on node (d). III. Describing storylines In this step, we described storylines by detailing key drivers chosen from the causal network. First, we set uncertainty and weight values of each element and our system calculated impact values of each element based on each weight. We select key drivers according to product of uncertainty and impact. Table 4 lists five highest elements out of all 20 elements in terms of the product. We selected “Form of city” as a key driver based on fourth in Table 4. In terms of “Form of city”, we identified two patterns of concentration and disintegration and we made two storylines, namely, the storyline of “Urban Centralization Sub Scenario” and one of “Compact City Sub Scenario” as shown in Table 5. IV. Describing sub scenarios In this step, we constructed two detailed causal networks by detailing the rough causal network so as to follow the two storylines. For example, since we considered that how to use cars is especially important in “Compact City Sub Scenario,” we put elements of the detailed causal network “How to use cars” and “Needs for cars” by detailing an element of “Users’ needs for mobility” in the rough causal network. As a result, we created 66 elements of the detailed causal networks in “Urban Centralization Sub Scenario” and 96
Uncertainty
Impact
Uncertainty X Impact
Global Political Cooperation
2
6.4
12.8
Global Economic Cooperation e.g. ETA
2
6.2
12.4
Energy Supply for Vehicles
5
1.4
7.0
Form of City
4
1.3
5.2
Energy Price
2
1.4
2.8
Element
Figure 4: Elements of the rough causal network (partially). 1
Full texts of the scenario designed in this case study can be downloaded from the 3S homepage (http://www-lce.mech.eng.osaka-u.ac.jp/3s/).
Table 4: Elements of the rough causal network (partially).
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Figure 5: A part of structured scenario of Compact City Scenario. elements in “Compact City Sub Scenario” from 20 elements of the common rough causal network. We described two sub scenarios from the detailed causal networks and the storylines. For example, Figure 5 illustrates a part of structured sub scenario developed from the causal network in “Compact City Sub Scenario”. Node (A) is a part of the storyline of the sub scenario and nodes (a) and (b) are a part of the causal network. For example, we wrote a sentence of node (B) “Since the density of population in each city is high, roads and passages are narrow.” by deploying node (A) and node (a). We express the relation between nodes (A) and (B) by a link of “logical_jump”, because node (B) is derived from not node (A) rationally but our assumption. This representation enables to distinguish logical part of a scenario from logically uncertain part in deriving hypotheses or conclusions, such as node (B), (C) or (D). Also, we express relations between the causal network and sentences of sub scenario by the link of “deploy”. Then, we made node (C) “Mid-sized cars are not useful in narrow roads and passages in cities.” by deploying nodes (B) and (b), and node (D) “Small cars will only be used in the compact city.” by derived from nodes (C) and (b). 5.2
Conclusions of the scenario
The conclusions of the designed scenario are summarized as follows. In “Urban Centralization Sub Scenario,” EVs and GVs are used in the same way as the current and Japanese automotive companies have advantages similarly to the current situation. Therefore, it is concluded that structural changes of automotive industry are moderate and CO2 emissions from the automobile sector does not decrease so much. On the other hand, in “Compact City Sub Scenario,” various manufactures develops small EVs and
each EV is owned not per family but per individual. In the end, in 2030, the number of automobiles increases and small EVs are diffused explosively. As a result, changes of industrial structure are large. Since EVs are diffused, CO2 emissions decreases compared with 2010. But, as a side-effect, the number of discarded automobiles will increase as the result of increased number of small EVs. It is found out that automotive related industry expands in this society in 2030, as the spread diffused EVs. About material industry, we concluded that it is necessary to secure rare metals and rare earths and to develop alternative materials. About energy industry, development of the electric power supply system adaptive to changes of electric demand of EVs is needed. 6
DISCUSSION
Using the proposed method, we succeeded in designing a scenario consisting of two sub scenarios as described in the previous section. The method supported constructing two levels of causal networks (i.e., rough and detailed), extracting key drivers from the rough causal network, making storylines, and deploying scenario texts from the causal networks and the storylines. By extracting key drivers from a rough causal network, we described storylines. As a result, we have shown two sub scenarios that indicate different worlds. Table 6 depicts the ratio of nodes in Expression Level deployed from the detailed causal network to all nodes in Expression Level, which consists of scenario texts. This ratio indicates how much we can describe the scenario texts systematically in this method. Table 6 shows that 90% of nodes in Expression Level in “Urban Centralization Sub Scenario” and 91% in “Compact City Sub
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Scenario” are deployed from the causal network. The result indicates that causal networks are useful in describing scenarios. Since the causal networks constructed in our method basically targets the present situation, we cannot describe scenarios that contains a quantum leap from the present. Proposing a method for composing such scenarios is one of our future works. A scenario design method based on backcasting is one of leading candidate of it
[7]
Alcamo, J. (2001): Scenarios as Tools for International Environmental Assessments, Environmental Issue Report No. 24, European Environmental Agency, Copenhagen.
[8]
Matsuoka, Y., Harasawa, H. and Takahashi, K. (2001): Scenario Approach on Global Environmental Problems, in: J. Environmental Systems and Engineering, No. 678 / VII -19, pp. 1-11. (in Japanese)
[9]
Jäger, J., Rothman, D., Anastasi, C., Kartha, S. and Notten, P. (2007): Scenario Development and Analysis, GEO Resource Book: A Training Manual on Integrated Environmental Assessment and Reporting, UNEP and IISD, Winnipeg, Canada.
[10]
Ward, E. and Schriefer, A.E. (1998): Dynamic Scenarios: System Thinking Meets Scenario Planning, in: Fahey, L. and Randall, R.M. (eds.), Learning from the Future, John Wiley & Sons, New York, pp. 140-156.
[11]
O’Brien, F.A. (2004): Scenario planning – lessons for practice from teaching and learning, in: European Journal of Operational Research 152, pp. 709-722.
[12]
Weihrich, H. (1993): Daimler-Benz’s Move towards the Next Century with the TOWS Matrix, in: European Business Review, Vol. 93, pp. 4-11.
[13]
Kishita, Y., Yamasaki, Y., Mizuno, Y., Fukushige, S. and Umeda, Y. (2009): Development of Sustainable Society Scenario Simulator – Structural Scenario Description and Logical Structure Analysis, in: Proc. of 16th CIRP Int. Conf. on Life Cycle Engineering 2009, pp. 361-366.
[14]
Kishita, Y., Mizuno, Y., Fukushige, S. and Umeda, Y. (2010): Development of Sustainable Society Scenario Simulator – Connecting Scenarios with Associated Simulators, in: Proc. of 17th CIRP Int. Conf. on Life Cycle Engineering 2010, pp. 402407.
By representing scenario texts by the structural scenario description method, we can utilize the representation as follows:
To trace reasons of scenario’s conclusion and therefore, to facilitate our review.
To make sure of consistency in scenarios by tracing relations between the causal networks and the scenario texts.
For example, when we verify the contents of node (D) in Figure 5, we trace the link of “logical_jump” and we check the hypothesis of node (C). We trace relation and finally we can reach node (A), which is the hypothesis of node (D). We believe that this method is useful for supporting modification of scenario texts by clarifying rationales for scenario descriptions. 7
CONCLUSION
Based on the structural scenario description method, we proposed a design support method for sustainability scenarios based on forecasting, which employs storyline and causal network. The case study clarified that the proposed method is useful for supporting scenario design process in a stepwise manner. The method can support developing scenario texts. Moreover, the relations between scenario texts and causal network enable the designer to trace rationales of scenario texts. Future works include development of a scenario design support method based on backcasting, which can support describing changes in a larger scale. ACKNOWLEDGMENTS This research is financially supported by Grant-in-Aid for Scientific Research (No. 20246130), JSPS, Japan. REFERENCES [1]
Duinker, P.N. and Greig, L.A. (2007): Scenario analysis in environmental impact assessment: Improving explorations, in: Environmental Impact Assessment Review 27 (2007), pp. 206-219.
[2]
Glenn J. and the Futures Group International (2003): Scenarios, in: J.C. Glenn and T.J. Gordon (eds.), Futures Research Methodology – V2.0, AC/UNU Millennium Project, Washington, D.C.
[3]
IPCC (2007): Climate Change 2007, Synthesis Report, Contribution of Working Groups I, II, III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC), Geneva, Switzerland.
[4]
International Energy Agency (IEA) (2009): World Energy Outlook 2009, IEA Publications, Paris.
[5]
Dreborg, K. (1996): Essence of Backcasting, in: Futures, Vol. 28, No. 9, pp. 813-828.
[6]
Bishop, P., Hines, A. and Collins, T. (2007): The Current State of Scenario Development: An Overview of Techniques, in: Foresight, 9/1, pp. 5-25.
Evaluating Trade-Offs Between Sustainability, Performance, and Cost of Green Machining Technologies 1
2
1
2
Moneer Helu , Jan Rühl , David Dornfeld , Patrick Werner , Gisela Lanza 1 2
2
Laboratory for Manufacturing and Sustainability, University of California, Berkeley, USA Institute for Production Science, Karlsruhe Institute of Technology, Karlsruhe, Germany
Abstract The growing demand to reduce environmental impacts has encouraged manufacturers to pursue various green manufacturing technologies and strategies. These solutions, though, may have a direct impact on several productivity metrics including availability, quality, service life, and cost. This study presents an approach to evaluate the trade-offs between the environmental, performance, and financial impacts of green machining technologies by combining green manufacturing principles into life cycle performance evaluation. The approach is validated by investigating the implications of reducing the processing time by increasing the cutting speed and chip load to green a horizontal milling process. Keywords: Life Cycle Cost; Green Machining; Data Acquisition
1
INTRODUCTION
Machine tool development has historically focused on reducing operating costs while simultaneously improving performance as measured by several metrics including availability, reliability, dimensional accuracy, and precision. To increase performance, machine tools have become more complex and automated in design, which has resulted in higher resource demands that have generally conflicted with rising resource costs due to growing scarcity and regulation. These trends have encouraged manufacturers and users to pursue various green machining strategies and technologies that enable increasingly efficient use of limited resources. Design and operation for the environment, though, has a direct impact on several factors including availability, quality, service life, and cost. Thus, it is important to quantify resource flows in machining to enable manufacturers to better guarantee performance as well as provide decision makers with tools that evaluate the tradeoffs between the environmental, performance, and financial impact of any potential technology decision. 2 2.1
CURRENT ASSESSMENT TECHNIQUES Environmental impact assessments
Life cycle assessment (LCA) has been traditionally used in the literature to quantify several resource flows (typically electrical energy) in machining. LCA is a quantitative tool that measures the resource and waste flows of a product from its fabrication to its endof-life [1]. Much of the early LCA work focused on assessments of different green machining strategies and technologies. There was also an observed need to establish a set of quantifiable dimensions to determine the trade-offs in process planning decisions. This area of work focused on the development of models to capture the effects of machining physics on environmental and public health impacts. Munoz and Sheng [2] provided the seminal model that comprehensively links machining parameters (e.g. speed, feed, depth of cut) to the environmental impacts of a machining process (e.g. energy consumption, process rate, mass flow of generated wastes). This approach was extended to energy by developing a
web-based energy estimation tool [3]. Subsequent work has connected LCA results from the literature to process planning to enable its use in the design of products [4]. While this research has provided initial tools for decision makers to evaluate current green machining strategies, these tools are based on theoretical models instead of captured data, which may limit the accuracy of these tools when gauging the trade-offs in process planning decisions [5], [6]. More recent work has addressed the deficiencies in previous process planning tools by developing methods to physically measure the environmental impacts of machine tools. Vijayaraghavan and Dornfeld [6] propose automated energy monitoring of machine tools using event stream processing to correlate measured energy consumption to machine tool usage. This enables full characterization of the machining process and the drivers of energy consumption. Similarly, Kuhrke, et al. [7] advocate a methodology to estimate the overall yearly energy consumption of a machine tool and any related costs by either measuring or calculating the energy consumption of individual components of the machine tool and considering the actual usage instead of assuming a constant power consumption. While calculating energy consumption inherently introduces inaccuracy into this approach, Kuhrke, et al. [7] argue that the feasibility of power measurements for highly specialized machine tools demands that calculations be used to simplify the approach for purchasing decisions. Dietmair and Verl [8] propose a nodular, component approach that also considers the actual usage of the machine tool to drive a modeling framework that estimates overall energy consumption using characteristic relationships between process parameters and energy consumption and other resource flows for each component. These characteristic relationships should be measured and can be found in the literature (e.g. Draganescu, et al. [9] modeled the specific energy of milling processes, and Klocke, et al. [10] modeled the specific energy of milling and drilling processes). 2.2
Performance and financial impact assessments
Increased competition and reduced profit margins have forced manufacturers to find ways to reduce operating costs and improve
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_34, © Springer-Verlag Berlin Heidelberg 2011
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the performance of their facilities and equipment. So, many manufacturers have used life cycle cost (LCC) analysis as the primary tool to evaluate the performance and financial impact of their equipment and facilities. LCC analysis is a quantitative tool that compares the cost-effectiveness of different investment options from the perspective of a manufacturer or other business decision maker [11]. It takes into account all business costs over the life of the investment option including those costs associated with acquisition (e.g. sales price), ownership (e.g. maintenance), and disposal. The automotive sector has been one of the biggest drivers of LCC analysis techniques to help establish warranty demands with their suppliers [12]. Many approaches that seek to optimize the LCC of manufacturing equipment have focused on maintenance since these activities typically dominate costs. This has driven the development of life cycle performance (LCP) evaluation, which relates the overall equipment effectiveness (OEE) – a performance metric that combines reliability, availability, and quality – to the total monetary costs from the initial investment to disposal to generate an efficiency metric for a manufacturing system [13]. Thus, LCP evaluation broadens LCC analysis by relating the results of an LCC analysis to the performance of an investment option. LCP evaluation has been primarily used to optimize technical services (e.g. maintenance intervals and spare part provisions) and assess and control the risks associated with establishing warranty and servicing contracts [12], [13]. While the development of LCP evaluation from LCC analysis represented an important first step towards combining performance and financial assessments, neither approach addressed resource flows or environmental impacts, which can be highly significant to the OEE and financial costs of a manufacturing system. So, it has become very important to capture environmental impacts within both LCP and LCC approaches. Early attempts addressed these limitations by integrating LCA into LCC analyses. One example of this approach is economic input-output LCA (EIO-LCA), which was developed at Carnegie Mellon University. This approach relates the environmental impacts of each sector of an economy to the monetary value of that sector as defined by government economic inputoutput tables [1]. Norris [11] describes other approaches referred to as “partial solutions” that combine a full LCA with a partial LCC or vice-versa. The first type of partial solutions (full LCA and partial LCC) involves adding cost flows to a traditional LCA. The second type of partial solutions (partial LCA and full LCC) adds a truncated LCA (e.g. some resource flows from the main facility plus some first tier suppliers) to a full LCC. Norris [11] also highlights two “full solutions,” PTLaser and TCAce. PTLaser includes dynamic LCA (i.e. time-varying flows), variable cost functions, and flexible investment, depreciation, accounting, and discounting tools in order to combine a full LCA with a full LCC. TCAce extends current LCA and LCC approaches to include all external costs including those that are less tangible (e.g. societal costs). Eco-efficiency is a last approach to combine LCA and LCC by normalizing the metrics from each analysis to enable an equal comparison [15]. 2.3
Combined assessments and proposed approach
The current literature contains many tools that incorporate financial costs into either an environmental or performance assessment. However, these tools do not generally address all three factors simultaneously, which neglects their inherent interdependence. Recent work, though, has begun to develop methods that enable consideration of all three impacts. For example, Kuhrke, et al. [7] and Dietmair and Verl [8] demand a full understanding of actual machine usage even though the aim of both is to measure overall energy consumption. Similarly, Thiede and Herrmann [16] evaluate production criteria as well as energy consumption and related costs,
but it is based on a simulation rather than a measurement approach. Also, Inamasu, et al. [17] include environmental (energy), performance (tool life), and financial factors when physically measuring the effect of cutting conditions on the energy consumption of a machine tool. Niggeschmidt, et al. [13] provided a basic framework to incorporate green manufacturing principles into LCP evaluation so that environmental, performance, and financial impacts could be simultaneously considered. This framework comprised three steps: 1. Design and integration of appropriately targeted process monitoring systems to measure relevant data sources. 2. Characterization of the manufacturing system based on the collected data. 3. Optimization of the manufacturing system based on the developed characterization. The goal of this paper is to apply the framework developed by Niggeschmidt, et al. [13] to develop a methodology that quantifies changes in environmental impact with respect to performance and cost. The approach is presented in Figure 1 and is based on data acquisition rather than modeling or simulation to ensure accurate calculation of impacts. By applying this approach to a baseline scenario (defined as “machining as usual”) and other alternatives that implement a green machining strategy, the true costs of these technologies can be determined.
Figure 1: Proposed methodology to combine environmental, performance, and financial impacts when evaluating investment options. 3
GREEN MACHINING TECHNOLOGIES
Many green machining approaches have focused on process and design level improvements through new technologies and machining strategies. To validate the approach described in Figure 1, though, this study focuses on processing time reductions [3]. The power demand of a machine tool can be divided into three components: constant, variable, and processing (or cutting) power (see Figure 2). Constant power demand is due to auxiliary components that are always powered and have a demand irrespective of the selected machining parameters (e.g. computer panels and lights). Variable power demand is due to those components that have a constant baseline demand but that may not always be active (e.g. spindle and drive motors). Finally, processing or cutting power demand is the power demand dependent on the material removal process. Constant and variable power demand are together referred to as tare power demand since this is the minimum amount of power that is required to run the machine tool whether or not chips are formed. Dahmus and Gutowski [18] found that the tare power demand increases with automation, which means that the energy consump-
Sustainability in Manufacturing tion of many modern machine tools is dominated by this constant power demand. Thus, reducing the processing time better amortizes these constant charges and may effectively reduce the specific cutting energy.
197 parameter was increased independently to better study the effects of each on the overall environmental and performance impacts. Two scenarios were considered for each parameter to initially validate our approach. The cutting speed was increased to 55 m/min and 60 m/min, which represented a feed rate increase of 10% and 20% respectively. Each chip load was increased by a 20% and 40% for the increased chip load scenarios, which represented a feed rate increase of 20% and 40% respectively. The chip load was increased further than the cutting speed due to stability limitations (increased cutting speed without an increase in feed rate typically induces chatter). 4.2
Figure 2: The typical power demand of a machine tool [3]. 4
METHODOLOGY
Machining experiments were conducted on a Haas VF-0 vertical milling machine tool to study the impacts of a reduced processing time strategy. A baseline scenario was selected as well as alternative scenarios that reduce processing time by increasing the feed rate. Relevant data for a performance and environmental analysis were collected during each machining experiment. 4.1
Energy based environmental assessment
The overall power demand of the machine tool was measured to evaluate the environmental impact of each machining scenario by determining the electrical energy consumption. A Yokogawa CW240 wattmeter was used in a three-phase, three-wire, three-current setup. The current transducers and voltage clamps were installed at the power input and a sampling frequency of 10 Hz was used. Figure 4 shows the measured power demand for the baseline scenario.
Baseline and alternative scenario design
The test piece chosen for this investigation was developed by Behrendt [19] (see Figure 3). This part is meant to compare the energy consumption of various three-axis machine tools that have a work 2 area (defined by the x- and y-travel) between 0.1 and 1 m . It has been designed to fully exercise the machine tool by requiring every combination of axes to create eighteen different features using four tools: a 50 mm, 5 insert face mill; an 8 mm end mill; a 16 mm end mill; and an 8 mm drill. The initial workpiece material is an 82 mm x 82 mm x 25.4 mm 1018 AISI steel blank. The cutting speed is kept constant at 50 m/min. The chip load is kept constant for every feature except the face cut, which requires a chip load of 0.1 mm/tooth, and the small and large grooves sets, which require a chip load of 0.5, 0.6, and 0.7 mm/tooth for each subsequent groove. The depth of cut is also incrementally increased for each of the three machining passes used to machine each feature except the drilled holes.
Figure 4: Measured power demand for the baseline scenario (a 2 second moving average was used to smooth the plot). The power demand was assumed to be constant for the 0.1 seconds immediately after a measurement. This allowed the total electrical energy consumption to be estimated using Equation 1: k 0.1Pi Etotal , i 1 3600
Figure 3: Standard part machined for each machining scenario (units in mm) [19]. The baseline scenario followed the standard presented by Behrendt [19]. To reduce the processing time, one may increase the number of flutes on the tool, increase the cutting speed, or increase the chip load. The latter two options were pursued in this study since they are typically the parameters that a machinist has most control over. While the cutting speed and chip load should be simultaneously increased to ensure a stable cut and good surface quality, each
(1)
where Etotal is the total electrical energy consumed (in kWh), k is the total number of data samples, and Pi is the ith measured real power demand. Real power (the portion of power used towards productive work) was used as opposed to apparent power (the total power including any losses in the electrical system) since power companies charge facilities based on real power as long as the power factor (a measure of efficiency defined as real to apparent power) of a facility is above a defined threshold (85% in California) [20]. Equation 1 was then used to estimate the change in total electrical energy consumption caused by each alternative scenario. 4.3
Load based performance evaluation
An LCP evaluation requires an understanding of the failure behavior of machine tool components. The failure behavior can be estimated using a reliability analysis that begins with the load profile on the machine tool components. Because these load profiles were difficult
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to obtain, the overall cutting forces were measured and assumed to affect all components equally. The cutting forces were measured for each scenario using a Kistler 9257A three-component dynamometer on which the workpiece was mounted. A Kistler 5004 dual mode amplifier set to a sensitivity of 200 N/V converted the dynamometer charge output to a voltage signal that was then recorded using LabVIEW Signal Express via a National Instruments NI USB-6009 multifunction I/O card. The load profile was generated using a sampling frequency of 1 kHz. Figure 5 shows the measured load profile for the baseline scenario.
where tj is the measuring interval, and Xij is the transformation of the characteristic load value measured over tj. Equations 2 and 4 were then used to estimate the change in cumulative damage caused by each alternative scenario. For a given conditional probability based on previous loads and a predicted future load profile, the cumulative damage then allows for a calculation of the remaining service life of the machine tool. 5
RESULTS
5.1
Energy based environmental assessment
The total electrical energy consumed during the baseline scenario was 0.387 kWh and the maximum power recorded was 13.1 kW. Table 1 summarizes the total electrical energy consumed and maximum power demanded during each alternative scenario relative to the baseline scenario. As expected, the total electrical energy consumed by the machine tool decreased for every scenario since it is most strongly dependent on the processing time. The slight difference between both 20% increase scenarios is likely due to measurement error. The greater decrease in energy consumption for larger increases in feed rate occurred because the effect on energy amortization per part is greater than that of the increased power required to run the higher feed rate. This suggests that an operating point exists that requires minimal specific cutting energy consumption [3], [10]. Also, the maximum power demanded by the machine tool increased for every scenario due to the increased power that the spindle and axes motors require for higher speeds. ∆Etoal Figure 5: Measured load profile for the baseline scenario (a 5th order Butterworth filter with cutoff frequency 0.005 Hz has been used to smooth the plot). A Weibull distribution approach is typically used to describe the stochastic failure behavior of machine tool components and statistically estimate their service life, but an alternative approach was required since the load profile on a machine tool is expected to vary with different machining processes [14]. So, the load-dependent reliability model was derived using an approach based on the Weibull Cumulative Damage Model, which relates the cumulative damage of varied loads to the Weibull distribution, and the Generalized Log-Linear Model. Cumulative damage, or cumulative exposure, is the effect caused by different stresses that decrease the service life of equipment. The general form of the Weibull Cumulative Damage Model is given by Equation 2:
W t,L Ft,L 1 e ,
(2)
where F is the probability of failure due to cumulative damage, t is time, L is the load vector, W is the normalized cumulative damage, and is the shape parameter. W is written in the Generalized LogLinear Model as given by Equation 3: t
W t,L
e
a0
a i X i t' i
dt',
(3)
0
where a0 and ai are model parameters, and Xi is a transformation of the load levels, Li, that depends on the type of load. For example, the power law offers that the natural logarithm is the appropriate transformation for mechanical stresses. The normalized cumulative damage presented in Equation 3 can be simplified to Equation 4:
W t,L
j
a 0 a i X ij e i t j ,
(4)
∆Pmax
Increased cutting speed scenarios 10% increase
-26.0%
+5.6%
20% increase
-29.7%
+12.6%
20% increase
-31.0%
+7.6%
40% increase
-40.7%
+13.8%
Increased chip load scenarios
Table 1: % change in total electrical energy consumption and filtered maximum power demand of the machine tool for each alternative scenario relative to the baseline case. 5.2
Load based performance evaluation
Table 2 summarizes the cumulative damage on each axis of the machine tool for each alternative scenario relative to the baseline scenario. These values were calculated by considering the stresslife relationship of bearings as an initial validation of our approach since bearings are an important part of many machine tool components. The cumulative damage on the x and y axes decreased with increased cutting speed. This was due to the slight decrease in cutting force created by the increased heat generation at the toolchip interface. Conversely, the cumulative damage on the z-axis increased with increased cutting speed despite the previously noted trend. This was due to the added stresses on the z-axis during the face mill cut created by run out of the face mill tool. Finally, the cumulative damage of all axes increased with increased chip load because of the strong, direct dependence of the cutting force on the chip load. The y-axis was unique in that increasing the chip load seemed to initially decrease the cumulative damage. This could likely be due to a suboptimal choice for the chip load for the baseline scenario from the perspective of cutting forces.
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∆Damagex
∆Damagey
∆Damagez
Increased cutting speed scenarios 10% increase
-8.7%
-37.9%
+39.8%
20% increase
-12.5%
-39.8%
+33.2%
Increased chip load scenarios 20% increase
+18.7%
-14.1%
+52.9%
40% increase
+31.2%
-0.2%
+113.2%
Table 2: % change in cumulative damage on each axis of the machine tool for each alternative scenario relative to the baseline case. 5.3
Total costs of alternative scenarios
Industrial facilities are typically charged for electricity based on both overall usage and peak power demand. In addition, the rates for both charges differ depending on the time of day (peak, off-peak, or partial-peak) and the time of year (summer or winter). Using the current rate schedule for the Pacific Gas and Electric Company (PG&E) in California [21], Table 3 shows how each alternative scenario may affect the electrical energy price per part. To determine the amount to which the increased power demand shown in Table 1 becomes amortized per part, it was assumed that a machine tool creates only the test piece for 12 hours per day (6 hours during peak and 6 hours during partial-peak in the summer; all 12 hours during partial-peak in the winter), 20 days per month, and that set up requires 30 seconds per part. Summer ∆Cost/Part
Winter
% Diff
∆Cost/Part
% Diff
Increased cutting speed scenarios 10% increase
-$0.001
-0.2%
-$0.008
-16.4%
20% increase
+$0.010
+4.3%
-$0.009
-17.6%
Increased chip load scenarios 20% increase
$0
0%
-$0.010
-19.9%
40% increase
+$0.004
+1.9%
-$0.013
-26.6%
Table 3: Electricity cost to produce 1 part using each alternative scenario relative to baseline scenario. The absolute cost difference per part was low for both the summer and winter pricing periods because the test piece is a simple part that is relatively cheap to produce. Also, the Haas VF-0 does not have too much auxiliary equipment, which means that the processing power is a relatively large portion of the overall power demand. So, reducing the processing time should not have had as significant an effect on the Haas VF-0 as it would have had on a larger or more automated machine tool where the processing power can be as little as 10% of the overall power demand [18]. Nonetheless, the percentage difference in electricity costs may still be substantial and did generally increase with increasing processing rate as seen in the winter pricing period. This trend should continue until a minimal operating point is reached due to the greater power required to operate the machine tool at greater loads. The summer pricing period did not have the same trend as the winter pricing period because of the relatively high demand costs ($12.67/kW and $2.81/kW for peak and partial-peak periods respectively [24]). Again, though, the summer pricing period would have more closely followed the winter pricing period if a larger or more automated machine tool were considered. The increase in damage that is shown in Table 2 also impacts costs because of its indirect relationship with the lifetime of a machine tool component. For example, the spindle bearings should be strongly affected by the increased damage in the z-axis for both of the increased cutting speed scenarios. So, a 20% increase in the
cutting speed increased the damage in the z-axis by 33.2%, which will decrease the service life of the spindle bearings by about 75% (that is, the machine tool will be able to machine 75% less parts before the spindle bearings will need to be replaced). In fact, because service life is measured in terms of parts produced in this approach, the relative change in damage is exactly equal to the cost of a component per part produced (e.g. if a spindle bearing costs $100 and has a life of 1000 parts, then they would cost $0.10/part for the baseline scenario and $0.133/part when the cutting speed is increased 20%, which is an increase of 33.2%). Thus, the increased cutting speed scenarios should generally decrease maintenance costs (if the damage in the z-axis is neglected since it was likely due to run out of the face mill), while the increased chip load scenarios should generally increase maintenance costs. 6
SUMMARY AND FUTURE WORK
This paper has presented an approach that considers environmental, performance, and financial impacts when evaluating green machining technologies and strategies. To begin the validation of this approach, a series of cutting experiments were performed to study the true costs of a reduced processing time strategy. The initial results indicate that such an approach may not provide great benefit for smaller machines or those will lower levels of automation such as the Haas VF-0 due to the increased loads on the bearings and other components of the machine tools and the marginal reductions in the electricity costs. However, these initial results do suggest that increasing the process rate could have significant benefits to larger and/or more automated machine tools were the processing power is a much smaller percentage of overall power demand and the machine tool components are designed to withstand greater forces. There are also other potentially significant costs that have yet to be included in this approach such as tool wear (which should be important for increased processing time strategies) and power factor. Furthermore, the performance evaluation should be improved to provide greater detail on the extent to which increased loads affect individual machine tool components. The preliminary results of this study suggest that power factors could be a promising green machining strategy since power companies provide many financial benefits to ensure a high power factor so that the electricity grid is most efficiently utilized. For example, for facilities that have greater than 400 kW demand, PG&E rewards power factors above 85% by reducing its fees by 0.06% for each percentage point above 85% [20]. Similarly, PG&E also discourages power factors below 85% by increasing its fees in the same manner. Many facilities generally operate at or above 85% when all powered systems are considered. However, machine tools tend to reduce the power factor due to the high resistive losses typical in motors. For example, a power factor of ~68% was typical for the Haas VF-0. There are two general ways to increase the power factor of equipment: use more power towards productive work, or change electrical components (e.g. motors) to higher efficiency models. Both are technologies that should be investigated further, especially the former option as many existing strategies may serve to promote this effect. Future work will focus on extending the current approach to consider other environmental impacts (e.g. water and machining fluid consumption, compressed air) and tool wear in addition to load and energy data on individual machine tool components to provide greater detail in characterizing the costs of green machining strategies. In addition, useful metrics such as energy reduction per Dollar of cost to implement the technology will be developed so that decision makers have the most relevant information when considering investment options. Through continued development, this approach
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may hopefully contribute towards existing cataloguing efforts such as the CO2PE! Initiative by providing true cost information for machining technologies and tools. 7
[11]
Norris, G. A. (2001): Integrating Life Cycle Cost Analysis and LCA, in: International Journal of Life Cycle Assessment, Vol. 6, No. 2, pp. 118-120.
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Fleischer, J.; Wawerla, M. (2007): Risk Management of Warranty Contracts in Machine Building Industry, in: International Applied Reliability Symposium, Frankfurt, Germany.
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Niggeschmidt, S.; Helu, M.; Diaz, N.; Behmann, B.; Lanza, G.; Dornfeld, D. A. (2010): Integrating Green and Sustainability Aspects into Life Cycle Performance Evaluation, in: Proceedings of the 17th CIRP International Conference on Life Cycle Engineering, pp. 366-371, Heifei, China.
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Lanza, G.; Niggeschmidt, S.; Werner, P. (2009): Optimization of Preventative Maintenance and Spare Part Provision for Machine Tools Based on Variable Operational Conditions, in: Annals of the CIRP, Vol. 58, No. 1, pp. 429-432.
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Kicherer, A.; Schaltegger, S.; Tschochohei, H.; Pozo, B. F. (2007): Eco-Efficiency: Combining Life Cycle Assessment and Life Cycle Costs via Normalization, in: International Journal of Life Cycle Assessment, Vol. 12, No. 7, pp. 537-543.
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Munoz, A. A.; Sheng, P. (1995): An Analytical Approach for Determining the Environmental Impact of Machining Processes, in: Journal of Materials Processing Technology, Vol. 53, pp. 736-758.
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Diaz, N.; Choi, S.; Helu, M.; Chen, Y.; Jayanathan, S.; Yasui Y.; Kong, D.; Pavanaskar, S.; Dornfeld, D. (2010): Machine Tool Design and Operation Strategies for Green Manufacturing, in: Proceedings of the 4th CIRP International Conference on High Performance Cutting, pp. 271-276, Gifu, Japan.
Inamasu, Y.; Fujishima, M.; Hideta, H.; Noguchi, K. (2010): The Effects of Cutting Condition on Power Consumption of Machine Tools, in: Proceedings of the 4th CIRP International Conference on High Performance Cutting, Vol. 1, pp. 267270, Gifu, Japan.
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Narita, H., Desmira, N., Fujimoto, H. (2008): Environmental Burden Analysis for Machining Operation Using LCA Method, in: Proceedings of the 41st CIRP Conference on Manufacturing Systems, pp. 65-68, Tokyo, Japan.
Dahmus, J. B.; Gutowski, T. G. (2004): An Environmental Analysis of Machining, in: Proceedings of the 2004 ASME International Mechanical Engineering Congress and RD&D Expo, IMECE2004-62600, Anaheim, CA, USA.
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Pacific Gas and Electric Company (2007): Economics of Power Factor Correction in Large Facilites >400kW, accessed 11/10/10, from: http://www.pge.com/includes/docs/pdfs/ mybusiness/customerservice/energystatus/powerquality/ power%20factor--revised-8-9-07.pdf.
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Pacific Gas and Electric Company (2010): Industrial (JUNE 1, 2010 – Present), in: Pacific Gas and Electric – Tariff Book, accessed 11/17/10, from: http://www.pge.com/tariffs/ IndustrialCurrent.xls
ACKNOWLEDGEMENTS
This work was supported in part by Mori Seiki, the Digital Technology Laboratory (DTL), the Machine Tool Technology Research Foundation (MTTRF), Sandvik and other industrial partners of the Laboratory for Manufacturing and Sustainability (LMAS). The authors would also like to thank Thomas Behrendt, Andrew Waterbury, Edwin Wibowo, and the UC Berkeley Mechanical Engineering Department’s Student Machine Shop for providing valuable insight and advice. For more information, please see http://lmas.berkeley. edu/. 8 [1]
[2]
[3]
[4]
[5]
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Klocke, F.; Schlosser, R.; Lung, D. (2010): Energy and Resource Consumption of Cutting Processes – How Process Parameter Variations can Optimise the Total Process Efficiency, in: Proceedings of the 17th CIRP International Conference on Life Cycle Engineering, pp. 111-115, Hefei, China.
Sustainable Production by Integrating Business Models of Manufacturing and Recycling Industries 1, 2
Christopher Jonsson
2
3
, Johan Felix , Anders Sundelin , Björn Johansson
1
1
Department of Product and Product Development, Chalmers University of Technology, Gothenburg, Sweden 2
CIT Recycling Development AB, Gothenburg, Sweden 3
CIP Professional Services AB, Gothenburg, Sweden
Abstract Future raw material shortage, high price levels and environmental regulations requires increased reuse of materials and components. This will substantially influence conditions for both manufacturing and recycling industries, which need to innovate their business models. Our hypothesis is that an integrated co-operation between organizations within these industries, employing symbiotic business models will increase the profitability and reduce the total environmental impact. Findings from interaction with manufacturing and recycling industries using a business model framework as baseline are presented. Results indicate that profit and decreased environmental effects can be achieved through symbiotic business models, but further development and implementation is needed. Keywords: Business Models; Sustainable Production; Industrial Ecology
1
INTRODUCTION
Future shortage of raw materials, high price levels and environmental regulations will require recycling of materials, as well as an increased level of re-use, repair and remanufacturing of components, than is currently the case today. This will substantially influence the conditions for both the manufacturing industry and the recycling industry where companies are starting to rethink their business models. Just as products can be designed for remanufacture, organizations can be designed to reduce the total environmental impact. The business model concept is based on the idea to illustrate how value is created and captured by an organization. It is a holistic concept that includes an organization’s value propositions and customers, how products and services are delivered, how revenues are generated, what resources, activities, and partners that is required, and the resulting cost structure [1]. It represents a new dimension of innovation and source of competitive advantage, and is seen as a new unit of analysis in addition to the product, firm, industry or network [2]. The purpose of this paper is to investigate if organizations within the manufacturing industry and the recycling industry could co-operate and align their business models, to increase the profitability and reduce the total environmental impact. The objective is also to identify incentives to work closer between the recycling and the manufacturing industry, and potential constraints to a closer interaction between the two industries. The definition used for the described closer collaboration in a business perspective is: symbiotic business model which will be used in this paper. The term symbiotic business model is used when two or more actors together strive to increase the total value creation and value capture over company boundaries. It is a hypothesis of the authors that an integrated co-operation between organizations within the manufacturing industry and the recycling industry can increase profitability and reduce the total environmental impact. The paper is organized as follows: Section 2 presents state of the art on manufacturing and recycling industries and their possibility to
work integrated in terms of business models and industrial ecology. Section 3 states the problem addressed in the paper. Section 4 describes the work done to achieve the results which are presented in section 5. Section 6 discusses the results in terms of closed and open loops of the business models. Section 7 concludes this work. 2
PROBLEM STATEMENT
The purpose of this paper is to investigate if organizations within the manufacturing industry and the recycling industry could co-operate and align their business models to increase the profitability and reduce the total environmental impact. The hypothesis stated for the research project from which this paper stems is: An integrated co-operation between manufacturing, refurbishing and recycling industries employing symbiotic business models will increase the profitability for the partaking parties and reduces the total environmental impact. The hypothesis was approached using the methodology described below. 3
BACKGROUND
The business model, how an organization creates and captures value, is often said to represent a new dimension of innovation and source of competitive advantage that spans the traditional models of process, product and organizational innovation. When a for-profit organization analyzes and changes its business model it assumedly does so with the intention to increase its own revenues, or reduce its costs or risk. Even though the business model literature has provided a more holistic and systemic perspective than traditional theory in strategic management the focal point is one organization, its resources, activities and position in the value chain, and not the system of stakeholders or the business models of other organizations involved in the full life cycle of the products or services provided.
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_35, © Springer-Verlag Berlin Heidelberg 2011
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The manufacturing industry and the recycling industry are generally thought of as separate entities with limited interaction and collaboration. Could this be an ancient way of looking at the industries? How well connected are the industries today? In order to examine this we have to explore how evolved they are today when it comes to recycling and reuse. 3.1
The manufacturing industry
The access of raw material and components to reasonable prices is going to be a challenge for manufacturing companies in the future. The supplies of natural resources are decreasing and the costs to extract those increases. Incremental prices of the raw material and components are to expect, which could trigger some companies to rethink their needs and therefore secure the access of material from different industrial areas. In order to secure the accessibility, the prices can be kept low and the price of the products can continue to be acceptable. The companies that do not secure the access of raw material or components will most probably have to increase the price of their products. Hence, the company will not be as competitive as others. One area which is well developed when it comes to reuse of components and recycling of material is in the end-of-life vehicles industry. Because of resent legislations, it is more important now than ever for the car industry to recycle more of the materials from cars (ELV-directive) [3]. End-of-life vehicles are transported to dismantling companies (i.e. scrapyards), where reusable parts are removed from the cars. The rest of the cars is sent to a recycling company to be recycled as material. This network consists of car production companies, dismantling companies, recycling companies and BIL Sweden. This network is a result of the ELVdirective, and it is called Refero [4]. This organization was developed to assure the 85% recycling regulation put by the ELVdirective. The idea is to make it more convenient for car users to get rid of their ELV. It will be even more important for the car companies to make this organization work hence the ELV-directive states that in the year 2015 the recycling regulation will be 95% [3][4]. 3.2
The recycling industry
Waste management is an important part of our modern society. The waste manage industry have evolved from collecting garbage from households to be put on a land fill, to be an important provider of “raw” material to the production industry. Today’s recycling companies usually collect waste from both households and industry. In 2009 about 98.5% of the waste from households was recycled, and 48.4% was recycled as energy through incineration in Sweden [5]. Unfortunately waste management companies are still looked upon by many as companies only handling waste that is put on a landfill instead of important companies with recycling capabilities. But as Pongrácz [6] argues that the description of waste management companies should be revised to “waste management control of waste-related activities, with the purpose of resources conservation and environmental protection”. Waste management companies are not only collecting waste but are also a source of raw material to the production industries. The main focus of the waste management industry is to recycle large and uniform flow of material to the industry as “raw” material. This is highly dependent on the price and market demand for the specific fraction of material. The highest demand of material has been, looking at a short time frame, the energy industry. It is more economically favorable to sell many of the waste fractions to the energy industry to become energy through incineration, than to sell it as “raw” material. To sort and separate some of the different materials from each other in a batch of waste, in order to create homogenous material fractions, and then sell it as raw material to a
production industry is very labor intense. It is usually more easily justified to sell it directly to an energy company for energy through incineration. From the manufacturing industries point of view, it is more “safe” to buy “new” raw material to produce their products with. One reason for the production industries to buy “new” raw material is the continuous access to material, which the recycling companies can’t secure. Another reason is the quality of the material that is provided by the recycling companies. If the quality isn’t high enough then the production companies can’t keep the same standard on their products as with using “new” raw material. This way of treating recyclable material is not a good foundation for a sustainable society. Unfortunately, waste that could be disassembled and where components could be reused, are not reused to a high extent. One of these waste categories is electrical and electronic equipment (WEEE). In general WEEE is recycled as material, e.g. shredded, crushed or treated in a similar way, and not recycled through reuse of components after disassembling [7][8][9]. This is very unfortunate, since a lot of components from WEEE could be reused in new products. This could decrease the environmental impact compared to producing new components from “new” raw material. There is an organization called Stålkretsloppet [10] that are working on closing the steel eco-cycle. The organization manages a program that is divided into different projects. The different projects is Improved steel scrap quality, Surface cleaning of steel scrap, New Metallurgical technologies, Product design and new steel products, Recycling of steel in the society, and Evaluation of environmental values. The program is financed by MISTRA and gives the opportunity to develop the steel industry [10] to further recycling and reuse of products in a constructive way. Symbiotic business models are one solution which might fit into Stålkretsloppet. 3.3
Integration
By integrating the different actors during a products lifecycle a more sustainable and holistic viewpoint is possible. When this integration is not present sub-optimization will occur and hence more easily resulting in additional environmental pollutions, higher cost, longer lead-times etc. Industrial ecology is a relatively new concept in the industry and the business world over all. Industrial ecology is defined as: “Industrial ecology is an emerging science which provides the conceptual tools to analyze and optimize the flow of energy and material in our production system” by the Interagency Working Group on Industrial Ecology, Material and Energy Flows of the U.S. federal government [11]. Symbiotic business models can be seen as a part of Industrial Ecology in the way that Cohen-Rosenthal describes it: “The power of industrial ecology as a conceptual framework is not the description of mass flow and their consequences but in applying human intention to explore potential connections so that we create interactions with more value and less waste” [11]. By making new connections between companies the evolution of business models can be made. Hence a more integrated involvement between companies can create a symbiosis which can lead to a decrease in environmental impact, secure flow of material, and increase the profit for everyone involved. One example which works great since a long time back is recycling of glass bottles in Sweden. They are recycled by rinsing/washing and then sent back to the brewery to be refilled. But those examples which are implemented in such a large scale are rare. Recycling and remanufacturing views waste as a resource. It is the hypothesis of the authors that taking an even more holistic
Sustainability in Manufacturing approach, to not only looking at the business model of one organization but many; new symbiotic opportunities could be identified. What one organization considers to be waste another organization might use as input for their processes. In addition reasoning about how to a route for recyclable products might most easily be created, maintained and used by actors throughout the product lifecycle results in a conclusion that the lesser number of actors involved the more possibility for success to recycle the product. If comparing this reasoning with Figure 1 one can say that the shorter route from the original product creation, the more easy will it be to maintain control for the products quality, location and hence its lifecycle. 4
METHOD
The presented paper and its results are based on state-of-the-art reviews, interviews with both manufacturing-, refurbishing- and recycling industries, as well as a workshop where key persons from manufacturing-, refurbishing- and recycling industries were invited to participate. State-of-the-art in each of the areas manufacturing, remanufacturing, refurbishing, sustainability over a product lifecycle and business models is each a very large area by their own.
203 However, when looking at the combination of these into one single multidisciplinary research area sets the scene for state-of-the-art to significantly less, see section 2 for some examples. The interviews were performed with company representatives from manufacturing-, refurbishing- and recycling industries which had good knowledge on the business models and products the companies manufactured, refurbished, and recycled respectively. The interviews with the company representatives were conducted as approximately one hour semi-structured interviews by using open but structured questions, according to Kvale [14]. The major categories of questions used were planned ahead of the interviews, one set for manufacturing companies and one set for refurbishing/recycling companies. A total of seven company interviews were conducted and during the majority of these interviews there were more than one person from the company present, which vouches for good insight on the business models and product lifecycles. The workshop was conducted after the interviews and the findings from them were discussed and summarized into one more generic consensus documentations on which all 22 workshop participants actively contributed. Contributions were for example business models which are working now, problem statements, possible solutions, law regulations as hindrance etc. Parts of these results are showed in section 5.
Figure 1: Schematics over possible product lifecycle routing options [12] adopted from [13].
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In order to investigate if, and potentially how, organizations within the manufacturing industry and the recycling industry could cooperate and align their business models we needed a business model framework. Using a business model framework we believe a more structured analysis of what elements of a business model that is relevant to further analyze and what elements to discard for future research, in contrast to a business case narrative. We decided to use Alexander Osterwalder’s business model framework based on nine different elements, to structure the analysis, discussion and recommendations [1]. The nine elements being: Customer Segments, Value Propositions, Channels, Customer Relationships, Resources, Activities, Partners, Revenue Streams and Cost Structure. In addition we use the business model framework as a structure for our observations and results in this article. However, this effort on symbiotic business models is fairly small research wise (half a man year in total effort), which is why this paper is more to be viewed upon as a pre-study where future research projects will stem from. It is also limited on the number of industries partaking in the interviews (seven) and in the workshop (ten), which makes the results more indication like than strong research based and validated. In any way the results shown below will shine light upon upcoming challenges present in industry and also some of the large potentials for future symbiotic business models, both in terms of economy and environmental effect. 5
RESULTS
Through the different interviews and workshops a number of observations were made regarding the business models of manufacturing and recycling industries. Below is a summary of the observations and discussions in relation to each of the nine elements of the chosen business model framework. 5.1
Customer segments
The customer segment element represents the different groups of organizations or individuals that the analyzed organization aims to reach and serve. It is the buyers of the products being manufactured, or the buyers of material or components being recycled. Both actors from the manufacturing industry and the recycling industry see challenges in the existing and potential customers segments. Actors in the manufacturing industry were most concerned with their customers’ perception that remanufactured and re-used components could mean a lower quality product. At the same time some of the respondents saw that remanufactured components could have an even higher level of quality due to manual inspection and adjustments. Recycling industries express the difficulty in knowing the customer demand and the stability in demand and prices. New potential customer segments are component buyers, renovators, spare part actors but also the manufacturing industry directly in some cases. 5.2
Value propositions
The value proposition element represents the bundle of products and services, offered by the analyzed organization, that create value for a specific customer segment. With an increased level of re-manufacturing and re-use components several actors in both the manufacturing industry and in the recycling industry saw potentially new value propositions to be offered to their existing and new customer segments. Recycling industries saw an opportunity to provide value adding services identifying, separating, renovating and quality assuring components and even supporting the design of new products. In some areas stable and secured material flow together with lower cost components was thought of as clear value propositions towards component buyers, renovators and spare part actors. Interviewed companies producing products saw potential
value propositions in providing “green” sustainable products at both premium but also potentially lower prices. Another discussion that got the attention of the manufacturing companies was the concept of providing products and services, maintaining ownership and control over products and components. One of the respondents expressed frustration that the market is not appreciating re-use, that green products are associated with low energy consumption and environmentally friendly materials, and re-manufactured products are perceived less valuable, “second-hand”. 5.3
Channels
Channels represent how the analyzed organization communicates with and reaches its customer segments to deliver a value proposition. For actors in the manufacturing industry the main issues that were discussed were the reverse logistics and the logistical challenges linked to getting products back from customers. Potential new types of intermediaries were discussed in the workshop in which some participants were concerned with the high costs, and the potentially increased costs with even more actors to split the pie with. For recycling industries new customer channels would potentially need to be set up, in order to reach and serve new customers providing value adding services. Existing and potential co-location of recycling industries within the facilities of the manufacturing companies were discussed. A concern raised by the recycling industries was the increased interest from manufacturing companies to bring more of the recycling services in-house to secure the access to raw materials and components. 5.4
Customer relationship
This element represents the types of relationships the analyzed organization establishes with its customer segments. With an increased level of re-manufacturing and re-use of components manufacturing companies increasingly turn into customers for the recycling industries. In some cases, such as in co-location situations or when one company is responsible to collect used products for the manufacturing company, closer relationship are being developed. One opportunity and risk that recycling industries foresee is the increased responsibility of being a component supplier. For the actors in the manufacturing industry they had limited expectations that their customer relationships would change. One exception that was discussed in interviews and the workshop was the concept of providing products as services and by that move from one-time transactions to long-term relationships. 5.5
Resources
This element represents the most important physical, intellectual, human and financial assets of the analyzed organization. Besides the investments needed by recycling industries in dismantling facilities and warehouses, the main resource that was discussed were human resources and the competencies needed. One respondent said that they shipped components for re-manufacturing from around the world to one country where they had the competence for disassembling and renovation the products. With the competence available in more locations, the level of shipping components around was expected to decrease. An interesting discussion regarding resources that manufacturing companies shared was their concern for the existing and future shortage of rare metals and the view that today’s products might need to be the raw material of tomorrow’s products. 5.6
Activities
This element represents the main things the analyzed company must do to make its business model work. For manufacturing companies examples of new activities that were mentioned in relation to re-manufacturing were design for remanufacturing, retake of products, warehouse logistics but also co-development
Sustainability in Manufacturing with partners. For recycling industries activities such as dismantling, separation, renovation and design support where mentioned. 5.7
Partners
This element represents the network of suppliers and partners that make the business model work. During the interviews and workshop different ideas were discussed on whom could partner with whom in the specific case of each respondent. Also, actors from both the manufacturing industry and the recycling industry saw room for new actors, new intermediaries and new types of relationships between them. Different forms of partnerships were discussed and the participants of the workshop were inspired by the development within industrial ecology and the partnerships that are being developed. 5.8
Revenue streams
The revenue model element represents the cash a company generates from each customer segment. For actors in the manufacturing industry the revenue models are not expected to change, even though the products might be less expensive to produce, or enables premium pricing. The exception being if moving towards products as services, as one-time transactions would be replaced by per-use payments, subscription models or other revenue models. Recycling industries mentioned new sources of revenues, and for example components instead of materials as products, but not new revenue models except for services that could be paid for in new ways. During the workshop there was an interesting discussion about who should own the material and component in the process of re-manufacturing or even re-cycling of materials. With an increased shortage of some raw materials one idea was that manufacturers would continue to own the material or component, and only lend it to recycling industries or remanufacturers. 5.9
Cost structure
This element represents all costs incurred to operate the business model of the analyzed organization. The obvious cost with increased level of re-manufacturing is the increased costs and the need for investments in equipment, warehouse space and personnel by the recycling industries. Actors from the manufacturing industry did not express any concern for an increased cost structure. A discussion that really got attention with the respondents was the idea that today most components are designed to sustain the life of the product, and by that the life of the least sustainable component. Knowing that components that could be made more sustainable would be re-used and that the value in some form returned to the manufacturer, more environmentally and economically sound components could be produced to sustain more than one product life. 6
DISCUSSION
Studying the results we can answer the questions that were asked in the introduction of this article: Is it an ancient way of looking at the manufacturing and the recycling industry as separate entities? To some extent this is true. But the industries are now changing and becoming more integrated, toward symbiotic business models. To be able to secure the resources to produce new products, symbiotic business models could be the best alternative so far. The connections between companies are pretty week and manufacturing companies’ changes subcontractors easy. Maybe this is the best way to trigger evolvement in different sectors, but is this a sustainable way of doing business when the natural resources are running out? Perhaps it is, perhaps it’s not. Even if manufacturing companies change subcontractors easy, the symbiotic business model should be in focus and implemented.
205 What come out of a number of discussions were open and closed loops of the products. Should the manufacturing companies own the products through the whole lifecycle or should the costumer own the product. Many companies think, when looking in to the future, that they see them self leasing out a product or only selling a service. This would secure the access of material and components. In this case they could even use more expensive material for some components to increase the lifetime of it, and they could also be able to reuse the component in a new product. This would change the current business model for both manufacturing and recycling companies. The recycling industries would get a different role in the products lifecycle. They would provide a service to be the experts in dismantling the product, sorting the different materials and secure the quality of the material and components. To be able to get a closer interaction between the recycling industry and the manufacturing industry, will put demands on both of the industries. Because of the lack of close collaboration between the industries, the long-term agreements that secure the market is of big concern. These agreements are very important otherwise the recycling industry doesn’t have the motivation to invest and implement new techniques for dismantling products. Since these investments are expensive it is also important to receive large volumes and that the price of the component doesn’t fluctuate much. To create a more sustainable recycling of components and material, the recycling companies should be more involved at an earlier stage of designing a product. The recycling companies are experts on recycling and it would be a natural stage in product development to take advantage of their knowledge. This would increase the recyclability of the products and strengthen the relationship between the companies. The manufacturing industry in general could be triggered to compete against each other by introducing a specific branding which proves that the specific product contains a high amount of reused components or material. A branding like this could provoke an increased use of components in products and hence create a more sustainable future for the companies. The government could affect the market for reuse of components through e.g. legislation, changes in tax or producer responsibility. Changes like this could generate a more wide use of refurbished component. The companies that have been involved in this project were intrigued by symbiotic business models. The project group is investigating the possibilities to concretize one or more pilot cases where symbiotic business models could be implemented. Discussions with interested companies are now underway to determine what value chain to investigate, which parties to invite to cover the full lifecycle of one product for a new research effort on symbiotic business models. 7
CONCLUSIONS
To conclude the findings from interviews, literature studies and the workshop we found the following to be most interesting: Some overall conclusions from the interviews:
Limited collaboration between manufacturer and recycling company is done today. The reason is explained as: The distance between the two parties is too far from each other in terms of the products lifecycle.
The responsibility to produce green products is on the subcontractor, which means that the assembler of sub components to a product for end user feels “stuck” in-between the subcomponent manufacturer and the recycling industries.
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There is no obvious incitement for the manufacturer to work closer to the recycling industries today.
[4]
REFERO. www.stenametall.com/refero, accessed 2010-1108.
The costumer’s interest in paying extra for a recyclable product is limited.
[5]
Svensk avfallshantering (2010). Report (In Swedish) by Avfall Sverige.
The producer does not get any extra benefits when producing products that are recyclable.
[6]
Environmental branding does not take recyclability in to account, most of the branding is done on a cradle to gate calculation, which means that recycling is not included.
Pongrácz, E. (2006) ”Industrial ecology and waste management: from theories to applications”. Progress in Industrial Ecology – An International Journal, vol. 3, Nos. 1/2.
Quality and access of components to reuse in new products is uncertain in most cases, which means that the manufacturer is not interested in taking risks on possible future remanufacturing businesses.
During the workshop discussions on how to integrate business models were lively discussed, a consensus on that it is needed and that it is possible was reached. However, several hindrances to reach the target were also identified, such as:
Laws and legislations hindering companies to take back sold products when they reach end of usable life e.g. when they were sold between two different countries.
Personal interests from employees who are not in line with what the companies actually are striving for, i.e. change is easy to plan but harder to perform in reality.
Traditions and current business models are set and the work procedures are defacto-standards for trading and communicating. Those traditions are not easy to change.
If it would be possible to create a positive trend among manufacturing companies to be able to reuse components, maybe in order to show off the quality of the refurbished products, it could change the consumption of raw material and reduce the pollution to the environment. However, to reach such a trend, a new view on recycled products is needed. The status of a recycled product needs to be increased to the eye of the beholder, or else a recycled component will always be second after a new one. When summarizing the findings on reviewing literature, conducting interviews with manufacturing, refurbishing, and recycling industries as well as findings from the joint workshop, we can see that there is a need for development of more symbiotic business models in order to utilize resources more efficiently, both for a more sustainable environment as well as for a more sustainable economy. 8
ACKNOWLEDGMENTS
The funding for this research is granted by VINNOVA (Swedish Agency for Innovation Systems, integrates research and development in technology, transport and working life.). This work has been carried out within the Sustainable Production Initiative and the Production Area of Advance at Chalmers. The support is gratefully acknowledged. 9
REFERENCES
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Osterwalder, A., Pigneur, Y. (2010): Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers. Modderman Drukwerk, Amsterdam.
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Zott, C., Amit, R., Massa, L. (2010): The Business Model: Theoretical Roots, Recent Developments, and Future Research. IESE Business School, Madrid, Spain.
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DIRECTIVE 2000/53/EC of the European Parliament and of the Council, on waste electrical and electronic equipment (WEEE), 18 September 2000.
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Ragnsells AB. www.ragnsells.se, accessed 2010-11-08.
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SITA. www.sita.se, accessed 2010-11-08.
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Cohen-Rosenthal, E. (2000) American Behavioral Scientist. Vol. 44 No. 2 (245-264)
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Amaya, J., Zwolinski, P., Brissaud, D. (2010): Environmental Benefits of Parts Remanufacturing: The Truck Injector Case. In: IMS Summer School on Sustainable Manufacturing, ETH Zurich, May 26-28.
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Gehin A., Zwolinski P., Brissaud D. (2008): A tool to implement sustainable end-of-life strategies in the product development phase, In: Journal of Cleaner Production, Vol 16, No. 5, pp. 566-576.
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Kvale, S. (1997): Den kvalitativa forskningsintervjun, Studentlitteratur AB, Stockholm, Sweden.
Life Cycle Engineering – Integration of New Products on Existing Production Systems in Automotive Industry 1
Waldemar Walla , Jens Kiefer 1
1
Daimler AG, Group Research & Advanced Engineering, Integrated Product Validation, Ulm, Germany
Abstract Today, the life time of production systems is often longer than the product life cycle of the products manufactured on these production systems. Hence, during the whole life cycle of the production equipment a, redesign and rebuilding of the production line is necessary again and again in order to integrate new products. This contribution will take a look on the whole life cycle of a manufacturing system, showing that major benefits of reusing the production equipment can only be achieved if new products can smoothly be integrated on existing production systems. Keywords: Life Cycle of Production Systems; Reuse of Production Equipment; Virtual Commissioning
1
INTRODUCTION
During the last decades, the model cycle in the automotive industry decreased from 10 to 6 years or even less [1]. At the same time the demand for individualized and configurable cars has increased. This has led to a huge number of product variants together with a high complexity of products.
2
The following chapter presents the life cycle of a typical production system for the body-in-white production. Figure 1 shows the single steps of the life cycle which contains the phases planning, operation (intermitted by product integrations) and deconstruction.
The huge number of product variants, the product complexity and the time pressure lead to big challenges in production. One of the commonly accepted solutions for the increasing product complexity is the improvement of production flexibility. Flexibility of production lines can be seen as the ability to manufacture different products on one production line. E.g. a sedan car, a station wagon or a SUV can be produced on one and the same line.
Planning Phase
Designing the production system Ramp up Maintenance
Operation Phase
In order to deal with the high product variety flexible technologies in production has been developed. Manufacturing systems offer a certain degree of flexibility in order to achieve optimal performance over their complete life cycle [2]. The life time of these production systems is often longer than the product life cycle of the products manufactured on these production systems. Hence, during the whole life cycle of the production equipment a redesign and rebuilding of the production line is necessary again and again in order to integrate new products. Major benefits of reusing the production equipment can only be achieved if new products can smoothly be integrated on existing production systems. For this, a methodology has been developed which collects the relevant requirements of the production system and influences the new product design in order to fit into the production system. But this does not ensure a smooth integration process. In order not to disturb the current production the production system and especially its ramp-up has to be assessed virtually. Therefore, a concept of virtual product integration will be described in this paper. According to the virtual commissioning, the interaction between mechanics, electronics and automation technology (e.g. Programmable Logic Controller, Robotics) of production systems has to be evaluated in an early stage of the product development process.
LIFE CYCLE OF A PRODUCTION SYSTEM
Integration of a new product Maintenance Integration of a new product Maintenance
Deconstruction Phase
Deconstruction Reuse of components
Figure 1: Life cycle of a production system. The different phases of the production life cycle will be discussed in the following chapter. 2.1
Designing the production system
Future oriented planning is essential in order to design a production system on which further products can be integrated. For the production process of the further products additional equipment and tools could be necessary. Examples for those can be:
Product dependent fixing and clamping devices
Robot tools (e.g. weld guns, fold tool, …)
Product dependent grippers
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_36, © Springer-Verlag Berlin Heidelberg 2011
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For this, additional equipment and free space in the factory layout is necessary. This space has to be taken into account already during the designing phase of the production system. Further, free space is needed for the additional raw material feed (e.g. part carrier and insertion gates). During the design phase of the production system the clear-sighted technologies have to be defined. The planners have to decide which materials (e.g. steel or aluminum) can be handled by the system (welding robots). Planning the production system the production planner has to decide which technologies (e.g. resistance or laser welding) will be used. This has an impact on future products which will be later integrated on this line. In order to be able to handle different product geometries the use of flexible grippers and fixing devices is recommended. 2.2
Operation Phase
After a successful manufacturing ramp-up the production system can be modified for various reasons. During the running production the engineers can assert that a modification of the production system is advantageous. If the advantages overweight the modifications costs, the production system can be changed and optimized. Furthermore, the production system can also be modified during maintenance and repair works. It is imperative for future reconstruction and adjustment works to document all changes during the whole life cycle of the production system. 2.3
Integration Phase
Due to the high complexity and the huge amount of the product variants car manufacturers introduce new products onto the market successively. In order not to disturb the running production the integration process has to be performed smoothly and quickly. 2.4
Deconstruction
Some day new product integrations are not profitable anymore. The effort for retooling and upgrading the production system is too big. Thus, a deconstruction of the production system is inevitable. Nevertheless, in certain circumstances every single production tool can be reused for future production systems. In this case all modifications of each tool have to be documented as well. 2.5
Traditional methods such as photogrammetry or conventional measurement reach their technical and economical limitations with complex object geometry which can occur in industrial systems. The use of high-performance 3D-laser scanners makes it possible to captures very large amounts of data in a short time, but the setup costs are quite high. 3D-laser scanning: Especially in the area of the “as-is” documentation, 3D-laser scanners are applied because of their high absorption rate and good geometry recognition. The laser beam scans the object vertically and horizontally with a constant step size. Characteristics of laser scanning:
Contactless recording during production is possible
The recording with the laser technology is not dependent on natural light (it works even in complete darkness)
During a scan up to 625 000 measurement points per second can be determined. Depending on the resolution a complete scan consists of 25 to 100 million data points
The accuracy of a scanned point is ± 3mm
3D-tacheometry: A tacheometer is an angle and distance measuring device. Laser tacheometers are used to measure horizontal and vertical angle and distances. Unlike laser scanning, the 3D-tacheometry is a single point measurement system. In industry 3D-tacheometry is used for precise measurement and control surveys. This includes the single point measurement of production halls, facades and very large objects. Characteristics of 3D-tacheometry:
Pointwise measurement of length and angles
Delivery of 3D coordinates with the highest accuracy
Precise detection of edges and corners of inaccessible objects
Combinations of tacheometer with other instruments, e.g. GPS, Bluetooth, additional lasers, cameras, etc.
