Lecture Notes in Electrical Engineering Volume 88
Tzyh-Jong Tarn, Shan-Ben Chen, and Gu Fang (Eds.)
Robotic Welding, Intelligence and Automation RWIA’2010
ABC
Tzyh-Jong Tarn Cupples 2, Room 103, Campus Box 1040 Washington University St. Louis, Missouri 63130 USA E-mail:
[email protected]
Gu Fang School of Engineering University of Western Sydney Locked Bag 1797 NSW 1797 Australia E-mail:
[email protected]
Shan-Ben Chen Intelligentized Robotic Welding Technology Laboratory School of Materials Sci. and Engg 800 Dongchuan Road Shanghai, 200240 P.R. China E-mail:
[email protected]
ISBN 978-3-642-19958-5
e-ISBN 978-3-642-19959-2
DOI 10.1007/978-3-642-19959-2 Lecture Notes in Electrical Engineering
ISSN 1876-1100
Library of Congress Control Number: 2011923801 c 2011 Springer-Verlag Berlin Heidelberg 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. Typeset & Coverdesign: Scientific Publishing Services Pvt. Ltd., Chennai, India. Printed on acid-free paper 987654321 springer.com
Preface
Robotic welding systems can be used in all types of manufacturing. They can provide several benefits in welding applications. The most prominent advantages of robotic welding are precision and productivity. Another benefit is that labor costs can be reduced. Robotic welding also reduces risk by moving the human welder/operator away from hazardous fumes and molten metal close to the welding arc. The robotic welding system usually involves measuring and identifying the component to be welded, welding it in position, controlling the welding parameters and documenting the produced welds. To develop an intelligent robotic welding system that can accomplish useful tasks without human intervention and perform in the unmodified real-world situations that usually involve unstructured environments and large uncertainties, the robots should be capable of determining all the possible actions in an unpredictable dynamic environment using information from various sensors such as computer vision, tactile sensing, ultrasonic and sonar sensors, and other smart sensors. From the existing successful applications, it can be concluded that emerging intelligent techniques can enhance and extend traditional robotic welding. This volume is mainly based on the papers selected from the 2010 International Conference on Robotic Welding, Intelligence and Automation (RWIA’2010), Oct. 14–16, 2010, Shanghai, China. We have also invited some known authors as well as announced a formal Call for Papers to several research groups related to welding robotics and intelligent systems to contribute the latest progress and recent trends and research results in this field. The primary aim of this volume is to provide researchers and engineers from both academic and industry with up-to-date coverage of new results in the field of robotic welding, intelligent systems and automation. The volume is divided into four logical parts containing twenty-five papers. In Part 1 (14 papers), the authors deal with some intelligent techniques for robotic welding. In Part 2 (16 papers), the authors introduce the Sensing of Arc Welding Processing. Various applications such as vision sensing and control of welding process are discussed. In Part 3 (9 papers), the authors describe their work on Modeling and Intelligent Control of Welding Processing. In Part 4 (8 papers), the authors exhibit their works on Welding Technics and Automations. In Part 5 (8 papers), the authors introduce some Special Robot Technology and Systems. Finally, in Part 6 (4 papers), the authors introduce some emerging intelligent techniques and their applications, which may contribute significantly to the further development of intelligent robotic welding systems.
VI
Preface
We would like to thank Professors R.C. Luo, J.L. Pan, B.S. Xu, S.Y. Lin, H.G. Cai, Z.Q. Lin, T.H. Song, L. Wu, P. Shan and Y.X. Wu for their kind advice and support to the organization of the RWIA’2010 and the publication of this book; to Na Lv, Ming-yan Ding, Hong-bo Ma, Shan-chun Wei, Zhen Ye, Yan-ling Xu, Huan-wei Yu, Tao Zhang, Ji-Yong Zhong, Cheng-dong Yang for their precious time to devote all RWIA’2010 correspondences and to reformat the most final submissions into the required format of the book, last but not least to Dr. Thomas Ditzinger for his advice and help during the production phases of this book.
October 2010
Tzyh-Jong Tarn, Washington University at St. Louis, USA Shan-Ben Chen, Shanghai Jiao Tong University, China Gu Fang, The University of Western Sydney, Australia
Contents
Part I: Intelligent Techniques for Robotic Welding Research Evolution on Intelligentized Technologies for Robotic Welding at SJTU . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S.B. Chen Image Processing for Automated Robotic Welding . . . . . . . . . . . Peter Seyffarth, Rainer Gaede
3 15
Automatic Seam Detection and Path Planning in Robotic Welding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kevin Micallef, Gu Fang, Mitchell Dinham
23
Error Compensation and Calibration of Inter-section Line Welding Robot Based on a Wavelet Neural Network . . . . . . . . . Su Wang, Xingang Miao, Yuan Yang, Xingai Peng
33
Autonomous Seam Acquisition and Tracking for Robotic Welding Based on Passive Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shanchun Wei, Meng Kong, Tao Lin, Shanben Chen
41
A Framework of Intelligent Remanufacturing System Based on Robotic Arc Welding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ziqiang Yin, Guangjun Zhang, Hongming Gao, Huihui Zhao, Lin Wu
49
A Fast GPI Line Detection Method for Robot Seam Tracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bowen Li, Shengqi Tan, Wenzeng Zhang
57
Mechanism Design and Kinematics Modeling of Irregular Cross-Section Pipeline Welding Robot . . . . . . . . . . . . . . . . . . . . . . . Xinghua Tao, Hongwu Zhu, Lili Xu
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VIII
Contents
Combined Planning between Welding Pose and Welding Parameters for an Arc Welding Robot . . . . . . . . . . . . . . . . . . . . . . . Huanming Chen, Yichen Meng, Xiaofeng Wang Optimal Digital Filtering for Tremor Suppression in a Master-Slave Robot Remote Welding System . . . . . . . . . . . . . . . . Hongtang Chen, Haichao Li, Hongming Gao, Lin Wu, Guangjun Zhang
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Coordinated Motion of Different Weld Robots Based on User Coordinates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Huajun Zhang, Chunbo Cai, Guangjun Zhang, Daming Shen
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Research on Information Transmission of a Welding Robot Based on Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . H.B. Wang, G.H. Ma, D.H. Liu, B.Z. Du
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Survey on Modeling and Controlling of Welding Robot Systems Based on Multi-agent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 Chengdong Yang, Shanben Chen Research on the Robotic Arc Welding of a Five-Port Connector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 Jiyong Zhong, Huabin Chen, Shanben Chen Mixed Logical Dynamical Model for Robotic Welding System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 Hongbo Ma, Shanben Chen
Part II: Sensing of Arc Welding Processing A Method of Seam Tracking Based on Passive Vision . . . . . . . . 131 Long Xue, Lili Xu, Yong Zou Research on a Trilines Laser Vision Sensor for Seam Tracking in Welding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 Zengwen Xiao An Optimal Design of Multifunctional Vision Sensor System for Welding Robot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 Yanling Xu, Xiangfeng Kong, Shanben Chen Wire Extension Control Based on Vision Sensing in Pulsed MIG Welding of Aluminum Alloy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 Lihui Lu, Ding Fan, Jiankang Huang, Jiawei Fan, Yu Shi
Contents
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Research on Track Fitting of Big Frame Intersection Line Seams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 Xingang Miao, Su Wang, Xiaohui Li, Benting Wan A Hybrid Approach for Robust Corner Matching . . . . . . . . . . . . 169 Fanhuai Shi, Xixia Huang, Ye Duan Depth Extraction by Simplified Binocular Vision . . . . . . . . . . . . 179 Gouhong Ma, J. Qin, F.R. Jiang, H.B. Wang Comparison of Calibration Methods for Image Center . . . . . . . 185 Xizhang Chen, Shanben Chen, Houlu Xue Seam Tracking and Dynamic Process Control for High Precision Arc Welding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193 Huabin Chen, Tao Lin, Shanben Chen Feature Selection of Arc Acoustic Signals Used for Penetration Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 Zhen Ye, Jifeng Wang, Shanben Chen Rough Set-Based Model for Penetration Control of GTAW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 Wenyi Wang, Shanben Chen, Wenhang Li A Study of Arc Length in Pulsed GTAW of Aluminum Alloy by Means of Arc Plasma Spectrum Analysis . . . . . . . . . . . 219 Huanwei Yu, Shanben Chen Arc Sound Recogniting Penetration State Using LPCC Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229 Jifeng Wang, Yantian Zuo, Yichang Huang, Bo Yang, Song Pan Investigation on Acoustic Signals for On-Line Monitoring of Welding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 Na Lv, Shanben Chen A Study on Applications of Multi-sensor Information Fusion in Pulsed-GTAW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245 Bo Chen, Shanben Chen Research on Image Process and Tracing of a Welding Robot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253 G.H. Ma, L. Wang, G.Q. Liu, M. Xiao
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Part III: Modeling and Intelligent Control of Welding Processing Predictive Control of Weld Penetration in Pulsed Gas Metal Arc Welding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263 Zhijiang Wang, YuMing Zhang, Lin Wu Study on the MLD Modeling Method of Pulsed GTAW Process for Varied Welding Speed . . . . . . . . . . . . . . . . . . . . . . . . . . . 271 Hongbo Ma, Shanben Chen Simulation of Decoupling Control of Pulsed MIG Welding for Aluminum Alloy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279 Ding Fan, Jiankang Huang, Lihui Lu, Yu Shi Modeling and Decoupling Control Analysis for Consumable DE-GMAW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285 Jiankang Huang, Yu Shi, Lihui Lu, Ming Zhu, Yuming Zhang, Ding Fan The Structure Design and Kinematics Simulation for Rotating Arc Sensor of TIG Welding Based on Pro/E 3D Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293 Jianping Jia, Hongli Li, Wei Jin, Shunping Yao Knowledge Model Building about a Motor Speed Regulation Fuzzy Control System . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299 Huimin Zhao, Mingyan Ding, Wu Deng, Xiumei Li, Wen Li Research on Surface Recover of Aluminum Alloy PGTAW Pool Based on SFS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307 Laiping Li, Xueqin Yang, Fengyan Zhang, Tao Lin Research on Fuzzy-Prediction-Control of GTAW Process Based on MLD Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315 Mingyan Ding, Hongbo Ma, Shanben Chen Application of Fuzzy Edge Detection in Weld Seam Tracking System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323 Zhenyu Xiong, Wen Wan, Jiluan Pan
Part IV: Welding Technics and Automations Application and Research of Arc Welding Automation in Korea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333 Suck-Joo Na
Contents
XI
Offline Programming for a Complex Welding System Using DELMIA Automation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 341 Joseph Polden, Zengxi Pan, Nathan Larkin, Stephen Van Duin, John Norrish Robot Path Planning in Multi-pass Weaving Welding for Thick Plates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 351 Huajun Zhang, Hanzhong Lu, Chunbo Cai, Shanben Chen Influence of the Bar Shape on the Welding Quality of Friction Hydro Pillar Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361 Xiangdong Jiao, Hui Gao, Hongwei Zhan, Canfeng Zhou, Jiaqing Chen, Yanqing Zhang Interference Analysis of Infrared Temperature Measurement in Hybrid Welding . . . . . . . . . . . . . . . . . . . . . . . . . . . . 369 Jifeng Wang, Houde Yu, Yaozhou Qian, Rongzun Yang Preliminary Investigation on Embedding FBG Fibre within AA6061 Matrices by Ultrasonic Welding . . . . . . . . . . . . . . . . . . . . . 375 Zhengqiang Zhu, Yifu Zhang, Chun Zeng, Zhilin Xiong Thermal Process Analysis in Welding Prototyping of Metal Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 383 Jian-ning Xu, Hua Zhang, Ronghua Hu, Yulong Li Study on Sub-sea Pipelines Hyperbaric Welding Repair under High Air Pressures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391 Canfeng Zhou, Xiangdong Jiao, Long Xue, Jiaqing Chen, Xiaoming Fang
Part V: Special Robot Technology and Systems The Mechanism Design of a Wheeled Climbing Welding Robot with Passing Obstacles Capability . . . . . . . . . . . . . . . . . . . . 401 Minghui Wu, Xiaofei Gao, Z. Fu, Yanzheng Zhao, Shanben Chen Anytime Ant System for Manipulator Path Planning . . . . . . . . 411 D. Wang, N.M. Kwok, G. Fang, Q.P. Ha Path Planning and Computer Simulation of a Mobile Welding Robot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 421 Tao Zhang, Shanben Chen The Control System Design of a Climbing Welding Robot Based on CAN Bus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 429 Xiaofei Gao, Minghui Wu, Z. Fu, Yanzheng Zhao, Shanben Chen
XII
Contents
An Implementation of Seamless Human-Robot Interaction for Pipeline Welding Telerobotics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 435 Na Dong, Haichao Li, Hongming Gao, Lin Wu Design and Experiment of a Novel Portable All-Position Welding Robot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443 Bin Du, Jing Zhao, Yu Liu The Power and Propulsion of Medical Microrobots . . . . . . . . . . 451 Xueqin Lv, Rongfu Qiu, Gang Liu, Yixiong Wu Mechanical Design and Analysis of an Articulated-Tracked Robot for Pipe Inspection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 461 Z.Y. Chen, G.Z. Yan, Z.W. Wang, K.D. Wang
Part VI: Intelligent Control and Its Applications in Engineering A Construction Method of Rational Approximation Model for Fractional Calculus Operators in Frequency Domain . . . . . 471 Wen Li, Guanghai Zheng, Bing Nie, Huimin Zhao, Ming Huang Research of Direct Discretization Method of Fractional Order Differentiator/Integrator Based on Rational Function Approximation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 479 Bing Nie, Wen Li, Haibo Ma, Deguang Wang, Xu Liang Blind Source Separation of Vibration Signal of Electric Motor Velocity Modulation System . . . . . . . . . . . . . . . . . . . . . . . . . . 487 Xiumei Li, Wen Li, Yannan Sun, Guanghai Zheng A Survey on Artificial Intelligence Algorithm for Distribution Network Reconfiguration . . . . . . . . . . . . . . . . . . . . . . . 497 Rongfu Qiu, Xueqin Lv, Shuguo Chen Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 505
Part I
Intelligent Techniques for Robotic Welding
Research Evolution on Intelligentized Technologies for Robotic Welding at SJTU S.B. Chen Intelligentized Robotic Welding Technology Laboratory, School of Materials Science and Engineering Shanghai Jiao Tong University (SJTU), Shanghai 200240, P.R. China e-mail:
[email protected]
Abstract. This paper addresses the new evolution of the study on intelligentized technologies for robotic welding based on our works in SJTU from 2006, which contains multi-information acquirement of arc welding process, such as extraction of weld pool image, voltage, current, and sound features; multi-information fusion algorithms for prediction of weld penetration by neural network and DS evidence theory; intelligentized modeling of welding process and robot system, such as welding dynamic knowledge models by SVM and RS methods, the MLD modeling of welding robot system; and intelligent control strategies for welding pool and penetration process, such as the adaptive inverse control scheme based on SVM-FRDS, control scheme based on the knowledge model by the RS theory, and the Model-Free adaptive control of pulsed GTAW; the 3-D seam tracking during robotic welding by combining arc sensing and visual sensing; and a new welding robot system for autonomously avoiding obstacles.
1 Introduction The most welding robots serving in practical production still are the teaching and playback type, and can not well meet quality and diversification requirements of welding production because this type of robots do not have the automatic functions to adapt to circumstance changes and uncertain disturbances during welding process [1-5]. In practical production, welding conditions are often changing, such as the errors of pre-machining and fitting work-piece would result in differences of gap size and position, the change of work-piece heat conduction and dispersion during welding process would bring on weld distortion and penetration odds [6]. Moreover, manufacturing of some large equipments, e.g., hull and container, need continuous and moving welding in long path and all position. In order to overcome or restrain various uncertain influences on welding quality, it will be an effective approach to develop and improve intelligent technologies for welding robots, such as vision sensing and multi-sensing of robotic welding process, recognizing welding surroundings, autonomously guiding and tracking seam, and real-time intelligent control of robotic welding process. Therefore, developing intelligentized technology for improving current teaching and playback T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 3–14. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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welding robot is necessary and exigent to meet high quality and flexible requirements for welding products and advanced manufacturing [3-7]. In our previous work [4-7], some research results on intelligentized technologies for welding robots were reported, the ref. [7] showed a functional realization of intelligentized welding robot systems it’s called as the locally autonomous intelligentized welding robot (LAIWR) systems, which could realize some primary intelligentized functions of welding robot systems. Based on our previous research [6-7], this paper presents some further evolutions of the intelligentized technologies for robotic welding [8-20], which contains multi-information acquirement of arc welding process, multi-information fusion algorithms for prediction of weld penetration; intelligentized modeling of welding process and robot system; intelligent control schemes for welding pool and penetration process; and the 3-D seam tracking during robotic welding by combining arc sensing and visual sensing. Further more, a new autonomous welding robot scheme with combined wheel and foot for getting across obstacles in all space position motion will be shown in this paper.
2 Characteristics Extraction of Multi-sensing Information During Arc Welding Process 2.1 Multi-information Acquirement of Arc Welding Process The experiment system for multi-sensing welding dynamical process is shown in Fig.1. The system consists of an electronic signal collecting module, a sound signal collecting model and a weld pool image collecting module.
Fig. 1. Schematic diagram of the experiment systems
By combining the three collecting modules, weld pool image, weld current, weld voltage and welding sound could be collected at the same time. Therefore the welding parameters could be controlled by the control module.
Research Evolution on Intelligentized Technologies for Robotic Welding at SJTU
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The Fig.2 shows the collected weld pool, current, voltage and sound information in five pulses. From the current, voltage and sound waveforms, it shows that the welding process can be divided into weld pulse peak period and weld pulse base period, the weld pool images were collected at the pulse base period to avoid the intense disturbance in pulse peak period by the welding arc to obtain the clear weld pool image [8,10].
Fig. 2. Weld pool, current, voltage and sound information in five pulses
2.2 Extraction of Multi-information Characteristics During Arc Welding Process 2.2.1 Extraction of Weld Pool Image Features The Fig.2 shows the topside and backside image of the weld pool, the geometric feature parameters can be obtained from the images. The special image processing algorithms for the topside and backside weld pool image can be found in [8]. 2.2.2 Extraction of the Weld Voltage and Current Features From Fig.3, it could be seen that the negative half-wave of weld voltage was bigger than the positive half-wave, this was caused by the cathode spot in pulsed GTAW. In this paper, the positive value of the voltage during a pulse period was used as the mean value of weld voltage during pulse peak period [8,9].
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S.B. Chen
Fig. 3. Weld current, voltage and sound during a pulse
2.2.3 Extraction of the Weld Sound Characteristics From Fig.2 and Fig.3, the weld sound intensity was used as a variable reflecting changes of welding status. The Fast Fourier Transform (FFT) was used to get the spectrum map of the sound to analyze the changes of the sound, and some other algorithms was developed for extraction of welding status, e.g. penetration characteristics [8,11].
2.3 Information Fusion Algorithms for Prediction of Weld Penetration by Neural Network and DS Evidence Theory The multi-sensor fusion model of the three sensors is shown in Fig.4. The information obtained by different sensors was first processed by back-propagation (BP) neural networks individually. Because welding process was influenced by heat inertia, responded to welding parameters with a time delay, the historical information should also be included to obtain more precise prediction results. The BP neural network was trained to obtain the BPA (Basic Probability Assignment) for each sensor [8]. The D-S evidence theory was used to combine the BPAs and obtain the final fusion BPA and obtain the prediction result. The algorithms details can be found in [8,9]. The experiment and analysis results showed that the multi-sensor could obtain better results than a single sensor. The prediction result of fusing three sensors was better than the prediction result of fusing two sensors. It shows that multi-sensor information fusion could obtain more information about the welding process and therefore describe the process more roundly and precisely.
Research Evolution on Intelligentized Technologies for Robotic Welding at SJTU
7
Fig. 4. Fusion model of electronic, image and sound signal
3 Intelligent Modeling of Welding Process and Robot System Modeling of the arc welding is one of the key techniques in automated welding. Real-time control of weld quality by the appropriate model, such as welding penetration, and fine formation of the welding bead, is still a perplexed problem faced by control engineers and welding technologists.
3.1 Knowledge Extraction of Arc Welding Dynamics by SVM Method One of our studies investigated to apply the Support Vector Machine-based Fuzzy Rules Acquisition System (SVM-FRAS) for modeling of the gas tungsten arc welding (GTAW) process [12,13]. The character of SVM in extracting support vector provides a mechanism to extract fuzzy IF THEN rules from the training data set. The fuzzy inference system using fuzzy basis function was constructed. The gradient technique is used to tune the fuzzy rules and the inference system. The schematic diagram of SVM-Based Fuzzy Rules Discovery System was depicted in Fig.5. Using the proposed SVM-FRAS method, we obtained the rulebased model of the aluminum alloy pulse GTAW process, shown as Table 1. Experimental results show the SVM-FRAS model possesses good generalization capability as well as high comprehensibility [12].
–
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S.B. Chen Step I: Extract Rules
Step II: Inferrence System Process Step III: Adaptive Learning
Adaptive Learning
Support Vector learning mechanism Extract Margin Support Vector S1,S2,...,Sm from the process
IF X is S1 THEN B1
Input X
Fuzzy inference system (Fuzzy basis function inference system)
IF X is S2 THEN B2
Output Y
M IF X is Sm THEN Bm
Fig. 5. Schematic diagram of SVM-Based Fuzzy Rules Discovery System Table 1. Part rules extracted by SVM in modeling for aluminum alloy pulsed GTAW process I t-3 I
t-2 WT
LT
WB
I
O t-1
WT
LT
WB I
t WT
LT
WB
I
WT
LT
WB
……… 191 10.5 17.3 8.2 216 10.5 17.2 8.6 171 8.9 17.3 5.6 192 9.5 16.1 5.7 164 9.9 15.9 6.8 223 11.9 22.2 9
165 10.6 18.5 6.6 194 10.8 17.2 8.5
226 10.5 19.3 12.3 160 9
172 9.6 13.1 5.2 183 10.1 13.4 3.2
16.8 7
219 11.1 18.7 9.4 220 11.7 19.6 9.4 230 11.4 18.2 10.8 163 10
20.7 8.9
………
3.2 Knowledge Model of Al Alloy GTAW Welding Process by the RS Method Based on our previous works on knowledge modeling of the welding process by the Rough Sets (RS) theory [5-7], the further research was completed and some knowledge rule models for welding pool dynamics of aluminum alloy GTAW by RS methods was developed in [6,14].
3.3 Mixed Logical Dynamical (MLD) Modeling of Pulsed GTAW During Robotic Welding The mixed logical dynamical (MLD) modeling method stems from hybrid systems described by interacting physical laws, logical rules, and operating constraints.
Research Evolution on Intelligentized Technologies for Robotic Welding at SJTU
9
The study in [17] presented a novel MLD modeling framework for robotic welding process and systems. The MLD model is then established and gives a good prediction quality of the back bead width of pulsed GTAW process with misalignment during robotic welding. The study [17] shows that the MLD framework is a good modeling method for pulsed GTAW process and robotic welding systems.
4 Intelligent Control Strategies for Welding Pool and Penetration Process Based on the knowledge models established in the above, some intelligent control scheme are shown as the following.
4.1 The Adaptive Inverse Control Scheme Based on SVM-FRDS for Pulsed GTAW Based on the SVM-FRAS model of the aluminum alloy pulse GTAW process in 3.2, the adaptive inverse control system for pulsed GTAW process was developed as Fig.6. Here the I-Controller means the inverse controller, the P-model means the process model for welding process, and the Predict-M means the predicted model of the backside width of welding pool during pulsed GTAW. The experiment results of the closed control system can be found in[12-13].
§
Wbd
SVM-FRDS SVM-FRDS Äæ¿ØÖÆ Æ÷ I-Controller
I
Wb
Pulsedº¸GTAW GTAW ½Ó ¹ý ³Ì P-Model
V-Sensing ´« ¸Ðϵ ͳ ep ec
£«
SVM-FRDS SVM-FRDS ¹ý ³Ì Ä£ÐÍ
£-
P-model
) Wb
£-
±³Predict-M ÃæÈÛ¿í Ô¤ ²â Ä£ÐÍ £«
Wb
Fig. 6. The adaptive inverse control scheme based on SVM-FRDS for GTAW process
4.2 Closed Control Schemes Based-on the Knowledge Model by the RS Ttheory Based on the obtained knowledge rule models for welding pool dynamics of aluminum alloy GTAW by RS methods, some control schemes were developed for
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realization of the closed real-time control of Al alloy welding pool during pulsed GTAW as follows: 1
)The controller based-on RS for the closed-loop control system of pulsed
GTAW The primary control scheme in Fig.7 was used to regulate the backside pool width by the RS controller during Al alloy pulsed GTAW.
Signal Conversion
Wbset +
e(t )
Δu
RS Controller
+
z −1 u
GTAW Process
+
Wb
Welding parameters
Wbpre
Welding pool sizes
Prediction Model
Sensing System
Fig. 7. The schematic diagram of RS closed-loop control system of pulsed GTAW
)
2 The compound control scheme with the RS and MS-PSD controllers for weld penetration and face-height of Al alloy pulsed GTAW. In order to control weld penetration and the height of topside seam during Al alloy pulsed GTAW at the same time, the advanced control strategy compounded the RS controller and MS-PSD controller for the closed control system scheme was developed as Fig.8. The controller algorithms and welding experiments can be found in [14]. H fset +
e1 (t )
MS-PSD Controller
ΔV1 f +
+
+
z −1
Signal Conversion
Wbset
+
e2 (t )
RS Controller
+
ΔI
Vf
V1 f
z −1
+ ΔV2 f
Feed forward Controller
Hf
g GTAW Process
Wb
I
+ Welding parameters
Wbpre H fpre
Prediction Model
Welding pool sizes
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Fig. 8. The compound control systems with the RS and MS-PSD controllers for weld penetration and face-height of Al alloy pulsed GTAW
Research Evolution on Intelligentized Technologies for Robotic Welding at SJTU
11
4.3 The Model-Free Adaptive Control of Pulsed GTAW Arc welding is characterized as inherently variable, nonlinear, time varying and having a coupling among parameters. In addition the variations in the welding conditions cause uncertainties in the welding dynamics. Therefore, it is very difficult to design an effective control scheme by conventional modeling and control methods. A model-free adaptive control algorithm has been developed to control the welding process [15], which only needs the observed input output data and no modeling requirement for controlled welding process. Thus, the developed model-free adaptive control provides a promising technology for GTAW quality control. The Model-Free Adaptive Control algorithms was introduced in [15].
5 The 3-D Seam Tracking During Robotic Welding by Combining Arc Sensing and Visual Sensing The seam tracking technique for three dimension (3-D) curve during robotic pulsed GTAW process was developed by the combination of arc sensing for torch height or arc length with passive visual sensing for correct an error of seam or torch deflexion [16]. The seam tracking scheme of robotic welding process is shown as Fig.9. Processing of welding pool image and identifying of seam and gap changes are realization through the image as Fig.10. The tracking control algorithms and experiments can be found in [16].
Fig. 9. Seam tracking scheme of welding robot system
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Fig. 10. Welding pool image during robotic welding
6 Some Applications of Intelligentized Robotic Welding Using the above intelligentized technologies for robotic welding, some significant applications are progressing in spaceflight, shipbuilding and automobile industries about 10 years [18-19], here is omitted
7 An Autonomous Welding Robot System for Whole Position Motion and Across Obstacle In many practice welding manufacturing sites, such as welding of shipbuilding and large tank, there is a need for the autonomous moving welding in a long distance and complicated space position. It also requires the welding robot with adsorbent and climbing functions for all position motion and flexible pose changes for various joints, such as the fillet, lap, vertical, inclined welding, and so on. So, a primary autonomous moving welding robot system with a combination of wheels and foot for adsorbent climbing and getting across obstacle was developed. It has some intelligentized functions to realize robotic welding, such as visual and ultrasonic sensing, automatic program of welding path and technical parameters, autonomous guiding and tracking welding. The scheme of the welding robot system for autonomous moving and across obstacle is shown as Fig.11. It integrates the above intelligentized technologies for robotic welding and realizes some welder’s intelligent functions, such as detecting and recognizing weld surroundings by visual sensing technology, identifying the initial position of weld seam, autonomously guiding weld torch to the weld starting and tracking the seam, real-time control of arc welding pool dynamics. The details are in [20].
Research Evolution on Intelligentized Technologies for Robotic Welding at SJTU
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Fig. 11. An autonomous welding robot system for all position moving and across obstacle
8 Conclusion Remarks Based on our previous work, this paper has shown some further research developments on intelligentized technologies for robotic welding in the Intelligentized Robotic Welding Technology Laboratory of Shanghai Jiao Tong University from 2006. It contains the multi-information acquirement of arc welding process, such as extraction of weld pool image, voltage, current, and sound features; multiinformation fusion algorithms for prediction of weld penetration; tracking seam based on combining arc and visual sensing, knowledge modeling; real-time intelligent control of weld penetration and welding pool dynamics. An autonomous welding robot systems with a combination of wheels and feet for all position moving and getting across obstacles has been developed, which will integrate the above intelligentized technologies for robotic welding and realizes some welder s intelligent functions.
’
Acknowledegment. This work was supported by the National Natural Science Foundation under Grant No. 60874026 and No. 51075268; National 863 plan of China under Grant No. 2009AAA042221, and supported by Key Foundation Program of Shanghai Sciences & Technology Committee under Grant No. 09JC1407100. The author wish to acknowledge the relative study works finished by Dr. Chen Bo, Dr. Huang Xixia, Dr. Lv Fenglin, Dr. Ma Hongbo, Dr. Fan Chongjian, Dr. Kong Meng, Dr. Wang Jifeng, Dr. Yezhen Dr. Shen Hongyuan, Dr. Chen Huabin, Dr. Wu Minghui, and etc.
References [1] Dilthey, U., Stein, L.: Robot System for Arc Welding—Current Position and Future Trends. Welding & Cutting (8), E150–E152 (1992) [2] Trailer: Manufacturer Depends on Robotic Welding to Boast Production. Welding Journal 74(7), 49–51 (1995)
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[3] Pan, J.L.: A survey of welding sciences in 21th century. In: Proceeding of 9th Chinese Welding Conference, Tianjun, China, vol. 1, pp. D001–D017 (October 1999) [4] Tarn, T.J., Chen, S.B., Zhou, C.J.: Robotic Welding, Intelligence and Automation. LNCIS, vol. 299. Springer, Heidelberg (2004) [5] Tarn, T.J., Chen, S.B., Zhou, C.J.: Robotic Welding, Intelligence and Automation. LNCIS, vol. 362. Springer, Heidelberg (2007) [6] Chen, S.B., Wu, J.: Intelligentized Technology for Arc Welding Dynamic Process. LNEE, vol. 29. Springer, Heidelberg (2008) [7] Chen, S.B.: On the Key Intelligentized Technologies of Welding Robot. LNCIS, vol. 362, pp. 105–116 (2007) [8] Chen, B.: Study On The Processing Method Of Multi-Sensor Information Fusion in Pulsed Gtaw, PhD Dissertation, Shanghai Jiao Tong University (May 2010) [9] Chen, B., Wang, J., Chen, S.: Prediction of pulsed GTAW penetration status based on BP neural network and D-S evidence theory information fusion. International Journal of Advanced Manufacturing 48(1-4), 83–94 (2010) [10] Wang, J.: Study on recognition method of penetration states based on welding sound feature, PhD Dissertation, Shanghai Jiao Tong University (September 2009) [11] Wang, J.F., Fenglin, L., Chen, S.B.: Analysis of arc sound characteristics for Gas tungsten argon welding. Sensor review 29(3), 240–249 (2009) [12] Huang, X.: Study on Support Vector Machine-based modeling methods and Their Application in Material Processing. PhD Dissertation, Shanghai Jiao Tong University (June 2008) [13] Huang, X., Shi, F., Gu, W., Chen, S.: SVM-based Fuzzy Rules Acquisition System for Pulsed GTAW Process. Engineering Applications of Artificial Intelligence 22(8), 1245–1255 (2009) [14] Fan, C.: Weld Pool Characters Extraction Visual Sensing And Intelligent Control During Varied Gap Aluminum Alloy Pulsed Gtaw Process. PhD Dissertation, Shanghai Jiao Tong University (September 2008) [15] Fenglin, L.: Study On Model-Free Adaptive Control Of Weld Pool Dynamic Proces in Pulsed Gtaw. PhD Dissertation, Shanghai Jiao Tong University (September 2008) [16] Meng, K.: Research On Process Control Method For Arc Welding Robot Based On Multi-Information Sensing Real-Time. PhD Dissertation, Shanghai Jiao Tong University (June 2008) [17] Ma, H.: Research On Mixed Logical Dynamical Modelling Of Aluminium Alloy Pulsed Tig Robotic Welding Process Based On Vision Feature Information. PhD Dissertation, Shanghai Jiao Tong University (August 2010) [18] Shen, H.-y., Lin, T., Chen, S.-b., et al.: Real-time seam tracking technology of welding robot with visual sensing. Journal of Intelligent and Robotic Systems 59, 283–298 (2010) [19] Chen, H.B., Lv, F.L., Lin, T., Chen, S.B.: Closed-loop control of robotic arc welding system with full-penetration monitoring. Journal of Intelligent and Robotic Systems 56, 565–578 (2009) [20] Chen, S., Wu, M.: The autonomous welding robot systems with combination of wheel and foot for climbing and getting across obstacle motion. The middle research report of the National 863 Plan of China (November 2009)
Image Processing for Automated Robotic Welding Peter Seyffarth and Rainer Gaede Ingenieurtechnik und Maschinenbau GmbH Rostock/ Germany Industriestr. 8, D-18069 Rostock Tel.: +49-381-793-450 e-mail:
[email protected]
Abstract. The construction of seagoing ships needs a lot of micro panels with maximal dimensions up to 16 x 4 m. About 2000 – 2300 micro panels with different sizes and about 20 – 25 various kinds of design are to weld for a medium size containership from 1700 – 2400 TEU. These are fillet welds mainly between stiffeners and the panel plate. There are two different techniques to make panels: For very long and parallel stiffeners are weld gantries in use for both side fillet welding, but for longitudinal and transversal stiffeners robotic welding is mainly used. Up to now the welding robot is linked and programmed by the CAD/CAM-system of the yard. This needs the offline programming more or less weeks before production and in this time the design of the panels can change. It is also an organizational problem to have the right CAD-program at the right time on the welding robot and to guarantee a flexible production process independent of the size and design of the micro panel. Therefore a way out is the flexible image processing. Developed by IMG and used by WADAN yards, MTW image processing system for welding robots delivers a very fast automatically generated program. The system, consisting of a laser line scanner with a scanning rate of 1 m/sec and a fast working calculation unit, gives a three-dimensional picture in less than 3 minutes all over the 16 x 4 m panel. After the shake hand process between the image processing system and the robot programming system the accuracy of the robotic positioning is ± 0.5 mm. This new developed 3-dimensional geometrically recognising and robot programming system allows a very fast and flexible production system for micro panels without any link to the central computer aided yard system. This “stand alone system” is independent, more flexible and shows, beside of other advantages, a high productivity.
1 Introduction The main parts in the prefabrication of ship hulls are flat and curved panels with dimensions up to 20 x 40 metres or more and so named micro panels. We meet especially the last one, the micro panels, in a large number of different forms an sizes beginning from 2 x 2 metres up to 4 x 16 metres. In a medium sized container freighter for 2000 containers there are about 2500 or more various micro panels. There are different production technologies for micro panels, consisting
T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 15–21. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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from plates and stiffeners. A modern micro panel production at a shipyard uses robots for mounting and welding as well. This is shown as an example in Fig. 1 and 2. Due to the various construction types of micro panels and their large range from unique single construction up to minimum series there is a high demand on the programming of robot systems including the movement of the robots in the 3dimensional coordinates and the welding parameters. In shipbuilding each micro panel needs its own welding program. All known programming procedures require either additional information on the work piece in the shape of CAD data or they need manual interaction. The classical programming of weld robots for micro panels takes places regardless of the really existing work piece and production scenario by a partly automatic analysis of CAD data in combination with demanding manual interactions. The programming takes place temporally very much in advance of the production and needs a high quality and relevance of the available construction data. Unfortunately this is in contrast to the flexibility of the production flow and can not take in account some changes of construction. That means, we have to take in account, that normally the programming is done a long time before production and very often the NC construction data are changed in the meantime.
Fig. 1. Mounting gantry for stiffeners in a micro panel line
Fig. 2. Welding station in a micro panel line using 2 robots before refitting
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Measurement of position Work. range
Weld robot
Gantry Scan head
Working area
Scan. range
Fig. 3. Principle of the 3 D image processing for robot programming
2 Refitting of an Existing Micro Panel Line by Image Processing To meet the needs for a very high flexibility and for an automated programming on demand for the existing panel line, which is shown in Fig. 1 and 2, the enterprise Ingenieurtechnik und Maschinenbau GmbH (IMG) designed, constructed and delivered recently a very fast working three-dimensional image processing system in cooperation with aviCOM and TSWE. The principle of this 3 D image processing system is shown in Fig. 3.and an overview about the system controlling units gives Fig. 4. The heart of the new industrial 3D scanner measuring system for micro panels is a camera head based on modern camera technology. The scan head measures 3D data according to the laser triangulation principle. Hence, to be able to measure 3D shape, an external line-generating laser source is used. The laser generator is mounted to the robot and projects its laser line on the working area from a distance of about 2 m in the height. The camera, that views the line from a different angle, sees a curve that follows the height profile of the object. By measuring the laser line deviations from a straight imaginary reference line, the height of the object can be computed. For scanning the robot moves with the scan head and the laser line along the working area, contour slices of the object are generated. The collection of such
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slices, or 3D profiles, is a description of the complete object shape as seen from the upper side of the object. The unique camera technology is capable of finding the position of the laser line by itself and reducing to whole image information into compact laser coordinates. These laser coordinates are transmitted to the PC. That is what the mounted 3D imaging technology makes very fast and reliable. Inside the scan head the camera offers several different methods for the generation of 3D profiles which differs in speed and height resolution. This flexibility of the camera was used to optimize results for the specific scanning task and material. The measurement principle gives geometrical limitation concerning the measurement of hidden parts in relation to the camera view. There are two kinds of limitations, camera occlusion and laser occlusion. Camera occlusion occurs when the laser line is hidden from the camera by an object and laser occlusions occur when the laser cannot properly illuminate parts of an object because of its projection angle. Adjusting the angles of the scan head and the laser can reduce the effects of occlusion. Additionally we use two scan heads with two laser sources illuminating the micro panels and especially the profiles from opposite sides. System controlling by industrial-PC
Scan head and image processingcomputer
Robot controller and robot
Image.processing software
Online programming
Rotrol-server macroadministration
Fig. 4. Overview about the controlling units of the image processing and robot programming system
The measurement system’s 3D field-of-view (FOV) is a trapezoid-shaped region where the laser line intercepts the FOV of the camera. It is only in this region that the camera generates 3D measurements. The camera FOV is given by the selected lens and camera software parameters. The height resolution of the measurements is dependent on the angle between the laser and the camera – as the angle is increased, the resolution is also increased – and on the selected 3D method. Generally, if the precision of the profiling algorithm is high, the maximum profile rate is limited compared to a less precise but fast algorithm. The maximum profile rate is dependent on a combination of the selected 3D method, the required measurement resolution, and the required height of the measurement region. By for instance decreasing the height region used for object
Image Processing for Automated Robotic Welding
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inspection the profile rate can be increased. Note, however, that the maximal usable profile rate also depends on the amount of light reflected from the object. The data stream of profiles was synchronized with the robot movement using an external encoder. This functionality will ensure that the length measurement and object scale in the movement direction is correct, even if the object speed varies, Fig.5. All parameters were optimized for the application of scanning micro panels under production conditions. The result is a scanning speed of 0.5 m/s with a maximum of independence from surrounding light conditions. The resolution of the 3D points is about 1mm in x and y direction and 2 mm in z (height). It is to consider that the resolution differs over the field of view in the relation to the height of points. The measurement range in z is actually 400 mm. The design goal was to optimize the overall scanning time, the sum of time to scan and process data. Although the scanning speed could be higher (up to 1 m/s) it is shown that the shortest process time is reached by processing the data parallel to the scanning process. The scanning is done in stripes of 1.20 m width and 16 m length in reversing order of forward and backward movement. Hence, the whole working area consists of a maximum of four stripes for each scanning head. 2. Scan-head
B
C Image processingcomputer
Scanhead
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D Systemcontrolling unit (industrial-PC)
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2 Robot controlling unit.
Position measurement system
Fig. 5. Concept of data processing for 2 robots from scan head to the welding robot
The measured 3D points in the laser plane of the scanner have to be processed with complex mathematical algorithms to calculate real world coordinates. This is done mostly during the scanning process parallel to the capture of 3D points. Additionally a first step of data reduction is done to extract object shape information as panel planes, profile planes and contours. The whole working area is scanned after about 3.5 minutes. At this time the extracted object shape information is available for high level processing in the final geometry extraction, Fig.6.
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Fig. 6. Visualisation of the micro panel on the user screen, ready for further high level processing
Fig. 7 shows the scan jib together with the two laser sources and the camera fixed at the refitted robot. The process of geometry extraction calculates the matching of all scanning stripes separately for each scan head. Additionally it calculates the matching of both scan heads to one description of the scene. This description consists of panels
Camera
Laser light
Fig. 7. Refitted robot with automated programming system by image processing in operation at the yard
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with 3D contour polygons, a plane approximation for profiles distinguishing different types such as HP, Flat, T- or L-Profiles and an information which profile belongs to which panel.
3 Advantages of the New Installed 3 D Image Processing System The main advantages of the recently installed new programming system by 3 D image processing are the following: z z z z z z
Programming on demand guarantees a very high production flexibility Programming is a very fast process, there is no remarkable production time lost. The scan rate depending on the profile height is 0.5 up to 1 m/s The process time for the whole process depending on the size of the panel and the number of stiffeners is 3.5 up to 12 minutes. This is very fast in comparison with some hours of welding time. The accuracy with 1 mm in x- and y-direction and 2 mm in z-direction is very high. It needs no operator for programming.
4 Conclusions The micro panel line is now successful in operation more than one year at one German shipyard. It needs only one operator for all processes including mounting and tacking of the stiffeners, changing the filler material for the two robots and so on. The automated micro panel line provides a high level of automation utilisation, production flexibility and an increase in efficiency. The experience at the yard shows, that the pay-back time for the investment is in a range of one year.
Automatic Seam Detection and Path Planning in Robotic Welding Kevin Micallef, Gu Fang, and Mitchell Dinham School of Engineering, University of Western Sydney, Locked Bag 1797, Penrith South DC, 1797, Australia e-mail:
[email protected]
Abstract. To make robotic welding systems more flexible, vision sensors are introduced as they provide large amount of information about the welding components. In this paper a method is introduced to automatically locate the weld seam between two objects in butt welding applications. The proposed method provides flexibility for robotic welding by having the ability to locate the weld seam on arbitrarily positioned work pieces. This method is also cost effective as it is developed using images captured from a low cost web-cam. Furthermore, the proposed method is able to plan a robot path along the identified seam. Simulation and experimental results show that the method can be used successfully in detecting and locating seams on variously shaped work pieces and robot paths can be successfully generated to follow the weld seams.