4
INTEGRATED PRODUCT DEVELOPMENT & PRODUCTION PLANNING
Conclusion
The importance of product integration on existing production lines plays a major role during the life cycle of a production system. For a smooth integration process a close collaboration between the product designers and production planners is essential. Furthermore, the digital validation of the integration process is necessary. This implies also a virtual integration. 3
conventional measurement with folding rule and capiler, photogrammetry, laser scanning and tacheometry. Depending on the task these methods have their advantages and disadvantages.
GEOMETRY CAPTURING OF THE PRODUCTION SYSTEM
The initial step in an integration procedure is the acquisition of the actual “as-is” state of the system. Thereby, the existing production system has to be analyzed regarding mechanical, electrical and software aspects. An overall methodology how that can be realized efficiently has not been developed yet [3]. However, the geometrical information is the starting point for the design and production planning process of the new product. This chapter summarizes the widely-used technologies for geometry capturing. For the three-dimensional measurements of objects, installations and factories, various measuring methods exists, such as
For smooth product integration the structure of the new product has to be similar to the old one. Hence, production requirements have to be taken into account in a very early phase of the product development process [4]. In this manner the effort for the reconstruction of the production line can be reduced. Nevertheless, in many cases the production system will have to be modified. In order to do this an efficient re-planning of the system is necessary. Figure 2 shows an overview of a product integration process on an existing production system. 4.1
Product development
In order to integrate a new product in an existing production system a methodology has been developed which collects the relevant requirements of the production system and influences the new product design in order to fit into the production system.
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Production Product A
Ramp up
Virtual commissioning
Production planning Product A
Production Products A & B
t Reconfiguration etc.
Layout
Cycle time
Production planning Product B (and A)
SOP Product A
Start Product development Product A
Accessibility
Product structure Material
Information
Product development Product B
etc.
Accessibility
Product structure Material
Product development Product A
Reconfiguration
Virtual product integration
Start Product development/ Production planning Product B
Start SOP Reconfiguration Product B
Figure 2 : Information exchange during the life cycle of a production system. As shown in Figure 2, the origin of these requirements can be found at any time in the life cycle of the production system. The possibilities and constraints of a production system are already defined in the system designing phase. The following information has to be considered in the product development process:
Technologies: If the product developer knows about the existing production system, he will design the new product accordingly to the available technologies.
Assembly sequence: The assembly sequence has a strong impact on the product structure. Hence, the number of components and the product structure of the new product have to be similar to the old one.
Geometry of the fixing, clamping and gripping devices: The geometry of the production resources which contact the product directly has to take into account. In order to fit the new product to the existing devices the product design has to be adapted to the geometry. Furthermore, the flexibility of the devices has to be known by the product developer.
Cycle time: The cycle time has to be identical for each production cell and can not be changed by implication. This constriction has to be shown to the product developer.
This Information can be provided to the product designer using an approach which is shown in Figure 3. 1. Collecting information about the current production • Documentation of the production system • Adjustment of the digital model of the production line • Data management 4. Identification of reconstruction activities for the production system • Redesign of the production system (if necessary) • Digital validation of the production system
2. Filtering, editing and analysing of the data • Which information is necessary? • Which consequences have to be deduced?
3. Supporting the product development process • Visualization of the consequences • Influence on the product design
Figure 3: Consideration of production requirements in the product design phase.
The concept can be divided into four steps that are directed in a circle. In order to integrate new products on existing production lines information about the production system has to be collected firstly. All resource geometries, process times or parameters must be known. In the second step, this information must be analysed. The results of the analysis have to be shown to the product development engineer. Knowing the manufacturing potential of the production system the product developer is able to influence the product design in order to realize an integration of the new product on an existing production line. In this manner the production system doesn’t have to be reconstructed. In some cases a reconstruction of the production line will be indispensable. This may be the case if the new product can not meet safety, lawful or styling requirements without special production systems. In this case the production planner has to trigger the re-planning process. The four steps approach supports the product developer with information like tool accessibility, sequence time, joining technology and factory layout. If another product needs to be integrated on this production line, the integration process will to be repeated starting with the first step. 4.2
Production planning
For the product integration on an existing line a modification of the production system can be necessary. This has to be planned accurately in order not to disturb the running production system at the integration process. A re-planning of the production system contains the following issues:
Change the factory layout
Install additional resources
Replace production equipment
Adapt the logistic concept of the parts
By validating the new designed production system virtually problems during the integration process can be avoided. For this the common known tool of the Digital Factory can be utilized:
Tool accessibility checks
Offline robot programming
Collision detection
Before rebuilding the production system and stopping the current production the re-planned system has to be commissioned virtually
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in order to avoid ramp up problems. In the final step, the production system will be reconstructed. 5
two separate data models (as depicted in Figure 4), which communicate over corresponding system interfaces during the execution phase (Tools: e.g. WinMOD & Invision).
VIRTUAL PRODUCT INTEGRATION
In this chapter, the simulation method of virtual commissioning as central technology to realize efficient integration processes is introduced. Apart from a general description of the topic virtual commissioning, the needed virtual data models, their development processes and the individual steps for accomplishing virtual product integrations used the method of virtual commissioning is presented. 5.1
Virtual commissioning – Goals and concept
As published in [5], virtual commissioning describes a methodology for validating and optimizing real control programs (e.g. PLC, RC, HMI) and the whole system behavior (e.g. the cross-function interaction inside a production system) without having the real production facilities. Due to the necessary hybrid data inputs in the form of digital product and resource as well as real control data, the simulation method of virtual commissioning is also frequently referred to a transfer from the digital to the real factory. Compared to the technology life cycle model published in [6], the method of virtual commissioning is a so-called key methodology that will be an important competitive advantage in the future. However, this new methodology is still an expert application that is currently only used for special production facilities in a selective way. Although much progress has been made in this topic in recent years, there are still some open issues that have to be solved in order to develop all benefits that can be gained by using this new simulation methodology. Relating to the entire production life cycle, the main benefits of virtual commissioning are for example:
More efficient PLC programming and debugging
Accelerated and more robust production ramp-ups
Higher degree of soft- and hardware maturity at start of production (SOP)
Operator trainings before SOP
Higher availability of the manufacturing systems during running production
More efficient integration processes during running production
As already mentioned, the key goal of virtual commissioning is the early validation and optimization of real control programs and the whole system behavior. To do this without having the real production facilities, a digital data model is needed that represents the mechanical, electrical and functional portions of the real equipment in a sufficiently exact way. Due to its contained information, this data model is to be called mechatronic cell model. 5.2
Mechatronic cell model – Components and development process
As shown in Figure 4, the mechatronic cell model is fundamentally composed of two corresponding parts: the extended 3D geometry model on the one hand and the control-oriented behavior model on the other hand. Depending on the company-specific strategy (goals of virtual commissioning, used IT architecture etc.), the mechatronic cell model can either consist of:
one integrated data model as integration of the extended 3D geometry model and the control-oriented behavior model in one common data model (Tools: e.g. DELMIA Automation, Process Simulate Commissioning) or
Figure 4: Components and interfaces of the mechatronic cell model. As presented in [7], each of these two strategies has their individual strengths and weaknesses. Last but not least, the strategic decision depends both on company- and tool-specific boundary conditions and can therefore only be made individually by each company. Today, the crucial factor in accomplishing virtual commissioning projects as profitable as possible is not the technical execution itself but the development process of the needed mechatronic cell model: This preparation phase is currently characterized by a mostly manual, error-prone and time-consuming process. At Daimler Research & Development, an engineering workflow for generating the mechatronic cell model as efficient as possible was developed, which main characteristics are illustrated in the following sections. Basis of this development process of the extended 3D geometry model is the existence of standardized resource components. According to a typical frontloading procedure, the 3D CAD models normally used in mechanical design (e.g. fixtures, grippers) have to be enhanced with additional mechatronic information. Base of this mechatronic-oriented design process is the existence of standardized mechatronic components (e.g. clamps, robots, sensors). In order to allow a company-wide access to these newly configured objects, all these mechatronic units are made available to the related departments in the form of a centrally organized mechatronic component library. In contrast to today's mechanical CAD models, the mechatronic components does not just solely consists of the pure 3D CAD model but also of kinematics (including end positions) and electrical information as for example the electrical name of the respective device. After taking the projectneutral mechatronic components from the library, the responsible department (e.g. the Tooling Design) only has to tailor them to their special installation situations in the respective production system (e.g. adaptation of the electrical name). The development of the control-oriented behavior model based on standardized behavior components, which describe the behavior of the production facility compared to the control equipment. Company-specific standards in the field of electrical/automation engineering have to be considered in developing the behavior models. According to their main tasks, these data models consist of organizational data as for instance names, behavioral description data as well as specific interface data. On the basis of so-called function group lists (output from the electrical design) that describes what kind and how many function groups are used in the respective robot cell, the preconfigured function group specific behavior
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models are combined to the control-oriented behavior model in an automatic way. More information regarding this development process is given in [5]. In a last process step, the extended 3D geometry model and the control-oriented behavior model are coupled to the overall mechatronic cell model. Basis of this connection are predefined interfaces of the two data models in the form of in- and outputs. Due to the same name conventions in both data models, this process step can be realized in an automatic way. 5.3
Capturing and feedback of real data as base for virtual product integrations
As published in [8], product integrations during the running production can be significantly accelerated using the method of virtual commissioning. In order to use virtual commissioning for integration processes in a most profitable way, in a first step, the current data of the real production system have to be captured and feedbacked to the respective data models in the digital world. In this context, as pictured in Figure 5, at least four different data types have to be distinguished: geometry, PLC, robot and HMI data. While the capturing of the needed control data (PLC, robot and HMI) is quite easy (copy of the real software), the situation to adapt the extended 3D geometry model according to the current situation of the real production system is much more difficult. To capture real geometry data (geometry and positions of used components), there are different approaches as for example 3D laser scanning. As already mentioned in chapter 3, each of these data capturing methods has individual strengths and weaknesses (e.g. costs, accuracies) and their efficient use depends on different conditions as for example the complexity and the accessibility to the real production system. In a second step, the captured geometry data have to be feedbacked into the existing 3D geometry model. According to the requirements, this process step can be either done in a manual or (semi-)automatic way. After adapting the mechatronic cell model according to the real production system, the mechatronic cell model can be coupled with
the respective control equipment and the virtual product integration can be simulated in a realistic way. So, real product integrations can be accomplished in shorter time periods, the degree of software maturity (PLC, HMI and robot programs) can be significantly improved and the re-ramp-up can be realized in a more robust and faster way. 6
SUMMARY AND OUTLOOK
Due to a soaring number of product variants with many product derivates manufacturing of several products on one production line is necessary. Therefore longer production life cycles with highly flexible and agile manufacturing systems are necessary. The single product variants come into the market at different times; hence an integration process of new products is necessary several times during the whole life cycle of the production system. In order to guarantee a smooth and quick integration process new methods are necessary which consider the production requirements from the whole life cycle. This paper takes a look on the whole life cycle of a production system and shows the importance of an early product influence in order to integrate a new product variant on an existing production system. Furthermore an approach of virtual product integration has been presented which is based on the virtual rump commissioning. Although much progress has been made in these topic fields, the following issues still remain to be addressed in future research activities:
Capturing and feedback of the current geometry and the real data has to be improved.
The product developer has to consider production requirements coming from the real production system. Hence, a structured and continuous support is required.
Production planning processes have to be modified. According to “brown field” the production planning process has to be modified.
Figure 5: Virtual commissioning as base for efficient integration processes.
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Parts of the presented activities and results are developed within the scope of the European Union research grant MyCar – Flexible Assembly Processes for the Car of the Third Millennium (NMP2-CT2006-026631). More information about this project is available at: http://www.mycar-project.eu. 8
REFERENCES
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Bär, T., Kiefer, J., Schmidgall, G., Burr, H. (2005): Objectives of a Seamless Digital Process Chain in the Automotive Industry, in: Proceedings of the 1st International Conference on Changeable, Agile, Reconfigurable and Virtual Production (CARV2005).
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Schuh, G., Wemhöner, N., Friedrich, C. (2006): Scenariobased Lifecycle Analysis of Manufacturing Systems, in: CIRP Journal of Manufacturing Systems, Vol. 35, pp. 129-135.
[3]
Reinhart, G., Meling, F., Zuber, E. (2009): Line Equipment in Asynchronous Life Cycles – Future Challenge fort he Automotive Body-Shop, in: Proceedings of the 3rd International Conference on Changeable, Agile, Reconfigurable and Virtual Production (CARV2009), pp. 642-650.
[4]
Mantwill, F. (2007): A New Approach to Applying Manufacturing Requirements within a Modern 3D-CAD System, in: Proceedings of the 2nd International Conference on Changeable, Agile, Reconfigurable and Virtual Production (CARV2007).
[5]
Kiefer, J., Ollinger, L., Bergert, M. (2009): Virtuelle Inbetriebnahme – Standardisierte Verhaltensmodellierung mechatronischer Betriebsmittel im automobilen Karosserierohbau, in: atp – Automatisierungstechnische Praxis, 7/2009, pp. 40-46.
[6]
Gausemeier, J., Ebbesmeyer, P., Kallmeyer, F. (2001): Produktinnovation, Carl Hanser Verlag, Munich.
[7]
Bergert, M.; Kiefer, J.; Höme, S.; Fedrowitz, C. (2009): Einsatz der Virtuellen Inbetriebnahme im automobilen Karosserierohbau – Ein Erfahrungsbericht, in: Tagungsband der 9. Magdeburger Maschinenbau-Tage, pp. 388-397.
[8]
Kiefer, J. (2007): Mechatronikorientierte Planung automatisierter Fertigungszellen im Bereich Karosserierohbau, Schriftenreihe Produktionstechnik, Band 43, Universität des Saarlandes.
Managing Sustainability in Product Design and Manufacturing 1
Klimis Ioannou , Alireza Veshagh 1
1
Manufacturing Department, University of Warwick, Coventry, UK
Abstract This paper identifies and discusses key management issues associated with the integration of sustainability criteria in the design of new products and manufacturing processes. Alternative strategies are compared and the business case is examined based on the drivers and barriers of sustainable product design and manufacturing. The paper also discusses the results of a survey conducted to examine the position of the manufacturing industry in the UK who have already implemented sustainability principles in their operations, and provides an insight into the salient features of sustainability management in terms of implementing and managing sustainability in the long run. Keywords: Sustainable Manufacturing; Survey; Management
1
INTRODUCTION
The unfavourable impact of development has led to the need for creating a balance between economic growth, environmental preservation and social equity. These three dimensions are equally important and they should be managed as a whole on the basis of a triple bottom line. Economic growth and environmental conservation have been traditionally regarded as two incompatible factors, but linked together on a trade-off basis. Sustainable development was introduced as a concept to reconcile this conflicting relationship. Since early 1970s environmental, social and economic criteria have been institutionalised as the guiding principles and therefore growth and development are not measured solely by the real income per capita or the gross national product; welfare is not perceived purely in economic terms, but emphasis is placed on quality of life, healthy living conditions, equal opportunities and social wellbeing. Sustainability is being increasingly integrated into business processes, activities and operations, such as product design and manufacturing to meet with environmental and social challenges faced by manufacturing industry. 2 2.1
SUSTAINABLE PRODUCT DESIGN Definition
A number of approaches to environmentally conscious product design have been developed and several new terms and terminologies have emerged. The most common terms appearing in bibliography are in chronological order green design, eco design or design for the environment, and sustainable product design. Green design mainly addresses single environmental issues and is based on design for “X” concepts, where “X” may stand for energy efficiency, recycling, modularity, disassembly.
by taking a wider view of the impact of product design process on humanity, and integrates ethical and social considerations alongside environmental issues into the design process. Drawing on the basic definition of eco design, SPD can be defined as the systematic consideration of design performance with respect to environmental and social objectives over the full product life cycle (Figure 1). 2.2
Principles of SPD
The principles of SPD can be summarised in the following rules:
Waste Equals Food: All products at the end of their life should become nourishment for something else.
Use Current Solar Income: SPD provides for efficient use of clean and renewable energy on a life-cycle basis, during manufacturing, transportation and use of products.
Respect Diversity: SPD accounts for the uniqueness of places, adjusts to local conditions, and considers diversity in human communities.
The five principles of SPD, namely products should be cyclic, efficient, solar, safe and social, can be mapped on a matrix table illustrating the relationship between the above approach, and the triple bottom line concept (Figure 2). 2.3
Strategies for SPD
There are an number of alternative approaches to SPD which can be divided into environmental, social and economic strategies. Environmental strategies include reduciton of materal usage, pollution prevention, energy efficiency and life cycle design. Product design strategies such as human factor engineering, health and safety, and fair trade aim to address the social aspect of design and manufacturing operations [1].
Eco design, which extends beyond green design, was introduced in the 1980s as a strategy to integrate environmental considerations in the product development process based on a life-cycle approach. Sustainable product design (SPD) widens the scope of eco design
Figure 1: Sustainable product design framework.
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_37, © Springer-Verlag Berlin Heidelberg 2011
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Sustainability in Manufacturing between manufacturing sub-systems, thus reducing unexploited energy and materials leaving production units. Cleaner Production aims to clean up industrial and manufacturing processes that take place in production units through the use of environmentally friendly machining technology and the utilization of renewable energy resources during manufacturing [7]. The Service-Oriented Business Model aims at decoupling economic growth from resource consumption and waste creation, by improving functionality of products [8], [9] and/or by increasing their utilization ratio [10]. Relevant strategies include leasing and renting services provided by manufacturers. Maintenance can also be considered as a service offered by manufacturers to improve and upgrade product’s functionality. Overall, alternative SM strategies can be mapped as shown in Figure 3. Closed-loop manufacturing links the production unit with final products at the end of their life; industrial ecology links the production unit with industrial waste; cleaner production takes place inside the boundaries of the production unit; finally, the serviceoriented business model integrates the whole process of manufacturing products and making them available to customers.
Figure 2: Principles of sustainable product design.
3 3.1
SUSTAINABLE MANUFACTURING Definition
4
Sustainable Manufacturing (SM) integrates sustainability life-cycle issues in the production process, and aims at effective use of materials, energy, knowledge and capacity for work [2]. Chronologically sustainable manufacturing followed green and lean manufacturing [3]. Quinn et al. [4] define SM as “systems of production that integrate concerns for the long-term viability of the environment, workers health and safety, the community, and the economic life of a particular firm”. 3.2
Principles of SM
The principles of SM can be summarised as follows [4]:
Reduce quantity of resources: energy, materials and water.
Improve environmental quality of resources: ecologically incompatible chemical substances.
Design for multiple and longer life-cycles: material loop systems; resource intensity and product’s service intensity.
Improve manufacturing technology: environmentally sound production technologies and processes.
Improve working conditions: ergonomic criteria, health and safety requirements; equity and fairness; employees’ personal development .
Support local communities: economical, social and cultural support.
3.3
toxic
The business case for integrating the principles of sustainability in a company’s operations is to create business opportunities by combating ecological and social problems threatening the world. Articulating the relationship between sustainability performance and profitability is crucial for integrating environmental and social aspects in business strategies. Integrating sustainability concerns in decision-making requires that efficient risk management is applied so that companies will protect themselves from sustainability threats and will capitalize on sustainability opportunities (Figure 4). 4.1
and
Strategies for SM
Alternative approaches to manufacturing have evolved from traditional to lean practices, and later to green manufacturing. Sustainable manufacturing includes strategies that go beyond lean and green practices by applying a holistic and innovative approach to manufacturing; key strategies are:
THE BUSINESS CASE FOR SUSTAINABLE PRODUCT DESIGN AND MANUFACTURING
Advantages of Manufacturing
Sustainable
Product
Design
Cost benefits and financial performance SPD allows cost benefits to be realised along the whole product life-cycle. Reduction in the quantity of resource requirements for goods to be produced results in reducing the cost of materials, the storage and distribution cost, the cost of production, and the quantity of waste to be managed. Manufacturing techniques that allow more efficient use of energy and materials, and require fewer consumables, reduce the cost of production and allow higher profit margins. Management of risks and opportunities Sustainability issues are at the forefront of the risk management agenda for most companies [11].
Closed-loop manufacturing is a manufacturing system where “products are made from used products, parts and materials takenback from market” [5]. This involves the 6R strategies, namely reduce, re-use, recycle, recover, redesign and remanufacture [6]. Recovery of products at the end of their life is the key link between conventional and closed-loop manufacturing. Modular design is a major enabler and requirement for closed-loop manufacturing. Industrial Ecology aims at closing the life-cycle by applying the closed-loop concept to industrial waste and by-products which is different to closed-loop manufacturing. Industrial residuals circulate
and
Figure 3: Sustainable manufacturing strategies
Sustainability in Manufacturing
215 Strict adherence to regulatory requirements Environmental legislation is a major driver for most companies, but when legal compliance is considered as separate from the opportunity to create competitive advantage, end-of-pipe solutions are preferred instead of holistic approaches that promote preventative strategies. Functional requirements
Figure 4: The business case for sustainability. Companies that apply carefully structured sustainability risk management systems are considered to be less risky businesses and are more attractive to insurance companies and financial organisations. Position in the market Public awareness has introduced ethical criteria to form customers’ purchase and consumption behaviour. The integration of sustainability principles in corporate strategy is important not only for expanding but also for retaining existing market share, as environmentally and socially conscious companies can participate in supply chain networks that set environmental and social requirements. Attract Investments Socially responsible companies are considered to pay off better and react more effectively in difficult economic times compared to their counterparts that focus solely on profit [12]. This makes these companies more attractive to socially responsible investors, who wish to invest in generating economic as well as sustainability values. Legal compliance Legal frameworks articulated at institutional level by governments and local authorities get increasingly strict requirements for manufacturers and retailers. Compliant companies are better prepared for future reviews of the environmental legislation. Development of innovative solutions Integration of sustainability principles in product design and manufacturing allows benefits to be realised by means of eco innovation [13]. 4.2
Barriers to Manufacturing
Sustainable
Product
Design
and
New innovative solutions that integrate environmental requirements are not always as functional as the conventional ones. Customers may be skeptical about the performance of green products or their poor quality [11]. Lack of support Lack of sufficient technical support and expertise prevents companies from realising sustainable solutions in product design and manufacturing. 5 5.1
SURVEY OF INDUSTRY Survey objectives
In order to compare and contrast literature review findings with industrial practice, a survey of manufacturing companies was conducted by means of questionnaire [15], [16]. This research focuses on a holistic approach to managerial issues and techniques that support sustainability in product design and manufacturing. The main objectives of the survey were:
To identify how companies regard sustainability issues in product design and manufacturing,
To identify what the most popular sustainability strategies in product design and manufacturing are,
To identify what the perception is about the favourable and unfavourable aspects of integrating sustainability in organisations, and
To identify what the managerial approach is to the integration of sustainability in product design and manufacturing.
5.2
Results and analysis
The survey was conducted by interviewing or sending the questionnaire to companies; 75% of which were small or medium enterprises.
The cost of development and long pay back period is regarded as a major barrier for companies to integrate sustainability in product design and manufacturing.
Over 90% of the sample companies had already integrated sustainability principles in their product design and manufacturing operations, which means that the survey results should be in line with the viewpoint of environmentally and socially conscious companies. Senior managers provided response to the survey questionnaire.
Market pull strategies
Alternative strategies for sustainable product design
Lack of awareness about market trends and the uncertainty about customers’ response to sustainability initiatives tend to prevent companies from integrating environmental and social considerations.
The most widely adopted strategies are those that deal with the consumption of resources during the life-cycle of a product (Figure 5).
The main barriers are [14]: Financial issues and commercial disadvantage
Time constraints The time required for the realisation of changes towards sustainability may be in conflict with fundamental activities of the company. Lack of accountability Owners of the company may consider that taking environmental and social initiatives is a matter of charitable action beyond their scope of responsibility. Figure 5: Most commonly adopted SPD strategies.
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Figure 6: SM strategies. Strategies aiming at improving the environmental quality and reducing the quantity of resources are of equal popularity, as selected by 74% of the companies responded to the survey. Such strategies involve practices to reduce the use of energy, materials, and water, as well as solutions that promote the use of recyclable, recycled, nontoxic, and nonpolluting materials. In addition, 57% of the companies participating in the survey take end-of-life issues into consideration during their product design process by providing for disassembly, recycling, reusability, and remanufacturing of products at the end of their life. Social issues receive less attention compared to other strategies with 43% but still remain a significant consideration. Alternative strategies for sustainable manufacturing Strategies aiming to reduce the quantity of resources used during manufacturing are highly popular, as 71% of the companies indicate that they have implemented strategies to reduce the use of materials, energy and water during manufacturing processes (Figure 6). However, fewer companies (55%) appear to have implemented strategies that improve the quality of resources used during the manufacturing process. End-of-life issues are more popular, as 61% of the companies apply strategies such as reuse, recycling and remanufacturing. The use of industrial residuals as input to manufacturing processes, and service-oriented business strategies are less widely applied, as only 26% of the companies apply such strategies. Finally, 71% of the companies have implemented measures to improve the working conditions of their employees in terms of health and safety. Business drivers for sustainability Market trends are considered to be the most dominant driver for sustainability (33%) as shown in Figure 7. Cost savings and financial benefits have influenced 24% of the companies towards sustainability, while 18% of the companies selected brand image and reputation of the firm. 14% of the companies identified innovation as a factor that encourages them to integrate sustainability in their business strategy.
Figure 7: Drivers to SPD and SM.
Figure 8: Barriers to SPD and SM. Legal requirements was the least strong driver towards sustainability, on the contrary to what was expected according to the literature review, as only 11% of the companies selected this barrier. This can be explained by the fact that most of the companies participating in the survey are compliant and strive to reach sustainability beyond legal compliance. Barriers to sustainability Financial constraints are the most important barrier to integrating sustainability principles in their strategy (31%) (Figure 8). The next more influential barriers to sustainability are time constraints (27%), lack of market awareness (15%), excessive drive towards shortterm profitability (11%), lack of technical support and expertise (7%), and weak involvement of stakeholders (4%). Some companies also regarded lack of governmental support as a major barrier to sustainability. Another point raised was the tough competition created by companies that enter market for a short period of time in an opportunistic way, offer their products in competitive prices and then disappear. Advantages of sustainability Developing innovative solutions is the benefit identified by most companies (24%) (Figure 9). 20% of the companies believe that they have managed to strengthen their position in the market by either growing their market share or creating new market opportunities. 18% of the companies participating in the survey state that they have gained financial returns from the reduced use of resources, and identify legal compliance as a benefit of integrating sustainability in their strategy. However, only 4% of the companies identify a relationship between sustainability actions and improvement of their relationships with stakeholders.
Figure 9: Benefits from SPD and SM.
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Management and practice This section of the questionnaire was designed to find out about the size of change in organisational aspects, such as strategy, culture and structure, in order companies to implement sustainability. A linear rating scale ranging from 1 to 4 was used in order to measure this change, where 1 indicates no change and 4 stands for a large change. The mean values for strategy, structure and culture are 2.4, 2.1, and 2.6 respectively (Figure 10). On average, this indicates a considerable change in all of these three organisational aspects for the companies participating in the survey. The highest level of change is for culture, followed by strategy and then structure. This can be attributed to the fact that structural changes require greater practical implications than changes in softer issues such as strategy and culture. In addition, changes in softer aspects are not easily measurable and thus managers may overestimate such transformations. Implementation and practice Companies were also asked to assess how the integration of sustainability principles in their product design and manufacturing strategies had affected their activities and operations such as supply chain management, risk management, performance management and human resources management. The scale of change was measured according to a four level scale and mean value results are 2.1, 2.1, 2.4 and 2.3 respectively (Figure 10). This indicates that the greatest change on average has occurred in performance management by integrating eco-social indicators, as well as in human resources aspects, such as recruitment, training and rewarding system. Activities concerning supply chain and risk management operations are considered to have changed at a lower scale. 6 6.1
DISCUSSIONS, CONCLUSSIONS AND RECOMMENDATIONS Industry survey
Considering the responses to questions about strategies in sustainable product design and manufacturing, more emphasis is given on strategies that focus on environmental aspects rather than on social issues. Strategies dealing with the quality and quantity of resources, as well as with end-of-life issues are amongst the most popular strategies for SPD and SM results.
Figure 11: Holistic approach to product design. Taking into account that this survey focused on companies that have already implemented sustainability, it can be argued that environmentally and socially conscious companies consider sustainability as a long-term strategy that goes beyond short-term legal compliance. Benefits from integrating sustainability principles in product design and manufacturing differ considerably from the drivers that push companies towards SPD and SM. Although innovation is low in the list of drivers, it is the benefit most companies gain. The managers’ assessment on the changes in organisational and management aspects indicate that although their companies have made a considerable progress towards sustainability, yet there is still progress to be made in order to achieve the highest level of integration and get the most out of sustainability. 6.2
SPD and SM: The link and integration
Sustainable product design requires that life-cycle thinking is projected to integrate economical, environmental and social considerations in product specifications as depicted in Figure 11. Product design and manufacturing are linked by sustainability through the life-cycle interface. Life-cycle design provides for product specifications such as modularity, recyclability and reusability, which enable sustainable manufacturing. On the other hand, sustainable manufacturing affects product life-cycle by means of the closed-loop and service strategies, which allow multiple life-cycles for products and extension of their useful life respectively (Figure 12). 6.3
Managing SPD and SM
Contrary to what was identified in the literature review, the survey results indicate that legal requirements and cost savings are not high in the list of drivers for integrating sustainability in product design and manufacturing. Market trends are considered as a stronger motive for companies to change course towards sustainability.
Sustainable organisations support product design and manufacturing by integrating economic, environmental and social considerations at both operational and strategic levels. The operational level includes tools, techniques and methodologies to enable sustainability in product design and manufacturing, while the strategic level refers to organisational issues such as product strategy, structure and culture of the company (Figure 13).
Figure 10: Scale of change in organisational aspects.
Figure 12: The link between SPD and SM.
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Soma, M, Kondoh, S. and Umeda, Y. (2003): Simulation of Closed-loop Manufacturing Systems Focused on Material Balances, in: Proceedings of Eco design 2003: Third Internatiorial Symposium on Environmentally Conscious Design and inverse Manufacturing, Tokyo, Japan, December 8-11, 2003.
[6]
Jaafar, I.H, Venkatachalam, A, Joshi, K, Ungureanu, A.C, De Silva, N, Dillon Jr, O.W, Rouch, K.E. and Jawahir, I.S. (2007): Product Design for Sustainability: A New Assessment Methodology and Case Studies, in: Kutz M, (Ed.) (2007): Handbook of Environmentally Conscious Mechanical Design, Wiley, USA.
[7]
Jayal, A.D, Badurdeen, F, Dillon, O.W, Jr. and Jawahir, I.S. (2010): Sustainable Manufacturing: Modeling and Optimization Challenges at the Product, Process and System Levels, in: CIRP Journal of Manufacturing Science and Technology, Vol. 2, No. 3, pp. 144-152.
[8]
Takata, S. (2007): Maintenance Essential for Life Cycle Management, in: Selinger, G. (2007) in: Sustainability in Manufacturing: Recovery of Resources in Product and Material Cycles, Springer, Germany.
[9]
Yoshikawa, H. (2008): Sustainable Manufacturing, in: Proceedings of the 41st CIRP Conference on Manufacturing Systems, May 27, 2008, Tokyo, JP.
[10]
Seliger G, Kim H, Kernbaum S, and Zettl M. (2008): Approaches to Sustainable Manufacturing, in: International Journal of Sustainable Manufacturing, Vol. 1, Nos. 1/2.
[11]
Esty, D.C. and Winston, A.S. (2006): Green to Gold: How Smart Companies Use Environmental Strategy to Innovate, Create Value, and Build Competitive Advantage, Yale University Press, London.
[12]
Beabout, G.R. and Schmiesing, K.E. (2003): Socially Responsible Investing: An Application of Catholic Social Thought, in: Journal of Catholic Thought and Culture, vol. 6, No 1, pp. 63-99.
[13]
Carrillo-Hermosilla, J, González P.R. and Könnölä, T. (2009): Eco Innovation: When Sustainability and Competitiveness Shake Hands, Palgrave Macmillan, London.
[14]
Van Hemel, C. and Cramer, J. (2002): Barriers and Stimuli for Eco Design in SMEs, in: Journal of Cleaner Production, Vol. 10, pp. 439-453.
[15]
Kuo, T.-C, Huang, S.H. and Zhang, H.-C. (2001): Design for Manufacture and Design for X: Concepts, Applications and Perspectives, in: Computers and Induastrial Engineering, Vol. 41, pp. 241-260.
Veshagh, A. and Obagun, A. (2007): Life Cycle Design and th CIRP International Management, Proceedings of 14 th Conference of Life Cycle Engineering, Tokyo, 11-13 June.
[16]
Westkamper, E. Alting, Arndt (2000): Life Cycle Management and Assessment: Approaches and Visions Towards Sustainable Manufacturing, in: Manufacturing Technology, Vol. 49, No. 2, pp. 501-526.
Veshagh, A. and Li, W. (2006): Survey of Eco Design and Sustainable Manufacturing in Automotive Industry, Proceedings of 13th CIRP International Conference on Life st nd Cycle Engineering, Leuven, 31 May-2 June.
[17]
Epstein M. (2008): Making Sustainability Work: Best Practices in Managing and Measuring Corporate Social, Environmental and Economic Impacts, Greenleaf Publishing, UK.
Figure 13: The sustainable organisation. The type of strategy to be adopted is determined by the sustainability targets set by top management, in concert with a sound business case to support them. According to Epstein [17] sustainability strategy encompasses three clearly discrete levels, which aim at: Managing regulatory compliance, Achieving competitive advantage, Complete social, economic, and environmental integration. Depending on the strategy followed, sustainability initiatives may focus on the value chain of the company, on its competitive context, or on general social issues (Figure 14). The holistic approach considers employees as key assets with emphasis on training and understanding of sustainability principles, and innovative thinking. In addition, the rewarding system should encourage sustainable practices and empower employees to take sustainability initiatives.
Figure 14: Sustainability integration strategies.
7 [1]
[2]
[3]
[4]
REFERENCES
Jawahir, I.S. and Dillon Jr O.W. (2007): Sustainable Manufacturing Processes: New Challenges for Developing Predictive Models and Optimization Techniques, in: Proceedings of the 1st International Conference on Sustainable Manufacturing, SM1, Montreal, Canada. Quinn, M.M, Kriebel, D, Geiser, K. and Moure-Eraso, R. (1998): Sustainable Production: A Proposed Strategy for the Work Environment, in: American Journal of Industrial Medicine, vol. 34, pp. 297-304.
8
CONTACTS
Klimis Ioannou, Mechanical Engineer, Beng, MEng email:
[email protected] Alireza Veshagh, Associate Professor 205 IMC, University of Warwick email:
[email protected]
A System for Resource Efficient Process Planning for Wire EDM 1
1
Sandeep Dhanik , Paul Xirouchakis , Roberto Perez 1
2
Institute of Production and Robotics, Mechanical Engineering Department, Swiss Federal Institute of Technology, Lausanne, Switzerland 2
R&D GF AgieCharmilles Technologies S.A., 1217 Meyrin 1, Geneva Switzerland
Abstract Efficient utilization of available resources for a particular machine tool technology is an important aspect of sustainable manufacturing. The objective of this paper is to present a method for efficient resource process planning for a Wire EDM Machine Tool System. For a given part information (required surface finish and workpiece height), the developed program automatically selects the feasible wires and generates the operation sequence and corresponding cutting conditions. The system then presents a multi objective framework consisting of both ecological criteria (waste generation, electricity consumption) and economic criteria (different costs and machining time) to suggest the wire which provides the best compromise solution for the considered criteria. Keywords: Wire EDM; Energy Consumption; Waste Generation
1
INTRODUCTION
Wire electro discharge machining (Wire EDM) is one of the important non-traditional machining processes, widely used in aerospace, nuclear and automotive industries [1]. It has been developed as an effective solution for a wide variety of materials including hard materials with intricate shapes. The Wire EDM process involves complex phenomena of generation of sparks and material erosion; these have been modeled by many researchers to solve the selection of optimum cutting conditions [2][3][4]. Further, considering the stochastic nature of the problem, a number of authors have tried to experimentally model the process for optimum process parameter selection using neural networks [5], genetic algorithms [6], and data mining techniques [7]. Although the minimization of process energy has been considered in some of these approaches, the consumption of the total electricity of the machine tool and other consumables like wire, filters and resins haven’t receive the same attention. Wire consumption is known to be one of the most important factors in Wire EDM machine tool utilization as it directly effects the waste generation and machine tool operation cost. Most of the efforts in past decades has been to increase machining speed without reducing the machining accuracy while considering various process related constraints like wire breakage. However, these approaches have not considered the impact of cutting conditions on the consumption of energy and wire, which are important for both economic as well as ecological points of view. Consideration of various consumables is especially important as the price of a wire EDM is small as compared to the cost of the consumed wire over the life of the machine tool. This paper presents a resource efficient manufacturing framework for Wire EDM which considers various economic and ecological factors to aid a process planner in selecting appropriate wire EDM consumables and cutting conditions. 2
LITERATURE REVIEW
Along with optimizing the Wire EDM process parameters, a number of researchers has identified and discussed the environmental
impacts of wire EDM machine tool utilization. Tonshoff et al. [8] reported on the environmental and safety aspects of EDM processes. Among the main potential environmental hazards they cited the hazardous gas, vapor and aerosols resulting from the high temperatures developed in the dielectric fluid. Also, heavy metals may be carried by the dielectric and the sludge. Since a great number of hazardous substances are generated during the EDM process it is very important to carefully treat and dispose this waste. Yeo et al. [9] proposed a method to assess the environmental impact of waste streams in die-sinking electric-discharge machining, transforming each of the three mass flows (electrode wear, part metal removal and dielectric waste streams) into a weighted mass flow by taking into account the rank value of the three considered hazard factors (toxicity, flammability and mass flow magnitude) of each mass flow and the relative importance (weights determined by pair-wise comparison of the importance between a pair of factors) of these hazard factors. The proposed method allows ranking from the environmental impact point of view alternative electrode-dielectric fluid combinations for die-sinking EDM machining. Yeo et al. [10] proposed a multi-objective method to select a dielectric fluid across several dimensions (machining time, consumed energy, part quality and electrode-part-dielectric fluid waste mass) in die-sinking electric-discharge machining. The best trade-off is obtained for the dielectric fluid with the highest utility calculated as a weighted linear combination of its utilities for each dimension; the weights are obtained from a pair-wise comparison of the relative importance of each pair of dimensions. Recently, the energy consuming wire-EDM system components and the energy consumption distribution was determined for the GF AgieCharmilles Eco EDM FI 440 cc machine tool [11]. It was found that a significant part of energy consumption was due to the water cooling, spark generator and water injection systems. Subsequently different operation modes were studied by variations in the part height, wire diameter, part material, wire material and cutting rates. The obtained results show that by changing the cutting conditions higher energy efficiency may be obtained. The machine, wire consumption and energy costs were further studied for a range of
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_38, © Springer-Verlag Berlin Heidelberg 2011
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process parameters. From these numerical examples it came out that the wire consumption costs are three to five times higher than the energy consumption costs.
machine tool builder GF AgieCharmilles SA, which can aid a process planner to achieve this goal. In summary, the objective of this paper is to summarize development in following areas:
It can be concluded that although various efforts are oriented towards the reduction of environmental impacts, there are the following shortcomings in the previously reported environmental impact assessment method (besides the fact that the method was developed for die-sinking EDM and not wire-EDM which is the focus of our study):
1. Energy consumption: To calculate the cost of machining based on consumable (EDM wire) consumption and electricity consumption by the following components:
The overall and component wise machine tool energy consumption of machine tool is not modeled.
The consumables waste streams are (electrodes, filters and de-ionizing resin) and
The influence of cutting conditions and part quality requirement is not taken into account.
not
considered
Since the inception of CNC machine tool technology, machine tool builders have created databases for cutting conditions which are developed for the practical operation of the machine tool. These databases are experimentally determined to achieve stable cutting conditions. Also, it is developed for the most common wires commercially available for the machine tool utilization phase. There exist at least 16 types of different for cutting steel according to GF AgieCharmilles Technology datatable[12]. Although machine tools are equipped with cutting condition tables for each wire, it is still a difficult task to decide which wires are feasible for a particular operation according to technical limitations and what is the best suitable wire among them. It is a daunting task to select the appropriate wire which adheres to the machining requirements as well as minimizes the machine tool energy and consumable utilization. Hence it is required to have a system, which can help the process planner to rank the different choices according to the predefined criteria or attributes. This paper presents a method and the developed computer tool in collaboration with a major EDM
Process related injection pump electricity consumption.
Process related spark generator input energy consumption.
Process independent (constant) energy consumption by the filling tank pump, auxiliary pump, and dielectric cooler.
2. Waste Stream Calculation: For a given application oriented user input (work piece material and thickness), the following direct waste streams are calculated:
Wire consumption mass
Workpiece debris due to cutting process
3. Development of a multi objective optimization system for best wire selection among various feasible wires for given part requirement. 3 3.1
WIRE EDM PROCESS CONSIDERATIONS Process Diagram
As a first step, a model for electricity and waste assessment is conceptualized in Figure 1. In this model, the flows of the different materials, energy consumption relevant utilities and materials can be observed. During the machine tool utilization phase, electricity is consumed by the spark generator, flushing pump (also known as injection pump), filtration pump and waste treatment system. The waste consists of treated waste and disposed waste. During the EDM process, deionised water is constantly getting contaminated with metal particles produced during the EDM process and water
Figure 1: Process Flow and System Model for Wire EDM.
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filter/resins are used to treat this waste. Wire is constantly unrolling during the wire EDM process and cannot be used again due to quality issues, and hence it represents a significant source of waste in wire EDM. 3.2
Process Level Considerations
In Wire EDM, process level issues involve the consideration of various machining stages and corresponding cutting conditions according to the developed technology rules. These technological rules are defined in Charmilles technological tables [12]. The EDM wires consist of many types and diameters. There can be more than one wire suitable for a certain application. Hence, it is very important to take into account various wires and to find out which wire among a set of wires represents the best compromise among various criteria (cost, energy consumption, waste generation etc.)
Figure 3: Power Consumption along the time horizon. The power consumption value of injection pump and spark generation unit depends upon the process (Workpiece material, wire type and diameter, cutting quality requirement). While, the other category of components (Filling pump, Filter Pump, Auxiliary pump, Cooler) are not affected by the process requirements. Hence, we need to take into account the Injection pump and Spark generation unit to calculate the total electricity consumption during the WEDM. Further, the spark energy output is formulated using the technology tables [12] and spark efficiency of the spark generator. Injection pump is found to be a direct function of a pressure index value (INJ) assigned by the machine tool controller and the relationship between the injection pump and INJ is established by experiments. Once spark generator efficiency is calculated, the input energy to the spark generator can be calculated with respect to the cutting conditions.
Figure 2: An example of a data sheet provided by GF Agie Charmilles for a workpiece made of steel and a brass wire [12]. Further, in order to achieve the specified workpiece surface finish by WEDM, a sequence of operations (one or multiple roughing, semi finishing, finishing passes) are required for a given wire. Each of these steps is an operation setting which contains a unique set of cutting conditions (various spark generation parameters, cutting speed and injection pump pressure) and the group of operation settings enabling the required surface finish is called operation sequence. There can be more than one operation sequence for a given set of surface finish, workpiece material, workpiece thickness, wire material and wire diameter. Therefore, it is needed to develop a system which selects the operation sequence according to the total machining time. For given part requirements, these details regarding operation settings and the cutting conditions can be useful for both energy and waste calculation as discussed in the following sections. 3.3
3.4
Waste Generation
For the disposable waste described in Figure 1, only the wire consumption can be accurately calculated. The filter systems are usually a quite simple installation, either paper-cartridge-filters or mineral filters. As there are no data available yet in terms of consumption for the recycling or burning of the filter, no definite conclusions can be drawn. However, the filter life is known to be affected by the eroded mass of workpiece due to the sparks. Wasted Workpiece material Calculation: During wire EDM, the material is removed from the workpiece by either thermal or mechanical action of sparks. The waste generation depends upon the wire diameter and the machining steps (see Figure 4). The type of wire and cutting conditions both affect this waste stream. For example, the bigger diameter wire can generate more waste than the smaller diameter wire for per mm of cut.
Electricity Consumption
The component wise power distribution during the various phases of a machining process on a WEDM machine tool system is shown in Figure 3. It can be seen that there are two types of components in the power distribution diagram:
Process related power consumption components
Injection pump power consumption
Spark generation unit
Process independent components
Filling Pump
Filter pump
Auxiliary pump
Cooler
(constant)
power
consumption
Figure 4: Workpiece Material Removal Calculation based on Operation settings. Waste Wire Mass Calculation: In wire EDM, the rolling of wire can be used to calculate the wasted mass as the wire is used only once due to quality issues. The wire is chopped using a chopping device
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and this waste mass is put in a waste bin. Hence, wire is a significant waste in the WEDM process. 3.5
Cost Calculation
The cost of different wires is shown in Table 1, although the wire cost depends also on the spool size, this cost data is taken for the purpose of roughly comparing different wire materials. The electricity cost is taken as 0.12 CHF per kWh. Serial no.
Wire Material
Wire cost (CHF per Kg)
1
Hard Brass
12
2
Zinc Coated Brass
15
3
Zinc Coated Copper XS
20
Table 1: Cost of different wires. 4
DEVELOPED METHOD
The details discussed in the previous sections are implemented for the assessment of different EDM wires for the user defined requirements. In WEDM, the user has to define the part requirements such as workpiece material, surface finish and workpiece thickness as shown in Figure 5.
Figure 5: User Input. Based on the workpiece material, the developed program automatically selects all the wires suitable for cutting that material. For example, for the case of steel, the 6 types of Hard Brass wire, 4 types of Zinc coated wire and 2 types of Zinc coated Copper XS wires are initially selected.
Figure 6: Summary of the Method developed for assessment of Energy and Waste. However, not all the wires are suitable for a particular application. For example, thin wires are not suitable for very high workpiece height (more than 80mm) while thick wire may not be suitable for low surface finish requirement. Once a wire is found feasible, the operation sequences are determined. There can be more than one
operation sequence for a given surface roughness. For example in Figure 2, there can be more than two operation sequences which may satisfy a medium surface finish requirement. Also, each operation sequence is a combination of roughing, semi finishing and finishing stages. There can be multiple semi finishing stages
Sustainability in Manufacturing between roughing and finishing stages. For each of these stages, the wire is travelling across the same cutting profile length, but with different cutting conditions. Hence, there is a need to evaluate the overall effect of all the operation settings in an operation sequence. Using the data tables, the cutting conditions, spark generator setting and injection pump pressure index are extracted and the machining time for per unit length, component level energy consumption and waste are calculated as discussed in the previous sections. Subsequently, the various attributes of all the possible operation sequences for a wire are evaluated, an operation sequences is selected which corresponds to the minimum time or minimum energy per unit cutting length. It was found that minimum time also corresponds to minimum total energy of a machine tool; hence either minimum time or minimum energy is suitable for selecting the operation sequence. This procedure is repeated for all the wires in the feasible wire dataset. The following entities are eventually evaluated for each feasible wire:
223
Figure 8: Spark Energy Consumption vs Workpiece height for different EDM wires. Further, the Injection pressure pump was measured and compared with the modeled power, this result is shown in Figure 9.
1. Total and component wise energy consumption 2. Waste Generation due to wire mass 3. Waste generation due to workpiece mass 4. Wire and electricity cost The flow diagram of the developed method is shown in Figure 6. Once the various entities for each wire are determined, it is required to find the wire which performs best among the different categories. Thus, it paves a way for multi objective optimization for best wire selection. In this paper, a weighted average method is suggested for the same. In order to implement this, weights and the baseline value for each of the attribute is required. The user has to define the weight of the objectives according to experience or requirements. The base line values are calculated as the average value of each attribute for all the feasible wires. This approach is shown in Figure 8.
Figure 9: Injection pump power vs INJ. 6
RESULTS
It can be seen from Figure 8 that the zinc coated copper XS wire is far more energy efficient for cutting large height of workpiece due to superior cutting speeds, while for small workpiece height the energy consumption is nearly the same for all the wires. However, if wires are compared with respect to various attributes, as shown in Figure 10, zinc coated copper XS wire has still the lowest energy consumption per mm length of cut, but the total cost is much higher due to the cost of wire consumption. The wasted mass of Zinc coated copper XS wire is also high as compared to the other wire.
Figure 7: Implementation of optimization framework. 5
EXPERIMENTS
A set of experiments are carried out on steel as workpiece material in order to identify the spark generation efficiency and verification of the adopted model for injection pump power. Spark generation efficiency is important as the output energy (process energy) is not the same as the input energy. Experiments are carried out on various WEDM wires and various parameters are monitored during the experiments. Spark energy obtained from the experiments is shown in Figure 8.
Figure 10: Spider chart of comparison of three wires in WEDM on steel (values are represented in percentage). In Figure 11, another machining scenario is presented with the same workpiece height, but different surface roughness. It can be seen clearly for this cutting scenario that Zinc Coated Copper XS wire can be regarded as a best solution in all the attributes. Thus,
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with the change in part requirement, the choice of the wire can change while considering different attributes.
choice of the best suitable wire, energy consumption and waste generation. There is no single wire which is best for all the attributes and hence there is a need of multi-objective framework to select the best compromise solution. 8
ACKNOWLEDGMENTS
This work is financially supported under the Swiss Innovation Promotion Agency CTI Project (CTIP-No.9933.2) “ReManufacturing: Concepts & Methods for Resource Effective Part Manufacturing”. The authors also thank the project team lead by Mr. Lukas Weiss of Inspire ETHZ for cooperation in energy measurement experiments. 9
Figure 11: Another example of steel machining using three wires with different required surface finish. The developed software presents such results for several wires. A sample example is shown in Figure 12. The wire index in Figure 12 corresponds to the list of wires as shown in Figure 5.In Figure 12, the missing index corresponds to the wires which are not suitable for the user defined application. It also shows that the contribution of cost of wire is much more significant than the electricity consumption. Further, the wire waste is much more significant than the waste of the workpiece mass.
Snoyes, R.; (1986): Current trends in non-conventional material removal processes, in: Annals of CIRP, vol. 35, No.2, pp. 467–480.
[2]
DiBonoto, D.D; (1989): Theoretical models of electrical discharge machining process—I. A simple cathode erosion model,in: Journal of Applied Physics Vol. 66, No. 9, pp. 4095– 4103.
[3]
Dhanik, S; Joshi, S.S.; (2005): Modeling of a single resistance capacitance pulse discharge in micro-electro discharge machining, in: Journal of Manufacturing Science and Engineering, Vol.127, pp.759–756
[4]
Das S., and Joshi, S.S.; (2010): Modeling of spark erosion rate in micro wire-EDM, in: International Journal of Advanced Manufacturing Technology (2010), Vol.48, pp. 581–596.
[5]
Kuriakose, S.; Mohan, K.; Shunmugam, M.S.; (2003) Data mining applied to wire-EDM process, in: Journal of Materials Processing Technology, Vol. 142, No.1, pp. 182-189.
[6]
Chen, H.C.; Lin, J.-C.; Yang Y.-Y.; Tsai, C.-H.; (2010): Optimization of wire electrical discharge machining for pure tungsten using a neural network integrated simulated annealing approach, in: Expert Systems with Applications: An International Journal, Vol. 37, No.10, pp. 7147-7153.
[7]
Kuriakose, S.; Shunmugam, M.S.; (2005): Multi-objective optimization of wire-electro discharge machining process by Non-Dominated Sorting Genetic Algorithm, in: Journal of Materials Processing Technology, Vol. 170 No. 1-2, pp. 133141.
[8]
Tonshoff, H.K.; Egger, R.; Klocke, F.;(1996): Environmental and safety aspects of electrophysical and electrochemical processes, in: CIRP Annals - Manufacturing Technology, Vol. 45, No.2, pp. 553-568.
[9]
Yeo, S.H.; H.C. Tan; and A.K. New; (1998): Assessment of waste streams in electric-discharge machining for environmental impact analysis, in: Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, Vol. 212, No.5, pp. 393-400.
[10]
Yeo, S.H.; and New, A.K.; (1999): Method for green process planning in electric discharge machining, in: International Journal of Advanced Manufacturing Technology, Vol. 15, No.4, pp. 287-291.
[11]
Deiss, C; (2009): Eco machine Study on Energy Consumption of wire and wire Erosion Machine (published in german), Master Thesis, RWTH Aachen University.
[12]
Charmilles; (2005): Technologies Manual Robofil 240 CC 440CC, GF Agie Charmilles SA.
Figure 12: A sample result window of software for 60 mm Steel cutting with 0.8 µm surface roughness. 7
CONCLUSION
The influence of wire diameter and material and part requirementsworkpiece height and surface finish, is investigated for machine tool use phase with different criteria: energy consumption, total machining cost, wire and workpiece waste generation. A computer program is developed to integrate existing technological database of a wire EDM machine, which calculates the abovementioned attributes. It can be concluded that the part requirements (workpiece height and surface finish) have significant effect on the
REFERENCES
[1]
Increased Trustability of Reliability Prognoses for Machine Tools 1
1
1
1
Gisela Lanza , Patrick Werner , Dominic Appel , Benjamin Behmann 1
Karlsruhe Institute of Technology (KIT), wbk Institute of Production Science, Karlsruhe, Germany
Abstract The estimation of trustable reliability figures for machine tools is a considerable challenge. The main reasons are the sparse availability of relevant components’ lifetime data as well as the load-dependence of reliability. The proposed paper presents a method to estimate increasingly trustable load-dependent reliability figures for machine tools using design information to estimate the reliability if no field data is given, service knowledge of an existing service department which monitors field maintenance of the products and documented field data of spare parts sales, service and maintenance activities. Keywords: Load-Depended Reliability Model; Trustability of Reliability Figures; Confidence Bounds
1
MOTIVATION AND OBJECTIVES
In recent times an increasing number of customers of the machine and plant industry ask for different kinds of special warranties [1], for example warranties for the maintenance costs over a long period of time, like 10 years [2], or warranted reliability figures for their production plants. Especially reliability improvement warranties with fixed reliability figures like a Mean Time Between Failure (MTBF) are often demanded. Currently, many companies in the machine and plant industry estimated these values purely on expert knowledge, mostly from the development department. But since the risks arising from these warranties could have a considerable impact on the company’s success, a trustable knowledge of the failure behavior of the machine components and the whole technical plants in the field is necessary. In most cases, this knowledge is best gained by statistically analyzing field data. This field data is achievable from service data of maintenance activities done by the operator’s maintenance or the manufacturer’s service department and to a lesser extent, from spare part sales. The main challenge of generating trustworthy predictions via statistical analysis in the machine and plant industry is the low data availability due to low production numbers and a comparatively low number of failures [3]. In this context the machine and plant industry differs considerably from industries with mass products where statistically analyzed reliability tests are common. Machine and plants are often individual products which are adapted to the buyers’ requirements. Moreover, reliability does not only depend on the technical characteristics of the considered components, it is also varying loads that have an essential impact on the failure behavior [3, 4]. Therefore, the success of the statistical analysis and the quality of the prediction strongly correlate with the inclusion of the load relations and the data quality. In order to address these challenges the paper in hand presents an approach to how systematically use different data sources in order to estimate trustworthy reliability figures using the developed methodology. Thus, the machine and plants industry is able to face the upcoming risk of extended warranties or reliability improvement warranties and the risk arising from the statistical variance of the
service life. Regarding high penalty payments up to 30 % of the machine price [5] the quantification of these risks is very important for the machine and plants manufacturer’s economical success. 2
RELIABILITY MODEL
Machines and plants in general are complex systems consisting of defined assemblies which can be, for their part, broken up in different components. Additionally, the varying applications of the components have a considerable influence on the machine service life. The installation position and the function of the components can thereby take in major constraints. Thus, it is essential to create a reasonable clustering of the machine components in order to systematically describe the machine structure and the components usage. To focus on relevant components for a further reliability consideration, clustering also has to include an analysis of the criticality of each component. Hence, a reduction of the machine structure complexity is realizable. The fault-tree analysis [6] constitutes an established tool to support this process. Depending on the data at hand, the statistical analysis is done at a different level of the tree. The more appropriate data available, the more detailed the level for the analysis can be chosen. For a trustworthy analysis, the level of detail should be as low as possible, ideally on the level of failure modes. However, in most cases, this is not possible since there are not enough failures per failure mode. In practice, it should be at least as detailed as the level of detail of the operator’s perception. For the analysis of the field data, mean values only, assuming an exponential distribution, are often used. Moreover, many companies start to use the two or three parametric Weibull distribution. But since reliability of machine tool components often highly depends on the operating conditions and the loads which result from these conditions, the Weibull distribution can be the wrong model. Therefore, the General Log-Linear model together with the Cumulative Damage model is used in this approach to describe this impact mathematically. [7] The advantage of this model is that it not only takes time-varying loads into account, but also different
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_39, © Springer-Verlag Berlin Heidelberg 2011
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applying loads at the same time. In the Weibull Cumulative Damage model the Weibull distribution is viewed in relation to a cumulative damage caused to the component by different loads rather than in relation to the component lifetime. The general form of the distribution function of the model is described in formula 1.
F (t; L) 1 e (W ( t ; L ))
W (t; L ) e
a0
a j X (t ') j j
(1)
dt '
(2)
0
a0 and aj are function parameters and t is the variable for time. Xj is a transformation of the stress levels Lj. The kind of transformation depends on the kind of load. For mechanical loads this is according to the Power-Law - the natural logarithm. For thermal loads this is - according to the Arrhenius law - the reciprocal value. [7] The model allows the calculation of the remaining service life at any time for any probability using the conditional probability based on the previous loads over time and a predicted future load profile. Formula 3 describes a remaining service life T, given a cumulated damage of W, a prognosis load collective of W f and a probability P for survival. [8] 1
T ( W ln( P ) W ) W 3
4 4.1
Form parameter β is considered to be load-independent and, therefore, constant as a characteristic parameter for a specific type of failure. W(t;L) represents the cumulated damage which is a function of time t and a load vector L. It replaces the term t/η in the Weibull distribution because η is considered to be load-dependent. In the General Log-Linear form it is written as in formula 2 [7]. t
distribution [e.g. 13] for certain machine components, but in most cases only a wide range is given.
f
(3)
At first, values for service lifes are often very conservative estimates. In an analysis of ball screws at the wbk Institute of Production Science, the actual service life of the least reliable of 25 ball screws was twice the service life which the respective standard predicted. [12] This is not advantageous for not-safety relevant parts since a too low reliability given for a component might result in lost sales in the bidding process. Secondly, most standards do not provide the probability of failure or a deviation of the service life. There are some sources for characteristic values of the shape parameter of the Weibull
Use of knowledge of the service department
If the service activities of an existing service department have not been sufficiently documented in the past, the knowledge of the service department staff can be used to estimate the parameters of the Weibull distribution. Jäger and Bertsche show in [14] an approach for a three parametric Weibull distribution. For a loaddependent view, this approach has to be modified. If the life-stress relationship is known, the service staff is only used to estimate the scale parameter of the service life distribution. The staff is prompted for the first (tmin) and the last failure (tmax) of a known number of components n. The service life around which most failures happen is also prompted for. Using the Benard Median Rank [15] one can get the following two formulas:
F tmin 1 e
t min L
F tmax 1 e
t max L
!
!
1 0,3 n 0,4
(4)
n 0,3 n 0,4
(5)
where η(L) is the load-dependent scale parameter of the Weibull distribution. By solving those two formulas, the value for the shape parameter β and the load level L (if not already known) can be estimated. Alternatively, the time at which most failures happen (e.g. the supremum of the distribution tsup) can be used together with the formulas 4 or 5.
df tsup dt
USE OF DESIGN INFORMATION
The estimation of parameters for the General log-linear model requires sufficient data about lifetimes of components and the respective loads they have been subjected to over time. This data is often not available in the machine tool industry and especially not in the case of a new component without any field data. However, there are sources for first estimates of the parameters. Important sources are S-N curves or Wöhler curves which are widely used in mechanical engineering. These Wöhler curves are often available for some parts of the components or can be artificially created by following the FKM guidelines [9, 10]. Furthermore, standards for the calculation of the expected service life of mechanical parts include life-stress relationships. One example is the standard regarding the service life of bearings. This standard also assumes an exponential relationship between mechanical load and lifetime with an exponent of 3 or 3.3 depending on the type of bearing [11]. Most of these data sources have two shortcomings.
USE OF SPARE PARTS AND SERVICE DATA
tsup
0 L
1
1
(6)
Since the first failure might be an early failure the use of formula 5 together with formula 6 might give the best results. The practical application of this approach has shown that its results can only be seen as a rough estimation. Especially the missing consideration of suspensions is a considerable source of error [3]. In this case, suspensions are lifetimes of components which did not fail up till the time of the analysis. 4.2
Use of spare parts sales
Spare parts sales are often seen as a usable data source in order to estimate reliability figures. But from the sales data alone the time and place of the actual assembly cannot be derivated and therefore the lifetime of a component is not calculable. It may be possible that the operator of the equipment informs the manufacturer about the use of spare parts. But even if not, it should be possible to calculate a simple MTBF value for a component by dividing the number of components in the field with the number of sold spare parts for a particular time period (e.g. year). In the machine and plants industry, most components are purchased by specialized suppliers and many customers buy their spare parts directly from them. Thus, the manufacturer might not know about all spare parts assemblies, if he is not informed. This could be addressed if the operator agrees to buy his spare parts only from the manufacturer e.g. as part of a larger service contract.
Sustainability in Manufacturing 4.3
Use of service data
The best source of failure data for many machine and plant manufacturers is the documentation of maintenance activities of their own service department. In order to use this information, the service actions performed have to be sufficiently documented. At least, the service life of the failed component has to be calculable. This is true if either the component can be uniquely tracked or the place of assembly (machine, position in the components tree) is fully known. The service life could be measured from assembly date to failure date, or even better in operating hours. Additional information could be the exact failure mode, the reason for the failure, the operating conditions, including all loads and repair expenses. As outlined before, the goal is to collect data about the service life of components together with the exact failure modes and the respective load collective. Predefined select lists for the documentation considerably simplify the statistical analysis compared to free text field. A major problem of the use of service data is that maintenance actions are often done by the operator’s maintenance department or a third party and the manufacturer is not informed. This could lead to wrong suspensions where it is assumed that a component is still in service while instead it has already failed. 5
USE OF OPERATOR DATA
Data from the maintenance department of the operator is seldom available for the machine or plant manufacturer. However, when the operator requires a reliability improvement warranty (RIW), he often shares parts of his maintenance data with the manufacturer in order to control the reliability figures. One example for these RIWs is the Maintenance – Total Cost of Ownership (M-TCO) model of the Daimler AG [16]. The analysis of the data can give a holistic view on the behavior of the machines and plants for this respective operator as long as the data meets the same requirements as the service data described earlier. Unlike the service data, the operator data should not include the problem of unreported failures. On the other side, it is often more difficult for the manufacturer to observe the operating conditions completely. 6
227 confidence bounds. If an operator or a data point has a high impact on the parameter or the confidence bounds, it is likely that the data is not as trustworthy as the rest of the data.
The usage of confidence bounds is combined with a jackknife method [17] to estimate the impact of certain data or the impact of the operating conditions on the estimated parameters and their
Key figures
Weibull distribution
Data with suspension
MTBF
100%
Upper confidence bound
213%
Lower confidence bound
59%
Difference
153%
MTBF
100%
Upper confidence bound
139%
Lower confidence bound
71%
Difference
69%
Data without suspension
Table 1: Comparison of the confidence bounds. Furthermore, there is the challenge of fixing the wrong distribution to the data. This could be addressed by fitting the Generalized Gamma distribution [18] which is a combination of the log-normal and the Weibull distribution. As seen in table 2 this method incorporates the uncertainty of the unknown distribution through a higher deviation of the distribution parameters and the resulting MTBF. Using a Monte Carlo simulation described in [19], the uncertainty in the parameters can be incorporated in the further calculations like e.g. in the prognosis of the warranty costs.
APPROACH FOR THE MEASUREMENT OF TRUSTABILITY
As outlined before, many factors reduce the trustability of failure data in the machine and plant industry. There are possibilities to reduce these factors organizationally, but considerable uncertainties remain, mainly due to the low number of products in field and hence the low number of failures. For a risk assessment in the bidding process of a new machine, these uncertainties have to be estimated as well as possible in order to calculate the risk of the whole bidding. In this approach, the confidence bounds are used to estimate the uncertainty of the field data. Table 1 shows the 90 % both-sided confidence bounds of the MTBF of an analysis of a field data set of a machine tool component. In the last column the MTBF is set as the reference value, the confidence bounds are expressed in relation to the MTBF. The difference quantifies the width of the confidence interval. The larger one includes the suspension which was documented in the system, the smaller one does not. While normally the inclusion of suspensions is necessary for realistic analyses of reliability data, in this case the inclusion increases the uncertainty regarding the parameters. It is suspected that the documented suspensions do not correspond with real components’ service lifes but were wrongly assumed because the failures of the components were not reported.
Data description
Key figures
Weibull distribution
Generalized Gamma distribution
MTBF
100%
100%
Upper confidence bound
139%
271%
Lower confidence bound
71%
37%
Difference
69%
234%
Table 2: Comparison of confidence bounds for different distributions. 7
SUMMARY AND CONCLUSIONS
There is a high demand for trustable reliability figures in the machine and plant industry in order to face demanded reliability warranties and, at the same time, to estimate the risk of upcoming costs in case of non-compliance. But the special circumstances in this industry, determined by small companies, low production numbers, short lifecycles and long warranty periods result in considerable challenges for the estimation of these reliability figures, like distribution parameters or MTBF values. The paper showed an approach to gain more trustable reliability figures and to assess their trustability. Therefore an application-oriented method is proposed to systematically use different data sources concerning reliability figures. As a result, the uncertainty of the estimated reliability distributions can be quantified using confidence bounds. In practice, the abstract concept of statistical uncertainties has to be
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made usable for the bidding process of small to mid-sized machine and plant manufacturers. Therefore, the assessment approach and a signaling system are currently in development at the wbk Institute of Production Science, which rank the reliability figures in three steps: Green (figures usable without consultation of a specialist), yellow (consultation mandatory for important projects), red (consultation of a specialist mandatory). The specialist would mainly be a development engineer or a service engineer who are currently in most cases the source of estimations for the reliability parameters of the machine and plant components. 8
ACKNOWLEDGMENTS
We extend our sincere thanks to the German Research Foundation (Deutsche Forschungsgemeinschaft) for the promotion of the research project LA 2351/7-1 “Development of a methodology for a failure oriented spare parts provision considering operation and load impacts” (“Entwicklung einer Methodik zur ausfallgerechten Ersatzteilbereitstellung unter Berücksichtigung von Betriebs- und Belastungseinflüssen”). 9 [1]
REFERENCES Fleischer, J., Niggeschmidt, S., Wawerla, M. (2007): Machine Life Cyle Cost Estimation via Monte-Carlo Simulation, in: Advances in Life Cycle Engineering for Sustainable Manufacturing Businesses, Proceeding of the 14th CIRP Conference on Life Cycle Engineering, pp. 449-454, Wesada University, Tokyo, Japan, June 11th-13th.
[2]
Verein Deutscher Ingenieure (1986): VDI-Richtlinie 4004, Blatt 4, Zuverlässigkeitskenngrößen – Verfügbarkeitskenngrößen, Beuth, Berlin.
[3]
Niggeschmidt, S. (2010): Ausfallgerechte Ersatzteilbereitstellung im Maschinen- und Anlagenbau mittels lastabhängiger Lebensdauerprognosen, Fortschrittsberichte Band 155, wbk Institut für Produktionstechnik, Karlsruhe.
[4]
Fleischer, J., Niggeschmidt, S., Wawerla, M. (2007): Optimizing the Life-Cycle-Performance of Machine Tools by Reliability and Availability Prognosis, in: Advances in Life Cycle Engineering for Sustainable Manufacturing Businesses, Proceeding of the 14th CIRP Conference on Life Cycle Engineering, pp. 329-334, Waseda University, Tokyo, Japan, June 11th-13th.
[5]
Fleischer, J.; Weismann, U.; Nesges, D.; Wawerla, M. (2004): Life-Cycle-Performance in der Produktionstechnik, in: VDI-Z 146 10, pp. 87-90.
[6]
Birolini, A. (2004): Reliability Engineering – Theory and Practice, 4th ed., Springer Verlag, Berlin.
[7]
Nelson, W.B. (2003): Applied Life Data Analysis, WileyInterscience, New York.
[8]
Lanza, G., Werner, P., Niggeschmidt, S. (2009): Behavior of Dynamic Preventive Maintenance Optimization for Machine Tools, in: Proceeding of the Annual Reliability and Maintainability Symposium 2009, pp. 315 – 320, Fort Worth, Texas, USA.
[9]
Forschungskuratorium Maschinenbau (2003): Rechnerischer Festigkeitsnachweis für Maschinenbauteile aus Stahl, Eisenguss und Aluminiumwerkstoffen, 5. Aufl., VDMA Verlag GmbH, Frankfurt am Main.
[10]
Fleischer, J., Niggeschmidt, S., Werner, P. (2008): Application Oriented Approach for the Integration of Load Impacts in Reliability Analysis and Lifetime Prediction, in: International
Applied Reliability Symposium 2008, Frankfurt, Germany, March 26th-28th. [11]
DIN Deutsches Institut für Normung e.V. (2007): DIN ISO 281 Rolling bearings – Dynamic load ratings and rating life (ISO 281:2007), Beuth Verlag GmbH, Berlin.
[12]
Munzinger, C., Schopp, M., Hennrich, H. (2010): Increasing maintenance efficiency - A strategy regarding condition and load-based approaches for diagnosis and prognosis, in: Conference Proceedings of the 2nd CIRP Process Machine Interactions 2010, Vancouver, Canada, June 6th-10th.
[13]
Bertsche, B., Lechner, G. (2004): Zuverlässigkeit im Fahrzeug- und Maschinenbau: Ermittlung von Bauteil- und System-Zuverlässigkeiten, Springer Verlag, Berlin.
[14]
Jäger, P., Bertsche, B. (2004): A new approach to gathering failure behavior information about mechanical components based on expert knowledge. in: Annual Reliability and Maintainability Symposium 2004, pp. 90-95.
[15]
Abernethy, R. B. (2006): The New Weibull Handbook. 5. Auflage, Robert B. Abernethy Publishing, North Palm Beach, Florida.
[16]
Nau, D. (2004): Total Cost of Ownership (TCO) bei DaimlerChrysler, in Conference Proceedings of the Conference Life-Cycle-Performance in der Produktionstechnik 2004, Karlsruhe.
[17]
Quenouille, M. H. (1949): Approximate tests of correlation in time series, in: J. R. Statist. Soc. B11 (1949), pp. 18–84.
[18]
Kao, J.H.K. (1958): A New Life Quality Measure for Electron Tubes. IRE Transaction on Reliability and Quality Control, PGRQC 13, pp. 15-22.
[19]
Lanza, G., Werner, P., Behmann, B. (2010): Prognosis of machine tool warranty costs considering arising risks through weak data sets and unknown load profiles, in: Conference Proceedings of the European Safety and Reliability Conference (ESREL 2010), pp. 1014-1018, Rhodes, Greece.
Hidden Aspects of Industrial Packaging The Driving Forces behind Packaging Selection Processes at Industrial Packaging Suppliers Sandra Silgård Casell 1
1
Division of Packaging Logistics, Department of Design Sciences, Lund University, Lund, Sweden
Abstract The choice of industrial packaging has an impact on activities throughout the supply chain in terms of costs, handling efficiency, transport efficiency and environmental considerations. The aim of this study is twofold: to gain insight into the processes employed by industrial packaging suppliers in packaging selection; and to understand the effect interactions between customer and supplier have on selection. In addition, bottlenecks in the packaging selection process are highlighted. The research findings emphasize the process mapping and logistics cost analyses performed. Three companies, all packaging suppliers to global manufacturing customers active in various fields have participated in the case study. Keywords: Industrial Packaging; Packaging Suppliers; Process Mapping
1
INTRODUCTION
“Packaging contributes to the success of product supply chains, enabling efficient distribution of products, and reduced environmental impact of products spoilage and waste.” [1] A literature review of the research in industrial packaging indicates a lack of focus on industrial packaging. In support of this, [2] claimed more than ten years ago that the literature in the area of packaging was dominated by consumer packaging from a marketing oriented perspective. This still holds true. [3] investigated packaging from a retail supply chain stance, where marketing aspects play a predominant role. [3] claimed that without examining the marketing aspects no considerations of the logistics aspects of packaging were possible. This research points towards a research focus on sales packaging. [4] includes sales packaging and industrial packaging in the basic theoretical aspects of packaging logistics. Research on packaging has also focused on the producing company’s (i.e., the packaging customer’s) processes of managing packaging matters. [5] put packaging in the light of sustainability research, claiming that “Packaging has a key role to play in sustainable development.” [6] showed that the interplay between the packaging, the logistics, and the product development functions can have a positive impact on logistics performance and cut costs. [7] elaborated this reasoning further, indicating that integrated packaging and product development that embrace logistics considerations enable a competitive and coordinated supply chain. [8] discussed the potentials of enhancing resource utilization by integrating the development of product and packaging to run in parallel. Industrial packaging is often addressed from an operational view of the producing companies, such as deficient handling aspects [4]. However, none of this research focused on the work of the industrial packaging suppliers, nor on the central interactions between the customer and packaging supplier in developing a packaging solution to serve customer satisfaction. [9] addressed the packaging development process at sales packaging suppliers, but did not emphasize the supply chain context and cost aspects. [10] investigated the packaging selection processes and the influences
on packaging decisions at two producing companies. Nevertheless, insight into the processes at industrial packaging suppliers is not reported on. To reduce this gap this research was carried out. The purpose of this paper is twofold; to gain insight into the processes employed by industrial packaging suppliers in packaging selection and to understand their interactions with manufacturing companies, in the context of the supply chain in order to find a packaging solution that satisfies the demands of these customer. The research questions used to fulfill this purpose are: What processes and rationales are used when packaging suppliers suggest an industrial packaging solution? ii. What is the nature of the ongoing dialogue and the state of involvement between the industrial packaging suppliers and the manufacturing companies to arrive at a satisfactory packaging solution? This paper presents a frame of reference followed by the research methodology and descriptions of the cases. The results are then presented, followed by the discussion and conclusions. i.
2 2.1
FRAME OF REFERENCE Industrial packaging and PPS set-up
The packaging type in focus is intended to fulfill the functions of protection, enable handling of the product, and be the vehicle used between factories, distribution centers etc., and not primarily for individual consumers (i.e., as sales packaging). In the packaging hierarchy [11], this type of packaging would correspond to secondary and in some cases tertiary packaging. Different terms are used in the literature for this type of packaging; business-tobusiness packaging, transport packaging, distribution packaging, and industrial packaging. The packaging term used for these functions in this study is industrial packaging. [1] denote the packaging which remains in an industrial supply chain industrial packaging. This view also applies to the study reported on here. As [9] pointed out, the terms packaging and package are considered synonyms. In this research, the abbreviation for product and packaging system is PPS, referring either to an existing PPS or to
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_40, © Springer-Verlag Berlin Heidelberg 2011
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a potential PPS. The term PPS set-up should be interpreted as the supply chain context in which the product and packaging system is handled and its encounters with the logistical layout. In turn, the logistical layout should be interpreted as the arrangement of the logistics activities on operational and executional levels. 2.2
Process mapping
The use of process mapping is important as a tool for process improvement by gaining an understanding of the activities performed by an organization [12,13]. This tool is powerful in discovering interactions and relationships between activities in different functions [13,14], in defining the current state of operations, and in identifying problem areas where improvements can be made [12]. Enhancement in process performance and thus customer satisfaction can be achieved by identifying activities that increase productivity and reduce inefficiencies, duplications, process cycle times and costs [13]. [14] describe a process as “a repetitively used network of activities linked in an orderly manner. The activities employ identified information and resources for transforming ‘input objects’ to ‘output objects’, extending from the point of identification to that of customer satisfaction.” The work of describing processes is commonly denoted process mapping, where previously invisible processes are brought forward [14]. 2.3
Packaging purchase
The packaging design can influence the productivity and cost efficiency of materials handling [4]. More than 10 years ago, [4] showed that the dialogue with the packaging supplier on new packaging solutions and decisions were taken care of by a purchaser or a designer. [4] noted a need for developing the organization around the transport packaging. The study also demonstrated that most companies view packaging as a cost driver, instead of realizing the cost savings it can give rise to along the supply chain. [4] also found that there is often a lack of knowledge on how packaging affects the logistics system and vice versa. 2.4
Theory on the packaging development/selection process
[9] identified a number of theories of the packaging development process and found that they were not generic, were inconsistent in the distinguishable phases of processes, were composed of fragmentary interrelations between activities in the process, differed in decision gates, and were too old to be compatible with today’s technology. Based on the review of existing literature on packaging development processes, [9] provided an interesting approach to them for packaging manufacturers (i.e., packaging suppliers). Her aim was to present an integrated packaging and product development process [15]. She proposed a generic packaging development that deals strictly with applying the traditional aspects of product development on the packaging development process [9]. Considerations of underlying aspects such as cost and lead-time aspects for each stage are absent. Neither supply chain and logistics considerations nor their associated costs are included in her reasoning to obtain a packaging solution. [8] claimed that the integrated processes had potential to enhance the utilization of resources, however, with regards to what resources and from what aspects are not elaborated on. In addition, [15] argued that an ultimate product and packaging system meets the demands on transport, handling and storage, however, the perspective of the demands are not clear. According to [4], few packaging are selected based on where and how the packaging is handled. Results from the [10] study discussed practitioners’ views of the processes used for packaging selection and indicated that the employment of an integrated product and packaging development process at manufacturing companies reduced costs. The empirical findings that packaging suppliers are involved after the manufacturing companies have decided on a PPS, are discussed
by [9]. This time of involvement is not in line with the theoretical rationales and benefits of a concurrent packaging and product development process discussed by [6,9]. [16] addressed the aspect of sub-optimization of functional areas: “If logistics were involved earlier, it would be much easier to make cost trade-off decisions between the logistics implication of a particular design.” The system-wide assessment of the trade-offs brought about by logistical and packaging activities are emphasized by [1]. A packaging that is incompatible with its product and encountered environment in terms of poor adjustment and insufficient protection causes consequenses such as poor filling rates and product damages, all of which have economical and environmental impacts on individual activities as well as on the overall supply chain. [16] provide illustrative examples of incompability. For the selection of industrial packaging [1] addressed consideration to efficient use of materials, low environmental impact, and product protection. However, compatibility with the encountered environment was not stressed in relation to the selection. 3 3.1
METHODOLOGY Research methodology
Case studies were used to gain insight into the industrial packaging suppliers’ processes and driving forces. This approach was selected given the scarcity of theory elucidating these processes in the literature. This gap in insight gave rise to research questions of an explorative nature and the decision to apply a case study approach with multiple cases. It was felt that such an approach would best answer the two research questions, since the case study approach is advocated when the present theory is insufficient or when a research field is new [17]. Case studies are highly suitable when the research focuses on contemporary phenomena in its real-life contexts, and when the boundary between the context and the phenomena is uncertain as [18] maintains. These circumstances are true for the processes investigated in this study. In order to answer the RQs empirical data was collected from three major packaging suppliers that offer industrial packaging on a global scale. It was carried out by the use of semi-structured interviews, secondary data (internal documents), and observations (field trips), which enabled validation through triangulation. There was a total of nine interviewees: four from Company A, two from Company B, and three from Company C. After completing the data collection, the interviews were transcribed and data analysis was carried out. Analysis was performed in line with [19] open coding and relational analysis. An inductive influenced research approach was used to investigate the processes and rationales behind the industrial packaging selection process. The case sampling was based on the desire to include packaging suppliers offering one-way packaging solutions, returnable packaging solutions, and both kinds of packaging solutions. This particular sample selection was selected to give insights into the reasoning behind diverse packaging selection processes. To ensure and evaluate the quality, relevance, and rigor as well as the reporting of the study per se, the author has striven to consider the framework developed by [20]. 3.2
Case descriptions
This section presents the characteristics and activities of the three participating case companies. Case Context Company A Case Company A provides traded products (i.e. packaging commoditites) and company designed products (i.e. tailored packaging). The company has grown from a local packaging
Sustainability in Manufacturing supplier to its current position as a global supplier to manufacturing companies. Company A has an abundant number of material suppliers spread around the world and its customers represent manufacturing companies active in most market segments such as automotive, electronics, apparel, and third-party logistics firms. Its design center is located in Scandinavia. Company A’s major strength is in its competence to develop highly specific customer tailored packaging solution. The offering of generic packaging solutions is limited. The packaging solutions offered are one-way and returnable B2B packaging solutions. Its packaging solutions are employed in the customers’ inbound flow of small items and outbound flow of larger processed items. Case Context Company B Case Company B provides returnable business-to-business packaging solutions to a number of different markets such as automobile, food and beverage producers, apparel industry, and delivery firms. It has its own design center located in Europe. Company B advocates that its deep knowledge of packagingrelated issues in numerous markets enables cross-fertilization and thus gives it a competitive advantage. Its packaging solutions are used for inbound transports, for example deliveries between the customers’ component supplier (tier 1 supplier) to its customer’s production plant. The packaging solutions provided by Company B are also frequently used for outbound transports, for example from distribution center to retailer. Case Context Company C Case Company C is a global market leader in its segment of packing solutions. The company provides business-to-business and sales packaging. Company C sells almost exclusively one-way packaging. Its customers and their products operate on a variety of markets, including food producers (B2C), appliances (B2C), and heavy industry (B2B). Often the industrial packaging has dual functions and is also used as sales packaging. The trend is towards sales packaging since the transport packaging is assigned a dual function in terms of fulfilling marketing and protective purposes. 4
RESULTS
The following section provides answers to the first research question: What processes and rationales are used when packaging suppliers suggest an industrial packaging solution? The answers to the second research question, What is the nature of the ongoing dialogue and the state of involvement between the industrial packaging suppliers and the manufacturing companies to arrive at a satisfactory packaging solution? are dealt with in 4.2. 4.1
Process mapping of the logistics flow
The particular activities observed in the process mapping for each of the industrial packaging supplier are presented in Table 1. The processes for finding an industrial packaging for a new product introduction or an existing product are not separated in this study. Company A Upon a request to find a packaging solution for a customer’s product, the approach is to identify the perceived needs or problems. This is followed by collection of facts by means of process mapping and assessment of the existing characteristics of the product to be packed and the PPS’s encounters and setups that are affected by the industrial packaging set-up. Aspects that include the PPS encounters and set-up are conditions during transport and handling (i.e., supply chain data collection). The more specific information the customer provides, the better the packaging solution. Following the mapping of customer information and an understanding at Company A for the current PPS set-up
231 (see Table 1 for investigated aspects) Company A contrasts the processes activities and associated costs with potential PPS setups that would mitigate the identified cost drivers. Company A experiences that its customers are often not in control of the costs of transport that arise due to packaging. Thus, the reduction of transport volumes, a concrete measure that implies reduction of the costs, is especially addressed by Company A. By means of the financial assessment cost drivers and potential cost reductions are identified, a final PSS set-up proposal is presented and further refinements of the solution are discussed with the packaging technician at Company A. The information provided by the producing company and the professional experience of the sales person directs him or her in the direction of one-way or returnable packaging. In those cases where the choice between a returnable and one-way PPS set-up is uncertain, the results of the logistics cost analysis displays the break-even point for the two set-ups and enables a factually based selection. In addition to the logistics cost analysis, Company A wishes to depict environmental savings in the processes of developing an industrial packaging explicitly for its customers. This is to be done by the use of an environmental tool that enables calculations of the amount of carbon dioxide emissions in relation to the different packaging solutions and their associated activities. The implementation of this system is meant to give the customer facts in order to make a conscious decision. The decision on a generic versus tailored industrial packaging is largely determined by the cost of constructing and manufacturing. From a Company A point of view the potential industrial packaging ought to be cost effective from the standpoints of the manufacturing per se, transport and handling. Employment of the logistics cost analysis includes these activities with associated costs. A number of 500 packing items is considered a large order and hence warrants a tailored packaging solution. Company B Company B’s efforts are initiated by identifying the problem or a changed/new flow and the associated causes and effects that the customer experiences. The company typically found that the customer’s description of the problem is most often not as comprehensive as needed for full insight and/or includes less relevant aspects. Hence, the information is extracted that is considered important. If needed, additional information is collected by Company B in order to understand the parts of the customer’s supply chain through which the PPS will pass. The information collected by Company B is classified in two categories; characteristics of the product to be packaged and flow analysis of the prevailing PSS encounters and operational activities. Company B often assesses and evaluates the existing activities in the supply chain by on-site observations. In the logistics cost analysis two or more PPS set-ups are scrutinized and compared to demonstrate the most profitable PPS. From a profitability point of view in terms of the operational activities mapped, either returnable set-ups with different features are compared with each other or a returnable and a one-way set-up is compared. Company B stresses that the customer is often not aware of the pay-off time and initial investment of the prevailing PPS. Going further into details, the process mapping of the flow analysis identifies the activities occurring at each supply chain actor. Subsequently, by means of a logistics cost analysis, the costs each activity incurs are identified and appraised. The costs for the same types of activities are aggregated and placed in the specific cost pools of operational cost, cost of handling, fixed costs, technical cost, and economical cost. The process mapping and the data on the product make up the basis for constructing and suggesting a first packaging solution to the customers. Further dialogue between the packaging supplier and the customer that
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result in the final PSS decision is described in 4.2. Company B stresses that customers often keep track of the costs of material and transport but lack knowledge on the costs of handling. The production tool needed for a new packaging solution consumes considerable resources in terms of initial investments and a long lead time. If the product to be packaged and its supply chain require extensive transport protection, the machinery for a new tool is invested in. On the other hand, if the product to be packed allows, Company B directs its customers to already existing packaging solutions enabling financial leverage. Additionally, evaluations of the needed annual amount of industrial packaging guide in the direction of generic or tailored industrial packaging. Company C Company C provides industrial packaging which often is synonymous with sales packaging, that is, the same packaging is used for transport as well as for display at the retailer. This combined field of use puts additional conditions on the packaging in terms of marketing features (graphics, information, etc.) and product protection. To succeed in developing a satisfying packaging solution, the customer has to reveal the goal for which it is striving for. Company C collects characteristics of the product to be packaged, maps information about its market place, and information on its supply chain from the origin to point of delivery with an emphasis on the functional areas at the customer company that are affected by the PPS set-up. Table 1 presents data collected about the product and information about the activities concerning the mapping of the supply chain set-up. The development of a packaging solution is partly dependent on the activities that emanate from the different functional areas in the customer company, (i.e., production, construction, logistics, marketing, and sourcing). Trade-offs of the activities serve as the basis for a proposed packaging solution (see more on this in 4.2). Hence, the packaging selection is based on function versus cost. Company C argues that the process of investigating and evaluating the prevailing packaging solution is an iterative process, and thus is after some time reinitiated. For Company C to meet its customers’ expectations, needs, and requirements and yet be cost effective, a tailored industrial packaging solution is considered the appropriate route. For a generic packaging solution the service dimension and tailored competence is somewhat lost. The annual amount and demand for industrial packaging determines whether a generic or tailored industrial packaging is ordered. An edition of 500 industrial packaging items per annum is considered a low number and the tailored packaging solution design may be too costly.
Product information Type of product Weight Dimensions Sensitive features Scratches Bumps Static electricity Mapping of supply chain Market Characteristics Type of market Market analysis Competitor analysis General Process Characteristics Overall mapping of SC activities Product encounters along SC
A X X X
COMPANY B X X X
X X X
X X X
C X X X X
X X
No. of items per packaging No. of packaging annually Project duration Savings post project Returnable packaging Initial investment Cycle time Cleaning Pay-off time Technical life expectancy Dim. of packaging solution Collapsible property Non-collapsible property Volume of return transport Moist milieu Transportation Mode of transport Filling rate Transport distance Transportation duration Cost due to packaging weight Dim. of transport vehicle Cost of transport Packaging pattern Third party logistics provider Handling Unfolding Filling/Packing Human Automatic Transporter Transhipment Distribution center Unpacking Human Automatic
X
X X
X X X X X X X X X X X X X X X
X X X X X
X X
X X
X X
X X X X X
X
X X X X
X X X X X X X
X X X X X X X X
X X
X X
X
X X X
X
X X X
X X
X X
X X X X X X X X
End of life Sales packaging Visual exposure Information Differentiated features
X X X
Table 1: A summary of the product characteristics and mapping of supply chain activities and parameters. 4.2
X X X
X
Unique characteristics Origin & Destination Packaging solution Mending Dim.of existing packaging Thickness of walls Volume Length/height/depth
The supplier involvement
–
customer
dialogue
and
phase
of
All case suppliers stress the need for a close and continuous dialogue with the customers for a successful PPS set-up. Additionally, all three suppliers emphasis the benefits of letting their competences work out a packaging solution in parallel with the construction of the product to be packaged, or at least before the intended date of launching the product. Often the customer companies provide specification of requirements of the product to be packaged and if there is a need of complementary data, the
Sustainability in Manufacturing packaging suppliers gather this. The more restrictions they are given, the less creative they can be. Company A Given the diversity of Company A’s customers and each product’s individual demands on its packaging solutions, an individual set of information is needed to serve these demands. The main orders at Company A are for tailored packaging solutions. To be able to construct a tailored solution the information provided by the customer needs to be comprehensive. Company A stresses that in cases where the customer does not provide the necessary information, the packaging supplier possesses skills and experience to estimate plausible parameters. However, lack of correct data extends the lead time. Often, the different functional areas at Company A discuss with the corresponding functions at the customer company. This approach reduces misunderstandings since there are no intermediaries. In this way, the response is immediate and the lead time decreases. Company A would like to be involved in collaboration with the customer company’s construction department at an early stage. Improvident orders may impose higher costs than necessary, particularly when there is a need for an advanced packaging solution and the material used is traded. Company B During the ongoing dialogue between the customer and Company B, the supply chain mapping falls into place piece by piece. In discussion between the two parties about concrete packaging solutions and their feasibility with the supply chain, light is shed on additional aspects that have not been taken into account earlier. This case often appears since the professionals working with them consider the PPS set-up to be general and well known and thus taken for granted. Further discussion and refinement of the packaging solutions enables a better degree of accuracy and finally the optimal packaging solution is identified. In case of insufficient information from the customer, Company B feels that the experience from diverse business areas many times enables them to make reasonable and often surprisingly accurate assessments and estimates of costs of supply chain activities. Additionally, Company B often experiences that the contact person at the customer company is not the correct one. The person shares the basic information about the product, but often lacks the logistics aspects. Company B is aware that the customer company’s sourcing function often terminates the business opportunity based on price arguments, even though the collaboration has proved to be fruitful. Company B is often involved late in the customers’ production phases, though more and more companies have realized the benefits of involving the packaging supplier early on in the chain to enable optimization of transports, production etc. Company C To open up for contemplation and to direct focus away from the cost of investment of the packaging in the customer company’s sourcing function, Company C has a tradition of actively involving the different functional areas of production, design, logistics, marketing, and sourcing in the customer company. In doing so, the needs and requirements of each of the functions are emphasized. By making the costs that are incurred along the supply chain activities visible during the conditions of different packaging systems, people in the sourcing function become aware that escalated costs for some activities result in reduced costs elsewhere in the supply chain, and that the total sum ends up lower than for the current packaging. According to Company C, this approach achieves the most optimal packaging solution based on the trade-offs of the different functions’ requirements and needs.
233 Company C perceives that the interest in largely involving the packaging supplier in the process has grown. Yet, the most common case is that the customer companies come to realize a need for packaging after the product has been developed. 5 5.1
DISCUSSION Process mapping at the packaging suppliers
In accordance with [9], the findings of this study identified that the packaging suppliers do not acknowledge the use of theoretical packaging development and selection processes. Instead, their experience provides good guidance on how to progress in the process of developing packaging solutions for customer satisfaction. Judging from the findings from the three packaging suppliers, there are differences in the way of mapping the supply chain activities. The companies provided different levels of details on the mapping of the supply chain (Table 1). In line with [13,14], all three packaging suppliers use the process mapping technique to comprehend interactions and relationships between the activities of different functional areas. All three packaging suppliers focus on the aspects of costs in the mapping, though from different angles. The desired output result is common for all three companies; to cut the customers’ direct and associated packaging costs as regards the supply chain activities. The need for the logistics cost analyses seem to originate from the lack of knowledge at the customer companies on what drives costs. This finding is in accordance with what [4] addressed. Company B categorizes different activities and associated costs. These costs are interpreted as being linked to different functional areas for the use of a particular PPS set-up. Company C employs a somewhat different approach in that the compromises agreed on between the different functional areas are the foundation for the packaging selection. Comparison of the current PPS set-up to a potential one suggested by the packaging supplier is the main focus in the three industrial packaging suppliers’ mapping processes. The rationale to involve supply chain actors, that are affected by the packaging in the packaging decision is in line with [1]. As found in the study, when there is an uncertainty whether to select one-way or returnable packaging, the output of the logistics cost analyses at Company A and B display the break-even point for the two alternatives. Except for the analysis of the cost driven activities the selection of one-way or returnable industrial packaging may be influenced by other driving forces, such as what set-up is most cost effective and profitable from a packaging supplier’s production point of view. None of the industrial packaging suppliers claim to be purely a provider of commodities, but of tailored packaging solutions. All suppliers stress that their added value comes with a tailored packaging solution and not with a commodity. The cost of construction, manufacture, and the requirements of the product to be packed partly determine whether a tailored or generic industrial packaging is to be used. It is constantly a matter of function versus cost. The three packaging suppliers can be split in two groups that represent differences in the opinion on what quantities of packaging items justify scale of economy. Interestingly, the sales packaging perspective at Company C clearly distinguishes itself from the industrial perspective at Company A. Company B indicates the same stance as Company A. Hence, it can be assumed that the cost level for developing an industrial packaging is higher per item than for developing a sales packaging. 5.2
Intercompany dialogue and involvement
Regarding the engagement of different functional areas in the development of an industrial packaging, Company B seems to have a reactive approach, Company C a proactive approach and
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Company A somewhere in between. The findings of [9] that the manufacturing company decides on the PSS and then involves the packaging supplier do not correspond to the findings of this study. Rather, depending on the degree of advancement of the packaging function at the customer, there is a continuum of information given by the customer. An advanced packaging function might provide a well-specified order and the need for the suppliers’ investigative competence is outplayed. At the other end of the spectrum, customers are found that possess little knowledge in packaging matters and rely on the packaging supplier to provide all input. The intercompany priorities and commitment to packaging, and the priority of its function often reflects the knowledge of packaging professionals dealing with the packaging suppliers and the level of existence of a logistics organization advanced in packaging matters. Companies A and B explicitly argue that not too seldom the contact person at the customer company lacks adequate and necessary insight to provide the answers needed. Often purchasers are asked to arrange for an industrial packaging solution; this experience is supported by [4]. The purchaser focuses on what suppliers qualify, but do not have skills in trade-offs that come with the different PPSs offered by different suppliers. The price of the packaging solution is often why the business opportunity fails; companies do not look for trade-offs in different areas. All case companies claim that their involvement early on in the development of the product is cost beneficial especially as regards the utilization of resources. This is in coherence with the findings of [8] that argued that an integrated product and packaging development process would increase resource utilization Company B expresses that one way to ensure early involvement is by building relations and trust. The manufacturing customer’s insight into the positive supply chain effects that come with the collaboration of the packaging and product development functions is supported by [6]. 6
CONCLUSIONS AND FURTHER RESEARCH
The lack of a PPS set-up perspective at the customer companies, in addition to the mindset of the function of the packaging and the importance of its impact, seem to play a role in the priorities of the packaging skills at the manufacturing company. How important is it to have a fully compatible PPS? The industrial packaging suppliers must ask the right questions to find out all the relevant conditions, to be able to design a packaging that satisfies all the needs for the intended encounters and activities in the flow. The data collection would speed up and be facilitated if the customer companies prioritize having people and functions with the right competence engaged in the work. Management needs to ensure that competent staff is dealing with packaging requirements and the dialogue with the packaging supplier. Otherwise, lead time and costs increase. Manufacturing companies that view packaging and the packaging selection as prerequisites to manage to get their products to the right place, in the right time, in the right condition, and at the right price, open the door to potential cost savings. For further research it would be interesting to investigate which function influences the industrial packaging selection the most. According to [21], the sales packaging decision is mostly influenced by the marketing function. Regarding the industrial packaging decisions, however, this study indicates that the opinion of the sourcing function seems to carry the main weight. If this is the case, is that the adequate way? 7
ACKNOWLEDGEMENTS
The author wishes to genuinely thank the participating case companies, the supervisors, and Vinnova, the Swedish Governmental Agency for Innovation Systems for funding.
8
REFERENCES
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Applying Functionally Graded Materials by Laser Cladding: a cost-effective way to improve the Lifetime of Die-Casting Dies 1
1
1
2
2
Sebastian Müller , Helge Pries , Klaus Dilger , Sörn Ocylok , Andreas Weisheit , Ingomar Kelbassa 1
3
Institute of Joining and Welding, Technische Universität Braunschweig, Braunschweig, Germany 2 3
Fraunhofer Institute for Laser Technology ILT, Aachen, Germany
RWTH Aachen University, Chair for Laser Technology, Aachen, Germany
Abstract The lifetime and economic methods of improving the lifetime of a die-casting die are key factors for the die-casting industry since the global competition and the competition with other production techniques have increased significantly. The article depicts the approach, technical realization, latest results and process limitations of the INNOGRAD research project. Here, powder-based laser-cladding is applied to combine two materials as a functionally graded material. By applying gradient structures it is possible to fit the material to the predominant type of exposure, resulting in an increase in the lifetime of die-casting dies. Keywords: Graded Material; Laser Cladding; Die-Casting
1
INTRODUCTION
Pressure die-casting is a cost-efficient technology for the massproduction of aluminum components and has experienced growing application over the last two decades. At present, the manufacturing process plays an important role for the development of weightreduced parts for the automotive industry. Parts, produced by high pressure die-casting are mostly characterized by a wide area of application and high integration functionality. At present, the die-casting industry is subjected to strong cost pressure, due to a highly competitive environment. To ensure the role as a widely-used manufacturing process in the upcoming decades, the high-pressure die-casting industry must further meet the challenges of an increasing competition with other manufacturing techniques. In this context, the lifetime of the diecasting die has an important influence on the competitiveness of the die-casting industry, since this is directly related to the manufacturing costs. Up to 50 % of these costs are spent on production, maintenance and repair of the die-casting die. This has led to an increasing interest in optimizing the lifetime of a diecasting die. Numerous efforts were made in order to enhance the lifetime of these tools. Special grades, tailored to the predominant failure mode of die-casting dies have been developed. However, these materials are cost-intensive because of the higher amount of alloy elements and/or advanced refining technologies (e. g. electro slag remelting). Another approach is the improvement of welding technology in order to achieve a high quality repair welding. Due to process limitations of conventional welding processes (e. g. TIG welding) welded areas often exhibit poor material quality. Applying functionally graded materials by laser cladding enables the combination of materials designed for a specific wear pattern with a smooth transition between base material and coating. In respect of this technology, materials can be applied which exhibit a distinguished high-temperature strength at layers close to the surface and fracture toughness at layers beneath the surface.
2
BASICS
2.1
Laser cladding process
The laser cladding process is a powder-based application to build up 3D parts layer by layer. The powder can be fed either off-axially or coaxially. Figure 1 (b) shows a discontinuous coaxial powder feed nozzle used for the experiments. Three individual powder gas streams are fed into a powder gas stream focus on the surface of the substrate [1].
Figure 1: Principle of laser cladding process (a); discontinuous coaxial-powder feeding (b). The powder is fed to the interaction zone by a carrier gas stream (helium or argon). The laser beam melts the powder material particles and a thin layer of the base material. After solidification, a layer with a metallurgical bonding to the substrate is produced. By adaption of the process parameters such as velocity, powder feed rate and laser power, the thickness of the built layer can be varied typically from 0.1 up to 3 mm in a single pass. With multi-layer cladding, thicker layers can be produced. This method is used for the production of graded layers. The main advantages of the laser cladding process are high precision, a minimized heat affected zone (HAZ), low distortion of the base material and the variety of materials [2]. Nearly every metallic material can be cladded. Low melting alloys based on aluminum can be used as well as cobalt-, nickel- or titanium-based alloys, intermetallics (e. g. titanium aluminide) and high melting point metals, such as tungsten.
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_41, © Springer-Verlag Berlin Heidelberg 2011
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236 2.2
Sustainability in Manufacturing Predominant failure modes of a die-casting die
In order to improve the lifetime of a die-casting die and to carry out repair welding works qualitatively, it is essential to know the predominant failure modes and their effect on the tool surface. Tools used for die-casting dies are subjected to high cyclic temperatures and mechanical loads. The high thermal loading of the die surface leads to a degradation of hardness and strength of the material [3]. Heating and cooling cycles cause thermal expansion and contraction of the material, generating strain fields which lead to a significant plastic deformation. The exceeding or accumulation of plastic deformation leads to failures at the surface of the die-casting die. The resulting failure modes are called heat checking and gross cracking [4]. Gross cracking is the result of a groove in the material. The groove can result from the geometrical shape of the tool surface or from the metallurgy of the material (e. g. segregation) [5]. The predominant case in die-casting applications is gross cracking, resulting from small radii, as shown in Figure 2 (a). Local stress exceeds the strength of the material; a total failure of the material is the result in these areas. Due to stress relief, only single cracks occur in critical regions. However, these cracks are relatively long and have a high crack propagation velocity.
The research project started with a review of boundary conditions for functionally graded materials. As a main outcome of phase one, a list of adequate materials is generated. The materials shall possess at least one mechanical property which exceeds the performance of conventional tool steels (e. g. the yield strength at elevated temperatures, fracture toughness, hot wear resistance). Phase 2 started with basic simulations and the production of onedimensional functionally graded materials. Subsequently, investigations took place in order to characterize the microstructure and the mechanical properties of the functionally graded material. Phase 3 contains thermal fatigues tests with selected material combinations. With the results of these experiments, it can be focused on promising material combinations and the risk of total failure in real die-casting dies is further minimized. In the final phase 4, long-term tests on die-casting dies are conducted.
Heat checking can be found in areas with a high thermal load. Here, the resulting strain fields at the surface lead to a fatigue of the material instead of exceeding the strength. Since the load is applied to a larger area, the amount of cracks is higher. Cracks, resulting from heat checking have a shorter crack length. Figure 2 (b) shows the surface degradation resulting from heat checking after 8500 cycles. The material, displayed in both figures is a 1.2343 (AISI H11) in a heat-treated condition.
Figure 2: Cross section of gross cracking (a) and heat checking (b) failure mechanism at the die-casting tool surface. Another failure mode is the degradation of tool surface due to abrasive wear mechanism. Here, fractions of particles in the melt mainly silicon - cause a wear of the tool surface. This failure mode occurs especially in ingate areas of the die-casting die, since there is the highest velocity and pressure of the melt. If a repair welding is requested, the described failure modes mean different challenges in the selection of materials and welding preparation. Gross cracking sections require a material with high ductility and fracture toughness for stress relief and low crack propagation rates. Areas, exposed to heat checking require a material with a high strength at elevated temperatures in combination with an adequate ductility and fracture toughness. 3 3.1
THE INNOGRAD RESEARCH PROJECT Aim and approach of the research project
The main goal of the INNOGRAD research project is improving the lifetime of die-casting dies by applying functionally graded materials to die-casting dies. The research project began in January 2008 and will take three years. Figure 3 depicts the four phases of the research project and the participating industrial companies.
Figure 3: Procedure of the INNOGRAD research project.
3.2
Potential of laser cladding with respect to time saving and cost reduction
Figure 4 depicts the procedures for a conventional tool manufacturing process and for an alternative manufacturing process, employing laser cladding. Considering the differences between both manufacturing processes, it becomes obvious that the alternative manufacturing process lacks four cost- and timeintensive steps: the production of raw steel blocks, rough machining, stress relieving and semifinish machining. The alternative manufacturing process, however, adds two additional steps: the machining of degenerated surfaces and the application of functionally graded materials by laser cladding. A tempering is appropriate in order to relieve weld residual stresses [6]. Unlike the heat treatment of the conventional production process, only one tempering is necessary.
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Build-up process of graded material by laser cladding
For building up graded layers, two different powders are fed simultaneously into the interaction zone, where both are melted by the laser radiation. The alloy composition of the solidified layer is a mixture of both materials. By changing the powder feed rates stepby-step, a smooth transition from one component to the other can be achieved. Here, Figure 5 serves to visualize the build-up process of functionally graded materials [1].
Figure 5: Schematic drawing of the build-up of graded layers. 4 4.1
RESULTS Metallographic results
During all steps of the experiments, the quality of the processed graded materials is investigated by various methods. The first step is the characterization of the microstructure by optical microscopy to ensure that the cladded layers are assembled without any cracks and only low porosity. Figure 4: Comparison of manufacturing processes. 3.3
Functionally graded materials for die-casting applications
Materials, designed for die-casting applications should exhibit the following properties: a high toughness and ductility in all regions, a high strength at elevated temperatures and a high resistance against tempering [3]. However, materials designed for a high strength often lack an adequate toughness and ductility. Conversely, materials with a high toughness and ductility are not able to achieve a high strength at elevated temperatures.
Figure 6 shows the combination of Marlok and Dievar in a onedimensional gradient. The gradient structure, mentioned in Figure 5, becomes visible in this cross section. The gradient structure exhibits a very low porosity and no cracks.
Thus, when designing a functionally graded material for die-casting applications, the idea is to combine materials with a high resistance against heat checking with materials, exhibiting a high toughness and ductility. Considering these boundary conditions, the use of a martensitic chromium hot work tool steel (Uddeholm designation DIEVAR) and a nickel maraging steel (Metso designation MARLOK) turns out to be a promising combination for die-casting applications. The DIEVAR material is designed and has been proven to exceed the properties of standard martensitic hot work tool steels as regards heat checking and gross cracking resistance [7]. Hence, it is predestined to serve as the outer layer of a functionally graded material. The MARLOK material is characterized by excellent fracture toughness, good strength at elevated temperatures and a good weldability [8]. In respect of its high fracture toughness, this material serves as the buffer layer between the top layers of DIEVAR and the base material (AISI H11). The following Table 1 provides the alloy composition of the two special grades and the alloy composition of the base material. material
Cr
C
Mo
Si
Ni
Co
Fe
Ti
V
Mn
1.2343
5,0
0,4
1,3
1,0
-
-
base
-
0,4
0,4
Dievar
5,0
0,4
2,3
0,2
-
-
base
-
0,6
0,5
Marlok
0,3 0,01 4,5
-
0,1
0,1 14,0 10,5 base 0,2
Table 1: Alloy composition of selected materials.
Figure 6: Cross section of one-dimensional gradient. After having assured the quality of the microstructure, the next step is the characterization of hardness. In order to get a twodimensional information of the hardness distribution, the ultrasonic contact impedance (UCI) hardness testing method is used. Figure 7 shows the two-dimensional hardness distribution before the heat treatment. The hardness of the base material is about 450 - 500 HV 1, which is a typical value for die-casting dies. At the transition between the base material and the buffer layer, the heataffected zone (HAZ) becomes visible. For a length of about 400 µm, the hardness rises up to 600 - 650 HV 1. This hardness increase is not critical since the area is located far from stressed regions at the tool surface. The following three buffer layers consisting of Marlok have a hardness of about 400 - 450 HV 1. The material is in a stage
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between a soft annealed condition (hardness would be in a range of 300 - 350 HV 1) and a precipitation-annealed condition. The three buffer layers are followed by four transition layers with a composition, mentioned in Figure 5. A slight gradient in hardness becomes visible here, resulting in a final hardness of about 600 650 HV 1 at the tool surface. The areas of a higher hardness at the surface result from boundary effects and can be neglected.
Figure 9: Stress-strain curves for distinguished die materials. Figure 7: Two-dimensional hardness distribution of a functionally graded material without heat treatment. Figure 8 depicts the hardness distribution of the same gradient structure after a heat treatment for two hours at a temperature of 580 °C. The heat treatment did not affect the hardness of the base material, which is essential in order to maintain its mechanical properties. The buffer layer is in a precipitation-annealed stage, resulting in a hardness of about 500 - 550 HV 1. The hardness of the three top layers is not affected.
5
SUMMARY
It has been shown that it is technically feasible to apply functionally graded materials by laser cladding. Graded layers were built up with a smooth transition of composition, only low porosity and without any cracks. The mechanical properties of the gradient materials are superior in comparison to the conventional base material. A wide amount of different materials can be processed, since the metal powder is manufactured by gas-atomizing conventional materials. Hence, this production technology is also sensible for other fields of application like cutting tools, forging tools or tools for continuing casting processes. However, due to the limited application rate of the laser cladding process, this application is predestinated for rather small volumes. Applying functionally graded materials by laser cladding bears the potential of contributing significantly to cost savings in terms of tool manufacturing. This derives from two aspects: the higher lifetime of die-casting tools and savings, resulting from a shortened manufacturing process.
Figure 8: Two-dimensional hardness distribution of a functionally graded material after heat treatment. 4.2
Mechanical properties
Test specimens have been prepared in order to characterize the mechanical properties of the material. These experiments have been conducted with different materials and temperatures. The geometry of the test specimens is according to DIN 10002 and the test specimens have undergone the same heat treatment for two hours at 580 °C prior to testing. First, tensile tests of the basic materials have been conducted in order to gain knowledge about the mechanical properties of the top layer and buffer layer structures. Moreover, in an advanced phase of the research project, temperature-dependent tensile tests of gradient structures have been conducted in order to reveal the mechanical properties of gradient materials. Figure 9 depicts the stress-strain curve of the base material 1.2343 (heat-treated condition) and two gradient materials, detailed previously. It becomes visible that the DIEVAR material considerably exceeds the yield strength, the ultimate strength and fracture elongation of the base material. The precipitation-hardened MARLOK material slightly exceeds the yield strength and the ultimate strength of the base material. However, it significantly exceeds the fracture elongation of both materials and thus proves its qualification as a buffer layer material.
Future activities of this research project will be concerned with the advanced application of graded layers on real die casting tools and its evaluation under operating conditions. 6
ACKNOWLEDGMENTS
The authors would like to thank the Federal Ministry for Economics and Technology (project no. 16IN0598) for funding this research project. 7
REFERENCES
[1]
Ocylok, S.; Weisheit, A.; Kelbassa, I. (2010): Functionally graded multi-layers by laser cladding for increased wear and corrosion protection, in: Physics Procedia 5, pp. 359-367.
[2]
Vedani, M. (2004): Microstructural evolution of tool steels after Nd-YAG laser repair welding, in: Journal of Materials Science, Vol. 39, pp. 241-249.
[3]
Klobcar, D.; Tusek, J.; Taljat, B. (2008): Thermal fatigue of materials for die-casting tooling, in: Materials Science and Engineering A, Vol. 472, pp. 198-207.
[4]
Sjöström, J.; Bergström, J. (2004): Thermal fatigue testing of chromium martensitic hot-work tool steel after different austenitizing treatments, in: Journals of Materials Processing Technology, Vol. 153-154, pp. 1089-1096.
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Müller, S.; Pries, H.; Dilger, K. (2010): Standzeiterhöhung von Druckgießformen durch Gradientenwerkstoffe, in: Assistentenseminar Fügetechnik und Schweißtechnik, Vol. 268, pp. 49-53.
[6]
Grum, J.; Žnidaršič, M. (2006): Residual stess analysis after laser surface alloying with various powdered materials, in: International Journal of Microstructure and Materials Properties, Vol. 1, No. 2, pp. 219-230.
[7]
Liluashvili, Z.; Pries, H.; Dilger, K. (2008): Untersuchungen zur Standzeitoptimierung von Aluminium-Druckgießformen, in: Schlussbericht AiF 14389 N, p. 139.
[8]
Klobcar, D.; Tusek, J.; Taljat, B.; Kosec, L.; Pleterski, M. (2008): Aging of maraging steel welds during aluminium alloy die casting, in: Computational Materials Science, Vol. 44, pp. 515-522.
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A Total Life-Cycle Approach towards Developing Product Metrics for Sustainable Manufacturing 1
1
2
Ankur Gupta , Anshu Dhar Jayal , Michela Chimienti , I. S. Jawahir 1
1
Institute for Sustainable Manufacturing, and Department of Mechanical Engineering, University of Kentucky, Lexington, KY, USA 2
Department of Mechanical and Management Engineering, Polytechnic of Bari, Bari, Italy
Abstract This paper presents a total life-cycle approach towards developing comprehensive product metrics for sustainable manufacturing including the triple bottom line: environment, economy and society. The developed generic metrics are grouped under different metrics clusters, and are categorized across the four life-cycle stages (pre-manufacturing, manufacturing, use and post-use) of a product. This gives an opportunity to develop a leveling system for the metrics based on the presence of different metrics across multiple life-cycle stages. The development and deployment of relevant product metrics ontology is shown as a prerequisite for the ultimate evaluation and improvement in product design for sustainable manufacturing. Keywords: Product Life-Cycle; Sustainable Manufacturing; Product Metrics Ontology
1
INTRODUCTION
Sustainable products are generally defined as those products that provide environmental, societal and economic benefits while protecting public health, welfare and environment over their full commercial cycle, from the extraction of raw-materials to final disposition [1]. According to the National Council for Advanced Manufacturing (NACFAM) in the U.S., sustainable manufacturing includes the manufacturing of sustainable products, and the sustainable manufacturing of all products [2]. This signifies the importance of developing product-level metrics towards fulfilling the goal of sustainable manufacturing. Further, the U.S. Department of Commerce defines sustainable manufacturing as “the creation of manufactured products that use processes that minimize negative environmental impacts, conserve energy and natural resources, are safe for employees, communities, and consumers and are economically sound” [3]. This statement indicates the close interconnection between product-based and process-based metrics for sustainable manufacturing. The National Institute for Standards and Technology (NIST) has a well-established sustainable manufacturing group actively involved in the development of metrics for sustainable manufacturing [4]. Development of product-based sustainability metrics has been going on for a considerable period of time, and researchers in the past have suggested different ways to assess the sustainability content of a product. A large number of indicators for product sustainability are available in the literature. The triple bottom line aspect of sustainability (considering the environmental, societal as well as economic factors) is well known and accepted in the academia as well as industry. The quantitative evaluation of tradeoffs among metrics across the triple bottom line is a difficult task, and this is one area where the ongoing research at the University of Kentucky is focused. It is a challenge to define and contain the system boundaries while trying to define the interrelationships among metrics across the triple bottom-line. A total life-cycle based approach helps to meet this challenge by developing metrics within the boundaries defined by the four life-cycle stages of a product.
2
TOTAL LIFE-CYCLE APPROACH TOWARDS PRODUCT METRICS DEVELOPMENT
A total life-cycle based approach towards developing indicators as well as metrics for product sustainability is the key element of the work presented in this paper. The categorization of metrics in this way provides an opportunity to develop a comprehensive list. The four life-cycle stages of a manufactured product in a closed-loop system considered here are: pre-manufacturing, manufacturing, use, and post-use [5]. Each life-cycle stage is defined in brief to understand its range of influence. This is critical because all the indicators/metrics are placed under different life-cycle stages based on these definitions. Pre-manufacturing: The foremost stage in the life-cycle of any product is the extraction of raw material from the natural reserves. Pre-manufacturing includes mining metal ores and smelting them into metal alloys, extraction of crude oil and processing it into hydrocarbons, cutting trees and transforming them into usable wood or paper, etc. Manufacturing: It is the phase where raw materials are transformed into finished products. A wide range of processing techniques is involved in this phase based on the desirable performance characteristics that are needed for the final product. Assembly (manual or automated), product packaging, etc., are also considered to be a part of the manufacturing phase. Use: The use phase pertains primarily to the amount of time the consumer owns and operates the product. During its use stage, the product needs to be energy-efficient, safe, reliable, easy to operate, maintain and repair, etc. Post-use: The post-use stage involves the final processing of a product for disposal, incineration, recycling, remanufacturing, or other end-of-life processing. Different end-of-life options can be considered during this stage to prolong the product life-cycle and also to ensure perpetual material flow in continuous development of next generation products from successive life-cycles.
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_42, © Springer-Verlag Berlin Heidelberg 2011
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PREVIOUS WORK ON INDICATORS/METRICS PRODUCT SUSTAINABILITY
FOR
Fiksel et al. [6] were among the earliest to develop product sustainability indicators and categorize these under environmental, societal and economic aspects. This work has a good aggregation of indicators, but with no total life-cycle consideration. Kaebernick et al. [7] and Ritzen and Beskow [8] developed procedures that consider environmental effects at the design stage of product development. Schmidt and Butt [9] developed a product sustainability index (PSI), which is implemented as a sustainability management tool in the Ford product development system. A significant part of the work in the product sustainability area is based on ISO 14000 standard series, which is pre-dominantly environmental, making it more of an environmental impact assessment. Further, no categorization across product life-cycle stages is performed. Consideration of four life-cycle stages across the triple bottom line has been an area of considerable emphasis from the early work done at the University of Kentucky [10]. 4
PRODUCT SUSTAINABILITY INDICATORS
EVALUATION
USING
The six major elements of product sustainability along with numerous sub-elements identified in our early work are shown in Figure 1. These sub-elements form the basis for developing generic product sustainability indicators. Based on the requirements of different products and industrial sectors, these indicators can be selected. However, there is a need to combine these indicators and estimate aggregate scores/indices that can help to evaluate or compare different products in terms of their overall sustainability content. Jawahir et al. [11] evaluated the sustainability content of a product using a generic product sustainability index (PSI), incorporating the three aspects of sustainability and categorizing the indicators (influencing factors) across the four life-cycle stages of a product. The weights assigned to the influencing factors are approximate numbers based on their relative importance and company priorities.
Figure 1: Product sustainability wheel [10]. Gupta et al. [12] used the analytic hierarchy process (AHP) to determine the relative importance of different influencing factors and compare the sustainability content of two similar products.
AHP is a widely used mathematical technique that can prioritize a mixed group of elements with both qualitative and quantitative nature, minimizing the subjectivity involved [13]. Again, the life-cycle based categorization of the indicators is a key towards developing an AHP-based product sustainability hierarchy, as shown in Figure 2 for two different designs of a consumer electronic product (MP3 player). The life-cycle stages of a product form the Level L2 of the hierarchical structure in Figure 2. The corresponding influencing factors under different life-cycle stages are placed at Level L3 and this helps in designing a more comprehensive and accurate questionnaire, leading to a better evaluation of product sustainability content. After performing the AHP based matrix calculations, it was established that Product I is more sustainable compared to Product II. The overall priority values for all the influencing factors at Level L3 are also determined that helps in establishing the indicators that need prioritized attention. Considerable interdependence is observed while developing the influencing factors or metrics for product sustainability. The interdependence refers to the influence of an indicator in one aspect over another indicator under a different aspect of sustainability (triple bottom line). Similarly, an indicator categorized under a certain product life-cycle stage can also have influence in other life-cycle stages. AHP methodology does not take this interdependence into account. The analytic network process (ANP), which is a generalization of AHP, can be used to study this interdependence and interaction of higher-level elements in a hierarchy on lower-level elements. The feedback structure used in this methodology helps in the decision making to attain a desired future [14]. The problem is structured as a network in ANP, rather than as a hierarchy. However, analytical calculations with ANP become very lengthy and complicated for a comprehensive set of influencing factors. 5 5.1
LIFE-CYCLE BASED LEVELING SYSTEM FOR PRODUCT METRICS Life-cycle based leveling system
An interesting observation while categorizing metrics across lifecycle stages is that some of these metrics have presence across multiple life-cycle stages. This provides an opportunity to organize metrics at different levels; for example, top level (Level 1) metrics can be the ones that are present across all four life-cycle stages. Level 2 metrics are the ones that are present across any three lifecycle stages. Similarly, Level 3 metrics are present across any two life-cycle stages and Level 4 metrics are only present in one lifecycle stage. Figure 3 shows a pyramid structure representation of these different levels of metrics. Further, Figure 4 shows the organization of metrics in the form of a Venn diagram. The four circles comprising the Venn diagram represent the four life-cycle stages of a product: pre-manufacturing, manufacturing, use, and post-use. Some example metrics (environmental, societal and economic) and the interdependence among these metrics are represented (through single and double headed arrows) in the Venn diagram. It can be easily noticed that the metrics presented as Level 1 need prioritized attention because of their influence across all life-cycle stages of a product. However, no weightings are assigned to any metric at this point which makes it difficult to ascertain if a Level 4 metric needs more attention than a Level 1 metric to enhance the overall sustainability content of a product. The metrics at four different levels set up the taxonomy of metrics, which can help in the future development of product metrics ontology. Ontology, which is an explicit specification of a conceptualization [15], and its challenging application in developing the metrics for product sustainability will be briefly presented in the last section.
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Figure 2: Product sustainability hierarchy [12]. Considerable subjectivity is involved in assigning the weightings to the metrics even in their current form as shown in Table 2. Further, any weighting system will be very product-specific. The application of AHP and ANP is a good approach that can be considered for assigning weightings to the metrics. A recent ANSI workshop also emphasized the need to have decision-making processes, such as AHP, that can be applied with flexibility based on varying needs for product sustainability standards [16]. The results obtained through AHP and ANP can be integrated with the data available for the metrics to derive more science-based weightings for a specific product. The recently undertaken case studies at the University of Kentucky involve automotive and aerospace products, and these case studies are expected to provide real-world examples for evaluating the actual product sustainability content. 5.2
different measurement method under corresponding life-cycle stages. For example, if an automobile is under consideration, the ‘energy use’ during the manufacturing stage can be based on different processes and machines used. For the use stage, the same ‘energy use’ metric can be based on the miles per gallon etc. It should also be noticed that the product metrics under premanufacturing and manufacturing stages are mostly related to the processes involved with these products. The measurement method under these two life-cycle stages can be correlated to the methods defined for the process metrics. The process metrics development is not a part of this work. However, it is worth mentioning here because the close interactions between process metrics and product (pre-manufacturing and manufacturing stage) metrics can help in eliminating the redundancy involved.
Metrics clusters and product metrics
Based on the six major sustainability elements and corresponding sub-elements of product sustainability as mentioned before, a product metrics system is developed for generic products. The metrics are grouped under different metrics clusters to make them more structured. These metrics clusters have an important significance because these will help in a systematic aggregation of data while considering the complete life-cycle of a product. The proposed metrics clusters are defined for the environmental, economic as well as societal aspects, as shown in Table 1. There are 13 metrics clusters in total, with ‘end-of-life management’ being a common metric cluster across all elements of the triple bottom line. Several metrics are identified and defined under different clusters. Some example metrics are shown (along with the clusters in which these metrics occur) in Table 2. All metrics are categorized across the life-cycle stages of a product to have a detailed understanding of the influence of a particular metric. Further, a generic measurement method is defined for each metric. This measurement method needs to be customized based on the product that is being evaluated. D/L stands for ‘dimensionless metric’ under the unit column in Table 2. The life-cycle based categorization of the metrics indicates the challenge involved in performing a complete sustainability evaluation for a product. The metrics that are present across multiple life-cycle stages will have a
Figure 3: Leveling system for product metrics.
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METRICS CLUSTERS
Figure 4: Examples of interdependence among indicators/metrics across life-cycle stages. ENVIRONMENT
ECONOMY
SOCIETY
Residues
Investment
Education
Energy Use and Efficiency End-of-Life Management Material Use and Efficiency
Innovation End-of-Life Management Quality
Customer Satisfaction End-of-Life Management Safety and Societal Wellbeing
Water Use and Efficiency Table 1: Product metrics clusters. 6
TOWARDS AN ONTOLOGY-BASED PRODUCT SUSTAINABILITY
APPROACH
FOR
The analysis of developed metrics and their categorization shows that considerable interdependence exists among them across lifecycle stages and with respect to triple bottom line. It is important to account for these interrelationships when evaluating the sustainability of a product. There is an increasing trend in developing software tools for product design and development. Most common among these are product life-cycle management (PLM) tools being developed by many companies. A significant amount of data for the pre-manufacturing and manufacturing life-cycle stages of a product overlaps with the manufacturing process-level data and a good integration between product master data and manufacturing master data would be needed. PLM tools provide a good environment to start and their use could be enhanced by including the various metrics clusters and developed metrics presented here. Furthermore, an ontologybased approach is proposed and considered as a solution towards improved product life-cycle management and thus improved overall product sustainability.
Fundamentally designed and used to improve communication between either humans or computers, ontologies are a good means to enable knowledge sharing and reuse. The main aim of ontology is to make explicit the knowledge contained within software applications, as well as that within an organization and the business procedures in a particular domain [17]. Considering that the metrics presented in the previous sections need to be defined and those definitions have to be adopted and understood by enterprise applications across the manufacturing system, the development of a product ontology which also takes these metrics into account is a sound solution for assuring and proceeding towards product sustainability. The ontology is often captured in some form of semantic network with nodes representing concepts or individual objects and arcs representing relationships or associations among the concepts [18]. The metrics clusters presented in Table 1 are good examples that can act as seed points for recognizing the ontology concepts. Also, the interdependence among metrics, as explained with the help of some examples in Figure 4, will help in representing the associations among the concepts of the ontology. This ontological approach will help in the development and deployment of relevant product metrics that will appropriately determine the overall product sustainability for sustainable manufacturing. 7
CONCLUDING REMARKS
This paper presents a generic product metrics system to assess the sustainability content of a product that can help in moving towards the goal of achieving more sustainable manufacturing. The metrics clusters and the corresponding metrics are developed so as to meet the needs across a broad range of industrial segments. An emphasis has been given to the life-cycle-based approach wherein the metrics are categorized across the four life-cycle stages of a product. Some analytical tools, such as AHP and ANP, are proposed along with data gathering that can help in assigning some weighting to different metrics.