1 Introduction There are many Robotic applications in the real world which range from industrial applications such as material handling or manufacturing to medical tasks such as surgical procedures [12]. Vision guided robotics is a topic of continued interest and recent advances in visual servo control and the technology that supports it have made it possible for the creation of accurate and robust vision control systems. Using vision to guide a robotic manipulator is, however, still a challenging task [2]. At present, there are many welding robots being used to maintain the safety of workers and the work area, as well as the quality of work. Current welding robots used in industrial applications are programmed through teach and play-back methods [12, 13]. Weld paths for these types of robots must be manually re-taught and re-programmed for different work pieces as they cannot self-rectify any offsets in the welding process. This makes the set up of these systems time consuming and expensive. To make the robotic welding more flexible in dealing with varying locations and shapes of weld pieces, vision is being introduced into robotic welding systems. Research has been reported where computer vision is used to detect and locate weld seams [1, 3, 9, 12, 13]. In addition, vision is also being used to control the weld quality by monitoring the weld pool dynamics [1, 5]. T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 23–32. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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Automatic weld seam detection and tracking is an important topic in robotic welding. It is an area of great interest as it is an important step in realising fully autonomous robotic welding [9]. Since vision systems can acquire information without interfering with the welding process, it is often used for seam tracking. There are a variety of approaches being adopted for seam detection and tracking in robotic welding. The use of specialised lighting is reported in [9]. In this method, a single laser line was projected onto planar work pieces and was used to detect the seam when the line is no longer straight. The introduction of a second laser line [6] generates more accurate information from the images. Another weld seam detection method using a single image is given in [13]. The image is manually segmented by only considering a central, predefined viewing window. This reduces computational time by not having to eliminate the background and reducing the overall size of the image. Once the seam is located from the grey level image, the start and end points are found by shifting a smaller window along the length of the seam until a corner is detected. This method assumes that the seam location will be in the central in the image, and large in comparison to the window. Also, the work piece is assumed to be planar with known depth and height. In [12] a method for real time seam tracking is proposed. The vision system is setup to take images slightly ahead of the welding torch which continually update the robot with the new position coordinates extracted from the image information. Since the viewing window is a significant distance away from the welding arc, it is difficult to continually track the seam around curves and sharp corners, therefore limiting this method to straight lines, significantly reducing the flexibility of the system. The methods presented above are suited mainly for the detection of simple seams. These methods are also developed to only deal with specific welding applications in mind. Significant modifications would have to be made to these seam detection algorithms before they could be implemented in multiple applications. In addition, these methods did not consider the effect that path planning plays on seam detection and tracking. Smooth and even paths are required to produce high quality welds. In this paper an automatic, low-cost flexible seam detection method will be developed which can be accurately and easily implemented in an industrial setting. Furthermore, the proposed method will also incorporate path planning into the algorithm to develop a smoother welding path for robot to follow. This paper is organised into four Sections. In Section 2, the methods used will be explained. Experimental results using an industrial robot are given in Section 3. Conclusions are then given in Section 4.
2 Methodology In this paper, a method for weld seam detection is introduced. The algorithm is capable of determining the shape of the seam as well as the start and end points on straight and curved seams. This method is also capable of reducing the number
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of points on the weld seam to smooth the robot welding path. This method uses a single image captured by a robot mounted camera referred to as the eye-in-hand configuration. The overall method contains 6 steps: Boundary Detection, Conner/Curve Detection; Determine Initial Seam; Select Initial and Final Position; Seam Point Reduction; and Final Seam Path. Each step is discussed in detail in the following subsections.
2.1 Boundary Detection An image containing the two objects in position for the butt-weld operation must first be captured. This image must view the entire seam. Appropriate image processing is performed to allow for the boundary detection of the two objects {B1, B2} to take place (as depicted in red and green in Fig. 1). This will result in a sequence of n1 and n2digital points describing the boundary of each object B1 and B2,
Bk = {Pi,m = (U i,m ,Vi,m ),i = 1, 2,...,n i , and m = 1,2}(0)
(1)
Where image point Pi+1,m is the neighbour point of the image point Pi,m, and (Ui,m,Vi,m) are the image coordinates of Pi of boundary m. An example of two objects with detected boundaries can be seen in Fig. 1.
Fig. 1. Object Boundaries Detected and Highlighted
2.2 Corner/Curve Detection Once the boundary pixel locations for each object are identified, the corner/curve pixels can then be determined. This is necessary as in a butt welding operation the two edges to be joined are relatively parallel and thus will begin and end on a sharp corner or curve. There are many methods available to determine corners in an image [7, 8, 10, 11]. In this paper, the corner detection method is based on the K-Cosine introduced in [15]. The K-Cosine detection technique is chosen for its low computational costs and simplicity. This boundary based corner detection technique determines the angle θ for each boundary pixel as shown in Fig. 2:
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K. Micallef, G. Fang, and M. Dinham ܲ݅ܭ ܽറ݅ ܲ݅
ɽ ܾሬറ݅ ܲ݅െܭ
Fig. 2. Vector Diagram
r r r r ai • bi = ai • bi • cos θ i r r C i = cos θ i = a i • bi
(ar
i
r • bi
(2)
)
θ i = cos −1 (Ci )
(3) (4)
Where ai and bi are the vectors between image points Pi,m and Pi+k,m, and Pi,m and Pi-k,m, respectively, θ i represents the angle between the two vectors ai and bi.
• is
the magnitude of vector y. The number of pixels between the points used in calculating vectors ai and bi is represented by the value K . It is clear from (2) and (3) that if pixels Pi-k,m and Pi+k,m, form a straight line, then Ci will be -1 or close to -1. One of the limits of the K-Cosine corner detection method is the assumed prior knowledge of the number of corners in an image and an estimate of the widest angle to occur in the image. This prior knowledge will allow for the threshold value T to be established to determine what Ci value constitutes a corner. This paper improves on the original K-Cosine corner detection method by allowing the number of corners in an image and angles of these corners to remain unknown. This improved method can also detect points that form curves in an image. To achieve this, a low threshold T, defined as -0.99(which is almost equal to a straight line θ = 172°) and a value of K = 3 was used in the method. This modification results in a large number of pixels being identified as corner or curve points. Based on these points, regions can be formed if points are connected. To reduce these regions to a single point, the value of θi is calculated for each pixel in the region using (2) – (4) and is then compared to the θ values of the neighbouring pixels in the same region. The pixel with the smallest angle θ is retained as the pixel with the greatest curvature for that region. The corner/curve detection method introduced above is more advantageous than the original K-cosine method as it not only identifies corner points but also identifies points around curves allowing for an accurate path planning along the seam. By determining the local maximums, a significant reduction in the number of points used in the preceding sections of this paper is achieved.
Automatic Seam Detection and Path Planning in Robotic Welding
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2.3 Determine Seam Edges The next step to this method is to identify the points that lie along edges which form the weld seam. This is done using the knowledge that the points along the weld edges are the points of interest when determining the seam location. To eliminate the redundant corner/curve points, the distance between all the corners of one object and all the corners of the second object is determined using the Euclidian distance: D=
(V2 − V1) + (U 2 −U1) 2
2
(5)
It is assumed that the corners are situated at coordinates (U1, V1) and (U2, V2). Each corner/curve point on one boundary is paired with the closest corner/curve point on the opposing object boundary. During this process, pairs that have separation greater than a set minimum pixel distance are regarded as redundant and will be eliminated. Once the above process is complete, the initial seam edge arrays are generated and can be used to determine the points that make up the initial seam. For the buttweld operation the weld seam is made of the mid-points between the two objects, therefore the location of the seam points can be calculated. The initial seam made up by these midpoints is then used to determine the start and end points of the weld seam and also in determining the accurate path for the robot to follow during the welding operation.
2.4 Initial and Final Position Selection and Seam Point Reduction Using the initial seam from Section C the initial and final points along the seam are determined using the process described below. Where U represents the x-axis in the image plane and V is the y-axis representation. This method is limited as the initial and final positions must be on opposite sides of the weld objects. Once the initial and final positions of the seam are determined, a path for the robot to follow is determined. The number of points found along the seam will vary significantly depending on the style of each individual seam. Find pixel with minimum and maximum pixels in both the U and V direction Calculate the distance between the min U and max U = dist U, Calculate the distance between the min V and max V = dist V, If dist u > dist V Initial position = min U, Final position = max U, Else If dist V is > dist U Initial position= max V, Final Position = min V
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The process of corner detection developed in this paper identifies not only points on a corner but also a significant number of points situated around curves. The disadvantage of detecting many points around curves is that each of these points will be used to drive the robot. This will affect weld quality and requires a great deal of memory space in the robot controller. However, by detecting all these points curves can then be identified along the seam that increases the overall flexibility of the system. A suitable method is developed in the paper to reduce the number of points whilst maintaining the shape and accuracy of the seam. The steps to perform this operation are shown below. 1.
2.
3. 4. 5. 6. 7.
8. 9.
Seam Edge Reduction Method Compare Cosine Value of Pi with Pi+1 i. If Cθi not equal Cθi+1, Go to (2); ii. If Cθi equal Cθi+1, Go to (3); Is Cθi = Cθi-1? i. YES, Go to (4); ii. NO, Store in Corner Array, Go to (5); Store in Curve Array Reduce Curve Array IF Number of points in Curve array = 2 or 3 Store Points as the Final Seam Edge, Go to (5); IF Number of Points >=3 Take the First, Middle and the Last points, store these points as the Final Seam Edge, Go to (5); Is this Final Point? i. YES, Go to (7); ii. NO, Go to (6); Next Point i. Go to (1); END
The above process results in a significant reduction of points along the seam edges. The final seam is then determined. Consequently the robot will be able to perform a smoother path along the seam resulting in an improved weld quality. The extent of this reduction is revealed in Section III.
2.5 Final Seam Points Input to the Robot The seam detection method results in a path made up of pixel locations in the image coordinate plane. This requires the transformation of the pixel locations from the image coordinate plane to the world coordinate plane. It is well known that the conversion of a 3-D point (x, y, z) into an image point (u, v) can be expressed as: w × (u,v,1) = P × (x, y,z,1) T
T
(6)
Where P is a 3x4 homogeneous transform and w is a scaling factor. To convert a point from the image plane (u, v) to the 3-D coordinate frame (x, y, z),one will need to know one of the values of x, y or z, alternatively, a stereo vision system is required to calculate the location of x, y and z from two images. In this paper z is
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assumed to be known, as is usually the case that a workstation is at a certain height. Using linear algebra, knowing u, v and z will allow the values of x and y to be calculated. To achieve this, the P matrix in (6) should be determined. In this paper, the P matrix is obtained using the method introduced in [4]. This hand-eye calibration technique was used as it is low cost, using a single web cam and has an accuracy in the x and y direction to within ±1mm. The accuracy achieved in the x and y directions are acceptable for the robotic arc welding process. With known seam points in image plane with their z values, the welding seam points’ world coordinate positions can be obtained. These real-world coordinates can then be loaded into the robot’s control system as a set of path points so that the robot can be operated to follow the seams.
3 Results This section contains the seam detection program test results, as well as the verification that the seam detection method is applicable to a real world system.
3.1 Seam Detection and Reduction Results To test the effectiveness of the seam detection program, objects with a variety of seam shapes were used. Case A contains a straight edged seam, Case B contains two straight edges creating a distinct corner, Case C requires the detection and navigation around various curves and finally Case D reveals a seam can be detected with parts placed in arbitrary orientations. Fig. 3 shows the points (in yellow) that make up the final seam for each individual situation. These seam points are by using the point reduction method along with the initial (green) and final (red) positions of the seam are shown.
3A. Case A
3B. Case B
3C. Case C
3D. Case D
Fig. 3. Initial Seam points for different welding shapes
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Fig. 4 reveals that a path for a robot to follow along the seam can be determined. The method proposed also meets the flexibility demands of seam detection. The resulting path for the horizontal seam in Figure 3D shows that not only is the seam detection method developed in this paper capable of detecting various shaped seams but can also determine the path if the objects of interest are placed in an arbitrary orientation in the workspace. Table 1 shows the results of the point reduction. Table 1. Point Reduction Results Number of Points On Initial Seam Case A Case B Case C Case D
16 125 157 110
On Final Seam 5 62 54 63
Reduction Percentage 68.75% 50.40% 65.61% 42.73%
The reduction results revealed in Table 1 are significant as this reduction will save significant memory space in the robots controller, doing so without the loss of path accuracy.
3.2 Experimental Results To verify the image processing results on a real application, further experiments were carried out using a robot. The robot used to verify that the seam path detected is applicable in a real life situation was a Fanuc M-6i Robot. The end effecter uses a brass pointer to simulate the position of welding wire in a welding application and the camera is mounted in an eye-in-hand configuration. The resulting motion of case B is shown in the Fig. 4.
4A. Motion 1
4B. Motion 2
4C. Motion 3
4D. Motion 4
Fig. 4. Case B Cornered Seam
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4 Conclusions In this paper a flexible seam detection method has been introduced. The developed method successfully detects different shaped objects in an image. In addition, the seam detection method is capable of generating smooth welding paths using minimum available point which can potentially improve the welding quality of the system. Experimental verification has validated the effectiveness of this seam detection method. The experimental results show that the seam detection method is capable of detecting various shaped seams made up of objects placed in arbitrary positions hence proving the flexibility of the seam detection method developed. Acknowledgement. This work is supported by the Australian Research Council under project ID LP0991108 and the Lincoln Electric Company (Australia).
References [1] Chen, S., Zhang, Qiu, T., Lin, T.: Robotic Welding Systems with Vision-Sensing and Self- Learning Neuron Control of Arc Welding Dynamic Process. Journal of Intelligent Robotic Systems 36, 191–208 (2003) [2] Corke, P.: Visual Control of Robots: High Performance Visual Servoing. Research Study Press, Somerset (1996) [3] Cook, G.: Feedback Control of Process Variables in Arc Welding. In: Proceedings of IEEE Joint Automatic Control Conference, vol. 2 (1980) [4] Dinham, M., Fang, G.: Low Cost Eye in Hand Camera Calibration. In: Proceedings of The 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO 2009), Guilin, Guangxi, China, December 18-22, pp. 1889–1893 (2009) [5] Dudek, G., Jenkin, M.: Computational Principles of Mobile Robotics. Cambridge University Press, New York (2000) [6] Gao, S., Zhao, M., Zhang, L., Zou, Y.: Dual-Beam Structured Light Vision System for 3D Coordinates Measurment. In: Proceedigns of the 7th Annual Conference, World Congress on Intelligent Control Automation, Chongqing, China (2008) [7] Lee, J., Sun, Y., Chen, C.: Multiscale Corner Detection by using Wavelet Transform. IEEE Transactions on Image Processing 4(1), 100–104 (1995) [8] Li, L., Chen, W.: Corner Detection Interprestation on Planar Curves using Fuzzy Logic Reasoning. IEEE Transactions on Image Processing 2(11), 1024–1210 (1999) [9] Liu, X., Wang, G., Shi, Y.: Image Processing of Welding Seam Based on SingleStripe Laser Vision Systems. In: Sixth International Conference, Conference on Intelligent Systems Design and Applications (2006) [10] Rattarangsi, A., Chin, R.: Scale-Based Detection of Corners of Planar Curves. IEEE Transactions on Pattern Analysis and Machine Intelligence 14(4), 430–449 (1992) [11] Rosenfeld, A., Johnson, E.: Angle Detection on Digital Curves. IEEE Transactions on Computers 22, 875–878 (1973) [12] Shen, H., Lin, T., Chen, S.: A Study on Vision-Based Real-Time Seam Tracking in Robotic Arc Welding. Robot. Weld Intelligence. & Automation, 311–318 (2007)
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[13] Shi, F., Zhou, L., Lin, T., Chen, S.: Efficient Weld Seam Detection for Robotic Welding from a Single Image. Robot. Weld Intelligence. & Automation, 289–294 (2007) [14] Sicard, P., Levine, M.: An Approach to an Expert Robot Welding System. IEEE Transactions on Systems, Man and Cybernetics 18(2), 204–213 (1988) [15] Sun, T.: K-Cosine Corner Detection. Journal of Computers 3(7), 16–22 (2008)
Error Compensation and Calibration of Inter-section Line Welding Robot Based on a Wavelet Neural Network Su Wang1, Xingang Miao1, Yuan Yang1, and Xingai Peng2 1
School of Mechanical Engineering and Automation, Beijing University of Aeronautics and Astronautics, Beijing 100191, China e-mail:
[email protected] 2 China Petroleum Pipeline Engineering Corporation Langfang 065000, China
Abstract. The main source of error of saddle-back coping welding robots is discussed in this paper. In view of the error source, a three-layered wavelet neural network compensation model is designed. Two steps are involved in using this model: the first step is to compensate the position error of torch point caused by axes 4 and 5 of a robot, the second step is to compensate all the five axes movement error respectively. The corresponding simulation and experimentation are conducted based on the compensation algorithm. Results show that this compensation model can greatly improve the movement precision of the robot. The average position error of torch point is reduced by 80%, and the average movement error of each axis is reduced by 60%.
1 Introduction Intersection line welding framework is widely applied in many fields. As for the working intensity is very hard in welding these space curves, more and more robots are employed in the welding process. A kind of saddle-back coping welding robot is innovated by Beihang University, and this robot can be fixed on an intersection cylinder [1]. However, because robot kinematics and dynamics are in a great complexity, and there are numerous unknown factors in multi-input and multi-output robot system which is highly non-linear and intensely coupling, geometry errors and gear wheel transmission errors do exist inevitably. All of the above will generate great errors between factual movement and theoretical program when the robot moves, and the position error can be 10mm indeed [2, 3]. Therefore the error compensation and calibration of robot are in a great demand, in order to make the robot work in the best state. T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 33–40. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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2 Saddle-Back Coping Welding Robot Framework and Error Analysis Figure I illustrates the framework of saddle-back coping welding robot. It is composed of five axes: the axis 1 is the waist joint which is to drive the whole robot to move around the intersection cylinder; axis 2 is the horizontal joint which can move horizontally; axis 3 is the vertical joint which can move in a vertical line; axis 4 is the big arc guiding track and axis 5 is the little arc joint. The axis 1,2,3 designed to control the welding point orbit and the other two axes are used to adjust the pose of the torch point. The axis 4 and axis 5 are two arc joints which are intersected uprightly sharing the same center, at which the touch point is located. In this way, the torch point pose can be adjusted while the two arc joints are moving, but the touch position will not be affected.
Fig. 1. Framework of saddle-back coping welding robot
If there is some geometry error in axis 4 or axis 5, serious problems will take place to the torch position when the two axes are moved, and the torch position and torch pose will be efficiently coupling, which is not we wanted. As a result, the main task of error compensation and calibration is to focus on the compensation of axis 4 and axis 5 to the whole structure especially the influence to the torch point position. In addition, each axis is driven by worm wheel, worm bar, planet retarder or screw rod, all of which will inevitably produce errors when the robot is moving, so this error compensation must be also included.
3 Algorithm of Intersection Line Welding Robot Error Compensation 3.1 Model of Error Compensation and Calibration Wavelet neural network (WNN) is a novel neural network based on wavelet analysis theory and artificial neural network. Because the WNN inherits the timefrequency localization of wavelet analysis and self-learning ability of neural
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network, it is strong in approaching and error-tolerating ability [4]. The number of nodes of hidden layer is equal to that of the wavelet base, and it can be worked out by the input signal. The relativity of wavelet neural enables the wavelet network to run in a faster convergence speed [5]. In this robot, the pose angle of torch point is adjusted by the axis 4 and axis 5. The two axes are intersected uprightly and share the same center, so theoretically speaking, when they are moving, the torch position will not be changed. But errors also exist in geometrical operation, size of the two axes, and the installation, and the torch position will be changed when the two axes move. The torch position error produced by the two axes can not be compensated by themselves, so it must be compensated by the other axes. One compensation method based on wavelet neural network is employed here. First, the torch position error produced by the axis 4 and axis 5 is compensated by the axis 1, axis 2 and axis 3, and then the compensation amount is obtained, so the total moving quantity of the each first three axes is obtained by their own moving amount adding to the compensation amount. Second, we can compensate the five axes moving error respectively. In the compensation, the first step and the second step adopt different wavelet neural networks. The theory map is shown in Fig. 2.
Fig. 2. Robot error compensation and calibration theory map
The number of input layer neural cells and output layer neural cells can be determined by practical demands, but the number of hidden layer neural cells does not only relate to the input and output cells but also relate directly to the number of training samples. According to the related references and experiments, it can be determined by the following experience formula [6].
N = [ I + O + 1 / 2 × O × ( I 2 + I ) − 1] / 2 +
P ±1
I--input number, O--output number, N--hidden number, P--sample number.
(1)
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3.2 Design of a Robot Error Compensation Algorithm In this wavelet neural network, input variables are joint angles of axis 4 and axis 5, output variables are joint angles of axis 1, axis 2 and axis 3 which are position compensation produced by pose change. The framework of WNNP based on one-step-front-predictive method is shown in Fig. 3, the input vector is
X (k ) = [θ k 4 θ k 5 ]T , the input layer is full channel; the output vector is Y (k ) = [ Δθ k1
Δd k 2
Δd k 3 ]T , ψ i is the wavelet inspiration function, the
output layer is the linear node. 25 training samples are examined here, and the number of wavelet base function N is 12 through the calculation from formula 1. So the 2-12-3 wavelet neural network is finally constructed.
Fig. 3. Framework of wavelet neural network
The second step of compensation is built on the first one. The input variables are the joints of the first three axes which are achieved after the completion of position error compensation, and the joints of axis 4 and axis 5, the output variables are the five axes joint angle already compensated, each axis has a wavelet neural network respectively.
4 Experiment of Error Compensation and Calibration 4.1 Experiment of Torch Point Error Compensation and Calibrate In the experiment, the laser interferometer (ML10) produced by Renishaw the U.K. is used to measure the position error caused by robot moving. According to the error compensation and calibration principle designed above, there are two steps in the case of calibration. First, an experiment to error compensation produced by axis 4 and axis 5 is done. In the work space of the robot, a point is chosen at random, and the reflector of laser interferometer is fixed to the torch point and is adjusted to parallel one axis of right-angle coordinate, then laser interferometer and spectroscope are adjusted, so that laser can parallel this axis too, please see Fig. 4.
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Fig. 4. Diagram of laser interferometer setting
This point is regarded as a reference starting point, there are 45 aiming points (including the starting point) are taught to axis 4 and axis 5 respectively. At each point, the linearity measurement mirror team is used to measure relative position distance changes, and the RenishawawML10 software is used to record the measurement data, but the joint angles of axis 4 and axis 5 in each point are obtained at robot controller. Then, laser interferometer and reflector are adjusted to parallel the other two axes of right-angle coordinate, and axis 4 and axis 5 are moved to every measurement point used in the last time, memorize the measurement data again. In the experiment, the data of each point is achieved by the average result of double direction for 3 times measurements. Then 36 points are selected as the training samples and the rest 9 will play the role of checking samples. The WNNP wavelet neural network is trained by the training samples, then the position error produced by axis 4 and axis 5 can be compensated by the trained WNNP. After that, the robot is tested again by the above method. Figure 5 illustrates the comparison of position errors in the three coordinate direction caused by axis 4 and axis 5’s movements, before and after WNNP compensation On the basis of the measurement data before and after compensation, the robot precision can be analyzed, please look at table 1. From this table, it can be observed that the average torch point position error in X direction of axis 4 and axis 5 declined by 84% compared to the one before compensation, and the maximum error declined to 19.8%. The average error in Y direction declined by 85.7%, and the maximum error declined to 18.8%. The average error in Z direction declined by 85.8%, and the maximum error declined to 21.9%. So this method can effectively decline the torch position error. Table 1. Torch point position precision before and after compensation
Error Ex Ey Ez
Before compensation(mm) average 0.4737 0.4393 0.2629
maximum 0.8958 0.9770 0.6792
After compensation(mm) average 0.07493 0.06239 0.03274
maximum 0.1778 0.1840 0.1485
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Fig. 5. Axis 4 and 5 error comparison before and after compensation
4.2 Error Compensation and Calibration of Each Axis The above compensation is just the first step of Fig. 2, on which the second step is gonging to be carried out. In this step, the five axes will be compensated separately, and the same wavelet neural network will be used. Limited by the length of this dissertation, we only take the axis 2 as an example to describe the experiment process, as for the other axes, comparison of precision and final conclusion will be listed. The axis 2 serves as the horizontal axis and it’s stroke is 500mm, so the laser interferometer measure step is set as 20mm. There is, include the reference point, 26 point in all. At each point, it is measured twice in positive direction and negative direction, and is repeated for five times. Each point measurement average value is shown in table 2. The average error serves as the training sample of WNNB wavelet neural network, then the axis 2 can be compensated by it. After the compensation, the same method is adopted to measure this axis again. The measurement result is shown in Fig. 6.
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Table 2. Average measurement value of axis 2 position error
position(mm)
0
20
40
60
80
100
average(µm)
0
-27.68
-41.9
-46.84
-48.9
-46.58
position(mm)
120
140
160
180
200
220
average(µm)
-41.72
-41.5
-40.1
-41.32
-42.24
-43.58
position(mm)
240
260
280
300
320
340
average(µm)
-49.2
-56
-66.52
-77.74
-89.48
-98.24
position(mm)
360
380
400
420
440
460
average(µm)
-102.58
-113.38
-116.56
-114.1
-101.72
-90.8
position(mm)
480
500
average(µm)
-76.14
-46.2
Fig. 6. Axis 2 curve of position error before and after compensation
It can be seen that the axis 2 position precision is greatly improved by the compensation, most position errors are within 15µm, the maximum error is 19.4µm, which declined by 83.8% compared to that before compensation, and the average value also declined by 72.4%. So the error compensation plays a favorable role.
5 Conclusions The source of error of saddle-back coping welding robot is analyzed. One kind of the error is the geometrical error from axes 4 and 5 which are intersected uprightly, the other kind of error is the manufacturing and transmission error of each axis. The wavelet neural network compensation model is designed according to the above error, and Matlab simulation and experimentation are made based on the compensation algorithm. The results show that this compensation model can
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effectively increase the robot’s movement precision, the position average error of torch point is reduced by over 80%, and the average movement error of each axis is reduced by over 60%. Acknowledgement. This work is supported by Construction Together Special Project of the Education Committee of Beijing (NO. 20091024).
References [1] Li, X., Wang, S.: Kinematic analysis and simulation of saddle-back coping welding robot. Journal of Beijing University of Aeronautics and Astronautics (08), 964–968 (2008) [2] Ma, L., Yu, Y., Ceng, W., et al.: Positioning error compensation for a parallel robot based on BP neural networks. Optics and Precision Engineering 16(05), 877–883 (2008) [3] Xia, K., Chen, C., Hong, T., et al.: A neural network model for compensating robot kinematics error. Robot. 17(03), 171–176 (1995) [4] Cong, L.: Path Following Control of Mobile Robot Based on Wavelet Neural Network. Coal Mine Machinery 29(02), 47–49 (2008) [5] Lian, Z., Wang, C., Zhang, J.: Research on improvement of BP algorithm based on WNN. Computer Engineering and Applications 43(2), 99–101 (2007) [6] Bao, J., Zhao, J., Zou, H.: Study on method of curve simulation based on BP network. Computer Engineering and Design 26(07), 1840–1841 (2005)
Autonomous Seam Acquisition and Tracking for Robotic Welding Based on Passive Vision Shanchun Wei, Meng Kong, Tao Lin, and Shanben Chen Intelligentized Robotic Welding Technology Laboratory, School of Materials Science and Engineering Shanghai Jiao Tong University (SJTU), Shanghai 200240, P.R. China e-mail:
[email protected]
Abstract. A method of autonomous seam acquisition and tracking for arc welding robot is proposed. The seam information is real-timely obtained by one CCD camera in front of the anterior seam, while the host computer is in charge of image processing and edge features extracting. Thus the deviation between the projection of the tungsten electrode and the central line of welding seam can be obtained. Simultaneously tracking trajectory is optimized by Kalman filter and revised to eliminate the deviation defined so as to track the weld seam. The method can also be used to finish seam acquisition and tracking in the whole welding process autonomously even if the robot is never taught. The results validate feasibility of the method and imply the improvement of autonomy and intelligence for robot welding.
1 Introduction Nowadays, a great number of industrial robots have been used in the fields of automatic arc welding. However, most industrial robots are still working in the teaching and playback mode which can’t meet the requirements of intelligent welding process and welding quality [1]. Hence, various sensors are applied in the welding process. Sensing technology is used to monitor the status of the process and acquire characteristic information. It is one of the most important steps for robotic welding automation. Many research papers, specific to different interferences and features of welding conditions, proposed various sensing methods, for example, ultrasonic method [2], infrared thermometry [3] and positive vision such as laser sensing [4]. With the development of the computer vision technology and image processing methods, numerous welding robots and some automatic welding machines are equipped with corresponding vision sensors to achieve different welding tasks in severe conditions. Kim [5] developed one welding robot system with both laser sensors and passive vision, which could be used for welding environment identification and seam tracking. Besides, K.Y. Bae [6] studied the online seam tracking of steel tube based on machine vision. But the weld gap was 2-4mm, which decreased the difficulty of image processing. J.S. Smith [7] studied GTAW real-time T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 41–48. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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top-face vision and used PID controller to assure the welding formation and quality. Due to its great reliability, small size and low cost, passive sensing technology with CCD sensors has been taken as one of the most common methods for robot welding sensing. This paper focuses on the autonomous seam acquisition and tracking for arc welding robot, presents a method of weld seam extraction and tracking with the CCD sensor. To some extent, it would be of importance to improve the level of autonomy and intelligence for robotic welding.
2 Experiment System Fig. 1(a) shows the main components and their relationships of the experiment system. Fig. 1(b) shows the robot welding system and scene photograph. RH6 robot is used in the experiment system which can accomplish motions of six joints in the process of trajectory planning. The interval that host computer could send data to the controller for robot motion is 16ms. The communication between the robot controller and the host computer is achieved by CAN bus. Welding torch is fixed at the end of the flange joint, while the CCD camera is fixed at the front of the torch. The images obtained by the camera are converted into digital signals through the CG400 image acquisition card. Since the host computer extracts the feature information of these images, such as seam track, corner and projection of tungsten, it can translate image information to robot-known data so as to control the robot to follow the seam track. During the movement, the coordinate values of the robot position are fetched by the CAN bus for the host computer’s record. Thus, the host computer could calculate the deviation between the projection of tungsten and seam central line, and revise the path of the robot in the form of incremental coordinate values.
(a)
(b)
Fig. 1. (a) Components and Communication of System; (b) Real Experiment System
Welding for butt plates is implemented to verify the effectiveness and adaptability. Before welding, the torch of the robot is guided to the initial welding position, while the welding path is not taught. The CCD camera is in charge of capturing the real-time images for seam information extraction so that the host
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computer can calculate deviation to help the robot planning the welding path and complete the welding task.
3 Image Process and Seam Extraction The image of welding seam and molten pool taken by the CCD camera is shown in Fig. 2(a). The images are captured on the moment of base pulse current. In order to reduce the redundant information and accelerate image processing speed, a small window in front of the molten pool is selected. In the small window, seam can be obtained quickly. Meanwhile, to eliminate the random noise interference in the image acquisition and transmission, the regular techniques of smoothing and sharpening are adopted in the window as pre-processing. Median filter with 3×3 template is adopted. Image sharpening is also used for enhancing the feature information. The pre-processing window image is shown in Fig. 2(b).
(a)
(b)
Fig. 2. (a) Origin Image; (b) Extracted Window
In order to improve the precision of the image processing and reduce the reflection interference of arc light, a new algorithm is presented to extract the feature of the seam edges. The basic procedures are expressed as follows: 1) Sobel-operator edge detection, aiming at extracting the edge information; 2) Threshold segmentation, converting the image into binary pattern easy to subsequent processing; 3) Image thinning, by which the main skeleton can be distinguished; 4) Adaptive area filtering, eliminating clutter points and edges in order to remove pseudo-edge; 5) Welding region locking, locking the most interested object for seam tracking; 6) Image restoration and Canny-operator extraction, here, processing the seam edges in the region locked as Canny operator detection contains more details which is necessary for seam tracking; 7) Area filtering again and fitting the seam edges, thus the central line of the weld seam can be obtained easily finally. The procedures and results are shown in Fig. 3, while Fig. 4 shows the final result. The whole algorithm costs less than 300ms to accomplish all steps. What’s more, different welding experiments prove the feasibility of the method as the average offset is less than two pixels, about 0.017mm.
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After seam edges extraction, two sets of data are stored to fit the curve. Linear fitting by least squares method is taken to obtain the linear equation of the central line of the weld seam, as shown in the Equation (1). f ( x ) = ax + b ; a = ( k1 + k 2 ) / 2; b = ( b1 + b2 ) / 2;
(1)
where k1 , k2 , b1 , b2 are the coefficients of the fitting curves for the upper and lower weld seam edges.
Fig. 3. Image Procedures and Results
Fig. 4. Result of the Image Processing
4 Seam Deviation Definition As shown in the Fig. 5, there are two coordinate systems, one is the image system, i.e. u-v coordinate system and the other is the base system, i.e. x-y coordinate system. Here, the image coordinate is defined as image captured by CCD camera. Its origin is the left lower point. The base coordinate system means the real coordinate system used for robot motion. In the image coordinate system, the distance in the v-axis direction, which is between the tungsten projection and the central line of the seam, is defined as weld seam position deviation d . Also, when
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the tungsten projection is above the seam in the image, d is defined as positive. Otherwise, it is negative. Defined that angle deviation of weld seam α is the angle between the tangent of the central line of the seam and the positive u-axis. Then, according to geometric relationship and ratio between actual distance and image solution, the weld seam position deviation d can be obtained as follows: d = s0 ( pv − ( apu +b ))
Where s0
pu
and
pv
(2)
are the u-axis and v-axis coordinates of the tungsten projection.
is the actual size of a pixel in the v-axis direction.
Fig. 5. Map of the Error Definition
Fig. 6. Map of Tracking Control
5 Seam Tracking Controller A control cycle of RH robot is 16ms. Incremental data, also called step size, determines the velocity of the robot. These data can be sent to the robot once per 16ms. Only one shot is carried out in every pulse period to capture images at the moment of base pulse current, which assures the stability and clarity of the captured images. The pulse frequency of 2 Hz is adopted. The multithread method is used to solve the asynchrony of two cycles, information processing and motion controlling. The main thread is in the charge of image acquisition, processing and deviation extraction, while the other is for sending incremental data and controlling the robot every 16ms. Here, define the control strategy as follows: Rule 1: If the deviation is zero, the robot follows the tangent line direction of r r the weld seam at a velocity of V f ; Rule 2: If not, Vb determined by the deviation is added to the ongoing movement of the robot to make the robot move towards the central line of the weld seam in the direction of the v-axis. r r According to the control theory, V f and Vb can be used as feed-forward velocity and feedback velocity respectively. The diagram of the robot tracking control is r shown in the Fig. 6. According to the geometry relationship, the formulas of V f and
r Vb
are as follows:
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V f < cos(D E ) Vb < sin E ; v y
V f < sin(D E ) Vb < cos E
(3)
The resultant velocity of the gun is: v = vx2 + v y 2 = V f 2 +Vb 2 + 2V f Vb sin α
(4)
That is to say, the step sizes could be obtained as long as the feedback velocity is determined. Considering that a seam tracking system is real-time and dynamic, the influence of steady-state errors could be neglected. This system adopts the PD controller to control the feedback velocity, which makes the algorithm simple and efficient. After the derivation of the PD controller, step sizes sent to the robot for x-axis and y-axis are: lx ly
Where
s1
1 V f cos(D E ) ( k p1d ( k ) kd 1 ( d ( k ) d ( k 1)))sin E ( )) V f sin(D E ) ( k p1d ( k ) kd 1 ( d ( k ) d ( k 1)))cos E 1 V f cos(D E ) ( k p1d ( k ) kd 1 ( d ( k ) d ( k 1)))sin E s1
s1
(5)
is the correction coefficient of converting image to actual coordinates.
In the tracking process, the Kalman filter is used to the result of the PD controller as it can get the optimal estimate value about the signal. Here, supposed that the weld seam detection formula with noise at the time(k) step is: X ( k + 1) = AX ( k ) + Gw ( k ) Y ( k ) = CX ( k ) + v ( k )
(6)
So, the recursive algorithm of the Kalman filter can be described finally as follows: ∧
∧
X ( k | k − 1) = A X ( k − 1) T T P ( k | k − 1) = AP ( k − 1) A + GQ ( k − 1) G T T −1 K ( k ) = p ( k | k − 1) C [ CP ( k | k − 1) C + R ( k )] ∧ ∧ X ( k ) = X ( k | k − 1) − K ( k )(Y ( k ) − CX ( k | k − 1))
(7)
P ( k ) = [ I − K ( k ) C ] P ( k | k − 1) ∧ s .t . X (0) = E [ X (0)], P (0) = var[ X (0)]
7 Experiments and Results Analysis The robot is guided to the initial welding position as well as the weld seam is not taught. Passive visual sensing is used for monitoring the whole welding process while PD controller is adopted to track the weld seam. Here the length of the weld
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seam is set in advance. Thus the motion cycle can be calculated and set based on the velocity and frequency of the welding. When the welding cycle equals the set motion cycle, both the robot motion and welding will stop automatically. The welding experimental conditions are carried out in Table 1. Table 1. Experimental conditions of pulsed GTAW Parameter
Value
Pulse duty cycle / Pulse frequency
50% / 2HZ
Weld speed
3mm/s
Peak / Base pulse current
230A / 50A
Argon flow
10L/m
Arc length
4mm
Welded joint
Butt joint
Material / Weld seam gap
Al alloy LD10 / 1-2mm
Fig. 7(a) displays the initial tracking error curve while Fig. 7(b) shows the error curve based on Kalman filtering. It is obvious that through Kalman filtering, the tracking trace appears much more smoothed. The error with and without the Kalman filtering is in the range of ± 0.4mm and ± 0.5mm separately. From the comparison, data process like Kalman filtering is important and necessary. 0.5
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(b) Fig. 7. Tracking Error Curves
Fig. 8 exhibits an example of welded work-piece. However, some abrupt motions occur due to the deviation mutation and instruction delay which influence the welding quality most. If one more advanced algorithm is adopted to quicken the image processing, it would improve the motion smoothness of the robot and raise the precision of seam tracking much better.
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Fig. 8. Welding Result
8 Conclusions A method of autonomous seam acquisition and tracking with passive vision for arc welding robot is proposed. By means of obtaining the weld seam information of anterior seam in the welding direction and generating corresponding seam coordinates, the robot can track the weld seam and finish the whole weld task with a PD controller. The experimental results after Kalman filtering show that the method can achieve the alloy butt weld task and the precision meets the production demands, which improved the level of autonomy and intelligence for robot welding. Acknowledgement. This work is supported by National 863 plan of China under Grant No. 2009AAA042221, and Shanghai Sciences & Technology Committee under Grant No. 09JC1407100, P R China.
References [1] Chen, S.: On the Key Technologies of Intelligentized Welding Robot. Robotic Welding, Intelligence and Automation, 105–115 (2007) [2] Fenn, R.: Ultrasonic Monitoring And Control During Arc Welding. Welding Journal (Miami, Fla) 64(9), 18–22 (1985) [3] Nagarajan, S., et al.: Control of the welding process using infrared sensors. IEEE Transactions on Robotics and Automation 8(1), 86–93 (1992) [4] Peiquan, X., et al.: An active vision sensing method for welded seams location using "circle-depth relation" algorithm. International Journal of Advanced Manufacturing Technology 32(9-10), 918–926 (2007) [5] Kim, M.Y., et al.: Visual sensing and recognition of welding environment for intelligent shipyard welding robots (2000) [6] Bae, K.Y., Lee, T.H., Ahn, K.C.: An optical sensing system for seam tracking and weld pool control in gas metal arc welding of steel pipe. Journal of Materials Processing Technology 120(1-3), 458–465 (2002) [7] Smith, J.S., Balfour, C.: Real-time top-face vision based control of weld pool size. Industrial Robot 32(4), 334–340 (2005)
A Framework of Intelligent Remanufacturing System Based on Robotic Arc Welding Ziqiang Yin, Guangjun Zhang, Hongming Gao, Huihui Zhao, and Lin Wu State Key Laboratory of Advanced Welding Production Technology, Harbin Institute of Technology, Harbin, China Tel.: +86-451-86415537 e-mail:
[email protected]
Abstract. Remanufacturing Engineering has become a research focus and been receiving growing attention. With the development of manufacturing industry, parts are becoming more and more complex and precision. However, traditional remanufacturing method cannot always meet requirements. The authors propose an intelligent remanufacturing system based on the “modeling-slicing-stacking” principle. First, establish a 3D model by structured light scanner; then, acquire the layers information by direct STL slicing; and finally, select the optimum tool path plan and welding parameters. Taking a half cylinder shell as a worn part, this paper proposes L25 (56) orthogonal experiments to select the optimum path and welding parameters. Results show that the remanufacturing surface smoothness mean error is less than 0.5mm under the selected optimum parameters.
1 Introduction Remanufacturing is the process of restoring the quality level of a used product to that of a new product [1]. Remanufacturing engineer is an important way to sustainable society development. Over the course of last decade, remanufacturing Engineering has become a research focus and been receiving growing attention [1, 2]. Researchers make many efforts on this topic [3-6]. However, remanufacturing engineering is a complex business because of the high degree of uncertainty in the production process. With the development of manufacturing industry, parts structures become more and more complex. Uncertainty worn position and worn form, diversity of materials and small-lot production are always the obstacles of remanufacturing. Traditional approaches such as riveting, plating cannot meet the requirement. Therefore some intelligent factors should be imported in remanufacturing system. This paper proposes an approach solution this problem. Different from traditional methods, the intelligent remanufacturing system proposed in this paper import RP(Rapid Prototyping) into remanufacturing engineering. Pat plan and welding parameters plan are conducted by human-machine interactive way. Its processing is following: First, set up three-dimensional model of the worn parts; Second, Slicing the model at a given thickness to reduce model dimension from
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3D to 2.5D; Third, according to the information of each layer, select appropriate welding parameters; finally, Stack layer by layer to restore the parts through robotic arc welding. It means that this system works by the principle ‘ModelingSlicing-Stacking’. This remanufacture approach reduces the parts complexity and the processing difficulty. Theoretically, it can be able to well restore parts of any shape.
2 The Intelligent Remanufacturing System Design 2.1 Composition of System The system mainly consists of four modules: 1) Motion controlling module, include a KUKA-R16 robot system and a controlling computer. 2) 3D modeling and shape monitoring module, include a structured light projector (ML-30), a CCD camera (MTV368-CB) and a black-white image grabbing card (DH-CG210). 3) Welding power supply (Fronius TPS4000). 4) A central control computer. The accessory parts include combination jig and fixture, etc.
2.2 System Description This paper proposes a novel modular intelligent remanufacturing system. The system composed of three levels, namely device level, data information level and intelligence level. The system scheme is shown in Fig. 1; it is obvious that the system include three functional modules: 1) Modeling module: Establish 3D remanufacturing model by 2D captured image in the camera. Modeling precision is a principal factor which will affect the quality of remanufacturing production. Structured light 3D sensing and modeling is a feasible way to acquire remanufacturing data information. Moreover, in oder to get contour of each layer and to ensure stack zone, the three dimensional model has to be slice in to layer. Layers’ thickness is determined by the stack height. This system employs direct slicing algorithm to save the time of building topology information and the triangles array. 2) Motion controlling module: conduct tool path plan and execute motion controlling program according to the sliced layers information and the feedback information of quality monitor and the welding parameter. People should select tool path and simulate the remanufacturing processing by off-line programming system. Robot’s task is to remanufacturing damaged zone of worn parts by stacking in layer. It can be seen that this module connect computer virtual environment with real robot workspace.
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3) Quality monitoring module: Monitor the remanufacturing process in real time, evaluate forming quality and send feedback signal to human-computer interface. This module can assist system to plan remanufacturing task by two modes: human-machine cooperation and automatic close-loop.
Fig. 1. Scheme of intelligent remanufacturing system
3 Experiment and Analysis The welding robot workspace is shown in fig. 2. The structured light scanner is installed on front of the welding torch. This paper supposes that the original part is a semi cylinder. It is severe wear to a half cylinder shell in working fig. 3(a) shows this “worn part”. Remanufacturing experiment is beginning at 3D modeling by structured light scanner.
Structured light scanner
Fig. 2. Welding robot workspace of the remanufacturing system
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3.1 Modeling The structured light stripe scan the mouse from left to right at the speed of 1mm/s, while the CCD camera captures images at the intervals of 0.3sec. After a series of image processing, the 3D point information of the mouse can be acquired, by which the STL model file can be constructed, shown in fig. 3 (b).
(a) the real hypothesis worn parts
(b) 3D model of hypothesis worn parts
Fig. 3. The hypothesis worn parts
3.2 Slicing The STL Format Models comprise thousands of adjacent triangular faces to reconstruct 3D surface. So, slicing is the processing of solving and connecting the intersection between slicing plane and triangle faces, as shown in fig. 4.
Fig. 4.Schematic diagram of contour generation
Without loss of generality, we analysis the condition shown in fig. 5, the slicing plane M intersect the triangle face V1V2V3 at point P(xp, yp, zp) and Point Q(xq, yq, zq). V1
M
P Q V2 V3
Fig. 5. Intersection between slicing plane and triangle face
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Let the slicing plane M:
ax + by + cz + d = 0
(1)
And the triangle face V1V2V3:
x − x1 y − y1 z − z1 = = x2 − x1 y2 − y1 z2 − z1
(2)
y − y1 z − z1 ⎧ x − x1 = = ⎪ x − x y − y z 2 1 2 − z1 Then, the intersection PQ: ⎨ 2 1 ⎪ ax + by + cz + d = 0 ⎩
(3)
⎧ ax1 +by1 +cz1 +d ( x2 − x1( ⎪ xp = x1 − a ( x − x 2 1) +b( y2 − y1) +c(z2 − z1) ⎪ ax1 +by1 +cz1 +d Therefore, The point P(xp, yp, zp): ⎪⎨yp = y1 − ( y2 − y1( a ( x − x 2 1) +b( y2 − y1) +c(z2 − z1) ⎪ ⎪ ax1 +by1 +cz1 +d ( z2 − z1( ⎪ zp = z1 − a ( x − x ⎪⎩ 2 1) +b( y2 − y1) +c(z2 − z1)
(4)
The Point Q(xq, yq, zq) can be acquired with the same method. The slice result of the half cylinder shell is calculated point by point.
3.3 Path Plan Different from rapid prototyping, remanufacturing works are always carried out in some special conditions. Therefore, on the premise of acquiring good formation, robot tool path plan should be made according to the users’ requirement, as shown in fig. 6. On the one hand, the requirements maybe include several aspects: fastest
Fig. 6. Path plan of welding robot based on user requirement
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stacking speed, highest forming precision, smallest welding heat input or smallest stress, and etc. In addition, the worn condition and the layer information are different from each other. All the information should be fused and analysis by central control computer, and finally, people can decide an optimum path plan scheme to generate program for arc welding robot. On the other hand, the manufacturing processing of each scheme is affected by many factors: 1) Current (I), voltage (V) and wire feed rate (Vf),denoted by ‘P’ because TPS 4000 is single regulating gas shielded welding machine;2) welding speed, denoted by ‘Vw’;3) wire extension, denoted by ‘L’;4) welding bead offset, denoted by ‘D’;5)welding sequence and etc. Researches show that normal welding operation will generate significant error in start and end portion due to the flow of molten metal[7]. In order to avoid this kind of error, reciprocating welding tool path is adopted in this system shown in fig. 7.
δ
h
Fig. 7. Tool path plan model Table 1. Factors and levels of orthogonal experiments Level
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Orthogonal experiments to select optimum parameter are necessary. The factors and their levels are shown in table 1. And optimum welding parameter is acquired by the orthogonal test (with L25 (56) orthogonal test table): P = 60; V = 0.15; L = 12; D = 6. The remanufacturing result is shown in fig. 8. Its manufacturing surface smoothness mean error is less than 0.5mm.
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Fig. 8. Result of the remanufacturing experiment
4 Summary The research develops an intelligent remanufacturing system based on robotic arc welding. The system composed of three levels, namely device level, data information level and intelligence level. “Modeling-slicing-stack” is the principle of this system. This paper acquires optimum welding stacking parameters by L25 (56)Orthogonal experiments. Taking a half cylinder shell as a worn part, remanufacturing experiment is conducted. Results show that the manufacturing surface smoothness mean error is less than 0.5mm. Further research will be carried out. Acknowledgement. This work was supported by National Natural Science Foundation of China, no 50675046 and 50775053.
References [1] Dong, S., Xu, B., Wang, Z., Ma, Y., Liu, W.: Laser remanufacturing technology and its applications. In: Proc.of SPIE, vol. 6825, pp. 1–6 (2007) [2] Mitra, S.: Revenue management for remanufactured products. The International Journal of Management Science 35, 553–562 (2007) [3] Zhu, S., Cui, P., Shen, C., Guo, Y.: Scanner external calibration algorithm based on fixed point in robot remanufacturing system. Journal of Central South University of Technology 12(2), 133–137 (2005) [4] Hu, Z., Teng, J., Shi, X., Yang, J.W., Shi, L.: Development and Key Technique of Virtual Remanufacturing Engineering. China Surface Engineering 21, 7–11 (2008) [5] Yang, P., Wu, L., Xu, B., Zhu, S.: Flexible remanufacturing system based on arc welding robot. Transactions of the China Welding Institution 9(27), 5–8 (2006) [6] Zhu, S., Meng, F., Dema, B.: The Remanufacturing System Based on Robot MAG surfacing. Key Engineering Materials 373-374, 400–403 (2008) [7] Zhang, Y., Chen, Y., Li, P., Male, A.T.: Weld deposition-based rapid prototyping a preliminary study. Journal of Materials Processing Technology 135, 347–357 (2003)
A Fast GPI Line Detection Method for Robot Seam Tracking Bowen Li, Shengqi Tan, and Wenzeng Zhang Key Laboratory for Advanced Manufacturing by Materials Processing Technology, Ministry of Education; Dept. of Mechanical Engineering, Tsinghua University, Beijing 100084, China e-mail:
[email protected]
Abstract. An improved line detection method, the fast GPI method, is presented in this paper. This method improves the GPI method, reduces computational time and memory storage. After the image is filtered by Sobel operator, the feature points are stored and a sequence of projection angles is calculated by these points. A preliminary GPI matrix is calculated by these angles and then the preliminary projection angle is obtained; more detailed calculation in the vicinity of the preliminary projection angle is performed. So the final GPI matrix can be calculated, and search the extreme of the final matrix, the straight line can be located in the image. This method can detect the position of the line more quickly than the GPI method and avoid massive calculations, and is useful in image understanding, especially for robot seam tracking in robotic welding.
1 Introduction 1.1 GPI Method The detection of line is an important research content in the image processing, and also one of the difficulties in the field of robotic welding research [1, 2]. In current, most straight line recognition methods apply taking the edge of the line by operators such as Sobel operator and then detect the line based on Hough transform [3]. The algorithm named as Gray Projection Integral (GPI) is different from the traditional Hough methods, this new method achieves straight line detection for the robot seam tracking with good degree of robustness and precision. The main steps of GPI Method are described as follows. A source image I with the width of w and the height of h is given, and a line with a tilting angle of lies on it. A 2-D frame {F:Oxy} is set in I, whose origin O is set on the top left corner, and the x-axis is along the top border to the right, and the y-axis is along the left border down. A rectangle sub-window whose width is b and height is 2d+1 is set in the image, and another 2-D frame {Fk:Okukvk} is fixed in the sub-window, whose origin Ok is set in the center of the sub-window, and uk-axis is parallel to the top border of the sub-window, and vk-axis is parallel to the left boundary of the sub-window. In the original frame {F:Oxy}, Ok’s homogenous cordinates are
β
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(x s,ys,1), and the angle between x-axis and uk-axis is α k, which is called projecting angle. In GPI method, k is a loop varible, and α k = α min + kθ . θ is the loop step length, and generally α min = 0° , and kmax = 180° / θ . The aim line Q is parallel to the yk-axsis, and all points’ gray value in the sub-window are projected onto it and we get GPI vector here.
Fig. 1. Main steps of GPI Method
For a particular point Pi, its homogenous coordinates in frame {F} are (xi,yi,1) and in frame {Fk} are (ui,vi,1). The transformation equation is shown below.
⎡ xi ⎤ ⎡cos α k ⎢ y ⎥ = ⎢ sin α k ⎢ i⎥ ⎢ ⎢⎣ 1 ⎥⎦ ⎢⎣ 0
− sin α k cos α k 0
xs ⎤ ⎡ui ⎤ ys ⎥⎥ ⎢⎢ vi ⎥⎥ 1 ⎥⎦ ⎢⎣ 1 ⎥⎦
(1)
The GPI value gvk is defined by the following equation.
gvk = The GPI vector
b
∑
ui =− b
f ( xi , yi )
(2)
uuv pk is defined as below. uuv pk = [ g1k | g 2 k | ... | gik | ... | g (2 d +1) k ]T
(3)
Put all GPI vector together in a matrix, and this matrix is called GPI matrix.
uuv uuv uuv uuuuuv G |(2 d +1)×k max = ⎡⎣ p1 | p2 | ... | pk | ... | pk max ⎤⎦
(4)
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In a gray picture, bright points have larger gray values, and dark points have smaller gray values, thus if the projecting angle α k is equal to the tilting angle β of the line, an extreme GPI value will exist in the GPI matrix, and a maximum GPI value means there is a bright line in the image with the tilting angle α k, and a minimum GPI value means there is a dark line in the image with the tilting angle α k. So to detect lines in a image is to search the extremum in the GPI matrix G.
1.2 GPI Method with a Circular Sub-window The sub-window in traditional GPI method is a rectangular one. In fact only the common part of the sub-window is used in the GPI method, computing the other part takes too much time and can bring in some noise. So an improvement to GPI Method is to change the sub-window into a circular one. The center of the sub-window is set in the center of the original image. The diameter of the circular sub-window is 90% of the smaller one between w and h.
Fig. 2. Main steps of GPI Method with a circular sub-window
In a circular sub-window, the amount of the points to integral is related to the vk coordinate.
s = d 2 − 4 × vk2 + 1
(5)
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And GPI value is redefined by the following equation. b
g vk =
∑
xi ' =− b
f ( xi , yi ) (6)
s
1.3 MSGPI Method In GPI method, a lot of reduntant calculation is processed. In fact, GPI method is a method of exhaustion, because in order to find out a line, each angle of the image is examined. The amount of calculation can be reduced by changing the searching strategy. MSGPI (Multi-Step GPI) method is put forward to solve this problem. In GPI method, the sensitive angle to detect a line is only around nine degrees, so GPI method can be executed in two steps. The first step is to examine the source image roughly using the sensitive angle as the loop step length to find out the general area where the line lies, and the second step is to make a fine check in this area using a small angle which completely satisfies the engineering precision requirement as the loop step length to detect the line’s exact tilting angle. The MSGPI (Multi-step GPI) method proposed in reference [5] overcomes the problems of the GPI method in some extent, but the velocity of this method for straight line detection is not satisfied. This paper presents a new fast GPI method for straight line detection based on the two methods, and the volume of calculation is reduced and the precision is preserved.
2 The Fast GPI Method 2.1 Principle of Fast GPI Algorithm The GPI method requires all points in the sub-window of the image to project onto a line along every direction and sums the gray values which are projected onto one same point to shape a special vector, and the vectors can form a matrix, the GPI matrix. By searching the maxima or minima in the GPI matrix, the line then can be calculated and located in the image. The principle of MSGPI method is presented in reference [5], this method detects the straight line by two steps using a first rough calculation and then an accurate calculation based on the first calculation results, and compared with the GPI method, the volume of calculation is reduced. But projection directions are still set to every direction of the image which wastes much calculation and memory space of computer. Based on the above analysis, this paper presents a new method which gets the projection direction through any two feature points which are stored after filtering. With these original projection directions, the preliminary GPI matrix can be got. By searching the extreme of this matrix, the preliminary calculation result, the initial projection direction, is got. So with the initial projection direction, an accurate calculation whose projection directions are within around 10°to the initial
±
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projection direction, and step size is 1°is performed. When all these calculation finished, the final GPI matrix is got and the location of the straight line in the image can be calculated by searching the extreme of the final matrix.
2.2 Specific Algorithm 1) After one image is filtered by Sobel operator [1], the feature points are stored in a 1 × n matrix. In order to calculate conveniently, the gray values of the feature points are set 1 while other points are set 0. 2) Chose m points in the stored matrix as initial calculation points, named as P10 , P20 ...Pm0 , After each initial point ( Pk0 , k = 1, 2...m ), chose l points as final cal-
culation points at a certain interval, named as Pk1 , Pk2 ...Pkl (k = 1, 2...m) , One projection angle can be calculated through an initial point ( Pk0 ) and its one final calculation point ( Pkr , r = 1, 2...l ) as follows:
⎧0o , if ( xk0 = xkr ) ⎪ θ =⎨ yk0 − ykr ), if ( xk0 ≠ xkr ) ⎪arctan( 0 xk − xkr ⎩
(7)
( xkr , ykr ) means the pixel position in the image. So a sequence of original projection angles can be got and stored in a 1 × N ( N = n × m) matrix. 3) Calculate the preliminary GPI matrix of the sub-window according to the angle sequence in step (2). By searching the extreme of this matrix, the preliminary result, the preliminary projection angle, is got. 4) Within the range of 10°around preliminary projection angle and a step of 1°, the final GPI matrix can be calculated, and search the extreme of the final matrix, the straight line can be located in the image.
±
2.3 Experimental Results In the field of robotic welding research, welding seam detection is very important. Fig. 3 shows a welding seam of two steel plates in a real-time welding process. The main goal is to detect the seam in the image accurately and precisely for realtime robotic welding. So a seam detection example is carried out using this figure by the fast GPI method. Fig. 4 shows the distribution of the maximum integral gray values corresponding to each projection angle. Fig. 5 shows the distribution of the GPI vector of the angle corresponding to the maximum integral gray value in Fig. 4. Fig. 6 shows the GPI matrix of the sub-window image. Fig. 7 shows the final result which the straight line is signed and located. Fig. 8a and Fig. 8b show two different experimental images and final detection results.
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Fig. 3. A welding seam of two steel plates
Fig. 4. Distribution of the maximum integral gray values
Fig. 5. Distribution of GPI vector
Fig. 6. GPI matrix
Fig. 7. Final result
(a)
(b)
Fig. 8. Experimental results
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Experiments above have shown that the fast GPI method can detect straight line accurately and because the first angle sequence is calculated by the feature points which mostly determine the direction of the line, so volume of calculation is reduced and precision can be guaranteed. This method can overcome the interference of scratches and rust to a certain extent and has good robustness. A large number of images with different degrees of rust and scratches which show welding seam of two steel plates have been detected. The computer's configuration is that: CPU Core2 2.00Hz, memory 3G, operating system is Windows XP, under MATLAB 7.1 [6]. The GPI method, the MSGPI method and the fast GPI method are programmed to detect straight lines respectively and the time each required is shown in table 1: Table 1. The results of three methods
Computational time(sec)
The GPI method 12.3698
The MSGPI method 1.7904
The fast GPI method 1.1849
3 Conclusions This paper presents an improved line detection method based on the GPI method-the fast GPI method. This method can detect the position of straight lines more quickly and avoid massive calculations. This method has been tested by images of real-time welding process and has been proved successful in straight line detection. This method is useful in image understanding, especially for robotic welding. Future research will focus on optimizing multi-line detection in one image and improving the speed of processing. More optimization algorithm based on GPI method will be proposed to improve the processing speed and accuracy for line detection. Acknowledgement. This work was supported by “Tsinghua Basic Research Foundation” (No. JC2007009) and the foundation of Key Laboratory for Advanced Material Processing Technology, Ministry of Education, P.R. China (No. 2008011) and Hi-Tech R&D Program of China (No. 2007AA04Z258).
References [1] Jain, R., Kasturi, R., Schunck, B.G.: Machine Vision. McGraw-Hill Inc., New York (1995) [2] Zhang, Y.: Image Engineering—Image Processing and Analysis. Tsinghua University Press, Beijing (1999) (in Chinese)
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[3] Bums, J.B., Hanson, A.R., Reseman, E.M.: Extracting Straight Lines. IEEE Transaction on Pattern Analysis and Machine Intelligence 8(7), 425–455 (1986) [4] Zhang, W., Chen, Q., Du, D., Sun, Z.: Gray Projection Integral Method for Line Detection. Journal of Tsinghua University (Science and Technology) 45(11), 1446–1449 (2005) [5] Zheng, Z., Zhang, W., Chen, Q., Du, D.: Multi-step GPI Method by Circular Subwindow for Weld Detection in Image. Transactions of the China Welding Institution 28(8), 77–80 (2007) [6] Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital Image Processing Using MATLAB. Pub. House of Electronics Industry; Pearson Prentice Hall, Beijing (2004)
Mechanism Design and Kinematics Modeling of Irregular Cross-Section Pipeline Welding Robot Xinghua Tao1,2, Hongwu Zhu 1, and Lili Xu3 1
School of Mechatronics Engineering of China University of Petroleum, Beijing 102249 e-mail:
[email protected] 2 SINOPEC Research Institute of Petroleum Engineering, Beijing 100101 3 School of Mechatronics Engineering of Beijing University of Chemical Technology, Beijing 100029
Abstract. An irregular cross-section pipeline welding robot is designed according to the current situation for irregular cross-section pipelines welding. The robot kinematic model is built based on the D-H rule, MATLAB simulation is used to verify the correctness of the model. The accurate movement of each joint is ensured and the foundation is laid for the future control study.
1 Introduction In the wellhead engineering of oil field, it is often required for the special crosssection pipeline field welding as shown in Fig. 1. A section profile in the shape of "8" is obtained by processing the round pipeline with cold pressing to result in a radial plastic deformation. The special pipe reaches the plug stratum position through the upper casing, and then is expanded into a round pipe by hydraulic pressure to plug complicated stratums, it is used to plug the stratum with malignant leak off primarily or separate other complicated stratums as temporary casing with unexampled superiority compared with intermediate casings [1]. At present, the primary method to connect various components of a special pipe is hand welding which can not guarantee the connection quality under the situation of a large amount of welding work due to various factors, that affects the reliability of the pipe plug construction greatly. The most effective way to solve those problems is the automatic welding [2] [3]. The pipe can’t be welded with conventional equipments on account of its irregular cross-section, therefore it yields great significance to carry out the research on an special pipe automatic welding equipment in improving welding quality, reducing production costs and shorting production cycle.
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Fig. 1. Irregular welded splice cross-section
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Fig. 2. The all position pipeline welding robot
This paper builds the kinematics modeling for the special pipe welding robot based on D-H rule quickly and accurately, and the simulation results show that the robot can track the irregular cross-section effectively, thereby the trajectory equation of the end executive body is determined which provides references for the following study.
2 Mechanism Design of the Special Pipe Welding Robot The all position pipe welding robot (Fig. 2) developed independently by Beijing Institute of Chemical Technology can perform automatic welding on conventional pipelines [4-6], The welding robot adopts a circular orbit and modular mechanism; a position sensor is installed for automatic seam position identification; welding parameters can be set arbitrarily along the seam; welding parameters can be adjusted automatically by linkage control with a welding source; the robot can adapt a variety of irregular seams with the function of seam position teaching on line; the robot has straight and angle swing modes for different demands for welding swing modes. This type of robots has been applied in a number of major engineering projects of many countries successfully. On that basis the research on a special pipeline welding robot mechanism was carried out combining with the irregular cross-section characteristics. The cross-section dimension is shown in Fig. 3 whose contour has the following characteristics: (1) Small in size, the prolate axis is 225mm, the minor axis is 178mm; (2) Large size variation, the length for necking is only 61mm; (3)The cross-section contour is formed by multi-segment curves and presents a shape of “8”. The mechanism must be elegant adequately to meet the minimum curvature requirement of the special pipe whose curvature radius minimum is small, and it must adapt to the curvature changes in a large range to meet the whole welding process demand. The whole mechanism consists of a shaping and correcting mechanism, a round orbital welding tractor, a seam tracking mechanism, a height regulation mechanism and a contour detection mechanism, etc. The simulation result is shown in Fig. 4.
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Fig. 3. Special pipe sectional dimension
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Fig. 4. System simulation result
The main part of the shaping and correcting mechanism is a circular orbit with four equispaced hydraulic cylinders installed on two truing pieces symmetrically whose radian is in common with the standard "8" shaped special pipeline. The shaping and correcting mechanism is installed on the special pipe with a shaping and correcting control system to control the movement of hydraulic power transmission system. This orbit is used not only for shaping but also for carrying the welding tractor. When the welding tractor moves in a circle on the orbit, the contour detection mechanism is fit to the pipe surface closely by controlling the height regulation slider. The height regulation slider is connected with the contour detecting mechanism flexibly which is a micro-wheel driven mechanism without a power fitting, there are a mini-swing mechanism, a welding torch clamping device and etc, on the mechanism followed by a welding torch whose trajectory always clings to the pipeline contour. The height pressure detection sensor is used for pressure detection between the contour detection mechanism and the special pipe surface, whose tracking control principle is shown in Fig. 5. P0 is the default pressure value between the contour detection mechanism and the special pipe surface, under which the contour detecting mechanism clings to the special pipe work piece surface and moves flexibly. The real-time pressure P is detected by a pressure sensor, the deviation between P0 and P is denoted with ΔP, the CW/CCW rotation of the height regulation motor is controlled according to the sign of ΔP.
Fig. 5. The control principle of the height pressure detection sensor
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3 Kinematics Modeling for the Special Pipe 3.1 Kinematics Modeling The cardinal movements of the special pipe welding robot include the turning of the welding tractor around the pipe’s central axis, the moving of the height regulation slider up and down along the radial axis, swinging in a certain range with the contour detection mechanism connected with the height regulation slider flexibly and the movement of the welding torch along with the contour detection mechanism. The position and pose between the welding torch end and the welding tractor can be obtained by kinematics modeling to verify whether the mechanism is able to achieve "8" shape tracking. To simplify the mathematical model, the follow hypotheses are proposed: (1) Since the welding tractor moves in circle along the orbit at a constant speed, which is equal to a connecting bar with a length of the orbital radius rotates around the central axis; (2) As the welding tractor has a rigid connection with the mechanism bearing a height regulation slider, it is assumed as a connecting bar between the centroid of the welding tractor and the junc with a length equal to the distance between the two points; (3) The contour detection mechanism can swing left and right in a certain range, but it is fitted with no power sets and moves along with the welding tractor and the height regulation slider, it is assumed that its centroid and the junc mentioned in (2) are in the same axis. According to the above assumptions, a fixed coordinate system is set up with the central axis of the special pipe as Z0 axis, the cross-section belongs to X 0Y0 Z 0 plane. The welding robot D-H frame of axes [7][8] is built up according to the relationship between joints as shown in Fig. 6.
Fig. 6. The welding robot D-H frame of axes
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The X 1Y1Z1 is a rotating reference frame, the connecting bar OA can be rotated
360° around Z1 axis, point A represents the centroid of the welding tractor, the X 2Y2 Z 2 is a movement reference system, the height slider can be moved along the axis Z2, point C represents the centroid of the contour detection mechanism, point D represents the welding torch end, OA’s length r = 170mm , AB’s length d1 = 20mm , CD’s length d 2 = 20mm , the slider length l between B and C is a variable. According to the D-H coordinates conversion method, the transform matrix of point D relative to the fixed coordinate system can be got by multiplication of each link’s transform matrix and thereby position and pose of the welding torch end can be obtained.
T 2 = 0T 1 1T 2 = Rot ( z ,θ ) Rot ( y, −90° ) Trans(d1,0, −r )
0
⎡ cos θ ⎢ = ⎢ sin θ ⎢ 0 ⎢ ⎣ 0
− sin θ cos θ 0 0
0 0⎤ ⎡ 0 0 0⎥⎥ ⎢ 0 ⎢ 1 0⎥ ⎢1 ⎥⎢ 0 1⎦ ⎣0
⎡0 − sin θ ⎢ = ⎢0 cos θ ⎢1 0 ⎢ 0 ⎣0
⎡ nDx ⎢n ⎢ Dy ⎢ nDz ⎢ ⎣ 0
− cos θ − sin θ 0 0
oDx oDy
aDx aDy
oDz 0
aDz 0
⎡0 − sin θ ⎢ = ⎢0 cos θ ⎢1 0 ⎢ 0 ⎣0
0 −1 0 ⎤ ⎡1 1 0 0 ⎥ ⎢⎢0 ⎥ 0 0 0 ⎥ ⎢0 ⎥⎢ 0 0 1 ⎦ ⎣0
0 d 1⎤ 0 0 ⎥⎥ 1 −r ⎥ ⎥ 0 1⎦
r cos θ ⎤ r sin θ ⎥ ⎥ d1 ⎥ ⎥ 1 ⎦
pDx ⎤ ⎡1 ⎥ pDy = 0 ⎢0 ⎥ T ⎢ 2 pDz ⎥ ⎢0 ⎥ ⎢ 1 ⎦ ⎣0
− cos θ − sin θ 0 0
0 1 0 0
(1)
0 0 d 2⎤ 1 0 0 ⎥⎥ 0 1 l ⎥ ⎥ 0 0 1⎦
( r − l )cos θ ⎤ (r − l )sin θ ⎥⎥ d1 + d 2 ⎥ ⎥ 0 ⎦
(2)
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The position and pose of the welding tractor (point A) is:
⎡ n Ax ⎢n ⎢ Ay ⎢ n Az ⎢ ⎣ 0
oAx
a Ax
oAy
a Ay
o Az
a Az
0
0
p Ax ⎤ ⎡1 ⎥ p Ay = 0 ⎢0 ⎥ T⎢ 1 ⎢0 p Az ⎥ ⎥ ⎢ 1 ⎦ ⎣0
0 0 r ⎤ ⎡cos θ ⎢ 1 0 0 ⎥ = ⎢ sin θ ⎥ 0 1 0⎥ ⎢ 0 ⎥ ⎢ 0 0 1⎦ ⎣ 0
− sin θ cos θ 0 0
0 r cos θ ⎤ 0 r sin θ ⎥⎥ (3) 1 0 ⎥ ⎥ 0 1 ⎦
It can be seen from Fig. 6 that ( r − l )involved in equation (2) is the distance between the special pipe center and points on the contour of the pipe cross-section, therefore:
(r − l ) = x 2 + y 2
(4)
The whole contour consists of multi-segment curves, so it is needed to found the equations piecewise. According to the sizes shown in Fig. 2, the equations are founded as follow:
⎧77.97 < y ≤ 112.5, x 2 + y 2 = 112.52 ; ⎪ ⎪32 < y ≤ 77.97,( x − 60.65) 2 + ( y − 58.91)2 = 282 ; ⎪ ⎨ ⎪27 < y ≤ 32,0.3x − y = −11.8; ⎪ ⎪0 ≤ y ≤ 27,( x − 58.5)2 + y 2 = 282 ; ⎩
(5)
When the welding tractor moves along the orbit, that is, the rotation angle of OA around the Z1 axis is θ , the corresponding y value is obtained based on the following orbit equations:
⎧ x = 170cos θ ⎨ ⎩ y = 170sin θ
(6)
Then the relationship between ( r − l ) and θ is got according to the equations (4) and (5). Finally the position and pose of the welding torch end relative to the base coordinate system is obtained by equation (2) and (3).
3.2 Kinematics Modelling Verification The spatial location of the welding torch end relative to the base coordinate system can be calculated with the welding tractor in the initial state to verify the accuracy of the coordinate system and D-H parameters. From one perspective, the initial position of point D in the base coordinate system
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( px , p y , pz ) = (58.5, 0, 40) while θ = 0 as shown in Fig. 6, the pose ⎡0 0 −1⎤ (n,o,a) = ⎢0 1 0 ⎥ ; from another perspective, when θ is substituted into ⎢ ⎥ ⎢⎣1 0 0 ⎥⎦ the position and pose matrix (2) of point D, it can be got:
⎡ nDx ⎢n ⎢ Dy ⎢ nDz ⎢ ⎣ 0
oDx
aDx
oDy oDz 0
aDy aDz 0
pDx ⎤ ⎡ 0 pDy ⎥⎥ = ⎢ 0 ⎢ pDz ⎥ ⎢1 ⎥ ⎢ 1 ⎦ ⎣0
0 −1 58.5⎤ 1 0 0 ⎥⎥ 0 0 40 ⎥ ⎥ 0 0 0 ⎦
(7)
It is thus clear that the welding torch end’s position and pose is in line with the actual situation in the initial state.
4 Simulation Verification Simulation is progressed with Matlab to verify the validity of the model [9]. Simulation parameters are: the orbital radius r = 170mm ; the distance from the centroid of the welding tractor to the height slider d1 = 20mm ;the distance from the welding torch end to the contour detection mechanism d 2 = 20 mm . The simulation result is shown in Fig. 7.
Fig. 7. The simulation trajectory of welding tractor and welding torch end
In Fig. 7, the red line indicates the welding tractor path and the blue line indicates the welding torch end path. The trajectory of the welding torch end is in accordance with the irregular cross-section while welding tractor moves along the orbit, which indicates the welding robot model fulfills "8" shape tracking and the expected result is reached.
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5 Conclusions The complete kinematic model for the irregular cross-section welding robot is set up and simplified according to the actual situation based on the D-H rule. The simulation result with Matlab shows the robot can track the expected trajectory accurately and verify the validity of the model. The kinematic model in this paper reflects the relationship between the executing agency and the robot body. The interrelated research on the control model will be the next research focus. Acknowledgement. We would like to express our heartfelt gratitude to Professor Xue Long,who has spent much of his precious time in offering valuable advice and guidance in our writing. Our thanks would go to our beloved families and friends for their loving considerations and great confidence in us. We would like to extend our heartfelt gratitude to the authors whose words we have cited or quoted, and to the scholars upon whose ideas we have freely drawn. Finally we wish to extend our thanks to the National "863 Program" project supplied me with this project (foundation item: 2009AA04Z208).
References [1] Zhu, Q.: The damage causes analysis and protective measure of deep well technology casing. West-china Exploration Engineering (4), 102–103 (2006) [2] Wang, W., Xue, L., Li, M.: Research on all position pipelines automatic welding equipment. Pipeline Technique and Equipment (4), 15–16 (2002) [3] Zhu, J.: The applications of welding robots. Maschinen arkt (12), 42–47 (2005) [4] Xue, L., Liang, Y., Zou, Y., et al.: Study on welding expert ystem for all-position welding robot of pipeline. Electric Welding Machine 38(8), 45–47 (2008) [5] Sun, Y., Cao, J., Jin, Y., et al.: Study on pipe welding robots based on PIC. Journal of Beijing Institufe of Petrochemical Technology 14(1), 28–31 (2006) [6] Jun, Y., Jiang, L., Zou, Y., et al.: Design and implementation of the interactive control system for pipe welding robot. Journal of Beijing Institute of Petro-Chemical Technology 14(1), 24–27 (2006) [7] Cai, Z.: Robotology. Tsinghua University Press, Beijing (2009) [8] Guo, H.: Industrial robot technology. Xidian University Press, Xian (2006) [9] Zhou, P., Zhao, X.: Mathematical modeling and simulation based on MATLAB. National Defense Industrial Press, Beijing (2009)
Combined Planning between Welding Pose and Welding Parameters for an Arc Welding Robot Huanming Chen, Yichen Meng, and Xiaofeng Wang Department of Welding Engineering, Nanchang Hangkong University, Nanchang 330063, P.R. China e-mail:
[email protected]
Abstract. To improve the programming efficiency of arc welding robots, the ANN (Artificial Neural Networks) technology was applied to the combined planning of welding pose and welding parameters. The series of CO2 arc welding experiments were performed by a Motoman UP20 robot, and the data of weld formation were collected under various welding poses and welding conditions. Thereby the relations between the weld formation and welding parameters were built. Then the experiment data were trained and simulated by an ANN using toolbox of MATLAB. The database of welding pose and welding parameters were established by the ANN self-learning function. After building the geometric model of the saddle-shaped weld, the information of welding pose was automatically picked-up by the MATLAB program, and then the job program of the robot was worked out according to the database. The results of experiments indicate that this planning method is feasible.
1 Introduction There are two methods of arc welding robot programming: online teaching programming and off-line programming [1]. At present, the online teaching programming is widely used in production lines, this method is simple and convenient, but takes a large amount of working time [2]. An effective way to solve this problem is to use off-line programming [3]. Welding planning is that computer technology is used to solve welding process problem. It belongs to the scope of computer-aided process planning. Welding planning means using off-line program techniques to free robot from online programming, it can improve efficiency and the level of automation in production process. Welding path planning, welding torch posture planning [4] and welding parameters planning are all parts of welding planning. They are the key contents of off-line programming [5]. In this paper, the spatial position relation between welding torch and weld is defined as welding pose. The main process of the combined planning involves the following stages. First, the influences of welding position and welding parameter on the weld formation are studied to construct the data tables of combined planning. Then, in order to get the spatial position information of the weld, the geometric model of the workpiece is established, and the curvilinear weld is divided into T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 73–80. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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several finite parts. So the curve weld can be supposed to consist of several straight welds. Combined with the planning tables after the workpiece angle and posture of every straight weld have been calculated, the welding parameter and the posture of weld torch can be extracted. Finally, the job program of the robot is compiled and the workpiece is welded for test.
2 Weld Position and Welding Torch Posture The welding tracks of arc welding robot are the space curves. The welding torch needs to deflect a certain angle because of geometric constraints and process requirements. Using off-line programming method, it means using feature-based model with some sets of welding parameters to arrange strategy for predicting the formation of weld. The saddle-shaped weld is a typical space weld, as shown in Fig. 1. The saddle-shape weld as an example was selected as model.
Fig. 1. Saddle-shaped welding model
In Fig. 1, the base coordinate system is X-Y-Z, the coordinates of point O is (0,0,0). The weld coordinate system is X1-Y1-Z1 coordinate system, O1 point is the weld point, O1X1 is the welding direction. The angle between weld coordinate system and reference coordinate system can be determined according to the spatial geometric theory. The parameters of weld location can also obtained by the coordinate system transformation. Two cylinders intersect in Fig. 1, the equation of intersecting line is represented as follows: ⎧⎪( x − d ) 2 + y 2 = r 2 ⎨ 2 ⎪⎩ z + y 2 = R 2
(1)
where R, r represent large and small cylinder radius respectively, d represents the distance between the origin of the coordinate system and the axis of the small cylinder.
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Equation (1) is transformed into the parametric equation as follows:
⎧ x = r sin t + d ⎪⎪ (0 ≤ t ≤ 2 π ) ⎨ y = r cos t ⎪ 2 2 2 ⎩⎪ z = R − r cos t
(2)
Weld location matrix includes the sampling point coordinates, Z-axis rotation angle of relative reference frame X-Y-Z, weld slope angle and weld rotation angle. Extracting weld seam location, namely, to find out the location parameters of the working point on subparagraph weld or to define weld seam coordinates transformation matrix. Each point of the weld seam on coordinates X1-Y1-Z1 is represented by the coordinate transformation matrix Tw.
Tw = [n o a
p]
(3)
where n, o, a are the unit vector of coordinate system for the rigid body, indicating the relative direction of the base coordinate system. p is the position vector. n, o, a, p are the functions of d, R, r and sub-degree angle t respectively. By making simultaneous solution of space angle vector of weld coordinates and transformation matrix, we can obtain an expression between the various parameters. Under such circumstances, as long as changing several parameters can substitute the existing matrix for the same structure with different sizes of saddle-shaped weld. The end-point of welding torch axis is at O1 point in the weld seam coordinates. Its axis in Z1-axis is the initial posture, the rotation angle around the X1-axis is working angle α, and the rotation angle around the Y1-axis is walking angle β. In the actual off-line programming, workpiece model is made first, and the model data (weld position and posture) are extracted, then welding process parameters are planned, the appropriate welding torch postures are selected. The point coordinates in the torch axis can be obtained by a simple coordinate transformation. In Fig.1, the position matrix Tg of welding torch coordinate system is as follows:
Tg = Tw T
(4)
where T is the rotation transformation matrix about α and β.
3 Welding Process Experiments The ultimate goal of the process experiment is to get the welding torch angles and process parameters which are fitted for each weld position. Then the satisfactory formation of weld can be obtained. The shape of weld is continuous space curve, and the angle between two cylindrical surfaces is also a gradual change within a certain range. If the weld curve is divided into many finite segments, each segment is close to flat fillet weld. And
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then the angles of workpiece, weld slope and weld rotation are extracted, thus the transformation of "curves to straight lines" can be completed. Each posture parameters of a weld segment is represented by its previous point. These parameters are substituted into the existing matrix, and then the whole group of welding posture matrix can be obtained. Each matrix represents the position and posture of a weld segment. Then the welding torch angles and process parameters for each weld segment position are found. The ANN(Artificial Neural Networks) is used to solve the practical problems of the combined planning of welding postures and welding parameters. Finally, a small step-length neural network of input matrix is built up in the limited scope, and then the parameters of the corresponding weld can be simulated in the existing network. The test conditions are as follows: Motoman Up20 arc welding robot system, TransPlus Synergic 4000 multi-purpose power source; welding wire H08Mn2SiA with a diameter of 1.0mm, CO2 shielding gas; welding fixture for a flat butt and fillet weld. Tested material is low-carbon steel plate of 2mm thickness as the same as the saddle-shaped workpiece. Table 1 lists the acquisition range of the experimental parameters. Table 1. The acquisition range of the experimental parameters Workpiece angle δ/°
Weld slope φ/°
Weld rotation θ/°
Walking angle β/°
Working angle α/°
Current I/A
Voltage U/V
Welding speed v /mm•s-1
[90,180]
[-45,45]
[0,45]
[-45,45]
[-45,45]
[71,139]
[18,22]
[7.5,11]
All welding experiments were conducted according to the limits of Table 1, more than 2000 specimens were used, and 1061 groups valid data were obtained after manipulating the experiment data. The actual main experimental method is factor rotation method, and the effects of separate parameters on the formation of weld can be validated. The purpose of this research is to identify the relationships between eight parameters within the range of experiment data in Table1 and the formation of weld. Every 5 units of angle parameters, current and welding speed is as a section, and every 0.5 units of voltage as a section. By taking the single factor method only, the times of experiment will be huge. It is far more than the actual number of 1061. Combined with a variety of experimental methods, neural network training and simulating data can solve this problem. A sample combination data sheets are obtained after data processed, and these sheets prepare the ground for building combined planning data.
4 Construction of the Combined Planning Data Sheet According to the corresponding relationship between eight parameters and formation of weld, non-linear problems in the field of welding can be solved by using
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the results of ANN. The content of the ANN toolbox of MATLAB is very rich. It contains many new existing neural network results. Carrying out loop calculations with the basic BP algorithm and BP neural model makes the actual output gradually close to the ideal output, so it can achieve the desired results. The structure of BP neural network has the hidden layer neuron transfer function which is tansig( ) and the output layer neurons which use logsig( ) transfer function. Feedforward networks usually consist of input layer, hidden layer and output layer, or more correctly, the network in this paper has 8 inputs, 2 outputs and 67 neurons in hidden layer. Neurons of hidden layer use S-transfer function, Output layer can use linear transfer function or S-transfer function. The structure of BP network was designed for the solved problems, each parameter of welding must be set properly. After BP network has been built and modified for several times, the network simulation results can be verified. The entire neural network can reflect the corresponding relation of workpiece angles, posture parameters, process parameters and formation of weld in the real welding process. The input and output 1061 matrixes were divided into four groups, three groups of them are as training data, one of them is for validation. After the neural network training was finished, the 8×265 matrix which is used for validation should be input. Errors between the experimental results and the 2×265 matrix satisfied the technological requirements after simulation. At first a small sub-step network input matrix must be created, and then the trained network can be used to simulate the network output. The input matrix and the output matrix constitute a combined planning data sheet. The input parameters range and the sampling step are customized according to the saddle-shaped weld dimensions and welding posture data. Programming according to some sampling methods, and an 8×10890 simulation matrix Xf is constructed. By using the existing neural network to emulate the normalized input matrix xf, the network output 2×10890 matrix yf can be obtained. The weld formation matrix Yf which is corresponding to the network input matrix was achieved after anti-normalized. The input parameter matrix and the formation of weld output matrix is transposed into a 10890×10 Excel table by sorted data, and the table is load into Table 2. In order to facilitate analysis and practical application, these 10 parameters are divided into two categories. One is related to the parameters of weld seam, including workpiece angle δ, weld slope φ, weld rotation θ, weld bead width B and penetration H. The former three are decided by the forms of weld seam, the latter two are welding requirements. They are variables which is not easy to be changed or do not want to be changed. And the other is robotic welding process parameters, including welding torch working angle α, welding torch walking angle β, welding current I, welding voltage U and welding speed ν. The process technician can select them according to weld seam parameters, after the weld seam parameters have been decided. In fact, when the weld seam parameters are determined, the problem of robot welding process parameters should be resolved. With the filtering function of the Access table, the robot welding parameters can be searched quickly.
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H. Chen, Y. Meng, and X. Wang Table 2. Combined planning data sheet Ordinal
δ/°
φ/°
θ/°
β/°
α/°
I/A
U/V
v /mm•s-
H/mm
B/mm
1
1
90
2
90
3
90
…
…
25 25 25 …
10890
135
25
45
0
0
80
18.0
9.2
0.230
3
45
0
0
80
18.5
9.2
0.564
3
45
0
0
80
19.0
9.2
1.101
3
…
…
…
…
…
…
…
…
45
0
0
130
22.0
9.2
1.494
3.671
5 Typical Workpiece Welding Test Writing the program by MATLAB, the parameters which are d, R, r as in Fig.1 and segmental number n were input, for example, d=0, R=43mm, r=59mm, n=72. After running, the output matrix was obtained, which contains all relevant data, and the origin of node coordinate axis is the intersection point of two cylindrical. The output matrix was arranged into the Access table which contains the weld point coordinates, weld slope angles, weld rotation angles and torch postures. Fig. 2 shows the result of a graphical simulation. In Fig. 2, the line segment arrangements of the saddle-shaped weld represent the angle and trajectory of welding torch. Arc welding robot system can control the welding current, voltage and welding speed by program. The geometric data of the selected saddle-shaped weld can be transformed into the welding torch posture and robot welding parameter (current, voltage). The job program of the robot can be worked out. By adjusting one or two parameters of bad weld point, the whole seam can be welded in contra-clockwise successfully. Fig. 3 shows the actual workpiece. The formation of weld reaches the Access table filtering requirements (weld penetration, weld width), and this proved the effectiveness of the combined planning data sheet.
Fig. 2. Image simulation
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Fig. 3. Actual workpiece
6 Conclusions The ANN technology was applied in combined planning of welding postures and welding parameters successfully. The welding posture can be described quantificationally. The saddle-shaped weld was selected for modeling, then the posture information of weld and welding torch were extracted. The continuous weld was divided into many finite segments, and the data of workpiece angle, weld slope and weld rotation of the segments were extracted. The data of weld appearance were collected under various welding poses and welding conditions, thereby determining the corresponding relationship between the weld formation and welding process parameters. By means of compiling program and image simulation with MATLAB, the data of the saddle-shaped weld and torch posture were calculated automatically. Then the job program of the robot was compiled according to the combined planning of welding postures and welding parameters. The results show that the planning method is feasible. Acknowledgement. This work is supported by the Science Foundation of Aeronautics of China under project No. 02H56007.
References [1] Chen, S.-b., Lin, T.: Intelligent welding robot technology. Chinese Mechanical Industry Press, Beijing (2006) [2] Tang, X.-h., Paul, D.: 3D visualized offline programming and simulation system for industrial robots. Transactions of the China Welding Institution 26(2), 64–68 (2005)
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[3] He, G.-z., Gao, H.-m., Zhang, G.-j.: Coordinated motion simulation in a robot arc offline programming system. Journal of Harbin Institute of Technology 37(6), 813–815 (2005) [4] Chen, Z.-x., Huang, Y., Yin, S.-y.: Analysis and design for offline welding robort programming system. Chinese Journal of Mechanical Engineering 37(10), 104–106 (2001) [5] Zhang, H.-j., Zhang, G.-j.: Self-defining path layout strategy for thick plate arc welding robot. Transactions of the China Welding Institution 30(3), 61–64 (2009)
Optimal Digital Filtering for Tremor Suppression in a Master-Slave Robot Remote Welding System Hongtang Chen, Haichao Li, Hongming Gao, Lin Wu, and Guangjun Zhang State Key Laboratory of Advanced Welding Production Technology, Harbin Institute of Technology, Harbin, China e-mail:
[email protected]
Abstract. Master-slave robot remote welding system requires the generation of an intermediate control signal which will be transmitted to the controlled subsystem: slave robot. The control signal is generated by human operation of the master manipulator such as a space-mouse, a joystick and a master hand. When a human operates the master robot, the human tremor signal will be transmitted to the slave robot, welding seam tracking precision will be decreased. Digital filtering could “clean” human tremor signals, so welding seam tracking precision will be improved if the intermediate signal is filtered before it reaches the slave robot. In this paper, a master-slave robot remote welding system was built consisting of a master manipulator with six degree of freedom (DOF), an industrial computer control system and a slave MOTOMAN robot, and an optimal digital filter designed to “clean” human tremor signal and to improve the welding seam tracking precision. Experimental results show that the digital filter indeed suppresses the operator’s tremor signal.