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Residues
INDIVIDUAL METRICS Emissions Rate (carbon-dioxide, sulphur-oxides, nitrous-oxides etc.) Solid Waste Stream Liquid Waste Stream
End-of-Life Management (Environmental)
Research and Development expenditure to enhance environmental sustainability/ Total research and development budget
$/$ (D/L)
Reused Product Ratio
Number of reused product units sold/ Total number of product units sold
D/L
Recycled Material Usage Rate
Recycled Water Usage Rate
Quality
Number of remanufactured product units sold/ Total number of product units Restricted material usage (Referenced from RoHS, REACH)/ Total number of product units made Amount of recycled material used (manufacturing phase)/ Total number of product units made Mass of recycled packaging material used/ Total number of product units made Net water consumption/ Total number of product units made Amount of reused or recycled water consumption/ Total number of product units made Cost of energy consumption/ Total number of product units made
PM
M
U
PU
mass/unit
Design-forEnvironment Expenditure
Energy Cost
Innovation
mass/unit
KWh/unit $/unit
Water Use Efficiency
End-of-Life Management (Economic)
Mass disposed into landfill/ Total number of product units made Liquid residues (cleaning agents, coolants etc.) generated/ Total number of product units made
Average maintenance (repair) energy per product unit over the use phase/ Total number of product units made
Packaging Material Usage Rate
Cost
mass/unit
Maintenance/ Repair Energy
Restricted Material Usage Rate
Water Use and Efficiency
Total Emissions (summing up different types of emissions and assign any weighting if possible)/ Total number of product units made
KWh/unit $/unit
Remanufactured Product Ratio
Material Use and Efficiency
UNIT
Net energy consumption/ Total number of product units made
Energy Efficiency Energy Use and Efficiency
MEASUREMENT METHOD
D/L mass/unit mass/unit mass/unit gallons/unit $/unit gallons/unit $/unit $/unit
Product Operational Cost
Costs involved during the product operation over its life-span per product unit
$/unit
Product Maintenance Cost
Maintenance/repair costs involved during the product use over its life-span per product unit
$/unit
Legal Cost
Costs incurred on legal disputes (involving employees and customers)/ Total number of product units made
$/unit
Average Disassembly Cost
Total disassembly cost/ Number of product units disassembled
$/unit parts/unit
Reused Product Profit
Average profit per reused product unit sold
$/unit
Remanufactured Product Profit
Average profit per remanufactured product unit sold
$/unit
Design Life
Average designed life-time of the product (compared with similar competing products)
Hours
Research and Development Cost
Research and Development costs on product sustainability related initiatives/ Total number of product units made
$/unit
Life-cycle Span
(Actual average life span of the product unit)(Designed average life span of the product unit)
Hours
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Education
Customer Satisfaction
End-of-Life Management (Societal) Safety and Societal Well-being
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Employee Training and Development
Average employee hours spent on training (related to product design, manufacturing, safety, quality etc.)/ Total number of product units made
Hours/unit $/unit
Repeat Customer Ratio
Number of repeat orders for the product/ Total number of product units sold
(D/L)
Product Return Rate
Average products units returned/ Total number of product units sold
(D/L)
Ease of Sustainable Product Disposal
Average cost involved in sustainable product disposal per unit of product disposed
$/unit
Ease of Product Takeback
Average cost involved per unit of product takeback after first-life of the product
$/unit
Product Processing Injury Rate
Average number of injuries during product processing/ Total number of product units sold
incidents/unit $/unit
Product Use Injury Rate
Average number of injuries during product use/ Total number of product units sold
incidents/unit $/unit
Table 2: Product metrics under different metrics clusters categorized across the life-cycle stages of a product. Case studies on major automotive and aerospace products are underway to apply the metrics system. Further, an ontology-based approach is suggested as a pre-requisite for continuous improvement in evaluating the sustainability content of manufactured products. 8
Schmidt, W.P.; Butt, F. (2006): Life-cycle tools within Ford of Europe's Product Sustainability Index, Int. J. Life-cycle Assessment, Vol. 11, No. 5, pp. 315-322.
[10]
Jawahir, I.S.; Dillon, O.W.; Rouch, K.E.; Joshi, K.J.; Venkatachalam, A.; Jaafar, I.H. (2006): Total life-cycle considerations in product design for sustainability: A framework for comprehensive evaluation, Proc. 10th Int. Research/Expert Conf. (TMT 2006), Barcelona, Spain, pp. 110.
[11]
Silva, N.D.; Jawahir, I.S.; Dillon, O.W.; Russell, M. (2009): A new comprehensive methodology for the evaluation of product sustainability at the design and development stage of consumer electronic products, International Journal of Sustainable Manufacturing, Vol. 1, No. 3, pp. 251-264.
[12]
Gupta, A.; Vangari, R.; Jayal, A. D.; Jawahir, I. S. (2010): Priority evaluation of product metrics for sustainable manufacturing, Proceedings of the 20th CIRP Design Conference, Nantes, France, April 19-21.
[13]
Saaty, T.L. (2008): Decision making with the Analytic Hierarchy Process, Int. J. Services Sciences, Vol. 1, pp. 8398.
ACKNOWLEDGMENTS
The authors acknowledge the project sponsorship by the National Institute of Standards and Technology (NIST). They also would like to extend their sincere thanks to other project team members, Dan Seevers, Tao Lu, Mohannad Shuaib and Chris Stovall for their contributions. Project guidance and continuous advice of Professors F. Badurdeen, O.W. Dillon, Jr., K.E. Rouch and M. Dassisti (Bari Polytechnic, Bari, Italy) are also gratefully acknowledged. 9
[9]
REFERENCES
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www.sustainableproducts.com/susproddef.html [accessed Jan 6, 2011].
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www.nacfam.org/PolicyInitiatives/SustainableManufacturi ng/tabid/64/Default.aspx [accessed Jan 6, 2011].
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Saaty, T.L. (2008): The Analytic network process, Iranian J. Oper. Res., Vol. 1, No. 1, pp. 1-27.
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Manufacturing Engineering Laboratory, National Institute of Standards and Technology (NIST), (2009): NIST Workshop on Sustainable Manufacturing: Metrics, Standards and Infrastructure, Gaithersburg, MD, USA, Oct 13-15.
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Gruber, T. (1993): Towards principles for the design of ontologies used for knowledge sharing, Technical Report KSL93-04, Knowledge Systems Laboratory, Stanford University.
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Jawahir, I.S.; Dillon, O.W. (2007): Sustainable manufacturing processes: New challenges for developing predictive models and optimization techniques, First Int. Conf. on Sust. Manuf. Montreal, Canada.
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American National Standards Institute (ANSI) (2009): ANSI workshop toward product standards for sustainability, Workshop Report.
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Fiksel, J.; McDaniel, J.; Spitzley, D. (1998): Measuring product sustainability, J. Sust. Prod. Des., Vol. 6: pp. 7-19.
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Denkena, B.; Shpitalni, M.; Kowalski, P.; Molcho, G.; Zipori, Y. (2007): Knowledge management in process planning, Annals of the CIRP, Vol. 56, No. 1, pp. 175-180.
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Kaebernick, H.; Kara, S.; Sun, M. (2003): Sustainable product development and manufacturing by considering environmental requirements, Robotics and Computer Integrated Manufacturing, Vol. 19, pp. 461-468.
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Huhns, M.N.; Singh, M.P. (1997): Ontologies for agents, IEEE-Internet Computing, Vol. 1, No. 6, pp. 81-83.
[8]
Ritzen, S.; Beskow, C. (2001): Actions for integrating environmental aspects into product development, J. Sust. Prod. Des., Vol. 1, pp. 91-102.
Carbon Footprint Analysis for Energy Improvement in Flour Milling Production 1
1
1
Chee Wai Patrick Shi , Fatida Rugrungruang , Zhiquan Yeo , Bin Song 1
1
Singapore Institute Technology of Manufacturing,71 Nanyang Drive, Singapore 638075
Abstract From the “cradle-to-gate” assessment of one ton of plain flour, wheat production is found to contribute most to the carbon footprint of plain flour with a percentage over 60%. In particular, the flour mill production, an energy-intensive lifecycle stage, accounted for approximately 30% of the final carbon footprint results — with the milling process resulting in 40% of the production emissions. Direct GHG emissions reduction strategies proposed include the purchased wheat from suppliers who demonstrate cleaner production and are of proximity. Alternatively, carbon footprint can be lowered indirectly through making the operations more energy efficient by improving the processes and equipment used. Keywords: Carbon Footprint Assessment; Energy Efficiency; Flour Mill Production
1
INTRODUCTION
The increasing concern of climate change globally has created a wave of carbon footprint programmes developed by different countries and organizational bodies to assess the environmental performance of products and services [1]. Carbon footprint, a subset of data from Life Cycle Assessment (LCA), is utilized to quantify the impact of Greenhouse Gas (GHG). In simple terms, this methodology focuses on the analysis of GHG emissions that is deemed to cause climate changes which is one of the key elements in LCA [2]. In the carbon footprint assessment, all major anthropogenic greenhouse gases engendered within the product or service system are converted to CO2 equivalent and summed up to determine the final global warming potential which can be adopted as the baseline for improvement. Having the knowledge of this baseline is vital for operational improvement, better management of resources and improvement of efficiency. As it is the aim for Singapore government to develop the country into a low carbon society, carbon footprint assessment can be a useful tool to facilitate such a move. The paper presents the carbon footprint analysis for plain flour manufactured in Singapore. Flour generally is one of the most essential ingredients found in our daily diet. Commonly found in bread, cakes, noodles and the list runs on, it is a food source vital to many around the world. Therefore, it is critical to ensure that the manufacturing of flour is carried out in sustainable manner. Although the wheat is imported, the study encompasses the production of wheat to provide the flour mill company a holistic view of the total product system. Flour can be a Business-to-Customer product (B2C) but for this study, Business-to-Business (B2B) would be the main concern. The results generated from the study created a baseline for their manufacturing operation improvement. Using the carbon footprint analysis, the company is able to understand their operation emissions, identifying the hot spots, and adopting the suggested recommendation from the study for further reduction in their carbon footprint. The used of carbon footprint analysis as an indicator to elevate the energy efficiency for producing a product is also is also explored and investigated.
A large amount of carbon emitted is generated within the flour milling production and is attributed indirectly by the use of equipment that consume electricity. This is similar to the majority of the manufacturing industries in Singapore. Therefore the reduction electrical consumption would translate to lower carbon footprint. 2 2.1
METHODS Guidelines
The study is conducted following the guidance of the International Standards Organisation (ISO) 14040 and 14044 which are well established framework for LCA [3]. The Publicly Available Specification 2050 (PAS 2050) [4] developed by the British Standards Institution (BSI) in partnership of Carbon Trust and Department of Environment Food and Rural Affairs (Defra) is also adopted for this study. Presently, there are no standards enforced globally for carbon footprint assessment but many carbon footprint initiatives use these standards mentioned as a point of reference [1] [5]. 2.2
Goal and Scope of Study
The purpose of this study is to provide a Life Cycle Inventory (LCI) that quantifies the GHG (carbon footprint) associated with the raw material extraction, production and transportation linked to flour mill production. These stages define the scope of the study which is cradle-to-gate. The results from the study should answer the following questions:
What is the carbon footprint to one ton of plain flour?
Where are the major contributors of the carbon footprint?
What are the corresponding recommendations to reduce the emission generated?
The work conducted is to aid the flour mill company to better understand how to improve their product system in terms of carbon footprint.
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_43, © Springer-Verlag Berlin Heidelberg 2011
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Shipment to Singapore
Electricity, Fuel, Fertiliser, Chemical
Wheat Production
Transportation
Preparation Unloading f rom vehicle
Storage in Silos
Cleaning Processes
Production Wheat Milling
Wheat weigher
Finishing
Delivery
Bran
Greenhouse Gas Emission
Raw Material Production
247
Pollard
Entoletor
Flour Sampling
Flour Bin
Bulk Flour
Delivery
Packing Additives
Mixing Premix
Legend Included in the assessment Excluded in the assessment
Figure 1: System boundary of plain flour production. 2.3
The Functional Unit
The functional unit of the product system is defined as one ton of plain flour delivered to a customer in bulk container. All inputs and outputs to the product system are normalised to the production of one ton of flour. 2.4
System Boundaries
Figure 1 shows a simplified life cycle process flow for the production of plain flour. The dashed lines represent the boundaries of the product system to the environment. The processes included in the system boundaries start with wheat cultivation. The cultivated wheat is harvested and transported from two main suppliers in Australia and USA. Once the wheat reaches the flour mill plant in Singapore, it undergoes several preparation processes before milling commences. These processes include unloading the wheat from the vehicle, storing and cleaning. The clean wheat then goes through the flour milling processes where the output – plain flour – is produced. Bran and pollard are the two co-products generated during the milling processes. Subsequently, the flour goes through a mixing process and is filled in a bulk tank ready to be delivered to the miller’s customers. There are two production lines working on hard and soft wheat. Both production lines were analysed and presented in this paper. 3
LIFE CYCLE INVENTORY
The life cycle inventory analysis of one ton of plain flour involves collating resource consumption and emissions data on wheat cultivation, transportation and flour mill production in the defined
system. The two types of inventory data are required in a carbon footprint assessment, mainly activity data (resource consumptions) and emission factors (conversion per unit of resource consumption) [4]. This assessment is carried out based on the inventory data collected from various sources including specific data provided by the company as well as peer-reviewed sources, publicly available models and reports, and databases. These data sources can be categorised as primary data source and secondary data source [4]. Primary data source is gathered directly from flour milling company which is mainly actual input-output data. For example, the data from the company includes energy consumption (electricity and diesel), production volume, bill-of-material, wastage produced and location of customers. Secondary sources are those from which generic input-output data were gathered and are representative of actual data. Examples include upstream production of wheat, electricity generation from the grid and transport of the wheat crop. Details of data collection and modelling are described in the following sections according to the sequence of the product lifecycle stages. 3.1
Raw Material Production
Wheat is purchased from Australia and USA. This study has relied on peer-reviewed sources for the secondary resource profile and emissions data of the upstream wheat production. The Global Warning Potential (GWP), on a 100 year time line, of wheat production studied in Australia [6] and USA [7] are 303.66 kg-CO2 eq/ton and 283 kg-CO2 eq/ton respectively. For the wheat production in Australia, the stage of raw material production covers the pre-farm activities, on-farm activities (seeding, spraying, harvesting) and post-farm activities (transport to
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silo, silo to port). Background data on production of fertilisers and pesticides, and electricity generation are also included. However, the carbon uptake during crop growing was omitted from the study. As argued by Biswas et al [6], CO2 uptake from crop growth is not considered as much of the plant material was retained on site following harvest. It is assumed that sequestered CO2 would be rereleased with time. Similarly, soil carbon (C) sequestration is considered insignificant, and soil CH4 emission uptake was not included because of the absence of data and is expected to be low from fertilized agricultural soils. Furthermore, the regional-specific value for the proportion of Nitrogen fertiliser emitted as Nitrous oxide (N2O) is used instead of the international default values such as from Intergovernmental Panel on Climate Change (IPCC) [8] and United States Environmental Protection Agency (US EPA) GHG inventory [9]. On the other hand, the research work on wheat production in USA [7] covers production of grain farming inputs and their transport, fuel use during grain farming, machinery production, and transport of milled wheat to the processing centre and point-of-sale. However, the research further emphasizes the transport infrastructure such that the impacts from fuel production, transport vehicle manufacturing and end-of-life were also included. The data employed into our study therefore omits the transportation section and only focuses on wheat production without wheat product transportation. Furthermore, the study was interpreted on the basis of IPCC conversion factors. It also considered the transferring of carbon from the atmosphere to soil matter which can be a CO2 sink. However, an impact of soil carbon storage was discussed as having a considerably large in the uncertainty ranges. The dataset was therefore employed in this study without modification. Transportation
The wheat is transported to Singapore from Australia and USA either by bulk vessel or container ship. The average distance from Perth, Australia is 4,588 km and Portland, USA is 13,178 km. The distances are obtained by an Internet calculation interface [10]. For this study, the bulk carrier would be the transportation mode used and the assumed load carried would be 105,000 tons. The -6 emissions therefore would be 2.41 x 10 kg-CO2e per kg-km [11]. 3.3
Delivery to Customer
The finished product, plain flour, is delivered to a customer in a 15 ton bulk tank truck. In this study, the flour mill’s customer on the west side of Singapore is selected as the destination of the plain flour. The assumption is also made that the return journey for the bulk tank is empty. 3.5
Energy Data
Electricity and diesel are two energy types involved in this carbon footprint study. They are common resource inputs to all process steps along the product lifecycle particularly in production and transportation stages. The use of electricity from the power grid in turn contributes to the global warming potential (carbon footprint). This is due to the burning of fossil fuels in the extraction and generation of electricity, and must be taken into account. Power grid emissions factor of Singapore was collected from [13]. Similarly, the emissions factor of diesel was obtained from [11]. 3.6
Allocation bran and pollards as co-products
During the milling of the wheat, co-products, bran and pollard, are generated. These co-products are used as inputs to another product system. When situation of such occurs, all associated emissions should be apportioned accordingly. As per stated in ISO 14040 and 14041 [3], an allocation of input and emissions of the sub-system shall be expanded to incorporate the burden from processing the co-products. In this case, the co-products are reprocessed into bio-packaging materials. 4
RESULTS AND DISCUSSION
4.1
Carbon Footprint Assessment Results
The carbon footprint of one ton of plain flour is calculated as a sum of the inventory data related to the acquisition of wheat production, transportation, flour mill production and delivery to customer. The overall carbon footprint for plain flour are quantified as 495.07 kgCO2eq/ton from hard wheat and 467.71 kg-CO2eq/ton from soft wheat. Figure 2 illustrates the carbon footprint in kg-CO2eq/ton of each stage. 350
Flour Milling Production
The flour milling production can be categorised into three sections namely preparation, production and finishing. All activity data associated with electricity and material usage related to these sections are collected directly from the company. In the preparation section, the wheat is firstly unloaded from the bulk vessel or containers and sent for cleaning. The wheat unloaded is transported to storage silos by various unloading mechanisms before undergoing through a series of cleaning processes. An insignificant amount of waste is produced during the cleaning processes which mounted up to approximately less than 0.5% of the throughput. Similar amount of waste generated is observed for two of the production lines. Milling and mixing of the cleaned wheat are carried out in the production section. The wheat is passed through a series of machines to break open the wheat kernels and reducing of the endosperm by grinding to obtain the final product – plain flour [12]. The two production lines that handle soft and hard wheat generate a total of approximately 26% and 25% of co-products (bran and pollard) respectively. Lastly, additives of less than 1wt% are added to the flour by a mixing process. In the finishing section, the mixed flour is sent to the storage silos and is filled in bulk tank truck for delivery upon request from customers.
300
289.39 288.91
Hard Soft
250
kg-CO 2 eq/ton
3.2
3.4
200
183.62 156.77
150 100 50
21.67
21.67 0.42 0.42
0 Wheat production Transport to f lour Production of f lour mill
Delivery
Figure 2: Overall carbon footprint of hard and soft plain flour. The carbon footprint distributions for each stage to produce plain flour from hard and soft wheat are further illustrated in Figure 3 and 4. The results show that wheat production makes the largest contribution to the overall product carbon footprint which is in the region of 60%, while the flour mill production gives an important contribution of approximately 30%. The emissions due to transportation are considerably small which is about 5% for wheat transport and almost negligible for delivery to customer. Due to the land constraint in Singapore, agriculture is not the major contributor to the economy. Therefore there is a great reliance on
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imported agriculture products from overseas and wheat is one of them. The results have shown that production of wheat is the highest. Studies carried out on wheat production are greatly debatable. The two examples [6] and [7] cited in this study for wheat production show difference in emissions although their approaches in their studies are similar in many ways. The dominant emissions N2O reported in the two studies are 9% [6] and 50% [7], which is generated from agricultural soils. Other reported emissions generated during wheat production do not have such large uncertainty. There have been many studies investigating the nature and extent of N2O emissions and approaches for improvements. However, the technical details and improvement potentials pertaining to the wheat production will not be discussed under the scope of this analysis. It will require revisiting as new research in this area develops. Delivery 0.08%
Production of flour 37.09%
Wheat production 58.45%
Transport to flour mill 4.38%
Delivery 0.09% Wheat production 61.76%
Transport to flour mill 4.63%
Figure 4: Distributions of product (plain flour) carbon footprint from soft wheat. Bulk flour filler 4.71%
Unloading 2.61%
Cleaning 7.58%
Flour bin 5.70%
Figure 5: Distribution of carbon footprint for flour milling of hard wheat. On the other hand, the focus has been directed at the flour mill production stage which is of particular interest to the flour mill company. The flour mill production process is a promising area where the company has full control over when it comes to implementing carbon footprint reduction strategies.
Cleaning 8.86%
Milling 42.96%
Figure 5 and 6 show the distribution of the carbon footprint of the flour mill production of the hard and soft wheat. Approximately 40% of the emissions is contributed by the flour milling process for the two production lines. 23% to 25% of which is contributed by the flour weigher and entoletor (high speed spinning disinfection machine used to destroy possible infestation found in the flour). These two processes amount up to approximately 65% of the total carbon footprint for the flour mill production. RECOMMENDATION FOR AREAS OF IMPROVEMENT Raw Material
The largest contributor to the overall emissions of the plain flour product comes from the wheat production. Biswas et al. [6] suggests that by reducing the emissions from fertilizer could significantly impact the overall emission for wheat production. It had also been suggested that approximately 80% emissions could be mitigated from the on-farm stage by substituting chemical fertiliser for organic fertilizer, but subject to variability and further research required. Reducing the use or selecting the right choice of transportation fuel can greatly reduce the emission. Advice in the use of cleaner production strategies is given and by considering plant capacity utilisation, technology used and maintenance of the plant. Similar recommendations have been made by Meisterling et al. [7]. The authors also highlighted food miles as an important parameter, but not necessarily the most important compared to the farming practices. This statement can also be supported by the findings obtained from this study where the wheat production emissions was over ten times greater than that of transportation. Nevertheless, the upstream (and downstream) production is not within control of flour mill company. The improvement can only be achieved through purchasing decisions and policies with low carbon footprint objectives. 5.2
Milling 40.40%
Unloading 3.05%
Figure 6: Distribution of carbon footprint for flour milling of soft wheat.
5.1
Production of flour 33.51%
Flour weigher and Entoletor 23.75%
Flour weigher and Entoletor 25.43%
5
Figure 3: Distributions of product carbon footprint (plain flour) from hard wheat.
Mixing 15.25%
Bulk flour Mixing filler 7.50% 5.52% Flour bin 6.68%
Hot spot identification
From the carbon footprint assessment in the milling process, weigher and entoletor have been areas identified as the key contributors to high carbon footprint. For this study, focus has been placed on identifying possible areas where improvement can be conducted to reduce the carbon footprint. For the flour milling production, the source of carbon footprint is generated solely by electricity. The way to reduce carbon footprint is to improve the energy efficiency of the processes or the equipment used. Figure 7 shows the overall distribution of electrical usage in the flour mill plant in the year 2009. The highest percentage of usages is accredited to the milling processes, weigher and entoletor of the hard and soft wheat, 32.79% and 26.42% respectively. Majority of the power generated comes for motors of various sizes employed in flour processing. Upon detailed investigation, the highest motor consumption is rated at 90kW used to power a large fan. The fan is
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DB-N 0.15%
DB-Q 1.07%
DB-M 0.16% DB-J 0.37%
DB-R 0.97% DB-S 2.24%
DB-T 2.89% DB-U 0.09%
DB-K 1.87%
DB-X 3.63% DB-A 2.35%
DB-B 5.44%
Milling - hard wheat 32.79%
DB-H 0.25% DB-G 5.34% DB-F 3.13% DB-E 4.75%
DB-C 6.07%
Figure 7: Distribution of electrical consumption for the flour milling plant.
Overall Product Carbon Footprint (kg-CO2eq/ton)
495.07 491.41
490
493.20
Hard 488.04
489.69
482.13
479.56
486.46
480 470
Soft
484.95
483.51
480.82
467.71 463.57 459.77
465.6
460
461.63
456.28
457.98
450
453.09
454.65
450.19
451.61
440 0
2
4
6
8
10
12
14
16
18
20
Percentage (%) of motor improvement
Figure 8: Possible overall product carbon footprint reduction upon implementation of motor improvement. That is approximately 1.74% and 2.08% of improvement compared to before any improvement carried out on the motors. The carbon footprint for processing hard and soft wheat on the flour mill production will be 175.01 and 147.05 kg-CO2eq/ton, respectively, which are approximately 4.96% and 6.20% improvement from before.
183.62
180
Hard
179.96 176.59
181.75
178.24
160
Soft
173.50
175.01
170
170.68
172.06
168.11
169.37
156.77 152.64
150
154.66
148.83
145.34
150.69 147.05
140
142.15
143.71
139.26
140.67
130 0
2
4
6
8
10
12
14
16
18
20
Precentage (%) of motor improvement
Figure 9: Possible overall flour mill production carbon footprint reduction upon implementation of motor improvement. It can be clearly seen that the improvements are more significant on the flour mill production compared to the total product system. From the study, evidences clearly show that carbon footprint can be reduced by a significant amount by simply improving the energy efficiency in operating the equipments within the production plant. 5.3
Using of co-products
It has been demonstrated in this study that the product carbon footprint can be reduced due to the allocation of co-products. The findings from this study show that when bran and pollard are utilised in other products such as packaging (becomes co-products), it absorbs about 4% of the product carbon footprint. The conversion of by-product into a useful and valuable co-product is an encouraging approach which not only results in environmental improvement, but is also prominent in the aspects of resource efficiency and economic benefits. This increases the utilisation of natural resources, reduces the disposal of waste and can be used as substitute material to non-renewable resources such as plastics and fossil fuels. 6
Milling - soft wheat 26.42%
500
190
Flour Mill Production Carbon Footprint (kg-CO2eq/ton)
required to run continuously during the whole production cycle and there are several numbers of these large fans and the purpose of these fans is to transport wheat and flour from point to point during the flour production. Recommendations were suggested to the flour mill company in methods to run the motor more efficiently thus reducing the overall production and product carbon footprint. Reduction methods encompass improvement in wheat and flour transportation through the piping hence reducing the speed of the fan, installation of variable speed drives and controllers to improve and to monitor the product flow, and wheat and flour transportation sequencing. Affinity Law states that the power of the motor is proportional to the speed of the fan cube therefore by reducing the fan speed the power of the motor will reduce in a greater magnitude. If the motor speed of the fan is reduced by just 10%, the reduction of the power of the motor will be approximately 30%. Therefore the 90kW motor will be now running at 65kW. Figures 8 and 9, illustrate reduction in carbon footprint for the overall product (1 ton of flour) and flour mill production versus the percentage of possible motor improvement implemented to the 90kW motors. Under the scenario, the flour mill company is able to reduce successfully 10% of the power consumption of the motors, the overall product carbon footprint from processing hard and soft wheat will be 468.46 and 459.98 kg-CO2eq/ton, respectively.
CONCLUSIONS
Carbon footprint assessment (life cycle approach) has been found to be a useful tool for quantifying GHG emissions from the “cradleto-gate” of one ton of plain flour, and for identifying which production stages are responsible for these emissions. The overall carbon footprint is quantified as 495.07 kg-CO2eq/ton for plain flour produced from heat wheat and 467.71 kg-CO2eq/ton for plain flour produced from soft wheat. Wheat production was found to be the most predominant stage in the carbon footprint of the plain flour product which accounts for over 60%. A foremost opportunity to improve the carbon footprint caused by upstream production is through the purchasing-decision of wheat where suppliers with cleaner production and closer location (minimised transport need) are chosen and sourced. The flour mill production was identified as an energy-intensive lifecycle stage which gives an important contribution of approximately 30% to the final carbon footprint results. It was recommended that efforts must be focused on improving the efficiency of energy use in the flour mill production. In particular, the milling process that causes approximately 40% of the production emissions. The emissions can be reduced by 1% to 11% if the wheat and flour transportation system’s efficiency is improved. Much of the emissions from the flour mill plant is contributed by the used of electricity. Therefore, proper energy management system should be enforced to ensure that the process and equipment used are energy efficient. Lastly, the use of co-products, bran and pollard, helps to maximise the utilisation of natural resources, minimise the disposal of waste and added value to the excess. By converting the co-products into bio-packaging materials can shared out the environmental impact by 23%.
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ACKNOWLEDGMENTS
The authors also would like to thank all relevant parties who contributed in one form or another to the project. 8
REFERENCES
[1]
Finkbeiner, M., (2009): Carbon footprinting – opportunities and threats, in International Journal of Life Cycle Assessment, Vol 14, pp. 91-94.
[2]
European Platform on Life Cycle Assessment European Commission – Joint Research Centre Institute for Environment and Sustainability (2007): Carbon Footprint – what is it and how to measure, pp. 1-2 http://lca.jrc.ec.europa.eu/Carbon_footprint.pdf. Last assessed 10 November 2010.
[3]
ISO, International Organization of Standardization – ISO 14040/44 (2006): Environmental management – Life cycle assessment – Principles and framework/Requirements and guidelines.
[4]
British Standard, (2008): Publicly Available Specification (PAS 2050) – Specification for the assessment of the life cycle greenhouse gas emissions of goods and services.
[5]
Pandey, D., Agrawal, M., Pandey, J. S., (2010): Carbon Footprint: current methods of estimation, in Environmental Monitoring and Assessment, DOI:10.1007/s10661-0101678-y.
[6]
Biswas, W.K., Barton, L., Carter, D., (2008): Global warming potential of wheat production Western Australia: a life cycle assessment, in Water and Environment Journal, Vol. 22, pp. 206-216.
[7]
Meisterling, K., Samaras, C., Schweizer, V., (2009): Decisions to reduce greenhouse gases from agriculture and product transport, in Journal of Cleaner Production, Vol. 17, pp. 222230.
[8]
Houghton, J.T., Ding, Y., Griggs,D.J., Noguer,M., Van der Linden,P.J., Dai, X., Maskell,K., Johnson, C.A., (2001): Climate Change 2001: the scientific basis. Contribution of working Group I to the third assessment report of the Intergovernmental Panel of Climate Change, Cambridge, UK; New York, USA: Cambridge University Press.
[9]
United State Environmental Protection Agency (US EPA) (2010): Inventory of U.S. Greenhouse Gas Emission and Sinks 1990-2008 EPA 430-R-10-006.
[10]
Sea distance calculator, in http://www.searates.com/ reference/portdistance. Last assessed 10 November 2010.
[11]
PE International, GaBi Echterdingen, Germany
[12]
Campbell, G. M.,(2007): Handbook of Powder TechnologyRoller Milling of Wheat, Vol 12, Chap. 7, pp. 383-419.
[13]
National Environmental Agency (NEA), Information on emission factors (for CDM projects in Singapore), in http://www.nccc.gov.sg/informationOnEmissionFactors. pdf. Last assessed 10 November 2010.
4.3,
LCA-software,
Leinfelden-
Modelling Machine Tools for Self-Optimisation of Energy Consumption 1
2
2
Robert Schmitt , José Luiz Bittencourt , Ralf Bonefeld 1 2
Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen, Aachen, Germany
Bosch Rexroth AG, Centres of Technology Competence and Innovation Strategy, Lohr am Main, Germany
Abstract This paper presents a novel approach for modelling machine tools and so preparing the basis for self-optimisation. Based on previous experiments and publications, the main energy consumers in typical machine constructions are identified and machine components are structured in a way that facilitates the definition of the optimisation problem. Also energy saving possibilities are illustrated together with the suggested structure. In addition, further planed research works for the implementation of self-optimised machine behaviour are presented. Keywords: Energy Efficiency; Machine Tools; Modelling
1
INTRODUCTION
The life cycle duration of manufactured products has been continually reduced in the last decades. As a consequence, the demand for more flexible production machines has increased. In the specific case of machine tools, this flexibility is often reached by oversizing machine components in the requirement and design phases. The side effect of this practice is a notable reduction of the machine’s energy efficiency and hence an increased TCO (total cost of ownership) [1]. In order to compensate this efficiency drawback, methods for machine tools’ self-optimisation in view of energy consumption have been developed. A self-optimising machine tool is an intelligent system that has the capability to react autonomously and flexibly to its surrounding environmental conditions, to the interference of external users/systems, or also to their own dynamic behaviour, modifying their local objectives and adapting their parameters/structure in response to these dynamic factors [2][3]. Regarding the specific problem of energy efficiency, such a machine tool is able to adapt itself to the current production requirements, plan the power balance of energy consumers and reduce energy losses [4]. In this paper, the main energy consumers in typical machine constructions are identified based on previous experiments and publications. Machine components are structured in a way that facilitates the definition of the optimisation problem. In addition, a novel approach for modelling machine tools in view of selfoptimisation is presented and further planed research works are described. 2
ENERGY CONSUMERS IN MACHINE TOOLS
There are many different classifications for energy consumers in cutting machine tools. According to [5], the most important consumers are spindles and feed axes together with their electronics. Other relevant consumers are periphery components for medium supply (e.g. hydraulic, cooling and coolant components). Following this concept, basically two groups of consumers can be defined: drives and periphery. A simple
monitoring application that divides consumers in this way is presented in [6]. Drives are considered to be the biggest energy consumers during production time. They are responsible for process logistics and shaping. Another classification divides them into three categories [7]: main drives, secondary drives and auxiliary drives. Within the group of auxiliary drives, pumps for several mediums can be found. However, this is an unusual classification, because pumps are often considered to belong to the periphery, due to their functional objectives. The periphery is mostly composed by the so called ancillary units and other machine components that assist machine operation, process logistics and process mastering. Some examples of ancillary units are the cooling unit, the hydraulic unit and the coolant unit. Measurements of power consumption distributions related to these machine components can be found in [8], [9] and [10]. In [11], main and auxiliary drives are classified separated (e.g. spindle and feed axes, respectively) and other consumers are also grouped as periphery. The load responsible for the energy consumption is divided into basic and operating loads. The energy demand can be primary or secondary – where the secondary demand is not involved in the modification of the work piece. The authors of [12] focused on the structure of the energy consumption combining operation states with machine components. A given example shows the consumed energy not only distributed between machine components (i.e. electric cabinet, hydraulics, drive control, main spindle and coolant), but also together with stand-by losses and the process. Although the presented classifications can be very functional and easy to understand, it is not clear, which consumers depend on each other. The cause-effect relationships of the energy consumption development are hidden. For instance, when a motorpump continues consuming energy to circulate cooling fluid, even though there is nothing to be cooled, it is evident that some relationships are not considered. In [13], consumers are not presented in parallel, but in three different scopes that determine
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_44, © Springer-Verlag Berlin Heidelberg 2011
253
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Sustainability in Manufacturing - Energy Efficiency in Machine Tools
the energy demand in cascade: the process, the main axes and the auxiliary components, which are responsible for coolant, cooling, pneumatics, exhaustion and hydraulics. In this case, cause-effect relationships can be easier identified and some measures to save energy are cited based on this structure. In terms of implementation of self-optimisation capabilities, the only drawback of this procedure is the complexity involved in the definition of the process for the machine controller.
maintenance and availability, and present therefore a great potential to save energy. Nevertheless, machine tools manufactures prefer to adopt this paradigm because of reduced investment costs and compact construction of components. Table 1 lists some pros and cons concerning the use of common suppliers at machine level:
A simple approach to classify machine components in view of energy consumption, to clarify relationships and to highlight energy saving potentials is shown in Figure 1. The superior part of the pyramid (i.e. operator, NC program and control system) triggers commands and operations for the machine components, which are supposed to execute some job. This is a simplification of the process description at machine controller level. The machine components need energy to do the job and receive it from the common supply systems. These systems provide the required energy to the machine components through a specific medium. The hydraulic components, for instance, use the hydraulic fluid provided by a hydraulic pump or accumulator (common supplier) to perform work. The necessary energy is obtained from the available hydraulic power.
commands, operations
energy demand
Operator, NC program, Control system
Machine components Main components
Auxiliary components
Common supply systems (cooling, hydraulics, pneumatics, electrical power supply, etc) Figure 1: Machine structure and development of energy demand. In the next subsections, the most important consumers following the proposed classification will be discussed. 2.1
Machine components
Figure 1 shows the division of machine components into main and auxiliary components. This is a functional classification that helps to identify the consumers in a macro point of view. While typical main machine components are spindles and feed axes, auxiliary components can be tool magazines, chip transporters, pallet changers and others. As explained in section 2, machine components need support from supply systems in order to perform their jobs. This functional relationship generates energy consumptions both from machine components (consumers) and suppliers. 2.2
Common supply systems at machine level
There are many types of common supply systems used in machine tools. Good examples are hydraulic systems, cooling systems, and electrical power supply systems. Pneumatic suppliers can also be considered a common supply system, but they are usually available in the factory plant and are not offered at machine level. Normally, common supply systems have a constant physical dimension as interface for all consumers (e.g. hydraulic pressure, voltage, etc.) and a variable physical dimension to dose power (e.g. hydraulic flow, electric current, etc.). They often consume more energy than necessary, because of oversizing, interface
Pros
Cons
Investment costs;
Operation costs;
Control simplicity of consumers;
Supply excess;
Energy exchange between consumers;
Oversizing;
Compact construction of consumers
More transport losses
Table 1: Pros and cons for the use of common suppliers. 3
ENERGY LOSSES AND SAVING POTENTIALS
The identification of losses and the systematic analysis of their causes are the basis for the development of energy efficient technologies. Measures to improve efficiency like the use of energy recovery, the adoption of efficient components with energetic convenient dimensioning as well as the shortening of processing time and the reduction of medium are cited in [13]. Dornfeld lists in [14] different strategies for energy reduction depending on the relation between processing power consumption and the so called “tare” power consumption (power consumption that is independent from the process). Operations are classified as “tare heavy” or “process heavy” in order to select measures like process optimisation (tooling, path, etc.) or cycle time reduction. In [11], energy losses of machine tool components are grouped in four categories: electric losses, damping losses, friction losses and flow losses. The authors distinguish component and system optimisation by mentioning respectively their direct and indirect effects for increasing energy efficiency. Another way to identify losses and uncover saving potentials is to analyse the direct handling with energy in view of energy transformation, distribution and storage. In the case of machine tools it is difficult to use this approach, once machine components can handle energy in several ways. Considering the structure proposed in section 2, energy losses in machine tools can be divided into three categories: consumer losses, supplier losses and supply excess. The following subsections will describe these losses. 3.1
Consumer losses
Consumer losses are related to the efficiency of the machine components (main and auxiliary components) and their elements (e.g. motors, frequency converters, gears, cylinders, valves, etc.). Possible measures to reduce these losses are:
optimisation of acceleration profiles;
optimisation of cutting fluid usage;
energy recovery and reutilisation;
optimisation of machine’s thermodynamics;
improvement of energy efficiency of components;
These measures can be implemented in different life cycle phases. While some of them can only be considered in the design phase, others are continuous processes that must be executed during the whole machine usage period.
Sustainability in Manufacturing - Energy Efficiency in Machine Tools 3.2
Supplier losses
4
Supplier losses are analogue to the consumer losses. They occur according to the energy efficiency of the supplier components and their distribution circuits under different working points. Some measures to improve efficiency are:
elimination of leaks in fluid distribution circuits;
reduction of the distribution circuits;
improvement of energy efficiency of supplier components;
The machine design defines predominantly, which of these measures are taken into account. 3.3
Supply excess
Common supply systems often consume more energy than necessary because of their design paradigm. Beside the frequently oversized system components, common suppliers are thought to maintain an interface to consumers, which can be very convenient (e.g. constant hydraulic pressure provided by a pump), but can consume much energy. In addition, common suppliers must be always available, because of a lack of information about when, which consumer will demand support. This generates a supply excess, which is related to the excess of supplied medium or unnecessary availability. The behaviour of a typical common supplier is presented in Figure 2. In this example, the operations A, B, C and D represent support requests from different machine components (consumers). These operations differ from each other in respect of time of arrival, duration and load, which is qualitatively plotted in the middle graph. In this case, the power consumption of the supplier (presented in the upper graph) does not directly correspond to the load progress. The reason is that this supply system contains an accumulator before the interface with its consumers. The power source of the supplier remains decoupled from the accumulator and is only coupled for a short period — when the accumulator reaches a predefined low level. This behaviour can be observed on the two peaks of the upper graph of the Figure 2. Between the peaks, a supply excess caused by unnecessary availability with duration Δt
Supply excess (stand-by)
SELF-OPTIMISING COMMON SUPPLIERS
Considering the discussed structures and categories of energy consumers, their energy losses and saving potentials, common supply systems can be modelled in order to facilitate a posterior system optimisation. The focus of this approach is to reduce the described supply excess by implementing supply controllers. Such controllers are able to switch suppliers into different modes/states pursuing a reduction of their total energy consumption. A similar modelling approach was presented by Dietmair and Verl in [15]. The authors focused on the assessment of machine energy consumption using operative state machines together with usage profiles. Each state represents a predefined power balance between the machine components (energy consumers) and can be achieved or not, depending on the usage profile. Although this concept facilitates the assessment of the machine energy consumption, the support for optimisation is not appropriate for the implementation of self-optimising systems. This occurs because the operational states contain sets of parameters, which must be optimised independently from the usage profile, ignoring usage variations. Another disadvantage of this approach is that idle and operational power consumptions are not handled separately, covering energy saving potentials like the reduction of supply excess. 4.1
Functional state machine for common suppliers
In order to tackle the problem of supply excess discussed in section 3.3 and prepare the basis for the implementation of self-optimising systems, a functional state machine for common suppliers is proposed. This state machine differs from that presented in [15] in some ways:
Transitions cost not only time, but also energy.
States do not represent what is currently done, but which operations are currently supported.
Each common supplier has its own functional state machine.
Operations are defined and also demand time and energy.
The idea is to model common suppliers more optimisation-oriented and help the decision making for the so-called sleep mode or shutoff strategies. According to this approach, the optimisation problem at supplier level can be defined by: “Find the best state/trajectory considering the required operations and time constraints in order to reduce idle power and total energy consumption”. To formulate the problem mathematically, an operation oi, belongs to a state si can be defined as:
t
Load at supplier
P input Supplier
is pointed. This occurs because the supplier keeps consuming energy (power source is decoupled but not shut off) even if there is no operation to support.
Operations
255
t
A B C D
oi, j (Tlead , Pdemand ),
where Tlead is the time taken to complete the operation and Pdemand the power demand caused by the operation at supplier level. An operation can be supported in one or more states and a state si is defined as:
Δt
Figure 2: Typical supplier with load progress and supply excess. To reduce losses related to supply excess, the following measures can be considered:
implementation of supply controllers;
elimination of bypasses;
shut-off strategies for suppliers;
that
(1)
s i Pidle , Oi , Oi o i, j | j 1,2,... , t
j
(2)
where Pidle is the basic constant power consumption presented by the supplier without load and Oi is a set of supported operations in the state si. Transitions between states can be given as a vector:
t i So , Sd , Ttrans , E trans ,
(3)
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Sustainability in Manufacturing - Energy Efficiency in Machine Tools
ΔE01, ∆t01
∆E31, ∆t31
Mode 0 Pidle,0
Pidle,1
∆E12, ∆t12
Mode 1
Mode 3 ∆E23, ∆t23
Mode 2
∆E10, ∆t10
A, C
Pidle,3
A, B
∆E21, ∆t21
Pidle,2
∆E32, ∆t32 A, B, C, D
Figure 3: Example of a functional state machine. where So and Sd respectively represent the origin and the destination states, Ttrans is the duration of the transition and Etrans is the amount of energy consumed during the transition. The optimisation problem to reduce losses related to supply excess can be solved by finding the best state trajectory for each set of previewed operations. This can be done by adding the energy costs for idle, transition and operation periods. Once the different possibilities for a specific usage profile (operations progress) are calculated, the minimal cost trajectory that satisfies both time and functional constraints can be selected and performed. Figure 3 shows an example of a functional state machine for a specific common supplier. The numbered states are named “Mode” and the supported operations are called A, B, C and D. In the state “Mode 0” no operation is supported. This could be a low power state used to save energy when machine components are idle and do not demand energy from supplier. For instance, an optimisation scenario could be described as followed:
Current state is “Mode 2”.
Operation “A” is foreseen beginning in Δtbegin seconds.
5
Further work will also focus on the reduction of consumer losses, as introduced in section 3.1. The main objects of study will be the optimisation of acceleration profiles as well as energy recovery and reutilisation. 6
Both transitions times Δt21 and Δt23 are shorter than Δtbegin.
Considering these constraints, the calculation of energy costs for the possible states is presented in Table 2. The total costs include also the transition costs, in the case of switching to “Mode 1” or “Mode 3”. The lowest total consumption corresponds to the best trajectory for the present scenario. State
Idle
Transition
Operation
Total
Mode 2
Pidle,2 . (∆tA+∆tbegin)
0
PA . ∆tA
2
Mode 1
Pidle,1 . (∆tA+∆tbegin ∆t21)
∆E21
PA . ∆tA
1
Mode 3
Pidle,3 . (∆tA+∆tbegin ∆t23)
∆E23
PA . ∆tA
3
Table 2: Example of energy cost calculation. The modelling and optimisation approach could be enhanced by expanding the definition of operations. It is possible to assign other characteristics like maximum and minimum expected power drains in order to define the best state also in view of transient behaviour.
FURTHER RESEARCH
In order to validate the usability and the completeness of the presented modelling method, simulations are being developed and experiments in typical real machines are planned. Moreover, efforts have been done to define machine operations that have some parameters, like duration or power demand, with a higher degree of uncertainty. For this case, machine learning algorithms are expected to be used, so that the system can learn with its experience, which is perceived by means of the already available data in the machine controller. Following the concept of selfoptimisation, the machine tool will be able to adapt itself to the current production requirements, plan the power balance of energy consumers and reduce energy losses.
SUMMARY
In this paper, energy consumers of typical cutting machine tool constructions were discussed and machine components were structured in order to facilitate the definition of the optimisation problem. After the identification and the classification of energy losses and saving potentials, a novel approach for modelling machine tools in view of self-optimisation of energy consumption was presented. In the proposed method, transitions between states/modes include time and energy consumptions, providing essential information for the optimisation. Instead of defining several operational states, functional states are used to represent the availability of subsystems, and therefore the current supported operations. State selection is done by a supply controller, which optimises the energy consumption depending on the usage profile of the machine tool. 7
ACKNOWLEDGMENTS
The authors thank the German Research Foundation DFG for the support of this research within the “Aachen House of Integrative Production” research initiative. The self-optimisation of machine tools’ energy consumption is being worked within the scope of the Cluster of Excellence "Integrative Production Technology for HighWage Countries" at the RWTH Aachen and at Bosch Rexroth AG.
Sustainability in Manufacturing - Energy Efficiency in Machine Tools 8
REFERENCES
257 [15]
[1]
Kühnle, J. (2008): Überdimensionierung Industrieanzeiger, Issue 15, pp. 102.
vermeiden,
[2]
Gausemeier, J.; Frank, U.; Giese, H.; Klein, F.; Oberschelp, O.; Schmidt, A.; Schulz, B.; Vöcking, H.; Witting, K. (2004): Selbstoptimierende Systeme des Maschinenbaus: Definition und Konzepte, HNI-Verlagsschriftenreihe.
[3]
Gausemeier, J.; Frank, U.; Schmidt, A.; Steffen, D.; Giese, H.; Klein, F.; Tichy, M. (2005): A Design Methodology for SelfOptimizing Systems, AAET 2005 - Automation, Assistance and Embedded Real Time Platforms for Transportation, Technical University of Braunschweig.
[4]
Gausemeier, J.; Kahl, S.; Radkowski, R. (2010): Selbstoptimierende Produkte – neue Perspektiven zur Steigerung der Energieeffizienz, in: Proceedings of the 1st International Colloquium of the Cluster of Excellence eniPROD, pp. 641-656, Chemnitz, Germany.
[5]
Brecher, C.; Boos, W.; Klein, W.; Kuhlmann, K.; Triebs, J. (2009): Ressourceneffizienzbewertung einer Werkzeugmaschine zur Steigerung ihrer Wirtschaftlichkeit, Werkzeugmaschinen ZWF, Issue 9, pp. 711-715.
[6]
Bittencourt, J.; Landgraf, G.; Bonefeld, R.; Schmitt, R.; Pavim, A. (2010): Model-based monitoring of machine tools energy consumption, in: Proceedings of the International Chemnitz Manufacturing Colloquium 2010, pp. 673-680, Chemnitz, Germany.
[7]
Götze, U.; Koriath, H.-J.; Kolesnikov, A.; Lindner, R.; Paetzold, J. (2010): Energetische Bilanzierung und Bewertung von Werkzeugmaschinen, in: Proceedings of the 1st International Colloquium of the Cluster of Excellence eniPROD, pp. 157-184, Chemnitz, Germany.
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Abele, E.; Kuhrke, B.; Rothenbücher, S. (2010): Maximierung der Energieeffizienz von Werkzeugmaschinen, MM MaschinenMarkt, Issue 9, pp. 26-29.
[9]
Gutowski, T.; Dahmus, J.; Thiriez, A. (2006): Electrical Energy Requirements for Manufacturing Processes, in: Proceedings of the 13th CIRP International Conference on Life Cycle Engineering, Leuven.
[10]
Brecher, C.; Herfs, W.; Heyers, C.; Klein, W.; Triebs, J.; Beck, E.; Dorn, T. (2010): Ressourceneffizienz von Werkzeugmaschinen im Fokus der Forschung, wt Werkstattstechnik online, Issue 7/8, pp. 559-564.
[11]
Neugebauer, R.; Wabner, M.; Rentzsch, H.; Kocourek, P.; Ihlenfeldt, S. (2010): Structure Principles of Energy Efficient Machine Tools, in: Proceedings of the 1st International Colloquium of the Cluster of Excellence eniPROD, pp. 207229, Chemnitz, Germany.
[12]
Dietmair, A.; Verl, A. (2010): Energieeffizienter Betrieb von Produktionsanlagen, in: Proceedings of the 1st International Colloquium of the Cluster of Excellence eniPROD, pp. 185206, Chemnitz, Germany.
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Abele, E.; Kuhrke, B.; Rothenbücher, S. (2010): Entwicklungstrends zur Erhöhung und Bewertung der Energieeffizienz spanender Werkzeugmaschinen, in: Proceedings of the 1st International Colloquium of the Cluster of Excellence eniPROD, pp. 99-120, Chemnitz, Germany.
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Dornfeld, D. A (2010): Sustainable Manufacturing – Greening Processes, Systems and Products, in: Proceedings of the International Chemnitz Manufacturing Colloquium 2010, pp. 99-113, Chemnitz, Germany.
Dietmair, A.; Verl, A. (2009): A generic energy consumption model for decision making and energy efficiency optimisation in manufacturing, International Journal of Sustainable Engineering, Vol. 2, No. 2, pp. 123-133.
Energy-Efficient Machine Tools through Simulation in the Design Process 1
1
Christian Eisele , Sebastian Schrems , Eberhard Abele 1
1
Institute of Production Management, Technology and Machine Tools, Technische Universität Darmstadt, Darmstadt, Germany
Abstract Machine tool manufacturers are increasingly challenged by their customers to quantify the energy consumption of their product during the design process. Therefore the availability of instruments to consider the energy demand already in the early phases of the machine tool conception is essential. This article describes an approach to simulate the energy consumption of machine tools by modeling the energetic interactions of the machine tools’ components. Machine Tool manufacturers can thereby identify the optimization potentials already early in the design process. Exemplarily, components of a machine tool’s coolant system will be modeled and the systems energy consumption will be evaluated. Keywords: Machine Tools; Design Process; Simulation
1
INTRODUCTION
Rising energy and raw material costs are only one of the challenges for manufacturing companies in order to be able to offer competitive products in a global and dynamic market environment. The demand for lower production costs moves also the energy consumption of manufacturing equipment into focus. Especially users in the automotive industry demand more often an indication for new acquisitions of how much energy a machine tool will expectedly consume during operation [1]. The factor “energy efficiency” is therefore another important evaluation criterion for new investment in machinery and equipment in addition to the classical parameters accuracy, performance, cost and reliability. Improving the energy efficiency of their products also leads to a competitive advantage for machinery and component manufacturers. Approaches to increase energy efficiency exist, for example the use of variable speed drives, but are generally associated with higher investment costs and depend significantly on the particular usage profile of the machine tool. Since reliable statements regarding the expected usage profile and the resulting energy demand are difficult to meet during the development process, efficiency potentials are often not fully exploited. Usually this results in the use of lower priced but also more energy-intensive components in machine tools. 2
SIMULATION TO CONSUMPTION
DETERMINE
THE
ENERGY
So far, no methods or tools are available to component respectively machine manufacturers in order to consider the energy consumption of a machine tools on component level already in the early stages of the machine development. Only with the knowledge of the energy consumption, it can be assured that the machine configuration and components with the highest benefit for the entire machines’ lifecycle are selected in the development process. Due to the complete modeling of the system, it can be adequately designed and optimization actions can be fitted specifically to the requirements.
Thus the often existing over dimensioning of components can be avoided. Since over dimensioned components operate below their nominal operating point their efficiency decreases which translates into significantly increased energy consumption. Simulation technologies are already widely accepted in other areas of the machine development and are used for a variety of tasks. Among other benefits, accuracy, speed and reliability were increased with decreasing development costs and time due to the application of simulation tools [2]. Therefore the use of simulation techniques seems to be an appropriate approach to estimate the energy consumption already in the early stages of the product development. In the following sections, the exemplary methods for modeling and simulation of a coolant system of a machine tool (Model VL7, manufacturer EMAG) are presented. The preparation of the simulation first requires an analysis of the system to be simulated, in order to identify those components that affect the energy consumption significantly. The subsequent modeling of the components aims to mathematically represent their energetic characteristics. Physical effects or components which only have a small impact on the energy consumption will be neglected to simplify the modeling process. The coolant system which is to be simulated is schematically displayed in Figure 1. The coolant medium is delivered through the pump and piping to the outlets, for example the tools. The 2/2-wayvalves allow to connect or disconnect individual functions of the coolant system. A direct connected three-phase AC induction motor drives the pump. 3
MODELLING SYSTEM
AND
SIMULATION
OF
THE
COOLANT
Primarily, the simulation models of the components must guarantee a proper handling and a sufficiently accurate quantification of the energy consumption. To reproduce the real behavior of the components exactly is not object of the simulation, only major factors that influence the energy consumption will be included. In addition, due to the practical usability of the simulation tool during
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_45, © Springer-Verlag Berlin Heidelberg 2011
258
Sustainability in Manufacturing - Energy Efficiency in Machine Tools the development process, all parameters for the simulation have to
259 The flux equations for rotor and stator were given by [3], [4]:
s Ls Lh i s L h i r
Energetic relevant system components
s Ls L h i s Lh i r
Turret
(2)
r Lr Lh i r Lh i s
Spray Nozzles
Lathe chuck
r Lr Lh i r Lh i s
Ls , Lr : Stator and rotor leackage inductances Lh : Magnetizing inductance
Main casting flushing
Working chamber flushing
With the calculated currents of rotor and stator combined with the following equation
Turret flushing
M el
3 p s is s is 2
(3)
M el : electrical moment p : number of pole pairs
Pipes Rohrleitungen
Induction Motor N = 3.0 kW n = 2840 1/min U = 400V / 50Hz
the electrical torque can be calculated [3], [4]. Using the equilibrium of moments, the load torque and the rotation speed dependent losses, e.g. friction, can be accounted for.
Centrifugal Pump 8bar 80l/min
Figure 1: Schematic figure of coolant system with energetic relevant components. be on hand. If appropriate and available, characteristic curves or mathematical equations were used for the modeling of the coolant system components’. Modeling
AC Induction Motor For modeling of the AC induction motor, the three stator voltages shall be used as input variables. The voltages are transformed in a rotor fixed coordinate system by Clarke transformation in order to have easier to handle equations for modeling. The voltage equations for stator and rotor were calculated by using the following formulas [3], [4]:
d s dt d U s R s i s s dt d 0 R r i r r r dt d 0 R r i r r r dt U s R s i s
U s , i s : axis stator voltage and current U s ,i s : axis stator voltage and current R s , R r : Stator and rotor resistance s , s : Stator and axis fluxes r , r : Rotor and axis fluxes : Angular velocity
(4)
M L : load torque
Coolant
3.1
d Me ML D dt
(1)
D : factor of speed dependent losses
: inertia The values for the motor parameters must be either requested by the motor manufacturer or can be calculated with type plate data which is not as exact as having the original values [5]. Centrifugal Pump The centrifugal pump is modeled by a characteristic diagram model, as the energetic and characteristic behavior is completely characterized by it. Characteristic diagrams for centrifugal pumps of pressure-dependent delivery heights and the corresponding required shaft power is provided in the data sheet of the pump published by the pump manufacturer. The implementation of the characteristic diagram is done by approximating the curve with interpolation methods. Therefore four equidistant nodes have to be extracted from each diagram. With the method of polynomial curve fitting, the coefficients of a third degree polynomial curve are determined. Beyond the range of the node data, linear extrapolation was used which is subject to a greater degree of uncertainty [6]. Since the provided characteristic curves in the data sheet were recorded at a certain reference density of the fluid and a certain reference angular velocity, it is necessary to use affinity laws in order to simulate systems with other velocities or fluid density. The delivery rate can be calculated with [7]:
q q ref
ref
q : pump delivery q ref : pump reference delivery
: angular velocity ref : reference angular velocitiy
(5)
260
Sustainability in Manufacturing - Energy Efficiency in Machine Tools
The pressure of the pump with different density and angular velocity was modeled according to the following equation [7]:
whereas the equation to model the friction factor for turbulent flow is
H H ref
ref
ref
2
(6)
based on the Haaland equation [9][10].
fT
H : pump delivery height H ref : pump delivery height reference
P Pref
ref
Spray Valves
3
(7)
The spray valves at the end of the pipes can be modeled as a local flow resistance with abrupt cross section geometry. The pressure losses can be modeled according to the orifice formula [10]. 2
Pref : referencepump brake power
p
Pipes The pressure loss in the pipes due to friction was modeled according to the Darcy-Weisbach-equation [8].
l q2 d 2 A2
(8)
f L : friction factor laminar flow Re : Reynolds Number
Δp : pressure loss
: flow coefficien t A : orifice Area
: fluid density
2/2-Way-Valves
The losses are a function of the flow rate and a friction factor which is dependent on laminar or turbulent flow. Also the pipe geometry and the fluid density is considered in the equation. The friction factor for laminar flow was modeled according to the following equation [8]:
k : geometric form factor (k 64 for circular pipes)
(11)
The value of the flow coefficient depends on the geometrical properties of the orifice and is usually provided in textbooks.
q : fluid flow rate
k Re
1 2 q 2 A
q : fluid flow rate
Δp : pressure loss f : friction factor l : length of pipe d : pipe diamter A : pipe cross Area : fluid density
fL
(10)
d : pipe diameter Re : Reynolds Number
P : pump brake power
p f
2
r : pipe surface roughness height
With the following formula, the brake power can be determined [7]:
ref
1.11 1.8 log 6.9 r 10 Re 3.7 d
f T : friction factor turbulent flow
: fluid density ref : reference fluid density
1
(9)
The valves in the system are used to regulate the flow to the spray valves. As the spray valve, they represent a flow resistance in the hydraulic system. For calculation of the flow rate through the orifice, the orifice formula (see 11) is used. The passage Area is dependent on the position of the valve. When the valve is closed, a small leakage area is assumed to exist which represents the clearance of the physical slide valve.
A A O AL
for valve open for valve closed
(12)
AO : Valve opening Area AL : Valve leackage Area The value of the flow coefficient is usually provided in manufacturer datasheets.
Sustainability in Manufacturing - Energy Efficiency in Machine Tools
261
Measurements
Simulation 3000 3
Electr. Power [kW]
Electr. Power [kW]
3000 3 2000 2
2000 2
1000 1
1000 1
0 0
50
100
150
200
250
300
80 60 40 20 0 0
50
100
150
200
250
150
200
250
300
50
100
150
200
250
300
50
100
150
200
250
300
60 40 20
10
Pressure [bar]
Pressure [bar]
100
80
0 0
300
10 8 6 4 2 0 0
50
100
Flow rate [l/min]
Flow rate [l/min]
100
00 0
50
100
150
200
250
8 6 4 2 0 0
300
Time [s]
Time [s]
Figure 2: Power measurements (left) and simulation results (right) for the machining cycle of a test workpiece. 3.2
Simulation
Subsequently, the components were computed with Simscape language, which is based on Matlab. The use of Simscape offers the advantage that simulation models of mechatronic systems can be built on the basis of their existing physical network. Thus an easy handling of the simulation model is assured for the designer when configuring the simulation model. In addition, Simscape components can be easily integrated into a library structure which ensures a good reusability of the simulation models. In order to determine the system’s energy consumption, a machining cycle of a test workpiece was simulated. As extensive performance measurements of various function modules during the machining cycle were part of the first project phase [11], this data can now be used for the verification of the simulation model. On the left side of Figure 2, the measurement results of the time profile of the measured flow rate and pressure of the centrifugal pump as well as the electrical power consumption of the induction motor are presented. For comparison reasons, the simulation results are displayed on the right side of Figure 2. As can be seen, a good conformity of the measured data and the simulation results can be recognized. When comparing the simulated electrical power consumption of the induction motor with the measured, the deviation is below 5%, which can be regarded as sufficiently accurate.
4
EVALUATION OF ENERGY-EFFICIENCY MEASURES
With the simulation model of the coolant system, it is now possible to select various component configurations and to use the simulation results of different variants in order to evaluate their potential savings in terms of energy efficiency. By the use of a centrifugal pump of another manufacturer and the application of an induction motor with the higher efficiency class Eff1, the potential savings of these actions will be exemplarily conducted. The requirement for selecting the components was, that the same performance measures, in terms of flow and pressure for the pump as well as speed and torque for the induction motor, as before can be met. Figure 3 shows the electrical power consumptions of both variants for the already known machining cycle of the test workpiece. For reason of comparisons, the power consumption of the system that was not optimized is also diagrammed in gray. In both cases the energy consumption of the system was reduced. If the average power consumption of 1.91 kW of the original system is used as basis, the demand of energy was reduced by 3.7% to 1.84kW during the machining cycle. By application of the optimized pump an improvement of 18.3% and an average power consumption of 1.56kW were achieved.
Sustainability in Manufacturing - Energy Efficiency in Machine Tools
Optimized Pump
3
2
1
0 0
Optimized System Initial System
50
100
150
200
250
Electr. Power [kW]
Electr. Power [kW]
262
2
1
0 0
300
Time [s]
Eff1-Motor
3
Optimized System Initial System
50
100
150
200
Time [s]
250
300
Figure 3: Simulation results of different component configurations. 5
SUMMARY AND CONCLUSIONS
The ability to determine the energy consumption of machine components already in the early stages of the development process is an important step on the way to the energy efficient machine tool. With this knowledge, it is possible to analyze and compare different machine configurations in their energetic performance and to optimize the machine tool accordingly. Based on the illustrated example of a machine tool coolant system, it was shown that the energy consumption of this system can be calculated with simulation models without the need of expensive and time intensive power measurements. Also different optimization measures can be evaluated in terms of cost and energy saving potentials. By modeling other components of the machine tools function modules’ as well, it will be possible to calculate the energy consumption of the entire machine tool in the future. Based on these results, further optimization potentials can be identified, such as the intelligent control of auxiliary units or a process customized component and equipment sizing. 6
Törnig, W.; Spellucci, P. (1990): Numerische Mathematik für Ingenieure und Physiker II - Eigenwertprobleme und numerische Methoden der Analysis, Springer Verlag, Berlin, München.