1 Introduction For welding tasks in extreme environment such as maintenance in nuclear power plants, construction of space stations, and pipes repairing undersea or underground, it is necessary to develop a flexible remote welding system based on telerobot technology to perform these tasks [1-5]. Up to now, the robot intelligence is not enough to support a totally autonomous remote welding operation, human supervision is still required. So combination of the intelligence and experience of human and the stability and accuracy of robot is a better way to perform the remote welding. The master-slave robot remote welding system could combine the intelligence and experience of human and the stability and accuracy of the robot, and could effectively improve the welding efficiency and success. Master-slave robot remote system has become a strong assistant for human interplanetary exploration, many countries did many researches on the interplanetary exploration, such as the American JPL/NASA ROBOT, Japanese MSMS ROBOT and German ROTEX ROBOT. T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 81–85. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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For master-slave robot remote welding system, a control signal is generated when operator manipulates master robot, and then is transmitted to the slave robot, so the operator’s tremor signal is transmitted to the slave robot with the control signal, thus the remote welding efficiency and success will be decreased. Tremor is a rhythmic uncontrollable oscillation, which includes physiological tremor and pathological tremor. Physiological tremor shows very small amplitudes with an energy distribution located at frequencies higher than 8 Hz [6]. In this paper, an optimal digital filter was designed to “clean” the operator’s physiological tremor signal from the control signal which is passed to the slave robot. The overall goal of this procedure is based on considering the human tremor as a distortion which is to be eliminated from the control signal by a digital filter based on MATLAB.
Fig. 1. Master-slave robot remote welding system
2 The Master-Slave Robot System The structure of master-slave robot remote system is shown in Fig. 1, it consists of a space navigator mouse, a Motoman HP3J robot, a “welding torch”, and an industrial computer. A space navigator mouse with 6 DOF is used as the master robot, and the Motoman HP3J robot is used as the slave robot, the HP3J robot controller is based on RTlinux operating system.
3 Welding Seam Tracking Experiment The operator was asked to move the system in such a way that the “welding torch” tracks the straight seam as precise as possible. The operator manipulates the space-mouse to generate the control signal, then the control signal is inverted to the position and orientation of the slave robot by kinematics. The position and orientation signal of slave robot is the tracking signal, which is accessed by the industrial computer, and the tracking target is the straight seam.
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4 Equalizer Design Digital filter is used for digital signal processing. The spectrum (Frequency/Phase) analysis is used for signal components analysis. In the master-slave robot remote welding system, the control signal, which includes operator’s tremor signal, is generated when operator operates master robot, so before “clean” operator’s tremor signal from the control signal, we should know the components of control signal. MATLAB is used to analyze the signal spectrum (Frequency / Phase) and to distinguish the operator’s tremor signal from the control signal, the tremor signal will be eliminated from the control signal by a digital filter. Fourier transform is the main method of spectrum (Frequency/Phase) analysis, which could achieve the conversion between the time domain and the frequency domain. Fig. 2 illustrates a two-dimensional system (i.e., x and y ) with digital filter, the digital filter, called equalizer, is used to “clean” the operator's tremor signal from control signal.
Fig. 2. General scheme of a two-dimensional man–machine system with digital filter compensation
The tracking deviation is shown in Fig. 3, it is obvious that the maximum deviation is -1.1602 mm and the deviation fluctuates significantly through the tracking process. So in order to improve the tracking precision, we should “clean” the operator's tremor signal from control signal. Fig. 4 demonstrates the distribution of the tracking signal power spectral density (PSD). In this paper, we use sampling frequencies is 40 Hz, and order-two Butterworth filter with cutoff frequency of 1.4Hz. 0.4 0.2
deviation(mm)
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[b, a ] = butter (n, Wn )
(1)
x f = filter (b, a, x)
(2)
y f = filter (b, a, y )
(3)
[b, a ] = butter (n, Wn ) designs an nth order lowpass digital Butterworth filter and returns the filter coefficients in length n + 1 vectors B (numerator) and A (denominator), filter filters the data in vector x and y with the Butterworth where
filter to create the filtered data
x f and y f , x and y are the vertical ordinate
and the horizontal ordinate of the tracking trajectory respectively,
x f and y f are
the vertical ordinate filtered and the horizontal ordinate filtered of the tracking trajectory respectively. As shown in Fig. 5, the deviation filtered lies in band of -0.13982--0.5467 mm, so the equalizer “clean” the operator's tremor signal from control signal, improves the tracking precision and the human performance in the master-slave robot remote welding system. 1 0.8 0.6
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Fig. 5. Tracking deviation after filtered
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5 Conclusions A master-slave robot remote welding system has been built, which consists of a master manipulator with 6 DOF, an industrial computer control system and a slave MOTOMAN robot. An optimal signal processing technique for suppressing tremor in the master-slave robot remote system has been developed and successfully tested. Experimental results show that the equalizer suppresses the human tremor signal and improves the welding seam tracking precision and the human performance in the master-slave robot remote welding system. Acknowledgement. This work was supported by contract (NCET-07-0233) from New Century Excellent Talents Support Program and by the National Natural Science Foundation of China (NSFC) under the Grant number 50905043. The authors also greatly appreciate the editor and the referees for their thoughtful comments which led to a substantial improvement on the presentation of this paper.
References [1] Raimondi, T.: Remote Handling in the Joint European Tonus _JET_Fusion Experiment. In: Proceedings of the 24th Conference on Remote SystemTechnology, pp. 188–195 (1976) [2] Agapakis, J.E., Masubuchi, K.: Fundamentals and Advances in the Development of Remote Welding Fabrication System. Weld. J. _Miami, FL, U.S 65(9), 21–32 (1986) [3] Launary, J.P.: Teleoperation and Nuclear Services Advantages of Computerized Operator-Assistance Tools. Nuclear Engineering and Design 180(1), 47–52 (1998) [4] Tung, P.C., Wu, M.C., Hwang, Y.R.: An Image-Guided Mobile Robotic Welding System for SMAW Repair Processes. International Journal of Machine Tools & Manufacture 44, 1223–1233 (2004) [5] Li, H.C., Wu, L., Gao, H.M., Zhang, G.J.: Review and Prognosis of State of Arts on Remote Welding Teleoperation. Transactions of the China Welding Institution 27(6), 108–112 (2006) [6] Gonzalez, J.G., Heredia, E.A., Rahman, T., Barner, K.E., Arce, G.R.: Optimal Digital Filtering for Tremor Suppression. IEEE Transactions on Biomedical Engineering 47(5), 664–673 (2000)
Coordinated Motion of Different Weld Robots Based on User Coordinates Huajun Zhang1,*, Chunbo Cai1, Guangjun Zhang2, and Daming Shen3 1
College of materials science and engineering, Harbin University of science and technology, Harbin 150040, China 2 National Key Laboratory of Advanced Welding Production Technology, Harbin Institute of Technology, Harbin 150001, China 3 Shanghai Zhenhua Heavy Industries Co., Ltd., Shanghai 200125, China 4 School of material science and technology, Shanghai Jiao Tong University, Shanghai 200240, China e-mail:
[email protected]
Abstract. Many different robots usually need to work together to execute one job in industry factories. A simple and practical master-slave coordinated model based on user coordinates is developed. In this study, a KUKA robot and a MOTOMAN robot are regarded as master and slave hand respectively. The central controller can communicate with the KUKA controller and the MOTOMAN controller by OPC Server and Motocom32 individually. Special user coordinates in respective weld plate are established in advance. According to the ending position and attitude of the welding torch of the master hand, the motion path of the slave-hand is deduced by kinematic coordinate conversion in user coordinates. Experimental results show that this method is simple and advisable.
1 Introduction With the quick development of industry robots, many different robots made in different companies are used in industry factory. It's difficult to coordinated control due to the different system and interface between different robots. Master-slave coordinated control system has many advantages such remote controlling, low cost, high efficiency, high quality and so on [1]. Many sensors will be removed for slave robot such as weld seam tracking sensor, arc length sensor and touch sensor. This system is suited for double V-groove, double-sided T joint and the same workpiece in different location [2]. At present, most researches are mainly focus on that master robot hold the workpiece and slave robot welding or assembling about double robots coordinate control mode [4-5]. Double robots are together tracking the trajectory edge [3]. Double robots handle objects together [6]. But research to double different coordinate welding robots has not been found basic on user coordinates. In this system, KUKA robot and MOTOMAN robot is master and slave hand individually. Centralize controller is communicated with KUKA *
Corresponding author.
T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 87–95. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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controller and MOTOMAN controller through OPC Server and Motocom32 individually. Special user coordinates in respective weld plate are established in advance. According to the ending pose of welding torch of master hand, Motion path of slave-hand is deduced by kinematics coordinate conversion in user coordinates. Experimental results show that this method is simple and advisable.
2 Double Different Robots System Double different weld robots system as shown in Fig. 1 is formed by double robots, double controllers, double welding system, external sensor system, coordination control center and auxiliary equipment and so on. As for the communication between KUKA robot and control center, the OPC (Open Connectivity) server is adopted in industrial automation. The position and other information of KUKA robot can be obtained through setting the DCOM (distributed component object model). The MOTOMAN robot can be controlled under no teaching jobs with Motocom32 communication. Therefore, the KUKA robot is master robot and the MOTOMAN robot is slave robot in our study. The OPC server is responsible for real-time access to the master robot pose. The MOTOCOM32 is in charge for realtime control the slave robot motion.
Fig. 1. Double different weld robots system
In order to realize the double robots coordinated motion, the coordinate model is developed through collecting the six pose parameters (X, Y, Z, A, B, C) of master robot under user coordinates. In this paper, the system can calculate the six corresponding pose parameters (X, Y, Z, A, B, C) of slave robot under user coordinates. Liner interpolation motion commands BSCMOVL can control the slave robot move to the target pose. Position feedback commands BSCPOS can obtain the slave robot pose.
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3 Master-Slave Coordinated Models 3.1 User Coordinates Establishment The conventional coordinated algorithm is complicated. This model requires high location accuracy between the robots and work piece, and requires high work piece assemble accuracy. In order to simplify the algorithm and meet the industry scene need, special user coordinates in respective weld plate are established in advance. Double coordinated welding robots include two types: welding the same work piece together (type I) or welding two work pieces individually (type II). In this study, double-sided double arc welding robot is suitable for double V groove butt joints and double-sided T joints as shown in Fig. 2a and Fig. 3. Double coordinated robots are suitable for two same type work piece welding as shown in Fig. 2b. The user coordinates of master and slave robots are established in the work piece surface seen from Fig. 2 and Fig. 3. As for type II, the user coordinates are set on the same position of two work pieces individually. As for type I, the work piece can be regarded as two same half work pieces, which divided from the middle section in thickness direction of butt plate or web. The user coordinates can set according to type II also.
web plate
midder surface
Ymu Ysu Xmu Xsu Omu Zmu Osu Zsu
base plate
(a)double-sided T-joint
Zsu Zmu
Ymu
Ysu
Osu Xsu
Omu Xmu
(b)the same workpiece Fig. 2. User coordinates selection mode
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{Omu} and {Osu} is the user coordinates of master and slave robot. As for type I, the space positions of Omu and Osu are equal as shown in Fig. 3. The plane XmuOmuYmu and XsuOsuYsu parallel each other. The direction Zmu and Zsu are opposite. For the same type robots, the tool coordinates are the same and mu su mtT = stT . But for two different robots, the tool coordinates are different also and
T ≠ sustT . The relative transformation matrix
mu mt
su st
R from master robot to slave ro-
bot should be obtained. The coordinated control center can real-time get the pose of master robot in tool mu mu mu coordinates OmuXmuYmuZmu ( mt p x , mt p y , mt p z , α , β , γ ). The tool positions of two robots are equal. The poses are unequal due to the tool coordinates of different robots. When the tool poses of two robots relative to the work piece are the same, the tool pose can be recorded through off-line simulation. The pose transformation matrix can be calculated.
Fig. 3. User coordinates selection of double-V groove
3.2 Modeling Inferential Reasoning In order to keep the slave robot with the same pose relative to the master robot, the tool pose of slave robot in tool coordinates OsuXsuYsuZsu is equal to OmuXmuYmuZmu. mu mt
Rxyz ( γ , β , α ) = R ( Z mu , α ) ⋅ R ( Ymu , β ) ⋅ R ( X mu , γ ) ⎡ cα = ⎢⎢ sα ⎢⎣ 0
− sα
⎡cα c β =⎢ ⎢ sα c β ⎣⎢ − sβ
cα 0
0⎤ ⎡cβ 0 ⎥⎥ ⎢⎢ 0 1 ⎥⎦ ⎢⎣ s β
0 s β ⎤ ⎡1 0 1 0 ⎥⎥ ⎢⎢0 cγ 0 c β ⎥⎦ ⎢⎣0 sγ
cα sβ sγ − sα cγ sα s β sγ + cα cγ c β sγ
0 ⎤ − sγ ⎥⎥ cγ ⎥⎦
cα s β cγ + sα sγ ⎤ sα s β cγ − cα sγ ⎥⎥ c β cγ ⎦⎥
(1)
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When the pose angle of master robot (KUKA robot): α = 180, β = 60, γ = 180 , the pose angle of slave robot (MOTOMAN robot): α = 0, β = 30, γ = 0 . The pose matrix of master robot as follows: mu mt
Rxyz (180, 60,180 )
⎡ c180c60 c180 s 60 s180 − s180c60 c180s60c180 + s180s180 ⎤ = ⎢ s180c60 s180s 60s180 + c180c180 s180s 60c180 − c180s180 ⎥ ⎢ ⎥ ⎢⎣ − s60 c 60s180 c 60c180 ⎦⎥
⎡ −0.5 0 0.87 ⎤ = ⎢⎢ 0 1 0 ⎥⎥ ⎢⎣ −0.87 0 −0.5⎥⎦
(2)
The pose matrix of slave robot as follows: su st
⎡c0c30 c 0s30s 0 − s 0c30 c 0s30c0 + s 0s 0 ⎤ Rxyz ( 0,30, 0 ) = ⎢ s0c30 s0 s30 s0 + c0c0 s0 s30c0 − c0 s0 ⎥ ⎢ ⎥ ⎢⎣ − s30 ⎥⎦ c30 s 0 c30c0
⎡ 0.87 0 0.5 ⎤ = ⎢⎢ 0 1 0 ⎥⎥ ⎢⎣ −0.5 0 0.87 ⎥⎦
(3)
The pose transformation matrix is calculated as following. mt st
R = sust Rxyz ( 0, 30, 0 ) mu mt Rxyz (180, 60,180 )
−1
⎡ 0.87 0 0.5 ⎤ ⎡ −0.5 0 −0.87 ⎤ ⎡0 0 −1⎤ =⎢ 0 1 0 ⎥⎢ 0 1 0 ⎥ = ⎢0 1 0 ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢⎣ −0.5 0 0.87 ⎥⎦ ⎢⎣ 0.87 0 −0.5 ⎥⎦ ⎢⎣1 0 0 ⎥⎦
Where
cα = cos α
, sα = sin α , cβ , sβ , cγ , sγ
the pose of master robot (KUKA robot) is matrix of master robot as follows: ⎡ cα cβ ⎢ mu ⎢ sα c β mtT = ⎢ −sβ ⎢ ⎣ 0
mu mt
px ,
mu mt
py ,
cα sβ sγ − sα cγ
cα sβ cγ + sα sγ
sα s β sγ + cα cγ cβ sγ
sα s β cγ − cα sγ c β cγ
0
0
mu mt
(4)
and so on. When
p z , α , β , γ , The pose px ⎤ ⎥ py ⎥ pz ⎥ ⎥ 1 ⎦
mu mt mu mt mu mt
(5)
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The pose matrix of slave robot is expressed as shown in formal (6).
mu st
⎡ 0 0 −1⎤ ⎡ cα c β R = ⎢⎢ 0 1 0 ⎥⎥ ⎢⎢ sα c β ⎣⎢1 0 0 ⎥⎦ ⎢⎣ − s β
⎡ sβ = ⎢⎢ sα cβ ⎢⎣ cα c β
cα s β sγ − sα cγ sα s β sγ + cα cγ cβ sγ
−c β sγ
cα s β cγ + sα sγ ⎤ sα s β cγ − cα sγ ⎥⎥ cβ cγ ⎦⎥
−c β cγ
⎤ sα s β cγ − cα sγ ⎥⎥ cα s β cγ + sα sγ ⎥⎦
sα sβ sγ + cα cγ cα sβ sγ − sα cγ
(6)
' ' ' Where α , β , γ is the pose angle of slave robot. ' ° If β = 90 , then α ' = 0
γ ' = A tan 2(
−cβ sγ ) sα s β sγ + cα cγ
(7)
' ' ° If β = −90 , then α = 0
−cβ sγ ) sα s β sγ + cα cγ
(8)
sα cβ ) sβ
(9)
γ ' = − A tan 2( ' If cos β ≠ 0 , then
α ' = A tan 2(
β ' = A tan 2(
γ ' = A tan 2(
−cα c β
( sβ ) + ( sα cβ ) 2
2
)
(10)
cα s β sγ − sα cγ ) cα s β cγ + sα sγ
(11)
In order to reduce the slag of slave robot motion, the moving speed of slave robot need to control also as follows.
( Px1 − Px0 ) + ( Py1 − Py0 ) + ( Pz1 − Pz0 ) 2
υ=
2
2
(12)
T
Where T is sampling period, Px0, Py0, Pz0 is the current time position of slave robot, Px1, Py1, Pz1 is the next time position of slave robot.
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3.3 Modeling Verification In order to verify above model, the pose of master robot and slave robot is compared when the pose of two robots are the same. When the pose angle of KUKA robot under user coordinates is α = 139, β = 49, γ = 131 . The corresponding pose angle of MOTOMAN robot under user coordinates is α = 30, β = 30, γ = 0 when the pose of two robots is the same. Whether the corresponding pose angle of MOTOMAN robot under user coordinates is α = 30, β = 30, γ = 0 or not through adopting above calculated model. The pose of master robot as follows. mu mt
Rxyz (139, 49,131)
−c 49 s131 −c 49c131 ⎡ s 49 ⎤ = ⎢⎢ s139c 49 s139s 49s131 + c139c131 s139s 49c109 − c139s131⎥⎥ ⎢⎣c139c 49 c139 s 49 s131 − s139c131 c139 s 49c109 + s139 s131⎥⎦
(13)
The calculated pose angle of MOTOMAN robot under user coordinates as follows. ⎛
β ' = A tan 2 ⎜ ⎜ ⎝
−c139c 49
( s 49 ) + ( s139c49 ) 2
2
⎞ ⎟ = A tan 2 ⎜⎛ 3 ⎟⎞ = 30 ⎜ 3 ⎟ ⎟ ⎝ ⎠ ⎠
⎛ 3⎞ ⎛ s139c 49 ⎞ ⎟⎟ = 30 ⎟ = A tan 2 ⎜⎜ ⎝ s 49 ⎠ ⎝ 3 ⎠
α ' = A tan 2 ⎜
γ ' = A tan 2(
c139 s 49s131 − s139c131 )=0 c139 s 49c109 + s139s131
The calculated results are consistent with the actual results, illustrated that the mathematical model is reliable.
4 Coordinated Motion Experiment Double robots system is shown in Fig. 4. When the master robot (KUKA robot) is moving as the path shown in Fig. 5, the slave robot (MOTOMAN robot) will follow the master trajectory with the same pose and position. However, the motion of the slave robot relative to the master robot has a certain slag. In order to realize the double robots coordinated motion, the coordinate model is developed through collecting the six pose parameters (X, Y, Z, A, B, C) of
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master robot under user coordinates. In this paper, the system can calculate the six corresponding pose parameters (X, Y, Z, A, B, C) of slave robot under user coordinates. Liner interpolation motion commands BSCMOVL can control the slave robot move to the target pose. Position feedback commands BSCPOS can obtain the slave robot pose. Workpiece
Master robot
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Fig. 5. Double weld robots position
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Seen from Fig. 5a and 5b, when the motion speed of the master robot is quick and the motion path is complex, the motion slag of slave robot seems obvious. However, the trajectory of two robots is overlapping shown in Fig. 5c. For doublesided arc welding, the welding speed is slow and the path is straight. So the system can basically meet the requirement of field welding production.
5 Conclusions For different arc welding robots, a simple and practical master-slave coordinated model based on user coordinates is developed and deduced. Communication with KUKA controller and MOTOMAN controller is realized through OPC Server and Motocom32 individually. Master-slave coordinated controlling experiment shows that this system can basically meet the requirement of field welding production. Acknowledgement. This research is supported by National Postdoctoral Foundation of China (No. 20090460616), by Harbin Creative Talent Science and Technology Foundation of China (No. 2010RFQXG005), by Heilongjiang Province Education Foundation (No. 20100503066) and by Shanghai Sciences & Technology Committee under Grant (No. 09JC1407100).
References [1] Zhang, H., Zhang, G., Wang, J., et al.: Effect of thermal cycles of DSAW on microstructure in low alloy high strength steel. Transactions of the China Welding Institution 10(28), 81–84 (2007) [2] Zhang, H., Zhang, G., Wu, L.: Effects of arc distance on angular distortion by asymmetrical double sided arc welding. Science and Technology of Welding & Joining 12(6), 564–571 (2007) [3] Chen, G., Huang, X., Wang, M.: Motion Planning for Two Cooperative Robots Tracking Complex Edge. Electrical Automation 28(5), 253–257 (2006) [4] Wang, Y., Tan, D.: Research status and development of cooperative robotics. Robotics 20(1), 69–73 (1998) [5] Piao, Y.: Research to multi-agent coordination control of welding flexible manufacturing cell. Shanghai Jiaotong University Doctoral Dissertation (2004) [6] Qu, D., Tan, D., Zhang, C., et al.: Double robots coordinated control system. Robotics 13(3), 6–11 (1991)
Research on Information Transmission of a Welding Robot Based on Network H.B. Wang, G.H. Ma, D.H. Liu, and B.Z. Du Mechatronics College, Nanchang University, Nanchang City, 330031 China e-mail:
[email protected]
Abstract. The hardware platform of a welding robot information transmission system was considered in this paper. Problems, such as time delay, information discard and so on were all given significant attention. In the end, a method of video transmission with high efficiency and reliability was proposed, and video transmission test also had been done. Experiments showed that the transmission method proposed in this paper based on network was feasible, the system information process speed was fast, and the video transmission was fluent, beside strong real-time, its robustness also was good.
1 Introduction In spring of 1994, since Ken Goldberg proposed the concept of network robot, the concept of teleoperation had been greatly extended. In Mercury Project, Ken Goldberg used an industrial manipulator installed CCD cameras and air bag system to do excavation work in a semi-circular work space. In 1995, Mercury Project was replaced by Telegarden Project [1]. At the same period, Kenneth Taylor of University of Western Australia developed a 6-DOF mechanical arm and installed a fixed camera which allowed network users to do the operation of the accumulation of wood in its work place [2]. Following Ken Goldberg and Kenneth Taylor, a lot of scholars are absorbed. Shanghai Jiaotong University has designed a web-based gas pipeline inspection robot [3] and Harbin Institute of Technology has designed a webbased virtual multi-robot teleoperation platform and network-based teleoperation clamp [4, 5]. To welding network robot system, welding video requires higher reality and higher video quality. While the bad video will affects remote client control decision-making directly. As a result, weld seam quality will be affected seriously. In this paper, aiming at welding robot network communication system, we had done some researches on method of welding robot real-time video transmission. T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 97–105. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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2 Communication System 2.1 Hardware 2.1.1 Network Topology The welding robot communication system is composed of Upper Computer and Lower Computer, whose hardware construction is show as Fig. 1. Integration control mode is used in this system. Welding robot control information transmits to the robot by the lower computer. When the lower computer receives the information from upper computer, it transmits the information related to the robot through the software integrated control platform, and tries to realize the control of the robot. Welding robot is separated from the upper computer network. On one hand, it can improve the safety of the system. On the other hand, it is also easy to increase more robots node in future.
Fig. 1. Welding robot network communication system hardware
2.1.2 Video Capture In the communication system, welding robot environment video and weld stream video are obtained by CCD and image acquisition card.SNA-M325 CCDs, produced by SHENZHEN SHINAIAN ELECTRONICS TECHNOLOGY CO., LIMITED, are used to obtain the weld stream video and environment video. DHCG410, produced by China Daheng Group, Inc., is chosen for image acquisition card.
2.2 Software 2.2.1 Protocol Choose There are two network communication protocols in TCP/IP (Transmission Control Protocol/Internet Protocol: TCP/IP) protocol group, one is TCP (Transmission Control Protocol: TCP), the other is UDP (User Datagram Protocol: UDP).
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The efficiency is high and the performance is steady in LAN. In order to improve the real time quality of the control system, UDP protocol is used in the communication system designed in this paper. 2.2.2 Multi-channel Mode Due to the otherness of each network, at different periods the network has different quality. In order to satisfy different transmission requirement of quality, layered transmission method is designed. To a given video frame sequence { V1,V2,V3,…,Vk },
Vk ∈ [0, 255]640×480 is a gray-scale video frame se-
quence, we use coding method E, then it can be mapped to n video frame of different quality levels.
E:Vk → {Ck 1,Ck 2,…,C k n } Different
quality
frame
{ N1 ,N 2 , ... , N k },
sequence
when
is
transmitted
transmitting,
chooses
on the
different channel
channel
Ni ,
N i ⊂ { N1 ,N 2 , ... , N k } according to the real network quality, coding the frame to the quality level fit to the channel and then transmits. At this time, in order to reduce the system overhead, other channels are closed and other quality levels aren’t allowed to coding. The receiving end also chooses to receive the video frame of corresponding channel, and other channel is closed. When it receives the i
frame sequence C k , decodes it use method D, then the required video frame sequence can been obtain. i 1 2 n '˖Ck i o Vk i ˈCk ⊂ {C k ,C k ,…,Ck }
With this method, unnecessary coding overhead is cut. The coding method is simplified and the response speed of the system is improved. When the system is working, the transmission control algorithm, which will be described in part 2.2, starts to evaluate the network status non-stop and chooses the corresponding video transmission channel for different quality of video transmission services. In this paper, control information and feed back information in welding robot network control system uses an exclusive information transmission channel, in order to simplify the transport protocol design and transmission processing. 2.2.3 Multi-thread Mode Thread is the description of the process executing, process is a static conception, and thread is a dynamic conception, one process needs at least one thread to execute the code in its address space. One process, if has no thread executes the code in its address space, then its existence will not make sense, the system will automatically clear it. When a process is created, the system will create a thread for it, this thread is called Primary Thread. One thread can create another thread.
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If there is only one thread in the communication system, then, when it is blocked for a socket communication wait state or doing a implementation of video capture, transmission, processing and other tasks, it will not able to respond to other operations, and it will cause choke to death, and affects the real-time. In order to solve this problem, multi-thread technology is used in this paper. In multithread programs, the system distributes a CPU Quantum to each thread, each thread gives the CPU control right to the next thread when its own quantum is time out. Because each quantum is too short, it seems to be multiple threads running in parallel, which is what we often say that multi-tasking. In multi-thread program, one thread collapse will not cause whole system collapse, when a thread runs into infinite loop, other threads can terminate it, rather than the whole system collapse. Multitask problems of running in parallel are successfully settled in this paper, the shortcomings of the system, that it can not respond to other operation when a socket is blocking is avoided. What’s more, threads take up less system resource, switching speed between threads is faster, which ensures the communication system compactness and efficiency.
3 Communication Method Design 3.1 C/S Communication Mode Client/Server mode, which technology is mature and whose transmission enfficiency is high, is used in this communication system. There are two interfaces for the software, one is for upper computer, the other one is for lower computer, who are showed in the part of network design. There are many upper computers in this communication system, they visit the lower computer through the network and read the related information of the robot, but in one time only one upper computer can gets the right to control the robot. When an upper computer in the network wants to control the robot, it must send a request to the lower computer first and get the right to control the robot. When the lower computer receives a control request, if the robot is free, then the upper computer will get the control right, otherwise, the upper computer will not get the control right.
3.2 Transmission Control Algorithm Transmission control algorithm block diagram shows as Fig. 2. The main idea of transmission control system is that the system supposes that the network quality is the best firstly. At the same time, it sets up the network quality evaluation system, and begins to network quality evaluating. If the evaluation system considers that the network quality is good, it provides high-quality video transmission service according to the current model. If the evaluation system considers that the network quality is poor, then the system will turn to the next quality level of video transmission mode and reduce the spending of network resources, and continues to evaluate network quality.
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Fig. 2. Transmission control system block diagram
When the transmission system has just started, the system assumes that the initial network state is the best, when it starts to transmit, the system sends the highest-quality video frame request firstly. At the same time, the network quality evaluation system starts to work, and the timer T starts to count. If it receives the video frame in expected time t after the high-quality video frame request is sent, then sends the next frame request, runs to the next cycle. If it dose not receive the video frame in expected time t, then the system considers that because of the poor network state the frame is lost. At this point, if the network evaluation system has not made the judgment that the network state can’t satisfy the requirement of highquality video frame transmission need, then records and goes to the next video frame request cycle. If the network evaluation system has judged that the network state can’t satisfy the requirement of high-quality video frame transmission need, then turns to the next quality level of video transmission mode. In the low-quality video frame transmission mode, the system sends low-quality video frame request, and continues to do evaluation of network quality, if the evaluation system considers that the network quality is good, it can satisfy the requirement of highquality video transmission, then turns to high-quality video transmission mode, otherwise, to send low-quality video transmission request. The principle of network quality evaluation system is as follows: at high-quality video transmission mode, in the evaluation time Tb , if the lost frame number n is less than N, or n
n d is less than N d , or n d < N d , then, the system considers that the
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network quality is good, it can satisfy the requirement of high-quality video transmission, otherwise, if n d ≥ N d , the system considers that the network quality only can satisfy the requirement of low-quality video frame transmission or turns to the lower video frame transmission mode.
3.3 Video Transmission Method Video transmission method show as Fig. 3.
Fig. 3. Video Transmission Method
The principle of video transmission showed in Fig. 3 is that, the upper computer sends transmission code to the lower computer, when the lower computer receives the transmission code it will set video transmission flag. Video capture process begins to work as the lower computer software starts to work. When a frame is captured, the lower computer will code and display it immediately, then judge, if video transmission flag is set, transmit the frame immediately, else capture another frame. When the system receives a video frame the upper computer will decode and display it immediately, then waits for another frame. Do as so circularly, the video transmission will be realized. The relationship of the process time of each part in the transmission system showed in Fig. 3 is as follow: When t2>t3+t4+t5 , Processing time of each part for the first frame
Video frame display interval T on upper computer can be calculated as follow: Processing time of each part for the next frame T= t2’+ t3’+ t4’+ t5’- t3- t4- t5= t2’=t2
(1)
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When t3+t4
Video frame display interval T on upper computer can be calculated as follow: T= t2’+ t3’+ t4’+ t5’- t3- t4- t5= t2’=t2
(2)
In the formula, t1= t1’ is request transmission time, t2= t2’ is capture-code time, t3= t3’ is decode-display time on lower computer, t4= t4’ is video frame transmission time, and t5= t5’ is decode-display time on upper computer. The transmission method showed in Fig. 3, its video frame display interval T on upper computer is equal to acquisition and coding time t2 on lower computer, its transmission efficiency is high, its video is fluent and its video transmission is steadier when there are some frames lost in the LAN, which can satisfy the welding monitoring requirements basically.
4 Test 4.1 The Environment of the Test The communication system has been tested in campus network of Nanchang University. The campus network is star topology, which has a gigabit bandwidth backbone network and stretches to every experiment building and institute from the network center. Sub-Gateway is set in every experiment building and institute, network resources are distributed to every lab by switches and every lab and share 10M bandwidth. Test time is 3:00-4:00 pm, when there are more users in the internet, the campus backbone network traffic is heavy, and the network stability is bad and will significantly affect the communication system. The upper computer and lower computer used in this test located in different subnets and the distance between the hosts is about 1.5km. Upper computer configuration: Intel Pentium CPU E2140, frequency is 2.4GHZ, EMS memory is 512M;lower computer configuration: Intel Pentium CPU E2140, frequency is 1.6GHZ, EMS memory is 512M, their control systems are both WIN XP.
4.2 Experiments Firstly, acquisition and coding time of the system are tested, the configuration of the host in the test is Intel Pentium CPU E2140, frequency is 2.4GHZ, EMS memory is 512M, and its control system is WIN XP. 10 teams of experiments have been done, continuous acquisition and encoding 60s in each team. Data is showed as Tsb.1.
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Average acquisition and coding time:
(s)*1000(ms/s)/Vc/=60*1000/1000=60ms (3) Take the data of Tab.1 into formula ⑴, and find out the video frame display interT2 =60
val T on upper computer.
T=t2=T2=60ms
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A number of tests have been done to test the processing speed of the communication system. Environment of the test is described in part 3.1, test data is recorded as tab.2. Data Processed is recorded in Tab. 3. Actual frame display interval on upper computer will be found, result is listed as Tab. 3 accordant to the one that calculated by formula . Result shows that the video frame display interval on upper computer is equal to the video capture card acquisition time on lower computer, which proves our transmission method designed is efficient.
⑷
Table 2. Communication System Process Speed (Vp) Unit (Frame/60s)
Table 3. Data Process
The test also proves system information process speed is fast, video transmission is fluent and real-time, which can satisfy basic need of welding robot network control system information transmission.
4 Conclusions This paper mainly aims at the bottleneck problem of welding robot video information transmission. A method of welding robot video information transmission is
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designed, it is high speed and real-time. With this method, some experiments of video transmission have been done. The tests prove that the transmission method is feasible, the system information process speed is fast and the video transmission is fluent and real time. Acknowledgement. Thanks to the Fund of National Science Foundation of China (No. 50705041) and anonymous viewers.
References [1] Zhuang, Y., Wang, W., Yun, W.-m.: Status and Development of Robotic Control Technologies Based on Networks. Robot. 24(3), 276–282 (2002) [2] Ma, G.-h., Chen, S.-b., Li, W.-h.: Development of Internet-Based Robot and Its Status Quo of Application to Welding. Welding & Joining 4, 5–8 (2004) [3] Yan, B., Yan, G.-z., Ding, G.-q., Zhou, B., Fu, X.-g., Zuo, J.-y.: On-line Detection of Gas Pipeline Based on the Real-Time Algorithm and Network Technology with Robot. Journal of Donghua University (Eng.Ed.) 21(5), 93–97 (2004) [4] Zang, X.-z., Zhao, J., Cai, H.-g.: Design of Multisensoration Gripper for Internetbased Teleoperation. Hingh Technology Lefttersi. 13(11), 23–26 (2007) [5] Wang, H.-b., Ma, G.-h., Deng, Y.-b., Wang, L., Zhang, H.: Construction of Welding Robot Network Control System. Journal of Shanghai Jiaotong University 42(sup.) (2008) (in chinese) [6] Xiao, L-x.: Teleoperation of a Network-enabled Mobile Robotic System. The University of Guelph (2005) [7] Zhang, J.-f., Fu, H.-l., Dong, Y.-m.: Novel 6-DOF Wearable xoskeleton Arm with Pneumatic Force-feedback for Bilateral Teleoperation. Chinese Journal of Mechanical Engineering 21(3), 58–65 (2008) [8] Li, M.-f., Li, S.-q., Zhao, D., Zhu, W.-g.: A Teleoperation System Based on Predictive Simulation and Its Application to SpacecraftMa intenance. International Journal of Plant Engineering and Management 13(1), 1–9 (2008) [9] Zhao, J., Gao, Y.-s., Cai, H.-g.: Safety Architecture of Internet based Multi-robot Teleoperation System. Journal of Harbin Institute of Technology (New Series) 12(6), 599–602 (2005) [10] Wang, S.-h., Xu, B.-g., Liu, Y.-h., Jia, Y.-y.: Multi-information Perception Aided Teleoperated Robot System over Internet. Robot 30(1), 47–55 (2008) (in chinese) [11] Gao, S., Zhao, J., Cai, H.-g.: Internet Based Design of Distributed Virtual Environment for Multi-robot Teleoperation. Journal of Harbin Institute of Technology (New Series) 13(1), 99–105 (2006) [12] Wang, S.-h., Xu, B.-g., Liu, Y.-h.: Internet-Based Real-Time Teleoperated Mobile Robot System. Journal of South China University of Technology(Natural Science Edition) 36(1), 81–88 (2008) (in chinese) [13] Gui, F., Quan, S.-h.: Analysis and test of time-delay in network control systems. Computer Applications 25(10), 2264–2266 (2005) [14] Zhang, Y.-j., Nie, Z., Liu, K.-y.: Test and Research of Time-delays in Remote Control System over Internet. Process Automation Instrumentation 29(3), 1–4 (2008)
Survey on Modeling and Controlling of Welding Robot Systems Based on Multi-agent Chengdong Yang and Shanben Chen Shanghai Jiao Tong University (SJTU), Shanghai 200240, P.R. China e-mail:
[email protected]
Abstract. This paper surveys on modeling and controlling of welding robot system based on multi-agent. It describes the ways of modeling based on BDI theory and Petri net, then the communication ways, middleware technologies and the cooperation methods were discussed respectively in the part of controlling. The application of welding robot systems based on multi-agent in abroad and domestic was introduced subsequently. At the end of the paper, we propose that the multiagent cooperation welding is an inevitable trend in the development of intelligentized welding, multi information acquirement and multi information fusion is a promising direction to realize controlling of welding robot systems based on multi-agent, gives a hierarchic architecture model of tentative experimental platform.
1 Introduction The notion of agent was put forward by Minsky firstly, agent was described as a hardware or a software which has the adaptability, autonomous ability, interactivity and intelligence [1]. Lots of scholars and research institutes have given different definitions of agent [2, 3], up to now there isn’t a uniform conception, but it is clearly that the agent has the basic attribute of autonomy, reactivity, social ability and pro-activeness. A robot, an expert system, a process or a model was can be considered as an agent [4]. Welding robot has the function of teaching and playback, all teaching operations can be reconstructed accurately. It has been widely applied in industrial production for the advantages of making the product quality stable, promoting productivity, improving working conditions, shorting the preparation period for new product development and realizing welding automation for small-batch products [5]. But sometimes, the signal agent doesn’t have enough knowledge, ability, resource and information to complete the problem independently in a complicated dynamic environment while facing the matter of overall importance, then multi-agent system was developed as a coupled network to solve those asynchronous problems, this system has strong fault-tolerant ability, high robustness, flexibility, adaptability, it can complete task efficiently and reliably under complex environment. Welding procedure is a complex process, which includes the complicated hardware, the complex multi-information interaction and the cooperation of different T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 107–113. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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actions. Multi-agent system can solve the problem of controlling material flow and information flow in welding process, multi-agent cooperation is a reasonable choice to accomplish the welding tasks especially when the weld-pieces are complex in shape and huge in size. BDI model [6] and Petri net [7] are the most useful tools which were used to model welding robot system based on multi-agent. The control of welding robot system based on multi-agent includes the choice of communication way, middleware technology and cooperation method.
2 The Modeling of Welding Robot System Based on Multi-agent 2.1 Modeling Based on BDI Model The BDI theory was established by Bratman in 1987, it contains belief, desire and intention, this theory believes that the problem can be solve effectively on the condition that keeping the balance of belief, desire and intention. Piao YongJie [8] has modeled the multi-agent in WFMC based on BDI model, presented a hybrid resource agent architecture and a hierarchy distributed multi-agent system architecture. Figure 1 shows the hardware and multi-agent network architecture of WFMC. This system includes three industrial robots (MOTOMAN UP6, MOTOMAN UP20 and SRH-6), two sensors, two welding power sources and a central control PC machine. The MOTOMAN UP6 was used for MAG welding has eight degrees of freedom, two of them were belonged to a positioner, the SRH-6 has six degrees of freedom was used for GTAW, the MOTOMAN UP20 was used for conveying and operating welding-pieces. Two kinds of sensors were used for real-time weld Welding Manipulator 1
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seam tracking in laser sensing system and monitoring weld pool dynamic processing in sensor system. The architecture of WFMC is bus-type, all the hardware resources were connected by Ethernet and TCP/IP protocol, the robot underlying control is completed through the communication between the MOTOCOM32 and the robot controller. The joint intention algorithm based on time restriction was proposed in this model, under the conduct of the joint intention which formed among agents, a joint cooperation action was established to realize the target P, the formal description of a joint cooperation action is shown as follows. JCACTION ({ a1 , a 2 , " , a n }
p) =
MBEL ({ a1 , a 2 , " , a n } BEL (( ∀ ai ,
JINTENT ({a1 , a2 , " , a n }
p )) ∧
ai ∈ {a1 , a2 , " , a n }) CONTRIBUTION ( ai
MBEL ({ a1 , a2 , " , a n }
({ a1 , a2 , " , a n }
JPGOAL ({ a1 , a2 , " , a n }
p )))) ∧
p ))
This equation indicates that the joint cooperation action among the agents is essential to achieve the group target P, at the course of system network communication, robot task date was uploaded and downloaded in the form of document between robot controller and PC machine. These files can be regarded as a set of robot motions and other instructions, in other words, a specific task of robot was determined by the ordered set.
2.2 Modeling Based on Petri Net Petri net was established by C.A. Petri in 1962, it is a useful tool for asynchronous system modeling and analyzing, Petri net gets extensive application because it is well in describing synchronization, concurrency and conflict in discrete event. Qiutao [9] has brought Petri net into modeling welding flexible manufactory system firstly, on the base of these researching works and according to the characteristics of intelligent welding flexible system, Maoguohong [10] has designed a modeling method (Timed Color Petri Net) for WFMS;WTCPN module is shown in Figure 2.
Fig. 2. Module of Multi-robot WFMS
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This hierarchic architecture model consists of three robots (a welding robot and two assisted robots), three sensors and a welding resource. It applies S (Place) and T (Token) to describe the state attributes set and the action attributes set of system actions respectively, the control rules and sensor response property are integrated in the action attributes to establish a mapping between model actions and outside responses. F (Directed) represents the relationship between the state attributes and the action attributes. The model was testified as a conservative system according to model conservation condition. Combine the characteristics of membership function with Petri net theory to redefine the simplified model and optimize information, simulation results showed the model performance is stable.
3 The Controlling of Welding Robot System Based on Multi-agent Choosing suitable communication way, middleware technology and cooperation method are very important for the controlling of welding robot system based on multi-agent. Cooperation is essential to keep the multi-agent system work together successfully, moreover, it is one of the key concept that distinguish multi-agent system from the related fields [11]. Communication ways can be divided into blackboard system and message system, in blackboard system, the agent can access the blackboard at any time, each agent can complete their own tasks independently, the communication among agents is indirect, blackboard system often used for tasks sharing and results sharing system. Compare to blackboard system, communication in message system by the way of exchanging messages directly between two agents, a unique address was specified by the message sender and only the specified address can receive the message. Message system is very popular because it is the base of realizing flexible and sophisticated coordination strategy, moreover, the message system can short the communication delay. Middleware is a kind of software designed to assist managing the complexity and heterogeneous distributed system [12]. Nowadays, the application areas of welding robot is extensive, but it is difficult to form a uniform standards because there are lots of robot producers, in addition, the uneven levels of openness and external interface is another reason to hinder the communication and the cooperation among different kinds of robot. In order to realize the communication and the cooperation, middleware technology is necessary to solve this problem. Middleware technologies such as CORBA, RMI, DCOM or MOM [13] can use to shield the different communication protocol and technology among different robot systems. CORBA and DCOM are the most popular middleware technology. Multi-agent cooperation is a result of the urgent demanding in production application, meanwhile, it is an inevitable trend in the development of intelligent agent technology [14]. The methods of cooperation in multi-agent system have contract net, blackboard module, the results sharing and functionally accurate cooperation, etc. Sometimes, a developed way which combined two ways can solve the problems more effectively, combining Petri net with contract [15]; Petri net with object oriented [16] are successful cases.
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4 The Application of Welding Robot System Based on Multi-agent At present, several foreign robot companies have realized multi-agent coordination in the automotive production line. Meanwhile, much research work about multi-agent cooperation have been studied deeply in domestic, Qiutao [9] has proposed automation Petri net model which can realize modeling analysis and scheduling control for welding flexible manufacturing cell center monitoring control platform with state sensing information. The scheduling realization of arc welding sensing process based on Petri net optimizing method is an initiation in robotic welding flexible manufacturing cell. Piao YongJie [8] has propounded an approach based on BDI model; this method could achieve multi-agent cooperation in welding flexible manufacturing system. Experiment results of aluminum alloy plate straight seam welding showed that through the cooperation between two resources agents to adjust welding torch motion track in real-time, this method meets the requirement of real-time controlling, corrects the deviation of arc length and welding torch trajectory effectively. Maoguohong [10] took the time and color into account, proposed TCPN model for WFMC to realize multi-agent cooperation in intelligentied welding. This model was used for aluminum alloy fillet welding, during the welding process, arc burning is stability, there is no conflict in the course of system operation, system running well, no collision and interference among agents, the abnormal phenomenon such as lack of weld, burn through and partial penetrated weld pool did not appear throughout the process of welding. L.X. Zhang [17] has established the agent model for TRWS which consists of welding robot, laser scanning welding seams tracking system, force sensor system, welding machine system, off-line programming system, tele-operation control human-machine interface, 2D vision system and stereo vision system, this architecture meet the requirement of real time control and the high intelligence of TRWS. Shiyi Gao [18] has constructed a hybrid control model based on architecture of multi-agent system, The production control system based on MAS improved real-time characteristics and robustness of the manufacturing system. Zhanghuajun [19] applied double robots coordination to realize double-side double arc welding and robot automatic welding for large thick plate of high strength steel.
5 Conclusions Modeling and controlling of welding robot systems with multi-agent depend on a reasonable way of modeling, the message system of agent communication, the middleware technology of DCOM or CORBA, the developed cooperation way. Multi-agent cooperation in robot welding is an inevitable trend in the development of intelligentized welding, multi information acquirement includes collecting weld pool image, weld seam image, back weld width image, weld current, weld voltage and welding sound, multi information acquirement and multi information fusion is a promising direction to realize controlling of welding robot systems based on
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multi-agent. Our tentative experimental platform is shown in figure 3, the weld pool image, weld seam image and back weld width image obtained by a vision sensor with three optical paths, establishing the relationship between the size of weld pool and back weld width will making the penetration control effectively. arc sound procesing Robot 2 controller
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Fig. 3. Layer figure of multi-robot WFMS Acknowledgement. This work is supported by National 863 plan of China under Grant No. 2009AAA042221, and Shanghai Sciences & Technology Committee under Grant No. 09JC1407100, P.R. China.
References [1] Minsky, M.: The Society of Mind, pp. 15–25. Simon and Schuster, New York (1986) [2] Russell, S., Norvig, P.: Artificial Intelligence:a Modern Approach, pp. 194–239. Prentice-Hall, Englewood Cliffs (2003) [3] Poslad, S., Buckle, P.: The FIPA-OS Agent Platform: Open Source for Open Standards. In: Proceedings of the 5th International Conference and Exhibition on the Practice Application of Intelligent Agents and Multi-agent Technology, Manchester, UK, pp. 355–368 (2000) [4] Walid, C.: Proposition of Formal Semantics for Multi-Agent Systems. Computer and Industrial Engineering 37, 453–456 (1999) [5] Lin Shangyang, M., Sanben, C.: Welding robot and application. China Machine Press (2000) [6] Bratman, M.: Intentions, Plans, and Practical Reason. Harvard University Press, Cambridge (1987) [7] Yuan Chongyi, M.: Petri Net, vol. 12, pp. 11–29. Southeast University Press (1989) [8] Piao, Y-j.: Study on Multi-agent Collaboration Controlling for Welding Flexible Manufacturing Cell. PhD Dissertation, Shanghai Jiao Tong University (June 2004) [9] Tao, Q.: System Integration and Optimizing Technologies for Arc Welding Robotic Flexible Manufacturing Cell. PhD Dissertation, Harbin Institute of Technology (June 2001)
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[10] Ma, G.: Research on Model and Control of Robotic Welding Flexible Manufacturing System with Petri Net. PhD Dissertation, Shanghai Jiao Tong University (November 2005) [11] Doran, J.E., Franklin, S., Jennings, N.R., Norman, T.J.: On Cooperation in MultiAgent Systems. The Knowledge Engineering Review 12(3), 309–314 (1997) [12] Bakken, D.: Middleware. In: Urban, J., Dasgupta, P. (eds.) Encyclopedia of Distributed Computing. Kluwer, Dordrecht (2001) [13] Zhuang, Y., Wang, W., Yun, W.: Status and Development of Robotic Control Technologies Based on Networks. Robot 24(3), 276–281 (2002) [14] Chen, Z., Lin, L., Yan, G.: An Approach to Scientific Cooperative Robotics through MAS (multi-agent system). Robot 23(4), 368–373 (2001) [15] Hsie, F.-S.: Developing cooperation mechanism for multi-agent systems with Petri net. Engineering Applications of Artificial Intelligence 22, 616–627 (2009) [16] Parher, L.E.: Current research in multi-robots systems. Artif Life Robotics 7, 1–5 (2003) [17] Zhang, L.: Study on Multi-agent Technique Based E-obotic Welding System. PhD Dissertation, Harbin Institute of Technology (December 2006) [18] Gao, S., Zhao, M., Zou, Y.-y., Liu, J.-w.: Production control modeling of manufacturing system based on MAS 13(6), 1067–1070 (2007) [19] Zhang, H.: New Technology of Double-sided Double Arc Welding and Robot Automatic Welding for Large Thick Plate of High Strength Steel. PhD Dissertation, Harbin Institute of Technology (April 2009)
Research on the Robotic Arc Welding of a Five-Port Connector Jiyong Zhong, Huabin Chen, and Shanben Chen Institute of Welding engineering, Shanghai Jiao Tong University, 200240, P.R. China e-mail:
[email protected]
Abstract. The uncertain factors of welding process, such as the welding distortion, alternate edges in the welded seams and the variable quantity of the gap, would affect the weld appearance and quality directly. For the robotic arc welding of a five-port connector it’s necessary to establish a welding quality control system, which can realize seam tracking and welding formation control. The system captures the visual signals and the voltage signals to realize three-dimensional seam tracking. Furthermore, it stabilize the shape of the weld pool under the conditions of the varied gap and alternate edge.
1 Introduction At present, most of the welding robots applied in the manufacturing are primarily “teach and playback” robots. The trajectory and welding parameters are preset, and the welding lacks of feedback of extraneous information and real-time control. So the traditional robotic welding needs extremely strict conditions. However in the actual welding process the environment and conditions will change inevitably. Such as welding joint location, gap and size dispersion caused by producing and machining of welding work piece, difference of trajectory by teach and actual seam, heat distortion during welding, weld penetration and formation and other factors will cause fluctuations of welding quality, and lead to the generation of welding defect [1-2]. For the welding of parts with complex structure, that require complex process, high quality and precision of welding and mass production, it’s especially necessary to adopt automatic and robotic intelligent technology which can completely replace manual welding [3]. With the development of sensor technology, arc sensing and visual sensing are applied in seam tracking. In order to achieve the high and low seam tracking control, the system captures voltage signals to obtain arc length information, and control the robot motion slider to adjust the height of welding torch [4, 5]. Vision sensing technology is widely used for seam tracking because it does not contact with the workpiece, not interfere with the welding process, and gets large amount of information and has versatility, etc. [6, 7]. Visual sensing is suitable for quality control of welding process [8-10] because it can get the two-dimensional or threedimensional information of dynamic weld pool, that directly reflect the dynamic behavior of molten metal in the welding process. T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 115–122. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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This paper studied the methods of real-time and quality control for robotic GTAW of five-port connector based on the arc and vision sensing.
2 Deformation Prediction and Fixture Design for a Five-Port Connector 2.1 Deformation Prediction for a Five-Port Connector Five-port connector will get distortion in the welding process because of its nature material and structure characteristics. That not only affects the size of the structure, but also reduces the local strength and stability of the structure, furthermore may affect the automatic welding quality. Therefore, scientific and quantitative prediction of welding deformation can provide important theoretical basis for subsequent welding fixture design and process optimization. Based on elastic-plastic theory Chen Huabin [11] from Shanghai Jiao Tong University created a five-port connector finite element model by Marc software. He used the elliptical Gaussian model as a surface heat source model for simulation of the welding heat, and got the welding deformation of five-port connector. Shown in Figure 1, the weld center gets compressive stress, however residual stress of the other nodes in the weld zone is tensile stress state. Radial deformation of the weld is significantly greater than circumferential one.
(a)
(b)
Fig. 1. Residual stress distribution for five-port connector (MPa) (a) Radial stress (b) Circumferential stress [11]
2.2 Fixture Design of a Five-Port Connector Combined with the preceding results of numerical simulation of five-port connector, in allusion to the weld pass features of five-port connector, we designed three sets of five-port connector robotic welding fixtures. Figure 2 is the
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three-dimensional figure of welding fixture for four small flanges welding. This fixture can realize the welding from inside of flange. It makes the trajectory be parallel with ground and reduces the difficulty of the coordination between robot and external axes, while only robot moves. However the outside welding fixture makes the external axes move and robot static.
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(b)
Fig. 2. Welding fixture of five-port connector for small flange welding (a) inside welding; (b) outside welding
Figure 3 is three-dimensional figure of welding fixture of five-port connector for hemisphere welding. This fixture has an umbrella structure which can make it conveniently go through the big flange. The screw structure props the umbrella up and down to effectively prevent the contraction of shell deformation of the welding process.
Fig. 3. Welding fixture of five-port connector for hemisphere welding
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3 Welding Quality Control System for Robotic Arc Welding of Five-Port Connector The welding control system for robotic arc welding of five-port connector is based on Motoman-UP6 robot of Shanghai Jiao Tong University Welding Engineering Institute. The system uses sensor that we designed ourselves, interface of robot arc welding monitoring, welding fixture, INVERTER 500P Dual Inverter AC square wave power and two-axis positioned and the corresponding software system to achieve automation and intelligence of welding process. The architecture of the system is shown in figure 4.
Fig. 4. Robot GTAW system’s architecture
The system which can realize real-time seam tracking and weld formation control with software system we compiled and the visual and arc sensing system, successfully transformed the original ‘teaching- playback’ robot into a welding robot system with independent function.
4 Acquisition and Processing of Welding Seam and Weld Pool Image Information Pool image features obtained by visual sensor, including the outline of the arc column, pool center, weld gap and seam center. In order to make arc robot achieve the seam tracking and weld formation control simultaneously, the weld pool and welding seam characteristic feature information must be obtained at the same time. The image from the welding seam always contains noise. For successive image processing, it’s necessary to denoise. Wavelet transform due to its low entropy, anti-relativity, flexibility, multi-resolution, is most successful in the application of image compression field. Through the accumulation of empirical data, due to the changes of the average gray value, image segmentation technology selects threshold automatically. Figure 5 is the image processing of Five-port connector welding
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[12]. Image processing includes restoration of degraded image, wavelet denoising, image segmentation, edge detection, curve fitting, and the feature information of weld gap, edge of welding seam and the weld pool center.
Fig. 5. Image processing of Five-port connector welding [12]
Shen Hongyuan [13] in his doctoral thesis, studied a ‘double window’ of the image processing method. The method takes image from the two windows in the weld area and arc column simultaneously, and that greatly reduces the image processing time and improves image processing accuracy.
5 Three Dimensional Seam Tracking of the Five-Port Connector Welding Image information obtained by passive visual sensing can realize acquisition and tracking of the weld path on the plane. Voltage signal obtained by arc sensing can realize height direction tracking. Using multi-sensor information fusion technology to process the information of visual and arc sensing, will enable the acquisition and real-time tracking of three dimensional welding seam.
5.1 Plane Seam Tracking The relative position between the projection pole position of tungsten on the image and weld centerline position is defined as the position deviation. And take the relative angle between the camera position and the tangent direction as the angle of deviation. Design a controller combined feedforward control and feedback control, which according to the image information can detect the deviation of tungsten pole and welding seam center, and control robot’ movement to keep forward along with welding seam.
5.2 Seam Height Tracking To achieve seam height tracking, the first step is to establish the relationship between the voltage signal characteristics and height of the welding torch. Voltage
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signal processing methods include mean filtering, filtering the negative half-wave voltage signal and mean calculations. Capture voltage signals with different height of torch, and establish the relationship of changes of voltage signal characteristics and changes of height of torch. Kong Meng [14] in his doctoral thesis presents a seam height tracking method with free calibration, and designed three welding process controller with seam height tracking. They are PID control, fuzzy control and fuzzy feedforward plus feedback control. And the last method not only can guarantee the height tracking, but also ensure the smooth movement of the robot.
6 Weld Formation Control Methods In order to ensure the stability of welding, we should adjust welding parameters as little as possible in the welding process. However, in robot welding process, there are gap and wrong side caused by the instable size of products and heat deformation, which will cause uneven formation and instability of the welding quality, without promptly adjusting the welding parameters. Under the condition that welding parameters and circumstance are constant, usually the gap becomes the principal contradiction to the welding quality in the welding process. You can adjust the welding current and wire feed speed to ensure the weld formation. Based on the volume conservation of welded joints metal, the document [13] analyses the difference between the amount of filler metal of welded joints and the need to fill. With theoretical analysis, it deduces the relationship of reinforcement and welding current, the wire feed speed, weld gap and the weld pool width, and sets a prediction model of reinforcement. An open-loop controller is established with the weld gap as input, meanwhile a wire feed speed closed-loop controller is created using the prediction model of reinforcement. The document [12] deeply analyses the factors impacting the weld pool width, and presents the quantitative rules of wire speed, welding current and the deviation of weld gap. Ip-Wb nonlinear self-tuning controller based on the Hammerstein model is designed. Experiments show that the controller can better overcome the external interference, and can well guarantee the stability of the back width of weld seam, with less adjustment compared with the conventional PID controller. In order to meet stability requirements of the back width of weld seam and the front reinforcement, a composite intelligent controller based on the presetting of parameters feedforward is designed. It adjusts the wire speed by fuzzy monitoring and adding gap feedforward, and it online adjusts the welding performance combining the artificial experience with weld pool characteristics.
7 Conclusions In this paper, the welding quality control system of robotic arc welding of a fiveport connector is studied. We get the prediction of deformation of five-port in the welding process, and design three sets of fixture for the robot arc welding of the five-port connector. The methods to realize the seam tracking and weld formation control for the robot arc welding of a five-port connector are studied to support
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theoretical basis for further research on the automatic welding quality control system of a five-port connector. Figure 6 is the flow chart of the robotic arc welding of a five-port connector. Acquire arc voltage
Capture image
Image processing and parameters acquisition
Robot control
Weld height tracking
Weld tracking and weld formation control
Arc off
Arc on
Start
Arc signals process
End
Fig. 6. Flow chart of the robotic arc welding of a five-port connector Acknowledgement. This work is supported by the National Science Foundation of China under the Grant NO. 60874026,and Shanghai Science & Technology Committee under Grant NO. 09JC1407100, P.R. China.
References [1] Paton, B.E., Wu, L.: Aerospace Welding Technology Development and Future. Aeronautical Manufacturing Technology 11(3), 37–42 (2004) [2] Lin, S.Y., Chen, S.B., Li, C.T.: Welding Robot and its Application. Beijing Industry Press, Beijing (2000) [3] Chen, S.B.: Advances in Intelligent Robotic Welding Technology. Robot Technology and Applications 3, 8–11 (2007) [4] Lai, X., Hong, B., Yin, L., Yue, Y.B.: Study on the High and Low Tracking Weld Method. Pipe 31(4), 35–37 (2008) [5] Luo, C.H., Zhang, F.J., Huang, Y.: Sensing Method Based on Narrow Gap Arc Welding Torch Height Adaptive Tracking System. Welding Technology 30(4), 40–41 (2001) [6] Shen, H.Y., Lin, T., Chen, S.B.: A study on vision-based real-time seam tracking in robotic arc welding. In: Proceedings of RWIA 2006. Series Lecture Notes in Control and Information Sciences, vol. 362, pp. 311–318. Springer, Berlin (2007) [7] Shen, H.Y., Fan, C.J., Lin, T., Chen, S.B., Xu, A.J.: Weld tracking technology of Square-wave AC GTAW for welding robot based on passive vision. Welding & Joining 11, 33–35 (2007) [8] Chen, S.B., Cao, J.M., Xu, C.M., Wu, L.: Visual sensing and real-time control of weld pool dynamics in pulsed GMAW. Transactions of The China Welding Institution 23(4), 17–23 (2002) [9] Bae, K.Y., Lee, T.H., Ahn, K.C.: An optical sensing system for seam tracking and weld pool control in gas metal arc welding of steel pipe [10] Shen, H.Y., Wu, J., Lin, T., Chen, S.B.: Arc welding robot system sith seam tracking and weld pool control based on passive vision. International Journal of Advanced Manufacturing Technology 39(7), 669–678 (2008)
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[11] Chen, H.B., Xu, T., Chen, S.B., Lin, T.: Numerical Simulation and Control of Robot Welding deformation of Five-Port connector. Lecture Notes in Control and Information Sciences 362, 203–209 (2007) [12] Chen, H.B.: The development of robotic GTA welding quality control sys tem for five-port connector on the launch vehicle propulsion [PHD Thesis]. Shanghai Jiao Tong University (2009) [13] Shen, H.Y.: Research on real-time seam tracking and control method of weld formation for arc welding robot based on vision sensing in aluminum alloy welding [PHD Thesis]. Shanghai Jiao Tong University (2008) [14] Kong, M.: Research on process control method for arc welding robot based on multiinformation sensing real-time [PHD Thesis]. Shanghai Jiao Tong University (2009)
Mixed Logical Dynamical Model for Robotic Welding System Hongbo Ma and Shanben Chen* Intelligentized Robotic Welding Technology Laboratory, Shanghai Jiao Tong University, 200240, China e-mail:
[email protected]
Abstract. Robotic welding system is normally modeled either by continuous model or discrete model. However, robotic welding system is naturally hybrid. Hence, it will be more appropriate to model the robotic welding system from the view of hybrid system. In this paper, mixed logical dynamical (MLD) modeling framework is adopted to study the modeling of robotic welding system and the robotic welding system mixed logical dynamical model is established through out the analysis of hybrid characteristics of the robotic welding system. Finally, welding experiment is conducted based on this model and the result shows the MLD model can give a more general description of the robotic welding system.
1 Introduction Robotic welding system is a kind of typical flexible manufacturing system (FMS). The manufacturing system is normally expressed as hybrid, which means the combination of continuous and discrete dynamics. Traditional modeling method, neither CVDS (continuous variable dynamic system) nor DEDS (discrete event dynamic system), are more conservative so that they cannot describe this kind of hybrid system. As a result, hybrid system methods are studied to apply for the manufacturing system [1-4]. Recently, mixed logical dynamical (MLD) model is also applied to the manufacturing system [5]. When controlling a robotic welding system, there are other factors that need to be considered which involve not only the controlling of the robot motion, but also involve the containing of welding devices [6]. Petri net (PN) theory has been adopted to model the welding flexible manufacturing system, especially robotic welding system [6-8]. However, the Petri net model can only describe the discrete part of the robotic welding system. The continuous part of the robotic welding system is not included during the modeling process. Usually, it is difficult to model the whole robotic welding system by a single model because of the coexisting of the continuous and discrete dynamics. Therefore, in the robotic welding area, it is important to establish how to build an appropriate model which supplies a foundation for managing and controlling the system. *
Corresponding author.
T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 123–128. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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Bemporad and Morari [9] presented a framework for modeling and controlling systems described by interdependent physical laws, logic rules, and operation constraints, denoted as mixed logical dynamical systems. This modeling framework can easily combine the continuous and discrete dynamics of the robotic welding system. According to the hybrid nature of welding process, mixed logical dynamical modeling method has been applied to the welding process [10]. However, the MLD model cannot describe the robot and welding devices. Therefore, the MLD framework will be used to model the whole robotic welding system. This paper is organized as follows. In section 2 a concise introduction of the welding equipment of the framework of MLD model is given. Section 3 gives the modeling process of the robotic welding system. Finally, some concluding remarks are made in Section 4.
2 Mixed Logical Dynamical Model Hybrid systems are dynamic systems that involve the interaction of continuous dynamics (modeled as differential or difference equations) and discrete dynamics (modeled by Petri Net or Automata). Hybrid systems have been a topic of intense research activity in recent years, primarily because of their potential importance in applications. Hybrid models are important to system analysis and control of hybrid systems. MLD systems generalize a wide set of models, among which there are linear hybrid systems, finite state machines, some classes of discrete event systems, constrained linear systems, and nonlinear systems whose nonlinearities can be expressed(or, at least, suitably approximated) by piecewise linear functions. In MLD modeling method, a binary variable is assigned to each statement. If and only if the statement is true, the value of the binary variable is true. For example, statement xi means “topside weld pool length is too short” for welding process. Thus, a binary variable
δi
is added to replace statement xi , expressed
as (3).
xi ≡ True ↔ δ i = 1
(1)
Combination of statements may be described with combination of binary variables, which can be suitably translated into linear inequalities involving binary variables δ i . Therefore, the system is modeled as an MLD through the following linear relations (4).
x (t + 1) = At x (t ) + B1t u (t ) + B2tδ (t ) + B3t z (t ) y (t ) = Ct x (t ) + D1t u (t ) + D2tδ (t ) + D3t z (t ) E2tδ (t ) + E3t z (t ) ≤ E1t u (t ) + E4 t x (t ) + E5t
(2)
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Where x is a state vector of the system and contains continuous and binary variables. y and u are respectively output and input vectors of the system, which consist of continuous and discrete parts. The vectors
δ ∈ {0,1}
rl
and
z ∈ R rc
are auxiliary binary and continuous variables, respectively.
3 Modeling of the Robotic Welding System The continuous process of robotic welding system can be described as equation (3).
⎧ x( k + 1) = x( k ) + u xTs ⎪ y ( k + 1) = y ( k ) + u T y s ⎪ ⎪ z ( k + 1) = z ( k ) + u zTs ⎪ ⎪a( k + 1) = a( k ) + uaTs ⎨b( k + 1) = b( k ) + u T b s ⎪ ⎪c(k + 1) = c( k ) + ucTs ⎪ ⎪ I = ub1 I p + (1 − ub1 ) I b ⎪V = k V + b Vf Vf Vf ⎩ f
(3)
x, y, z , a, b, c are the position and status of the robot, u x , u y , u z , ua , ub , uc are the velocity of the robot, Ts is the sampling time, I is
where
the welding current, speed,
I b is background current, I p is peak current, V f is feed
VIb ,VI p ,VV f are the control voltages of background current, peek current
and feed speed respectively.
k Ib , k I p , kV f , bIb , bI p , bV f are the parameters of the
equation. ub1 is a discrete input to differentiate the background current and the peak current. Hence, a model selector variable i ( k ) is added, expressed as equation (4) and the last two lines of the continuous model (3) will change according to the model selector, shown in equation (5).
⎧i (k ) = 1 if ub1 = Ture ⎨ ⎩i (k ) = 2 if ub1 = False
(4)
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⎧⎪ I = k IbVIb + bIb if i( k ) = 1 ⎨ ⎪⎩V f = kVf VVf + bVf
(5)
⎧⎪ I = k IpVIp + bIp if i( k ) = 2 ⎨ ⎪⎩V f = kVf VVf + bVf
According to different working mode of the robotic welding system, the robotic welding system can be divided into four stages. They are motion before welding, welding motion, motion after welding and fault mode and four discrete states xb1 , xb 2 , xb3 , xb 4 are introduced to represent these four modes. In the welding motion stage, another three different modes of the welding power are divided. They are stand-by mode, ready mode and arc mode and three discrete states xb 5 , xb 6 , xb 7 are also introduced to represent these three modes. Seven discrete input variables
ub 2 , ub 3 , ub 4 , ub 5 , ub 6 , ub 7 , ub8 are introduced to describe the
states of different switches and discrete fault input, such as welding switch, arc established, water tank switch, gas valve switch, welding power switch, visual computer result and robot fault input. There are also two discrete events δ1 , δ 2 , which will be triggered when the robot arrive the start point and end point of the weld seam. Those discrete dynamics can be expressed as the automata model, shown in Fig. 1.
b7
b8
ub 8
u b6
xb 5 u b2 ∧
¬u b 3
u b8
∨u
∧(
) u b5 ∨¬
¬u b 6 ¬u b 4
∨ u b3 ¬
¬u
xb 3
xb 4
¬ub 2 ∧ ub 3
¬ub 6
xb 6 ¬ub 2 ∧ ¬ub 3
xb 2
ub 4 ∧ u b 5 ∧ u b 6
δ2
ub 2 ∧ ub 3
xb1
δ1
xb 7
Fig. 1. Discrete automata model
Benefiting from the software HYSDEL (hybrid system description language) [11], the MLD model for robotic welding system can be easily obtained, shown in (6). Due to the limited space, this paper does not give the numerical values of the MLD model (6).
Mixed Logical Dynamical Model for Robotic Welding System ⎡ xr (t + 1) ⎤ ⎡ Arr Arb ⎤ ⎡ xr (t ) ⎤ ⎡ B1rr B1rb ⎤ ⎡ur (t ) ⎤ ⎢ x (t + 1) ⎥ = ⎢ A ⎥+⎢ ⎥ ⎥⎢ ⎥⎢ ⎣ b ⎦ ⎣ br Abb ⎦ ⎣ xb (t ) ⎦ ⎣ B1br B1bb ⎦ ⎣ub (t ) ⎦ ⎡ B2rb ⎤ ⎡ B3rr ⎤ +⎢ ⎥ d (t ) + ⎢ B ⎥ z (t ) B ⎣ 2bb ⎦ ⎣ 3rb ⎦ ⎡ yr (t ) ⎤ ⎡ Crr Crb ⎤ ⎡ xr (t ) ⎤ ⎡ D1rr D1rb ⎤ ⎡ur (t ) ⎤ ⎥+⎢ ⎥ ⎢ y (t ) ⎥ = ⎢C ⎥⎢ ⎥⎢ ⎣ b ⎦ ⎣ br Cbb ⎦ ⎣ xb (t ) ⎦ ⎣ D1br D1bb ⎦ ⎣ub (t ) ⎦
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(6)
⎡ D rb ⎤ ⎡ D rr ⎤ + ⎢ 2 ⎥ d (t ) + ⎢ 3 ⎥ z (t ) D ⎣ 2bb ⎦ ⎣ D3br ⎦ ⎡ur (t ) ⎤ ⎡ xr (t ) ⎤ E2 d (t ) + E3 z (t ) ≤ E1 ⎢ ⎥ + E4 ⎢ x (t ) ⎥ + E5 u t ( ) ⎣ b ⎦ ⎣ b ⎦
Based on the robotic welding system MLD model, the welding experiment is conducted. The discrete inputs, discrete outputs and discrete events of the robotic welding system are recorded during welding. Table 1 shows several sampling times of the discrete variables by binary codes. The binary codes match the discrete automata model shown in Fig. 1. As a result, the effectiveness of this MLD model is demonstrated by this experiment. Table 1. Sampling results of discrete variables
xb1 , xb 2 , xb3 , xb 4 , xb 5 , xb 6 , xb 7 ub1 , ub 2 , ub 3 , ub 4 , ub 5 , ub 6 , ub 7 , ub 8 , δ1 , δ 2 1000000
0000001000
0100000
0000001010
0010000
0000001001
0001000
1101111100
0000100
0001101010
0000010
000111010
0000001
1111111000
4 Conclusions In this paper, a novel MLD modeling method is studied to describe the robotic welding system. This study will make the modeling of robotic welding process more easily and give a novel understanding of robotic welding process from hybrid system. In the future, more dynamics in robotic welding process will be included by using MLD modeling method. Acknowledgement. This work is supported by the National Natural Science Foundation of China under the Grant No. 60874026, and Shanghai Sciences & Technology Committee under Grant No. 09JC1407100, P.R. China.
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References [1] Hitchcock, M.F., Baker, A.D., Brink, J.R., et al.: The role of hybrid systems theory in virtual manufacturing. In: IEEE/IFAC Joint Symposium on Computer Aided Control System Design, Los Alamitos, CA, pp. 345–350 (1994) [2] Cassandras, C.G., Liu, Q., Gokbayrak, K., et al.: Optimal control of a two-stage hybrid manufacturing system model. In: Proc. 38th IEEE Conference on Decision and Control, Phoenix, Arizona, USA, pp. 450–455 (1999) [3] Pepyne, D.L., Cassandras, C.G.: Optimal control of hybrid systems in manufacturing. Proceedings of the IEEE 88(7), 1108–1123 (2000) [4] Balduzzi, F., Menga, G.: A state variable model for the fluid approximation of flexible manufacturing systems. In: IEEE International Conference on Robotics and Automation, Leuven, Belgium, pp. 345–350 (1998) [5] Cheng, S., Zhang, H.: Research on modeling method for manufacturing systems based on mixed logical dynamic. Computer Engineering and Application 41(32), 193–195 (2005) [6] Ma, G.H., Chen, S.B.: Modeling and controlling the FMS of a welding robot. International Journal of Advanced Manufacturing Technology 34(11), 1214–1223 (2007) [7] Qiu, T., Chen, S.B., Wu, L.: Petri net based modeling and analysis for welding flexible manufacturing cell. China Welding 10(1), 1–6 (2001) [8] Ma, G.H., Chen, S.B.: Model on simplified condition of welding robot system. Journal of Advanced Manufacturing Technology 33(9), 1056–1064 (2007) [9] Bemporad, A., Morari, M.: Control of systems integrating logic, dynamics, and constraints. Automatica 35(3), 407–428 (1999) [10] Ma, H., Wei, S., Lin, T., et al.: Mixed logical dynamical model for back bead width prediction of pulsed GTAW process with misalignment. Journal of Materials Processing Technology 210(14), 2036–2044 (2010) [11] Torrisi, F.D., Bemporad, A.: HYSDEL-a tool for generating computational hybrid models for analysis and synthesis problems. IEEE Transactions on Control Systems Technology 12(2), 235–249 (2004)
Part II
Sensing of Arc Welding Processing
A Method of Seam Tracking Based on Passive Vision Long Xue1,3, Lili Xu2, and Yong Zou3 1
School of Mechatronics Engineering of China University of Petroleum, Beijing 102249 2 School of Mechatronics Engineering of Beijing University of Chemical Technology, Beijing 100029 3 Optical-mechanical-electronic Technology Key Laboratory of Beijing Institute of Petro-chemical Technology, Beijing 102617 e-mail:
[email protected]
Abstract. In the light of the welding image characteristics obtained by passive vision sensing, the mathematical morphology is introduced, combined with image segmentation, wave filteration, edge detection, feature extraction for seam centerline extraction and welding quality adjusting. It is shown that the method has good effect on seam centerline extraction and quality adjustment.
1 Introduction A variety of physical energies produced during the welding process always cause great deformation of welding parts, bring about a seam position deviation and create some difficulties for welding [1]. Therefore, the accurate seam tracking is critical to ensure welding quality in the welding process. Currently the visual methods used in seam tracking include passive and active optical vision. The former acquire welding zone images directly by using the system composed of an arc or ordinary light source and a CCD camera, the latter obtain images with an auxiliary light source such as laser projection onto the surface of a workpiece. There is no advanced errors because the arc itself is located in the monitoring area in passive vision sensing, and it gets numerous information of the joint and welding pool with a simple equipment and low cost [2], therefore it is widely used. This study adapts the passive vision sensing.
2 Experiment Setup Figure 1 shows an all-position pipeline automatic welding robot system, the mechanical system includes a rail and a walking trolley which is installed on the rail through the clamp system, the walking trolley with the welding torch goes around in circle along the outer wall, the tilting mechanism and slider mechanism on the walking trolley swing horizontally and reciprocate up and down to track the seam. In the experiment, the direct observation is vulnerable to the serious arc T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 131–138. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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interference, so a specific wavelength optical filter is added in front of the CCD to filter some noises, but it’s still difficult to obtain useful seam information directly because of the great amount of noise presented in the image. Therefore, a series of image processing technologies such as image segmentation, wave filteration, edge detection, feature extraction must be used.
Fig. 1. Experiment setup
3 Seam Image Processing 3.1 Image Segmentation Image segmentation is used to extract useful features from images. Threshold is used most commonly while there is a great gray level differentia between the seam and surrounding. The raw image is set as f ( x, y ) , the appropriate threshold T is selected to divide image into two parts [3]:
⎧1 f ( x, y ) > T g ( x, y ) = ⎨ ⎩ 0 f ( x, y ) ≤ T
(1)
The seam image in the research system was selected as the raw image, it can be seen from Fig. 2(a) that the contrast between the welding zone and background is great and then a large threshold 0.8 was selected, the Fig. 2( b) shows the seam is separated clearly from background.
(a) Raw image
(b) Binary image
Fig. 2. Image binarization
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3.2 Wave Filtering Binarization filters off some noises, but it requires further de-noising because sinks still exist in the middle and edge of the welding pool and there are spot and blocky noises in the surrounding of the welding pool. Wave filtering includes the filtering of spatial and frequency domain. To spatial filtering, a neighborhood operation is carried out in virtue of template, each pixel in the output image is obtained by calculating the template and corresponding input image pixel. Spatial filtering methods include commonly the linear filtering, median filtering and adaptive filtering. Frequency filtering contains the ideal, Butterworth, exponential and other forms of low pass filtering, which make low frequency component pass and filter the high frequency component. Generally, the image edge and noise correspond to the high frequency part of Fourier transform. The above-mentioned methods have good effects on Gaussian noise, saltpepper noise and multiplicative noise, but they are unfitted for this study because the various noises exist in the surround of the weld pool and the edge is jagged with the seam drowned by arcs and noises, so morphological filtering is adopted. Mathematical morphology is a nonlinear image processing method, which uses a certain shape structure element to measure and extract corresponding shape in the image. It can simplify the image data, maintain the basic shape characteristic and remove the irrelevant structure. There are four basic operations: dilation, erosion, opening and closing [4]. Regard Ω as two-dimensional Euclidean space, image A and structural element B are subsets in the space, point b ∈ Ω , the four basic operations are defined as follow: (1) Expansion:
A ⊕ B = {a + b a ∈ A, b ∈ B} = ∪ Ab b
Each element in A is a transformation result of the corresponding element from A to the new coordinate system centered by b . Expansion expands boundary by merging all contiguous background into the object, such as filling the hollow. (2) Erosion:
{
}
AΘB = z ∈ Ω B z ⊆ A = ∩ Ab
Erosion eliminates the small and meaningless objects such as the boundary points. The results of expansion and erosion are related to the structure elements. (3) Opening: A° B = ( AΘB ) ⊕ B It means A is eroded and then expanded by B, which can eliminate the small branches and smooth the boundary of large object with no obvious change in the size of the original object. (4) Closing: A • B = ( A ⊕ B )ΘB It means A is expanded and then eroded by B, which can fill up the small cavity in object, connect adjacent objects and smooth the boundary with no obvious change in the size of the original object.
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The different structure elements and operations are used to detect useful information from images and noises with different characteristics. It can be seen from Fig. 2 (b) that the edge of welding pool is jagged and there are spot and blocky noises around the pool, the first step is to design a structure element whose scale is larger than the noise blocks and carry out opening operation to eliminate them, as shown in Fig. 3 (a);the second step is to use a structure element whose scale is larger than the hollow blocks and carry out closing operation to fill the edge, as shown in Fig. 3 (b);it can be seen clearly from Fig. 3 (b) that there is a clear groove in the welding pool corresponding with the seam whose lateral length is much shorter than its neighborhood, so a rectangular element with a lateral length a little longer than the welding pool is designed to carry out opening operation to reveal the covered part of weld pool [5], as shown in Fig. 3(c);the third step is to design a flat element with a longitudinal width a little longer than the seam to carry out closing operation to fill up Fig. 3(c), then the image Fig. 3(c) is subtracted from the operation result, that is, ( A • B − A) , to remove welding pool part in the image and extract the seam, as shown in Fig. 3(d); If there are still some small noises in Fig. 3(d), a element with lateral and longitudinal sizes slight larger than noises is designed to do opening operation, then the straight and clear seam is obtained as shown in Fig. 3(e).
(a) A1
A o B1
(d) A4
(b) A2
A3 B4
(c) A3
A1 B2
A3 (e) A5
A2 o B3
A4 o B5
A is a raw bianry image 2(b), A1 is 3(a), A2 is 3(b), A3 is 3(c), A4 is 3(d), A5 is 3(e), B1, B2, B3, B4, B5 are five different structure elements. Fig. 3. Seam extraction processing
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3.3 Edge Detection Edge detection is the key to extract the seam information. Most classical approachs take into account of the gray scale change for the area adjacent each pixel based on the raw image and the first or second derivatives change law near the edge are used for edge detection. The usual operators include differential operator, Laplacian operator, Canny operator, morphology, etc [1]. Differential operator is based on the first derivative, but it needs some post processes such as the connection and refinement, Laplacian and Canny operators are two improved algorithms in common use. Laplacian operator falls into the second-order edge detection, which is realized by finding the zero crossing of the second derivative from the image gray values, Canny operator can achieve a balance between reducing noises and detecting the edge, which is better than other operators. Morphological can also be used in edge detection according to the characteristics that expansion operation expands the image edge and erosion operation reduces it. For a binary image, there are three detection operators [6]:(1) A ⊕ B − A ;(2) A − AΘB ;(3) A ⊕ B − AΘB . A small size element 3 × 3 is selected because it is optimal that the detected edge is composed of the single point pixels for the seam centerline extraction. The (1) can be used to get the outer edge with a pixel expansion;(2) is just used to get the edge;(3) is used to get the outer edge and inner edge. Fig. 4 is the result by using (2).
3.4 Feature Extraction A clear seam edge is got through above mentioned processes. In order to guarantee the welding torch to track along the seam precisely, the accurate seam centerline position must be obtained. Only a simple algorithm is needed here: To scan column by column, once a point whose pixel value is equal to 1 (that is the coboundary point A(i1 , j ) ) is found, the scan is stopped at once, and then to scan from bottom to top in this column to find the first point whose pixel value is 1 (that is the lower boundary point A(i2 , j ) ), the average value is calculated by
i = (i1 + i2 ) / 2 (that is the center point horizontal coordinate),the result line is got by connecting all center points as shown in Fig. 5.
Fig. 4. Seam edge detection
Fig. 5. Seam centerline extraction
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4 Seam Adjusting The welding quality needs to be estimated. By observing a large number of seam images, we find a common ground for all the dis-qualified seams that the deviation between the welding pool coboundary and the centerline is much too different from that between the welding pool lower edge and the same centerline. So the following method is taken: (1) To scan the filtered binary image(Fig. 3 (a)) from left to right to find the first point A(i1 , j ) whose pixel value is 1(to get the coboundary point coordinate corresponding to this column), then to scan this column from up to bottom and get A(i2 , j ) (the lower limb point coordinate corresponding to this column). (2) The point coordinate in the centerline corresponding to this column is (i , j ) (the variable h which is initialized to 0 is increased with 1 automatically if the top and bottom points are found on each scan), selecting a threshold p , then comparing the D-value between (i1 − i ) and (i2 − i ) , (i1 − i) − (i2 − i ) < p shows the D-value does not exceed the scope, the variable k which is initialized to 0 is increased with 1 automatically; if (i1 − i ) − (i2 − i ) > p , the variable m which is initialized to 0 is increased with 1 automatically when (i1 − i ) > (i2 − i ) , and the variable n which is initialized to 0 is increased with 1 automatically when
(i1 − i ) < (i2 − i ) . (3) To compare k h with g after previous steps( g is the ratio of k h for qualified welding), k h < g indicates the welding pool deviates from the seam center in scope, the welding is confirmed as " qualification "; in contrast, k h < g indicates the deviation is oversize, the welding is confirmed as " disqualification ", simultaneously, m > n denotes downward bias and m < n is upward bias. After above judgment, the results are given to the executing agency to do appropriate readjustments. Fig. 6 shows the analysis results of several seam images.
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(b) Downward bias
(c) Downward bias
(e) Upward bias
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Fig. 6. Analysis results
5 Conclusions As for the seam tracking system based on passive vision sensing, the seam centerline must be extracted accurately from the welding zone and the welding quality must be evaluated to achieve accurate seam tracking in real time. But many severe noises exist in CCD images due to splash, dust, electric arc light and other effects in welding process. This paper adopts an effective seam image processing algorithm to extract the seam centerline and realizes qualitative adjustment. The next task will be to analyze the deviation accurately and send the deviation to an executing agency to achieve quantitative regulation through a large amount of experiments and research. Acknowledgement. I would like to express my gratitude to all those who helped me during the writing of this paper. A special acknowledgement should be shown to the following authors, from whose lectures I benefited greatly. I am particularly indebted to my family, who have been assisting, supporting and caring for me all of my life. Finally I wish to extend my thanks to the National "863 Program" project supplied me with this project (foundation item: 2009AA04Z208).
References [1] Liu, X., Ai, X., Hu, X.: Study on Welded Seams Image Processing Based on Structure Light Vision Sensing. Jiangxi Science 27(2), 215–218 (2009) [2] Gao, X., Liu, M., Chen, J.: Application of Vision Sensing and Image Processing Technique in Welding Control. Modern Welding Technology (11), 15–18 (2006)
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[3] Luo, J., Feng, P., Halidan, A., et al.: The Application of MATLAB 7.0 in Image Processing. Mechanical Industry Press, Beijing (2005) [4] Sun, Z.: MATLAB6.x Image Processing. Tsinghua University Press, Beijing (2002) [5] Li, X., Gao, X.: Application of Gray Scale Morphology to Image Processing of Weld. Welding & Joining (10) 44–46 (2007) [6] Li, X., Gao, X., Ding, D., et al.: Weld Image Processing Based on Binary Morphology. Welding Technology 35(6), 9–11 (2006)
Research on a Trilines Laser Vision Sensor for Seam Tracking in Welding Zengwen Xiao School of mechanical Engineering, Nanjing Institue of Technology, Nanjing, 211167 e-mail:
[email protected]
Abstract. In recent years, laser vision sensors have been widely used to track the weld seam. However, they could make errors when the camera rotating around the welding torch. A trilines (triple laser lines) laser vision sensor (TLVS) was used in this work to overcome the problem. Three laser lines, which were made by an aspheric lens with a point laser source, provided important information to find the weld seam lines. Clearer images were obtained through the medium filter, a thinning transformation, fitting straight lines, and hence, the enhanced range data were obtained by the extraction method. Through the proposed method, 10 images per second could be processed. Experiments were performed with the TLVS attached to a robot. It was found that this TLVS was very reliable for tracking the weld interface.