[7]
Gülich, J. F. (2010): Kreiselpumpen - Handbuch für Entwicklung, Anlagenplanung und Betrieb, Springer Verlag, Berlin, München.
[8]
Watter, H. (2008): Hydraulik und Pneumatik – Anwendungen und Simulation, Vieweg + Teubner, Wiesbaden.
[9]
Haaland, S. (1983): Simple and Explicit Formulas for the Friction Factor in Turbulent Flow, Journal of Fluids Engineering (ASME), Vol. 103, No. 5, pp. 89–90.
[10]
Jelali, M.; Kroll, A. (2004): Hydraulic Servo Systems – Modeling, Identification and Control, Springer Verlag, London.
[11]
Rudolph, M.; Abele, E.; Eisele, C.; Rummel, W. (2010): Analyse von Leistungsmessungen als Beitrag zur Untersuchung der Energieeffizienz von Werkzeugmaschinen, Zeitschrift für Wirtschaftlichen Fabrikbetrieb: ZWF, Carl Hanser Verlag, München, Vol. 110, pp. 876-882.
ACKNOWLEDGMENTS
The authors gratefully acknowledge the financial support of the Federal Ministry of Education and Research (BMBF) and the project supervision of the Project Management Agency Forschungszentrum Karlsruhe, Divison Production and Manufacturing Technologies (PTKA-PFT) for the project e-SimPro – Energieeffizienz von Produktionsmaschinen durch Simulation in der Produktentwicklung (Energy efficient production machines through simulation in the product development process. 7
[6]
REFERENCES
[1]
Kuhrke, B.; Erdle, F. (2010): Energieeffizienz als Investitionskriterium, in: Werkstatt + Betrieb : WB, Carl Hanser Verlag, München, Vol. 143, No.1-2, pp. 30-32.
[2]
Zirn, O.; Weickert, S. (2009): Modellbildung und Simulation hochdynamischer Fertigungssysteme, Springer Verlag, Berlin, München.
[3]
Schröder, D. (2009): Elektrische Antriebe – Grundlagen, Springer Verlag, Berlin, München.
[4]
Fuest, K.; Döring, P. (2007): Elektrische Maschinen und Antriebe, Vieweg Verlag, Wiesbaden.
[5]
Quang, N. P., Dittrich, J. A. (1999). Praxis der feldorientierten Drehstromantriebsregelungen, Expert-Verlag, Ehningen bei Böblingen.
8
CONTACT
Christian Eisele Institute of Production Management, Technology and Machine Tools (PTW), Technical University of Darmstadt, Germany.
[email protected]
Energy Consumption Characterization and Reduction Strategies for Milling Machine Tool Use 1
1
1
Nancy Diaz , Elena Redelsheimer , David Dornfeld 1
Laboratory for Manufacturing and Sustainability, University of California at Berkeley, USA
Abstract Since machine tools are used extensively throughout their functional life and consequently consuming valuable natural resources and emitting harmful pollutants during this time, this study reviews strategies for characterizing and reducing the energy consumption of milling machine tools during their use. The power demanded by a micromachining center while cutting low carbon steel under varied material removal rates was measured to model the specific energy of the machine tool. Thereafter the power demanded was studied for cutting aluminum and polycarbonate work pieces for the purpose of comparing the difference in cutting power demand relative to that of steel. Keywords: Green Machine Tools; Energy Consumption Reduction; Specific Energy Characterization
1
INTRODUCTION
A product undergoes three life-cycle stages: manufacturing, use and end-of-life. Consumer products whose environmental impact is dominated by the use phase include light fixtures, computers, refrigerators, and vehicles, in general products that are used extensively during their functional life. All the while these products consume resources, in particular energy in the form of electricity or fuel. The machine tool is one such product. The use phase of milling machine tools has been found to comprise between 60 and 90% of CO2-equivalent emissions during its life cycle [1]. This study presents a method for predicting the electrical energy consumed in manufacturing a product for the purpose of reducing its environmental impact. In conducting a life cycle assessment, product designers may choose to opt for a process, economic input-output (EIO), or hybrid approach. The drawback of the process LCA, though, is that because this method entails acquiring process-specific data it is time consuming and therefore resource intensive. An alternative to measuring the machine tool’s electrical energy consumption directly, for example, is to use aggregate data as is done with EIOLCA [2]. An EIO-LCA, therefore, is not specific to the design of a particular product. The strategies presented herein provide a method for more quickly generating manufacturing energy consumption estimates for a particular product. 1.1
Cutting load profile
As described by Diaz et al. in [3] the power demand of a machine tool is comprised of cutting, variable, and constant power components. The cutting power is the additional power drawn for the removal of material. The machine tool used in this analysis, the Mori Seiki NV1500 DCG, is a micromachining center with a relatively low standby power demand when compared to large machining centers. Therefore, the cutting power can comprise a large portion of the machine tool’s total power demand. Energy consumption for high tare machine tools was found to be primarily dependent on the processing time of the part, which is dictated by the part geometry, toolpath, and material removal rate.
One such method for optimizing the tool path for minimum cycle time was presented in [4]. This paper is concerned with the effect of the material removal rate on energy consumption. The material removal rate for a 3-axis machining center can be varied by changing the feed rate, width of cut, or depth of cut. Since increasing the feed rate was found to have dire consequences on the cutting tool life [5], the experiments conducted herein varied material removal rate through width of cut and depth of cut experiments for the purpose of analyzing the material removal rate’s effect on cutting power and more importantly, energy consumption. Although increases in the material removal rate translate to faster machining times, the loads on the spindle motor and axis drives increase as well, resulting in higher power demand. Since our main interest is energy consumed in product manufacture, the trade-off between power demand and machining time was analyzed to confirm that the increased loads due to faster material removal was not increasing the total energy consumed. 2
POWER DEMAND FOR VARIED M.R.R.’S
Since machine tool programmers and operators have an array of options when defining the process plan for part production, this analysis strives to reduce energy consumption by process parameter selection of a machine tool. Specifically, the parameters concerning material removal rate (M.R.R.) were varied on a Mori Seiki NV1500 DCG while selecting appropriate tooling. The power demand was measured with a Wattnode MODBUS wattmeter. In previous work, experiments were conducted in which spindle speed, feed rate, feed per tooth, and cutter type were varied to analyze the change in energy consumption while milling a low carbon steel, AISI 1018 steel [5]. Also, [6] conducted experiments on face milling, end milling, and drilling operations in which the energy consumption, machining cost, and tool wear were compared for increased cutting speeds. Tool wear and, consequently, cutting tool cost increased significantly when the process parameters veered away from the recommended cutting conditions. So in the
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_46, © Springer-Verlag Berlin Heidelberg 2011
263
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Sustainability in Manufacturing - Energy Efficiency in Machine Tools
following experiments the cutting tool type was changed to maintain the recommended process parameters, but reduce energy consumption while machining, nonetheless.
Figure 2 shows the average power demand of the NV1500 DCG for cutters (1) – (3). The relationship between power and M.R.R. shifts from parabolic to linear in moving from the conditions imposed on cutter (1) to cutter (3). The increase in power demand is the greatest for cutter (3), but the load on the spindle motor and axis drives is also much greater than that of the 2 flute cutting tools since the feed rate is twice as large or greater.
2.1
Width of Cut Experiments
Given the energy savings from changing the cutter type this project focused on varying material removal rate. First the width of cut was increased while machining with a: 1. 2 flute uncoated carbide end mill, 2. 2 flute TiN coated carbide end mill, and 3. 4 flute TiN coated carbide end mill. Peripheral cuts were made along the y-axis at a depth of cut of 2 mm with an 8 mm diameter end mill over a length of 101 mm in a 1018 steel work piece. The width of cut was varied by 1 mm increments between 1 mm and 7 mm, in addition to a 7.5 mm width of cut. Table 1 summarizes the cutting conditions used. The chip load was maintained at approximately 0.03 mm/tooth to avoid excessive tool wear and breakage. Spindle Speed Cutter [
rev ] min ute
Feed Rate
[
Chip Load
mm ] min ute
[
mm ] tooth
M.R.R.
[
mm 3 ] sec ond
(1)
5426
330
0.033
11 - 83
(2)
7060
430
0.030
14 - 108
(3)
7060
860
0.030
29 - 215
Table 1: Process parameters for width of cut experiments. Once the power was measured for each width of cut experiment, the power demand was measured for the machine tool while air cutting, that is, while running the toolpath without material removal. This way the power associated with the material removal process could be extracted, known hereafter as the cutting power demand. The average air cutting power demand was found to be 1510 W for the cutter (2) process parameters, so it was subtracted from the average total power demand. Figure 1 shows the cutting power demand as a function of the M.R.R. for cutter (2). This plot has a slightly parabolic trend with a point of inflection at approximately 75 3 mm /s. The cutting power demand for the 7.5 mm width of cut was almost nine times greater than the 1 mm width of cut. Since the total air cutting power demand was only 1510 W, though, the resulting increase in total power demand of the machine tool was only 28%. Thus in terms of energy consumption, the operator still experiences energy savings with the increase in M.R.R.
Cutting Power [W]
450
430 W
400
367 W
350 300
281 W
250 200
206 W
150
149 W
100 0
Depth of Cut Experiments
Depth of cut experiments were also conducted on a 1018 steel work piece 101 mm in length. Cuts were made along the y-axis using 8 mm diameter, 2 flute uncoated and TiN coated carbide end mills under near slotting conditions (a width of cut of 7.5 mm). The power demand was measured at depths of cut of 1, 2, 4, and 8 mm. The chip load was maintained constant across the various cutters at 0.051 mm/tooth. The spindle speed and feed rate were varied, though, to account for higher loads on the machine tool during the depth of cut experiments (see Table 2 for a summary of the processing conditions). Spindle Speed Cutter [
rev ] min ute
Feed Rate [
mm ] min ute
Chip Load [
mm ] tooth
M.R.R. [
mm 3 ] sec ond
(1)
2500 - 3200
254 - 325
0.051
40 - 250
(2)
3250 - 4160
330 - 425
0.051
50 - 330
Table 2: Process parameter ranges for depth of cut experiments.
500
50
Figure 2: Average total power demand as a function of M.R.R. 2.2
106 W 34 W 0
58 W 50
100
MRR [mm^3/s] 2 flute TiN Coated Carbide
Average
Figure 1: Cutting power demand using cutter (2) while cutting 1018 steel.
Figure 3 summarizes the power demanded by the NV1500 DCG for the 2 flute TiN coated end mill (cutter (2)) and the energy consumed as a function of material removal rate. Although the power demand increases with load the energy consumption still drops drastically with the increase in material removal rate. The machine tool experiences a power demand increase of approximately two-thirds, whereas the energy consumption reduces to less than one-third of its original value. This shows that the decrease in processing time effectively dominates over the increase in power demand due to increased loads. Since the power demand was shown to increase with load, and experimentally this increase in load was not enough to increase the overall energy consumption, the trade-off between power demand and processing time will be analyzed.
Sustainability in Manufacturing - Energy Efficiency in Machine Tools 200
3,000
180
Energy [kJ]
140
2,000
120 100
1,500
80
1,000
60 40
500
20 0
Power Demand [W]
2,500
160
0 0
100
200
300
400
MRR [mm^3/sec] Energy
Power
Figure 3: Energy and power demand as a function of M.R.R. for depth of cut experiments with cutter (2). 2.3
Trade-off Between Power Demand and Processing Time
The machine tool’s electrical energy consumption is dependent on the power demand, pavg, and processing time, ∆t, as seen in Equation 1. Since the power demand shows some variability due to the internal cooling unit of the machine tool, the average power demand, pavg, will be used. As was mentioned previously, the average power demand is composed of a cutting, pcut, and air cutting, pair, component; consequently the energy consumption can be expanded as follows:
e pavg * t ( pcut pair ) * t
(1)
Two scenarios will be compared. Scenario (1) is the base scenario, while scenario (2) will be the scenario in which the material removal rate is increased for the purpose of reducing processing time. The constants, α and β, were created to represent the increase in pcut and decrease in ∆t, respectively (see Equations 2 and 3). Note that both constants are less than unity.
pcut 1
(2)
pcut 2
t 2 t 1
(3)
Equation 4 shows the relationship between pavg1 and pavg2, which assumes that the air cutting power demand, pair, remains relatively constant for both scenarios.
pavg 1 * pavg 2 pair * (1 )
265 with large work volumes which have a high standby power demand. Further work can be conducted in which the assumption that the air cutting power demand does not stay constant to expand the applicability of the power and processing time trade-off analysis. 3
CHARACTERIZING THE SPECIFIC ENERGY
The specific energy of various manufacturing processes was previously summarized by Gutowski et al. [7], but for any given manufacturing process the data was limited to only a sample of process rates. This study, though, will focus on milling machine tools and the operable range of the machining center when characterizing the specific energy. In characterizing the energy consumption of a machine tool, as the M.R.R. approaches infinity the specific energy is expected to reach a steady state of zero. But, given the work volume, spindle speed, and table feed constraints of a machine tool as well as the maximum loads that can be applied without deforming the main body frame or breaking the spindle motor, the operator will never reach a M.R.R. anywhere near infinity. So under the constraints of the M.R.R. a curve of the following form:
ecut k *
1 b M .R.R.
was fit to the data from the width of cut and depth of cut experiments. Note that the constant, k, essentially has units of power and b represents the steady-state specific energy. The total specific energy, which accounts for cutting and air cutting power demand, was indeed found to have an inverse relationship with the M.R.R. (see Figure 4). The air cutting power demand dominated the specific energy. The impact of the cutting power demand on the specific energy was minimal since at high loads (i.e. at high M.R.R.’s) the machining time decreased significantly. The specific energy decreases rapidly until a M.R.R. of 3 approximately 75 mm /s is reached. For M.R.R.’s lower than 75 3 mm /s, a slight increase in the material removal rate causes a sharp drop in the specific energy because machining time improves 3 dramatically. At M.R.R.’s greater than 100 cm /s, the gain from increasing the process rate is minimal since the specific energy begins approaching a steady-state value. This gain could be significant for work pieces requiring a substantial amount of material removal, but since the machine tool used in this study is a 3 micromachining center a M.R.R. greater than 100 mm /s would show only a minor decrease in energy consumption given standard work piece sizes.
(4)
If the relative size of the air cutting power demand is denoted by:
i
pair i pavg
(5) i
where i is 1 or 2 for scenarios 1 and 2, respectively, then the inequality presented in Equation 6 shows the condition that must be met in order for the energy consumption of scenario (2) to be smaller than that of scenario (1). 2
1
(6)
So if β is less than α, then e2 will always be less than e1. Also, as η2 increases (i.e. if the air cutting power demand comprises a large portion of the total power demand) then the probability of e2 being less than e1 increases. This would be the case for machine tools
(7)
Figure 4: Specific energy as a function of M.R.R.
266
Sustainability in Manufacturing - Energy Efficiency in Machine Tools
The best fit model was found to be:
results. The process parameters used in the experiment are outlined in Table 3.
e cut 1481 *
1 3.678 M.R.R.
(8)
where the first constant, a, is similar to the average air cutting power demand values. As was expected, the specific energies at low M.R.R.’s had such large variations (due to the internal cooling unit) that they surpassed the bounds of the model, but at high M.R.R.’s the specific energies were well within the bounds. Upper and lower bounds with a 95% confidence level are provided below: 1 e cut 1478 * 3.541 M.R.R.
(9)
1 e cut 1488 * 3.853 M.R.R.
The machine tool analyzed in this paper is a micromachining center. Larger machine tools can process material at higher rates, therefore shifting the specific energy curve to the right. But these machine tools will also have higher standby power demand due to the peripheral equipment [8] causing an upward shift in the specific energy curve (see Figure 5).
Specific Energy
Macro
Material Removal Rate
The aforementioned experiments were conducted with a low carbon steel work piece. The type of material being machined is also a factor in the cutting power demand of the machine tool, though. A plastic work piece, for example, is expected to generate a smaller load on the spindle motor than a metal work piece and therefore result in a lower cutting power demand. Since the cutting load is expected to vary with the work piece material, the following experiments were conducted to measure the power demand of the Mori Seiki NV1500 DCG while machining peripheral cuts on 1018 steel, 6061 aluminum, and polycarbonate. A depth of cut and width of cut of 2 mm and 4 mm, respectively, was used. The chip load of 0.0254 mm/tooth was maintained constant across the experiments, to allow for the comparison of the
Polycarbonate
[
mm ] tooth
0.0254
0.0254
0.0254
[
mm ] min ute
248
621
310
Spindle Speed
[
rev ] min ute
4889
12223
6112
M.R.R.
[
mm 3 ] sec ond
44
82.8
41.3
Table 3: Process parameters for power demand experiments with multiple work piece materials. The recommended cutting speed varied with the work piece material. Aluminum was cut at the highest speed, followed by polycarbonate, then steel. The use of coolant while machining aluminum was recommended by the cutting tool manufacturer due to the material’s ductility and its tendency to build-up on the cutting tool. Coolant was also recommended for polycarbonate to prevent it from melting because of the high temperature at the cutting tool and work piece interface. Steel can be cut without coolant (which would greatly reduce the total power demand of the machine tool), but since cutting fluid aids with chip exit and this study is primarily concerned with the cutting power demand, coolant was used when cutting all material types. The power demand of the NV1500 DCG is shown in Figure 6, and is broken down into cutting and air cutting power demand. The air cutting power demand is approximately the same across the three processing conditions. The difference is due primarily to the change in spindle speed, the highest of which was used while cutting aluminum. The difference in the power demanded by the axis drives was found to be negligible even though the feed rate for aluminum is more than two times that of steel. The cutting power demand shows greater variability for the three work piece materials. The cutting power was the greatest while machining the steel work piece. In fact, it was approximately 7% of the total power demand. This may be due to the fact that it has the highest tensile strength, followed by aluminum, then polycarbonate. The cutting power while machining the polycarbonate work piece was the smallest and almost negligible, only 1% of the total power demand. 1800
Power Demand [W]
EFFECT OF WORK PIECE MATERIAL ON POWER DEMAND
6061 Aluminum
Feed Rate
Figure 5: Shift in specific energy plot for larger machine tools. 4
1018 Steel
Units
Chip Load
(10)
This specific energy model can be used to estimate the total energy consumed while cutting. The part features and tolerances would dictate the size and type of machine tool required for part manufacture. The optimal M.R.R. can be determined using standard process parameters based on the work piece material and the appropriate cutting tool for the feature creation. Therefore, the total energy consumption while cutting can be calculated by multiplying the specific energy estimate by the volume of material removed.
Nano Micro
Parameter
1600 1400
117
87
14
1450
1500
1470
1200 1000 800 600 400 200 0 Steel Cutting Power
Aluminum
Polycarbonate
Air Cutting Power
Figure 6: Power demand of NV1500 DCG for steel, aluminum, and polycarbonate work pieces.
Sustainability in Manufacturing - Energy Efficiency in Machine Tools
267
A particular work piece material can be machined at a range of process parameters while maintaining minimal tool wear and good surface finish. So future experiments should be conducted in which the material removal rates overlap as much as possible across the work piece materials under study when calculating the cutting power demand for the purpose of comparison. Also, the power demand of the spindle motor and the axis feed drives should be measured directly since presently the cutting power demand is obtained by subtracting the air cutting power demand from the total power demand of the machine tool.
Machine Tool Technologies Research Foundation (MTTRF2009) 2009 Annual Meeting, pp. 47-50, Shanghai, China.
5
The specific energy model allows a product designer to estimate the manufacturing energy consumption of their part’s production without needing to measure power demand directly at the machine tool during their part’s production. Since the specific energy as a function of M.R.R. for the micromachining center presented herein varied by as much as an order of magnitude, it is important to use process parameters and machine tool-specific data to determine accurate electrical energy consumption. This model could therefore be used in place of aggregate embodied energy values for manufacturing processes as provided by [9] or to replace process estimates with great uncertainty when conducting hybrid life cycle assessments. ACKNOWLEDGMENTS
This work was supported in part by Mori Seiki, the Digital Technology Laboratory (DTL), the Machine Tool Technologies Research Foundation (MTTRF), Kennametal, and other industrial partners of the Laboratory for Manufacturing and Sustainability (LMAS). The authors would like to thank the UC Berkeley Mechanical Engineering Department’s Student Machine Shop for providing valuable insight and advice. For more information, please visit lmas.berkeley.edu. 7
Inamasu, Y.; Fujishima, M.; Hideta, M.; Noguchi, K. (2010): The Effects of Cutting Condition on Power Consumption of Machine Tools, in: Proceedings of the 4th CIRP International Conference on High Performance Cutting (HPC2010), Vol. 1, pp. 267-270, Gifu, Japan.
[7]
Gutowski, T.G.; Liow, J.Y.H.; Sekulic, D.P. (2010): Minimum Exergy Requirements for the Manufacturing of Carbon Nanotubes, IEEE International Symposium on Sustainable Systems and Technology (ISSST2010), Washington, D.C.
[8]
Behrendt, T. (2010): Development of a Simulation-Based Application to Derive and Estimate Potentials of Efficiency Measures for Diverse Machine Tool Processes, Diploma Thesis, Braunschweig University of Technology.
[9]
Ashby, M.F. (2009): Materials and the Environment: Ecoinformed Material Choice, Butterworth-Heinemann, Burlington, MA, USA.
CONCLUSIONS
This study has shown that the machining time dominates energy demand for high tare machine tools. Additionally, it has provided a method for characterizing the specific energy of a machine tool as a function of process rate, which can be extended to other types of manufacturing processes.
6
[6]
REFERENCES
[1]
Diaz, N.; Helu, M.; Jayanathan, S.; Chen, Y.; Horvath, A.; Dornfeld, D. (2010): Environmental Analysis of Milling Machine Tool Use in Various Manufacturing Environments, IEEE International Symposium on Sustainable Systems and Technology (ISSST2010), Washington, D.C.
[2]
Carnegie Mellon University Green Design Institute. (2010): Economic Input-Output Life Cycle Assessment (EIO-LCA), Available from: http://www.eiolca.net/
[3]
Diaz, N.; Choi, S.; Helu, M.; Chen, Y.; Jayanathan, S.; Yasui, Y.; Kong, D.; Pavanaskar, S.; Dornfeld, D. (2010): Machine Tool Design and Operation Strategies for Green Manufacturing, in: Proceedings of the 4th CIRP International Conference on High Performance Cutting (HPC2010), Vol. 1, pp. 271-276, Gifu, Japan.
[4]
Rangarajan, A.; Dornfeld, D. (2004): Efficient Tool Paths and Part Orientation for Face Milling, in: Annals of the CIRP, Vol. 53, No. 1, pp. 73-76.
[5]
Diaz, N.; Helu, M.; Jarvis, A.; Tönissen, S.; Dornfeld, D.; Schlosser, R. (2009): Strategies for Minimum Energy Operation for Precision Machining, in: Proceedings of the
An Investigation into Fixed Energy Consumption of Machine Tools 1,2
1,3
1,2
Wen Li , André Zein , Sami Kara , Christoph Herrmann
1,3
1
Joint German-Australian Research Group in Sustainable Manufacturing and Life Cycle Management
2
School of Mechanical and Manufacturing Engineering, Life Cycle Engineering and Management Research Group, The University of New South Wales, Sydney, Australia
3
Institute of Machine Tools and Production Technology (IWF), Product- and Life-Cycle-Management Research Group, Technische Universität Braunschweig, Germany
Abstract Improving energy efficiency of manufacturing processes requires knowledge about the energy consumption as a function of the machine tool and cutting process itself. Both theoretical and empirical models of unit process energy consumption have emphasized the relevance of fixed energy consumption which ensures the machine readiness. However, the machine tool behavior during the stand-by mode is lack of thorough study. This paper presented the investigation of fixed energy consumption from definition and description to improvement strategies. Six machine tools covering different manufacturing processes are selected for this investigation in order to evaluate the future savings. Keywords: Machine Tools; Fixed Power; Energy Consumption
INTRODUCTION
The reduction of electrical energy demands in the use phase of machine tools is an essential key to improve the environmental performance over the entire life cycle. Preliminary environmental studies for machine tools used in discrete part manufacturing (e.g. turning and milling) indicate that more than 99% of the environmental impacts are due to the consumption of electrical energy [1]. Although the use of electrical energy is crucial from the environmental and also the economic point of view, studies are primarily based on rough estimates considering averaged demands. In order to increase the reliability, case-specific energy demands of machine tool processes are presently measured and transformed into models supporting the quantification of energy demands [2]. Traditional estimation of cutting energy for turning and milling processes is based on process parameters, which has been a starting point for the optimization of machining processes under energetic aspects as well as the derivation of capacity requirements for the machine tool [3]. As this method is restricted to the cutting process and solely capturing the energy for material removal, the unavoidable energy demand ensuring an operational readiness of the machine tool is yet disregarded. Recent theoretical exergy framework and empirical models of unit process energy consumption have emphasized the relevance of fixed energy consumption which ensures the machine readiness [45]. More importantly, the fixed power demands continuously accumulate the total energy consumption throughout the work shift. However, the machine tool behaviour during the stand-by period is lack of thorough studies. This paper investigates the energy consumption of the machine tool for reaching and remaining operational readiness. The initial point therefore is the definition of a fixed power demand of a machine tool. Starting from this definition, the fixed power demands of six machine tools covering three different manufacturing processes are physically measured and analysed. The fixed power demands are described on a component basis by considering the machine configuration and given power demands of components. Within the detailed information, the energy requirements of machine
availability are discussed as an enabler to promote energy efficiency of machine tools. 2
FIXED ENERGY DEMAND OF MACHINE TOOLS
2.1
Definition of Fixed Energy Demand of Machine Tools
The energy consumption of a machine tool results from the temporal power demand which is not static but rather dynamic throughout a machining process. It is influenced by the design of the process and the selected machine tool. Power meters enable to capture the specific power demand of a machining process which consequently provides a basis to recognize actions (e.g. acceleration of spindles). Reviewing the power profile of an exemplary turning process in Figure 1, the start-up of the machine, spindle and material removal as well as the resulting power states can be determined [4]. With consideration of these states the power demand can generally be differentiated into a variable and a constant portion [5]. 8000
7000
6000
Effective Power (W)
1
Spindle Accelerate
5000
4000
Spindle Start
3000
Material Removal start
Unproductive Power Tool Tip Power
Ready for Operation
2000
Operational Power Fixed Power
Machine Start
1000
Machine Switch Off
0 1
101
201
301
401
501
Time 601
701 (0.1S)
Figure 1: Power profile of a turning process [4].
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_47, © Springer-Verlag Berlin Heidelberg 2011
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269
The variable power considers the process-related demand to conduct the machining operation. This includes not only the power required for removing material but also the process-depended operation of components (e.g. spindle rotation and movement of axis). Apart from that, the constant power demand resumes the fixed, machine-related power ensuring a functional mode of operation (ready for operation) [3]. While the variable power aggregates the power demand of motors and spindles enabling the material removal, the fixed power demand also includes the power demand of operating components as motors and spindles [6]. These devices are energized to maintain in position. With regard to components and supporting a better understanding of the power demands (see Figure 1), an extension to the given power classification is proposed considering the following four power segments [4]:
Time for operational readiness: duration from machine start-up until all the indispensable components are activated to ensure operational readiness.
Energy consumption for start-up: the total energy consumption during the start-up period.
Fixed power: as defined before, it can be used to estimate energy consumption of remaining machine availability.
Time for machine power-off: duration from machine switch-off until every component is inactivated.
Energy consumption for power-off: consumption during the power-off period.
3
DESCRIPTION OF FIXED POWER DEMAND
Fixed power: power demand of all activated machine components ensuring the operational readiness of the machine tool;
Operational power: power demand to distinctively operate components (e.g. move axis or rotate spindle) enabling the cutting as performed in air-cuts;
In order to derive the energy index of machine readiness and availability, energy metering and monitoring is essential to obtain authentic information of each individual machine. It requires a high resolution to capture the instantaneous power peak and the rapid changes of machine states. Therefore, the sample rate should be less than 0.5s.
Tool tip power: power demand at tool tip to remove work piece material;
Unproductive power: power converted to heat mainly due to frictions during the material removal.
2.2
Technologies of Reducing Fixed Energy Consumption
As mentioned before, the fixed energy consumption is highly relevant for reducing energy consumptions of manufacturing processes throughout different machine states. Different energy efficiency measures aim at reducing the fixed energy demands through improved machine tool design as well as optimized process design. From the machine tool builder’s perspective, the improvement of machine tool design includes for instance from substitution of inefficient electrical motors to use of break for non moving axes [1]. As a result, the instantaneous fixed power could be minimized. For the existing manufacturing plant, process control measures enable the optimal utilization of the process by eliminating nonvalue adding tasks. However, standby state of each machine tool is unavoidable in most scenarios. The smart energy saving technologies has thus been introduced by control suppliers such as DMG Energy Save, which automatically power off or switch machine tool into hibernate mode once the standby period exceed a customer defined limit [7]. In order to achieve an optimal result, the configuration of the smart control system requires detailed information about machine readiness and availability. Therefore, the energy requirements of ensuring operational readiness are focused in this paper. 2.3
The Energy Index for Machine Readiness and Availability
As shown in Figure 1, machine tool passes different state to achieve operational readiness. During this period, machine tool not only consumes electrical energy but also requires a certain amount of time to achieve operational readiness. The same situation is applied to machine power-off stage. Therefore, the following energy related indicators are selected for evaluating machine readiness and availability.
Peak Power: maximum instantaneous power requirement during the start-up period, which is relevant to the additional energy cost due to power peaks.
the
total
energy
A component-based description of fixed power consumption is also important to offer insights of machine behaviour. The starting point is to identify all the electrical components as well as their characteristics. Then, the constitution of the fixed power should be described. Preliminarily, Others like Dietmair et al used finite approach to assign the energy consumption to each component according to different operation sequences [2]. However, only the hydraulics can be quantified based on observed energy curve. Owing to the physical constraints of power measurement at a component level, alternative information resources were utilized, such as wiring scheme. It should be noted that the rated power given in machine documentations is an estimated value under either nominal or extreme conditions, which initially indicates the capacity of the electrical motor. Therefore, the calculated fixed power breakdown is compared with real data to provide approximate information of each component. The analysis comprehends six machine tools covering three different manufacturing processes (Table 1). Machine tool
Type
Studer S40
Universal grinding machine (3 spindles)
Studer S120
Internal grinding machine (1 spindle)
Colchester A50
CNC lathe (1 spindle)
MS NL 2000MC/500
CNC lathe with milling functionality (2 spindles)
MS Dura Vertical 5100
Vertical milling machining centre (1 spindle)
DMU 60 P
5-axis milling machining centre (1 spindle)
Table 1: Machine selection for conducting the study. 3.1
Experimental Consumption
Measurements
of
Fixed
Energy
For each of the above listed machine tools, the power demand was measured at the main switch with a National Instrument (NI) system, which is developed on a LabVIEW interface in conjunction with a NI data acquisition device. The voltage and current signals are captured and recorded every 0.1 second via NI module 9225
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and 9229 respectively. Three Fluke current clamps were applied to convert current signals into voltage signals. The captured signals are processed simultaneously to the LabVIEW interface via NI compact chassis Cdaq-9172. The power measurement started from turning on the main power switch until each machine tool was ready for operation. The above measured period varies in terms of duration due to different start up procedures. Although the power consumption during standby-mode fluctuates within a small derivation, the fixed power is relatively static comparing to machine start-up and operation periods. In order to indicate the power behaviour, the start-up to operation for each device is illustrated in Table 2.
periphery system, cooling and lubrication system, control system and auxiliary system [3].
Pfixed =3.69
3 0
Drives
6
Studer S40
Spindle
Effective Power (kW)
9
Based on the operation of components, the power profile of a machining process represents the accumulation of the individual power demands for each component (see Figure 2). The research scope is limited to a machine tool with its integrated components; disregarding additional peripheral devices as coolant filter systems. Other components, such as coolant pump motor, chip conveyer motor, and tool library systems are only activated when they are commanded during processing stage. In this case, they are thus excluded for fixed power description.
Pfixed =1.69
Studer S120
2 0
Effective Power (kW)
Pfixed = 1.16
Colchester Tornado A50
2
Besides rotary motion, holds as well as centres work piece
Rotary Tool Spindle Motor
Rotary motion for cutting tool
i-Axis Motor
Hydraulic Unit Motor
Rotary motion for pump to supply clamping pressure
Lubricant Pump Motor
Rotary motion for pump to supply lubricant
Oil Cooler Pump Motor
Rotary motion for pump to supply oil cooler circuit
Turret Motor
Effective Power (kW)
4
Mori Seiki NL2000MC/500
Pfixed = 1.58
2
2
Mori Seiki DuraVertical 5100
Pfixed = 1.02
1
Auxiliary System
0
Effective Power (kW)
9 6 3 0
Spindle Amplifier/ Frequency Converter Servo Amplifier /Frequency Converter Computer and Display
DMU 60P Pfixed = 5.45
Table 2: Measuring fixed power for each machine tool. Deduction of Fixed Power Demand
Machine tools can be characterized as assemblies of components ensuring a specific function [6]. Each component performs a particular act enabling the entire machine to perform more complex, useful functions. Table 3 summarizes briefly the individual functions of electrical components in machine tools which can generally be classified into spindle drives, servo drives, hydraulic system
Periphery System
Effective Power (kW)
3
Control System
0
3.2
Cooling Lubrication System
0 6
Linear motion for cutting tool towards i-axis Besides rotary motion, holds as well as centres work piece at tailstock Rotary motion for cutting tool change
Tailstock Spindle
6 4
Main Spindle Motor
Servo Drives
4
Function
Hydraulic System
Effective Power (kW)
6
Component
Lightning Fan Coolant Pump Motor Chip Conveyer Motor Tool Change Arm Motor
Transfer numerical control signal for spindle rotation speed into adjusted electrical signal Transfer numerical control signal for servo feed into adjusted electrical signal Processing and visualization of program Lightning the working area Air flow generation for cooling electrical components Rotary motion for pump to supply coolant circuit with pressure Rotary motion for chip conveyer Rotary motion for tool change
Table 3: Electrical components in machine tools. Considering the power characteristics of components two types can generally be identified. The first type considers components that are either fully activated or not. The auxiliary components are generally under this category. Although the power demand of hydraulic pump depends on the desired pressure, the oil pressure remains constant
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throughout the stand-by and processing stages. The change of hydraulic pressure only occurs when unclamping the chuck. Hence, the hydraulic system can be classified as a static component. The same reason can be applied to cooling and lubrication system due to the continuous operation with a constant load. The second type covers components that are operated with a dynamically adjusting load. Generally, the spindle and servo motors rotate at variant speed. The dynamic torque and load on the drive system requires frequent adjustments [8]. The power demands for the control system (e.g. frequency converter) thus vary accordingly. Therefore, the spindle drives, servo drives and associated control system are grouped as adjusted components, as illustrated in Figure 2.
Instead of relying on real time measurement, the wiring scheme, circuit diagram and machine specifications were utilized for the fixed power estimation of each component. According to EN 60204 wiring schemes document integrated components as well as their power demand in detail in order to facilitate awareness about safety, function and maintenance of electrical equipment used in machines [9]. Thus, the power demand of components at nominal operational conditions can easily be derived for these documents. With other documents (e.g. circuit diagram, machine manuals), the rated power or nameplate power can be obtained for the major electrical components. This given value is the power output of a device under specific or nominal operating conditions, which indicates the capacity of each motors and drives with a continuously load [8]. Table 4 is an exemplary list of component rated power for Colchester A50.
0
1 activated (t)
0
0
Cooling & Lubricant
1 activated (t)
0
Power demand due to component activation and operation
Auxiliary System
1 activated (t)
Pconstant
Pconstant
Pconstant
PFC(load)
PS(load)
PM(load)
Control System
Spindle Drives
100% load (t)
0
100% load (t)
Servo Drives 0
100% load (t)
Power (W)
Hydraulic System
t
Time (s)
Figure 2: Power agglomeration of components to the power demand of the machine tool.
3.3
Component-based description of Fixed Power demand
Physical measurements at a component level require access to each sub-system. Conventional energy metering system can only capture the voltage and current data with constant frequency, which is not suitable for the devices with variant frequency such as amplifier, frequency converter and servo drives. The metering point at the main switch also limits the transparency of machine behaviour, since only significant power changes can be detected from the observed curve. Category
Component
Rated Power (kW) [10]
Auxiliary System M1 Cross Slide Fan
0.040 0.020
Lightning
0.036
[10]
Computer and Display
-
Hydraulic System Hydraulic pump motor
0.550
Cooling and Lubrication
Cooling Pump
[10-11]
-
Lubricant Pump
Sub-total Control System
0.646
Spindle Amplifier Module
0.120
[12]
Servo Amplifier Module
0.093
[12]
[10-11]
X-Axis Servo Motor
0.500
Z-Axis Servo Motor
0.500
Turret Servo Motor
0.300
Spindle Drive
Spindle Motor
5.500
Periphery
Coolant Pump
Servo Drives
[10]
Spindle Motor Fan
[10-11] [10-11] [10-11] [10]
0.370
Table 4: Rated power list of each component in Colchester A50.
For most of the tested machines, all the static systems are activated, such as hydraulic system, auxiliary system, cooling and lubricant system. The hydraulic, cooling and lubrication system have been identified as the main electrical consumers for machine readiness. For those major components, the rated power values highly agree with real measurements. For instance, the hydraulic pump of Colchester A50 is rated as 0.55 kW. The power curve of machine start-up indicates a significant power increase due to hydraulic activation from 0.45kW to 1.05 kW. Another example is for the complex 5-axis machine centre DMU 60P. The cooling unit normally remains idle until the temperature exceed a certain degree, while the power demand increases from 5.45 kW to 6.8kW. The rated power values of all the cooling components which includes compressor, pump and fans sum up to 1.25 kW. However, the aggregation of available rated power of static components (e.g. 0.646 kW) does not fit the real measurements (e.g. 1.16 kW). The differences are mainly due to following reasons: 1. Lack of information about several components; 2. Difference between input power and output power; 3. Disregards the adjusted power demand for the servo drives and the control system. For some reviewed machines, the rated power is not provided for each electrical component. Generally, the machine specifications only include the nameplate power for the main drive motors. As shown in table 4, the energy related information for computer, cooling and lubricant system is yet absent. Certainly, the input power is not equal to output power as the energy transmission efficiency is constantly less than 1. In addition, the rated power is estimated under nominal operating conditions which may not be the same situation as machine standby. Nevertheless, the difference is relatively small as the static components operate under constantly low load. Apart from the components with constant power demand, the servo motors and the associated control system are also activated [13]. For the turning and milling machine tools, the servo motor holds the cutting tool at the home position against the gravity of the cutting tool, which requires the servo motor constantly adjusting the tool position with relatively low loads. The servo amplifier or associated frequency converters are hence activated accordingly. However, the specific loads on the servo motors are unlikely to be quantified, especially during the stand-by stage. The self-consumed electrical energy of frequency converters or amplifiers is even more difficult to derive. The further investigation of the Fanuc amplifier (used for Colchester A50) showed that the heat dissipation of the servo amplifier is 93W under normal operational loads [12]. Since the servo motor is only positioning, all the rated power demands of servo motors and associated control units are scaled down accordingly.
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Sustainability in Manufacturing - Energy Efficiency in Machine Tools
By comparing the experimental power demand at different machine states with the rated component specific power features, a component-based description of fixed power demand is therefore obtained, as summarized in Figure 3 [10-12, 14-19].
DMU 60P
Servo Drives 17% Control System 6%
Σ 5.45
MS Dura Vertical 5100
Σ 1.02
Auxiliary System Cooling & Lubrication System
MS NL2000MC/500
Σ 1.58
Hydraulic System
Σ 1.16
Studer S120
Servo Drives
Σ 1.69
Figure 4: Average fixed energy breakdown of reviewed machine tools.
Σ 3.69
Studer S40
0
1
2
3
4
5
6
Figure 3: Component-based description of fixed power consumption. 4
STRATEGIES FOR FIXED ENERGY REDUCTION
Energy consumption reduction generally includes two strategic approaches. One is by reducing the instantaneous power demands, which requires an energy oriented design of machine tool at a component-base; the other one is by shortening the period of fixed power integration, which can be achieved by both increasing machine utilization as well as switching-off the machine to reduce the standby time. To practice above energy saving strategies, it is essential to acknowledge the energy performance of the machine tool during start-up, standby and power-off stages. However, the existing machine documentations do not provide sufficient information for energy consumption estimation. Moreover, energy metering and monitoring of each individual machine is time consuming and costly. Generally, it requires 1.5 hours for applying the metering system, measuring the process and analyzing the data with hourly costs of US$50. The initial installation cost is even more expensive since specialized and certified technician is required for connecting the high voltage. For a large plant, there are normally hundreds machine tools differentiating from age to capacity. It is nearly impossible to derive energy profile for each individual machine tool. In order to avoid further physical measurements at machine level, it is important to include power demand of the electrical consumer at different stages in the machine manuals. 4.1
Cooling Lubrication System 31%
Hydraulic System 27%
Contol System Colchester A50
Auxiliary System 19%
Componented-based demands
improvements
of
fixed
power
A reduction of fixed energy consumption can directly be achieved by technical measures that reduce the fixed power demand of components through applying energy efficient devices. Figure 4 illustrates average fixed energy breakdown of the reviewed machine tools. It suggests that the improvements of hydraulic system, cooling and lubrication system can save up to 58% of fixed energy consumption.
The self-regulatory initiative lead by Cecimo has listed potential measures for energy improvements of machine tools. For instance, the hydraulic unit and cooling lubricant unit are suggested to be coupled with control system, and be activated only on operating stage. For the auxiliary units, the line connected motors can be replaced by inverter motors. The feed axes during non moving axes should be also switched off and clamped by a brake [1]. In order to precisely estimate the leverage of the energy efficiency measures, the component-based description of fixed power consumption needs to be improved in terms of its authenticity and level of detail. 4.2
Process optimization consumption
for
reducing
fixed
energy
As mentioned earlier in this paper, the fixed power adds the total energy throughout different machine states. Different strategies should be applied according to the status of the machine tool. During the operation stage, the fixed power sets the power base for functional performance. According to the studies about specific energy consumption (SEC), the share due to fixed power consumption (specific fixed energy) can be reduced by increasing the process rate [4]. In other words, a higher production speed results less fixed energy consumption per product. For the non-continuous manufacturing processes, such as turning and milling, the standby time is unavoidable but feasible to be minimized. The main strategy here is to switch-off the machine if standby time is long enough. It is critical to notice the functionality of the fixed power, which ensures the machine readiness for operation. Once the machine is completely switched off, a specific time is needed to regain operational readiness. Therefore, the energy saving measures should not sacrifice the availability of the machine tool. As defined in section 2.3, the energy related index is compared among selected machine tools (see in Table 5). The study cycle starts from machine power-off until the machine readiness is retrieved. A 30 min idle period is assumed for the comparison between temporarily switching off the machine and keeping machine standby.
Sustainability in Manufacturing - Energy Efficiency in Machine Tools
273
4
9
2.4
9
Time to power-off (s)
15
10
10
15
10
15
Energy to power-off (kWh)
0.012 0.004 0.001 0.005 0.002 0.025
Fixed Power (kW)
3.69 1.69 1.16 1.58 1.02 5.45
Time to standby (s)
250
Energy to standby (kWh)
0.256 0.036 0.004 0.038 0.021 0.096
Studer S40
110
30
150
110
100
Cecimo (2009): Concept Description for CECIMO’s SelfRegulatory Initiative (SRI) for the Sector Specific Implementation of the Directive 2005/32/EC, available online: http://www.ecodesign-info.eu/documents/Machine_tools _VA_20Oct09.pdf, last revised 8/2/2010.
[2]
Dietmair, A., Verl, A. (2009): A generic energy consumption model for decision making and energy efficiency optimisation in manufacturing, in: International Journal of Sustainable Engineering, 2: 2, 123-133.
[3]
Kalpakjian, S; Schmid, S. R. (2006): Manufacturing Engineering and Technology, Ed. 5, Prentice Hall, S. 220.
[4]
Li, W., Kara, S. (2010): An Empirical Model for Predicting Energy Consumption of Manufacturing Processes: A Case of Turning Process, in: Proceedings of the Institution of Mechanical Engineers, Part B, Journal of Engineering Manufacture. In Print.
[5]
Gutowski, T., Dahmus, J., Thiriez, A. (2006): Electrical Energy Requirements for Manufacturing Processes; in: 13th CIRP International Conference on Life Cycle Engineering, Leuven, Belgium.
[6]
Weck, M., Brecher, C. (2006): Werkzeugmaschinen Konstruktion und Berechnung, 8. Ed., Springer.
[7]
DMG. Energy Save. Online: http://za.dmg.com/ino/mailing_ energysave_10/en/energysave.htm.
[8]
Crowder, R. M. (1995): Electric Drives and their Controls, Oxford Science Publications.
[9]
International Electrotechnical Commission (2009): IEC 602041, Safety of machinery - Electrical equipment of machines.
[10]
Colchester Co. (2000): Circuit Diagram of Colchester Tornado A50.
[11]
Colchester Co. (2000): Manual of Colchester Tornado A50, T5F-E02-11/98.
[12]
Fanuc Co. Fanuc AC Servo Amplifier Alpha Series Descriptions. B65162E/03. Online: http:// www.docstoc.com docs/28703564/Fanuc-AC-Servo-Amplifier-Alpha-SeriesDescriptions.
[13]
Nailen, R. L. (2002): When is a motor idle?, in: Electrical Apparatus, January 2002, Barks Publications.
[14]
Fritz Studer AG (2007a): Technical Manual for Studer S40.
[15]
Fritz Studer AG (2007b): Technical Manual for Studer S120.
Total Energy Cost for Temporary Switch Off (kWh) 0.268 0.040 0.005 0.043 0.023 0.121 Energy Saving if machine idle for 30min (kWh)
1.577 0.805 0.575 0.747 0.487 2.604
Table 5: Comparison of energy index of machine readiness. Firstly, the peak power has been detected during the machine startup period. Although the peak value is less than the spindle acceleration or other rapid changes of motor states, this instant drawing current may cause additional energy cost due to grid quality. The time from machine power-off to standby differentiates among the reviewed machine tools. While grinding machine Studer S40 requires 3.5 mins for pre-lubrication, the Colchester A50 can regain operational readiness in half minute. When applying an automatic energy saving system, the default switch-off time should be selected according to the cycle-time from machine switch-off to standby. Based on the assumed scenario, the machine tool with high fixed power demand, such as DMU 60P, Studer S40, can result significant savings by temporally switch-off the machine. It should be noted that the machine is completely powered off in this estimation. Recent technologies may allow machine to be easily restarted through the receipt of the familiar emergency shut-off operating mode [7]. Both power-off and hibernate mode requires customized analysis of fixed power consumption. Since the energy saving device cost more than ￡1,099 pre device, the cost efficient estimation should be also included in practice. 5
SUMMARY
This paper presented the investigation of fixed energy consumption from definition and description to improvement strategies. The awareness of fixed power demands have been raised due to the considerable share of the total energy consumption. While physical measurements derive the authentic power behaviour during different machine states, the machine documentation should also provide sufficient information of energy consumption to improve the transparency of the machine tool. The strategies for fixed energy reduction are discussed and evaluated considering both machine design measures and operational process optimizations. 6
ACKNOWLEDGEMENTS
The lead author kindly acknowledges the funding provided by the Advance Manufacturing Cooperative Research Centre for this research. This paper was compiled by the Joint German-Australian Research Group “Sustainable Manufacturing and Life Cycle Management” funded by the BMBF under reference AUS 09/AP1 and managed by the International Bureau of the BMBF at DLR.
REFERENCES
[1] DMU 60P
MS Dura Vertical 5100
6
Peak power (kW)
Colchester A50
9
Studer S120
MS NL2000MC/500
7
[16]
Mori Seiki Co. (2004): Technical Manual of NL Series.
[17]
Mori Seiki Co. (2004): Service Information Folder – Electrical Circuit Diagram NL Series, 148037B05.
[18] Mori Seiki Co. (2008): Service Information Folder – Electrical Circuit Diagram Dura Vertical, 148440A22. [19]
Deckel MAHO Co. (2001): Circuit Diagram of DMU_60P.
Energy Efficiency Measures for the Design and Operation of Machine Tools: An Axiomatic Approach 1,2
1,3
1,2
André Zein , Wen Li , Christoph Herrmann , Sami Kara
1,3
1
Joint German-Australian Research Group in Sustainable Manufacturing and Life Cycle Management
2
Institute of Machine Tools and Production Technology (IWF), Product- and Life-Cycle-Management Research Group, Technische Universität Braunschweig, Germany 3
School of Mechanical and Manufacturing Engineering, Life Cycle Engineering and Management Research Group, The University of New South Wales, Sydney, Australia
Abstract Due to environmental legislation and increasing customer demand, the development and deployment of energy-related improvement measures for machine tools has intensified. These measures centre different aspects of machining as integrating energy efficient components or applying start-stop strategies. Although the measures aim for a reduction of energy demand, guidance on the selection and prioritization of efficiency measures is necessary in order to identify adequate methods and create awareness about the effects and interdependencies. Using axiomatic design a matrix is developed that relates functional requirements of improving energy efficiency of machine tools to design parameters. Keywords: Machine Tools; Energy Efficiency; Axiomatic Design
1
INTRODUCTION
The reduction of electrical energy demands in the use phase of machine tools is an essential key to improve the environmental performance over the entire life cycle. Preliminary environmental studies for machine tools used in discrete part manufacturing (e.g. turning and milling) indicate that a proportion of more than 99% of the environmental impacts is due to the consumption of electrical energy [1]. As a consequence, the improvement of the environmental performance of machine tools is enforced by European environmental legislation through the preparatory initiation of an ecodesigndirective and moreover approached by self-regulatory initiatives of the machine tool industry [2, 3]. Measures to improve energy consumption of machine tools provide substantial leverage to reduce the associated environmental impacts in the use phase. The development of those measures comprises organizational as well as technical aspects. While organizational measures focus on the mode of operation of a machine tool, technical measures for instance address specifically the substitution of components through energy efficient alternatives. Although all measures are determined to improve the energy consumption, a systematic concept that provides structured guidance for the implementation of energy efficiency measures and associated impacts is yet absent. Based on the initial functional requirement to reduce the energy consumption for a machining cycle and in accordance with the axiomatic design theory, a decomposition matrix is developed which decomposes functional requirements and design measures for improving energy efficiency of machine tools. The proposed concept intends to successfully guide the implementation of energy efficiency measures for machine tools. 2
IMPLICATIONS OF THE ECODESIGN DIRECTIVE ON MACHINE TOOLS
The ecodesign directive provides an EU wide framework that defines ecodesign requirements for products which either directly (energy-using) or indirectly (energy-related) impact the environment through the consumption of electrical energy in the use phase of the
life cycle [4]. The directive is applicable for products which can be characterized by the following three criteria. Products should have significant sales and trade relevance with more than 200.000 units sold in the EU per year, considerable environmental impacts due to the use of energy (anticipated as 1000 PJ of primary energy) and a notable potential for improvement in terms of its environmental impact without entailing excessive costs [2]. As a result of the initiation of an ecodesign directive, rules and criteria are determined that limit the energy demand of products made available to markets in the European Union. Moreover, rating schemes can be defined that classify products according to the energy efficiency. An example therefore is the energy efficiency rating for electrical motors which evaluates the energy efficiency through relating mechanical output power to electrical input power in defined performance test procedures [5]. Preliminary studies which elaborated the extension of the ecodesign directive to new product groups estimated the associated environmental impacts of machine tool usage and pointed out the existence of eminent improvement potential. Within the analyzed product groups, machine tools ranked third place with a primary energy demand of 17.475 PJ per year [6]. In addition, promising saving potentials have been identified as improving the power factor, reducing the power demand in idle mode as well as integrating variable speed drives [6]. As a consequence of these preliminary studies, the implementation procedure described by the ecodesign directive was opened in 2008 aiming at the improvement of the environmental performance of machine tools and commenced with the initiation of a machine tool related product group study in 2010. Triggered by these efforts and industry self-regulatory initiatives, the development and deployment of energy-related improvement measures have further intensified starting, for instance, from the development of energy efficient components to the integration of energy-management principles into machine tool controls [1]. Prior to the analysis of improvement measures, the associated energy demand of machine tool operation is described in order to derive the objectives and scope for the axiomatic decomposition.
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_48, © Springer-Verlag Berlin Heidelberg 2011
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ENERGY EFFICIENCY OF MACHINE TOOLS
3.1
piece (so called air-cut) and the material removing capacity [13].
Power Demands of Machine Tools
Machine tools represent stationary assemblies that are fitted with (or intedend to be fitted with) a drive system other than directly applied human effort. They consist of joined parts and moving components enabling the entire machine tool to perform a complex, useful function which is the geometric shaping of workpieces made of arbitrary materials using appropriate tools and technologies [7, 8]. In machine tools electrical energy is transformed into mechanical or other desired forms of energy. The energy consumption of a machine tool results from the temporal accumulation of the individual power demands for each component (see Figure 1) [9]. Thus, the power demand is not static but rather dynamic throughout the machine tool operation. It is influenced by the design of the process and the selected machine tool configuration. Machine tool Sensor systems
In this case, the power measurement for the grinding machine shows that more than 3.5 kW are required as fixed demand and that the power demand increases up to 5.2 kW throughout processing including the variable power demand. These power demands do not consider the power demand of the mandatory filter unit which is providing coolant to the machine and continuously operating. However, the power profile enables to evaluate machining processes with regard to their energy demand [14]. The two displayed grinding processes differ only in the value of the MRR and are given in Table 1. The processing times and energy demands are derived from the data of the power measurement; initially considering the activation of the spindle to the final stop of the spindle for each process. The st results show that the 1 process with higher MRR prevails in terms of energy consumed per removed material. If the energy demand is allocated to the removed material volume and processing time, the resulting specific energy demand for the processes decreases with reduced MRR.
Power demand due to operation of components
st
Control
P1(t)
P2(t)
P3(t)
P4(t)
P5(t)
P6(t)
Frequency converters
Spindles
Power (W)
Signal elements
275
Servo motors
2 Processing
Processing time (s)
121
246
Total energy (Wh)
321
482
110
253
211
229
3600
3600
0.089
0.134
0.00074
0.00054
- Fixed energy (Wh) Time (s)
- Variable energy (Wh)
t
3
Material removed (mm ) Total energy per removed -3 material (Wh mm )
Figure 1: Agglomeration of power demands - according to [9, 10]. Power meters enable to capture the dynamic power demand of a machining process which consequently provides a basis to recognize actions and associated power demands. Reviewing the power profile of an exemplary internal cylindrical grinding process in Figure 2, the start-up of the machine tool and spindle as well as two machining processes with varying material removal rates (MRR) can be determined. Based on the resulting power demand, a variable and fixed portion can generally be differentiated [11, 12]. While the fixed power covers the constant demand, which is necessary to ensure a functional mode of operation (ready for
nd
1 Processing
-3
-1
Specific energy (Wh mm s )
Table 1: Comparing machining processes according to the energy demands. With regard to the total energy demand for a machining process, optimization measures should aim at maximizing the MRR in order to reduce the impact of the fixed power. However, while increasing the MRR it is also absolutely essential not to neglect the resulting process conditions and work piece quality.
operation), the variable demand power considers the power for carrying out the machining operation without touching the work
Power
Power factor
1,0
6
0,8
5 4
0,6
Variable demand
3
0,4
2
Fixed demand
0,2
1 0 0 Machine start up
100
200
Initial spindle lubrication
300 Spindle start
400 1st Processing
500 Idle
600
700
2nd Processing (with 50% MRR)
Figure 2: Power demand of a grinding machine (two processing cycles).
800
0,0 Time900 (s)
Machine turned off
Power Factor
Effective Power (kW)
7
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Sustainability in Manufacturing - Energy Efficiency in Machine Tools
In addition to the power demand, Figure 2 also displays the power factor which indicates how efficiently the machine tool is using the power supplied to it [15]. While the power factor generally remains at 0.8 in the example of the grinding machine, the start-up and shutdown of the machine tools leads to power factors less than 0.5. Thus, only 50% of the power supplied is used as effective power.
Reusing invested energy
With regard to the power demand and power factor resulting from the operation of the grinding machine tool, the indicated saving potentials given in the EU directive can easily be identified. This includes the improvement of the low power factor and most importantly the reduction of power demands in non-processing times (e.g. reduce fixed power during spindle lubrication and in idle mode). Although saving potentials can be determined to improve the energy consumption, a systematic concept that provides structured guidance for the selection and implementation of energy efficiency measures and the associated impacts is yet absent.
Recovering energy
Classification Scheme to Improve Energy Efficiency of Machine Tools
In general, energy efficiency is defined as relation of output to energy input. Energy efficiency can furthermore be specified using a variety of indicators based on physical, economic or thermodynamic reference parameters [16]. Due to the complexity of defining a functional reference for machine tools, an overall indicator for the valuation of energy efficiency of machine tools is yet obsolete [1]. Thus, as a basis for deriving improvement measures the energy efficiency of machine tools is in this paper described as the amount of electrical energy invested to perform a complete machining operation consisting of the procedures start-up, set up, processing of a distinct amount of material and shut down (as displayed in Figure 2). Consequently, improving energy efficiency for the above mentioned scope requires either maximizing the output for a given input or minimizing the energy required to provide a given output. In the case of machine tools, the minimization of energy is encompassed by improvement measures that initially reduce energy demands and subsequently reuse invested energy or finally recover energy losses of transformation processes [according to 17]. Reducing energy consumption With regard to the power demand in Figure 3, a reduction of energy consumption can directly be achieved by technical measures that reduce the power demand of components through applying energy efficient devices. In addition, the enhancement of energy efficiency can indirectly be realized through organizational measures [18]. While technical measures include for instance the replacement of hydraulics and motors with energy-efficient ones, organizational measures focus on the optimization of process times based on energy-oriented process planning (compare Table 1). Reduce energy demand by technical measures
Apart from the consideration of energy consumption, another important aspect is the conversion of electric energy within the machine tool and the resulting energy liberation. Recovering energy losses through heat recuperation techniques may be beneficial once a heat potential is present. Energy recovery potentials are generally approached by thermal management (e.g. apply heat exchanger to control cabinets) [1, 21]. In addition to the effect of an improvement measure, the point of action can be classified according to the process, specific components or the entire machine tool design. Hence, this classification takes into account the implementation ability of the measure. While a process optimization can be applied instantaneously, changes in the design of the machine tool are more beneficial for the subsequent machine tool generation. Based on this classification scheme, every improvement measure can be clustered and prioritized according to the effect and the implementation ability. Thus, this builds the basis for the subsequent decomposition of saving potentials and interlinking with adequate measures. 3.3
Improvement Measures
Triggered by the necessity to improve the environmental performance of machine tool usage, measures to minimize the energy demand of machine tools in the use phase have been developed and deployed. Initially, a catalogue of measures has thus been established based on the available measures developed and collected within the research project Prolima, the initiative Blue Competence as well as the self-regulatory initiative lead by Cecimo [1, 22, 23]. All in all, a set of more than 190 improvement measures has been listed and classified according the developed classification scheme. It has to be pointed out that not all measures are categorized excluding those which do not provide an energy-related effect. Hence, in Figure 4 the selection of measures which could clearly be assigned to the given set of categories is displayed. 120 102
Amount of improvement measures
3.2
Apart from the reduction of energy consumption, improvement measures can furthermore aim at reusing energy. Especially motors enable to reuse energy once the motor acts as a generator in braking mode. Examples include for instance the energy balancing of multi-motor drives or kinetic buffering of energy [20].
100 80 60 40
Power
20
Reduce energy demand by organisational measures
14
8
0 Reduce
Reuse
Recover
Effect of improvement measures
Time
Figure 3: Strategies to reduce the energy demand of machine tool usage [according to 19].
Improve machine design
Improve components
Improve processing
Figure 4: Classification of energy-related improvement measures. Based on the classification, the relevance of measures to reduce the energy consumption was determined. Although more than 40% of these measures aim at reducing the energy demand through
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improved machine tool design, 22% of the measures are pursuing the same effect by improved process design.
decomposition are displayed in Table 2. Based on the initial FR 1, the subsequent level resolves the three FRs to reduce, reuse and recover energy with the DP to analyze specific saving potentials to fulfill the requirements.
As a result of the classification, more than 100 energy-reducing measures have been identified. However, due to couplings and interrelations between the measures and the resulting effects on the energy consumption, a systematic concept is required that provides structured guidance for the selection and implementation of energy efficiency measures. Thus, a design guide is developed with regard to axiomatic design theory which decomposes improvement measures as design parameters to fulfill the functional requirements (representing saving potentials). 4 4.1
DECOMPOSITION OF ENERGY EFFICIENCY MEASURES TO IMPROVE MACHINE TOOLS Axiomatic Design
Axiomatic design is a systematic tool that structures and clusters measures within a design process through mapping of functional requirements and design parameters. A functional requirement (FR) can be defined as a set of functional needs of a system (e.g. product or process). A related design parameter (DP) represents a response which fulfills the FR, hence leading to a structured design process [24, 25]. The relationship between FR and DP is defined in a vector which displays the decomposition of the FR with unique and preferably uncoupled DP. The decomposition has for that reason to consider two axioms in order to obtain an optimal design process [24]: 1. Independence Axiom: Maintain the independence of the functional requirement. 2. Information Axiom: Minimize the information content. In accordance with the axiomatic design theory, the decomposition demands to define an initial FR which states the objective and scope for the design process. At the main decomposition level, the related DP is rather extensive and will lead with ongoing decomposition to more specific and detailed solutions that fulfill the requirements [26]. Hence, the axiomatic design methodology enables to link energy saving potentials for machine tool usage with optimal improvement measures. Moreover, by ordering the results in a systematic topdown structure and integrating path dependency (reading from left to right) the decomposition vector provides guidance for the implementation of measures to minimize the energy demand of a machining cycle (according to [27]). 4.2
Objective and Scope
With regard to the definition of energy efficiency, the objective of the decomposition focuses solely on the minimization of the electrical energy demand to perform a machining cycle. Moreover, the scope is limited to a machine tool with its integrated components; disregarding additional peripheral devices as coolant filter systems. In contrast to the use of indicators like the specific energy consumption or energy per manufactured part, the scope is set to cover the power demand of a full machining cycle without additional references. This enables to consider the characteristic energy consumption of a machine tool with value-adding and also non-value-adding activities (e.g. idle mode). Moreover, this rather extensive focus avoids considering the product material and processing technology specifications. 4.3
Mapping of Parameters
Functional
Requirements
to
Design
The energy efficient machine tool decomposition focuses solely on energy-related objectives and thus the contribution of FRs on minimizing the energy demand. The resulting FR and DP of the initial
To extend the decomposition, the branch of the FR 11 (minimize energy) is decomposed into FRs aiming at the reduction of machining time (FR 111) prior to reducing the power demand of components (FR 112). While the machining time involves organizational measures based on energy-aware process planning, the FR 112 can be fulfilled by technical means to avoid or reduce the power demand of energy using components. FR 1: Minimize energy demand of a machining cycle DP 1: Energy efficient machine tool decomposition FR 11: Reduce energy input DP 11: Identify and explore potentials for energy reduction FR 111: Minimize operation time of machining process DP 111: Energy-oriented process planning FR 1111: Minimize processing time DP 1111: Perform process at maximum material removal rate ensuring target quality FR 1112: Minimize time of non-value adding tasks DP 1112: Reduce time non-value adding tasks FR 11121: Avoid non-value adding tasks DP 11121: Eliminate non-value adding tasks FR 11122: Reduce time of non-value adding tasks DP 11122: Plan process with minimal non-value adding tasks FR 112: Minimize power demand of a machining cycle DP 112: Improve power demand of the machine tool FR 1121: Operate components efficiently DP 1121: Use components only when required FR 1122: Minimize power demands of components DP 1122: Substitute inefficient components with efficient ones FR 1123: Minimize power demand to operate machine DP 1123: Reduction of moved masses FR 12: Reuse energy DP 12: Identify and explore potentials for energy reuse FR 121: Ensure energy feed back DP 121: Integrate energy feed back system FR 1211: Reuse kinetic energy to power the machine tool DP 1211: Feed back the braking energy to power the machine tool FR 1212: Conserve kinetic energy DP 1212: Integrate kinetic energy buffering systems FR 1213: Maximize energy potential DP 1213: Transform energy into other useful forms FR 13: Recover energy losses DP 13: Identify and explore potential energy losses FR 131: Ensure efficiency of energy transformation DP 131: Eliminate non-efficient transformations FR 132: Minimize energy losses of transformation DP 132: System to prevent and minimize losses FR 133: Maximize energy recovery DP 133: Integrate energy recovery system FR 1331: System to directly recover electrical energy DP 1331: Apply thermal management to recover electrical energy FR 1332: System to indirectly recovery energy DP 1332: Apply thermal management to conserve energy losses Table 2: Derived FR and DP of the energy efficient machine tool decomposition. The decomposition of the branch FR 12 shows just one direct link to reuse energy of accelerated or differently powered components which could be satisfied by applying feed back or buffering measures.