1 Introduction Laser vision sensors are widely used in automated manufacturing processes in noisy environments, such as welding, because such sensors are robust even in the presence of extreme noise. Smati et al [1]., Clocksin et al. [2], and Agapakis [3] obtained the feature point from the range data of a base metal using a conventional laser vision sensor, and they suggested that the seam can be found and tracked. Since then, Suga and Ishii [4] have applied a laser vision sensor in welding process control and automatic welding inspection. Recently, many kinds of welding processes have been introduced in an effort to improve productivity. Laser vision sensors using a charge-coupled device (CCD) to obtain accuracy performance have been investigated. If the position and direction of the camera relatively to the rectangular coordinate system of the welding torch are constant, the traditional laser visions behave well. However, it could make errors produced by advanced control when the camera rotating around the welding torch along a curving seam, because the laser beam line must leave a distance ahead of the torch. In this study, a method was developed to process triple-range data simultaneously using three laser patterns in an image. This method produced a high data acquisition rate, even while using a standard CCD. By employing a 3-laser-line pattern using a single laser source and lens, a simple, lightweight sensor structure could be fabricated. The prototype for the trilines laser vision sensor (TLVS) was constructed in this way and applied to tracking the seam for real-time welding. We T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 139–144. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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inserted the entire welding part into the laser area and imaged the laser lines to find the seam at once. This method can find the seam very quickly.
2 TLVS System The TLVS system consists of a laser source, aspheric lens, and CCD camera. The laser used for TLVS was a IIIb-class diode laser with power of 100 mW and wavelength of 650 nm. An aspheric lens was used to generate three laser strips. This aspheric lens can generate three laser lines at 120 mm, which are 7 mm apart. A 1⁄2-in. CCIR CCD with 640 × 480 pixels was also used to capture the maximum 20 frames per second. A 650-nm band pass filter with band width of 10 nm was installed in this device. To process the incoming images in the CCD, image grabber Meteor MC was used. The digital images were obtained from analog images with this grabber. An industrial computer was used to process images and calculate robot motions. The image processing program was written on the basis of C++. Also, for real-time tracking, the image processing program was created in such a way as to be integrated into the positioner control program. The horizontal and vertical resolutions of TLVS were 0.07 mm. The TLVS software includes an image processing program that is capable of processing 10 frames per second. In terms of a conventional single-line laser vision sensor, this would be the equivalent of more than 30 frames per second.
2.1 Basic Principles of TLVS The laser vision sensor obtains range data through optical trigonometry. A TLVS can acquire data for triple laser lines using the same method employed by traditional single-line laser vision sensors. Figure 1 shows the basic principles of a laser vision sensor. Figure 1 depicts a laser vision sensor, which projects a trilines-shaped structural laser onto the workpiece and acquires range data by measuring the form of this laser plane with a CCD. The trilines patterned laser vision sensor utilizes the principle to obtain range data. The image from CCD contains triple laser lines. Therefore, this method requires the additional process of extracting and discriminating triple laser lines included in the image.
Fig. 1. Basic Principles of TLVS
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In addition, when the CCD is structurally perpendicular to the ground in the TLVS, each laser plane has a different incidence angle to the workpiece and distance from the starting position to the sensing position, causing the light intensity to change greatly. In such cases, differences in laser line intensity transmitted to the workpiece will increase, and the thickness and intensity of the laser lines generated on each laser plane will vary. Therefore, the TLVS image processing algorithm should take this phenomenon into account. This new image processing algorithm is shown after this section. In order to be convenient for subsequent calculations, the distances between the top of the torch and the nearest laser line or between the adjacent lines are equal, such as “L”, shown in Figure 2. The images of the laser lines are acquired and processed at a constant interval. As a result, three seam positions at the three laser lines could be recorded after every image is processed. The torch moves at a constant velocity. So after a constant time the torch will across a constant distance, such as “L”. This is similar to that the three positions (the points O1i, O2i, and O3i,) move towards to the torch and the torch top (the point O) take the place of the nearest position (the point O1i) of that three. If the distance is equal to the distance that between the adjacent laser lines, such as “L”, we can locate the position of the torch according to the two positions at the nearest two laser lines by contrast to the former recorded three positions when the three lines are at these positions. y3i y2i
o 3i x3i o2i
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Fig. 2. Reference frames in the image
3 Seam Tracking System Structure Figure 3 shows the seam structure of the tracking system. The laser lines are vertical to the “V” shape seam of the workpiece. The image of the laser lines at the seam is acquired by the CCD camera through the band pass filter. Then the image is processed with software and the position of weld seam is calculated. According to the calculating results, the positioner setup takes the torch to the correct position controlled by movement control card and computer.
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Fig. 3. Seam tracking system structure
3.1 Image Preprocessing and Acquisition of Range Data Laser lines are extracted from the acquired image, and within the laser lines, the range data are determined by processing the line position. To extract laser lines, image preprocessing, laser-line extraction, and feature extraction are generally implemented. It is similar to that of a conventional laser vision sensor but differs by having the capability to acquire triple laser lines from an image, corresponding to triple-range data. Since the TLVS acquires triple range data from an image, the TLVS requires a more complex processing system than conventional systems, for example, sorting the laser lines after image preprocessing. Nevertheless, in our study, image processing was accomplished within an interval of 0.1s.
3.2 Image Preprocessing The images obtained from the CCD include not only laser lines but also noise due to spatter, arc light, or overlapped reflection. To extract the laser lines precisely, noise in the CCD images must be removed. This process is referred to as image preprocessing. A smoothing filter and a convolution mask are generally used for this in conventional laser vision sensors. In this study, advanced image preprocessing techniques are proposed. For the first step of preprocessing, an advanced median filter (Ref. 15) was used to remove or reduce the noise within the image. Specialized masks were used for this filter to intensify the laser lines and to weaken the noise. The filter is expressed in Equation 1.
I new ( X ,Y ) =
Y +m
X +n
j =Y −m
i = X −n
∑
∑I
raw
(i, j )
(2n + 1)(2m + 1)
where X=n…(640-n), Y= m…(480-m) (1)
In Equation 1,Iraw is the raw image intensity from the CCD in the image coordinate, and Inew is the image intensity after advanced median filtering. (2n + 1) indicates the pixel number within the mask in the x-axis direction, and (2m + 1) shows the pixel number within the mask in the y-axis direction.
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Through this process, spot noise and relatively low-intensity line and area noise were removed. The line noise due to spatter in the orthogonal direction against the laser line was merged into spot noise. To achieve accuracy position of the seam, a thinning transformation is taken make the laser lines clear. Equation 2 is a thinning transformation.
A ⊗{B} = ((L((A ⊗ B1 ) ⊗ B2 L) ⊗ Bn )
(2)
where ⊗ is erosion operation, Bi is the turn of Bi-1 Furthermore, we fitting the thinned laser lines into several straight lines to acquire more accurate positions of the seam.
4 Results of Seam Tracking Experiments were conducted. Original image, thinned image and the fitted image are shown in Figure 4. Experiments results show that the tracking error can be less than 0.6mm.
(a) Original image
(b) Thinned image
(c) Fitted image
Fig. 4. Image processing
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5 Conclusions In this study, we developed a trilines laser vision sensor that improved the tracking capability and reliability of conventional laser vision sensors so as to apply accurate seam tracking. New algorithms applied in the TLVS enabled triple range data to be obtained concurrently within a single image by using triple laser stripes. The image is processed by thinning transformation and fitting straight lines are applied to achieve reliable and accurate seam position. Acknowledgement. In writing the paper, Jifeng Liu and Fei Hao have given the authors great help to prepare the welding workpiece for tests. For this reason, the authors acknowledge them sincerely.
References [1] Smati, Z., Yapp, D., Smith, C.J.: An industrial robot using sensory feedback for an automatic multi-pass welding system. Proceeding British Robot Association 6, 91–100 (1983) [2] Clocksin, W.F., Bromley, J.S.E., Davey, P.G., Vidler, A.R., Morgan, C.G.: An implementation of model based visual feedback for robot arc welding of thin sheet steel. The International Journal of Robotics Research 4, 13–26 (1985) [3] Agapakis, J.E., Katz, J.M., Friedman, J.M., Epstein, G.N.: Vision-aided robotic welding: An approach and flexible implementation. The International Journal of Robotics Research 9(5), 17–34 (1990) [4] Suga, Y., Ishii, A.: Application of image processing to automatic weld inspection and welding process control. The Japan Society of Precision Engineering 32(2) (1998)
An Optimal Design of Multifunctional Vision Sensor System for Welding Robot Yanling Xu, Xiangfeng Kong, and Shanben Chen Welding Engineering Institute, Shanghai Jiaotong University, Shanghai 200240, P.R. China e-mail:
[email protected]
Abstract. To meet the requirements for automation of welding robot, the author suggests an optimal design of multifunctional vision sensor system for welding robot, which have many features, such as remote controling, resisting disturbance from arc, welding environment identifying, initial welding seam guiding, seam tracking, penetration controlling and so on. The vision sensor is used to acquire some ideal images of stainless steel welding process experiments. It will set up the foundation for the later robot welding quality control. The multifunctional vision sensor presented in the paper is showing important practical value as it is simple in construction, low in cost and strong in practicability.
1 Introduction The quality control of robot welding is one of the difficult and hot spots in welding research, which is an important study that scientific workers in welding area devote themselves to research it [1-4]. In practice, the quality of weld formation is often influenced by distortion, ways of spreading heat, variability of gap, stagger edge. In order to overcome these uncertainties affecting the quality of welding, There has grown up an urgent need for new technologies of information feedback and intelligent control to improve the flexibility and intelligent level of welding robot [5-8]. To achieve accurate intelligent controlling during welding, the accurate information which is used to describe welding process should be obtained first, which means sensors can play an important role. It has important theoretical significance and practical value on the quality control of welding process by using sensors to acquire useful information in the process of welding. At present, there are various ways sensor by using on the welding robot. The vision sensor is applied by many welding researchers, because the vision sensor has more advantage than other sensors, such as huge information content, non-contact, fast, high precision and high degree of automation and so on. At present, the vision sensor has become the research hotspot on the third generation of welding robot [9-12]. Therefore, the sensor with reasonable design and efficient is particularly important in the intelligent robot of the welding process. T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 145–152. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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2 The Design Principle and Structure of Vision Sensor System Vision sensor system is an important part of the robot welding detection system. It will directly affect the welding process control on precision and forming quality. If the acquisition of image is not ideal, it will make the initial position guiding, weld seam tracking and penetration controlling very difficult, even leading directly to the poor quality of the welded joint. Fig 1 is the robot welding system with a vision sensor.
Robot Controller
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General input/output General input/output
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Fig. 1. Experiment system chart
The vision sensor systems can be divided into two classifications according to the imaging light source: the active method (imaging with another high-intensity light source) and the passive method (imaging with arc light illumination). In the passive sensing method, the weld pool and root gap images can be illuminated by the arc light emission; therefore, this method has fewer pieces of hardware, a simpler light path of imaging and lower application cost. And, passive vision sensor method has a higher precision to detect the seam than the active vision sensor, because some detection error is often occurred by distortion, ways of spreading heat, variability of gap, edge displacement disturbance in using active vision sensors. So the paper chooses the passive vision sensor. It must have many features, such as welding environment identifying, initial welding seam guiding, seam tracking, penetration controlling, remote controlling, simple, practical, beautiful, reasonable price, etc. Vision sensor hardware is mainly composed of CCD camera, image acquisition card, automatic transmission mechanism, optical filter system, secondary reflector frame, etc. Fig 2 is the design and physical system, respectively.
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Fig. 2. The design and real object chart
3 The Multi-functional Optimal Design of a Vision Sensor 3.1 The Design of Automation Control over Long Distance When a welding robot works in adverse conditions, such as in dust, nuclear radiation and poison, prior to welding and under the natural light, we need the transmission to remove dimmers and filters which are in front of the camera automatically to recognize and to guide the robot to the initial position of the welding seam. However, during the welding process, because the light condition has evolved from the natural light to the arc light, the dimmers and filters should be placed in front of the camera, which can filter out arc light for reducing disturbance on taking images, to collect images and to control in the process of welding. Faced with this situation, the automatic transmission of sensor can remove and place the dimmers and filters automatically over long distances. The automatic transmission of sensor presented in paper includes a micromotor, a pair of gear rack and a pair of linear guide rail. Compared with traditional stepper motor, cylinder and electromagnet, the micro-moment DC electromotor is much lighter, smaller and cheaper under the guarantee of the need of transmission. The design for changing itinerary of gear rack has been adopted to reduce the internal space of the transmission, which can maximize in reducing the volume of vision sensor. The linear rail system has been used to ensure the accuracy of movement and the minimization of friction, which is advantageous to protect the micro-motor. By taking the advantages of simple structure, compact size, light weight and strong usefulness, the mechanism of transmission which can remove and place the dimmers and filters automatically over long distances, can control the dimmer-filter system to remove the dimmers and filters before welding and place them during welding when welding robot works in adverse conditions. Fig 3 shows two 3D visualizations about placement and removal of the dimmer- filter system.
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CCD locking
Dimmer-filter
linear guide
Fig. 3. 3D Visualizations about placement and removal of the dimmer- filter system
3.2 The Design for Resisting Disturbance from Arc The main issue of using the Passive vision sensor left to be solved in the process of seam tracking and welding is how to eliminate the disturbance of arc light. The sensor presented in paper which is improved in the structure of the dimmers and filters system, is designed as drawer-type structure for stratification. The dimmers and filters system is divided into three tiers, which are shading layer, dimming layer and filter layer. A lens of CCD camera (Charge Coupled Device camera) is embedded in the shading layer to avoid obtaining the intense reflection without diminishment and filtration. In order to get sharp images, The dimming layer and filter layer are designed as drawer-type structure, because this structure can load the dimmers and filters in different drawers to eliminate the disturbance of arc light and reflection from different areas. The design which makes it easy for changing the dimmers and filters, for the purpose is to make the welding robot adapt to many sorts of environments, can help get the accurate information and images about welding position.
3.3 The Design of Guiding of the Initial Welding Position The vision sensor plays an important role in guiding the welding robot to the initial welding position of weld, which is one of the significant steps of intelligence of welding process, can make teaching-playback robot more intelligent and adaptable when they works. teaching-playback robot. In general, the detailed process of using vision sensor can be divided into many steps, such as vision sensor calibration, image acquisition, feature extraction, image matching and Three-dimensional Reconstruction. People usually use two cameras placed in different positions to shoot the same scene, then extract the corresponding feature points between two images and match the images in order to
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calculate the depth information of the feature points, which is also called the 3D coordinate (three dimensional coordinate). Due to that the end joint of welding robot can only bear limited weight and for reducing the size of vision sensor, the vision sensor with a single camera is adopted in the paper. By using a camera to shoot the same scene in different places and using stereovision and image matching, the welding robot can recognize the the initial welding position after the MOTOCOM sends the 3D information to the the host control computer. The design of the vision sensor with a single camera not only meets for requirement of reorganization and guidance about the initial welding position, but also minimize the size and the weight, saving a great deal of cost.
3.4 Design of Seam Tracking and Weld Penetration Control In practice, accurate seam tracking and weld penetration control, which are key techniques for guaranteeing the quality of welding, are important research trends for automation in the process of welding. This requires that vision sensor should be designed not only for seam tracking, but also for weld penetration control. The Fig 4 shows the images of welding process gathered by the new sensor. From these images, we can distinguish the edge of molten pool, the centre of arc, the gap and angle of weld and the tungsten-nozzle, so the qualification of the vision sensor referred to the paper perfectly matches the need of seam tracking and weld penetration control.
Fig. 4. The images of welding process
4 Experiment and Results Whether the design of vision sensor is good or bad, the key is to obtain the clear image of seam and weld pool during robot welding automation. The object of butt
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seam of stainless steel is studied. According to the spectral character of the stainless steel welding, as shown in Fig 5, one wideband filter and two dimmer glasses are used for the sensor. The transmission of the windband is 590-720 nm. The attenuation of the two dimmer glasses is 89and 96%, respectively. Fig 6 shows the form of welding current and the times of capturing image, where T is the cycle time of the welding current, Tp is the pulse level duration and Tb is the background level duration. In this paper, the pulse frequency is 2 Hz, and the time of capturing image is 20 ms after the fall edge from pulse level to background level. The Table 1 is experimental conditions of pulsed GTAW for stainless steel.
Fig. 5. The spectral character of the stainless steel welding
Fig. 6. Welding current
Table 1. Experimental conditions of pulsed GTAW for stainless steel Parameter type
Parameter value
Parameter type
Parameter value
Pulse frequency f(Hz)
2
Electrode φ(mm)
2.4
Peak current Ip(A)
150
base current Ib(A)
20
Flow of Ar (l/min)
10
wire feed speed
0
Workpiece size
300×100×2
Weld speed (mm/s)
2.5
Material
stainless steel
()
Dutyratio %
50
Using the vision sensor system, we can get some clear image during the robot welding automation process. Figure 7 shows the image of the weld seam, and the figure8 is the image of weld pool. Between the weld seam and the weld pool background, the difference in the grey level is obvious. It will be beneficial to extract the weld seam edge and the weld pool edge.
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Fig. 7. The time-consecutive seam trcking image of the stainless steel welding
(a)
(d)
(b)
(e)
(c)
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Fig. 8. The time-consecutive penetration controlling image of the stainless steel welding
5 Conclusions It becomes one of the important research fields by observing the weld area with machine vision, which has particular advantages in seam tracking, penetration control and the stability monitoring during welding. The vision sensor referred to in this paper is simple in structure, low in cost, strong in practicability, which
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solves the problem of how to acquire the welding images in narrow gap of closedloop control. Meanwhile, it can process images acquired from welding area rapidly. Therefore, it has important practical value. Acknowledgement. This work is supported by National 863 plan of China under Grant No. 2009AAA042221, and Shanghai Sciences & Technology Committee under Grant No. 09JC1407100, P.R. China.
References [1] Chen, S.B., Wu, L.: The Theory and Technology of Intelligent Robot Welding. Robot 19(sup.), 4–7 (1997) [2] Lin, S.Y., Chen, S.B., Li, C.: Welding Robot and Its Application, pp. 3–11. Mechanical Industry Press, Beijing (2000) [3] Huang, z.-y.: Application Status and Technology Prospect of Welding Robot. Equipment Manufacturing Technology (3), 46 (2007) [4] Chen, S.B., Lin, T.: The Technology of Intelligent Welding Robot. Mechanical Industry Press, Beijing (2006) [5] He, Y., et al.: The Information Fusion and Application of Multi-sensor. Electronic Industry Press, Beijing (2000) [6] Luo, Z.Z., Jiang, J.P.: Robot Vision and Information Fusion. Science Press, Beijing (2002) [7] Yang, W.H.: Multi-sensor Data Fusion and Its Application. Xidian University Press, Xian (2004) [8] Chen, Q., Sun, Z.G.: The Application of Computer Vision Sensor Technology in Welding. Journal of Welding 22(1), 83–91 (2001) [9] Guo, Z. M.: Study of Autonomous Guidance of Initial Welding Position Based on Vision Sensing [ph.d.thesis], Haerbin, Harbin Industrial University (2002) [10] Zhu, Z.Y.: Identification and Initial Guiding of Welding Robot on Vision Sensor [ph.d.thesis], Shanghai, Shanghai Jiaotong University (2004) [11] Chen, S.B., Zhang, Y., et al.: Welding Robotic Systems with Vision Sensing and Selflearning Neuron Control of Arc Weld Dynamic Process. J. of Intelligent and Robotic Systems 36(2), 191–208 (2003) [12] Chen, S.B., Chen, X.Z., Li, J.Q.: The Research on Acquisition of Welding Seam Space Position Information for Arc Welding Robot Based on Vision. J. of Intelligent & Robotic Systems 43(1), 77–97 (2005)
Wire Extension Control Based on Vision Sensing in Pulsed MIG Welding of Aluminum Alloy Lihui Lu, Ding Fan, Jiankang Huang, Jiawei Fan, and Yu Shi Lanzhou University of Technology, Lanzhou, China e-mail:
[email protected],
[email protected]
Abstract. Research work of wire extension control based on vision sensing was done in pulsed MIG welding process of aluminum alloy. In order to resolve the actuality problem of traditional PID control, a self-tuning fuzzy PID controller was designed and a rapid prototyping control platform was established for pulsed MIG welding of aluminum alloy using real-time target environment based on xPC. With vision sensing of welding zone image and corresponding image processing algorithm, a fuzzy PID closed loop control system for wire extension was designed on the basis of the built rapid prototyping control platform. Experimental results show that the method of vision sensing can meet the control requirements of wire extension stability, the rapid prototyping control system built with fuzzy PID controller based on vision sensing can realize well the control of the wire extension in pulsed MIG welding of aluminum alloy and also has strong robustness and quick response ability.
1 Introduction In pulsed MIG welding, stabilization of arc length and droplet transfer is a key of ensuring welding quality. Many studies have shown that the change of wire extension has significant influence on arc length, mode of metal transfer and so on [1-3]. The results of literature [4] show that droplet transfer is quite sensitive to change of wire extension in MIG welding of aluminum alloy. Therefore detecting wire extension to control arc length and match the welding parameters simultaneously, such as welding current and voltage, is an important method to ensure the stability of welding process. Aiming at this problem, scholars did some research; literature [5] realized sensing of wire extension by detecting the change of resistance with change of wire extension. Literature [6] show that short circuit voltage between the torch and work piece is in direct proportion to the wire extension, when welding current keeps constant during the medium term of short circuit. Literature [7] preliminarily realized the pattern recognition of wire extension change through combination of arc sound signal and SVM (support vector machine). However, the method through detection of indirect physical quantity to realize sensing of wire extension has difficulty in pulsed MIG welding of aluminum T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 153–159. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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alloy. One hand, this is because aluminum alloy welding wire has good conductivity which results in electrical parameters such as resistance change not obvious. On the other hand, when adopting pulse power supply, the mode of metal transfer is free transfer such as one droplet per pulse or many droplets per pulse but not short circuiting transfer. Here the detection of electrical signals will be disturbed by droplet transfer, thus make the electrical and sound signals severely polluted. Collecting the characteristic signal will also become difficult. Aiming at this problem, the method of vision direct sensing was adopted in this paper to control the wire extension in pulsed MIG welding of aluminum alloy. On the basis of realizing vision sensing, using wire extension signal as input and wire feed speed as output, a rapid prototyping control system was established for pulsed MIG welding aluminum alloy using real-time target environment based on xPC. Then aiming to the actuality problem of traditional PID control, a selftuning fuzzy PID controller was designed, finally wire extension control test was carried out, and obtaining stable welding process.
2 Experiment System Composition The experiment system is shown as figure1. Welding part includes Germany DELEX VIRIO MIG-400L-digital control welder, X, Y biaxial worktable; image acquisition part mainly contains Panasonic CP-230 type CCD camera and NI PCI1405 image acquisition card; electrical signal acquisition and control signal output part contains: industrial control computer, Advantech PCL-812PG supporting xPC, NI PCI-6221 data acquisition card, Advantech PCL-728D/A data output card with isolation, closed current sensor CSM400FA, standard voltage isolation module Advantech ADAM-3014. Software platform is real-time target environment supporting xPC of Matlab/Simulink. Rapid Controller Prototyping (RCP) technology was adopted for the control system. RCP can express the control algorithm by block diagram, and transform the block diagram into executable code directly, which is downloaded automatically to target computer to be executed. RCP technology can verify the feasibility of the hardware and software scheme by Fig. 1. The schematic diagram of rapid repetitious and real-time on-line exprototyping control system for pulsed MIG periment [8]. welding of aluminum alloy In the welding process, keep the relative position of CCD camera and welding torch invariant, and keep the welding torch fixed while make worktable walk to weld.
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3 Wire Extension Signal Extraction The software system of the image acquisition is compiled by LabView. Figure 2 is the typical image of weld zone captured by CCD camera. In the image, the distance from welding torch mouth to end of welding wire is the length of wire extension. The flow chart of designed image processing algorithm is shown as figure 3.
Fig. 2. Typical image captured by CCD camera
Fig. 3. Detection process of wire extension
4 Design of Fuzzy PID Controller A fuzzy controller with double inputs and three outputs was adopted for the control system. The deviation e and deviation variation rate ec are as the input variable of the controller. The variation range of deviation e and deviation variation rate ec were defined as the universe of fuzzy set {NB, NM, NS, ZO, PS,PM,PB). The universe of e and ec ,
e = [−3, −2, - 1, 0, 1, 2, 3],ec = [−3, −2, - 1, 0, 1, 2, 3] The fuzzy control rule table was established by summarizing technical knowledge and actual operation experience of engineering designers, shown as table1, table2 and table 3. After establishing the fuzzy control rule table for Δk p , Δki , Δkd on-line adaptive correction of PID parameters could be carried out with fuzzy rules. Assumed that e , ec , Δk p , Δki , Δk d obeyed normal distribution, so membership of fuzzy subset could be obtained, then according to membership assignment table
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NP
NM
NS
ZO
PS
PM
PB
NB
PB
PB
PM
PM
PS
ZO
ZO
NM
PB
PB
PM
PS
PS
ZO
NS
NS
PM
PM
PM
PS
ZO
NS
NS
ZO
PM
PM
PS
ZO
NS
NM
NM
PS
PS
PS
ZO
NS
NS
NM
NM
PM
PS
ZO
NS
NM
NM
NM
NB
PB
ZO
ZO
NM
NM
NM
NB
NB
Table 2. Fuzzy control rule table for Ki Ki
NB
NM
NS
ZO
PS
PM
PB
NB
NB
NB
NM
NM
NS
ZO
ZO
NM
NB
NB
NM
NS
NS
ZO
ZO
NS
NB
NM
NS
NS
ZO
PS
PS
ZO
NM
NM
NS
ZO
PS
PM
PM
PS
NM
NS
ZO
PS
PS
PM
PB
PM
ZO
ZO
PS
PS
PM
PB
PB
PB
ZO
ZO
PS
PM
PM
PB
PB
Table 3. Fuzzy control rule table for Kd Kd
NB
NM
NS
ZO
PS
PM
PB
NB
PS
NS
NB
NB
NS
NM
PS
NM
PS
NS
NB
NM
NM
NS
ZO
NS
ZO
NS
NM
NM
NS
NS
ZO
ZO
ZO
NS
NS
NS
NS
NS
ZO
PS
ZO
ZO
ZO
ZO
ZO
ZO
ZO
PM
PB
NS
PS
PS
PS
PS
PB
PB
PB
PM
PM
PM
PS
PS
PB
of fuzzy subset and fuzzy control model of the parameters, designed fuzzy matrix table of PID parameters with fuzzy synthetic reason, and then got the correction value and substituted the value in the equation 1, as follows:
,
,
k p = k p ' + Δk p ki = ki ' + Δki kd = kd ' + Δkd
(1)
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5 Wire Extension Control Experiment In order to test the control effect of wire extension using fuzzy PID controller by vision sensing method, control experiment was carried out on the basis of rapid prototyping control platform for pulsed MIG welding of aluminum alloy. Because wire feed speed determines the feed of welding wire directly and has small time delay, so wire feed speed was chosen as controlled quantity of wire extension control. Base metal is aluminum alloy 50581-H321, its specification is 300mm × 100mm × 6mm; adopted welding wire is Al-Mg welding wire 5356, its diameter is 1.2mm. Welding parameters are shown as table 4. Table 4. Test parameters welding parameters
value
welding speed
15cm/min
base current
25A
peak current
200A
pulse frequency
40Hz
pulse duty cycle
50%
Figure 4 is the wire extension control program based on vision sensing developed by MATLAB/Simulink. Figure 5 is the weld appearance after wire extension control. Figure 6 is the typical signals in pulsed MIG welding control process of aluminum alloy, including welding current and welding voltage. Figure 6(c) is the change of wire feed speed. Figure 6(d) is the change of wire extension obtained by vision detection. The results show that wire feed speed changes real-timely under fuzzy PID control, and fluctuation of wire extension is small, obtaining stable welding process.
Fig. 4. Wire extension control program based on vision sensing
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Fig. 5. Weld appearance
(a) Welding voltage
(c) Change of wire feed speed
(b) Welding current
(d) Change of wire extension
Fig. 6. Typical welding signals in control process
6 Conclusions In order to resolve the actuality problem of traditional PID control, a self-tuning fuzzy PID controller was designed. With vision sensing and corresponding image processing algorithm, a rapid prototyping vision sensing and control platform was established for pulsed MIG welding of aluminum alloy using real-time target environment based on xPC. In pulsed MIG welding of aluminum alloy process, the method of vision sensing can meet the control requirements of wire extension stability, and the rapid prototyping control system built with fuzzy PID controller based on vision sensing can realize well the control of the wire extension in aluminum alloy pulsed MIG welding and also has strong robustness and quick responseability. Acknowledgement. This research work is supported by National Nature Science Foundation of China (50805073).
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References [1] Yan, C., Hu, S., Guo, Y.: Arc length control model in CO2 shield welding. Transactions of the China Welding Institution 26(6), 69–72 (2005) [2] Zhu, Z., Bao, Y., Yu, S.: Real-time control system of torch-height based on throughthe-wire sensor. Transactions of The China Welding Institution 23(3), 26–28 (2002) [3] Zhang, H., Huang, S., Yang, S.: Effect on projected transfer in MAG welding with the pulsed wirefeeder. New Technology and New Process (08), 39–40 (2004) [4] Shi, Y.: Study of Robotic Intelligent Control System for Aluminum Alloy Pulsed MIG Welding Process. Lanzhou University of Technology (2005) [5] Orszagh, P., Kim, Y.C.: Short-circuiting transient phenomena in GMA/ CO2 welding. Trans. of JWRI 26(1), 43–60 (1997) [6] Bao, Y., Zhu, Z., Wu, W.k.: Through-the-Wire Sensor of Short-ircuit CO2 Welding. Transactions of the China Welding Institution 22(3), 55–58 (2001) [7] Ma, Y., Qu, M., Chen, J.: SVM classifier for wire extension monitoring using arc sound signal in GMAW. Transactions of the China Welding Institution 27(5), 21–26 (2006) [8] Burns, D., Sugar, T.: Rapid embedded programming for Robotic Systems. Submitted to Proceedings of the 2002 ASME Design Engineering Technical Conferences and Computers in Engineering Conference 2002 (2002) [9] Huang, Y.J., Kuo, T.C., Lee, H.K.: Fuzzy-PD Controller Design with Stability Equations for Electro-hydraulic Servo Systems. In: Proceeding of International Conference on Control: Automation and Systems 2007, pp. 2407–2410 (2007)
Research on Track Fitting of Big Frame Intersection Line Seams Xingang Miao1, Su Wang1, Xiaohui Li2, and Benting Wan3 1
School of Mechanical Engineering and Automation, Beijing University of Aeronautics and Astronautics, Beijing 100191, China e-mail:
[email protected] 2 Beijing Institute of Control Engineering, Beijing 100190, China 3 Jiangxi University of Finance and Economics
Abstract. When big frame intersection line welded structural assemblies are produced and assembled, certain errors always come to being, so generally speaking, both instructional and fitting methods are applied to the seam track in the welding robot, in which welding quality is largely decided by the precision of the track fitting. A detailed analysis about the acquisition and fitting of saddle-back welding seam is given in this article, in which the authors put up ellipse fitting method based on the general rules of intersection line deformation, and three-coordinate separated least square algorithm fitting based on curve fitting. In the guidance of some instructing points in key position, simulations are made to verify the two methods separately. The result shows that the three-coordinate separated least square algorithm method is more precise than the other one. Therefore, it is fit for big intersection line welding frames.
1 Introduction Intersection line welding framework is widely applied in many fields, such as large storage sphere, automobiles, vessels, aviation, petroleum pipelines, etc. As the working intensity is very hard in welding these space curve tracks, besides the welding quality is not easily ensured, the welding robot is more and more employed in such cases in recent years. A kind of saddle-back coping welding robot is innovated by Beijing university of Aeronautics and Astronautics, which can be fixed on the intersection cylinder by a four-jaw chuck, so the D-H coordinate system is set up through a reasonable simplification of the robot structure, and both the positive and negative equations of kinematics are derived. However, limited by manufacturing and assembling errors, in practice the track of welding seam is usually obtained by both instruction and fitting at the same time. T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 161–168. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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2 Analysis on Errors Produced in Intersection Line Structural Assemblies As for big frameworks, considering the fact that the size of intersection line frameworks is quite large, and the prophase processing and assembling precision is rather low, the welders cannot make sure that the two cylinders are orthogonal indeed. Fig. 1 illustrates the distortion structure. If orthogonal intersection line is set as the standard in the process of track planning, a huge error can be brought in practical welding, which will result in welding defects or quality declination, and in some cases even rejects. Therefore, whether to obtain an appropriate welding track that coincides with the practical seam becomes the key factor in determining the quality of submerged-arc welding.
Fig. 1. Errors produced in intersection line structural assemblies
3 Research on Intersection Line Track Fitting As for the seams in complex space, the instruction method is commonly adopted in obtaining seam tracks. Nevertheless, as the diameter of big frame intersection line seam is much larger, the distance between instructing points would not be very short. Through instructing certain key points, the whole seam track can be obtained by fitting. In this paper, two fitting methods are assumed, and a more effective one is chosen after the simulation comparison.
3.1 Ellipse Fitting Method According to the analysis of practical application and ordinary rules, an ellipseshaped tube is easily produced when the cylinder is distorted, and circle is a special case of ellipse, so ellipse can be set as a standard shape. A threedimension-curve is produced when two cylinders are intersected to each other; and the points of this curve would form an ellipse when they are projected to xy and yz plane. As a result, the intersection line can be disassembled as a combination of two two-dimension curves. In the following part, these two-dimension curves will be formulated.
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An ellipse is on xy plane as is shown in Fig. 2. Its parameters are the center coordinate ( xc , yc ), long axis obliquity δ , the value of long and short axes ( a, b ). In search of a convenient calculation, description and function parameterization, reference system x1 y1 is introduced, and the origin is just located in the center of ellipse.
Fig. 2. The parameters of ellipse
The equation of ellipse in reference coordinate
:
x1 y1 can be denoted
as follows
x′ 2 y ′ 2 + =1 a 2 b2
(1)
Suppose the above equation is: Ax + Bxy + Cy + Dx + Ey + F It can be solved from the above equation group: 2
2
=0
1 B BE − 2CD BD − 2 AE ⎧ ⎪δ = 2 arctan A − C , xc = 4 AC − B 2 , y c = 4 AC − B 2 ⎪ ⎪⎪ 2 ( A + C ) + ( A + C ) 2 − 4( Axc 2 − Cyc 2 − Bxc yc − F ) ⎨a = 2 ⎪ 2 ⎪ ( A + C ) − ( A + C ) − 4( Axc 2 − Cyc 2 − Bxc yc − F ) ⎪b 2 = ⎪⎩ 2
(2)
Apply the least square fitting rule based on algebra distance [3], the equation is: U ( A, B , C , D , E , F ) = n
∑ ( Ax i =1
2 i
(3)
+ B x i y i + C y i 2 + D x i + E y i + F ) 2 = m in
In consideration of the ellipse equation must meet the restriction 4 AC − B 2 < 0 , and the ellipse parameter cannot be affected by calibration factors [4], we can
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simplify the above inequality constraint to an equality constraint: B 2 − 4 AC = 1 . It can be denoted as aT Ca = 1 . Introduce the Lagrange factor λ and meanwhile solve the derivative [5]:
2 DT Da − 2λ Ca = 0, ⎡ x12 In the formula: D = ⎢ # ⎢ ⎢ x82 ⎣
x1 y1 # x8 y8
y12 # y82
a Ca = 1 T
x1 # x8
(4)
y1 1⎤ ⎥ # #⎥ y8 1⎥⎦
So formula (6) can be donated as: n
U ( A, B, C , D , E , F ) = ∑ x ⋅ a = Da i i i =1
2
(5)
= aT D T DaT = λ aT CaT = λ = min After deduction, the former question is turned into solving the matrix’s best extensive feature vector, which can make formula (5) come in to existence [6]. The feature vector can be solved by formula (4). Similarly, the ellipse equation projected on plane yz can also be solved.
3.2 Three-Coordinate Separation and Least Square Algorithm Method Least square is also named as curve fitting. It is adopted to solve the question of how to find a trust value from a group of measured values. The radical principle of least square is to find a best fitting curve from the measured value xi , yi (i = 1, 2" , n) , and the quadratic sum of difference between each point of this fitting curve and measured value is kept least along this fitting curve. Suppose relation between physical quantity y and independent variable x is y = f ( x; a0 , a1 ," , an ) , and a0 , a1 ," , an are n+1 parameters needed to be established. The parameters a0 , a1 ," , an can be solved by least square, and make formula m
s = ∑ ( yi − yi′ ) 2 as minimum, in the formula, yi is measured value, yi′ is i =1
solved function value from
a0 , a1 ," , an and y′ = f ′( x; a0 , a1 ," , an ) .
In applying least square to fitting curves, the key factor to increase precision is to choose a suitable base function. In order to fit the ellipse track better, the relation curve of intersection line axis value and waist rotation angle must be researched first. From Fig. 3 we can see that no matter the relation between axis
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x, y, z value and rotation angle is sine or cosine curve, the period of axis z is
π.
Suppose fitting curve is
u (θ ) = a sin(ωθ + θ 0 ) + b , and use this formula as
lease square function which can fit the instructing points.
、、
Fig. 3. Relation of axis X Y Z and waist rotation angle
From Fig. 3, it can be observed that the parameter
ω
is known, parameters
a,θ0 , b are unknown. Because this question is a nonlinear function, which will be very complicated if we use least square algorithm, it must be linearized [7]. Based on trigonometric function, it can be illustrated as:
u (θ ) = a sin(ωθ + θ 0 ) + b = a cos θ 0 ⋅ sin(ωθ ) + a sin θ 0 ⋅ cos(ωθ ) + b Suppose
(6)
C = a cosθ0 , D = A2 − C 2 = a sin θ0 , B = b , the above formula can
be transformed into:
u (θ ) = C sin(ωθ ) + D cos(ωθ ) + B
(7)
In this way, the former nonlinear question can be turned to a linear question, so we can fit the measured data and make minimal the quadratic sum of error that is denoted by the following formula: n
σ = ∑ [ui − C sin(ωθi ) − D cos(ωθ i ) − B ]
2
(8)
i =1
So we can solve parameters C, D, B by linear least square method, and the matrix can be formed as:
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⎡1 sin(ωθ1 ) cos(ωθ1 ) ⎤ ⎡ u1 ⎤ ⎡B⎤ ⎢u ⎥ ⎢1 sin(ωθ ) cos(ωθ ) ⎥ 2 2 2 ⎥ U = ⎢ ⎥ x = ⎢C ⎥ A=⎢ ⎢ ⎥ ⎢#⎥ ⎢# ⎥ # # ⎢⎣ D ⎥⎦ ⎢ ⎥ ⎢ ⎥ 1 sin( ωθ ) cos( ωθ ) u n n n ⎦ ⎣ ⎣ ⎦ Based on the equation A Ax = A U , it can be solved: T
T
x = ( AT A) −1 AT U
(9)
So the fitting intersection line curve can be shown by the parameter equation below:
⎧ x(θ ) = Cx sin(θ ) + Dx cos(θ ) + Bx ⎪ ⎨ y (θ ) = C y sin(θ ) + Dy cos(θ ) + By ⎪ ⎩ z (θ ) = Cz sin(2θ ) + Dz cos(2θ ) + Bz
(10)
4 Comparison of Simulations of the Two Fitting Methods In order to compare the defaults and advantages of the two methods, a group of error sample data is obtained from intersection line formula (11), a sample data is obtained per certain fixed angle, then the two fitting methods can be realized in Matlab programing.
⎧⎛ x + 50 ⎞2 ⎛ y + 35 ⎞2 ⎪⎜ ⎟ +⎜ ⎟ =1 ⎪⎝ 590 ⎠ ⎝ 610 ⎠ ⎨ 2 2 ⎪⎛ y + 35 ⎞ ⎛ z + 20 ⎞ ⎪⎜⎝ 1995 ⎟⎠ + ⎜⎝ 2003 ⎟⎠ = 1 ⎩
(11)
A sample data can be obtained per π 4 , so there are totally 8 groups of data listed in table 1. There are three axes fitting curves and fitting error curves derived from the two fitting methods, which is shown in figure 4 and figure 5.
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Table 1. 16 groups of sample data
θ(rad) 0 π/4 π/2 3π/4 π 5π/4 3π/2 7π/4
x(mm) -51.819 366.8267 538.8162 365.5098 -51.8968 -466.4389 -638.4222 -467.3962
y(mm) 575.929 397.5219 -33.9335 -466.3280 -643.7891 -467.1107 -35.3753 394.8515
z(mm) 1884.6997 1931.3565 1978.5966 1932.7896 1885.2553 1933.5325 1979.8691 1933.7310
Fig. 4. Fitting curves and error curves of ellipse
Fig. 5. Fitting curves and error curves of three-coordinate separated least square algorithm
From horizontal comparison of the simulation results of the two groups, it can be seen that three-coordinate separated least square algorithm fitting is more precise than ellipse fitting with the same sample number and precision. The error of every axis in ellipse method is more periodic than the former method. From vertical comparison, it can be observed that the number of error is going to decrease as the number of sample data increase in both methods. But the error absolute value and error variety range of three-coordinate separated least square algorithm method become smaller.
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5 Conclusions In this paper, track fitting on big frame intersection line seam is researched in detail. Ellipse fitting and three-coordinate separated least square algorithm fitting are both designed and deduced. Simulation based on some instructing points in key positions is made using Matlab, and the result shows that the three-coordinate separated least square algorithm method is more precise than the ellipse method, so a natural conclusion is drawn that this method is not only fit for big frame intersection line trace fitting but also fit for other intersection line frames. Acknowledgement. This work is supported by Construction Together Special Project of the Education Committee of Beijing (NO. 20091024).
References [1] Li, X., Wang, S.: Kinematic analysis and simulation of saddle-back coping welding robot. Journal of Beijing University of Aeronautics and Astronautics (08), 964–968 (2008) (in Chinese) [2] Wang, S., Zhu, X., Li, X.: Research of the algorithm for the five-axis intersection line welding robot. Materials Science & Technology 12, 61–65 (2006) [3] Peng, Q.: Least-square Fit to the Edge of Jupiter and Saturn for its Precise Position. Publications of Yunnan Observatory 7(2), 77–78 (2007) [4] H S. Technique note: least-squares fitting with spheres, pp. 191–199 (1998) [5] Varah, J. M.: Least squares data fitting with implicit functions, pp. 558–578 (1994) [6] Zheng-You Z.: Parameter estimation techniques:a tutorial with application to conic fitting, pp. 59–76 (1997) [7] Jüttler, B.: Shape preserving least-squares approximation by polynomial parametric spline curves. Computer Aided Geometric Design 14(8), 731–747 (1997)
A Hybrid Approach for Robust Corner Matching Fanhuai Shi1, Xixia Huang2, and Ye Duan3 1
Welding Engineering Institute, Shanghai Jiao Tong University, China e-mail:
[email protected] 2 Marine Technology & Control Engineering Key Lab, Shanghai Maritime University, China 3 Computer Science Department, University of Missouri-Columbia, USA
Abstract. Robust and high accuracy corner matching plays an essential role in many applications in computer vision such as camera calibration, 3D reconstruction and robot localization. In this paper, we describe a hybrid approach that can automatically detect and match image corners with high accuracy. Our approach is based on SIFT structure information and sub-pixel Harris corner localization, which is rotation invariant and is localized directly on true image corners detected by the enhanced curvature scale space method. Experimental results show that the proposed method offers an effective solution to automatic robust corner matching.
1 Introduction To date, researchers have proposed various types of features [13] suited for different applications. For example, for object recognition, blob features such as SIFT [7] are commonly used because of their robustness with respect to scale changes and rotations. For applications such as 3D reconstruction, camera calibration and robot localization, corner features are often used because of their better localization accuracy and higher repeatability [10]. These different types of features are sometimes complementary to each other, and as a result are often used together. For example, blob detectors are in a sense complementary to corner detectors [13]. By using several complementary feature detectors simultaneously, the image is better covered and the performance becomes less dependent on the actual image content. The combination of multiple feature detectors has been exploited for object matching and detection [8][12]. In this paper, we follow the same spirit and propose a hybrid approach for corner matching that can robustly match corresponding corner features from two images automatically. Our approach integrates the robustness of the SIFT features with the localization accuracy of corner features. The SIFT features will provide the spatial context for the corner feature, as well as a good estimation of the orientation of the local neighborhood around the corner feature, which are essential for the robustness of the corner matching. T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 169–177. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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1.1 Background A common approach for corner matching is to take a small patch of pixels around the detected corner features such as the Harris corner [2], and compare the similarity between this patch and patches around each of the candidate corners in the other image. The most popular similarity measure for corner matching is the normalized cross-correlation (see, for example, [14]). The normalized crosscorrelation, however, can be sensitive to rotation and scale changes and thus may not perform well if there are significant rotation and scale changes between the two images. To overcome this limitation, recently, Zhao et al. [15] proposed a new normalized cross-correlation based image matching method that is rotation and scale invariant, where they adopt the Harris-Laplacian detector [9] for interest point detection and determine the size and orientation of the correlation window by Laplacian characteristic scale selection [6]. In this paper, the proposed method is also rotation invariant, however unlike [15], it utilizes the robustness of the SIFT feature to achieve the rotation invariance of corner matching indirectly.