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In contrast, the third branch of FR 13 entails three major FRs. Based on the second law of thermodynamics, the recovery of energy has to ensure that transformation of electrical energy is done efficiently, hence avoiding the losses in first place (FR-DP 131). Based on this, the inevitable losses should be minimized (FR-DP 132) before the application of recovery measures is considered (FR-DP 133).
5
In alignment with the classification of improvement measures in Figure 4, the decomposition of FR and PD displayed in Figure 5 confirms the increased availability of improvement measures to reduce the energy consumption of machine tool usage in contrast to reusing or recovering energy. With regard to improving the energy demand of a machine tool, this initial decomposition enables to identify and structure potentials and improvement measures guiding the successive deriviation of implementation sequences.
SUMMARY
Against the background of the increasing availability of energyrelated improvement measures for machine tools, this paper presented axiomatic design as a systematic tool to structure improvement potentials and provide guidance for the optimal implementation sequencing of measures. Based on the initial functional requirement to minimize the energy demand of a machining cycle, design measures are derived aiming at the reduction, reuse or recovery of energy. Based on the first decomposition, the decomposition vector will be detailed in future work and extended in detail to describe the couplings and interrelation between the FR and DP.
FR-1
Minimize energy demand of a machining process DP-1
Energy efficient machine tool decomposition
FR-11
Minimize energy input
DP-11
Identify and explore potentials for energy reduction
FR-111
Minimize operation time of a machining process
DP-111
FR-112
Minimize power demand of a machining process
DP-112
FR-1111
FR-1112
FR-1121
FR-1122
Minimize processing time
Minimize time for non-value adding tasks
Ensure effective usage of component
Minimize power demands of components
DP-1111
DP-1112
DP-1121
DP-1122
Perform process with maximum MRR
Reduce time for non-value adding tasks
FR-11121
Avoid nonvalue adding tasks
DP-11121
Eliminate nonvalue adding tasks
Use components only when necessary
FR-13
Reuse energy
Recover energy losses
DP-12
DP-13
FR-121
FR-131
Identify and explore potentials for energy recovery
Identify and explore potentials for energy reuse
Improve power demand to perform the machining process
Energyoriented process planning
FR-12
Substitute inefficient components
Ensure energy feed back
Ensure efficiency of energy transformation
DP-121
DP-131
Eliminate nonefficient transformations
Integrate energy feed back system
FR-1123
Minimize power demand to operate the machining process DP-1123
Reduce moved masses through machine design
FR-1211
Reuse kinetic energy to power machine tool
DP-1211
Feed back braking energy to power machine tool
FR-1212
FR-1213
Conserve kinetic energy
Maximize energy usage potential
DP-1212
DP-1213
Integrate kinetic energy buffering system
Transform energy into other useful energy forms
FR-11122
Reduce time to perform nonvalue adding tasks
DP-11122
Operate process with minimal nonvalue adding tasks
Figure 5: The first 4 levels of the energy efficient machine tool decomposition.
FR-132
Minimize energy losses of transformation
DP-132
System to prevent and minimize losses
FR-1331
Directly recover energy
DP-1331
Apply thermal management to recover energy
FR-133
Maximize energy recovery
DP-133
Integrate energy recovery system
FR-1332
Indirectly recover energy
DP-1332
Apply thermal management to conserve energy losses
Sustainability in Manufacturing - Energy Efficiency in Machine Tools 6
ACKNOWLEDGEMENTS
This paper was compiled by the Joint German-Australian Research Group “Sustainable Manufacturing and Life Cycle Management” funded by the BMBF under reference AUS 09/AP1 and managed by the International Bureau of the BMBF at DLR. 7 [1]
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European Commission (2007): Study for preparing the first Working Plan of the EcoDesign Directive, available online: http://ec.europa.eu/enterprise/policies/sustainablebusiness/files/workingplan_finalreport_en.pdf, last revised 11/11/2010.
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European Commission (2010): Eco-design of Energy-Using Products, http://ec.europa.eu/energy/efficiency/ecodesign /eco_design_en.htm, last revised: 8/18/2010.
[5]
de Almeida, A. T., Ferreira, F. J. T. E., Fong, J., Fonseca, P. (2008): EUP Lot 11 Motors Final Report, online available: http://www.ecomotors.org/files/Lot11_Motors_1-8_280408 _final.pdf, last revised 11/11/2010.
[6]
European Commission (2008): Establishment of the working plan for 2009-2011 under the Ecodesign Directive, available online: http://eur-lex.europa.eu/LexUriServ/ LexUriServ.do? uri= COM: 2008:0660:FIN:EN:PDF, last revised 11/11/2010.
[7]
Schischke, K. Hohwieler, E.; Feitscher, R., König, J., Duchstein, B., Nissen, N. F.: Energy-Using Product Group Analysis Executive Summary, available online: http://www.ecomachine tools.eu/typo/reports.html?file=tl_files /pdf/EuP_Lot5_Task1_draft_V01.pdf, last revised 11/11/2010.
[8]
Weck, M., Brecher, C. (2006): Werkzeugmaschinen Konstruktion und Berechnung, 8. Ed., Springer, Berlin.
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Devoldere, T., Dewulf, W., Deprez, W., Willems, B., Duflou, J. (2007): Improvement Potential for Energy Consumption in Discrete Part Production Machines; in: Advances in Life Cycle Engineering for Sustainable Manufacturing Businesses, Springer.
[10] Dietmair, A., Verl, A. (2009): A generic energy consumption model for decision making and energy efficiency optimisation in manufacturing, in: International Journal of Sustainable Engineering, 2: 2, pp. 123-133. [11] Gutowski, T., Dahmus, J., Thiriez, A. (2006): Electrical Energy Requirements for Manufacturing Processes, in: 13th CIRP International Conference on Life Cycle Engineering, Leuven, Belgium. [12] Herrmann, C., Zein, A., Winter, M., Thiede, S. (2010): Procedures and Tools for Energy Metering of Machine Tools, in: Manufacturing 2010 - 3rd International Scientific Conference, Poznan.
279 [13] Eckebrecht, J. (2000): Umweltverträgliche Gestaltung von spanenden Fertigungsprozessen, Shaker, Aachen. [14] Anderberg, S. E., Kara, S., Beno, T. (2010): Impact of energy efficiency on computer numerically controlled machining, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, Volume 224, Issue 4, 2010, pp. 531-541. [15] Saidur, R. (2010): A review on electrical motors energy use and energy savings, Renewable and Sustainable Energy Reviews, Volume 14, Issue 3, pp. 877-898. [16] Patterson, M. G. (1996): What is energy efficiency? - Concepts, indicators and methodological issues, in: Energy Policy, Volume 24, Issue 5, May 1996, pp. 377-390. [17] Kaebernick, H. (2008:, Reuse, Recycling and remanufacturing impediments for industrial implementation, 15th CIRP International Conference on life cycle engineering, 2008, Australia [18] Herrmann, C., Thiede, S., Zein, A., Ihlenfeldt, S., Blau, P. (2009): Energy Efficiency of Machine Tools: Extending the Perspective, in: 42nd CIRP Conference on Manufacturing Systems, Grenoble. [19] G.E. Fanuc (2008): The Environmental and Economic Advantages of Energy-Efficient Motors, available online: http://leadwise.mediadroit.com/files/2928energy%20savin g_wp_gft688.pdf, last revised 11/11/2010. [20] Siemens (2010): Blue Competence, available online: http://www.automation.siemens.com/mcms/mct/de/motio n-control-events/metav-2010/blue-competence/Seiten/ Ausstellung%20%E2%80%9EBlue%20Competence%E2%80%9C. aspx, last revised 11/11/2010. [21] Diaz, N., Helu, M., Jarvis, A., Tönissen, S., Dornfeld, D., Schlosser, R. (2009): Strategies for Minimum Energy Operation for Precision Machining, Proceedings of MTTRF 2009 Annual Meeting, available online: http://sustainablemanufacturing.biz/media//DIR_17101/95 43103a2878 dfb8ffff83b7ffffd524.pdf, last revised 11/11/2010. [22] Prolima (2010): Environmental Product Life Cycle Management – Best Practice Manual, available online: http://www.prolima.eu, last revised 11/11/2010. [23] VDW (2010): Blue Compentence, available online: http:// http://www.vraagenaanbod.nl/download/VDW%20Blue%2 0Competence%20Concept%20EMO%20Milaan.pdf, last revised 11/11/2010. [24] Suh, N. (1990): The Principles of Design, Oxford University Press, New York. [25] Reynal, V. A., Cochran, D. S. (1996): Understanding Lean Manufacturing According to Axiomatic Design Principles, Lean Aircraft Initiative, Report Series #RP96-07-28. [26] Babic, B. (1999): Axiomatic design of flexible manufacturing systems, in: International Journal of Production Research, Vol. 37, No. 5, pp. 1159-1173. [27] Cochran D. S., Arinez, J. F., Duda, J.W., Linck, J. (2001): A Decomposition Approach for Manufacturing System Design. In: CIRP - Journal of Manufacturing Systems, 20(6), pp. 371389.
Analyzing Energy Consumption of Machine Tool Spindle Units and Identification of Potential for Improvements of Efficiency 1
1
1
1
E. Abele , T. Sielaff , A. Schiffler , S. Rothenbücher 1
Institute of Production Management, Technology and Machine Tools, Technische Universität Darmstadt, Darmstadt, Germany
Abstract This paper describes the results of the analyses of a representative machine tool spindle unit and the thereof identified potentials for increasing the energy efficiency. This is done within the project “EnergieMSP”, whose aim it is to develop an energy optimized spindle unit with an adapted electric drive train. The holistic analysis of the system is done in a machine tool to determine the consumption of pressurized air, the energy demand for the tool change and hydraulic stand-by losses. The frictional losses in the bearings and rotary union and the electrical drive train where separately investigated at test benches. Keywords: Machine Tools; Spindle Units; Efficiency
1
INTRODUCTION
Public environmental awareness and the sociopolitical debate regarding the climate change lead to tightened political regulations with the aim to reduce the emission of greenhouse gases. Thus the producer’s responsibility toward environmental impact of their products will increase. This regards also machine tools as they are part of the work program within the framework directive for energy using products of the Council of the European Union [1]. In the course of that the energy cost will increase as well and affect the manufacturing costs of producing industry. Considering cutting processes the energy consumption of machine tools have the biggest part of the total energy needed for the process as a study at the Heidelberger Druck conducted by the PTW [2] has shown. Figure 1 shows the complete allocation of the electrical energy consumption for machining a turning work piece.
for acquisition and the annual accruing costs for a period under consideration of ten years. The allocation of the annual operating costs of this machine tool is given at the right column of figure 2.
10 9 8 7 6 5 4 3 2 1
490,000 € Operating Costs of a Machine Tool p.a. (without labor costs, tool costs and overhead) lubrication for cutting 39,000 €
pressurized air energy costs occupancy costs repair
Invest: 100,000 €
maintanance capital commitment
Figure 2: Operating cost of a machine tool.
Figure 1: Breakdown of Energy consumption for machining a turning work piece. This big amount of the energy consumption causes up to 20% of the operating costs of machine tools per year [3]. Figure 2 shows the operation costs for an exemplary machine tool according to COSTRA study conducted by the PTW. The left column of figure 2 illustrates the total cost of ownership, including the investment costs
To reduce energy costs the requirement of low energy consumption becomes more and more the main focus of research and development activities in manufacturing engineering as well as for the machine tool manufacturers. The authors of [4] also observe that besides the need of an improved efficiency the potential of savings has to be determined during development and design phases, so that they could be reflected with the mostly higher invest costs. The return on investments of energy saving measures strongly depends on the rate of capacity utilization. Because of that it becomes necessary that users of machine tools define the user profile of the machine for the manufacturer, so they can review potential measures for improved efficiency [5]. Therefore the need to identify and analyze the main energy users is given. The fact that in an average machining center the main spindle mostly unit has the biggest connection power shows that this core component with its peripheral systems takes one of the main parts of the energy consumption of the machine tool during the primary processing time. But also during the secondary processing time the spindle units and added systems like the supply for pressurized air for
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_49, © Springer-Verlag Berlin Heidelberg 2011
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281
sealing air and oil-air-lubrication as well as hydraulics for tool clamping need a considerably amount of energy [6,7]. In order to achieve the aim of a spindle unit for machining parts with minimized energy demand a holistic improvement of the mechatronic system is necessary. For investigation of the spindle unit three topics were defined, which are energy efficient electrical drive train, improvement of efficiency through lightweight design and loss minimized spindle components.
specific thermal capacity of the coolant medium. The cooling system needs electrical power (PRec) for the recirculation of the cooling fluid. With the EER of the chiller between electrical input power and delivered cooling power (PWater) the total power consumption for cooling is:
2
SYSTEM BOUNDARIES
To analyze the system and determine the power consumption and the energy efficiency ratio (EER) of the system and its components one has to define reasonable system boundaries. Therefore all components which are in general necessary for operating the machine tool spindle unit were considered and the interaction with the machine tool analyzed. The border of the electrical drive system is chosen at the intermediate direct current link. This was caused by the fact that a network of drives often has only one central direct current link for the power distribution to several consumer loads in the network. Therefor the inverter and the smoothing inductances are part of the tool spindle system. On the tool side of the spindle unit the mechanical power (Pme) output as product of rotating speed (n in rpm) and torque (T in Nm) is defined:
Pme n
30
T
(1)
PCooling
PWater PRec EER
(2)
The tool clamping mechanism is mostly hydraulic actuated. The needed power is determined by flow and pressure measurements on the fluid side and a power measurement on the electrical side. Typical energy efficiency ratios for hydraulic pumps are in the range of 30% to 40%. As the hydraulic power can be provided local as well as central for other consumers, the definition of a system border is more difficult. In addition to that there are some alternative concepts which use pressurized air, coolant lubrication or electromechanical actuators for tool clamping. To achieve comparability to each other, especially electromechanical, systems it will be calculated on how much electrical energy is needed to generate the demand of hydraulic energy. For the given system one needs 1 kWh of electric energy to generate 0.35 kWh of hydraulic energy. With those definitions and the specified system borders the significant energy consumptions of the spindle system where systematically analyzed to find out starting points for improvements of efficiency. Figure 3 shows the machine tool spindle unit with the named peripheral components and the described system border.
The EER of the converter can be determined by the ratio of the effective power of the motor and the power of the direct current link, which can be measured. The EER of the motor follows as ration of mechanical power to effective power of the motor. The compressed air conditioning is outside the defined border, as this is often centrally realized for many consumers of pressurized air in the plant. The consumption of pressurized air itself is regarded and set together by the following parts:
Bearing lubrication (optional): Oil air lubrication of the bearings 6 is an option, as with a speed parameter of 1.5•10 mm/min the bearings are lubricated with grease and therefore need no additional energy during operation. For higher values oil air lubrication is used which needs pressurized air and electrical energy.
Sealing air: To protect the spindle unit against chips, particles, fluids and condensate formation.
Exhaustion (optional): Surplus oil in the machine tool spindle housing is exhausted by the venturi-effect
Taper cleaning air: During the tool change the taper is cleaned by pressurized air to ensure that no particles are at the contact surfaces between tool holder and interface.
The consumption of pressurized air is measured in the supply line by calorimetric transducer in standard cubic meters per hour (Nm³/h). The costs for one Nm³ are one euro cent for modern air supply units. As the costs for compressed air conditioning are dominated by electrical and the costs for one kWh can be assumed to 10 euro cent, one need approximately 0.1 kWh of electrical energy for one Nm³ of pressurized air [8]. For cooling the machine tool spindle unit cooling channels are in the section of the drive and the bearings. Therein a cooling fluid circulates, so that heat and with that an energy exchange from the spindle unit to the heat exchanger is realized. With that the temperatures of the spindle components are limited and thermal deflections in axial direction are mostly avoided. The needed cooling power (PWater) can be calculated as the product of mass flow, temperature difference between forward and back flow and the
Figure 3: Definition of System Boundaries. 3
ENERGY TRANSPORT MECHANISMS MACHINE TOOL SPINDLE UNIT
INSIDE
THE
Besides the EER of the machine tool spindle unit the energy transport inside the spindle is important for the precision and the energy compensation. All losses of the spindle system appear as heat in the system and cause a rise in temperature of the system. This leads to unwanted extensions of components e.g. the spindle shaft. Therefore, besides the economic aspects it is of interest to minimize the losses with respect to the machining accuracy. Irrespective which kind of motor is used the electric drive of the spindle unit is a heat source. Figure 4 shows the location of the heat sources and cooling exemplary for a spindle unit with synchronous motor, where additionally a shaft cooling is shown, which is only used for particular applications. All copper, eddy current and hysteresis losses of rotor and stator are converted to heat and have to be conducted out of the spindle. Therefore the system has cooling channels in the stator housing at which the heat is transferred to the cooling fluid by forced convection. Those cooling channels are often around the bearings too (see Figure 4), so that the heat which is generated by the
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friction in the bearings is directly transferred to the cooling fluid. The heat of the rotating part has to be transferred by heat conduction through the bearings and convection in the air gap to the stator part and therefore also to the cooling fluid. Another small part is transferred by heat radiation to the ambient air, but this could by neglected due to the small temperature difference between spindle housing and ambience.
4.2
Pressurized air
The consumption of pressurized air was measured on the existent machine tool. Sealing air holds the largest proportion with 9.27 3 Nm /h, followed by the bearing lubrication with 7.89 Nm³/h and exhaustion with 1.49 Nm³/h. The consumption of lubricating air decreased at a maximum speed by 3% as opposed to idleness. This dependence was however disregarded in the process and a value adopted at mean speed. The consumption of pressurized air, as defined in the paragraph System boundaries, was recalculated into an equivalent electrical power input and amounts to 1874 W. A 3 metered flow of 23.3 Nm /h was determined for the cone cleaning 3 air at a tool change. A consumption of 0.01 Nm of air equals to an equivalent electrical energy of approximately 1 Wh per tool change. 4.3
Hydraulics
In [8] empiric models are determined and validated with the help of extensive test benches, which allow an estimation of the breakdown of the energy transport. With that one gets the following distribution for the heat of the rotor for the used reference spindle unit:
The energy demand for a tool change in the reference system was determined by gauging pressure and flow in the supply pipe of the hydraulic cylinder of the tool releasing device. The hydraulic peak output accounts to 1.6 kW and the hydraulic energy for a complete cycle of releasing/clamping can be specified with 0.2 Wh. An electrical energy of 0.59 Wh per tool change results with consideration of efficiency (see paragraph 2 System Boundaries). Hydraulic systems will always hold losses in the leakage, in order to maintain the system pressure. This Standby-consumption was determined by the reference machine, whose hydraulic system runs in a storage charging circuit, as amounting to 36 W (electrical).
4.4
Figure 4: Location of heat sources and sinks within spindle units [9].
70% are transmitted by convection via air gap to the stator within the area of the drive.
16% are transmitted by convection via air gaps outside the area of the drive to the stator or housing
10% are delivered to ambient air
4% are removed from the rotor via convection and conduction through the bearings.
This estimation was done for a permanent exited synchronous machine in field weakening operation at 20,000 rpm and a load of 5.4 Nm. This estimation is not valid in general and has to be considered for each different system and operating point separately. However, due to the relation of a small air gap to a large surface in the area of the drive in all machine tool spindle units, the biggest amount of heat is transferred via convection in the air gap of the drive. This is supported by the high rotational speed, because this leads to a turbulent air stream, which is advantageous for energy transport. Generally the heat transfer coefficient increases for small air gaps and high circumferential speed. Therefore, the reduction of the air gap is of benefit for the achievable drive power as well as for good heat transfer in the spindle unit. 4 4.1
DETERMINING THE ENERGY EFFICIENCY RATIO OF THE REFERENC SYSTEM Reference System
In order to determine the energy efficiency a reference system was chosen, which will be optimized along the progress of the project by the identified potentials. The reference system can be identified by the following benchmark data: maximum number of revolutions -1 6 28,000 min ; speed parameter of 2.4•10 mm/min (oil-air lubrication); tool interface HSK 63; maximum torque 38 Nm (S1); maximum continuous output 40kW (S1) and weight 52 kg. The machine tool spindle unit is currently available in varying bearing configurations, thus creating the possibility to compare different systems. The spindle is available for inspection in a machine tool DMG V75 and as a single spindle in the test stand.
Friction in rotating Components
In an attempt to measure friction and the resulting losses in the spindle bearing, a separate test bench was constructed. This complex approach is needed in order to exactly calculate the frictional torque produced by the bearing. Especially repercussions of the electrical drive and of the rotary union have to be excluded from the friction moment. Therefore a bearing for the spindle shaft in a spring-loaded O-array was designed in the test bench. This shaft is driven by a separate spindle. Figure 5 shows that the spindle is mounted on an air bearing and has a degree of freedom along its rotation axis, which is mounted free of friction; thus providing the precise measurement of the actual torque on the bearings with a constant number of revolutions. In order to provide the opportunity for further inquiries on the bearing, the possibility of measuring the temperature on the inner and outer ring during operation was created. A change in temperature can be made possible by adjusting the coolant flow rate and the heating of the shaft. Furthermore, the axial preload force can also be varied, in order to determine the effect of the temperature, the axial preload force and the number of revolutions on the friction moment. In cooperation with the bearing manufacturer of the project consortium any further effects of different osculation and cage geometries will be examined. For a first analysis of the distribution of energy consumption to be created with qualitative results, measurements were taken on different operating points on common spindle bearings with diameters of 70 mm. The results in Figure 5 show that with 24,000 rpm, an output of 1 kW is needed to keep two bearings revolving with constant rotational speeds. These first results initially provide the dimension in which the frictional losses of the bearing fall. These dimensions have also been determined by previous studies in [11]. The rotary union, which also has bearings and rotates with same speed as the spindle, is necessary for the transfer of high pressure lubricating coolant onto the revolving shaft. An exemplary rotary union, was examined in the test bench in the interest of the friction moment. In order to determine the dependence on pressure and rotational speed, these values were varied, ranging from 0 to 80 bar and 0 to 30,000 rpm. The most unfavorable case was at 80 bar and
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q
30,000 rpm, resulting in a friction output of 140 W. Without pressurization a friction output of 20 W at 3000 rpm was measured. 1000 Power [W] / Torque [Nmm]
900 800
LIq
Torque sensor
500
Conection of cooling Coupling Air bearing 300
UP
Uq PM
Ud PM
400
200
5000
Electrical Drivetrain
To analyze the electrical drivetrain with its components motor, converter and motor choke the reference spindle was mounted on a test bench and loaded with a swing frame machine. There it is possible to set different operating points and to measure rotational speed and torque. For the determination of the EER one has to measure the power of intermediate direct current link, the effective power at the motor terminals and the mechanical power at synchronized times. At high rotational speeds the spindle units with permanent magnet synchronous machines operate in field weakening mode. That means one needs very high field current (Id) to compensate the field of the permanent magnets on the rotor, which causes the linked air gap flux (ΨP). Otherwise the speed (Ω) proportional counter electromotive force (EMF) will rise to the voltage limit (ULim) of the inverter and therefore a further increasing of speed would not be possible. One has to consider that the speed in this context is the speed of the rotary field, which is mechanical speed multiplied with the number of pole pairs. This can be seen in the equation of the voltage per phase (UP) of the permanent magnet synchronous machine in steady state operation according to [12]:
U d jU q R I
j
(R) P
d
Id
Ud
d
b) field weakening
Figure 6: Vector diagrams for different operating points.
Torque Power
(R) P P
Iq
IP
a) base speed
10000 15000 20000 25000 rotational Speed [min-1] Figure 5: Test Bench for investigation of friction losses in bearings end exemplary results.
U
Uq
U Lim
IP Iq
0
(R) P
PM
UP
600
100
4.5
q
LId
U Lim
Spindle bearing Telemetric temperature measurement
700
LIq
As can be seen in Figure 6 in field weakening operation, one gets especially at low loads (small torque current Iq) a high phase angle between voltage and current and therefor a power factor (cos(φ)) near to zero. Power measurements at those operating points are very difficult and one needs very precise measurement equipment [13]. Figure 7 shows exemplary the results for the analysis of the system at a load of 5 Nm. Therein are the losses and EER for a speed range from 6000 rpm to 28,000 rpm. The motor of the reference system starts, depending on the load, approximately at 11,000 rpm to operate with field weakening. One can see in the diagram of Figure 7 that above that speed the EER becomes lower.
(3)
This is the voltage equation in rotor reference frame, which is indicated by the superscript (R). Wherein Ud is the voltage in the direction of the main flux of the permanent magnets and perpendicular to that one has the voltage Uq. The inductance (L) is the sum of motor inductance and of the motor choke. Dividing equation 3 in real and imaginary part one gets the equations for Ud and Uq.
U d R P I d LI q
(4)
U q RP I q LId PM
(5)
With those equations one can draw the vector diagram for different operating points, which is shown in Figure 6, where the operation in base speed range and in field weakening is compared. The resistance (RP) is neglected due to its less impact.
Figure 7: Results of loss power and EER measurements of the reference spindle unit. 4.6
Other Components with Energy Consumption
As described in section 2 and shown in Figure 3 there are a lot of components needed to operate the machine tool spindle units. For a holistic consideration all of them have to be taken into account. Without a detailed description they are given additionally to the named operating point dependable ones in Figure 8.
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Sustainability in Manufacturing - Energy Efficiency in Machine Tools POTENTIAL FOR IMPROVEMENTS AND DEPENDENCY OF OPERATING POINT
5.1
Comparison of operation Points
With the results of the previous section two different operation points were summarized and compared to each other. Figure 8 shows the sankey diagram for operating points in base speed range compared to one in field weakening range. For both operating points the load was 5.4 Nm. One can see on the left side in Figure 8 that for lower speed the pressurized air, which is independent of the operating point, dominates the losses. Although the losses of the electrical system rise significantly at high speeds (see Figure 7) the EER of the whole system is with 56% higher than at lower speeds where only an EER of 50% could be reached. The reason for that is a big part of the energy consumption is determined by the peripheral components, which is constant for all operating points. The worst case therefore is the system when it’s ready for use and controls to zero speed at no load. To keep this state a total power of 2485 W is needed, whereof 75% are used for pressurized air.
Figure 8: Allocation of losses for two different operating points. 5.2
Potential for Improvement of Energy Efficiency
Figure 9 shows the determined system energy efficiency ratio for a constant load over the complete speed range, including all necessary components to operate the machine tool spindle unit. The system has its maximum EER of 60% at a speed of 16,000 rpm. In the graph of figure 9 three additionally scenarios for improvements are inscribed. The first of them is to reduce the load independent consumption of peripherals like pressurized air and hydraulic leakage by 30%. This reduction has especially at lower speeds, where the electric drive system has high EERs, a remarkable potential to improve the efficiency.
The second scenario provides a reduction of the losses of the electrical drive (converter, inductance and motor) about 15%. This measure has the biggest impact at high speeds. The shape of the curves in figure 9 shows how the scenarios effect on the EER depending on the operation point. One can see that the curves of the two scenarios cross nearly at the maximum of the measured EER. Hence, it is absolutely necessary to consider the whole system to achieve an improvement for all operation areas. Thus, the third case shows the combination of the first two ones. The reduction of pressurized air consumption offers very high potential for increasing the energy efficiency of the system. Therefore concept for lubrication and bearings have to be developed, which allow operation at characteristic speed values of 6 2.5x10 mm/min and higher without pressurized air. An approach could be seen in grease reservoir lubrication for high speed spindle bearings. Also for the sealing air one can think about alternatives through complex designed labyrinth-sealing, so that the consumption of sealing air could be reduced or completely avoided. However this could result in higher costs and has to be compared with the life cycle costs but could be a benefit after a short amortization time. For further possible savings the user profile of the machine tool and spindle unit has to be regarded. For machining with many tools and short primary process times the secondary process times caused by tool changes consume a considerable part of the machining time. The reduction of the chip-to-chip time, e.g. at the machining of crankcases for engines, could rise productivity and efficiency. The chip-to-chip time [14] includes the movement of the axes, the tool change itself and the run-up and breaking of the spindle unit. Depending on the required speed the acceleration and deceleration of the spindle holds the biggest part of the tool changing time. Because of that in many machining centers the drive capacity of the spindle unit is oversized. That means the maximum torque is chosen two to three times higher than it would have been necessary for the cutting process to reduce run-up times. Figure 10 shows the power consumption of the electric drive during such a machining cycle.
Figure 10: Duty cycle for a machine tool spindle unit. The objective in the project “EnergieMSP” is to reduce the inertia of the spindle shaft by lightweight design with carbon fiber composites so that the same accelerations could be achieved with much lower torque. One aim is to have a direct connection to the bearings so that the utilization of the advantageous material density of the composite would be compensated through added steel sleeves. Preliminary studies with prototypes are actually done.
Figure 9: Measured system energy efficiency ratio and scenarios for improvements.
As an alternative to the conventional tool clamping with springs and hydraulic disengagement an electro-mechanic system was tested on a prototype. This show clearly advantages in the energy consumption, which is only regarded for clamping and releasing the tool. The electro-mechanic solution needed 0.11 Wh per tool
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change whereas the hydraulic system needs 0.59 Wh. However there is a disadvantage during the clamped time, because the electro-mechanic system requires 287 W of auxiliary energy compared to 36 W of the hydraulic system. Hence, this is only useful in cases with a high number of tool changes per hour.
Anwendung von Lebeszykluskostenanalysen von Werkzeugmaschinen, Gabler, pp. 51-80.
Furthermore the concept for the electric drive train is to use additional components for energy storage (capacitors) to buffer power peaks, which occur at frequent tool changes. Measures like this provide also the possibility for the recovery of the kinetic energy at a deceleration of the spindle unit for e.g. tool changes [15]. Thus, power supply could be reduced in design size, which results in lower cost for connection power. Regarding the improvement of the drive itself it is also important for which using profile the spindle unit is designed. For a universal usage or the described application above permanent magnet synchronous machines with field weakening ratios of 3:1 to 4:1 are preferred. The reason is the high EER up to nominal speed and the high specific power. A possibility for improvement is an better adjustment between motor and converter. For given voltage and current output limits one has to minimize field weakening so that only the minimum reserve for compensation of load steps without big breaks in speed remains. Those measures were supported with improvements of the motor through other winding schemes (double layer windings, single coil winding, higher filling factors) and alternative materials for the magnetic circuit of the machine. If the spindle unit is mainly used for long terms at high speeds asynchronous machines could be the better alternative. Moreover for machining at highest speeds or at highest speed parameters for bearings the minimization of the friction within the bearings could deliver a significant proportion to increase the system EER. Therefore spindle bearings with modified geometries of bearing races and cages were developed from the project consortium of “EnergieMSP”. 6
Kuhrke, B.; Erdle, F. (2010); Energieeffizienz als Investitionskriterium in Werkstatt und Betrieb 1, 2/2010.
[5]
Abele, E.; Kuhrke, B.; Rothenbücher,S. (2010): Entwicklungstrends zur Erhöhung der Energieeffizienz spanender Werkzeugmaschinen, in:1st International Colloquium of the Cluster of Excellence eniPROD, pp. 99-120.
[6]
Gutowski, T., Dahmus, J., Thiriez, A. (2006) "ElectricalEnergy Requirements for Manufacturing Processes",Proceedings of the13th CIRP International Conference on Life Cycle Engineering, May 31st-June2nd, 2006, Leuven, pp. 623-628.
[7]
Dahmus, J., Gutowski, T. (2004) An environmental analysis of machining, Proceedings of IMECE2004, 2004 ASME International Mechanical Engineering Congress and RD&D Expo, November 13-19, Anaheim,California USA.
[8]
VDMA 24378 (2009): Prognose des Energieverbrauchs von Lackieranlagen (Entwurf), 03, 2009.
[9]
Abele, E.; Brecher, C.; Altintas, Y. (2010): Machine tool Spindle Units, in: CIRP Annals - Manufacturing Technology, Volume 59, Issue 2, pp. 781-802.
[10]
Gebert, K. (1997): Ein Beitrag zur thermischen Modellierung von schnelldrehenden Motorspindeln. Dissertation TUDarmstadt.
[11]
Steinert, T. (1995): Das Reibmoment von Kugellagern mit bordgeführtem Käfig, Dissertation RWTH Aachen.
[12]
Schroeder, D. (2009): Elektrische Antriebe – Regelung von Antriebssystemen, Springer-Verlag, Berlin-Heidelberg.
[13]
Huber, W. (2010): Elektrische Verluste und Wirkungsgrade, in Elektronik Praxis, No.8 2010.
[14]
VDI 2852: Kenngrößen numerisch gesteuerter Fertigungseinrichtungen; Span-zu-Span-Zeit bei Automatischen Werkzeugwechsel.
[15]
Diaz, N.; Helu, M.; Jarvis, A.; Tonissen, S.; Dornfeld, D.; Schlosser, R. (2009): Strategies for Minimum Energy Operation for Precision Machining. Proceedings of MTTRF 2009 Annual Meeting, Shanghai.
SUMMARY
This article referred about the results of the analysis of a representative machine tool spindle unit and the identified possibilities for improvement of energy efficiency. The identified potentials were used as a base for the design and realization of an energy efficient spindle unit. The results show that it basically makes sense to reduce the consumptions of the peripherals like pressurized air, hydraulics and stand-by consumption of power electronics, which are independent from the operating points. Those measures also provide enormous potential for the improvement of the complete machine tool. Regarding the electric drive train, bearings and tool change system the possible improvements strongly depend on the using profile of the spindle unit and have to be reflected with the additional costs, which those measures cause. 7
[4]
REFERENCES
[1]
Council of the European Union, Directive 2005/32/EC of the European Parliament and the Council of 6 July 2005 establishing a framework for the setting of ecodesign requirements for energy-using products and amending Council Directive 92/42/EEC and Directives 96/57/EC and 2000/55/EC of the European Parliament and the Council, Official Journal of the European Union, Brussels 22.07.2005.
[2]
Schiefer, E. (1999): Analyses of the energy consumption of a Traub TNS 65/80D within the machining production of the Heidelberger Druck AG, PTW.
[3]
Abele, E; Dervisopoulus, M.; Kuhrke, B. (2008): Schweiger, S. (Editor) Lebenszykluskosten optimieren Bedeutung und
Energy Consumption as One Possible Exclusion Criterion for the Reuse of Old Equipment in New Production Lines 1
1
1
1
Lars Weyand , Helmut Bley , Martin Swat , Kirsten Trapp , Dirk Bähre 1
1
Institute of Production Engineering, Saarland University, Saarbruecken, Germany
Abstract The reuse of production equipment represents a promising approach to saving investment costs. Therefore, more and more companies are taking the topic of reuse into consideration. However, there are also risks and disadvantages concerning the reuse of old production equipment. In some cases, reusing old equipment can eventually be more expensive than buying new resources. Therefore, an adequate reuse assessment is necessary. Not only technical factors and investment costs have to be considered, life cycle aspects such as energy consumption play a crucial role as well. In this paper, an approach to a risk-reduced reuse of old equipment is presented. Keywords: Production Equipment; Reuse; Energy Consumption
1
INTRODUCTION
If manufacturers decide to reuse resources from previous production lines, they are theoretically able to take important competition advantages [1], [2]. Investment costs regarding the purchase of new equipment can be reduced and moreover, problems during the ramp-up phase can often be avoided due to the knowledge regarding old resources within their own company. Therefore, more and more companies are taking the topic of reuse into consideration. However, there can also be disadvantages concerning the reuse of old production equipment. Uncertainties regarding the suitability of the resources and regarding the spare parts supply present a risk and warranty claims regarding old equipment usually do not exist. There is partly no sufficient experience with the issue of dealing with the topic of reuse and business processes in this area are often not fully developed [3]. In some cases, reusing old equipment can eventually be more expensive than buying new resources due to life cycle aspects. Table 1 summarizes the discussed pros and cons regarding the reuse of old production equipment.
Reuse of assembly equipment
In order to adequately deal with the topic of reuse, a detailed reuse assessment is necessary. This reuse assessment has to take lots of different aspects into account. Not only technical factors and investment costs have to be considered, life cycle aspects and the resulting life cycle costs play a crucial role as well. One important life cycle aspect is the topic of energy consumption [4], [5]. Energy is a requested, limited and increasingly expensive commodity that exerts economic and ecological pressure on production companies [6], [7], [8]. This aspect has to be considered in connection with modern production planning [9] – especially if equipment from old production lines is to be reused. This paper deals with the topic of reuse and energy efficiency in the area of production planning. A special approach which allows performing a selective reuse planning is presented. This approach also considers the topic of energy consumption in an explicit way. It is explained how energy aspects can be taken into account at the early stages of production planning and a method allowing product variant-oriented energy calculations is introduced. 2
EXISTING APPROACH EQUIPMENT
TO
REUSING
PRODUCTION
- Reduction of the necessary investments in new resources
A planning workflow for a selective reuse planning has already been presented in [10], [11]. The approach mentioned is a 3-step approach which considers reuse aspects already in the planning process so that reusable equipment is automatically identified. Consequently, there is no need to start the planning process by considering reuse restrictions, whereas this is possible too.
- Feasible from a technical point - In some cases, more efficient of view, problems during the resources could be available ramp-up phase do not have to on the market be expected
In a first step, a so-called early resource calculation has to be executed. The early resource calculation determines the equipment which would be bought today considering the latest technical achievements and innovations available on the market. It is a planning activity in which new equipment which fulfils the requirements of the planning task in the best way is identified. Due to the fact that the final need of resources – the number of necessary resources – can be determined after the line balancing only, this obviously is only a first, rough calculation.
Advantages
Disadvantages
- Uncertainties concerning the suitability of the old resources and concerning the spare - Knowledge concerning the use parts supply of the resources within the company - No warranty claims
Table 1: Advantages and disadvantages of reusing resources.
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_50, © Springer-Verlag Berlin Heidelberg 2011
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Sustainability in Manufacturing - Energy Efficiency in Process Chains
In a second step, a so-called reuse calculation has to be taken into account. It has to be checked whether old equipment, which fulfils the general requirements too, is available. As to the decision whether to favor old or new equipment, not only technical aspects and cost calculations regarding initial investment costs have to be taken into consideration. Life cycle aspects and life cycle costs have to be considered as well and both aspects are included in the developed approach. As to the calculation of the remaining life time, some publications have already dealt with this topic – for instance in the publications [12], [13], [14], [15]. Regarding big companies, it is rated as reasonable to put experts from the maintenance and repair department in charge of calculating the remaining life time of the resources. Weibull analyses are necessary and talks with the resource suppliers make sense too. The third step is called final resource planning. As explained in [11], this activity is necessary due to the fact that the line balancing finally determines the number of resources needed. Figure 1 shows the elaborated reuse planning approach including the three steps described above dealing with resource planning activities. As to the reuse calculation, lots of data regarding the old equipment has to be available (performance data, installation data, data regarding performed modifications, data in respect of completed hours of operation, data of former activities concerning resource availability and malfunctions, in the best case a CAD model, additionally recent photos etc.).
Planning data
Early resource calculation2
Process planning1
not okay
Preliminary resource list
Processes
Iterative improvements
okay
not okay
Line balancing
Reuse calculation
No. of stations, assembly sequence, …
Preliminary Preliminary reusable new equipment equipment
okay Final resource planning
not okay
Final resources (old + new equipment) okay Digital validation checks
not okay
Checked PPR models
Further planning activities 1,2 Based
on digital graphs PPR = Product-Process-Resource Figure 1: Developed planning workflow according to [11], [16].
In general, the old resources have to be assessed from many different perspectives. It is proposed to involve experts from different departments in the assessment activity. These experts have to have special knowledge and experience concerning the relevant areas. It is expected that at least one expert from the quality department, one from the maintenance and repair department, one from the occupational safety and the environment protection department, one from the control engineering department and one expert from the cost department will be needed. Every expert has to give a detailed, written statement about the suitability of the equipment initially rated as reusable and has to guarantee by his or her signature that all statements are made to the best of his or her knowledge. In general, every expert needs his or her own digital assessment form which contains particular fields to be filled in. These digital forms can consist of several pages. Finally, it could be reasonable to integrate all of the digital forms into one final form containing only the most important data. This final digital form presents the basis for making the final decision. From our point of view, the whole assessment process has to be supported by software. A suitable digital working environment including a reuse library and possibilities for cross-functional communication has to be available [16]. The general suitability of the approach has already been checked based on a separate example taken out of the automotive industry. A scenario of a side door assembly has been discussed in detail [10], [17]. By means of the discussed side door example, it was shown that the developed concept is feasible, whereas the created software prototype still has to be improved in order to be able to guarantee fast and comfortable reuse assessments. The obtained results are promising, whereas it is clear that further examples have to be investigated. There are still some points which have to be discussed in more detail. One aspect is the topic of how to deal with energy aspects more explicitly. Section 3 shows how energy aspects can be considered in a local way in connection with the reuse calculation in which old and new resources have to be compared. In contrast to that, Section 4 explains how the energy consumption of the whole production line, which is still being developed, can be calculated. Moreover, a method for calculating variant-specific energy consumptions is introduced. 3
DETAILED CONSIDERATION OF WITHIN THE REUSE APPROACH
ENERGY
ASPECTS
Within the developed approach, it is differentiated between the two following values of energy consumption: “energy consumption of the resource Rj per hour in the case of use (||Euse, Rj||)“ and “energy consumption of the resource Rj per hour in the case of stand-by ((||Eidle, Rj||)“. As to the approach presented in Section 2, the energy consumption of new and old resources can be immediately compared by production planners during the reuse planning. It is a paired comparison analysis of energy values regarding ||Euse, Rj|| and ||Eidle, Rj||. The results of this comparison have to be checked regarding their acceptability. Strong deviations can lead to an exclusion of old resources even if all other requirements – the requirements regarding quality, safety etc. – are fulfilled. Interesting examples of significant improvements regarding the energy consumption of resources can be found in recent publications. In respect of laser welding, the improvements regarding laser sources – the change from rod to disk lasers – are impressive. More than half of the costs for cooling and electricity can be saved with modern disk lasers. In [18], a quantitative comparison of a modern 4 kW disk laser and a modern 4 kW rod laser is presented. The comparison is based on the following boundary conditions: the laser sources are in use during 8 years, 3shifts per working day and the average duty rating is 75%. The disc
Sustainability in Manufacturing - Energy Efficiency in Process Chains laser finally causes lower operating costs than the rod laser. As to the disc laser, the operating costs per hour are approximately 18 €, whereas the rod laser causes expenses of over 20 € per hour in the same situation. From the point of view of investment costs, this result is surprising due to the fact that the disc laser is more expensive than the rod laser. The final reason for the better result of the disc laser is its lower energy consumption. The electricity costs per operating hour for instance are approximately 1.4 € in comparison to 7.5 €. The energy savings of the disc laser are so important that it surpasses the higher investments costs in the total calculation. In this case, a reasonable final decision would be to purchase the disc laser instead of the rod laser. A similar progress regarding energy savings as mentioned in connection with the laser sources has been reached in the area of industrial furnaces [19]. Moreover, cleaning processes have become more efficient too [20]. The above-mentioned comparison of laser sources considers new resources. In case of comparing old and new equipment, it can be assumed that the differences regarding the energy aspects can be even more extreme – e.g. in case of an old rod laser which has to be compared with a new disc laser. From our point of view, there is no doubt that energy consumptions can play an important role within reuse discussions. If there are old, energy-consuming resources which would have to be uninstalled, transported, reinstalled, adapted and commissioned with much effort in order to fulfill new tasks in reuse scenarios, the purchase of new, energyefficient resources could be the better solution. As to production planning – including reuse planning –, it is proposed to use planning software. The general aim is to support
289 production planners as much and as good as possible so that suboptimal solutions can be avoided. In the case of using planning software, the comparison of the energy values could be performed by software and the identified deviations could be classified automatically. The impact of the deviations could be visualized by means of different colors – for instance green for small deviations, yellow for medium deviations and red for strong deviations. The limits of the colored areas naturally have to be defined in advance. In our model – a classic product-process-resource model –, the energy values are eventually transferred from the resources to the processes in connection with the process-resource allocation. The equipment-specific values regarding the energy consumption are then becoming attributes of the processes, whereas it does not matter whether an old or new resource is allocated to a process. In order to simplify the issues discussed in this paper, it is defined that only one resource can be allocated to one process. Moreover, it is assumed that a process needs a resource full time (100% of the process time) in the case of a process-resource allocation. In the simplest case, each process Pi consequently contains five energyrelevant attributes: a process time (ti), a frequency of use (fi), an energy consumption per hour in the case of use (||Euse, Pi||), an energy consumption per hour in the case of stand-by (||Eidle, Pi||) as well as a rate (ri) defining the energy costs in Euro per consumed kilo joule, which depends on the operating method of the resource. Furthermore, the process contains information about the product variants that need to make use of this special process. Figure 2 shows what happens in the case of a process-resource allocation and it summarizes all the points discussed above.
Process without resource allocation
P1
Process with resource allocation
Process attributes t1 f1 r1 ||Euse, P1|| = 0 ||Eidle, P1|| = 0 …
Process attributes t1, f1, r1 ||Euse, P1|| = ||Euse, R3|| ≠ 0 ||Eidle, P1|| = ||Eidle, R3|| ≠ 0 …
Resource attributes ||Euse, R3|| ≠ 0 ||Eidle, R3|| ≠ 0 …
R3 P1
Processes in a process graph Process step 1 f1 = 100% t1, r1 ||Euse, P1||, ||Eidle, P1|| …
Process step 2
Process step 3
R2 P2
R1
R3
f2 = 70% t2, r2 ||Euse, P2||, ||Eidle, P2|| …
1,2
P1 f4 = 30% t4, r4 ||Euse, P4||, ||Eidle, P4|| … V
V
R4
1,2,3
V
P3 1,2,3
P4 3
1,2,3 Product variants
f3 = 100% t3, r3 ||Euse, P3||, ||Eidle, P3|| …
Variant elements which include alternative processes (here: P2 and P4 are alternative processes; either P2 or P4 is executed during the production; it is impossible that both processes are executed during the same time) Regarding a process step which contains two variant elements V , the rule is: n fi, if the variant elements V contain the process Pi ∑ fi, alternative = 100% fi, alternative = 0, in all other cases i=1
Figure 2: Transfer of resource attributes to process attributes within the model.
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In general, it is not enough to look at the energy consumption in a local way only. In fact, a paired comparison analysis of energy values within the step of reuse planning is a first necessary check in respect of acceptable energy consumptions but further analyses regarding the energy efficiency of the whole production line and regarding the products and product variants which are produced in this line have to be performed – at least at this point of time at which a complete draft of the production line is available. This is an important point especially due to the fact that production lines are often divided into sections and planned section by section by different production planners so that a complete overview of the production line and its energy consumption is often missing at early planning stages. Therefore, it is proposed to perform further global analyses if a complete draft of the production line is available. Such global energy analyses are necessary particularly if the management has defined limits regarding the allowed energy consumption. One reasonable target of the management could define that new lines would have to be less energy-consuming than former similar production lines. Section 4 deals with the topic of calculating the energy consumption of production lines in a more detailed way. 4
CALCULATING THE PRODUCTION LINES
ENERGY
CONSUMPTION
optimization step could also lead to the result that fewer resources could finally be reused due to their suboptimal energy consumption. In general, it is proposed to automatically calculate the average energy consumption of the production line as well as the variantspecific energy consumption by software. A manual calculation of values that is time-consuming and error-prone should be avoided. Moreover, the use of software offers further advantages. Results could be pictured in a clear way by means of digital diagrams and an intelligent digital environment could automatically generate proposals regarding improvements. An unbalanced situation regarding the energy consumption of product variants can be dangerous even if the average energy consumption of the production line per piece is lower than the defined target. One reason for that might be that the sales forecasts regarding product variants are uncertain. Customers could eventually order more product variants that need lots of energy in the production process and in this case, the average calculations performed in the step of production planning would be wrong. Therefore, it is not enough to perform average calculations only – variant-specific analyses are necessary as well. It should always be tried to reach a balanced situation.
OF
Symbols
Regarding a first assessment of the production line, it is reasonable to consider the average energy consumption of the production line per piece. The aforementioned average energy consumption of the production line per piece can be calculated – considering the assumptions of Section 3 – based on equation (1).
n E ti || Euse, Pi || f i i 1
(1)
G ti || Eidle, Pi || 1 f i i kJ i 1 i1 py n
Description
Unit
E
Average energy consumption of the production line per piece
[kJ]
E(x)
Variant-specific consumption of energy regarding one product variant x
[kJ]
n
Number of processes within the process graph
/
m
Number of additional energy consumptions which are considered
/
ti
Process time of the process Pi
[h]
Process time of the process Pi in case this process is part of the analysed product variant x
[h]
m
However, it can be dangerous to work with average calculations only. An explicit examination of energy aspects regarding single product variants has to be performed too. The goal of each company should be to produce each of their product variants in an energy-saving way and to make a progress regarding the energy consumption from one product generation to the next. The variantspecific consumption of energy regarding one product variant x can be calculated based on equation (2). All symbols of equation (1) and (2) are described in Table 2.
E ( x ) vi ( x) || Euse , Pi || i 1 n
ti || Eidle, Pi || i1 n
vi ( x) || Eidle, Pi || i1 n
(2)
Gi kJ i 1 py m
Equation (2) delivers the variant-specific energy consumption, whereas the results can be used for comparisons and conclusions. In case a target regarding the energy consumption is not met, possibilities for reducing the variant-specific consumption have to be taken into account, whereas it has to be considered that the average energy consumption of the production line will be affected as well. Moreover, production planners have to be careful in respect of optimizations due to the fact that more aspects than only the energy aspects mentioned have to be considered. However, an
vi(x)
vi (x) = ti, if Pi necessary for x vi (x) = 0, if Pi not necessary for x ||Euse, Pi||
Energy consumption of the process Pi per hour in the case of use
[kJ/h]
||Eidle, Pi||
Energy consumption of the process Pi per hour in the case of standby
[kJ/h]
fi
Frequency of use of the process Pi
x
Product variant analysed
Gi
Energy consumptions that are not considered within the equations (In accordance with [21], energy per year used for lighting, heating etc.)
py
Predicted number of pieces which will be produced within the production period of one year Table 2: Description of symbols.
[%] /
[kJ]
/
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291
In order to show that average calculations often have to be treated with caution, one simple example dealing with drilling processes and their energy consumption is discussed. The example is not connected to a reuse scenario, but it is sufficient to show that focusing on average values exclusively can lead to risks. The analyses have been performed at the Saarland University in connection with a student research project [22]. Different parameters have been varied in the analyses and their impact on energy consumption has been investigated. However, only the impact of the diameter will be discussed in this section in a bit more detail.
Energy costs can be calculated by means of equation (3) and equation (4). Cø represents the average energy costs of the production line per piece, whereas C(x) represents the variantspecific energy costs of one specific product variant x.
In our case, there are two different product variants which get different drill holes. Product variant 1 gets a drill hole with a diameter of 7.5 mm, whereas the diameter is 15 mm regarding the second product variant. As shown in Figure 3, the realization of the different holes causes different energy consumptions concerning the drilling machine. In order to realize a hole with a diameter of 7.5 mm, an electrical energy of 15.456 kJ is needed. As to the hole with a diameter of 15 mm, 30.831 kJ are necessary. The sales forecast states as follows: It is assumed that 70% of the yearly total production will deal with product variant 1, whereas the remaining 30% will be needed for product variant 2. Based on this forecast, the average energy consumption can be calculated. In this example, 20.063 kJ per piece are needed on average. At this point, the question occurs whether this result is reliable.
n C ( x) vi ( x) || Euse , Pi || ri i 1
By concentrating on the variant-specific results and comparing the obtained values, it is obvious that there is an unbalanced situation: product variant 2 consumes nearly twice the energy needed for product variant 1. Consequently, it is clear that the average result is not reliable. If the demand situation is different from the one defined in the forecast, the manufacturer will probably be confronted with higher energy consumption and higher costs. If 70% of the yearly total production will deal with product variant 2 for instance in reality, an average energy consumption of 26.206 kJ per piece will result out of the drilling process. This means that the consumption would be approximately 31% higher than calculated within the planning phase. Of course, this is a simple example, but it shows that focusing on average values is not reliable in unbalanced situations. Therefore, it is proposed to use not only equation (1) in order to assess the energy consumption of production lines – from our point of view, further variant-specific analyses are necessary. The variant-specific analyses especially become interesting if mixed-model production lines which consist of a high number of variant-specific processes and resources are considered at the planning stage.
Energy consumption [kJ]
E (1) 1 Sales forecast:
70%
E (2)
2 30%
n m G ti || Eidle , Pi || ri 1 f i i rgi € py i1 i1
n ti || Eidle, Pi || ri i 1
(3)
(4)
n m G vi ( x) || Eidle , Pi || ri i rgi € py i1 i1 Within equations (3) and (4), information about the rates ri defining the energy costs of the processes Pi per consumed kilo joule as well as information about the rates rgi defining further general energy costs of the production line per consumed kilo joule, which are not considered in other terms of the equations, is necessary. The unit of the rates ri and rgi is Euro per kilo joule. The calculation results can finally be visualized again in diagrams which contain a high transparency. The resulting diagrams are basically similar to the diagram shown in Figure 3, whereas in this case the axis of the ordinate considers energy costs, not energy consumption. Based on the favored diagrams, product variants with high energy consumption and/or high energy costs can be easily identified. The clear presentation of the existing conditions regarding energy consumption and energy costs has some advantages. Misinterpretations and information losses in case of information transfer can be avoided or at least reduced. Moreover, comparisons with other production lines become much easier through the use and superposition of the diagrams. Regarding intelligent digital environments, it can be said that such systems would offer the possibility of automatically identifying the most energy-intensive processes. In the case that process graphs, as described in [16], are used, a feedback regarding suboptimal processes could be executed again in a visual way. Inadequate processes regarding the energy consumption can be highlighted in terms of color in process graphs. All in all, it is rated as necessary to support the whole production planning process by software. In the future, production planners will need simple and transparent software tools which can be easily used and which offer the possibility for performing general as well as variant-specific assessments [16]. This type of software tool is also required as far as energy analyses of production lines are concerned.
30.813 E =20.063 15.456
n C ti || Euse , Pi || ri f i i1
Product variants
Product variant 1: D = 7.5 mm, Product variant 2: D = 15 mm Material: 16MnCr5, Drilling depth: 10 mm E = (15.456 * 0.70) kJ + (30.813 * 0.30) kJ = 20.063 kJ Figure 3: Drilling process and its energy consumption depending on varying diameters in accordance with [22].
5
SUMMARY AND OUTLOOK
Energy efficiency becomes a more and more important topic in the manufacturing industry. In this paper, it has been discussed how energy aspects can be considered in connection with a reuse planning approach which aims at reusing old production equipment in new production lines in order to save costs. In this context, it has been clarified that an unacceptable energy consumption of the old equipment can finally lead to the decision to purchase new equipment. However, it has also been mentioned that it is not
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sufficient to look at energy aspects from a local perspective only. A paired comparison analysis of energy values of old and new resources within the stage of reuse planning is only the first step that is necessary. Further analyses regarding the energy efficiency of the whole production line and the products and product variants which are produced in this line certainly are necessary.
[8]
Takata, S.; Kimura, F.; van Houten, F.J.A.M.; Westkämper, E.; Shpitalni, M.; Ceglarek, D.; Lee, J. (2004): Maintenance: Changing Role in Life Cycle Management, CIRP Annals Manufacturing Technology, Volume 53, Issue 2, pp. 643-655.
[9]
Jawahir, I.S.; Holloway, L.; Rouch, K.E.; Hall, A.; Dillon, JR., O.W.; Knuf, J. (2005): Design for Sustainability (DFS): New Challenges in Developing and Implementing a Curriculum for Next Generation Design and Manufacturing Engineers, 3rd SME International Conference on Manufacturing Education.
[10]
Weyand, L.; Bley, H. (2010): Automotive Final Assembly Planning and Equipment Reuse, International Conference on Competitive Manufacturing (COMA), pp. 343-348.
[11]
Weyand, L.; Bley, H.; Merl, D. (2010): Boundary Conditions for a Selective Reuse Planning in the Automotive Industry, 7th CIRP International Conference on Intelligent Computation in Manufacturing Engineering (ICME).
[12]
Kara, S.; Mazhar, M.; Kaebernick, H.; Ahmed, A. (2005): Determining the Reuse Potential of Components Based on Life Cycle Data, CIRP Annals - Manufacturing Technology, Volume 54, Issue 1, pp. 1-4.
[13]
Anityasari, M.; Kaebernick, H. (2008): A concept of reliability evaluation for reuse and remanufacturing, International Journal of Sustainable Manufacturing, Volume 1, Number 1-2, pp. 3-17.
[14]
Weule, H.; Buchholz, C. (2001): Method for assessment of reuse suitability within modular assembly systems, Assembly Automation, Volume 21, Number 3, pp. 241-246.
This research activity was partly supported by the research project “Flexible Assembly Processes for the Car of the Third Millennium (MyCar)” funded by the Commission of the European Union and by the research project “Ganzheitliche Gestaltung energieeffizienter technologischer Prozessketten (enPROchain)” funded by the program „Zentrales Innovationsprogramm Mittelstand (ZIM)“ of the German Ministry of Economics (BMWi).
[15]
Fitzgibbon, K.; Barker, R.; Clayton, T.; Wilson, N. (2002): A failure-forecast method based on Weibull and statisticalpattern analysis, Annual Reliability and Maintainability Symposium, pp. 516-521.
[16]
Weyand, L. (2010): Risikoreduzierte Endmontageplanung am Beispiel der Automobilindustrie – Risk-reduced Final Assembly Planning in the Automotive Industry, Dissertation, Universität des Saarlandes.
7
[17]
Weyand, L.; Bley, H. (2010): Planning of Final Assembly Lines which are Flexible in Respect of the Output, 3rd CIRP Conference on Assembly Technologies & Systems (CATS), pp. 7-12.
[18]
Hammer, T.; Brockmann, R. (2006): Scannerschweißen mit dem Scheibenlaser – Remote Laser Welding by Disc Laser, Laser Technik Journal, Volume 3, Issue 3, pp. 36-38.
Methods of calculating the energy consumption of a production line have been presented. In this context, it has been mentioned that the results of average calculations partly have to be handled with caution. Average calculations that are based on uncertain sales forecasts usually deliver hints regarding the annual energy consumption of the line, but they are not reliable in unbalanced situations and they do not give any information about the energy efficiency of single products and product variants. Therefore, additional variant-specific analyses are necessary. All in all, first promising results have been obtained, whereas there is no doubt that further examples have to be discussed in the future and that the presented equations still have to be enhanced where necessary – at this point, the underlying assumptions included in the equations should not be forgotten. Moreover, it has to be discussed in the future how available software prototypes can be modified so that energy aspects can be addressed as explicitly as mentioned in this paper. Production planners need transparent software tools which are easy to use and which offer the possibility for performing general as well as variant-specific assessments – also regarding the topic of energy efficiency. 6
ACKNOWLEDGMENTS
REFERENCES
[1]
Ko, J.; Hu, S.J.; Huang, T. (2005): Reusability Assessment for Manufacturing Systems, CIRP Annals - Manufacturing Technology, Volume 54, Issue 1, pp. 113-116.
[2]
Weule, H.; Buchholz, C. (2001): Re-use of Assembly Systems: A great ecological and economic potential for facility suppliers, Proceedings of SPIE, the International Society for Optical Engineering, Volume 4193, pp. 44-55.
[3]
Fleschutz, T.; Harms, R.; Seliger, G. (2009): Valuation of Assembly Equipment Reuse with Real Options, 20th Annual Conference of the Production and Operations Management Society.
[4]
Dewulf, W.; Duflou, J. (2003): Simplifying LCA Using Indicator Approaches - A Framework, CIRP Seminar on Life Cycle Engineering.
[5]
Grote, K.-H.; Antonsson, E.K. (2009): Springer Handbook of Mechanical Engineering.
[6]
Herrmann, C.; Zein, A.; Thiede, S.; Bergmann, L.; Bock, R. (2008): Bringing sustainable manufacturing into practice – the machine tool case, Sustainable Manufacturing VI: Global Conference on Sustainable Product Development and Life Cycle Engineering.
[7]
Jeswiet, J.; Duflou, J.; Dewulf, W.; Luttrop, C.; Hauschild, M. (2005): A Curriculum for Life Cycle Engineering Design for the Environment, 1st Annual CDIO Conference.
[19] Irretier, O. (2010): Energieeffizienz in Industrieofenbau und Wärmebehandlung – Maßnahmen und Potentiale, Energy efficiency in industrial furnace engineering and heat treatment – provisions and potentials, in: elektrowärme international, pp. 37-44. [20]
Krebs, M.; Deuse, J. (2009): Parts cleaning in the value stream, 3rd International Conference on Changeable, Agile, Reconfigurable and Virtual Production (CARV), pp. 800-809.
[21]
Rahimifard, S.; Seowa Y.; Childs, T. (2010): Minimising Embodied Product Energy to support energy efficient manufacturing, CIRP Annals - Manufacturing Technology, Volume 59, Issue 1, pp. 25-28.
[22]
Latz, C.; Jost, S. (2010): Energieverbrauch in der spanenden Fertigung am Beispiel Bohren – Energy consumption in metal-cutting manufacturing based on the example of drilling, Institute of Production Engineering, Student research project, Saarland University.
Optimizing Energy Costs by Intelligent Production Scheduling 1
Agnes Pechmann , Ilka Schöler 1
1
Department of Mechanical Engineering, University of Applied Sciences Emden/Leer, Emden, Germany
Abstract Small and medium sized enterprises tackle the problem of high energy consumption and costs. Reducing both by fixing leaks, improving the thermal insulation of buildings and switching of unnecessary heating/cooling are first steps. More advanced steps are reengineering machinery and processes. Within this paper results of a research project are presented. Unused potential for increasing energy efficiency can be found by optimizing the production schedule. A full scale PPC software was developed which is able to schedule production without energy peaks and furthermore is able to create a 24h energy forecast to hand over to the energy supplier. Keywords: Production Planning and Controlling; Energy Peaks; Load Management
1
INTRODUCTION
With Technical solutions the full potential to increase energy efficiency is not exploited, organizational aspects need to be discovered as well. An optimized production schedule is a further step towards increasing energy efficiency. First steps in reducing the energy demand for production sites are to collect information regarding the current status of the energy consumption and to analyze the potential to avoid or reduce it [1]. Technically easy but very effective steps to avoid energy consumption are e.g. fixing leaks (pressured air), improving the thermal insulation of buildings, and switching off unnecessary lighting and heating/cooling. Technically more advanced steps to decrease the energy consumption are elevating the energy efficiency of machinery and processes, integrating renewable energy and helping to stabilize the energy grid by avoiding energy peaks. Research has been done concerning the question on how to reduce the energy consumption within a production site covering thermal insulation of facilities, reduction of conversion losses, optimization of the production processes (e.g. reducing the production temperature or pressure) [2, 3]. Little has been researched on savings (energy, CO2, costs) due to organizational optimization on the shop floor itself. [4, 5]. Is there still potential to reduce energy consumption and energy peaks when considering regular restriction set by the market and or the customer like short delivery times? How does this change when considering non-omissible interferences caused by machinery, material or humans? How is it possible to handle the necessary plan and control data? Industrial planning and controlling generally focus only on the direct economic and technical aspects, neglecting so far the role of energy consumption and its possibilities of cost savings. In order to preserve the environment and develop a sustainable industry (in terms of economic and natural resources), the scarce and efficient use of energy must be included among the target values for industrial intelligent production scheduling and controlling.
The latest German energy concept of September 2010 underlines the need for environmentally friendly electrical power supply. In times of climate change due to high greenhouse gas concentration, raising need of energy and consequently, a higher need of energy import, it is necessary to provide a dependable and affordable electrical power supply in Germany [6]. The supply is one side; the demand is the other side to look at when talking about reducing CO2 emission and the dependability on foreign energy sources. We will focus on reducing and optimizing the demand. The main focus within the BMBF sponsored research project called Energy (E-)PPS (support code 01LY0813A) is to develop a production planning and controlling software which is able to handle regular PPC tasks and to reduce energy consumption by intelligent scheduling and organization of production processes. The software is targeted toward small and medium sized production facilities with a wide range of standard products with small to mid size series. Controlling energy consumption of production processes and optimizing its use in respect to timing by key energy indicators creates the necessary visibility of the energy demand and its related costs. Then as the second and main step of optimization the avoidance of cost intensive energy peaks through intelligent production scheduling is targeted. The very practical oriented approach within the project leads towards a better understanding of the energy demand and its related costs as stated above. The E-PPS software is a full scale PPC-software and is developed for small and medium production sites with a high variety of products. Beyond that the scheduled production runs without high energy peaks. Furthermore the E-PPS creates a 24 hour energy forecast to hand over to the energy supplier. Any short notice change in production such as “emergency” orders or machinery break down is implemented into the production schedule corresponding the 24 h forecast. After this first introductory part, some basic, relevant facts of the energy power market will be presented in chapter two, followed by aspects regarding energy consumption of manufacturing processes (Chapter 3). In chapter four we will list established approaches on how to decrease energy consumption as such, and we will point out
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_51, © Springer-Verlag Berlin Heidelberg 2011
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possibilities of a more holistic approach on energy cost savings. In order to control the changes required for a successful cost saving program energy key performance indicators (KPIs) are of value. The identification of these KPIs is the focus of chapter five. The purpose and use of the E-PPS software and the requirements for such production planning and scheduling software is stated in chapter six, followed by the necessary requirements of production processes for its use (Chapter 7). The paper concludes with an outlook on the future of E-PPS and next steps and a short summary. 2
IMPORTANT ENERGY (-MARKET) FACTS
The energy market as such is a highly developed and dense market not only in Germany. One of the main points to know as an energy consumer with interest in optimizing energy costs is how the electricity costs sum up, based on the following charges:
Customer service charge (covers e.g. billing related costs and standby costs of the power system).
Energy charge (depends on the actually used kWh per billing period).
Power charge (depends on the peak demand in kW used within the billing measuring cycle time, usually a 15 min interval) [7].
Power factor charge (depending on how effectively the provided power is used by the consumer, also called regulating power charge).
With the knowledge of the different parts of the electricity bill and its related costs, a decision can be made where to start cost cutting projects. The different charges offer different potential for cost cutting. The customer service charge is a candidate for lowering costs by renegotiating the present contract conditions with the electricity supplier; sometimes with surprisingly positive outcomes. To reduce the energy charge, direct energy consumption has to be cut down; one way is replacing older, not as efficient equipment with newer and more efficient one. The result will be less energy consuming production processes which lowers the energy charge. Whether this is a feasible solution has to be decided by the company based on return on investment decisions and financial resources available for these kinds of investments. The power charge (charge for the peak demand) is the total sum of energy demand of all energy users within the company, e.g. all production equipment / machinery, computers, light, and air conditioning. The main share usually comes from the shop floor in the case of a manufacturing company. High energy consuming industries such as paper or metal have tools, the so called load management in place to limit energy consumption. If the peak power per 15 min interval gets too high, machinery is shut down according to a pre-defined plan. Usually either the user with the highest peak demand is shut down or the ones which are the least critical to the production process. Though this is a very effective way to cut down on short-term power demand and therefore lowering the peak, the main purpose lays not in energy cost savings but in not crossing the technical available power limit which would result in stress on the whole power grid. A further effect of “just” shutting down machinery is the lowered productivity of the production. Standard conditions for electronic power contracts are getting less and less common for production companies. While every production company has to negotiate its own detailed contract conditions with the power supplier, the negotiations can be positively influenced for the consumer if its own future demands are known. With the capability to present energy load forecasts, displaying the power
demand for the upcoming 24 hours on a quarterly hour basis, the power supplier is enabled to optimize its overall power and regulation power production. In addition to that the power supplier is able to optimize its purchase demand since it knows more exactly how much power it needs to provide to its customers. The more exact it can build its own aggregated power supply profile, the lower is the safety span it needs to have available in case of unaccounted demand. Having correct forecasts for the peak demand is especially important since it is the main concern of the grid operators. The knowledge about the exact time of the peak demand as well as the avoidance of high peak demand (meaning to have lower peak levels) helps to stabilize the energy grid and to avoid the just in case portion of the regulation power reserve. In short: known and/or lower high peak demand leads to less regulating power and, thus, to less energy costs for the supplier and the consumer. And, in addition, the less regulating power is used, the more CO2 emission is avoided. 3
SOME ASPECTS CONSUMPTION
ON
ENERGY
EFFICIENCY
AND
First, though speaking about energy, we focus in this paper on electricity. Secondly, the definition of energy efficiency needs to be specified since it is not consistent in literature and is used in an unusual way. A common definition is: energy efficiency is the ratio between benefit and cost. The benefit will be the output and the cost in this specific case will be the amount of energy used for this specific output. The output can be a single piece or a certain number of pieces. The energy used for this certain amount of product is called specific energy [8]. Energy efficiency within this paper is used in respect to balanced power profiles; ideally having minor or even non peak demands. However, the amount of energy per unit produced is very important, too, but not the main focus in the project. Electricity consumption as such can be classified from different perspectives; here we look at it from a manufacturing consumer perspective as shown in Figure 1. In order to optimize the power load, here: balancing the power demand to a minimum possible around the average demand, the dependencies of the power consumers have to be identified. Action to cut down energy demand can and has to be considered for the two general split ups, the base load and production dependable energy. The first part, the base load consumption, consists of energy used for facility and the serviceability. Overall consumption
Base load
Facility
Serviceability
Production dependable Product dependent
Product independent
Figure 1: Electricity consumption. The facility includes mainly buildings and heating systems/air condition for offices. To cut down the energy consumption within the building, it is recommended to call in a specialist on energetic factory building refurbishment. Serviceability includes the air conditioning systems, e.g. in cleanrooms, and coolingsystems (for
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lubrication) for machine tools, e.g. for a hydraulic press. To cut down on energy consumption in these areas a closer look has to be taken into the off-side machinery within the shop floor. Special care has to be taken not to endanger the required ambient conditions for production by ad-hoc actions.
profile which each individual production process step for each individual product shows. The overall energy profile of the company is the sum of each individual process step at each given time.
The second main electricity consumption part is the energy used directly in the production processes, therefore directly related to operational steps, here referred to as production dependable energy consumption. The production dependable energy consumption has two fractions, the product dependable and the product undependable fraction. Intelligent production scheduling such like E-PPS is focusing on the product dependable energy consumption since the software is scheduling and organizing the production on defined products. By looking at the product dependable energy the main interesting and often forgotten aspect is the power profiles. Each single machine has a distinguished power profile for each defined production step for each product/material. The energy profile of a single production process is likely to change if the material is changing, e.g. a milling machine processing steel vs. copper. The product undependable energy consumption is more or less a constant consumer such as vacuum pumps.
Machinery used.
Product being produced.
Process step measured.
Most of the times companies do not know the base load and the production dependable energy consumption of the factory; for most of the small and medium sized companies the topic energy consumption is, more than less, a black box. Now some forwardlooking companies are investing in electricity measurement equipment for production while investing in new machinery. But gathering of data is only of help if analysis and evaluation are made and the appropriate actions are taken. 4
OPTIMISATION APPROACHES
In industry, especially in the heavy energy consuming branches like metal, paper or plastics, a few approaches to limit energy consumption can be found. The most common approaches will be named, followed by a new holistic approach. 4.1
Implemented common approaches to cut down energy consumption and costs
Three different approaches can be found within the industries to reduce energy consumption and energy costs.
The measured energy profiles are defined through the following information:
Figure 2: Energy profiles with a standard PPC software. Figure 2 shows exemplary energy profiles. Two processes (process A and process B) are running at the same time on the shop floor. In total the energy profile shows an energy peak demand at 5:05 since both processes have a peak demand at that time. This peak demand will cause a tremendous peak in the energy grid as well. And, unfortunately, if the peak demand is higher than the allowed value in the energy contract the peak will result in additional cost, too. If it is possible to postpone one of the processes, then the peak demand can be lowered; costs for the extreme peak are avoided! To transfer this idea – product and process dependable energy profiles used in a PPC software – the project E-PPS was set up to have the following four major steps within the workflow:
1. Optimization of single machinery (investing in new and less energy needing machinery).
Developing a method to identify main energy efficiency indicators.
2. Energy cut offs to reduce energy peak demands (as described in chapter 2).
Defining specific requirements for a software system for energy efficiency planning and controlling.
Developing the software system E-PPS.
3. Energy load forecasts on an aggregated level to cut down cost due to a better position in the contract negotiation. None of the above solves the problem of the high energy consumption in the industry nor is it achieving the overall best result for the companies. The price of each single product might increase and the productivity of the plant will go down due to shut downs of single machinery. Also the forecast of the energy consumption at an aggregated level does not give the possibility to react appropriately if a peak demand is occurring. 4.2
The holistic optimization
Acknowledging that the common approaches do not tap the full potential to cut down energy cost, the idea was born for a more holistic approach with the ability to manage energy consumption of the production through an intelligent production planning and controlling system. The potential to increase the energy efficiency due to a better production planning and controlling is possible through the energy
The testing of measurements and calculations of the energy consumption and efficiency indicators were made in a pilot plant. The project focuses on small and medium sized companies preferable with a very high variety on non standard products. The pilot plant is a company within the semiconductor field. The product spectrum was sufficient enough to test the E-PPS software. 5
KPIS TO MEASURE CONSUMPTION
AND
CONTROL
THE
ENERGY
Key Performance Indicators (KPIs) are a set of measures which can be used to look back and see how well the company in various fields (purchasing, sails, production etc.) at a certain time performed but also to see, if the direction one is heading to is still right and even further, what has to be done to improve even more [9]. In general, a set of KPIs is a control tool which can be adapted for each company and for each question. In this particularly case they
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should help the energy manager to see what has to be done to reduce the energy consumption dramatically. Similar to the continuous improvement process such as the PDCA Cycle (Plan, Do, Check, Act) an eight step procedure was developed during the runtime of the E-PPS project. To have the possibility to get suitable KPIs matching the needs of the shop floor, this eight-step procedure was developed [10] and is shown in Figure 3:
1. Facility and Equipment Inventory
6. Quantifcation of Indicators
7. Monitoring and Controlling
2. Process Analysis
5. Identification of Causes and Factors
8. Review and Evaluation
3. Initial Energy Measurements
4. 20/80 Identification
6
E-PPS SOLUTION FOR ENERGY AND COST SAVING
The main question for being able to organize and schedule the production considering all planning criteria (e.g. delivery time, capacity) including energy efficiency is to define the input data of the software. The requirements differ depending on the industry and its specific needs. Independent of the differences the following information is necessary in every case for the E-PPS software:
Detailed information about the existing energy contract.
Detailed information about each individual production process.
Detailed information about the energy consumption profile of each production machine (production dependable part).
Detailed information about further energy consumer (base load part).
The energy contract information includes the energy and power charge, power factor charge and the customer service charge. The production process information includes the single production resources (first, second and n-choice, products and product variation on each production resource), production times (set-up, processing time), and cost centers.
Figure 3: Finding procedure of KPIs. The eight steps are: 1. Facility and equipment inventory. 2. Processes analysis (including identification and characterization of the processes and identification of the equipment and machinery). 3. Initial energy measurements (including gathering the energy bills and profiles from the supplier and measuring the main equipment and machinery involved) 4. 20/80 identification (including individual and global analyses). 5. Identification of causes and factors (for the significant high energy consumption). 6. Quantification of the developed indicators. 7. Monitoring and controlling (including deciding which data, from which source at which frequency). 8. Review and evaluation (to prove that the chosen indicators are still valid and useful). This set of customized energy KPIs allows not only the energy manager to get an overall look at the performance of the production and, therefore of the energy consumption, but also this set of KPIs works as an alarm system in the case something does not operate correctly. Since the KPIs are numbers determined by the main energy consumer they will react accordingly to the performance of the consumer. Furthermore, while developing these KPIs, the gain of knowledge about the production processes, the consumption of energy per unit and the composition of the resulting energy costs are of tremendous use. To sum up the implementation of the customized energy efficiency indicators within a production company leads to the following:
Customized set of energy efficiency indicators.
Specific and detailed knowledge about all energy consumers.
Detailed knowledge about each specific product, process step and its own energy profile.
Detailed knowledge about the product specific cost profile as a function of the energy consumption for each product.
Figure 4: Process stages. The energy consumption profile of each production machine includes the load profile with detailed information about the stage of the process [11] and the product which is produced. An example of a load profile is shown in Figure 4. The load profile is subdivided into five different parts. Each part has its specific profile. Especially the standby and idle profiles can be used within the planning routine to calculate the difference between e.g. different starting times of the production. The amount of time needed to gather all energy profiles depends on different factors: a) number of measurement equipment sets available b) pre-installed or integrated measurement equipment on the machinery c) number of single production/operation processes and d) duration of the production/operation processes. 7
REQUIREMENTS FOR THE USE OF E-PPS SOFTWARE
The main question after implementing the power profiles into the software is under which conditions is the use of the E-PPS software feasible? Certain requirements are needed within the production processes in order to work according to the production plan of EPPS. The main question is: how flexible is the production process? Does the production have the flexibility
to halt a running process?
to delay a process step?
to pull forward a process step?
Sustainability in Manufacturing - Energy Efficiency in Process Chains The potential to halt, delay or pull-forward a process differs from industry to industry, from product to product and from process to process. A very flexible production site will be able to use all three methods to move a processing step to avoid peak demands. The result will be a desired production output with a well-balanced energy profile. An example of a well-balanced energy profile can be seen in Figure 5.
297 industry or is the cost saving potential high enough for an implementation in other industries with less energy consumption? Feedback on recent exhibitions shows that a considerable interest exists concerning energy consumption and reducing energy costs. Yet the energy price today seems still too low for putting further efforts on reducing energy consumption which goes beyond the common solutions as stated in section 4.1. In the future, with higher costs on energy power consumption but even more for stabilizing energy grids, it can be expected that companies are willing to invest in a more holistic approach like the E-PPS software. Next to cost aspects, aspects regarding awareness and capabilities of dealing with energy questions have to be considered, and – the simple, first steps in optimizing energy consumption (e.g. insulation of buildings, avoiding leaks) have to be carried out. Then, the future is open to invest in more advanced energy optimizing tools like E-PPS. 9
Figure 5: Energy profiles with E-PPS software. The comparison between the energy profiles of a standard PPC software (Figure 2) with the application of the E-PPS software (Figure 5) reveals the peak demand at 5:05. This energy peak can be reduced to a lower level when using the E-PPS software. Though with E-PPS we can bring the power peaks to a lower level, it has to be noted that the overall direct energy consumption stays the same. Nevertheless, the declined peak demands reduce the power peak charge. Furthermore the effect on cost saving due to the energy forecast is a question of negotiation with high cost saving potential. As mentioned before, the main question whether the E-PPS software is valuable or not is, from this point of view, a question of flexibility on the production and processes involved. The process steps have to be flexible enough to be planned according to needs of the market/production dependencies and according to energy efficiency criteria. To achieve a reduction of energy costs through the E-PPS software, it is necessary to be able to halt, delay or predraw a process. If that is not possible for whatever reasons, the EPPS is not the right tool to reduce the cost for the power charge displayed on the energy bill. On the other hand, the E-PPS software is a great tool to generate the 24 hour energy load profile forecast for negotiation purposes. 8
FUTURE WORK WITHIN THIS THEME
Further questions have to be discussed to define requirements to be fulfilled by the production company to achieve the attained value out of the use of the E-PPS software. The following main points have to be considered and further analyzed:
Order type requirements.
Manufacturing type requirements.
Shop floor scheduling requirements.
The question of which production companies are ideal users of the E-PPS system has to be answered. Right now the system is implemented and tested in a medium sized enterprise which operates in the semiconductor field. Is the cost cutting potential of the E-PPS software only interesting for high energy consuming
SUMMARY
The simple and application oriented idea of the E-PPS project to reduce energy costs by reducing energy peak demands resulted in the question of how much energy is actually used at each given time during the production of a single piece. The usage of the eight step procedure to define customized KPIs leads to a lot of needed information and to an order of importance of the different machinery concerning energy use. The results of the E-PPS project show that it is possible to reduce energy costs by intelligent planning, organizing and scheduling of production if the specific load profile of each production step is considered while creating a production schedule. Reducing peak demands and creating a 24 hour energy load profile forecast helps reducing the energy costs for small and medium production sites. On an aggregated level, CO2-reduction is possible through the reduction of regulation power on the power provider’s side. 10 REFERENCES [1]
Lackner, P. et al (2007): Handbuch Schritt für Schritt Anleitung für die Implementierung von Energiemanagement; Wien, Österreichische Energieagentur – Austrian Energy Agency.
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Kühnert, C. et al (2009): Optimierung der Prozessfrührung komplexer verfahrenstechnischer Prozesse mit Support Vector Machines; Frauenhofer-Institut für Informations- und Datenverarbeitung, Karlsruhe.
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Steinko, W. (2008): Optimierung von Spritzgießprozessen; Carl Hanser Verlag, München.
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Schiefendecker B. (2006): Energiemanagement-Tools – Anwendung im Industrieunternehmen, Springer-Verlag, Berlin Heidelberg.
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Junge, Mark (2007): Simulationsgestützte Entwicklung und Optimierung einer energieeffizienten Produktionssteuerung, Dissertation; kassel university press GmbH, Kassel.
[6]
Deutsche Bundesregierung (2010): Energiekonzept für eine umweltschonende, zuverlässige und bezahlbare Energieversorgung, Berlin.
[7]
Müller, E. et al. (2009): Energieeffiziente Fabriken planen und betreiben; Springer Berlin Heidelberg.
[8]
Irrek, W, Thomas S. (2008): Definition Energieeffizienz, Wuppertaler Institut für Klima, Umwelt, Energie GmbH, Wuppertal.
298 [9]
Sustainability in Manufacturing - Energy Efficiency in Process Chains Parmenter, D. (2007): Key performance indicators. Developing, implementing, and using winning KPIs, John Wiley & Sons, Inc., Hoboken, NJ.
[10] Aragon Sandoval, M. J. (2009): Selecting and Defining Indicators for Energy Consumption Monitoring in a Production Facility, Master Thesis, University of applied science, Emden/Leer, Emden. [11] Weinert, Nils (2010): Vorgehensweise für Planung und Betrieb energieeffizienter Produktionssysteme, Dissertation, Frauenhofer Verlag Stuttgart.
Methodology for an Energy and Resource Efficient Process Chain Design 1
1
Sebastian Schrems , Christian Eisele , Eberhard Abele 1
1
Institute of Production Management, Technology and Machine Tools, Technische Universität Darmstadt, Darmstadt, Germany
Abstract In consequence of rising costs for raw materials and energy, as well as the customers’ increasing awareness of environmental protection the question of a sustainable production is becoming more and more important. A considerable part of the energy and resource demand in manufacturing is determined during the production planning process. Thus the chosen production technique and the consequential process design have a big influence on the consumption values. This paper presents a methodology to assess different manufacturing technologies, analyzing and estimating their energy and resource consumption and finally choosing the process chain that fits the best to the company’s requirements. Keywords: Technology Assessment; Process Chain Design; Resource Efficiency
1
INTRODUCTION
During the past years a consequent increase of costs for raw materials and energy intensifies the question how to minimize energy and raw material consumption in the production process. With 47% of the overall electrical energy consumption in Germany, the industry has a promising potential to reduce the energy demand and therewith the energy costs [1]. A recent study specifies the total reduction potential for energy costs in Germany with 53 billion Euro with a 10 billion Euro share in the industry [2]. Another aspect which intensifies the industry’s ambitions to shift towards a more energy and resource efficient production is the customer’s increasing awareness of sustainability and sustainable products. An important contribution for this development is caused by the debate about global warming and the negotiated environmental agreement of the Kyoto protocol [3]. Based on this agreement to reduce the emissions of greenhouse gases within the next decades, many industrial companies have declared to reduce their emissions significantly in the upcoming years as well. An essential point to achieve these ambitious objectives is the reduction of energy and resource consumption which concerns especially electrical energy of production machines. For this reason the potential of optimizing the energy consumption along the process chain in the production process is assessed and a methodology leading to an energy efficient production system is developed. 2 2.1
OBJECTIVE Energy and resource consumption in process chains
In order to optimize energy and resource consumption in production systems, two different approaches are commonly used. It is either possible to optimize an existing production system by replacing systematically inefficient machines or components against more efficient ones or to identify the energetic optimal way of production already in early phases when the production process is planned. Figure 1 shows the relation between the upcoming resource costs
(including energy) and the scope of influence on these costs during different phases starting with the production planning and ending in the phase of manufacturing according to [4] [5]. Hence the greatest influence and saving potential is located in the early phases of the planning process.
possibility of influencing resource* costs
cumulative resource* costs
high
*Ressources: - energy - comp. air - raw materials - supplies
low product development
process chain design
manufacturing
time
Figure 1 : Resource costs and influencing possibilities during the production planning [4] [5]. A lot of research dealt already with the optimization of energy and resource consumption in existing production systems. With the high energy consumption in production and the political and industrial objectives to reduce CO2 emissions in mind, these approaches show a very important step to minimize energy and resource consumption. But as the influence on energy and resource consumption is much higher in early phases of the product development process, a new approach supporting the production engineer is necessary. Energy and resource consumption was no decision criterion for alternative process chains in production planning so far [6] [7] [8]. This is for example attributable to the former low energy and resource costs and thus the non-existing economical pressure, the uncertain data in the conceptual phase of the process planning and the associated difficulties in predicting future consumption values. As the demand for an energy and resource efficient production
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_52, © Springer-Verlag Berlin Heidelberg 2011
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today is rising, energy and resource consumption of production processes is becoming a more and more important factor which is already taken into account when purchasing a machine [9]. Life cycle inventory databases (LCI) which are commonly used for life cycle assessments or energy and resource consumption estimations contain only little information about the consumption of different production processes which can strongly vary by different production machines or scenarios [9]. Thus it is necessary to develop a methodology which enables a reliable estimation of the energy and resource consumption not depending on LCI Data. 2.2
Preceding research activities
The inclusion of ecological information during the product development process has already been treated in different research projects. The collaborative research centre (CRC) 144 at the RWTH Aachen dealt with the assessment of energy and resource consumption of materials and technologies. The methodology included a comparison of alternative processes focusing on the embodied energy. Still, a general modeling procedure of manufacturing processes, a capturing of the required data as well as a strategy to predict the energy consumption of processes in the planning stage is not covered [10]. Main topic of the CRC 392 and the following transfer area 55 was to develop methodologies for prospective estimations of the ecological influence of products along their whole life cycle. Based on lifecycle inventory data, an estimation of the influence of a product was conducted in the product development process. A methodology to predict the influence of alternative manufacturing processes is not included in this method [11] [12]. As lifecycle inventory databases are too generic and neglect many driving parameters such as the influence of the machine configuration or the periphery, they are generally not suitable for a prediction of manufacturing processes yet. Former approaches dealing with the assessment and selection of alternative process chains were mainly oriented to technological or economical selection criteria [13] [14] [15]. In a few cases ecological criteria have been taken into account [7] [8], but the emphasis were mainly on the processing of the ecological data and did not include the acquisition or the anticipation of expected data. Current studies concentrate particularly on the energy consumption of production processes. Taken into account that the energy consumption of production machines has been brought into focus recently, only a few methodologies have been developed. Kuhrke and Dietmair have both developed different methodologies to forecast the energy consumption of machine tools. Kuhrke’s model bases on the cooperation of the machine tool manufacturer and the customer who define together use cases of the future operating conditions. He develops a model enabling the precalculation of the machine components’ energy consumption basing on these use cases and targets an optimal, demand actuated, machine design [9]. Dietmair describes use-case specific models basing on previous energy measurements of the regarded machine. He defines different machine states which can be combined for an arbitrary machining cycle and finally predict the energy consumption [16]. A methodology focused on the production process is introduced by Schultz. He develops a methodology to assess different manufacturing processes by an input / output process model. The methodology concentrates on the merging of the ecological assessment by a LCA and the economical assessment by a costbenefit analysis. He does not describe the way how to determine
the necessary data or how to predict the energy of processes which do not exist yet [17]. Weinert introduces a model which can calculate the energy consumption of different processes using so called energy-blocks. On the basis of previous measurements he defines different machine states and interpolates a function which approximates the energy consumption curve shape. Like in Dietmair’s model, previous measurements are necessary to be able to calculate the energy consumption of a process. To predict the energy consumption of machines he assumes that the energy consumption of similar machines is comparable but a model to predict or transfer energy calculations is missing [18]. The described methodology in this paper concentrates on the early phase of the production planning and aims to allow the production engineer to choose the most efficient way to produce a certain product. An assessment of alternative process chains towards their energy and resource efficiency builds the fundament for the selection of the process chain which fulfills most suitable the requirements regarding the different ambitions (e.g. technological and economic) including the predicted need of energy and resources. Models are developed to predict the consumption values of different production processes and to handle the uncertainty of data in production planning. The models are designed in a way that they work with the available data and a low parameterization effort to deliver calculation results as exact as possible. Subsequently the calculation results of these models have to be utilized in a decision model which combines the ecological decision parameters with other existing parameters respecting user specific decision demands. 3
PROCESS MODELS
In order to be able to determine the energy and resource consumption of a manufacturing process, models are needed which describe the process and the necessary parameters as exact as possible and help prognosticate the expected consumption values. The constraints for these process models are the consideration of the manufacturing machine, the manageability of the models and the necessity of a prognosis of the energy and resource consumption of the regarded process as reliable as possible. The modeling of the processes is oriented on a three-stage reference model which classifies the process from a conceptual process chain to a concrete process layer (see Figure 2).
Observation layer
Transfer
Production Process chain structure analysis …
Manufacturing technology Turning Drehen
•primary forming •forming •cutting…
Specific Proces Varnishing Lackieren
Analysis of single processes
Assessment of alternative process chains Process models of exemplarily chosen main processes
A A B B C C
… varnishing Lackieren Spanende machining Bearbeitung Pa a r m e te r
Z e ti
L e i st u ng
E in h ie t
F o rm
s
x *y + z
W k
x *y + z
e l
Da t e n t y p
F uz z y
F uz z y
Ka r ft
N
x *y + z
Z ah l
…
…
…
…
Software environment for an integrated process chain assessment
Figure 2 : Detailed structure of the models.
Proz es s
Prozes skette
Ferti gun gs verfahren +… #…
Prozes s datenb an k
Ma Msa chi sch n ie ne
Ressourc en
Sustainability in Manufacturing - Energy Efficiency in Process Chains
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Turning process Parameter Material part diameter secondary time factor cutting depth main time … neccessary process energy
Unit steel 4,000 3,500 4,000 6,351 … 0,565
4,000 3,500 4,000 6,351 … 0,565
4,000 3,500 4,000 6,351 … 0,565
4,000 mm 3,500 4,000 mm 6,351 min … … 0,565 kWh
Cathegory double double double double … double
process specific parameters
Machine tool
… machining varnishing Parameter Time Power Force …
Unit s kW N …
Formu la Data x*y+ z Fuzzy x*y+ z Fuzzy x*y+ z Double … …
Parameter chip-to-chip-time idle time set-up time … energy consumption machine tool cutting tool waste
Unit 3,000 3,000 3,000 3,000 s 0,100 0,100 0,100 0,100 s 120,000 120,000 120,000 120,000 min … … … … … 5,717 8,482 14,010 16,508 kWh 0,005 0,005 0,006 0,007 kg
Cathegory double double double … fuzzy fuzzy
cooling lubricant supply Parameter Value Unit … W energy consumption cool. lub. supp. 0,0000 0,0000 0,0000 0,0000 kWh water consumption 16,0096 22,5908 35,7532 42,3344 kg
Cathegory fuzzy fuzzy fuzzy
Parameter
… Value
Unit
machine specific parameters
peripheral components
Cathegory
Figure 3 : Section of a process model to calculate the consumption values of a machining process. In a first step of the approach generic process models of five different production processes have been developed. Based on measurements and literature research the determining factors and fundamental energy calculating equations have been evaluated for each manufacturing technique. In this step the focus is primarily set on the process itself and less on the production machine. Aside from raw materials and supplies, the energy consumption of the processes represents the most important key figure. Figure 3 shows a section of an exemplary process model for the machining process.
consumed less energy per piece than the new one, these two parameters do not permit a reliable estimation of the energy needed. It is rather more important to consider the respective machine configuration with the installed components for the consumption prediction. Therefore the machine specific parameters play an important role in the calculation in the process models. The accuracy of the calculation depends on the estimation of unknown process and machine data in the process planning. Thus the models have to be adapted in the way that they can easily be modified and parameterized, using predefined databases.
The structure of the process models is divided into two parts. Influencing factors affiliated to the process on the one side and influencing factors affiliated to the production machine on the other side.
In other research projects of PTW, Maxiem [9], Esimpro [22] and the research group Ecomation [23], assessment methods of energy efficient machine tool components and simulation models for the use-specific energy consumption of defined machine tools and its components are developed. These models are designed to deliver an exact simulation of the energy consumption of a specific machine tool on which the detailed machine and work piece parameters are already common. As these models are especially developed for the calculation of the energy consumption of machine tools, their transfer to other manufacturing processes is not provided.
The strategy to develop the models is depending on the regarded process and its energetic conditions. For a varnishing process for example, the process depending energy cannot be determined by specific equations as for a machining process. No energy is introduced to the work piece at the operating point. Nevertheless the process parameters (like the varnish thickness) influence the resource consumption and have also to be considered. For this reason empirical determined data is used to calculate the consumption values for such processes. For the example of a machining process, the energy consumption has been calculated using the equation of Viktor and Kienzle [19] to approximate the necessary chip removal energy. Measurements have shown that the energy consumption of a process is strongly influenced by the operation of the production machine itself and less by the specific process parameters [20] [21]. An analysis of two machine tools in the project has shown that the same machining process on two comparable equipped machines of different age and connected load by the same manufacturer can cause a difference in energy consumption up to almost 40% per piece. As the older machine with a higher connected load
As much information about the processes and the machines is still uncertain in the production planning phase, the simulation of specific machines is not possible. The above described process models will be advanced to use dynamic simulation models to be able to calculate the energy and resource consumption of different production processes. The models base on general machine models which are able to deliver the needed accuracy to be able to serve as decision making basis. These general models cover different production machines and their components. Each model is predefined in different standard power classes which enable a primarily calculation without the necessity of detailed information about the process and the machine.
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Sustainability in Manufacturing - Energy Efficiency in Process Chains
Lösung A
EerFer Drehen
Härten
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100
100
80
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40
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Para1 Para2 Para3 1 1 0 2 1 0 1 2 1 4 4 1 Σ P.weight 0.4 0.4 0.2 Para1 Para2 Para3
point value 0 1 2 3 4
characteristic impossible still possible sifficient good ideal
Alt. 1
Alt. 2
Alt. 3
Preselection of feasible process chains
Preselection of alternative manufacturing technologies
Prior elimination of technological and economical unsuitable solutions in the production planning
Calculation of the predicted energy and resource demand
Calculation of the energy and resource consumption of alternative process chains using the predefined process models
Weighting factors for energy, resources and other decision parameters
Determination of a scala of assessment values
Determination by experts opinion and a paired difference test of all decision factors
Subdivision of the characteristic of each attribute along a ten-stage scale
Calculation of comparable values of different process chains
Calculation of the overall value of a process chain including all decision parameters and the corresponding wheight
Figure 4 : Main steps of the decision model. The modular design enables an easy calculation of the influence of alternative components or whole machines. The models are stored in a database. The use of that database permits the production planner to check alternative production processes easily. Combined with the specific use case and the corresponding machine and component states, a calculation of the needed energy and resources can be optimized. 4
DECISION MODEL
As used in the product life cycle assessment (LCA) following [24], a decision model to identify the preferred alternative has been developed. In contrast to a LCA, the decision methodology needed for the choice between several production alternatives has not only to include the ecological decision parameters but also technological or economical determining factors. Therefore the described decision problem is assigned to multi criteria decision models which are characterized by:
Multiple criteria to be assessed
Multiple decision parameters
A comparison of alternatives [25]
Similar to the LCA, energy and resource consumption but also e.g. the technological and economical attributes serve as a decisionmaking basis. Beside the decision parameters, the methodology has also to consider the fact that every user may impose different standards on the assessment methodology and attaches different importance to the decision parameters. Another fact to be considered is that alternative process chains may consume different kinds of resources or different forms of energy (electrical energy or gas e.g.). The comparability of these alternatives has nevertheless to be preserved and the methodology to be designed in order to allow a reliable decision. In a first step the determination of technological decision parameters is not included and the parameters are assumed as given data.
Figure 4 describes the general steps of the decision methodology. The decision methodology is oriented on [26] and begins with the prior elimination of technical and ecological not considerable solutions. Thus it can be assumed that only suitable solutions have to be assessed. The second step builds the foundation for the final selection of the process chain, the calculation of the expected energy and resource demand. Using the predefined process models the energy and resource consumption of the process chain will be calculated and serves as data base for the further assessment. Once these values are determined, they can be combined with other decision parameters like technical or economical ones. The actual assessment starts in step three. Generally, different companies may assign a different importance to the decision parameters. Thus a point rating system has been introduced based on different weighting factors for each parameter, following, a paired difference test is used to calculate these factors. For all parameters, a cross checking matrix A is developed, that describes the weighting of all parameters among themselves (see Figure 5).
compared to
p1 p2 p3 … pn
parameter 1 parameter 2 parameter 3 …
1 2 2
parameter n
2
0 1 1
0 1 1
0 0 0 …
2
2
=A
ann
Figure 5 : Cross checking matrix for the parameter evaluation.
Sustainability in Manufacturing - Energy Efficiency in Process Chains
The weighting factors Fj are subsequently calculated by the normalized point weight of each parameter:
Fj
i 1 n n aij j 1 i 1 n
aij
(1)
In order to be able to compare substitutable process steps, a value scale for each single process step has to be defined. Some of the parameters appear in multiple alternative processes (the energy consumption in almost every process for example) and some of the parameters may appear in only one of the alternatives (like special operating supplies). For the comparison of resources which are consumed by multiple decision alternatives, the 5-point scale can be spread equally over the range between the smallest and highest resource demand following equation (2) where Opt stands for the optimal and Pes for the most unfavorable value.
(2)
For other decision parameters, the distribution of the value scale has to be determined by an expert’s opinion. After the calculation of the corresponding point-values of every process step, the normalized total value of the process step PPi is calculated by the multiplication of each parameter’s point value PRj and the corresponding weighting factor Fj divided by the number of decision parameters n.
PPi
1 n
n
P
Rj
Fj
(3)
CONCLUSION AND OUTLOOK
In this paper, a methodology is introduced to assess alternative manufacturing process chains towards their energy and resource efficiency. It can be noticed that the main levers for an energy and resource efficient production are in the phase of the production planning. Especially in this early phase the main challenge is the uncertainty of the needed data and the associated prospective calculation of the consumption values. LCI data which have been used in the past for the energy consumption estimation of production processes only contain very generic information about the processes and are not transferable to a particular production process. The presented methodology builds a suitable basis for a transfer into practical use. In this first step the process models and the methodology set a starting point for further work which includes a transfer into practical use in industrial environment. The quality of
ACKNOWLEDGMENTS
The authors gratefully acknowledge the financial support of the German Research Foundation (DFG) for the project AB 133/40-1: EerFer “Entscheidungsunterstützung zur energieund ressourceneffizienten Fertigung” (Decision support for an energy and resource efficient process chain design). 7
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United Nations, (1998): Kyoto Protocol to the United Nations Framework Convention on Climate Change, Kyoto, Japan.
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1
Finally the point value of the whole process chain can be calculated by the addition of the point value of each process step. The overall point value builds finally the decision base for the process planner to be able to compare and select the process chain which fulfills the defined requirements at best. 5
the predicted consumption values, the manageability as well as the algorithms are to be optimized in cooperation with industrial partners for the transfer into practice. Further development on the decision methodology will not only include the influence of each decision parameter, but also the influences between the parameters themselves. 6
In the next step, the characteristic of all decision parameters, for example the results of the prior energy and resource consumption calculation are transferred into a general scale of values going from 0 (unsatisfactory) to 4 (very good).
4 x Pes PRj Opt Pes
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[10] SFB 144 RWTH Aachen (ed.) (1996): Energie- und Rohstoffeinsparung – Methoden für ausgewählte Fertigungsprozesse, Düsseldorf: VDI-Verlag (VDI-Z Integrierte Produktion, Sonderpublikation). [11] Abele E.; Anderl R.; Birkhofer H. (2005): Environmentallyfriendly Product Development Methods and Tools, SpringerVerlag Berlin Heidelberg. [12] Abele, E.; Anderl, R.; Birkhofer, H.; Rüttinger, B. (2008): EcoDesign – Von der Theorie in die Praxis. Springer-Verlag Berlin Heidelberg. [13] Ullmann, C. (1995): Methodik zur Verfahrensplanung von innovativen Fertigungstechnologien. Dissertation, RWTH Aachen, Germany. [14] Fallböhmer, M. (2000): Generieren alternativer Technologieketten in frühen Phasen der Produktentwicklung. Dissertation, RWTH Aachen, Germany.
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[15] Knoche, K. (2004): Generisches Modell zur Beschreibung von Fertigungstechnologien. Dissertation, RWTH Aachen, Germany. [16] Dietmair, A.; Verl. A. (2010): Energy Consumption Assessment and Optimisation in the Design and Use Phase of Machine Tools, in: Proceedings of the 17th CIRP International Conference on Life Cycle Engineering (LCE2010), pp. 76-82, Hefei, China. [17] Schultz, A (2002): Methode zur integrierten ökologischen und ökonomischen Bewertung von Produktionsprozessen und – technologien. Dissertation Universität Magdeburg, Germany. [18] Weinert, N. (2010): Vorgehensweise für Planung und Betrieb energieeffizienter Produktionssysteme, Dissertation, TU Berlin, Germany. [19] Victor, H.; Kienzle, O. (1952): Die Bestimmung von Kräften und Leistungen an spanenden Werkzeugmaschinen, in: VDIZ, Vol. 94, pp. 200-305, Germany. [20] Abele, E.; Schrems, S. (2010): Ressourcenorientierte Bewertung alternativer Prozessketten - Herausforderungen und Möglichkeiten zur Prozesskettenbewertung im Produktionsplanungs-prozess, in: ZWF, Vol. 105, p. 542-546, Carl Hanser Verlag München, Germany. [21] Abele, E., Dervisopoulos, M., Kuhrke, B. (2008): Bedeutung und Herausforderungen der Lebenszyklusanalyse am Beispiel Werkzeugmaschine, in: Schweiger, S. (ed.): Lebenszykluskosten optimieren. Gabler Verlag Wiesbaden, pp.51-79, Germany. [22] Rudolph, M; Abele, E., Eisele, C., Rummel, W. (2010) : Analyse von Leistungsmessungen – Ein Beitrag zur Untersuchung der Energieeffizienz von Werkzeugmaschinen, in: ZWF, Vol. 105, pp. 876-882, Carl Hanser Verlag München, Germany. [23] Abele E.; Schrems, S. (2010): Determination of energy consumption control factors of machine tools by component oriented simulation, in: Conference Proceedings International Chemnitz Manufacturing Colloquium (ICMC 2010), pp. 711 718, Chemnitz, Germany. [24] DIN EN ISO 14040: Umweltmanagement – Ökobilanz – Grundsätze und Rahmenbedingungen. Berlin, Beuth, Germany, 2009. [25] Hwang, C.-L., Masud, A. S. M. (1979): Multiple Objective Decision Making – Methods and Applications. Lecture Notes in Economic and Mathematical Systems, No. 164. Springer Verlag, Berlin Heidelberg, Germany. [26] VDI-Richtlinie 2225 (1997): Technisch-wirtschaftliches Konstruieren. Beuth Berlin, Germany.
Sustainability in Manufacturing - Energy Efficiency in Process Chains
A New Shop Scheduling Approach in Support of Sustainable Manufacturing 1
1
2
Kan Fang , Nelson Uhan , Fu Zhao , John W. Sutherland 1
2
2
School of Industrial Engineering, Division of Environmental and Ecological Engineering and School of Mechanical Engineering, Purdue University, West Lafayette, USA
Abstract Shop scheduling is a classic manufacturing decision-making problem. A new approach to scheduling that considers peak power load and energy consumption (and associated carbon footprint) in addition to cycle time is presented. A general multi-objective mixed-integer programming formulation is provided and examined using a simplified case study that considers the scheduling of a variety of components on two machines. The challenges of finding a solution to this scheduling problem are discussed, which motivates the need for specialized algorithms for these new scheduling problems. Computationally efficient algorithms that compute near-optimal solutions and optimal solutions under specific assumptions are examined. Keywords: Scheduling; Peak Load; Carbon Footprint
1
INTRODUCTION
Over the last 60 years, the consumption of energy by the industrial sector has almost doubled. The industrial sector is the largest energy consumer and currently accounts for about one-half of the world’s total energy consumption. In addition, industrial energy consumption, which was at 175 quadrillion Btu in 2006, is projected to increase 40% by 2030. In the United States, approximately 34% of all the end-use energy consumption was associated with the industrial sector, and the associated energy cost in 2006 was about $100 billion. Since the U.S. energy supply is dominated by fossil fuels (more than 85% of the energy comes from such sources such as coal and natural gas), the industrial sector contributes 27% of the U.S. greenhouse gas (GHG) emissions. This makes the industrial sector second to the transportation sector in terms of GHG emissions. Manufacturing enterprises in the U.S. are facing ever-increasing pressure to reduce their reliance on fossil fuels and reduce their carbon footprint. This pressure is driven by concerns related to climate change, energy costs, and energy security. Energy issues will assume an even more important role in the future, owing to ever-increasing demand for energy from developing countries, and the likely implementation of a carbon tax or carbon cap-and-trade policy. These economic and environmental drivers motivate the need for initiatives directed at substantially reducing energy consumption and GHG emissions from manufacturing enterprises. Most previous research on manufacturing energy consumption has focused on developing more energy efficient machines/processes [1]. However, the energy requirements for the active removal of material can be quite small compared to the background functions needed for manufacturing equipment operation [2]. Drake et al. [3] showed that there are significant amounts of energy associated with machine start-up and machine idling. As a result, in a mass production environment, more than 85% of the energy is utilized for functions that are not directly related to the production of parts [4]. This suggests that energy saving efforts focusing solely on updating individual machines or processes may be missing a significant, and perhaps a bigger, opportunity, i.e., system-level changes. Unfortu-
nately, such approaches have not yet received much attention. Current shop floor scheduling strategies employed by manufacturing enterprises focus mainly on productivity and throughput time, with energy and environmental factors often ignored. Although energy/environment related criteria have not been usually considered as major factors in manufacturing systems, minimizing energy consumption has been an area of interest in computer and embedded electronics systems for several years [5,6]. Given the scale of industrial activities – 175 quadrillion Btu energy consumed, $100 billion in costs, and 1.5 billion tons CO2 equivalent produced – relative to computer/electronics system activities, this suggests that a tremendous opportunity exists with respect to industrial activities. Clearly, similar research is needed for manufacturing enterprises. In addition, since manufacturing plants are often charged based on the peak power demand (referred to as “power consumption” to distinguish it from energy consumption) from the energy provider instead of actual electricity consumption, optimized operation schedules could further reduce energy costs. Compared to machine or process redesign, implementation of optimized shop floor scheduling and plant operation strategies only requires a modest capital investment. Given this backdrop, this paper presents a general formulation of the shop floor scheduling problem that considers productivity, carbon footprint, and peak load simultaneously. A simple job shop with two machines is used to demonstrate potential trade-offs among these criteria and the complexity inherent to this new scheduling challenge. 2
BACKGROUND
Existing research on production operation methods to reduce environmental impacts is sparse. At the equipment level, a literature search only resulted in a handful of papers. One of the most relevant studies is one by Mouzon et al. [7], who investigated the problem of scheduling a single machine to minimize total energy consumption. In particular, they looked at the scheduling of a CNC machine in a machine shop for a supplier of small aircraft parts. They pointed out that if instead of leaving the non-bottleneck ma-
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_53, © Springer-Verlag Berlin Heidelberg 2011
305
306 chines idle, they could be turned off until needed; such a change would result in an 80% savings on total energy consumed. They showed that the ability to predict the next arrival of a job (e.g., the inter-arrival times between jobs) greatly affects the effectiveness of various dispatching rules (for example, batching vs. non-batching). Mouzon and Yildirim [8] examined the scheduling environment and proposed a metaheuristic framework to compute schedules that minimize the total energy consumption and the total tardiness on a single machine. Considering the hierarchical structure of a manufacturing plant and associated planning functions, shop floor schedules have an impact on schedules at the equipment level. As a result, shop floor schedules could significantly affect energy consumption as well as other environmental impacts of an individual machine. Unfortunately, although a variety of performance measures have been considered for shop scheduling, these efforts have largely focused on economic, time, or operational considerations. For example, one typical objective in such scheduling problems is to minimize the incurred setup cost or setup time, which arises from the reconfiguration that often needs to take place between jobs. For just-in-time manufacturing systems, a common objective is to schedule jobs so that the usage rate of all parts is as constant as possible, or so that the load of each workstation is as constant as possible. In contrast, research on scheduling with environmentally-oriented objectives is relatively scarce. However, research on energy-aware scheduling is growing. For instance, Subai et al. [9] incorporated energy and waste considerations into the hoist scheduling problems that arise from surface treatment processes. Wang et al. [10] proposed an optimal scheduling procedure for vehicle sequencing in order to reduce energy consumption in an automotive paint shop. By selecting appropriate batch and sequence policies, they also found that the paint quality can be improved and the number of repaints can be reduced. Nevertheless, these efforts on scheduling research, especially those for flexible manufacturing systems (FMS) in use by many market-leading enterprises, provide a starting point for introducing environmental considerations into schedule optimization, particularly from a model and algorithm development perspective. An FMS can be viewed as a production system that is capable of producing a variety of part types. The system consists of a number of machines or workstations, each of which can perform different operations with the appropriate tooling setups. Motivated by customer demands to produce greater part variety, large OEMs are moving to FMS technology since an FMS is better suited to producing large component variety than dedicated equipment such as transfer lines. Since an operation may be performed at any one of a number of machines, the routing of a job through the system is flexible, and needs to be considered when determining a schedule. In addition, the use of material handling systems and limited buffers imposes additional constraints on the starting times of jobs. Flexible assembly systems are similar, except the routing of a job is more or less fixed. Most of the research on scheduling for flexible manufacturing and assembly systems has focused on specific problems that arise in industry, and as a result, the existing literature is vast [11]. Various optimization-based approaches have been considered for scheduling in flexible manufacturing and assembly systems, including agent-based approaches [12], metaheuristics [13], constraint programming [14], and mathematical programming [15]. None of these studies addressed energy related objectives in modeling the scheduling problem. The next section presents an optimization model with productivity and environmental objectives.
Sustainability in Manufacturing - Energy Efficiency in Process Chains 3
MATHEMATICAL PROGRAMMING FORMULATION FOR MULTI-OBJECTIVE SHOP SCHEDULE OPTIMIZATION
Consider a set of machines 1,2, … , , which are ordered so that a job cannot start on machine until it is completed on machine 1 , for 2, … , . In addition, consider a set of jobs 1,2, … , that need to be processed on each machine in . These constraints can be relaxed or modified to reflect other types of process/job interdependencies (e.g., a job shop). For many processes, the time required to perform a job may be reduced by increasing the machine speed, with the consequence generally being increased peak power demand. To consider power consumption/peak load as part of our formulation, a finite and discrete set of machine speeds 1,2, … , is considered; on each machine, every job runs at a speed to be determined. The processing time of job ∈ on machine ∈ at speed ∈ is 0. In addition, the power consumption (peak power demand) of job ∈ on machine ∈ at speed ∈ is 0. The following ten sets of decision variables are associated with the problem:
max
is the makespan of the schedule:
max
is the peak total power consumption;
max
is the GHG emissions (carbon footprint);
denotes the completion time of job on machine ; is equal to 1 if job is processed on machine with speed , and 0 otherwise; is equal to 1 if job precedes job , and 0 otherwise;
on machine is , is equal to 1 if the start time of job before the completion time of job on machine , and 0 otherwise;
on machine , is equal to 1 if the completion time of job is after the completion time of job on machine , and 0 otherwise;
, is equal to 1 if the completion time of job on machine occurs during the processing of job on machine , and 0 otherwise;
is equal to 1 if
,
1 and
1, and 0 otherwise.
,
The following multi-objective mixed integer linear program seeks to minimize: (i) the makespan, (ii) the peak total power consumption, and (iii) the total carbon footprint, and is inspired by the work of Manne [16]. Below, represents a very large constant and is the carbon footprint of electricity consumed by the shop which can be found using EPA’s eGRID2007 Version 1.1 by specifying a facility’s 5-digit zip code. For example, electricity consumed by a manufacturing facility located in West Lafayette, IN (zip code 47907) has a carbon footprint of 0.616 kg/kWhr (1.538 lb/kWhr). minimize
max
minimize
max
minimize
max
subject to , ∈ ,
max
∑
,, ∈ ,
∈
∑
,
∑
∑
∈
∖ 1 ; ∈ ,
, ∈
∈
,
, ,
,
1
,
, ,
∈ ∈
; ,
, , ; , ; ,
(3)
∈ :
, ∈
1
∈
1 ,
(2)
, ∈
∈
∑
,
(1)
∈ ∈ , ∈ ,
; , ; ,
1, ∈ : ∈ ,
(4) 1, (5) (6) (7) (8)
Sustainability in Manufacturing - Energy Efficiency in Process Chains 1
,
∑
,
1, ∈
∈
1, ,
, ,
∈
∑
∈
∈
(9)
; ∈ ,
(10) ,
∈ :
(11) ,
2, , , ∈ : ∑
∈ ; ∈ ,
; ,
∈ :
∑
∈
∑
∈
(12) ,
max ,
; ∈ (13)
∑∈ ∑
∈
∑
max ,
∈
∈ 0,1 ,
,
∈ ; ∈ ,
∈ 0,1 , ∈ 0,1 ,
∈ 0,1 , , ∈
;
∈ 0,1 ,
,
∈ 0,1
(14)
Constraint (1) ensures that the makespan of the schedule is greater than the completion time of any job. Constraints (2)-(3) ensure that a job cannot start on machine until it is completed on machine 1. For each pair of jobs: , ∈ , either job is processed before job , or vice versa. Constraints (4)-(5) ensure that the ordering of the jobs is consistent across machines. Constraints (6)-(9) ensure that the binary decision variables , , and take on only the intended values. Constraint (10) ensures that each job is processed with exactly one speed on each machine. Constraints (11)-(12) ensure that the decision variables correspond to a permutation on . Finally, constraint (13) ensures that the total power consumption across machines at any time instant is at most max , while constraint (14) ensures that the total carbon footprint for completing all the jobs is at most max . For the multi-objective schedule optimization model formulated above, one can search for Pareto efficient schedules, i.e., feasible schedules that are not dominated by any single performance measure. It should be noted that the traditional scheduling problem – that is, finding a schedule that minimizes the makespan for a flexible manufacturing system – is already computationally difficult. Thus, it is expected that introducing additional decision variables, objectives, and constraints related to power/energy consumption and carbon footprint will make the scheduling problem even more computationally challenging. To gain a better understanding of the problem, a case study for a simple hypothetical flow shop is shown below. 4
CASE STUDY: A TWO MACHINE JOB SHOP
A shop is assumed to make cast iron plates with slots. Starting with cast iron plates, two operations are required (with each operation performed on a separate machine): face milling and profile milling. The plates (Figure 1) can have different lengths, different face milling depths of cut, different slot depths, and different numbers of slots. In total, the case study considered 36 different parts to be produced, which correspond to the combinations of three different plate lengths, two different face milling depths of cut, three different numbers of slots, and two different slot depths. Cutting speeds for both face milling and profile milling are independent variables that are within the range recommended by the Machinery's Handbook [17]. Table 1 shows the geometry of the parts and cutting conditions. The shop is organized so that each plate must be face milled before it undergoes profile milling. For a job processed on one of the machines, the load and energy consumption can be calculated based on the equations provided below. Figure 2 provides additional insight into the meaning of several of the variables. ∙ ∙ ∙
∙
(15)
∙ ∙ ∙
(for face milling) ∙
307
(16)
∙
(for profile milling)
∙ ∙ ∙
(17) (18)
∙
∙
(19)
∙
In the relations above, the subscript “basic” corresponds to the lowest power level of machine tool operation. For the basic level, machine tool energy is consumed by lighting, the NC controller, chiller system, oil pump, and way lube system. Activities performed while the machine tool is at the basic power level include workpiece loading/unloading, positioning, and fixturing. The subscript “idle” subscript corresponds to a power level higher than the basic level. For the idle power level, the main spindle is turned on and power is also provided to the automatic tool changer and cutting fluid pump. Activities performed while the machine tool is at the idle power level include the tool approaching the workpiece, the tool retracting from the workpiece, tool motion between features, adjustments in the machine settings, and tool change operations. Finally, the subscript “cutting” corresponds to a power level and the period of time when material is actually being cut. In the equations represents electricity consumption (in MJ or kWhr), represents power consumption (in kW), w, d, and V represent the cutting width, feed per tooth (or flute), and cutting speed, respectively. represents the specific cutting energy, represents the length of the workpiece, represents the cutter diameter, ( ) represents the number of teeth (flutes) per cutter, represents the spindle speed, and represents the number of slots. If the cutting energy is the product of the specific energy and the amount of material removed, then it may be noted that for a given operation on a part, the energy consumption will be constant regardless of the cutting speed, assuming the specific cutting energy is independent of feed rate and cutting speed. Part Geometry Width (cm)
20
Length (cm)
35/45/55
Thickness (cm)
10
Depth of milling on surface (mm)
2/4
# of slots
4/6/8
Slot depth (cm)
0.5/1.0
Slot width (cm)
3.0
Cutting conditions Face milling Cutter diameter (cm)
30
# of teeth
15
Feed per tooth (mm)
0.2
Cutting speed (m/min)
18/24/30/36/45
Profile milling # of flutes
4
Feed per flute (mm)
0.1
Cutting speed (m/min)
18/24/30/36/45
Table 1: Part Geometry Settings and Cutting Conditions.
308
Sustainability in Manufacturing - Energy Efficiency in Process Chains flexible manufacturing and assembly systems is already notoriously difficult to solve.
Figure 1: A Cast Iron Plate with Slots.
Figure 2: Power Profile for a Representative Machining Operation (e.g., Face Milling).
Figure 3: Pareto Frontier Showing Tradeoff Between Peak Load and Makespan. With the mathematical programming model for the scheduling problem and cutting process relations established, and a set of jobs to schedule, an optimal schedule can be determined. Attempts were first made to use the commercial optimization software packages IBM CPLEX, in conjunction with goal programming techniques to identify points on the Pareto frontier. Unfortunately, for this twomachine 36-job problem, a provable optimal solution could not be found within 24 hours with a computer of two 2.5 GHz Quad-Core AMD 2380 processors and 32 GB of RAM. This is not surprising since it is known that job scheduling with only a single objective for
With hard-to-solve mathematical programming models, it is often advantageous to study related, but easier-to-solve models. A relaxation of a mathematical programming model is an auxiliary mathematical programming model that is formed by weakening the constraints or the objective function of the model. Researchers have successfully created near-optimal schedules for various real-world scheduling problems represented by hard-to-solve mathematical programming formulations, using information from computationally tractable relaxations of these hard-to-solve models [18]. For the two-machine flow shop problem, algorithmic and structural results for some fundamental special cases of the problem are shown below. Suppose the flow shop does not have intermediate storage between the two machines. That is, Machine 1 (face milling in this case) cannot start processing the next part if the part it has just completed cannot start being processed on Machine 2 (profile milling). This is sometimes known as “blocking” in the scheduling literature. In this case, it can be shown that this scheduling problem is equivalent to an asymmetric traveling salesman problem. This transformation was used to solve the particular instance described above. Using the Concorde TSP solver, the Pareto efficient frontier was computed for this relaxed case with peak load and complete time (makespan) as the two objectives considered. The Pareto frontier was generated by specifying 21 different upper bounds on the peak total power consumption and then searching for the minimum makespan. Each schedule corresponding to the minimum makespan was computed in 4-5 seconds. Figure 3 shows the Pareto efficient frontier for this case. It can be seen that there exists a trade-off between makespan and peak load. If the makespan is the dominant consideration, a complete time as short as approximately 10,000 seconds (~2.75 hours) can be achieved at a corresponding relatively high peak load of 15 kW. On the other hand, if we can tolerate a longer complete time, the peak load can be reduced to less than 6 kW, with a corresponding makespan of approximately 75,000 seconds (~20.8 hours). Figure 3 also shows the carbon footprints corresponding to the 21 minimum makespans identified. It can be seen that carbon footprint decreases as the upper bound of power consumption increases. In other words, the carbon footprint and makespan have a similar behavior; this suggests that the carbon footprint can be reduced by minimizing the makespan. Suppose the constraints on available cutting speeds are relaxed and it is assumed that the cutting speed can take on any value within the recommended range. In particular, job ∈ on machine units of work, and a job that requires units of ∈ requires work and is processed at speed has a processing time of / . We also assume that the power consumption at speed is for some constant 0. When 0 1, the optimal schedule is simply to process all the jobs without overlap at the fastest possible speed. When 1, the following results hold: 1. Suppose the flow shop does not have intermediate storage between the two machines. In this case:
Suppose the work required is consistent across machines—that is, for any two jobs: , ∈ , implies . To be specific, this means that if a part has a relatively small processing time on Machine 1 (face milling in this case), then it also has a relatively small processing time on Machine 2 (profile milling). For this case, an optimal schedule can be computed in polynomial time. This is accomplished by transforming the scheduling problem into an equivalent asymmetric traveling salesman problem that satisfies the so-called Demidenko [19] conditions (the Demidenko conditions are a set of triangleinequality-type relations).
Sustainability in Manufacturing - Energy Efficiency in Process Chains
If in addition to the conditions above, the work required for each job is the same on each machine – that is, for any job ∈ , – then, an optimal schedule can be computed by a simple priority-list algorithm. In particular, if ⋯ then scheduling the jobs according to the permutation 1,3,5, … , n, … ,6,4,2 yields an optimal schedule.
2. If the flow shop does have intermediate storage between the two machines, that is, Machine 1 (face milling in this case) can start processing the next part immediately after completing the current part, no matter whether or not Machine 2 is ready to accept the part being processed, then the following holds. For simplicity, we assume the power consumption at speed is .
Assuming the total power consumption on the two machines is max at any time in the schedule, and given a fixed permutation of the jobs, an optimal schedule (i.e., job completion times and job/machine speeds) can be found in polynomial time. In addition, the sequence of jobs can be decomposed into subsequences of jobs that are processed at the same speed. To illustrate, suppose we have 4 jobs 1,2,3,4 , and it is decided in advance that the jobs are to be processed according to the order 4,2,1,3 . Then we can determine the optimal speeds and completion times for each of the jobs in polynomial time. This kind of result on the mathematical structure of the scheduling problem is useful in the design of neighborhood search algorithms to find near-optimal solutions.
5
SUMMARY, CONCLUSIONS, AND FUTURE WORK
A general multi-objective mixed integer linear programming formulation has been presented for optimizing the shop schedule that considers both productivity (e.g., makespan) and energy (e.g., peak load and carbon footprint) related criteria. Different from the current schedule optimization approaches where productivity is the only objective (and thus the highest possible operation speed is preferable), this new formulation considers the operation speed as an independent variable, which can be varied to affect the peak load and energy consumption. The constraints associated with the formulation can be relaxed or modified to reflect other types of process/job interdependency (e.g., a job shop). A simplified case study – a two-machine flow shop producing cast iron plates with slots is used to demonstrate the complexity of the problem. It was found that even for this simplified case, the computational time could be so significant that it is almost time prohibitive to compute Pareto frontiers. Several special cases that were more theoretically and practically tractable were examined. The findings from this examination include:
If there is no intermediate storage between the two machines the problem can be transformed into an equivalent traveling salesman problem. The resulting Pareto frontier suggests that there exists significant trade-offs between makespan and peak load.
If cutting speed can be treated as a continuous variable, under the conditions that there is no intermediate storage and the work required is consistent across machines, the problem can be transformed into an equivalent asymmetric traveling salesman problem, the solutions to which can be computed in polynomial time.
If cutting speed can be treated as a continuous variable and there is intermediate storage, an optimal schedule can be found in polynomial time for any given permutation of the jobs.