1.2 Overview To better localize and robustly match corner features, in this paper, we propose to explore blob features such as SIFT to provide structural information for the corner features. Our approach is not only rotation invariant, but also localized directly on true image corners detected by efficient enhanced curvature scale space (CSS) method [4]. There are two stages in our approach. First, by utilizing the SIFT features and its matching results, an initial set of matched corner pairs are chosen from all the CSS corners. Next, the localization of the selected corners are further refined into sub-pixel accuracy, and corners with low location stability or with lower similarity measurement will be removed. The remainder of the paper is organized as follows. Section 2 describes how to build the initial set of corner pairs by SIFT structure and matching information. Section 3 refines the initial matched corners by location accuracy, location stability and similarity measurement. Some experimental results on real images are presented in Section 4. Section 5 concludes the paper.
2 Initial Corner Matching by SIFT Information In this section, we will demonstrate how to build an initial set of corner pairs by SIFT structure and matching information. The basic idea is first assign each interest corner point to its nearest SIFT point, and then select the initial corner pairs from the two sets of corner points corresponding to a SIFT pair in two images. Figure 1 shows the flow chart of this procedure.
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First, interest corner points in the two images are detected. Our methods can be applied to all kinds of corner detectors such as Harris corner [2] and Keep SIFT matches steerable filters [5]. In with big scale this paper, we employ the enhanced CSS corner deAssign corner tector proposed in [4] to points to the nearest detect “true” image SIFT points in each corners. Next, SIFT feature points are detected and Compute normalized matched in the two imcoordinate of the corner ages. For efficiency in point in the related SIFT matching, it is computacoordinate system tionally cheaper to comFind initial corner pute the dot products pairs by minimal Euclid between the SIFT dedistance in the same scriptor vectors rather SIFT point pair than to calculate the Euclidean distances between Fig. 1. Flow chart of building initial set of corner pairs the SIFT descriptor vecby SIFT structure and matching information. tors. Thus, similar to the strategy suggested in [7], we keep matches in which the ratio of the vector angles from the nearest to the second nearest neighbor is less than a specific threshold, for example 0.65 in this paper. In order to guarantee the reliability of SIFT matching and the later corner matching, we only keep SIFT matches whose blob structure has a bigger scale for example the top 30 biggest scales or greater than a certain threshold. Finally, given a set of corner points and a set of selected SIFT feature points in an image, assign each corner point to its nearest SIFT points. We will then normalize the coordinates of each corner point with respect to the local coordinate system defined by the location and orientation of its associated SIFT feature point. By computing the normalized coordinates of the corner point, the corner point can be localized relative to the related SIFT structure and therefore achieve invariance to scale and image rotation relative to the SIFT blob. More specifically, let’s assume a corner point c = I ( x, y ) is assigned to a Interest detection
corner
SIFT feature detection and matching
p = {I ( xs , ys ), σ , θ } where both ( x, y ) and ( xs , ys ) are image coordinates, σ and θ are the characteristic scale and dominant direction of the corresponding SIFT feature point p respectively. Figure 2 indicates the
SIFT feature point
relation among corner point c, the corresponding SIFT point p, the local coordinate system X’pY’ and the image coordinate system XOY. Then the normalized
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coordinates
cnorm = ( x , y ) of the local coordinate system centered at the corre-
sponding SIFT feature point can be obtained:
⎛⎛ x ⎞ ⎛ x ⎞⎞ RT ⎜ ⎜ ⎟ − ⎜ s ⎟ ⎟ ⎛ x⎞ ⎝ ⎝ y ⎠ ⎝ ys ⎠ ⎠ , R = ⎡cos(θ ) − sin(θ ) ⎤ ⎜⎜ ⎟⎟ = ⎢ sin(θ ) cos(θ ) ⎥ σ ⎣ ⎦ ⎝ y⎠
(1)
Since the normalized coordinates of the corner point are obtained in the corresponding SIFT coordinate system, initial corner matching can be obtained by comparing their Euclidean distance. Suppose there are m interest corners in the first image and n interest corners in the second image, which are all connected to the same pair of matched SIFT features. Consider a matrix G ∈ Dm , n such that its element Gij stands for the Euclidean distance between the i-th interest corner in the first image and the j-th interest corner in the second image. In order to establish the one to one interest corner correspondence, two interest corners will be accepted as a match only if their distance Gij is both the minimal element in its row and the minimal element in its column. After selecting all such elements in the matrix G, the initial set of interest corner matches between the two images can be established. Due to the accuracy limitation of SIFT feature localization and orientation assignment, there might still exist some incorrect corner pairs in this initial stage. In the subsequent refinement stage to be described next, we will further refine the localization accuracy of the initial set of corners, and remove those incorrect corner pairs.
3 Refined Corner Matching In this section, we’ll refine the initially selected corner pairs into sub-pixel localization accuracy, and remove those corner pairs that have lower location stability or similarity measurement.
3.1 Localization of Stable Sub-pixel Corner Pairs Given the initial locations of the corners obtained in Section 2 as an initial guess, we will proceed to calculate the sub-pixel corners on the image based on sub-pixel Harris finder in Bouguet’s Camera Calibration Toolbox1. Note that, if we change the size of detection window used to detect the corner, the localizations of the detected corner might also change. This phenomenon is called as location drift. Thus we can use the extent of the location drift as a
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measurement of the stability of the detected corners, i.e. if the location drift of a corner is large in comparison with the related size variance of the detection windows, then we consider it as an unstable corner will be removed. In our implementation, the sub-pixel locations of a corner will be calculated in two detection windows W ' and W '' of increasing window size that are centered at the same initial guess position. Square detection windows with size 9 pixels and 13 pixels are adopted, and the location drift should be less than 0.6 pixels. Y c( X
p(x
O
X
Fig. 2. The relation among corner point c, the corresponding SIFT point p, the local coordinate system X’pY’ and the image coordinate system XOY. θ is the dominant direction of SIFT feature point p .
3.2 Similarity Measurement of Corner Region To better match the corresponding corner regions between images, a rotation and scale invariant strategy is adopted in similarity measure to estimate the difference between the initial set of corner points. In contrast to the approach in [15], the orientation of the correlation windows are determined according to the dominant direction of the corresponding SIFT feature point. The matching algorithm is described in detail as follows: Let c1 = I1 ( xi , yi ) be a corner point in one image with σ 1 and θ1 the characteristic scale and dominant orientation of the corresponding SIFT point respectively, and c2 = I 2 ( x j , y j ) be the initially matched corner point in the other image with σ 2 and θ 2 the characteristic scale and dominant orientation of the corresponding SIFT point respectively. Without loss of generality, let’s assume and define r as the scale ratio r = σ 1 / σ 2 > 1 and θ = θ 2 − θ1 as the angle difference.
W1
and
W2
are
the
two
correlation
windows
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(2 w + 1) × (2 w + 1) centered on each of the matched corner points, where w is a constant. W1 is rotated by | θ | around c1 . W1 and W2 can then be represented as two arrays of pixel intensities A and B :
Auv = I1 ( xi + ru cos θ + rv sin θ , xi + rv cos θ − ru sin θ )
(2)
Buv = I 2 ( x j + u , y j + v ) u, v ∈ [− w, w] . The similarity measure between the two corner points can then be defined as the cosine of the vector angle ϕ between A and B . where
Sc1 ,c2 = cos ϕ =
A⋅ B || A |||| B ||
(3)
where || ⋅ || means norm of a vector. By utilizing the SIFT feature structural information, the similarity measure is robust against rotation and small scale variance. The similarity measure decreases from 1 to 0 with the increase of difference between the local neighborhoods around the two corresponding corners. In our implementation, we will calculate the similarity measure between all the initially matched corner pairs. Only those corner pairs whose similarity measurement is greater than a certain threshold can be considered as valid matches. The threshold of similarity measurement in this paper is set to be 0.98.
4 Experimental Results and Discussion In this section, we will demonstrate some experimental results on real images of various content. These images are under different imaging conditions, such as rotation, translation, small-scale changes, indoor/outdoor, etc. Some of the images are from the public domain resources of INRIA and others are collected by our laboratory. Figure 3-Figure 6 show the final matching results for four different image pairs under different camera motions or image conditions. Figure 3 and Figure 4 show the matching results for image pair Office and Town with translation and rotation changes, which are taken indoor and outdoor respectively. There also exist some self-similarity structures in the two pair of images. Furthermore, image pair Town are taken from the aircraft, which produce some viewpoint changes. Most of the matched corners in Figure 3 and Figure 4 are salient and location stable in the test scene, and the number of matched corner pairs is enough for algorithm of camera pose estimation, such as five-point algorithm [11].
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Fig. 3. Corner matching result for indoor image pair Office (from our lab) with translation and rotation changes
Fig. 4. Corner matching result for aerial image pair Town (from our lab) with translation and rotation changes
Fig. 5. Corner matching result for image pair NewYork (from INRIA) with rotation changes
Fig. 6. Corner matching result for image pair Residence (from INRIA) with rotation and scale changes
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Figure 5 shows the matching results for image pair NewYork with small rotation changes. Figure 6 shows the matching results for image pair Residence with large rotation and scale changes. From the results in Figure 5 and Figure 6, we note that the number of matched corners will decrease when the rotation and scale changes increase. The obtained corner matches occasionally still contains a few false matches due to the inaccuracy characterization of SIFT dominant orientation or the improper matches established in the matching procedure. In the case of matching two uncalibrated images or camera pose estimation, the epipolar geometry can be used to reject the false matches [3][4][15]. The estimation of epipolar geometry from interest corner correspondences is usually performed by the robust estimator RANSAC 0 when dealing with static scene2. Then the interest corner matches that are not consistent with the estimated epipolar geometry are identified as false matches and rejected. For instance, we perform RANSAC algorithm on the aerial images in Figure 4 for epipolar geometry computation, and as a result remove the false matches.
5 Conclusions In this paper, we propose a hybrid approach for image corner matching. Our approach is based on SIFT structure information and sub-pixel Harris corner localization, which is not only rotation invariant, but also localized directly on true image corners. The experimental results showed that the proposed method offers an effective solution to robust corner matching. Acknowledgement. This work is supported in part by National Natural Science Foundation of China under Grant No. 60805018, National 863 plan of China under Grant No. 2009AAA042221, and Shanghai Sciences & Technology Committee under Grant No. 09JC1407100, P.R. China.
References [1] Fischler, M.A., Bolles, R.C.: Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Communications of the ACM 24(6), 381–395 (1981) [2] Harris, C., Stephens, M.: A combined corner and edge detector. In: Alvey Vision Conference, pp. 147–151 (1988) [3] Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2000) [4] He, X.C., Yung, N.H.C.: Curvature scale space corner detector with adaptive threshold and dynamic region of support. In: Proc. of ICPR, pp. 791–794 (2004) [5] Jacob, M., Unser, M.: Design of steerable filters for feature detection using Cannylike criteria. IEEE Trans. Pattern Anal. Mach. Intell. 26(8), 1007–1019 (2004) [6] Lindeberg, T.: Feature detection with automatic scale selection. Inter. J. Comput. Vision 30(2), 79–116 (1998)
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[7] Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Inter. J. Comput. Vision 2(60), 91–110 (2004) [8] Mikolajczyk, K., Leibe, B., Schiele, B.: Multiple object class detection with a generative model. In: Proc of CVPR 2006, pp. 26–36 (2006) [9] Mikolajczyk, K., Schmid, C.: Scale and affine invariant interest point detectors. Inter. J. Comput. Vision 1(60), 63–86 (2004) [10] Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(10), 1615–1630 (2005) [11] Nistér, D.: An efficient solution to the five-point relative pose problem. IEEE Trans. Pattern Anal. Mach. Intell. 26(6), 756–770 (2004) [12] Sivic, J., Zisserman, A.: Video google: A text retrieval approach to object matching in videos. In: Proc of ICCV 2003, pp. 1470–1478 (2003) [13] Tuytelaars, T., Mikolajczyk, K.: Local Invariant Feature Detectors: A Survey. Foundations and Trends in Computer Graphics and Vision 3(3), 177–280 (2007) [14] Zhang, Z., Deriche, R., Faugeras, O., Luong, Q.-T.: A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry. Artificial Intelligence Journal 78(1-2), 87–119 (1995) [15] Zhao, F., Huang, Q., Gao, W.: Image Matching by Normalized Cross-Correlation. In: Proc. of ICASSP 2006, Toulouse, France, May 14-19, vol. 2, pp. 729–732 (2006)
Depth Extraction by Simplified Binocular Vision Gouhong Ma, J. Qin, F.R. Jiang, and H.B. Wang Mechatronics College, Nanchang University, Nanchang City, 330031 China e-mail:
[email protected]
Abstract. In order to control welding robot to trace welding seams well, a binocular vision method is researched in this paper according to three dimension rebuilding theory. This sensor system adopted two JC-BP650 vision sensors. At the same time, an algorithm of binocular vision image match with competitive match theory is designed. The algorithm included a series of image feature extraction, and image feature match. Image experiment showed Canny operator is more suitable than other image process operators after comparing image process effectiveness. With image feature matching process, best match point is oriented and calculated easily along the real depth. A flow chart of image are processed and a software program is developed, too. Depth extraction experiments indicated that image algorithm has good ability of real process and it can detect the depth information well. Experiment shows different errors of the system and the error rate is in the lowest condition when the depth is 27.5 cm.
1 Introduction Binocular vision is one kind of three-dimension vision. It simulates vision function of eyes with two CCD sensors to gesture different digit image in different way. With theory of space vision, we can rebuild three-dimensional shape and position of surrounding photographic field [1]. With this method, it can be used to plan robot path and to trace welding seams and rebuild three-dimensional welding pool and so on. It plays an important role in welding area. Now in China, there are some universities that had done important works, including Shanghai Jiaotong university, Tsinghua University, welding key lab of Herbin institute of technology and center university of Taiwan and so on [2-4]. This paper will focus on depth extraction of welding piece in intelligent tracing of welding robot system.
2 Image Processing To the image acquired by the two CCD, in order to acquire the image character better, and to pre-match it, in this paper, with a sign on the steel plate, serials of image processing was be done also (shown as Figure 1). T.-J. Tarn et al. (Ed.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 179–183. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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Fig. 1. The images collected by the two sides CCD
Fixed threshold segmentation was used (Shown in Figure 2).
Fig. 2. Fixed threshold segmentation
To the image above, how to extract the edge is very important to acquire the character of the image. Four operators, such as Prewitt operator, Roberts’s operator, Kirsch operator and Canny operator, were used to processing the image in this paper. We had compared the edge detection effect, and found that Canny operator is the best to the situation studied in this paper. The effect shows as Fig. 3. So we use Canny operator to extract the edge character of the white sign.
(a) Processed with Prewitt operator (b) Processed with Roberts operator
(c) Processed with Kirsch operator (d) Processed with Canny operator Fig. 3. Comparison of edge detection effect
3 Matching Mechanism In order to match the image better, three constraint condition of stereo matching was proposed by Marr [5-6]: consistent bindings, uniqueness constraint and
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smooth constraint. It can be further divided into the two types of constraint: Local constraint and global constraint. According to local constraints, a matching matrix was constructed, show as Figure 4.
Fig. 4. Matching matrix
、
In the matrix: N L N R is edge feature number of the image, in the matrix, that the edge of the left image l matches the edge of the right image r . The white circle is on behalf of an effective match, the black circle is on behalf of invalid match. The effective match, as the candidate, will participate in competitive matches. CSM lr is matching degree, CSM lr value is greater that the degree of match is higher. With judging each candidate of the point with above rules, we can find the match way. The chart is the flow chart of the entire process of matching competition. M l , r means
Fig. 5. Competitive matching process
4 Experiment As shown in Figure 6, the hardware of the system constructed of CCD camera, the image acquisition card, PC and other components. Its basic working principle is that the two CCD acquire surface image of the welding seam at the same time. The image grab card, which receives the video signal sent by CCD and transform the signal into digital, transmits the digital signal into the computer. Image processing was completed and the results were measured in a PC and final results will show in the display, eventually, the depth of the object was extracted.
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Fig. 6. Structure of the system
A pair of JC-BP650 cameras produced by Jichuan Company and DH-CG400 image grab card was used in the experiment, and the software platform used in Windows XP environment was produced by VC++ 6.0. In the experiment, the two CCD were installed as show in Fig. 1. The experiment, in which the two CCD were fixed at different position, was conducted. Every data of the experiment was recorded as show in Table 1. In the experiment, the size of the image grabbed by the CCD is 736 ∗ 376(8bit). Table 1. Experimental data f binocular distance measurement unit: cm position
Real distance(cm)
1 2 3 4 5 6
16.8 18.6 21.5 25.5 34.5 40.5
binocular distance measurement (cm) 25.14 24.75 31.37 25.14 36.41 36.83
7
58.0
50.29
deviation (cm)
− 8.34 − 6.15
error rate
-9.87 0.36 − 1.91 3.67
49.6% 33.1% 45.9% 1.41% 5.54% 9.06%
7.71
13.29%
According to Table 1, an error curve of the same depth can be obtained, as shown in Figure 7.
Fig. 7. Relation of error rate and depth
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According to above table we can find that when the distance between the two CCD and object is about 27.5cm the error is the least. We consider that listed reasons lead to generate error. (1) Optical sensor error, this is the system error, it can be compensated through correction. (2) Image registration error, the algorithm may not find a match point exactly of two images.
5 Conclusions In this paper, the binocular vision system is designed to get the depth and a method based on the mechanism of competitive matching is designed. Image process experiments have been done and it was found that the canny operator is more suited to real process. Experiments indicate there is a best depth for vision system. Image process experiments also show that the vision system has simple geometric relation of the two cameras; there are some errors because it is difficult to adjust the camera in ideal collocated relation. Acknowledgement. Thanks to anonymous peer reviewers and editors and fund of National Science Foundation of China under Grant 50705041.
References [1] Wang, K.H., Cao, H., Liu, Y., et al.: Binocular stereo vision with laser to reconstruct welding seam. Transactions of the China Welding Institution 27(8), 1–4 (2006) [2] Chen, S.B., Lin, T., et al.: Intelligentized welding robot technology. China Machine Press, Beijing (2006) [3] Chen, Q., Guo, S.Z.: Computer Vision Sensing Technology and Its Application in Welding. Transactions of the China Welding Institution 22(1), 83–91 (2002) [4] Tung, P.-C., Wu, M.C., Hwang, Y.-R.: An image-guided mobile robotic welding system for SMAW repair process. International Journal of Machine Tools & Manufacture (44), 1223–1233 (2004) [5] Ma, S.D., Zhang, Z.Y.: Computer vision - Calculate Theory and Algorithm Basis. Science Press, Beijing (1997) [6] Wang, L.-c., Xiong, C., Wang, C.-y., Lu, Q.-y.: Simplified Binocular Stereo DistanceMeasurement Algorithm and a System Based on Competition. Chinese Journal of Sensors and Actuators 20(1), 150–153 (2007) (in Chinese) [7] Xiong, C., Tian, X., Lu, Q.: Real-time Binocular Matching and Depth Measurement Based on Competing Schema. Computer Engineering and Applications 1, 83–85 (2006)
Comparison of Calibration Methods for Image Center Xizhang Chen1, Shanben Chen2, and Houlu Xue1 1
School of Material Sci. and Eng., JiangSu Univ., ZhenJiang 212013, China 2 Inst. of Welding Engineering, Shanghai Jiaotong Univ., Shanghai 200030, China e-mail:
[email protected]
Abstract. The calibration of image center is necessary in computer vision. It is divided into two categories in this paper, one is independent of other intrinsic parameters, and the other is not. Category is further devided into 3 groups, and Zhang’s method is taken as an example in category . The theory and experiments of each method are given. Through comparison of different methods, the characteristic of each calibrated method is also discussed. The results provide the readers a reference to select or advance calibrated methods according to different applications, requirements and experimental conditions.
Ⅰ
Ⅱ
1 Introduction Camera calibration is a necessary step in 3D computer vision. It determines the internal camera geometric and optical characteristics (intrinsic parameters) and/or the 3D position and orientation of camera frame relative to a certain world system (extrinsic parameters) [1]. In many cases, the overall performance of the computer vision system strongly depends on the accuracy of the camera calibration. For an ideal lens camera calibration would involve modeling 3D to 2D projection through a simple center of perspective projection. Unfortunately, models for real lens should take into account additional imaging properties that vary radially in the distance from center of the image. To capture these properties we need to know their center. Willson R.C. [2] Shows an inaccurate image center can have a significant effect on the calibration accuracy (in this case for a standard Tsai-Lens camera mode1) [2], which would also effect the accuracy of the whole computer vision. Thus it is very important to calibrate accurately the image center. In computer vision the most commonly used definitions of image center are the focus of expansion and the center of perspective projection. The numerical center of the image coordinates is also commonly used [2]. It is usually used as the origin of the imaging process and appears in the perspective equations. In high accuracy application, it also serves as the center or radially modeled lens distortion. For its importance, many researchers have focused on this topic. Reimar K. [1] divided the methods of determining image center into three groups: direct optical method, method of varying focal length, radial alignment method and model fix method. T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 185–192. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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Zhu [3] applied laser method to the calibration of image center and got accurate result. Shivaram G [4] presented a technique using spheres as calibration objects for finding the image center. Li [5] and Zhu [6] used one lens and multi-lens to calibrate the image center. These methods calibrate the image center independent of the whole camera calibration process, are realized before the camera calibration. There are also some methods involve the calibration of image center into the whole camera calibration, such as the methods in Ref. [7-9]. All these methods to calibrate image center have their advantages and disadvantages. In this paper, we compared with the different methods in common use. The experiments and analysis showed their characteristics, which make us select the proper method easily according to special work condition and special applications, also is the foundation to develop and advance new methods.
2 The Camera Model To calibrate a camera, a model for the mapping of the 3D points of the world to the 2D image generated by the camera, lens, and frame grabber is necessary. The pinhole and telecentric camera model are two typical models in common use. Here we only discuss the former. It effects a perspective projection of the world coordinates into the image, just like the human eye does. Fig. 1 shows the perspective projection effected by a pinhole camera. The world point P is projected through the optical center of the lens to the point P’ in the image plane, which is located at a distance of f (the focal length) behind the optical center. As a habit for human, we pretend the image plane lies in front of the optical center, as shown in Fig. 1. There are 4 coordinate systems in this model: world coordinate system (WCS), camera coordinate system (CCS), image coordinate system and image plane coordinate system. All the coordinate systems are shown in Fig. 1.Point P is given in WCS, we should transformed it into CCS in order to make the projection into the
Fig. 1. The pinhole camera model
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image plane possible. The CCS is defined so that its x and y axes are parallel to the column and row axes of the image, respectively, and the z axis is perpendicular to the image plane. If the camera coordinates and image plane coordinates of point P are ( X c Yc Z c )T , and ( xu yu )T , respectively. Then according to pinhole model, we have:
xu = f
Xc Zc
yu = f
Yc Zc
(1)
The coordinates ( xu yu ) are the value in ideal pinhole model. But the effect of lens distortion makes the point deviate from ideal points, if the real coordinates are T ( xd yd ) , we have: T
xd = (1 + krd2 ) xu , yd = (1 + krd2 ) yu
(2)
Where rd2 = xd2 + y d2 , k is the distortion coefficient. Modelling lens distortion is very important for accurate computer vision. For the image coordinate system and image plane coordinate system we also have:
u = C x + S x xd
v = C y + S y yd
(3)
Where (C x , C y ) is the image center. Sx and Sy are the number of pixels in unit distance along x and y direction. All these parameters are marked in Fig. 1. Thus the parameters (C x , C y ) , Sx and Sy, f, k make up the interior parameter of camera.
3 Calibration of Image Center The image center is the frame buffer coordinates of the intersection of the optical axis with the image plane. It is the center of frame buffer in theory but not actual value. Thus we should calibrate the image center. There are two categories to calibrate image center, one is the methods independently of all the other camera calibration, the other is those methods involve in the whole camera calibration. The former can also be divided into 3 groups as introduction part shows. All the experiments are done in an intelligent welding robot system. Two WAT902 micro CCD cameras are fixed on the end-effector of robot, whose dimension of the target plane is 1/2 inch and pixels of the imaging plane are 768( H ) × 576(V ) . Images are captured by image capture card CG-400 that produced by DaHeng Corporation in Beijing.
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Fig. 2. Experimental setup
3.1 Category
Ⅰ
In this part we introduce the 3 groups methods to calibrate image center that independently of other camera calibration parameters.
3.2 Direct Optical Method This method is seen as the most accurate, it is often use as a reference in other method to calibrate image center. The principle is shown as Fig. 3. diaphragm
lens image plane laser beam
laser generator reflect beam principal point
Fig. 3. Schematic illustration of direct optical method
When the laser beam is pointed at a lens assembly through a hole on light screen, part of the light comes into being image in the CCD chip and display on the image plane. And part of the light is reflected to the surface of the light screen, which is a light spot in image plane. When incident light coincides with optical axis, the reflect lights also coincide with the incident lights. The reflect light is pointed at the hole of light screen. We get only one image point in image plane, and this image point is just we want to know, i.e. the image center. Adjusting the pose and position of the camera and observing the position of reflected light spot, we can confirm the position of image center. In order to get more accurate result, the experiments are done in darkroom. The results are shown in Fig. 4. From (a), we can see that the incident light doesn’t coincide with reflect light. After proper adjustment, the incident and reflect light coincide with each other, the image spot can be seen as the image center.
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(a)
(b)
Fig. 4. Diagram of experiment process
If the incident intensity of laser is high, maybe the image is a little light circle around the incident light spot, as shown in Fig. 3. Mass center of image spot is got by image processing, which is the image center. The calculated formula is:
Cx =
1 N
i= N
∑u i =1
1 N
Cy =
i= N
∑v
(4)
i =1
The mean value of 10 times repeat experiments is the last results. It is (368.27, 282.84) And the experimental data show that this method has good robustness, and the error is less than 2 pixels.
3.3 Method of Varying Focal Length If the focal lengths change, the image in camera with pinhole model will appear zoomed. In the changing image, only one point, i.e. the center of zoom, is unchanged. This point is often seen as the intersection of the optical axis with the image plane, which in turn is the image center. Supposing the effective focal length of camera change from f1 to f 2 , from Eq. (1) to (3), we have: u f 1 − Cx u f 2 − Cx
v f 1 − Cy v f 2 − Cy
=
=
xdf 1 xdf 2
ydf 1 ydf 2
=
xuf 1 (1 + krdf2 1 ) xuf 2 (1 + krdf2 2 )
=
yuf 1 (1 + krdf2 1 ) yuf 2 (1 + krdf2 2 )
=
=
f1 (1 + krdf2 1 ) f 2 (1 + krdf2 2 )
f1 (1 + krdf2 1 ) f 2 (1 + krdf2 2 )
(5)
(6)
Then we have: Cx (v f 1 − v f 2 ) + C y (u f 2 − u f 1 ) = u f 2 v f 1 − u f 1v f 2
Where
(7)
(u f 1 , v f 1 ) and (u f 2 , v f 2 ) is the coordinates of a feature when the focal
length is
f1 and f 2 , respectively.
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If we know the coordinates of more than 2 feature points, we can calculate the image center using Eq. (7) and least square method. According to the theory above, we can realize the calculation by two ways, one is variation of the imaging distance method using one lens [1, 6], the other is variation of the effective focal length using 2 lens [1]. We just discuss the later because the former has been introduced in our Ref. [6]. There are some errors due to the inaccuracies in lens manufacturing and mounted and dismounted of lens. When the difference of focal length is proper, such as 6mm and 12mm, the accidental error can be reduced to infinitesimal [10]. Here we use 3 lens, their focal length is f1 = 12mm, f2 = 6mm and f3 = 4.8mm, respectively. Assembled lens in pair, The mean values is image center (We call it multi-lens method later.). Images snapped by three different lens are shown as Fig. 5 and the results are show in Tab.1.
=12
=6
(a) f1
(b) f1
(c) f1
=4.8
Fig. 5. Snapped images by 3 different lens Table 1. Calibrated results by varying focal length lens f1 & f2
f1 & f3
f 2 & f3
Mean value
Cx 333.35
340.60 354.48
345.22
Cy 255.42
276.17 313.78
281.79
3.4 Radial Alignment Method This technique uses a physical setup. It is accurate but requires calibration points with known coordinates, and image feature extraction should within a small systematic error. It uses the simple Radial Alignment constraint to compute the image center. This experiment is yet to be done. Related theory and results see Ref. [1].
3.5 Category Ⅱ
Ⅰ
Not as category , this category calibrates all the intrinsic parameters in a procedure. Here we take the method Z. Zhang [7, 9] presented as example to introduce
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the calibrated process. We call it Zhang method in this paper. The technique only requires the camera to observe a planar pattern shown at more than 2 different orientations [9]. The motions need not to be known in advance. This method lies between photogrammetric calibration and self-calibration. Detailed theory and deduction can see Ref. [9], we don’t repeat it here. We give the simplified calibrated procedure according to the Ref. [9].
(a) Template used in experiments
(b) Images captured in different pose and position
Fig. 6. The standard template and its images
(1). Print a pattern and attach it to a planar board, the pattern is composed of 9×13 black and white chess board, the size of each lattice is 20×20mm, as shown in Fig. 6 (a). (2). Take a few images of the model plane under different orientations by moving the camera on robot, as shown in Fig. 6 (b). (3). Detect the feature points in the images. (4). Estimate all intrinsic parameters and get the image center, the last result is (352.49, 276.84).
4 Comparison and Discussion The experimental data have been given in part 2.Tab. 2 gives the comparison of different calibrated methods for the image center. Some of the results are cited from References. From the table, we can know their characteristic clearly. The concept of direct optical method is the simplest and the results are very reproducible and accurate, here we take it as reference. But it needs laser and good experimental skill. The method of varying focal length is simpler to perform but is not so accurate as other two groups. Radial alignment method is the most general purpose technique among the three groups in category . It is also accurate and the error is within 6 pixels. But accurate feature extraction algorithms are needed. And more computation time is needed. Zhang’s method in category also has better results, but it needs to extract corner point coordinates and calculated error is produced during the calibration. Calibration of image center is necessary in computer vision. Different applications, requirements and experimental conditions may require different calibration methods. We compare the methods in common use and give the results and their characteristics, which can be used as a reference to select or advance calibrated methods.
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Techniq ue used
Buffer center
Category Ċ
Category ĉ Direct optical method
Variation of focal length Onelens method[1
Multi-lens method
Radial alignmen t method[1]
Zhang method
, 6]
Image center
384,288
368.27,282. 84
N/A
345.22,281. 79
N/A
352.49,27 6.84
Max Offset
15.73,5.16
reference
21,28
23.05,1.05
17.5,8.5
15.78,6
Reprodu cibility
N/A
±3
±20
±8
±6
±6
Acknowledgement. The work is supported by Research Foundation for Talented Scholars of Jiangsu University No.07JDG085, Innovative Research Team of Jiangsu University, and Shanghai Sciences & Technology Committee under Grant No. 09JC1407100, P.R. China.
References [1] Lenz, R.K., Tsai, R.Y.: Techniques for Calibration of the Scale Factor and Image Center for High Accuracy 3D Computer vision Metrology. IEEE Trans. on Pattern Analysis and Machine Intelligence 10(5), 713–720 (1988) [2] Willson, R.C., Shafer, S.A.: What is the center of image? In: Proceedings CVPR 1993, New York, pp. 670–671 (1993) [3] Zhu, Z.-y., Piao, Y.-j., Lin, T., Chen, S.-b.: Research of laser calibration method for the image plane center of the CCD camera. Laser Journal 25(1), 35–36 (2004) [4] Shivaram, G., Seetharaman, G.: A new technique for finding the optical center of camera. In: Proceedings of International Conference on Image Processing, Chicago, pp. 167–171 (1998) [5] Li, J.-q.: The Research on Acquisition of Welding Seam Space Position Information for Arc Wedling Robot Based on Vision. Harbin Institute of Technology, Harbin (2003) [6] Zhu, Z.-y.: Research on welding robot recognition and guiding to the inital welding position with visual method. ShangHai Jiaotong Univ., ShangHai (2004) [7] Zhang, Z.: Camera calibration with one-dimentional objects. IEEE Trans. on Pattern Analysis and Machine Intelligence 26(7), 892–899 (2004) [8] Ma, G., Wei, S.: Implicit and explicit camera calibration: theory and experiments. IEEE Trans. on Pattern Analysis and Machine Intelligence 16(5), 469–480 (1994) [9] Zhang, Z.: A flexible new technique for camera calibration. IEEE Trans. on Pattern Analysis and Machine Intelligence 22(11), 1330–1334 (2000) [10] Fang, Y., Gao, L., Lin, Z.: The research on calibration method of camera intrinsic factors in the intelligent CMM inspection system. Robot 20(2), 148–152 (1998)
Seam Tracking and Dynamic Process Control for High Precision Arc Welding Huabin Chen, Tao Lin, and Shanben Chen Welding Engineering Institute of Material Science and Engineering, Shanghai Jiao Tong University, Shanghai, China e-mail:
[email protected]
Abstract. A local intelligent welding robot system with low-cost vision sensor was developed. Especially, adaptive control for the correction of welding path deviation, welding current and wire feeding velocity in real time were constructed. For the seam-tracking module, an improved adaptive threshold segmentation algorithm was applied to detect the welding curve. The results show that direct imaging with advanced image processing algorithm can provide precise and robust information of the weld groove. At the same time, a nonlinear self-corrected controller was constructed. Input parameters including geometric parameters of the weld pool were captured from dynamic image acquired by visual sensing system. In order to keep a sound weld appearance, the variation of the weld gap compensatory feed-back module was added into the control system. This local intelligent welding robot system was verified on the face flange of aluminum alloy.
1 Introduction Welding automation is the trend of the welding technical development. Since the current ‘teaching and playback type’ robot lack external information sensor feedback and function of the real-time adjusting, it could not meet the demands for precision welding of the complicated seam in special high-tech structure. In engineering practice, welding conditions, such as the pre-machining tolerance of the work-piece, fit-up precision and unfitness of the joint, change continually, which would result in poor welding appearance and defect possibly. Consequently, an automatic-welding system without feedback and control may easily result in poor welding quality, especially when the joint preparation and fitup precision are far away from ideal requirements. In order to overcome various uncertain factors influencing welding quality, develop and improve intelligent technologies for welding robots will be an effective approach, such as vision sensing and real-time intelligent control [1-5]. In recent years, significant achievements have been gained in weld pool geometry sensing, modeling and intelligent control of the weld quality and process monitoring. However, more accurate and reliable techniques are still needed due to complexity of welding process, real-time and opening limits in robot systems [6-7]. T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 193–201. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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Vision sensors are widely used for quality control of robot arc welding process. However, the on-line monitoring and control techniques are extremely important research topics in robot arc welding. In this paper, a real-time visual image feedback with adaptive control on the face flange for the robot welding (gas tungsten arc welding, GTAW) has been conducted.
2 The Robotic Welding Systems Description With the main control computer as its core, a vision sensor and the interface circuit box are designed to realize the autonomy and intelligence of the arc welding robot by existing welding source power, and the correspondent software developed. The welding torch can be rectified in the horizontal direction along the seam path through setting the deviation voltage signal (-10V to 10V) in the robot controller. The general software flow chart for the arc welding robot system is shown in Fig. 1. In this system, it could monitor the information of the weld pool and gap during welding process. We can adjust the correction of the welding path deviation and welding parameter according to the vision feedback information in realtime.
Fig. 1. General software flow chart
This work is done to provide an online real-time manipulation system for the arc welding robot and satisfy its local intellectualized welding requirements. The welding parameters and dynamic welding pool information are well inspected and controlled through the vision sensors. The whole arc welding robotic system adopted the multi-thread paten during welding process. It could capture the weld image by the main thread. In additional, the image processing and welding parameter adjusting were conducted by the other worker thread side-by-side.
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3 Image Processing Algorithm The information of the weld pool and gap image is captured by the CCD camera with narrow-band composite light filter. From the analysis to the characteristic of the light spectrum of welding for aluminum alloy, it is distributed from 580nm to 720nm. Based on the above analysis, the narrow-band filter centered at 630nm and neutral glass slices of 10%+30%+50% are selected, which could reduce the arc noise to the weld pool. The main control computer captures the images, extracts the information of the weld pool and the gap through the specific image processing algorithms, and calculates the voltage values for the seam tracking and welding parameters (pulse current and the wire feed rate) adjusting. Although the face flange has been assembled well, an uneven gap still exists in some places, as shown in Fig. 2.
Fig. 2. The seam gap viewed without arc
The vision sensor captures the images of the weld in the top face front. Figure 2 shows the weld image of the pulse square-wave AC GTAW for the face flange weld joint with ‘V’ type groove. Since the size of the image is 768×576 pixels, in order to get a clear contour image of the weld pool and gap, and then calculated the geometry rapidly, a window, rather than the whole image is used to reduce the data which would be processed. Weld pool and the gap were defined as window 1 and window 2, respectively (Shown in Fig. 3).
Fig. 3. Typical image of the weld pool and gap
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From the three-dimensional grey level distribution (Shown in Fig. 4), the variation of the pixel gradient between the weld pool boundary and background is not significant, traditional edge detector should produce an edge indication localized to a single pixel located at the midpoint of the slope. Although this form of edge detection performs reasonably well, the detailed information is very poor in the weld pool image.
Fig. 4. The image of the weld pool and grey distribution
In this paper, we selected improved adaptive threshold segmentation algorithm, which could solve the uneven background of the weld pool image segmentation. More specifically, this method can be described as the following algorithm. Determine
δi
sub-rules,
⎧1, f ( x, y ) > T g ( x, y ) = ⎨ , where g ( x, y ) is the ⎩0, other
output and T is the threshold. Search the set pixel matching the sub-rules. Ensure the approximate solution
δ
of the
δi
sub-rules,
⎧1, f ( x, y ) ≥ T and g( i , j ) >f ( x, y) × M%, g ' ( x, y ) = ⎨ ⎩0, other
a) Original image b) Adaptive threshold segmentation c) edge detection
Fig. 5. Windows1 image processing
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Note that, the image of the weld pool using above processing algorithm is incomplete. The further processing is necessary, such as removal of the false edges, thin edge algorithm and so on. In this paper, this section part would not be discussed.
Fig. 6. The image of the weld groove and grey distribution
At the same time, the processing flow for window2 was similar to window1, and the result is shown in Fig. 4. In addition, considering the demands of the seam tracking, in this section part we should fit curve of the weld groove from the CCD camera.
a) Original image b) edge detection d) curve fitting
Fig. 7. Windows 2 image processing
Meanwhile, the total image processing time is 155ms, which could meet the real-time control requirements.
4 The Precise Control of the Arc Welding Robot 4.1 Seam Tracking Control Module In order to realize the seam path tracking, the most important is to determine the projection point of the tungsten. The deviation error is related to the welding speed, correction voltage and response time. It could be expressed as follows, δ = f (V , vW )Δt , where δ is the deviation, V is the correction voltage and Δt is the response time. From the above expression, it can be seen that the deviation equation is a black box model. We must research the suitable seam path tracking control strategy. In this paper, we adopted the past mature seam tracking
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algorithm which is developed by our research group [8]. A simple proportional, integral and derivative (PID) controller has been introduced into the experiments. The input variable is the offsets and the deviation voltage is the output variable. It is given in the following equation, k
v(k ) = k p e(k ) + ki ∑ e(k ) + kd [e(k ) − e(k − 1) ]
(1)
j =0
Where v( k ) is the deviation voltage, e( k ) is the offset. In our experiment, the value of k p ,
ki , kd is 1.65, 0.18 and 0.02, respectively. Fig. 8 is the seam track-
ing results for the face flange. The seam track path is obtained through the detection on the weld pool image, and the maximum error is ±0.6 mm.
ordinate of the seam / mm
80
desired trajectory
tracking trajectory
60 40 20 0
-20 -40 -60 -80 -80 -60 -40 -20
0
20
40
60
80
abscissa of the seam / mm
Fig. 8. Seam Tracking results of the face flange
4.2 Control Scheme for the Face Flange Due to the influence of the variation of the heat dissipation and gap on the welding process, it is difficult to adjust the welding process parameters using conventional control strategy. Thus, in this paper, a peak current self adaptive regulating controller with weld gap compensation system is made to control the welding process. The block diagram of controlling the penetration depth is shown in Fig. 9. From this diagram, the vision system was employed as the feedback mechanism, and then the actual top-side bead width and the gap were input into the back-side bead width dynamic neural network model (BWDNNM). Where δ represents the state of the gap,
I p is the peak current, V f is the wire feed rate, Wb is the target back-
side bead width, and the
Wb is the output variable from the BP. Meanwhile, in
order to produce the corresponding compensation, the disturbance quantity was introduced to the feed forward link. Let the desired back-side bead width be
Wbset .
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Fig. 9. Block diagram of self adaptive compound controller
The compound controller of the welding penetration is divided into the feed forward part and feedback part. On the one hand, the feedback part is the peak current self adaptive regulating controller. The adaptive welding current adjuster based on the minimum variance theory is defined as I ( k ) = − 1 / b 0 ( a )W b ( k ) + a 2 W b ( k − 1) + a 3 W b ( k − 2 ) + a 4 W b ( k − 3 ) (2) + b 1 I ( k − 1) + b 2 I ( k − 2 ) + b 3 I ( k − 3 )
On the other hand, the feed forward controller is conducted from the corresponding knowledge, i.e., the variation of the wire feed rate is determined by the disturbance variation of the gap. The deviation e and variation ce of the gap for the input variable are defined. The rule at this situation is described in the following if-then form: if e is A and ce is B, then ΔV f is C, where A, B, and C are the fuzzy variables. Let [-6mm/s ship functions
,6mm/s] is the universe of ΔV , and the memberk
ΔV f of the fuzzy variables are adjusted based on the constant
welding process parameters. If the deviation of the gap is beyond the universe of [-2mm 2mm], it is difficult to achieve a sound welding joint. The if-parts are determined from the control knowledge of experts in the situation. The control rule table can be found in our past publication [9]. The practical experiment has been carried out in developed system using above closed-loop control strategy. Here, the minimum adjustment step of the peak current I p and wire feed rate are 5A and 1mm/s respectively. In this study, the de-
,
sired value of the back-side width is set to 7mm. Since the welding process become steady after 5 pulse cycles, the controller keep constant during the initial period. Fig. 10 is the curves of the parameters in closed-loop controller. The backside bead width was controlled and kept steady regardless of the variation of the gap. In addition, we can see that the actual back-side bead width varied around the average values. In this paper, we adopted the residual standard deviation (RSD) to evaluate the developed controller. The RSD from the desired value of the backside width of the weld pool is 0.84mm.
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Fig. 10. The control curve of the face flange
The top and back side appearance of the face flange weld was shown in Fig. 11. The results showed that the adopted controller has good adaptability, and the appearance is uniform and sound.