Although the two-machine flow shop problem has some interesting structural and algorithmic properties, these may not hold as the scale of the shop increases to industrial conditions where perhaps
309 hundreds of machines are involved. Moreover, the computation time will be prohibitive for such industrial-sized problems. This suggests that in order to find practical schedules that balance economic and environmental performance, the following activities are needed: (i) construct simplified, adequate shop floor models for which optimal schedules can be found, and (ii) design specialized algorithms (e.g., relaxations of the general scheduling problem or neighborhood searches) for which acceptable schedules can be found with reasonable computational effort. It should be noted that in addition to cycle time or makespan other factors such as tardiness, cost, and quality, are generally considered in shop floor scheduling. From an environmental performance perspective, although only energy related criteria, e.g., peak load, energy consumption, and carbon footprint, have been introduced as new objectives here for shop floor schedule optimization, in the future other objectives could be considered. For example, the scheduling formulation could address such other environmental and occupational health impacts as water consumption, air emissions, and noise. Considering all of these objectives simultaneously is expected to introduce nonlinear correlations among some decision variables and constraints thus making the scheduling problem even more difficult. It should also be noted that the scheduling optimization discussed here is “static”. In reality, due to unforeseen disturbances on the shop floor during the execution of a schedule, an originally optimal schedule may become sub-optimal or even unacceptable; thus, a new schedule will often have to be found in a relatively short time frame [11]. Integrating environmental considerations into this kind of dynamic scheduling or real-time scheduling problem clearly represents a significantly larger challenge. Since environmentally responsible shop floor scheduling has the potential to significantly reduce the energy and environmental footprint of manufacturing facilities without incurring large capital investment, numerous opportunities exist in this new area which are expected to have a broad and significant impact on manufacturing enterprises. We hope that the work presented herein related to environmentally conscious scheduling will serve as a starting point for others to make contributions to this emerging area. 6
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Drexl, A., Kimms, A., Matthiessen, L., (2006): Algorithms for the Car Sequencing and the Level Scheduling Problem, in: J of Scheduling, Vol.9, No.2, pp.153–176.
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Manne, A.S., (1960): On the Job Shop Scheduling Problem, Operations Research, Vol.8, No.2, pp.219-223.
[17]
Oberg, E., Jones, F.D., Horton, H.L., Ryffel, H.H., McCauley, C.J., Heald, R., (2008): Machinery’s Handbook, Industrial Press, New York, NY.
[18]
Möhring, R., Schulz, A.S., Stork, F., Uetz, M., (2003): Solving Project Scheduling Problems by Minimum Cut Computations, in: Management Science, Vol.49, No.5, pp.330-350.
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Demidenko, V.M. (1979): The Traveling Salesman Problem with Asymmetric Matrices (in Russian), in: Vestsi Akad. Navuk BSSR Ser. Fiz.-Mat. Navuk Vol.1, No.1, pp.29-35.
Comparison of the Resource Efficiency of Alternative Process Chains for Surface Hardening 1
1
1
1
Gunther Reinhart , Saskia Reinhardt , Tobias Föckerer , Michael F. Zäh 1
Institute for Machine Tools and Industrial Management (iwb), Technische Universität München, Munich, Germany
Abstract As negative implications of resource exploitation and further undesirable ecological developments, e.g. climate change, increase, companies are confronted with novel challenges. Especially during the production stage of a product’s lifecycle companies have the possibility to influence the resource consumption by choosing the most efficient process chain for a certain manufacturing task. To compare accumulated resource flows of different process chains, a new valuation method using a resource efficiency index was developed. Exemplarily, this paper will analyze the resource consumption of two alternative process chains for surface hardening of a workpiece resembling a guide rail. Keywords: Production Planning; Sustainable Manufacturing; Machining
1
INTRODUCTION
Resources, e.g. raw materials, are getting scarce as a result of the rapidly rising demand. In order to prevent the negative implications of resource exploitation and further undesirable environmental developments, e.g. climate change, legislation is starting to restrict and influence both companies and consumers in their behavior. In addition, the image and consequently the competitiveness of a producing company are strongly influenced by its efforts regarding the establishment of resource efficient and sustainable products and processes [1]. These developments have led to the consideration of environmental effects of the usage and disposal phase of the product lifecycle during product design. But in most cases not only using and disposing of a product cause resource depletion and waste, but also the manufacturing phase can be held responsible for a large share of the environmental burden [2]. Therefore, it is important to consider the manufacturing process chain specific resource consumption during the production planning phase, as 80% of the environmental impact of a manufacturing system is fixed at that time [3]. In particular, if several alternative manufacturing process chains are able to fabricate the product a decision will have to be made. Apart from the decision criterion cost, the criterion resource efficiency should be taken into consideration for the reasons mentioned above. In spite of these findings, the resource flows of manufacturing processes are not as well known as they should be. Furthermore, research studies state that available methods supporting the evaluation of resource efficiency are still insufficient [4]. Main approaches that aim to quantify resource flows and to evaluate the resource efficiency of manufacturing process chains will be discussed shortly in the following. Current approaches that aim to quantify resource flows of a manufacturing process either use a material and energy flow based analysis or attempt to derive resource consumption in a predominantly analytical way. Especially machining processes have been analyzed concerning the production of waste material, the consumption of cutting fluid and the use of energy [5, 6]. However, the quantification of resources used in the process is based solely on theoretical calculations, rendering the approach inept for the
prediction of overall resource flows that include the manufacturing equipment as well. A variety of valuation methods utilizing an expost material and energy flow based analysis, also known as inventory analysis, exists. Most of these methods also include the assessment of the environmental impact of the regarded object [7, 8, 9]. The approaches mentioned above focus on an ex-post analysis, which does not show the predictive character needed in the production planning phase. Recently, a few approaches sought to develop a general method for resource efficiency evaluation [10, 11]. The goal of this paper is to further develop and apply a general method for evaluating resource efficiency of manufacturing process chains based on the approach introduced by [11]. Therefore two alternative process chains, one using only traditional manufacturing processes and one including a new hybrid manufacturing process, will be compared and their resource efficiency will be evaluated. The remainder of this paper is set out as follows. The next section presents the modeling of the material and energy flows and the evaluation of the resource efficiency. The modeling procedure is then applied to two alternative process chains for surface hardening in the third section. Finally, the resource flows are calculated and the material and energy efficiency are determined for each process chain. Using the resulting resource efficiency index a quantitative comparison of the alternative process chains is possible. 2 2.1
METHODOLOGY Modeling of Resource Flows
A manufacturing process chain consists of different manufacturing processes that are needed to change a product from one defined state to another. In this paper a manufacturing process is modeled as a composition of the manufacturing technology used, the manufacturing system the technology runs on and the additional peripheral systems. The manufacturing system and more complex peripheral systems can be subdivided into their components, which is indicated by the circles surrounding the manufacturing system in Figure 1.
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_54, © Springer-Verlag Berlin Heidelberg 2011
311
312
Sustainability in Manufacturing - Energy Efficiency in Process Chains
peripheral systems manufacturing system manufacturing technology
1
manufacturing system
5
2 system 4
Figure 1: Manufacturing process model. Generally every resource ri can constitute an input ri IN and an output. Whereas output is divided into output going into the product ri PROD and output that is wasted ri OUT. Input resources used in a cutting process are for example energy, material, water, auxiliary material, coolant etc. The quantity of every ri IN, ri PROD and ri OUT required by the examined manufacturing process for one manufactured product can be described by corresponding resource vectors rIN, rPROD and rOUT (see Figure 2).
rPROD rIN process
rOUT Figure 2: Resource flows of a manufacturing process. In general resources used in manufacturing process chains can be divided into two main categories: material and energy. This subdivision of resources facilitates both the quantification of resource flows and the evaluation of resource efficiency. For the quantification of indirect resource flows (e.g. the proportion a manufacturing process has regarding the power consumption of a central compressed air system) the produced quantity per time unit is needed as supplemental information. The following subsections describe the quantification of rIN, rPROD and rOUT using the manufacturing process model. Input Resource Flow Quantification The input energy flow consists of the manufacturing system and the peripheral systems. The energy consumption of the technology is part of the manufacturing system. The required power can be calculated theoretically for many standard technologies. As the technology is executed on the manufacturing system, the theoretical value has to be multiplied with a correction factor due to interaction between technology and manufacturing system. Generally, the input energy flow of manufacturing systems and peripheral systems can be calculated using the power consumption of defined system states and the time during which the system is in one of the defined states based on the work schedule of the product. Accordingly, the input energy flow is set together as follows:
rEnergy IN rEnergy IN manufacturing system rEnergy IN peripheralsystemk
(1)
k
The power consumption of system states usually varies due to the composition of active and inactive components of the manufacturing
3
pow er consumption
system or peripheral system. Figure 3 shows the calculation of the power needed in the system states A to D depending on the system’s active components.
44 3,5 [kW] 33 2,5 2.5 22 1,5 1.5 11 0,5 0.5 00 A
B C systemstates
D
Figure 3: Power consumption of different system states. For each defined state of a system the required power has to be assessed. The best way to determine the values is by conducting power measurements of the system. As the method should be applicable in the planning phase the power data has to be a requirement when soliciting a quotation. If there is no data available the required power values have to be estimated using measurement data of similar systems if possible. If only a fraction of the peripheral system’s input energy flow can be assigned to the manufacturing process, the energy flow can usually be allocated to the manufacturing process based on the manufactured product quantity and relative energy intensity of the product. The input material flow consists of all material going into the process. Generally there are there different categories of material used in manufacturing processes: workpiece materials, auxiliary materials and tool materials. The workpiece materials and some auxiliary materials can be directly allocated to the product. Other centrally provided auxiliary materials and tool materials can be allocated to the manufacturing process based on the manufactured product quantity and relative material intensity of the product. Product and Output Resource Flow Quantification The product energy flow is either the ideal energy used to perform a certain manufacturing process or the minimal energy of all the technologies in the alternative process chains that can be used to perform the process step. The product material flows equal all workpiece material flows and the auxiliary material flows that go into the product. Typically, the required workpiece material can be calculated using product design data. The output energy and material flows are established by subtracting the product resource flows from the input resource flows. In the case of the workpiece, the output material flow is determined by subtracting the product geometry from the unmachined part geometry. 2.2
Resource Efficiency Evaluation
Resources are usually understood as natural resources subdivided into raw materials such as minerals, environmental media such as air, water or earth and flowing resources such as wind or solar energy [12, 13]. These categories already include most of the resources that are potentially used in a manufacturing process. In order to make the term even more practical for industrial application, resources such as electrical energy have to be integrated. The term ‘efficiency’ is defined in various ways depending on the particular context. In general, efficiency can be understood as the ratio of benefit and effort. Relating this definition to resources either implies that, given a certain amount of resources used, the amount of produced units has to increase in order to obtain a more resource efficient situation. Or, on the other hand, the provided the amount of
Sustainability in Manufacturing - Energy Efficiency in Process Chains produced units is fixed and the amount of resources used has to be decreased. Whereas the use of resources includes contamination, damage and waste represented by ri OUT. In order to allow a resource efficiency calculation, all resources have to be standardized to one consistent unit. In this paper, the above-mentioned resource categories may be used. The unit for the category material is kilogram [kg], requiring the unification of solids, gases and liquids being substances in different states of aggregation. For the category energy the standard units are Joule [J] or kilowatt hours [kWh]. Having assessed the meaning of efficiency for manufacturing processes and eliminated the unit calculation problem, the material efficiency of a manufacturing process ωMaterial is defined as follows [11]:
i 1 ri PROD Material n n i 1 ri IN i 1 ri OUT
313
process chain ‘induction hardening’ 1. milling (d = 3 mm) 2. induction hardening 3. grinding (d = 0.1 mm)
hardened surface layer (hardening depth = 0.4 mm)
process chain ‘grind-hardening’
n
(2)
d = depth of cut Figure 4: Alternative machining of workpiece.
The benefit of a manufacturing process is the product resource flows and the effort put in which consists of the input resource flows and all output resource flows that do not go into the product. This definition implies that the more input flows directly into the product the higher the resource efficiency. Efficiency of 100% is only attained, if all resource input goes into the product itself, leaving no additional output. Also, the more auxiliary resources are needed for the process, the more the efficiency decreases. Consequently, resources that do not go into the product are accounted for twice in the denominator, representing an input resource on the one hand and waste or contamination on the other hand.
The two alternative process chains, one using only traditional manufacturing processes and one including a new hybrid manufacturing process, is compared and their resource efficiency is evaluated. As shown in Figure 4 and Figure 5, the traditional process chain includes a milling process, an induction hardening process [14] and a grinding process. As customary, grinding is used as a finishing process after several steps of soft machining and a subsequent heat treatment at the end of the process chain.
process chain ‘induction hardening’ induction milling..... hardening
As the output energy flow has no know detrimental effect on the environment it does not have to be accounted for as effort. Accordingly, the energy efficiency of a manufacturing process ωEnergy can be reduced to:
i1 ri PROD n i1 ri IN
3 3.1
Material Energy 2
heat treatment
milling..
grind-hardening
finishing grinding
(3) change of manufacturing system
The resource efficiency is based on both material and energy efficiency. The ratio of material efficiency to energy efficiency may vary according to the company’s focus. In this paper it is assumed that material and energy efficiency are equally important and the resource efficiency index is calculated as follows:
Resource
soft machining
grinding
process chain ‘grind-hardening’
n
Energy
1. milling (d = 2.5 mm) 2. grind-hardening (d = 0.5 mm) 3. grinding (d = 0.1 mm)
(4)
APPLICATION Alternative Process Chains
As application this paper analyzes the resource consumption of two alternative process chains for surface hardening of a workpiece resembling a guide rail. Both process chains start from the same semi-finished part, a bar with a square cross section (150 mm length, 28 mm width, 18 mm height) consisting of soft-annealed 100Cr6. The two alternative process chains both modify the workpiece by machining a 10 millimeter wide slot with a depth of 3.1 millimeter and realizing a hardened surface layer with a hardening depth of 0.4 millimeter as shown in Figure 4.
Figure 5: Comparison of the two alternative process chains and classification of the process steps concerning production phases. The alternative process chain employs the “grind-hardening” process, which is an innovative approach to cut down the process and auxiliary time by substituting conventional hardening processes [15]. Therefore, grind-hardening is a hybrid manufacturing process, which can be classified as a soft machining and heat treatment process at the same time. Grind-hardening enables a process integrated heat treatment by grinding with subsequent finishing in one clamping. The large amount of heat in the contact zone between the grinding wheel and the workpiece, which is generated by deformation, shearing, friction and separation while grinding, is used for surface layer hardening by means of a short time austenization of the machined part. The martensitic hardening is mainly achieved by self quenching [16] supported by the convective heat transport of the used coolant [17]. 3.2
Modeling of Resource Flows of the milling process
In the following, the different resource flows will be modeled exemplarily with the milling process (see Figure 6) of the grindhardening process chain.
314
Sustainability in Manufacturing - Energy Efficiency in Process Chains
d w vf vc
vf vc
= = = =
depth of cut = 2.5 mm width of cut = 10 mm feed rate = 7.97 mm/s cutting speed = 1.67 m/s
w d Firstly, the product energy flow is assessed. The power necessary for the process Pc can be determined by multiplying cutting force Fc and cutting speed vc:
Pc Fc vc
(5)
König et al. [18] describe how the cutting force can be calculated theoretically, so that no previous measurements are required. In this case the cutting power is 894 Watt. The product energy flow results from multiplying power and cutting time tc:
rEnergyPROD Pc tc 894 W 18.9 s 16,896.6 J
1 1.0
f (d) 0.9423 d 0.165
0,8 0.8 0,6 0.6 0,4 0.4 0,2 0.2 00 0
1
2
axes movement incl. main spindle on
Pc, real f (d ) Pc 0.810 894 W 724.14 W
5
2 milling
1
4
3
0 0
10
20
30
40
50
60 time
70
80
power approximated Wirkleistung angenähert Figure 7: Measured power consumption of the milling process. In order to variably model the milling process, the power consumption of the main components is assessed. As the machine’s axes movement only occurs when the spindle is turned on, the power consumption of the axes movement has to by derived by subtracting the power consumption of the spindle from the measured value. With these component power values the resulting power consumption of the system states can be calculated independently, making further power measurements unnecessary. Only the power needed to execute the technology itself has to be assessed separately. When determining the technology power consumption it is again possible to calculate the power consumption independent of measurements by using the theoretical process
(7)
3 [kW] 2 1 0 A
[s] 110 90 100
power measured Wirkleistung gemessen
6
On the basis of the now given information about the power consumption of the components, the different system states can be calculated as depicted in Figure 9. In this case, component 1 is not one physical consumer but subsumes all small components that consume power when the milling machine is turned on, including e.g. transformers or lighting.
standby
2
[mm] 5
Figure 8: Correction factor of technology power consumption.
1
3
3 4 depth of cut d
The grind-hardening process chain has a depth of cut of 2.5 millimeter and a correction factor of 0.810. Accordingly, the power consumption of the technology as one component of the manufacturing system is calculated as follows:
power consumption
power consumption
4
in process (milling)
f(d) based on measurements trendline
1,2 [-]
(6)
The input energy flow of the considered manufacturing system, the milling machine, can be derived from the different system states of the process as described in Section 2.1. Figure 7 shows the measured power of the milling process over time and visualizes the four states of the milling machine. The power graph displays the actual power measured on the one hand and the approximated power consumption on the other hand. The approximated power represents the ideal run of the power curve neglecting singular power peaks. 6
main spindle on
correction factor f(d)
1.4 1,4 Figure 6: Face-milling process.
[kW] 5
power described in Equation 5 and multiplying the value with a correction factor, that has to be derived from empirical studies. These empirical studies include a variation of parameters and simultaneous measurement of the electrical power consumed. For the milling process considered in this paper a non-linear correlation of depth of cut and power consumption was ascertained concerning the correction factor. The discovered correlation is visualized in Figure 8.
components: 1: basic consumption 2: machine control 3: feed drives 4: main spindel 5: technology
B C D system states
system states: A: standby B: axes movement C: main spindle idle D: in process
Figure 9: Power consumption of the states for the milling machine. The input energy flow can be determined by multiplying the system states of the milling machine and the time during which the machine is in one of the defined states based on the work schedule for the milling process. The assumed waiting time is a result of the difference between process time and the cycle time of the production line. The energy for each process step and state is listed in Table 1 and the final input energy flow of the manufacturing system results in 138,816 Joule.
Sustainability in Manufacturing - Energy Efficiency in Process Chains step
system state
time [s]
energy [J]
clamping
state A
60
42,000
state B
7
5,250
state C
3
4,500
state D
18.9
42,036
state C
3
4,500
state B
7
5,250
unclamping
state A
20
14,000
cleaning
state A
10
7,000
waiting
state A
20.4
14,280
149.3
138,816
machining
sum
Table 1: Work schedule of the milling machine. The only peripheral system used during the milling process is the compressed air system. In this case, instead of modeling the entire system only the energy needed to produce the estimated amount of compressed air is calculated. Based on the machine that was examined and its work schedule the input energy flow of the peripheral system is determined for a compressed air consumption rate of 1.5 liters per second at 6 bar for 10 seconds (step ‘cleaning’ in Table 1). The resulting energy flow is 13,072 Joule. The entire input energy flow according to Equation 1 is:
rEnergy IN rEnergyIN milling machine rEnergy IN compressedair system 138,816 J 13,072 J 151,888 J The input material flow of the workpiece corresponds to the semifinished part of 0.5935 kilogram of soft-annealed 100Cr6. The product material flow is made up of 0.557 kilogram of 100Cr6 forming the machined workpiece. The output material flow is established by subtracting the product material flow from the input material flow resulting in 0.0365 kilogram. The milling tool material flow is an input and output flow but does not go into the product. Also the fraction of the milling tool, which is allocated to one workpiece results from distributing the entire tool material equally over the expected tool life. The resource flows of the other processes can be modeled and assessed in a similar way. Especially, for the grinding processes the energy flows of the coolant system, as a complex peripheral system, are modeled using a state based approach as introduced above for entire manufacturing system. In addition, the coolant consumption per workpiece is calculated by dividing the entire coolant residing in the coolant system by the quantity of products that are machined during a coolant-renewal interval. 3.3
Resource Efficiency Evaluation
Having assessed all resource flows based on a weekly production quantity of 800 pieces, the resource efficiency of the alternative process chains can be determined. Table 2 shows the material flows of the induction hardening process chain. The material flows of the grind-hardening process chain are displayed in Table 3. Due to the fact that the semi-finished part and the final workpiece geometry are the same, the material flows are identical. The tool waste of the induction hardening process chain is less than the tool waste of the grind-hardening process chain, because the tool wear of grind-hardening is added. The coolant flow is independent of the utilization ratio of the grinding machine and therefore identical for both process chains. In this case a coolant system with a capacity of 800 liters of water based coolant solution and a coolant-renewal interval of 26 weeks are assumed.
315 rMaterial IN [kg]
rMaterial OUT [kg]
rMaterial PROD [kg]
tool waste
0.0043
0.0043
0
workpiece
0.5935
0.0365
0.557
0.05
0.05
0
coolant
0.0435
0.0435
0
sum
0.6913
0.1343
0.557
cooling water
Table 2: Material flows of process chain ‘induction hardening’. rMaterial IN [kg]
rMaterial OUT [kg]
rMaterial PROD [kg]
tool waste
0.0143
0.0143
0
workpiece
0.5935
0.0365
0.557
coolant
0.0435
0.0435
0
sum
0.6513
0.0943
0.557
Table 3: Material flows of process chain ‘grind-hardening’. In order to compare the energy efficiency of the two process chains a benchmark for each production phase has to be found. Table 4 displays the ideal energy flows concerning the three phases introduced in Figure 5, which will be used as the basis of comparison in the following evaluation. For the heat treatment phase the energy needed for the austenitization is applied, assuming a low heating rate [19]. Further, the minimal energy of the analyzed technologies is used for the soft machining and finishing phase. production phases
energy [J]
soft machining
20,280
heat treatment
2,491
finishing
6,807
sum
29,578
Table 4: Ideal energy flow concerning the production phases. In order to determine the input energy, Table 5 and Table 6 show the different process steps. Each step relates to the energy consumed by the corresponding manufacturing system and peripheral systems. The energy of all process steps added up finally forms the energy of the entire process chain. Before each change of manufacturing system and at the end of each process chain the workpiece is cleaned for ten seconds using the compressed air system. The additional cooling system in the induction hardening process step is needed for quenching the workpiece to realize surface hardening. step milling
induction hardening
grinding sum
system
energy [J]
milling machine
126,790
compressed air
13,072
induction hardening machine
160,700
cooling system
87,450
compressed air
13,072
grinding machine
336,127
coolant system
157,120
compressed air
13,072 907,403
Table 5: Input energy flow of process chain ‘induction hardening’.
316
Sustainability in Manufacturing - Energy Efficiency in Process Chains
The values concerning the milling process step in Table 6 are derived from the resource flows modeled in Section 3.2. step
system
energy [J]
milling machine
138,816
Erhöhung der Energieproduktivität, in: Zeitschrift für wirtschaftlichen Fabrikbetrieb (ZWF), Vol. 105, No. 10, pp. 870-875. [3]
Griese, H.; Müller, J.; Sietmann, R. (1997): Kreislaufwirtschaft in der Elektronikindustrie: Konzepte, Strategien, Umweltökonomie, VDE-Verlag, Berlin, Germany.
[4]
Neugebauer, R. (Ed.) (2008): Energieeffizienz in der Produktion: Untersuchung zum Handlungs- und Forschungsbedarf, Fraunhofer Gesellschaft.
[5]
Munoz, A. A.; Sheng, P. (1995): An analytical approach for determining the environmental impact of machining processes, in: Journal of Materials Processing Technology, Vol. 53, No.3-4, pp. 736-758.
[6]
Bley, H.; Nickels, T.; Schmidt, J. (1997): Mutual Effects in a Sequence of Cutting and Cleaning, in: Proceedings of the 29th CIRP International Seminar on Manufacturing Systems, pp. 125-132, Osaka, Japan.
[7]
Goedkoop, M.; Spriensma, R. (2001): The Eco-indicator 99: A damage oriented method for Life Cycle Impact Assessment, retrieved March 16, 2009, from http://www.pre.nl/download/ EI99_methodology_v3.pdf.
[8]
DIN EN ISO 14040 (2006), Beuth Verlag, Berlin, Germany.
[9]
Salonitis, K.; Tsoukantas, G.; Drakopoulos, S.; Stavropoulos, P.; Chryssolouris, G. (2006): Environmental Impact Assessment of Grind-Hardening Process, in: Proceedings of the 13th CIRP International Conference on Life Cycle Engineering (LCE 2006), Vol. 2, pp. 657-662, Leuven, Belgium.
[10]
Neugebauer, R. (Ed.) (2010): Energy-Efficient Product and Process Innovation in Production Engineering, Verlag Wissenschaftliche Scripten, Auerbach, Germany.
[11]
Reinhardt, S.; Reinhardt, G. (2009): Resource Efficiency Valuation of Manufacturing Processes, in: Quaderni della XIV Summer School 'Fancesco Turco', pp. IV.84-IV91, Monopoli, Italy.
This paper introduced an approach for evaluating the resource efficiency of manufacturing process chains. The procedure was applied to two alternative process chains for surface hardening. The material and energy flows of both chains were determined and evaluated – identifying the new hybrid manufacturing process ‘grind-hardening’ as a resource efficient alternative to the traditional hardening process using induction. Further analysis of possible transport and handling processes should be conducted to expand the scope of the process chain valuation. Furthermore a weighting scheme for resources has to be developed in order to reflect the special characteristics of different materials and energy types and their ecological effects.
[12]
Commission of the European Communities (2003): Towards a Thematic Strategy on the Sustainable Use of Natural Resources (No. COM(2003) 572), Brussels, Belgium.
[13]
Rogall, H. (2008): Ökologische Ökonomie, VS Verlag für Sozialwissenschaften, Wiesbaden, Germany.
[14]
Benkowsky, G. (1990): Induktionserwärmung: Härten, Glühen, Schmelzen, Löten, Schweißen, Verlag Technik, Berlin, Germany.
[15]
Zäh, M. F.; Brinksmeier, E.; Heinzel, C.; Huntemann, J.-W.; Föckerer, T. (2009): Experimental and numerical identification of process parameters of grind-hardening and resulting part distortions, in: Production Engineering – Research and Development, Vol. 3, No. 3, pp. 271-279.
5
milling grind-hardening
grinding
compressed air
13,072
grinding machine
348,450
coolant system
158,400
grinding machine
168,577
coolant system
98,400
compressed air
13,072
sum
938,787
Table 6: Input energy flow of process chain ‘grind-hardening’. Applying Equation 2 to the calculated material flows (Table 2 and Table 3) the material efficiency of the process chains can be determined. The energy efficiency of each process chain is calculated by dividing the accumulated ideal energy by the entire input energy of the respective process chain as implied in Equation 3. The specific efficiencies of the different process chains are listed in Table 7. The resource efficiency is calculated according to Equation 4, indicating the grind-hardening process chain as the most resource efficient alternative. process chain ‘induction hardening’
process chain ‘grindhardening’
material efficiency [%]
67.47
74.70
energy efficiency [%]
3.26
3.15
resource efficiency [%]
35.37
38.93
Table 7: Resource efficiency of both process chains. 4
SUMMARY AND OUTLOOK
ACKNOWLEDGMENTS
[16]
The authors would like to thank their colleagues at the Foundation Institute of Materials Science (IWT) for their support concerning the grinding machine data and the Deutsche Forschungsgemeinschaft (DFG) for funding the project ‘Grind-Hardening Process Simulation’.
Brockhoff, T., (1999): Schleifprozesse zur martensitischen Rand¬schicht¬härtung von Stählen, Shaker Verlag, Aachen.
[17]
Brinksmeier, E.; Minke, E.; Wilke, T., (2005): Investigations on Surface Layer Impact and Grinding Wheel Performance for Industrial Grind-Hardening Applications, in: Annals of the WGP, Vol. 12, No.1, pp. 35-40.
6
[18]
König, W.; Essel, K.; Witte, L. (1982): Specific Cutting Force Data for Metal-Cutting, Verlag Stahleisen, Düsseldorf, Germany.
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Shrivastava, P. (1995): Environmental technologies and competitive advantage, in: Strategic Management Journal, Vol. 16, No. S1, pp. 183-200.
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Reinhart, G.; Karl, F.; Krebs, P.; Reinhardt, S. (2010): Energiewertstrom – Eine Methode zur ganzheitlichen
[19] Orlich, J.; Rose, A.; Wiest, P. (1973): Atlas zur Wärmebehandlung der Stähle, Verlag Stahleisen, Düsseldorf, Germany.
Synergies from Process and Energy Oriented Process Chain Simulation – A Case Study from the Aluminium Die Casting Industry 1
1
Christoph Herrmann , Tim Heinemann , Sebastian Thiede 1
1
Institute of Machine Tools and Production Technology, Technische Universität Braunschweig, Braunschweig, Germany
Abstract Due to the significant ecological relevance and constantly rising prices, energy consumption more and more gets into the focus of manufacturing companies which strive to consciously consider energy consumption when planning and managing production facilities. Thereby it is important to take into account the interdependencies on different hierarchical levels in a production system (between single processes and the whole process chain). Against this background this paper presents an approach for a combined application of an energy oriented process chain simulation and a detailed process simulation. This approach enables an integrated evaluation of the interactions of parameter variations on both levels. Keywords: Energy Efficiency; Simulation; Aluminium Die Casting
In order to increase the efficiency of production processes and process chains especially in energy intensive industries like the aluminium die casting industry, a systematic approach against the background of a comprehensive system understanding is necessary [4] [5]. It is not sufficient to focus on only single selected system elements of the production. To avoid focusing on minor relevant aspects and local optimisation as well as problem shifting the whole production system including all relevant input and output flows has to be taken into consideration. Against this background,
Figure 2 shows fields of action in the context of energy- and resource efficiency in production. One can differentiate between the machine- or process perspective and the view on process chains or production/factory system, yet both are directly connected as depicted in the figure. Based on either singular or permanent data collection, the understanding of interrelations through modelling as well as on suitable methods for the evaluation and prediction of operating behaviour, it is the ultimate objective to integrate energyand resource consumption as a further dimension into operational decisions (besides conventional objectives like for instance
a)
Aluminium Price (USD/Ib)
INTRODUCTION
In modern automotive design lightweight construction plays a major role for reducing fuel consumption and improving the driving characteristics of automobiles. An extensively applied way for reducing the weight of automobiles is the substitution of steel parts through aluminium parts. Many of these parts have a very complex structure and still they need to be produced in mass production. While being able to fulfil these strict requirements in terms of quality and quantity aluminium high pressure die casting is a well established production technology. This is also reflected in the quantity of produced aluminium die casting parts. In 2008 only in Germany over 413,000 tons of die casted aluminium parts were manufactured causing over one million tons CO2eq in the foundry. [1] [2] Having overcome the impacts of the recent global financial crisis the aluminium die casting branch recovers to well-known growth rates. By the year of 2005 the yearly growth rate in this industry branch in Germany was about 10%. Nevertheless the current growth rates in the aluminium die casting industry cannot hide the fact that this whole industry is under heavy pressure of reducing its overall costs in order to fulfil the requirements of its customers which are mainly automotive OEMs. As major cost drivers in this industry energy and aluminium prices need to be considered. Steadily increasing prices for raw materials (Figure 1, a) and energy (Figure 1, b) before the recent crisis and again a trend to increasing prices for aluminium and energy supplies after the financial crisis exert significant pressure to reduce energy and material consumption within the aluminium die casting process chain.
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b)
electricity oil 200
gas gross domestic product (GDP)
price index (2000=100)
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0 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
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Figure 1: Development of a) Al (10 year progression 2000-2010, graph from infomine.com) and b) Energy Prices (compared to GDP) [3].
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_55, © Springer-Verlag Berlin Heidelberg 2011
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Sustainability in Manufacturing - Energy Efficiency in Process Chains maintaining a defined temperature within the metal before it is charged into the casting chamber. Usually natural gas is used for smelting the metal in the smeltery whereas electricity is used for maintaining the temperature in the dosing oven. An alternative solution for the supply of the casting cells with molten aluminium is the supply of liquid metal directly from the metal supplier to the casting plant and the casting cells. In this case there is no need for smelting furnaces in the casting plant and the energy for resmelting the aluminium after it has already been smolten during the alloying process can be saved. However in this case specific infrastructure needs to be implemented at the metal supplier and the casting plant. Furthermore the molten metal needs to be heated during the transportation between metal supplier and casting plant.
Integration
Scenario Simulation / Improvement
Analysis / Evaluation
Modeling
Data Acquisition / Measuring
Figure 2: Fields of action for energy- and resource efficiency in production [5]. utilization, cycle times, quality rates). Within this systematic approach it is increasingly important to develop scenarios for improving the focussed process or process chain and to evaluate the impact of these measures based on a simulation approach before their individual implementation. The simulation of improvement scenarios becomes necessary as the energy and resource consumption behaviour of production machines is usually highly dynamical and also complex on process chain level due to the manifold interactions of the single subprocesses and also because of the their interdependencies with connected peripheral equipment and technical building services. [5] The effect and synergy of a combined application of a process simulation (Magmasoft®) and a process chain simulation (energy oriented process chain simulation, developed by IWF, Technische Universität Braunschweig) will be discussed in this paper. As a basis the underlying aluminium die casting process chain will be introduced in the following. ALUMINIUM DIE CASTING PROCESS CHAIN Subprocesses and material/energy aluminium die casting process chain
flows
in
the
According to the focus of the BMBF-funded research project ProGRess (www.progress-aluminium.de) the aluminium high pressure die casting process chain can be described as depicted in Figure 3. The process chain consists of four major steps in order to produce parts in a defined quality: smelting of aluminium, the die casting process itself, heat treatment to set up certain metal properties and one or several machining processes to realize the final geometry and surface quality. The first step in the in-house process chain in aluminium die casting companies is a centralized smeltery in which different furnaces smelt large quantities of solid aluminium ingots to liquid aluminium that is transported in crucibles via fork-lift trucks to the diverse die casting cells in the casting plant. At the casting cells the molten aluminium gets filled into dosing ovens that are also used for
As it is illustrated in Figure 3 the described process steps are usually divided by quality gates where scrap material is assorted. This assorted material as well as swarf and in particular the gating system and sprue waste that are stamped off the work piece directly after the casting are transferred to the smeltery again and get resmelted and supplied to the casting cell afterwards as cycle material. Additionally, as mentioned, there are diverse auxiliary flows that are needed to ensure stable casting and machining processes like cooling water, release agents, lubricants, etc. that need to be taken into consideration when it comes to a holistic assessment of this process chain.
emissions, energy, auxiliary materials (e.g. cooling water, release agents) chips, (recyling) material (e.g. sproud spill) , waste
liquid metal
casting process form
spraying
machine
Machining
Heat Treatment
Die Casting quality gate
solid smelting furnace
dosing oven
Smelting
Self-hardening alloys furnace / process
Figure 3: Aluminium die casting process chain.
machining process machine
quality gate
2.1
quality gate
2
The die casting process itself takes place in casting cells that usually consist of the mentioned dosing oven, the die casting machine with its dies, tempering units, parts-removal robots, spraying devices for applying release agents and saws or stamping presses for removing e.g. gating systems and sprue waste from the work piece. Inside the casting cells the liquid aluminium is casted into solid semi finished parts. In this process electricity is consumed by the dosing oven, the die casting machine, the spraying device, the involved robots, the tempering units, the spraying robot and the saws/stamping presses. Additionally release agents, water and compressed air are used by the spraying device. As process output there are the semi finished parts and aluminium (gating systems, sprue) that is stamped off the semi finished parts. After a visual quality check the semi finished parts are processed by a mechanical treatment. As these operations can be manifold also the input and output flows can be very versatile. Besides diverse auxiliary flows (e.g. coolants or compressed air) at least the electrical energy consumption by the value adding processes and the peripheral equipment like e.g. exhaust air systems needs to be analyzed. In some cases the die casted work pieces also get a heat treatment in order to influence the material properties. Depending on the intended function of the product or by using self hardening alloys this process step is not necessary.
die casted parts in defined quality
Sustainability in Manufacturing - Energy Efficiency in Process Chains 2.2
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Challenges in aluminium die casting in the context of energy and resource efficiency
assessing the impact of material efficiency in foundries in relation to their total energy consumption.
The aluminium die casting process chain is strongly depicted by significant losses of energy as well as material. A major part of the energy input is needed for heating of material/parts (e.g. very high relevance of waste heat) [6]. But especially the heat balance of the die casting process itself is characterised by high energy losses. A large part of the energy input is lost in the process in terms of heat or by cooling processes. On the whole, the extensive heat losses in the die casting process are extremely dissatisfying in terms of environmental performance respectively energy efficiency. Figure 4 shows the main thermal energy flows in the die casting process of aluminium. Besides the heat losses in the value adding process also the heat losses in non value adding peripheral equipment cannot be ignored.
Furthermore, material losses of 2-5% occur, which cannot be reintegrated into the process again and which are simply lost. Whereas from the company’s point of view those losses are mainly essential in terms of material costs, the energy-intensive and environmentally damaging exploitation of aluminium is especially critical from an ecological point of view (waste which potentially endangers the environment, large areas are necessary for the winning of bauxite, electrolysis for winning aluminium). Thus, from a global point of view, besides an extensive usage of recycled secondary aluminium an enhancement of material efficiency and therefore a reduction of aluminium consumption indirectly also leads to a considerable decrease of energy demand [8] [9].
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Pheripherieger Peripheral devices äte
Against this background measures for increasing the energy and material efficiency can be devided in two categories: organizational and technology/design oriented measures. Organizational measures adress measures like cycle time reduction and the reduction of unproductive idle modes whereas technology or design oriented measures adress measures that focus on the improvement of the production process itself (e.g. by using energy effcient drives or reducing scrap material) or the improvement of the product or the corresponding machine tools (e.g. through volume reduction of the product and its gating systems).
4,8 kW Environment
6,2 kWh W Hea ärmet verl lo sses uste
Ger Deävice tevelorl sses uste
Figure 4: Thermal energy flows in the die casting process of aluminium [6]. Compared to the casting process itself even more heat is needed in the smeltery for the liquidation of aluminium. In this particular step of the aluminium die casting process chain the energy demand is directly linked to the amount of material that needs to be smelted and therefore can be reduced by reducing the amount of material that needs to be processed in the casting process [7]. However, it is exactly the bad exploitation of the raw material aluminium that is problematic in the process. Spillover and sprue (which can make up up to 50 % of the cast form and are determind by the product and tool design) as well as finished components, which do not meet the required quality demands, and parts from the start-up process are partly remelted into ingots as cycle material and have to run through the whole energy intensive process again. Depending on the production parameters or component this applies to 30-70% of the originally input material (Figure 5) [2]. The impact of the continuos resmelting of cycle material can be estimated regarding the fact that in an average aluminium smeltery 930 kWh of energy are needed for smelting one ton of aluminium. This value is based on the total energy consumption of a smeltery that uses shaft furnaces and can vary depending on the smelted alloy but still can be used for Umschmelzen resmelting in toMasseln ingots
Recycling-Material cycle material 30%30-70% 70%
scrap Ausschuss solid metal Blockmaterial
3
SIMULATION APPROACHES TOWARDS ENERGY AND RESOURCE EFFICIENCY IN ALUMINIUM DIE CASTING
8,9 kWh
3.1
Process simulation in aluminium die casting
Within the research project ProGRess the company Magma Gießereitechnologie GmbH simulates the effects of changing the geometry of e.g. gating systems and the effects of a variation of process parameters (e.g. cycle time and injection speed) on the quality of the casted products. By doing this optimized process parameters can be found through conducting manifold simulation runs. So e.g. the cycle time, quality rate or gating system volume can be reduced/increased in iterative simulation runs and the corresponding product quality (in terms of porosities, solidification behaviour, etc.) can be predicted in order to define the best process parameters according to the individual target function that still guarantee high quality products. To illustrate the potential of a process simulation for the aluminium die casting process Figure 6 compares the original design of an aluminium die casting product and its gating system with an improved version in which the volume of the gating system has been reduced significantly. Through a process simulation that has been conducted with the software Magmasoft® it can be ensured that the quality of the product in terms of entrapped gas, solidification behaviour, etc. is not negatively affected by this measure. By applying this measure 25% of the metal in the gating system can be saved which leads to a decrease in the total aluminium shot weight of approx. 12% [10]. As described above this also directly affects the energy efficiency in the smeltery as there is no smelting energy needed for the saved metal. original design
improved design
material 5% - 7% 5-7%
casting
Gießprozess process
Bauteil product
liquid metal Flüssigmetall
Materialverluste metal loss 2-5% 2% - 5%
Figure 5: Cycle material in aluminium die casting [2]. Figure 6: Design variation of gating system [10].
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Sustainability in Manufacturing - Energy Efficiency in Process Chains
In this sample simulation run it has also been shown that besides the reduction of the gating system volume also the cycle time can be reduced by 8%. Although this is a respectable result it cannot be predicted whether this measure would lead to bottlenecks in the downstream process chain and therefore would flatten the effect on the production line.
focus on application areas that will lead to real improvements also on a process chain level. In this way a target-oriented improvement of production systems can be ensured.
3.2
The structure and characteristics of the interlinked processes of the underlying case study for the simulation and pre-evaluation of parametervariations is shown in Figure 7. In this case three casting cells with peripheral equipment serve two identical CNC milling machines for mechanical treatment before they are being transported automatically by a conveyor in a one piece flow into an abrasive blasting machine and finally to a palletizing device.
In contrast to detailed process simulation approaches process chain simulation focusses more on the interdependencies and interactions of multiple processes within a factory. Referring to previous publications (e.g. [11] [12] [13]), the architecture (input, logic, user and evaluation layer) of the simulation that is used for this paper shall not be explained in detail here. Basically it is a modular, flexible approach which allows a realistic representation of the production system with all the interdependencies and dynamics of involved technical equipment. As an extension of well-known material flow simulators all energy related input and output flows are explicitly considered. This means that based on real metered consumption data the machine behaviour can be depicted with state charts – each operating state has a definable duration (e.g. based on certain time or trigger events) and is connected with a certain consumption of a resource (described as value or equation, e.g. depending on process parameters). Thus, with this technique the dynamic consumption behaviour of e.g. all forms of energy, any (auxiliary) materials or even emissions can be modelled. Furthermore an energy oriented process chain simulation can predict the overall energy consumption of whole process chains that result from single process parameter variations [11]. Sample load profiles (for electricity as well as compressed air consumption) that can be generated this way are illustrated in Figure 8. The derivation of the electricity consumption that is caused by the generation of compressed air is simulated in an integrated module in which the behaviour of compressors can be modelled. [12] 3.3
Synergies from a combined application of process and process chain simulation
The impact of organizational or technological or design oriented improvement measures regarding economical criteria (lead times, product-related costs, etc.) as well as ecological criteria (energy and material consumption) underlies dynamical effects within the process (e.g. thermodynamical behaviour of material flows) as well as along the process chain (e.g. dynamic energy demand, bottle neck situations). As these effects often depend on partial reciprocal interrelations of the individual subsystems of a production process or on interactions of subprocesses within a process chain a simulation approach is needed for the evaluation of improvement scenarios that are proposed to be implemented in the process chain. Especially detailed process oriented simulation approaches face the challenge that they can only serve with a very limited perspective when it comes to a holistic evaluation of a process chain. Still their potential for delivering a detailed view into the order of events of the main processes within a process chain is often more promising than the exclusive use of a process chain simulation. As it is often practically not feasible to run a detailed process simulation for every subprocess and as it is also not feasible to simulate the effect of every single process parameter in every kind of peculiarity due to restrictions of computing capacities a combination of the advantages of process and process chain simulation approaches is needed. One approach for the preevaluation of the effect on a process chain that comes from parametervariations within one single process will be introduced in the following. This pre-evaluation will set the boundaries for a parametervariation in a detailed process simulation that is used for the improvement of die casting operations. With these restrictions as input for detailed process simulations those simulation runs can
CASE STUDY
Elements of the process chain that are not depicted in Figure 7 are beyond the system boundary of the case study. Thus effects on upstream processes like smelting ovens are calculated based on the simulation results afterwards. Casting cell
Saw, Robot
Casting cell
Saw, Robot
CNC-milling CNC-milling Casting cell
Abrasive blasting
Palletizing
Saw, Robot 90000 80000
Die casting machine
W
70000 60000
peripheries
Process chain simulation for aluminium die casting
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40000 30000
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20000
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10000
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t
0
Figure 7: Process chain for simulation case study. The simulation model that has been deduced from the process chain description is depicted in Figure 8. It is parameterized with real metered data like cycle times, (energy) load profiles depending on operation modes, process dependend material efficiency, etc. and focuses on the consumption of electrical energy in this case. Based on this model and the underlying parameterization for every subprocess of the process chain the effect of changing process parameters in the die casting process on the total energy consumption per part and the required throughput time per part can be estimated. The manipulated process parameters in this case are the cycle time and quality rate. Furthermore the effect of the quality rate in comparison to the material volume of the product and its gatingasystemainatermsaofamaterialaefficiencyacanabeaevaluatedain 800
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Figure 8: Simulation model based on case study process chain and sample load profiles.
Sustainability in Manufacturing - Energy Efficiency in Process Chains order to evaluate levers for decreasing the embodied energy per part. As this is a process chain simulation only the effects on the process chain are considered. The effects of parametervariations on the intra-process behaviour (e.g. in terms of wear or differing load peaks through speed variations) need to be evaluated afterwards by a detailed process simulation. 4.1
Varied parameters on process and process chain level
The objective in this case is the production of 40 flawless parts.This output is set fixed. Through the variation of single process or design parameters manifold scenarios can be simulated. Table 1 depicts the single parameters (cycle time, material volume, quality issues, etc.) that also can be manipulated in detail in the process simulation Magmasoft®. In this case the shot weight is directly linked with the material efficiency. As the product design and the product weight are set fixed the aluminium shot weight can still be varied because of possible design variations of the gating system. As the gating system gets stamped off the product after the casting process it becomes cycle material and the volume of the gating system determines the material efficiency of the die casting process. Regarding the two simulation approaches the volume of the gating system has a direct impact on the mold filling and therefore on the product quality that can be simulated in the process simulation. On the process chain level the material efficiency as well as the quality rate that determines another part of the cycle material have an impact on the total energy consumption regarding the smelting energy in the shaft ovens. The cycle time can be manipulated by changing the clamping time of the die casting machine (DCM). On process level this is linked with the simulation of the solidification behaviour and the shortest required cycle time for continuous solidification. On the other hand the effects of manipulated cycle times can lead to new bottleneck situations or changes in the length of standby or operative modes of succeeding processes on process chain level. It is assumed that the characteristic of the underlying load profile of the DCM is not affected by changes in the cycle time in this case as the varied clamping times represent only changes in idling times and no changes in the dynamic power consumption behaviour of the machine. 4.2
Simulation scenarios based on parameter variations
The described parameters are varied in a realistical and technical feasible range in different scenarios (see Table 1) in order to preevaluate their effect on the process chain as an input for a succeeding detailed process simulation. They are individually compared to a base run of the process chain simulation. Scenario 1 (S1) to scenario 5 (S5) describe an increase in material efficiency through an improved design of the gating system which leads to a decrease in the aluminium shot weight. S6 to S13 describe a variation in the cycle time whereas a decrease is considerd as well as an increase. S14 and S15 deal with a variation in the quality rate, which also affects the overall energy consumption as energy is wasted through scrap material. S16 is a combination of S5 and S9 and therefore describes the effect of reduced cycle times in combination with reduced aluminium shot weight. This combination Scenario Base run Scenario
Shot weight 100%
Shot Material weight Efficiency
S1
98%
S2
96%
S3
94%
Material Efficiency 71,10%
Scenario Cycle Time
73%
S6
74%
S7
76%
S8
94%
Scenario
98%
S14
96%
S15
S4
92%
77%
S9
92%
S5
90%
79%
S10
102%
S11
104%
S12 S13
Quality Rate
Cycle Time 100%
Quality Rate 100%
Material Cycle Scenario Shot weight Efficiency Time
Quality Rate
95%
*1
S16
90%
79%
92%
100%
98%
S17*2
90%
79%
92%
100%
*3
90%
79%
92%
100%
*4
90%
79%
92%
100%
*5
90%
79%
92%
100%
S18 S19
S20 *1
: Combination of S5 and S9
*4
106%
*2
108%
*3
: Abrasive Blasting: Lot Size 1 : Only 2 DCMs and S16 : Abrasive Blasting: Lot Size 10 and S16
: Only 2 DCMs
*5
Table 1 : Scenarios and parametervariations for process chain simulation case study.
321 is noteworthy as usually a reduction of shot weight enables a reduction of cycle times as the solidification of the metal takes less time when less material is casted. S17 and S18 describe the effect of selected organizational measures for an improvement of the process chain. Lot sizes of a process with high energy consumption during standby modes (abrasive blasting, see Figure 7) are varied and the process is simulated to be shut down during idling times in order to avoid standby energy consumption. S18 combines S17 with S16. S19 describes the reduction of DCMs in the process chain from three to two as another orgainzational measure. S20 combines S19 with S16. 4.3
Process chain simulation results as boundary conditions for process simulation
Figure 9 shows the simulated energy consumption per flawless part in relation to the result of the base simulation run which is paramterized with real metered process data and parameters. The base run itseld shows that the die casting cells are the main electricity consumers with a share of about 73%. The abrasive blasting process as well as the compressors for the generation of compressed air have a share of 11% each whereas the saws, robots and CNC-milling-machines have a share of only 5% in total. 110,00% 105,00% 100,00% 95,00% 90,00% 85,00% 80,00% Base S1 Run
S2
S3
S4
S5
S6
S7
S8
S9
S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20
Figure 9: Total energy consumption per flawless part. It is obvious that the reduction of casted material leads to a decrease of the energy consumption per part as the saved metal does not need to be smelted. In comparison to that the variation of cycle times does not lead to a strong increase or decrease of the energy consumption on process chain level. On the contrary a decrease of the quality rate leads to a significant increase of energy consumption as it is also connected with additional scrap material that needs to be smelted. Regarding that a decrease in the volume of casted metal often allows a decrease in the cycle time of the process it is shown with the results of S16 that this combination of varied parameters leads to a decrease in the energy consumption per flawless part of about 10% compared to the base simulation run. S17 and S18 show that the change to a batch production at the abrasive balsting process has an additional effect of only about 2%. The reduction of DCMs (S19 and S20) show only a marginal effect as the production load has to be transferred to the remaining DCMs in order to produce 40 flawless parts – but it shows that the investment in the third machine would not necessarily have been needed. Regarding the time per part (Figure 10) the simulation results demonstrate that none of the described parameter variations lead to a significant effect on process chain level. The throughput time is only affected by organizational measures like the batchwise production in the abrasive blasting process and the reduction of DCMs. 110,00% 105,00% 100,00% 95,00% 90,00% 85,00% 80,00% Base S1 Run
S2
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S4
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S9 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20
Figure 10: Production time per part. Figure 11 illustrates the lever that single parameters have on the total energy consumption per part and the production time per part on process chain level. As the same parameters can be simulated in a detailed process simulation like Magmasoft® for the optimization of the die casting process the results of the process
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Sustainability in Manufacturing - Energy Efficiency in Process Chains
chain simulation can span an area of parametervariations in which a detailed and time-consuming process simulation can lead to promising results. Therefore this pre-evaluation of parameters can decrease the effort that is needed for a holistic improvement of processes and process chains.
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Sensitivity: Energy per flawless part
[2]
Institute of Joining and Welding, TU Braunschweig.
[3]
German Federal Ministry of Economics and Technology (2007): Energy Statistics, http://www.bmwi.de/ BMWi/Navigation/Energie/ enenergiestatistik.html.
[4]
Schultz, A. (2002): Methode zur integrierten ökologischen und ökonomischen Bewertung von Produktionsprozessen und technologien, Dissertation, Magdeburg, Germany.
[5]
Herrmann, C., Thiede, S., Heinemann, T. (2010): Ganzheitliche Ansätze zur Erhöhung der Energie- und Ressourceneffizienz in der Produktion, in: 10. Karlsruher Arbeitsgespräche Produktionsforschung 2010, Karlsruhe, Germany, 2010.
[6]
Optimierung der Energiebilanz beim AluminiumDruckgießprozess, final report, Deutsche Bundesstiftung Umwelt, support code AZ 22197, 2007.
[7]
Soldering, P., Petku, D., Mardan, N. (2009): Using simulation for more sustainable production systems - methodologies and case studies, in: International Journal of Sustainable Engineering, Volume 2, No. 2.
[8]
Bayrisches Landesamt für Umweltschutz (2005): Effiziente Energieverwendung in der Industrie – subproject „Metallschmelzbetriebe“, Augsburg.
[9]
Anders, U., Pries, H. Dilger, K. (2003): Ökologisch und ökonomisch optimierter Trennstoffeinsatz beim AluminiumDruckguss, BMBF, 01RW0055, Braunschweig, 2001-2003.
[10]
Magma Gießereitechnologie GmbH, presentation at 10. Karlsruher Arbeitsgespräche Produktionsforschung 2010, Karlsruhe, Germany, 2010.
[11]
Herrmann, C.; Thiede, S. (2009): Process chain simulation to foster energy efficiency in manufacturing, in: CIRP Journal of Manufacturing Science and Technology, Elsevier, ISSN 17555817.
[12]
Thiede, S., Herrmann, C. (2010): Simulation-based Energy Flow Evaluation for Sustainable Manufacturing Systems, in: Proceedings of the 17th CIRP International Conference on Life Cycle Engineering (LCE2010), pp. 99-104, Hefei, China.
[13]
Herrmann, C., Thiede, S. (2009): Towards Energy and Resource Efficient Process Chains, in: Proceedings of the 16th CIRP International Conference on Life Cycle Engineering (LCE2009), pp. 303-309, Cairo, Egypt.
110,00% Shot weight Cycle Time Quality Rate
105,00%
100,00%
[1]
95,00%
90,00% 90%
95%
100%
105%
110%
Sensitivity: Production time per part 110,00% Shot weight Cycle Time Quality Rate
105,00%
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Figure 11: Sensitivity anlaysis of simulation results 5
SUMMARY / OUTLOOK
Against the background of the increasing relevance of considering energy and resource consumption in producing companies the paper introduces the aluminium die casting process chain as example for highly energy intensive process chains. Two simulation approaches are presented for the evaluation of parametervariations on process and on process chain level. Both simulation approaches are applied to case studies from the aluminium die casting industry. Within this case study the process chain simulation is used for a pre-evaluation of effects that occure on process chain level an are affected by variatons in the die casting process. Through this preevaluation the area of necessary parametervariations within the process simulation can be reduced in order to decrease the necessary computing capacity and to enable a more target oriented improvement of the process chain. As result of the process chain simulation a potential for decreasing the energy consumption per part by 10-15% seems to be possible. In upcoming research work the results of the process simulation have to be used as input on process chain level. By doing this the results of the detailed optimization on process level can be finally evaluated on process chain level for achieving a severe improvement in the observed production system. 6
REFERENCES VAR – Verband der Aluminiumrecycling-Industrie e.V., Key Figures – Production of Aluminium in Germany; http://www.aluminiumrecycling.com/en/recycling/eckdate n.php.
ACKNOWLEDGMENTS
The introduced analysis of the aluminium die casting process chain is based on the research project ProGRess (Design of Resource efficient Process Chains using the Example of Aluminium Die Casting; www.progress-aluminium.de) that is funded by the German Ministry of Education and Research (BMBF) and managed by the project management agency Karlsruhe (PTKA). All data has been anonymized and alienated and does not show the exact results that have been elaborated within the project consortium.
8
CONTACT
Christoph Herrmann
[email protected]
Tim Heinemann
[email protected]
Sebastian Thiede
[email protected]
Institute of Machine Tools and Production Technology (IWF), Product- and Life- Cycle-Management Research Group, Technische Universität Braunschweig, Germany
Context-Aware Analysis Approach to Enhance Industrial Smart Metering 1
2
1
1
2
2
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C. Herrmann , S.-H. Suh , G. Bogdanski , A. Zein , J.-M. Cha , J. Um , S. Jeong , A. Guzman 1
1
Institute of Machine Tools and Production Technology (IWF), Product- and Life-Cycle-Management Research Group, Technische Universität Braunschweig, Germany 2
Center for Ubiquitous Manufacturing, POSTECH, Pohang, Gyeongbuk, South Korea
Abstract With consideration of the increasing relevance of energy consumption and rising energy costs, strategies to improve energy efficiency in manufacturing environments have to be developed. As the energy consumption results from the temporal accumulation of individual power demands of subordinate devices, energy metering enables to capture these demands and display the dynamic behaviour. A fundamental precondition for the identification and implementation of improvement measures is nevertheless transparency about energy demands as well as related couplings and interdependencies. In this paper, the context-aware analysis approach is proposed to provide additional information supporting the understanding of energy consumption in manufacturing environments. Keywords: Manufacturing System; Energy Efficiency; Context Awareness
1
INTRODUCTION
In manufacturing systems energy and resources are used in order to obtain operational readiness (e.g. through compressed air provision or heating) and conduct transformation processes for value creation. Hence, manufacturing is simultaneously value creating and resource-consuming. Due to increasing electricity and raw material costs, the usage of energy and resources is increasingly exerting pressure on the management of manufacturing companies. Industrial operations have consumed about 29 % of the final energy in Germany in 2007 [1]. Considering the electrical energy proportion, more than 556 PJ are used in industrial application areas. This corresponds to 23 % of the total industrial final energy consumption [2]. In the Republic of Korea the industrial sector is even responsible for 56 % of the final energy consumption in 2006. When calculating the share of the electrical energy consumption, the industrial sector accounts for more than 50 %, representing about 598 PJ [3]. Assuming 3500 kWh yearly electricity consumption per 4-member household, this equals to more than 92 million households. With regard to the increasing relevance of energy consumption in manufacturing systems and the related costs, strategies to improve energy efficiency have to be developed. In accordance with the principles of a sustainable development, these strategies have instantly to consider economic, ecological and social aspects from a life cycle perspective in order to avoid problem shifts from one life cycle phase to other life cycle phases [4]. ‘Organizations of all kinds are increasingly concerned to achieve and demonstrate sound environmental performance by controlling the impact of their activities, products or services on the environment […].’ (Introduction of DIN EN ISO 14001 [5]). In this statement the aspect of control, as an enabler to improve sustainability, is highlighted. In order to realize a sustainable development through control, management systems have to be enabled to define objectives and derive strategies to efficiently design, plan and operate all elements in manufacturing systems [6]. An important precondition for this is the availability of adequate and prompt information about the energy and resource demands and
resulting consumption patterns of all elements in manufacturing systems in order to facilitate optimal decision making of improvement measures [7][8]. 2 2.1
INDUSTRIAL SMART METERING Energy consumption of manufacturing processes
Manufacturing processes transform semi-finished inputs into valueadded outputs. Energy consumption becomes a physical necessity to perform that transformation. Theoretical calculations can be performed to estimate minimum energy expenditures of energy to perform processes (for example metal cutting or metal forming), but due to several losses in forms of mechanical friction, plastic and elastic deformation as well as the resulting heat losses a fairly higher amount of necessary energy expenditures is needed to perform the actual transformation process. Gutowski et al. have shown in early studies of manufacturing processes that the actual consumed energy for machining processes is overwhelmed by over five times as much energy needed for auxiliary processes like coolant pumps, lubrication supply and technical air ventilation [9]. To draw that conclusion Gutowski et al. had to meter the actual power demand of all subordinate devices in regard to the overall demand of the process related to the number of produced units. Another study concerning the energy efficiency of manufacturing processes was performed by Devoldere et al., showing that the regarded hydraulic press brake’s actual processing time, used for discrete work piece bending added up for a time share of less than 13 % of the total active time of the machine, resulting in a unproductive time share of over 87 %, consuming a high amount of unnecessary energy expenditures [10]. In this study Devoldere et al. have clearly indicated the need to approach an analysis of energy consumption behaviour by considering the aspect of time. Consumed energy is the integrated demand of electrical power over a certain period of time and is usually not considered as being static in its behaviour. In order to gain a better understanding of energy consumption of manufacturing processes a metering strategy has to be applied
J. Hesselbach and C. Herrmann (eds.), Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, DOI 10.1007/978-3-642-19692-8_56, © Springer-Verlag Berlin Heidelberg 2011
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Sustainability in Manufacturing - Methods and Tools for Energy Efficiency
taking the aspect of time-dynamic behaviour and of subordinate devices into consideration. 2.2
Metering of energy consumption
To gain an understanding of the energy and resource consumption behaviour of manufacturing processes a time-related measurement of the power demand of a selected system is to be applied initially. The electrical power actually demanded by a process is imposed indirectly from the metering values of voltage and current of all active electrical supply lines of the system. Figure 1 shows the power profile P(t) of an exemplary production machine.
Figure 3 shows exemplarily how the linkage of expert knowledge to the power profile from Figure 1 can be applied. The grey horizontal and vertical lines indicate a rough division of power demand ranges of different operation modes of the processes observed by the process engineer from the operation of the selected grinding machine. maximum process power P/W
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steady idle mode power demand
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Figure 3: Power (P) demand of a grinding process over time (t).
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Figure 1: Power profile P(t) of a production process. The amount of informational degree that can be derived from the given metering data is highly dependent of the granularity of a measurement in time and in amplitude [11]. This granularity is a result from the metering system’s sampling rate, metering signal resolution and the capacity of the digital processing system in the measurement chain. Analysing Figure 1 under the aspects of a better energetic system understanding, one can define maximum power demands and draw a conclusion about the dynamic behaviour of the process by estimating the variance of the profile, but can hardly allocate any power demand level to any operation mode. Neither can one tell which amount of consumed energy was related to value adding and which to non-value adding operation modes. In order to gain a better understanding of the power profile the lack of information has to be compensated with expert knowledge of operators or engineers or further informational sources. The selection, dimensioning and operation of the metering system as well as the realisation of a metering data acquisition, data processing and the provision of the pre-processed metering data is what we have related to in this paper as Industrial Smart Metering. The emphasis on ‘smart’ is related to the coupling to an integrated data pre-processing and communication interface.
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Products Emissions
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Figure 2: Generic metering model for better energy and resource aware system understanding (adapted from [7]). Industrial smart metering adopts the generic model of energy and resource metering of industrial manufacturing processes as depicted in Figure 2, in order to create a foundation for a better system understanding by taking all inputs and outputs as well as the time aspect into consideration. When applying also expert knowledge to the gained metered data, a certain degree of allocation of power demands and time shares is possible by either logging process observations or known events to the metered data.
The higher informational degree of Figure 3 in relation to Figure 1 is determined by the allocation of time-spans to events like start-up, machine setup and processing. Each time-span contains a specific field of individual power demand behaviour. But nevertheless, a true allocation of subordinate power demands is hardly possible on the given informational background not mentioning the time and effort to manually allocate visual observations to the time axis of the metered power profile. Today’s market provides smart meters known from the applications in private households, implemented with the focus to enable a certain level of transparency of the energy consumption of a household. By giving the user a visual feedback through various means of information ranging from power profiles over different time periods or accumulated energy counts or even calculated performance indicators. The provided information is generated in order to overcome the consumer’s habitual consumption behaviour by raising his awareness for the energetic impact of his behaviour [12]. The new awareness is ideally causing a motivation in the consumer to rethink his routine behaviour and finding a more optimal solution while taking energy as a new optimization criterion into consideration. The concept of Industrial Smart Metering is trying to adapt the same approach as smart metering in private households and is applying it to the requirements of a manufacturing environment [11]. Obviously, the requirements of a manufacturing environment are a lot more ambitious. The target group of the provided energy-related information does not only consist of one or two persons with quite the same interest, skill, and competence level as in a private household, but is rather divers, ranging from the shop floor operator and facility manager up to the process engineer and management staff. Each single target person has a different expert knowledge and a separate range of operational competency which leads ideally to a very case specific information provision for each receiver of the information. Against this background the us