Fig. 11. The weld appearance of the face flange
5 Conclusions In this paper, the vision sensing and the controlling of the welding quality have been presented, which demonstrated the robust method of the seam tracking and intelligent control scheme for traditional arc welding robot. The tracking accuracy is in the range of ±0.6 mm for the production. Meanwhile, good quality welding joints can be obtained regardless of the variation of the gap on the face flange work-piece. For this purpose, a peak current self adaptive regulating controller with weld gap compensation system is developed to control the welding process. The RSD from the desired value of the backside width of the weld pool is 0.84mm. Acknowledgement. This work is supported in part by National Natural Science Foundation of China under Grant No. 51005153, and Shanghai Sciences & Technology Committee under Grant No. 09JC1407100, P R China. Many thanks are also given to Dr. Hongyuan Shen and Hongbo Ma from Shanghai Jiao Tong University. The authors wish to thank for these supports in order to carry out this feasibility study.
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References [1] Tsai, C.-H., et al.: Fuzzy control of pulsed GTA welds by using real-time root bead image feedback. Journal of Materials Processing Technology 176, 158–167 (2006) [2] Luo, H.: Robotic welding, intelligence and automation, laser visual sensing and process control in robotic arc welding of titanium alloys. LNCIS, vol. 299, pp. 110–122 (2004) [3] Valavanis, K.P., Saridis, G.N.: Intelligent Robotics System: Theory, Design and Applications, Boston, pp. 12–18 (1992) [4] Miller, M., Mi, B., Kita, A., Ume, C.: Development of Automated Real-Time Data Acquisition System for Robotic Weld Quality Monitoring. Mechatronics 12, 1259–1269 (2002) [5] Song, H.S., Zhang, Y.M.: Measurement and analysis of three-dimensional specular gas tungsten arc weld pool surface. Welding Journal 87(4), 85–95 (2008) [6] Cook, G.E., Barnett, R.J., Andersen, K., Strauss, A.M.: Weld modeling and control using artificial neural networks. IEEE Transactions on Industry Applications 31(6), 1484–1491 (1995) [7] Adolfsson, S., Bahrami, A., Bolomsjo, G., Cleason, I.: On-line quality monitoring in short-circuit gas metal arc welding. Welding Journal 78(2), 59–73 (1999) [8] Shen, H.-y., Wu, J., Lin, T.: Arc welding robot system with seam tracking and weld pool control based on passive vision. International Journal of Advanced Manufacturing Technology 39(7-8), 669–678 (2008) [9] Chen, H.B., Lv, F.L., Li, J.Q.: Closed-loop control of robotic arc welding system with full-penetration monitoring. J. Intell. Robot Syst. 56(5), 565–578 (2009)
Feature Selection of Arc Acoustic Signals Used for Penetration Monitoring Zhen Ye1, Jifeng Wang2, and Shanben Chen1 1
Institute of Welding engineering, Shanghai Jiao Tong University, 200240, P.R. China 2 Shanghai Institute of Special Equipment Inspection & Technical Research, Shanghai, 200062, P.R. China e-mail:
[email protected]
Abstract. Investigation on the arc sound has been made to find the features which can represent the welding penetration. We first acquired original features of the signal by using wavelet packet decomposition. By the Branch and Bound (BB) method, we reduced the number of features and finally obtained a combination of features with certain representation. The result suggests that energy characteristics in some frequency ranges might be effective features to describe the acoustic signal which can be used for predicting welding penetrations.
1 Introduction The welding process is a complicated real-time process. This brings great challenges to the development of intelligent welding. Up to now, the study of intelligent welding mainly focuses on the visual information. However, sometimes it is difficult to grasp useful welding phenomena because of spattering, fuming, and electromagnetic noise. But the acoustic signal of the arc welding seems to provide available and valuable information to cover the shortage of the visual information. Some studies have suggested that the arc sound is an important criterion for the welding quality control [1-6]. Our study here is to try to search for a combination of acoustic features which can represent the conditions of welding penetration. Therefore, we first acquired original features of the signal by using wavelet packet decomposition. After that, we reduced the number of the features based on the Branch and Bound method. And finally we obtained a combination of features.
2 Experiment System and Signal Preprocessing 2.1 Experiment System Acoustic signal acquisition system for Pulsed TIG welding includes welding source, wire feeder, water-cooled welding torch, welding table and acoustic sensor. The experiment system is depicted in Fig 1. T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 203–210. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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Fig. 1. Experiment System Chart
2.2 Welding Condition Aluminum alloy sheet is welded by AC-pulsed TIG welding, some welding conditions are depicted in Table 1. Table 1. Welding Conditions of AC-pulsed TIG welding Rate of feed
2.5mm/s
Dwell time for arc starting
10s
Gas-flow rate
Ar 15ml/s
Pulse frequency
2Hz
We acquire different welding penetration by changing welding speed and welding current, experiment conditions are depicted in Table 2. The sampling frequency of the acoustic signal is 40 kHz. Table 2. Experiment Conditions of AC-pulsed TIG welding Welding Speed (mm/s)
Welding Current(A)
Condition of Penetration
8mm/s
235
Complete penetration
12mm/s
235
Complete penetration
16mm/s
235
Incomplete penetration
8mm/s
230
Complete penetration
10mm/s
230
Partial penetration
12mm/s
230
Incomplete penetration
8mm/s
225
Complete penetration
10mm/s
225
Partial penetration
12mm/s
225
Incomplete penetration
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2.3 Preprocessing of Acoustic Signal 2.3.1 Wavelet Packet Decomposition Traditional wavelet transform can only decompose the low-frequency information after each transform, but wavelet packet decomposition cannot only decompose the low-frequency information, but also decompose the high-frequency information as well. Therefore it can provide us more valuable information to predict the conditions of welding penetration. Daubenchies (dbN) series function is a widely used wavelet function in engineering field. We chose db3, db5, db7 wavelet to analyze the acoustic signal. 2.3.2 Feature The formulas [3] are as follows which are used to calculate the feature of arc sound of each frequency band after wavelet packet decomposition. n
Short-time energy (SRT)
E =
Mean square root (MSR)
x rm s =
Waveform indicator (WI)
1 n
x
i=1
2 i
n
1 n
∑
x i2
i =1
FSHA = xrms (
1 n ∑ xi ) n i =1
F C R E = m a x (| x i |) x r m s
Peak factor (PeF) Pulse factor (PuF)
1 n ∑ xi ) n i =1 1 n = max(| xi |) ( ∑ xi ) 2 n i =1
FIF = max(| xi |) (
Margin factor (MF)
FCLF
Kurtosis factor (KF)
⎡1 N ⎤ K = N ∑ ⎢ xn ∑ xn ⎥ n =1 ⎣ n n =1 ⎦ N
P =
⎡ N ⎢∑ ⎢⎣ n = 1
4
2 ⎡N ⎡ 1 N ⎤ ⎤ ⎢ ∑ ⎢ xn − ∑ xn ⎥ ⎥ n n =1 ⎦ ⎥⎦ ⎢⎣ n =1 ⎣
∑
⎡1 ⎢ n xn ⎣
2
3
⎤ xn ⎥ n =1 n =1 ⎦ 3/2 2 N ⎤ ⎡ ⎤ 1 ⎢ xn − n ∑ xn ⎥ ⎥ n =1 ⎣ ⎦ ⎥⎦ N
N
Skewness factor (SF)
∑
N
∑
2.3.3 Normalization
In the acoustic signal of arc welding, the variation range of different features is quite large. Some change from 0 to 1, others from 100 to 200, or even 100 to
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400. And in the feature selection, the differences would affect the final result of selection. To avoid such unfavorable effect, we normalized the data before the feature selection. The formula of normalization is as follows xij ' =
xij − Min( xi ) Max( xi ) − Min( xi )
(1)
:the jth element of the ith feature after normalization; x :the jth element of the ith feature; Max(X ):the maximum element of the ith feature; Min(Xi):the minimum element of the ith feature. xi j ' j
i
i
3 Feature Selection After data preprocessing, we acquired the original features for predicting the conditions of welding penetration. Since it is necessary to calculate the features referred in 2.3.2 for each frequency-band after wavelet packet decomposition, so we would obtain 72 features if the signal were decomposed by 3 layers, and obtain 136 features if by 4 layers. If all these features were applied to predict the condition of the welding penetration, then not only the computation time would be too long to make real-time control unpractical, but also the precision of the prediction might be affected as well. Therefore, it is necessary to reduce the number of features.
3.1 Evaluation Function We had three classes of samples {X(1th), X(2th), X(3th)} which corresponds respectively to three conditions of penetration. X(ith) is the D-dimension vector of the ith class of sample, Xk(ith) is the kth sample of X(ith), and ni is the ith sample size. The evaluation function is as follows
J = min ⎡⎣ J (Bi , j ) J iw ⎤⎦ i , j =1,2,3,
(2)
i≠ j
The larger the J is, the longer the distance between two classes is. JB(i,j) is the distance between class X(ith) and class X(jth), the formula is as follows,
J (Bi , j ) = (mi − m j )T • (mi − m j )
(3)
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J iw is the distance within class X(ith), the formula is as follows, Jiw =
1 ni
ni
∑(X k =1
( ith ) k
− mi )T • ( X k(ith) − mi )
(4)
mi represents the mean vector of the ith sample, the formula is as follows,
mi =
1 ni
ni
∑X k =1
( ith ) k
(5)
3.2 Feature Selection Based on Branch and Bound Method Suppose l is the number of features to be selected, Fl is the combination of l features, O is the number of original features, and FO is the original feature combination. Then the problem can be described as follows: Max J(Fl)
∈
St. Fl FO d≤l≤d+num d and d+num corresponds to the maximum and the minimum number of features to be selected. We chose d as 3 and num as 4 in here. We extracted features based on the Branch and Bound (BB) method [7]. But the traditional BB requires J(Fl) reduces with the decrease of the number of features, and this is not satisfied with the result of the computation (see Fig 2). A possible reason for this might be that most features are partially or completely unrelated to the conditions of penetration, and some of them even have unfavorable effect on prediction. So at first, with the abandonment of unfavorable features, the criterion increases. But with the reduction of features, the truly valuable features began to operate. When all the unfavorable features were discarded, the combination left would be best.
Fig. 2. Criterion Curve of some Coefficient Signals
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To avoid the problem brought by non-monotonicity mentioned above, it is necessary to modify the BB algorithm as follows. Bound Definition Rules
Suppose Fk is a combination of k features when the Node j is reached;{Fk} is all the combination of k features when the Node j is reached; B is the bound of the algorithm; Bk is the maximum J when the number of features is k. then Bk=max J({X}) B=min(Bk) , d≤k≤d+num Branch Cutting Rules
Suppose Ji(Fk) is the J of Fk in the ith level of the search tree;Ji+1(Fk+1) is the J of Fk+1 in the (i+1)th level of the search tree; then the rule of branch is as follows, If Ji+1(Xk+1)- Ji(Xk)≥0, no cutting; Elseif Ji+1(Xk+1)- Ji(Xk)<0 && B< Ji+1(Xk+1), no cutting; Elseif Ji+1(Xk+1)- Ji(Xk)<0 && B<>Ji+1(Xk+1), cutting.
4 Experimental Results After feature selection by BB method, we obtained results as follows. Table 3. Computation Results
-
Reconstituted signal
(the largest criterion)
wavelet coefficients
(the largest criterion)
Number of features
criterion
Number of features
criterion
Db3 (decomposed by three layers)
4
1.7422
4
1.7356
Db3 (decomposed by four layers)
5
1.8487
5
1.8489
Db5 (decomposed by three layers)
3
2.0574
3
2.1097
Db5 (decomposed by four layers)
4
2.0665
4
2.0725
Db7 (decomposed by three layers)
4
1.9387
3
1.9644
Db7 (decomposed by four layers)
3
1.9914
3
1.9790
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Table 4. Feature combination when signal is decomposed by three layers Frequency range(KHz) 2.5~5 12.5~15
Reconstituted signal db3 db5 db7 MSR MSR MSR
Wavelet coefficient db3 db5 db7 MSR MSR MSR
MSR
MSR
MSR
MSR,S RT
MSR, SRT
15~17.5 17.5~20
MSR MSR,S RT
MSR,S RT
MSR, SRT
MSR
Table 5. Feature combination when signal is decomposed by four layers Frequency range(KHz)
Reconstituted signal db3
db5
db7
db3
db5
db7
3.75~5
MSR
MSR
MSR
MSR
MSR
MSR
12.5~13.75
MSR MSR, SRT
MSR
MSR
MSR
MSR
MSR
MSR
MSR, SRT
MSR
MSR
16.25~17.5 17.5~18.75
MSR, SRT
Original signal
MSR
Wavelet coefficient
MSR
MSR
Table 3 shows the number of selected features and its criterion. From the table, db5 wavelet function is better than the others. The wavelet coefficient is better than reconstituted signal, and the four-layer wavelet is better than three-layer wavelet. Table 4 and 5 shows that mean square root and short-time energy are two statistics only selected. This suggests that the energy might be an effective feature to describe the acoustic signal which is used for predicting welding penetrations. Additionally, from Table 4.2 and 4.3, we can also see that not every frequency range is selected. The features selected are mainly concentrated on 0~2.5KHz, 12.5~15KHz, 15~17.5KHz and 17.5~20KHz when the original signal is decomposed by three layers, and mainly on 3.75~5KHz, 12.5~13.75KHz, 16.25~17.5KHz, 17.5~18.75KHz and original signal when decomposed by four layers. It is probably because the acoustic signal of these frequency bands is more sensitive to the change of welding penetrations.
5 Conclusions An improved BB method is applied to feature selection of arc sound. Using the method, we found that energy characteristic might be an effective feature to describe the acoustic signal which can be used for predicting welding penetrations.
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It is also found that signals in some frequency ranges are more representative than others. Acknowledgement. This work is supported by the National Natural Science Foundation of China under the Grant No. 51075268, and Shanghai Sciences & Technology Committee under Grant No. 09JC1407100, P.R. China.
References [1] Research Data, No 72-178, joint Committee with Arc Physics and Welding Metallurgy (November 1976) (in Japanese) [2] Futamata, M., Toh, T., Inoue, K., Arata, Y.: Investigation on welding arc sound-effect of welding method and welding condition of welding arc sound (report 1). Transactions of JWRI 11, 25–31 (1979) [3] Saini, D., Floyd, S.: An investigation of gas metal arc welding sound signature for online quality control. Welding Journal 77(4), 172–179 (1998) [4] Tam, J.: Methods of Characterizing Gas-Metal Arc Welding Acoustics for process Automation. The University of Waterloo [5] Wang, Y.-w., Chen, Q., Sun, Z.-g., et al.: Relationship between sound signal and weld pool status in plasma arc welding. Trans. Nonferrous Met. Soc. China 11(1), 54–57 (2001) [6] Wang, J.F., Fenglin, L., Chen, S.B.: Analysis of arc sound characteristics for Gas tungsten argon welding. Sensor Review 29(3), 240–249 (2009) [7] Somol, P., Pudil, P., Ferri, F., Kittler, J.: Fast branch and bound algorithm in feature selection. In: Sanchez, B., Pineda, J., Wolfmann, J., Bellahsense, Z., Ferri, F. (eds.) Proceedings of SCI/ISAS 2000 World Multi Conference on Systemics, Cybernetics and Informatics (SCI), vol. VII, pp. 646–651 (2000) [8] Wang, J.F., Chen, B., Chen, S.B.: Feature Extraction in Welding Penetration Monitoring with Arc Acoustic signals. The International Journal of Advanced Manufacturing Technology (Review NO. #IJAMT3966)
Rough Set-Based Model for Penetration Control of GTAW Wenyi Wang1, Shanben Chen1 and Wenhang Li2 1
Institute of Welding engineering, Shanghai Jiao Tong University, 200240, P.R. China e-mail:
[email protected] 2 School of Materials Science and Eng., Jiangsu University of Science and Technology, Zhenjiang Jiangsu 212003, China
Abstract. In this research work, the rough set method, a relatively new artificial intelligent approach, is studied to model the weld bead geometry in terms of the welding parameters selected to produce the welds during the pulsed gas tungsten arc welding (GTAW) process. The basic concepts of rough set theory are presented. The rough set-based modeling method is introduced. Experiments are conducted to verify the developed methods. A double-side visual sensing system is used to capture, from both the topside and the backside, the weld pool images simultaneously. The pool image is processed by a real time algorithm and thus the characteristics of the weld pool surface are extracted from the single-item image. Based on the acquired geometrical parameters of the weld pool and the set welding parameters, the prediction model of the weld geometry is developed by the proposed rough set modeling method. The performance of the model is presented and evaluated. It can be concluded that although the accuracy of the rough set model is not fully comparable with that of other modeling scheme, it is computationally efficient which is preferable for real time modeling. It should also be pointed out that as an emerging AI method, rough set based modeling method is a promising approach for the precise prediction of the welding process.
1 Introduction Modeling of GTAW process is an important issue in welding community, especially in automated welding. It provides advanced knowledge on the mechanics of weld processes and how they can be best controlled and utilized. Total automation of welding has not yet been achieved largely due to the model of welding process is not fully understood [1-2]. The physical geometry of the molten weld pool is a major factor in determining the final quality of the weld, such as its strength. These parameters include topside weld width (denoted as Wt), topside weld length (Lt), backside weld width (Wb), penetration of the weld pool, reinforcement and transverse cross-sectional area, etc. These parameters are governed by welding parameters, which include welding current (I), torch travel speed (V), wire feed rate (W), welding voltage, electrode T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 211–218. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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tip angle, shielding gas type, flow rate and so on. These geometry parameters are also influenced by such parameters as contamination, workpiece heat absorption and various environment conditions. The task of weld modeling is to describe the geometrical parameters in terms of other ones. This task, however, is nontrivial by two challenges. First, the multivariable system is closely coupled. The adjustment of any single geometry parameter will affect the others. Second, no all the parameters influencing the final quality of welds can be quantified or observed. The weld models can also be classified into two categories, namely static models and dynamic ones. The static models describe the relations between process variables in equilibrium states while the dynamic ones provide additional information of the process during transition. Hence, the dynamic models are preferable for the realtime control purpose. The capture of dynamic responses requires pass process state memory. A rudimentary way to accomplishing this is by means of autoregressive feedbacks. Several inputs are added to the model, each one accepting one of the welding variables delayed by a fixed time lag. se lower and upper approximation of a concept to discover fundamental patterns in data. By interpreting the obtained results, it can be used in many ways. When applied it to generate system model, the system variable data is broken into a limited number of meaningful ranges, then, the tables of data are reduced to a minimum set of decision rules for predicting the next state or measurement. Thus, a usable system models from tables of systems data is produced. These days, it has been widely used in many fields where a large number of data is obtained and a system model is needed such as in medical data analysis Chen et.,al (2000) [3] established a neural network model of the GTAW process for predicting the backside width with the welding parameters and topside size parameters. Some advanced methods, such as neural networks, are devised to tackle the problem. But the physical meaning of neural networks is not clear, and the networks should be rebuilt when new samples are entered. The main objective of this work is to explore the applications for rough set theory in arc welding modeling, and in particular the GTAW process. A rough set based model is developed for the prediction of the backside width of weld pool. This model can be further used in the automatically control of the weld pool penetration during GTA welding. The paper consists of six sections: the first one presents introductory remarks, next, the basic idea of the rough set is introduced, the third section presents the experimental system, the experiment is referred in the fourth section, the results of numerical experiments are shown in fifth section, while the last one presents the most important concluding remarks.
2 Experimental System The experimental system including a welding power source, a wire feeder, an image sensor and other equipment is used to perform the random current experiment. The welding current, wire feed speed and welding speed were controlled by the host computer. A scheme of this system is shown in Figure 1.
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Fig. 1. The Welding System
3 Proposed Method The rough set concept proposed by Z. Pawlak [4-5] in 1982 is a mathematical method to imprecision, vagueness and uncertainty. The hypothesis is that with every object of the universe of discourse, some information (data, knowledge) is associated [2]. Objects characterized by the same information are indiscernible, i.e. similar, in view of the available information about them. The indiscernibility relation generated in this way is the mathematical basis of rough set theory. The relation of data is discovered by approximation concept. The redundant information is reduced by reduct technique. In this section, the basic concepts of the rough set approach are briefly introduced. The modeling process is discussed.
3.1 Basic Concepts of Rough Set Theory Objects characterized by the same information are indiscernible in view of the available information about them. It is called the indiscernibility relation, which is the central notion of rough set theory. It can be presented as follows. Let U be a finite set of objects, called the universe. Let A be a finite set of attributes. With every attribute aA, set of its values is associated. Each attribute a determines a function: → Va. With every subset of attributes B of A, an indiscernibility relation on U is associated, For the sake of intuition, it can be defined by an information table called an information system of an attribute-value table. A simple information table is presented in Table 1.
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Condition attributes
1 2 3 4 5
decision attributes
a1
a2
a3
D1
1 2 2 3 2
1 1 1 2 2
1 1 1 1 2
1 2 1 2 2
In this information table, five samples can be distinguished, i.e, discerned, employing information provided by all attributes. Thus samples 2 and 3 are indiscernible with respect to attributes a1, a2 and a3, since they have the same values of these attributes. Similarly, samples 1, 2 and 3 are in terms of attributes a1 and a2, etc.
3.2 Approximations Let U be the universe, X be a subset of the universe, x be the element of the universe, B be a subset of A. With every X U, two sets is associated and defined as follows: x X}
⊆
⊆
∈U :[ ] R RX = {x ∈U :[ ] R RX = {x
(1) (2)
RX and RX are called the R-lower and R-upper approximations of X, respectively. R-lower approximations of X contains elements definitely belongs X, R-upper approximations of X contains elements probably belongs X. X BnR () = RX −RX
(3)
(3) is called the R-boundary region of X. Rough set can also be characterized by accuracy of approximation RX αR ()X = RX. Where denotes the cardinality of X when X the approximate sets is shown in Figure 2.
≠ ∅. An intuitive graph to explain
Fig. 2. Upper and Lower Approximation of Set
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3.3 Reduction of Attributes Some data can be removed from an information table without losing essential classificatory information. This idea can be expressed as reduct. A reduct is a minimal subset of attributes that enable the same classification of elements of the universe as the whole set of attributes. Let B be a subset of A , let a belong to B. aa is superfluous in B if I ()B = IB ( −{ }) ;otherwise a is indispensable in B. Set B is independent if all its attributes are indispensable.
3.4 Modeling Step The first step is to clean the collected data by removing obvious outliers and completing the missing data. The second step is to discretize the data to get smaller sets of values of attributes. These two steps will lead to a better result. The third step is to compute the reducts by rough set theory. Based on the reducts, the ‘ifthen’ rules can be generated. The last step is to validate the effectiveness of the proposed model, if it can not meet the required accuracy standard, one or several steps should be executed repeatedly.
Fig. 3. The System Modeling Procedure
4 Experiment and Results 4.1 Data Preparation Welding current (denoted as I), top weld width (Wt), top weld length (Lt) and top weld area (At) are selected as the condition attributes, while back weld width (WB) is selected as the decision attribute. Part of the collected data is shown in Table 2.
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1 2 … 760
Condition Attribute I(t) 210 168 … 209
Wt(t) 11.1 9.4 … 11.2
Lt(t) 16 13.8 … 16.1
At(t) 177.6 129.72 … 180.32
Decision Attribute Wb(t) 7.6 8.7 … 7.7
Wb(t+1) 8.7 6.2 … 7.9
For the sake of the dynamics of the welding process, the value of the previous two samples times are considered, which is preferable for real-time control purpose. Hence, the original decision table contains 15 condition attributes and 1 decision attribute. To verify the effectiveness of the prediction model, the dataset is then separated into two sets: one contains 608 samples and another contains 152 samples.
4.2 Rules It is possible to generate rules from a set of all reducts. This is done by overlaying each reduct over the Original decision table, and reading off the values. In this case, 3476 deterministic rules are generated. Deterministic rules have only one descriptor in rule’s consequent parts. By applying or inspecting if-then rules it is possible some decision to be derived. Part of the rules can be seen in Table 3. Table 3. Rules Rules
If
Then
1
I(t) ([210,211] ] and Wb(t) ([7.5,7.8] ]
(over-penetration)
2 3 4
I(t) ([210,211] ] and Lt(t) ([16.0,16.2] ] Lt(t) ([*,14.1] ] and Wb(t) ([8.1,9.0] ] Wt(t) ([9.3,98] ] and A(t) ([*,168.73] ] and Wb(t) ([8.1,9.0] ] I(t) ([168,173] ] and Wt(t) ([9.3,9.8] ] and Wb(t) ([8.1,9.0] ] … I(t) ([165,168) ) and Wb(t) ([5.7,6.4] )
(over-penetration) (full-penetration) (full-penetration)
5 … 64
(full-penetration) … (partial-penetration)
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The rules can be interpreted by follow way. Rule 1, for example, can be interpreted as if the controller receives a value of Wb(t-1) between 0.4987 and 0.5000 and a value of I(t-1) between 12.8348 and 13.2161, then the output of the controller should be 7.9338. Rule1, for example, can be interpreted as follows: if the value of I(t) is between 210 and 211, and the value of Wb(t) is between 7.5 and 7.8, then it is predicted that the weld pool is over-penetration. Other rules can be explained in the same way.
4.3 Prediction Accuracy Table 4 shows the prediction accuracy of the proposed modeling method. For partial penetration, 15 samples are correctly predicted, the accuracy is 55.6%. For full penetration, 43 samples are correctly predicted, the accuracy is 76.8%. For over penetration, 28 samples are correctly predicted, the accuracy is 40.6%. Table 4. Prediction Accuracy (Classification performance) for Weld Pool Penetration Predicted Actually Partial Full Over Undefined
Accuracy
Partial
Full
Over
15 6 0 0
10 43 41 0
2 7 28 0
undefined 0 0 0 0
0.556 0.768 0.406
4.4 The Stability of the Modeling Method To demonstrate the stability of the proposed modeling method, two series of experiments were performed. In the first phase, one set was used for training, whereas the trained system was tested using the other set. In the second phase, the set used previously for testing was used for training and vice versa. The accuracy for both phases of the experiment is shown in Table 5. Table 5. Prediction Accuracy for Weld Pool Penetration
First
(%)
Phase Second
(%)
Accuracy
48%
40.8%
Undefined
0
3
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5 Conclusions This paper is aimed to get a knowledge-based model for automated weld penetration control. To meet this goal, the rough set is introduced into this field. The experiment result of the proposed method shows that it can be effectively used in this field. Acknowledgement. This work is supported by the National Science Foundation of China under the Grant NO. 60874026, and Shanghai Science & Technology Committee under Grant NO. 09JC1407100, P R China. Natural Science Foundation of Jiangsu Higher Education Institutions (09KJD460002) National Natural Science Foundation (51005107).
References [1] Kristinn, A., Ge, C., Gabor, K., Kumar, R.: Aritificial Neural Networks Applied to Arc Welding Process Modeling and Control. IEEE Transactions on Control Systems Technology 26(5), 7 (1990) [2] Zhang, Y.M., Li, Y.C.: Control of Dynamic Keyhole Welding Process. Automatica 43, 9 (2007) [3] Chen, S.B., Lou, Y.J., Wu, L., Zhao, D.B.: Intelligent methodology for measuring, model-ing, control of dynamic process during pulsed GTAW —part I Bead-on-plate welding. Welding Journal 79(6), 15 (2000) [4] Pawlak, Z.: Rough set. International Journal of Computer and Information Science 11(5), 341 (1982) [5] Pawlak, Z.: Rough set approach to knowledge-based decision support. European Journal of Operational Research 99, 48 (1997)
A Study of Arc Length in Pulsed GTAW of Aluminum Alloy by Means of Arc Plasma Spectrum Analysis Huanwei Yu and Shanben Chen Institute of Welding engineering, Shanghai Jiao Tong University, 200240, P.R. China e-mail:
[email protected]
Abstract. This paper studied the arc length in pulsed GTAW of aluminum alloys by means of arc plasma spectral analysis. The intensity of Ar I spectral lines and electronic temperature Te of arc plasma were extracted to detect the change of arc length. The average Te signal in the period had a linear relationship with the arc length and was also quite sensitive to the collapse in welding seam caused by thermal accumulation. The average intensity of Ar I spectrum had better antijamming capability than Te, but was less sensitive than Te. Probably, analysis of these signals by synthesis could achieve a better effect on detecting the arc length.
1 Introduction Arc welding is a complex physical and chemical process and multiple factors affect the quality of welding and the formation of defects [1]. Arc plasma transfers welding energy from the weld source to workpiece, and is on dynamic equilibrium with a heat output stably in an ideal welding. Arc plasma closely related to the welding process contains abundant of spectral information, such as electric temperature Te, pressure and particle density parameters, etc. [2-3]. When the input of welding power and welding position changing, such as welding current changing, tungsten position offset or workpiece misaligning, welding defects may occur [1,4], which could be monitored by means of arc plasma spectral analysis [5-9]. A CCD sensor is the key component of the spectroscopic analysis technique, which records the intensity and wavelength of the spectrum. Through traditional extensive non-destructive testing (NDT) techniques (penetrant liquids, magnetic particles, ultrasonics, etc.) used to verify defects within the seam were quite effective [5], but in some critical applications spectroscopic sensor system could provided an real-time, lower costs detection of defects in the weld seam. In GTAW, the arc length determining heat input distribution is critical factor of weld quality, but due to the difficulty in measuring the arc length in practical process, precision sensing and control of arc length have been the bottlenecks in achieving good quality. Arc sensor, based on the theory an arc voltage is the sum of voltage drops in the anode, the cathode, and the arc column, have been used in T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 219–227. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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sensing and control of arc length [6], but the arc voltage depended not only on the arc length but also on the tungsten electrode and weld joint [2, 7], which resultant variation in arc voltage limited the sensing and control resolution. Researchers have found that the arc light offers a more accurate measurement of the arc length for both GTAW and GMAW [7-9]. This is because the intensity of the arc light is primarily determined by the arc column, rather than the anode or the cathode.
2 Theoretical Background In this paper, we monitored the welding by spectroscopic analysis of the arc plasma trying to find a correlation among the welding quality, arc length and the electronic temperature of the arc plasma (Te) was calculated with the relative intensity of some emission lines belonging to a particular atomic species (e.g. Ar) in the plasma [10-11]. The arc plasma generally assumed to be optically thin (no absorption spectrum) and in local thermal equilibrium (LTE) [12], which implied that particles had Maxwellian energy distributions and collisional processes dominated over radiative ones. This assumption was fulfilled when the critical electron density satisfied this condition given by the criterion (1), and the typical electron densities in the plasma plume over the surface of these metals (Fe, Al), exceed these critical electron density by at least one order of magnitude [13].
N e ≥ 1.6 × 1012 Te
1/ 2
( ΔE ) 3
(1)
Where Ne was the electronic density, and ΔE was the largest energy gap in the atomic energy level system. When LTE was satisfied, the population of an excited level belonging to a particular atomic species was determined via the Boltzmann equation (2):
Nm =
⎛ − Em ⎞ N ⎟⎟ g m exp⎜⎜ Z ⎝ κTe ⎠
(2)
Where m is signed the excited level state, N the population density of this particle, Nm the density of the particle on the excited state m, Z the partition function, gm the statistical weight, Em the excited energy, k the Boltzmann constant and Te the electronic temperature. Imn the intensity of spectral line induced by a transition from the level m to the level n, can be determined by Nm, the population density of the upper level m, as followed equation(3):
I mn =
N m Amn hc
λmn
Where Amn was the transition probability from level m (upper) to n (lower),
(3)
λmn
the wavelength associated with the transition, h the Planck constant and c the light velocity in vacuum. Substitute the right-hand side of Eq. (2) for Nm in Eq. (3), Eq. ( 4) was obtained:
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⎛I λ ⎞ ⎛ hcN ⎞ Em In⎜⎜ mn mn ⎟⎟ = In⎜ ⎟− ⎝ Z ⎠ κTe ⎝ Amn g mn ⎠
(4)
If there are two emission lines from the same element in the same ionization stag, signed Im(1), Im(2), the radio of the two lines intensities is described by Im(1)/Im(2). From the radio we could obtain the expression (5) of Te: E ( 2) − E m (1) ⎡ ⎛ I m (1) Am ( 2)λm (1) g m ( 2) ⎞⎤ ⎟⎟⎥ Te = m ⎢ In ⎜⎜ k ⎣ ⎝ I m ( 2) Am (1)λm ( 2) g m (1) ⎠⎦
−1
(5)
3 Experimental Setup and Results The schematic diagram of the experimental system was shown in Fig 1. The ‘OTC INVERTER ELECON 500P’ welding source was selected (with a TIG AC welding current ranging from20 to 500A). The torch in our system fixed to a step motor by which we could change the tungsten in vertical direction with 0.002mm resolution didn’t move in welding direction, while the workpiece driven by step motor motion in welding direction. The distance of tungsten electrode (3.2mm diameter) away from workpiece could be changed in welding process by a step motor. The optical instrumentation of the system was formed by a collimating lens receiving the light emitted from the arc plasma and a optical fiber (Ocean Optics P400- UV-SR) used to guide the light from the collimator to the spectrometer. A 3648 linear CCD spectrometer HR4000 UV-NIR, with spectral resolution of 0.02 nm (FWHM) and spectral range from 200 to 1100 nm was employed. The system was controlled by an industrial control computer through control module included an amplifying circuit, a remoter and a motion control board. The amplifying circuit was used to amplify the output signal of the computer to control the welding current and the motion control board was used to control welding speed and adjustment of the tungsten electrode.
Fig. 1. Schematic diagram of experimental system
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In our experiments only LD10 plates of 4mm thickness were welded with no wire feeding and no groove. The experiment conditions were tabulated in Table 1. The welding current was pulsing alternating current with base current and peak current by turns described in Fig 2. The welding current period T equaled to 1s, while Tb the base current time and Tp the peak current time both equaled to 0.5s. Fiber optic probe was fixed at about 30cm away from the arc plasma and parallel to the workpiece surface. The spectral data of arc plasma obtained by spectrometer with an integration time of 35ms was immediately saved into computer for offline analysis. Table 1. Experiment conditions
Pulse frequency f (Hz) 1
Peak current Ip (A)
Base
170
50
current Ib (A)
Welding speed Vw(mm/s)
Electrode diameter
3
3.2
Ф(mm)
Ar flow rate (L/min)
Pulse duty ratio (%)
15
50
Fig. 2. Form of welding current
Fig 3 was the spectrum diagram of arc plasma at the moment of base current and peak current. The waveform and peaks position of (a) and (b) spectrums in Fig. 3 were similar, but the intensities of these spectrums were different because of the difference of currents. The arc spectrum mainly concentrates in the 380900nm, and that the stronger characteristic spectral peaks with few overlaps in 650-950nm spectral region were well distinguishable. Spectra suite software was used to perform localization of the spectrum peaks, which elements characteristic spectral line these spectrum peaks belong to was confirmed by comparing the peaks to Atomic Spectra Database element standard spectrum data in NIST [14]. The spectrum peaks in the 650-950nm were confirmed of mainly Ar I spectral line, while the excited energy Em, the statistical weight gm and the transition probability Amn were also obtained in NIST.
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Fig. 3. Spectrum of arc plasma in base and peak current
Adjustment of arc length was performed through the step motor attached to the tungsten electrode. Experiments of different arc length were performed as shown in Fig 4. The arc length was 3mm, 4mm, 5mm and 6mm from left to right of Fig. 4a in turn. The welding time was 8 seconds every time, and the workpiece was cooled to room temperature after every welding in order to avoid inconsistency of width and depth in welding seam zone caused by the heat accumulation. Spectral data from the spectrometer were normalized according to formula (6):
xnor. (i ) =
x (i ) − xmean xmean
(6)
Where x(i) was the i-th value of raw data, xnor . (i ) the normalized value of x (i ) ,
x mean the mean of raw data. The Normalized average intensity of Ar I spectral lines detected in spectrum were shown in Fig 4b, while the abscissa corresponded to the welding time, vertical axis corresponding to the normalized average intensity. The electronic temperature Te of arc plasma was calculated by a pair of Ar I spectral lines according to the formula (5), then normalized as shown in Fig 4c. Three vertical dotted lines divided Fig 4a and Fig 4b into 0-8s, 8-16s, 16-24s and 24-32s zones, corresponding to the different arc length in Fig 4a. The average intensity and Te assumed periodic square-wave along with the period of welding base current and peak current. When the tungsten electrode was elevated, the arc length elongated correspondingly, the average intensity and Te also ascended, nevertheless, average intensity ascended both in base and peak current, while Te ascended only in peak current. The power supply to arc per unit length equaled to the loss of that, when arc length elongated, the resistance enlarged led to power input increasing, as a result arc plasma had higher temperature and made stronger spectral radiance to the surrounding space.
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(a) Welding seam of different arc lengths
(b) Average intensity of Ar I spectral lines; (c) Electronic Temperature of arc plasma Te
Fig. 4. Experiments of different arc length
In order to eliminate the influence of square-wave caused by pulse current, adopted the mean of average Ar I spectral lines and Te in every current period for analysis, as showed Fig 5. The change was obvious when arc length elongated 1mm. The relationships between arc length and the mean in period were obviously consistent; therefore, the mean in period as characteristic signals of arc plasma could be used to reflect arc length.
(a) Mean of average Ar I spectral lines in period (MArP); (b) Mean of Te in period (MTeP)
Fig. 5. Arc length versus the mean of signals in period
An experiment that arc length was adjusted during welding process was performed as shown in Fig 6. At the beginning the distance between tungsten electrode and workpiece was 4mm, adjusted the distance to 5.5mm 10 seconds later and 7.0mm after 20 seconds. Fig 6a, 6b respectively was front and back of the welding seam, and at about half seam position collapse occurred which was further exacerbated at the terminal. In Fig 6b and 6c, at 10s and 20s position where tungsten electrode was elevated, MArP and MTeP signal also ascended with arc length’s elongation. However, MArP and MTeP signal were not steeply ascending but with 1 second slope for transition, this was because elevation of the tungsten
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electrode was stepping and time-consuming. During 1 to 20 seconds of welding, MTeP was smoother than MArP corresponding to good weld, but more fluctuant than MArP after 20 seconds corresponding to collapse as shown in Fig 6b, and these showed that MTeP signal better reflected the welding quality.
(a) Front of a welding seam
(b) Back of a welding seam
(c) Intensity of Average Ar I spectral lines;
(d) Te
Fig. 6. Experiment of arc length adjusted during welding process
Fig 7 depicted the relationship between arc length and Te, where the value of Te was obtained by means of averaging the Te in the entire welding time including base and peak time. Due to current fluctuations and collapse of weld seam caused by heat accumulation, the dispersion of points was greater, but still a linear relationship could be seen, and that a liner equation was fitted as following:
L = 5.12 ⋅ Te + 4.8
(7)
Where L was the arc length (unit mm), Te was the normalized average electronic temperature.
Fig. 7. Average normalized Te versus Arc length
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A mass of experiments indicated that the error in experiment mainly caused by the accuracy of Te and welding conditions. When only two emission lines were considered for the calculation of Te, the detection of arc length was less precise, especially if the excitation energies of those lines were similar. The excitation energies of Ar I emission lines used in this paper with a small difference 0.1561eV might cause a less accuracy, furthermore a slight float of spectral peaks probably caused by electromagnetic interference discovered in welding tests sometimes affected the spectral line detection, but a clear relationship was still discovered between arc length and the Te. Conditions in experiment, such as heat dissipation, deformation of workpiece, the shape of tungsten electrode and so on, also affected the accuracy. The average intensity of Ar I spectrum had better anti-jamming capability, but was less sensitive than Te on detection of welding defects. Probably, analysis of these signals by synthesis could achieve a better effect on detecting arc length. In general, spectral information of arc plasma including intensity of Ar I spectral lines and electronic temperature Te well reflected the change of arc length, moreover electronic temperature Te was also quite sensitive to the collapse in welding seam, therefore, Te could be performed on line to monitor arc length and welding heat input for stable welding seam.
5 Conclusions The spectral information of pulsed GTAW of aluminum alloy was studied by means of spectrometer, and the intensity of Ar I spectral lines and electronic temperature Te of arc plasma were extracted to reflect the changes of arc length. We found that the average Te signal in the period had a linear relationship with the arc length and was also quite sensitive to the collapse in welding seam caused by thermal accumulation. Probably, analysis of Te and average intensity signals by synthesis could achieve a better effect on detecting arc length while the signals could detect useful information of welding process, but also had anti-jamming ability of error factors. Acknowledgement. This work is supported by the National Natural Science Foundation of China under the Grant No. 51075268, and Shanghai Sciences & Technology Committee under Grant No. 09JC1407100, P.R. China.
References [1] Yin, Y.S.: Metal welding defects and prevent. Heilongjiang Science and Technology Press, Harbin (1995) [2] Hiroping, A., Shi, Y.X.: Translated. Welding electric arc phenomenon. China Machine Press, Beijing (1985) [3] Li, J., Yang, Y., Li, H., et al.: Technology of Measurement and Control by Welding Arc Spectral Information and Its Application. Engineering Science 3(7), 21–29 (2001)
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[4] Zhou, M.H., Yu, M.P.: Welding Defects and Countermeasures. Shanghai Science and Technology Literature Press, Shanghai (1989) [5] Meola, C., Squillace, A., Memola, F., et al.: Analysis of stainless steel welded joints: A comparison between destructive and non-destructive techniques. Journal of Materials Processing Technology 155(1-3), 1893–1899 (2004) [6] Ushio, M., Mao, W.: Sensors for arc welding advqntages and limitations. Trans. JWRI 23(2), 135–141 (1994) [7] Richardson, R.W., Edwards, F.S.: Controlling GT arc length from arc light emissions. In: Proceedings of the 4th Trends in. Welding Research, Gatlinburg, TN, pp. 5–8 (1995) [8] Wȩglowski, M.S.: Utilization of the arc light emission emitted during TIG welding to monitoring this process. Welding in the World 53(1-2), 2–12 (2009) [9] Li, P., Zhang, Y.M.: Robust Sensing of Arc Length. IEEE Transactions on Instrumentation and Measurement 50(3), 697–704 (2001) [10] Marotta, A.: Determination of axial thermal plasma temperatures without Abel inversion. Journal of Physics D: Applied Physics 27(2), 268–272 (1994) [11] Sforza, P., de Blasiis, D.: On-line optical monitoring system for arc welding. NDT & E International 35(1), 37–43 (2002) [12] Griem, H.R.: Principles of plasma spectroscopy. Cambridge University Press, Cambridge (1997) [13] Ying, M., Xia, Y., Sun, Y., et al.: Plasma properties of a laser-ablated aluminum target in air. Laser Part. Beams 21(1), 97–101 (2003) [14] National Institute for Standars and Technology (NIST) atomic spectra database, http://www.nist.gov/physlab/data/asd.cfm
Arc Sound Recogniting Penetration State Using LPCC Features Jifeng Wang, Yantian Zuo, Yichang Huang, Bo Yang, and Song Pan Shanghai Institute of Special Equipment Inspection & Technical Research, Shanghai 200062 e-mail:
[email protected]
Abstract. Welding is a complex heat working process and it will generate sound, light, electricity, soot and so on, which contain abundant information about welding quality and penetration. As the quick development of industrial production, it is necessary to meet more and more requirements from both welding automation and precise seam forming. The penetration of seam is a common important index of the welding quality. As to Gas Tungsten Argon Welding (GTAW), using welding sound signals to monitor the welding pool state has the potential in the practical application. The arc sound channel which like speech pipeline model was described as a linear prediction all-pole model. This paper adopted linear prediction cepstral coefficients technology in speech recognition to extract the features of arc sound and employs an artificial neural network to classify the different penetration state, with 79% accuracy in test data.
1 Introduction The sound induced from the welding process includes acoustic emission and audible sound. Acoustic emission belongs to the elastic pressure wave, which is produced by energy releasing in solid materials, during deforming or fracturing processes with frequency over 20 kHz [1]. Audible sound, with frequency of 20~20000 Hz, is produced from arc in welding process. In this work, the sound only represents audible sound. Welding sound reflects the behavior of the arc and the welding pool, so the sound is an important feedback quantity for detecting arc stability, and welding quality [2-4]. Joseph found that there was a spectral reliance for professional welders and sound through psycho-acoustic experiments [5]. In practice, an experienced welder usually justifies a right welding process by hearing the arc sound. Also, it is comparatively simple and easy to get important information from the audible arc sound. Therefore, the arc sound signal is a potential and available resource for on-line monitoring and controlling welding quality. However, for gas shielded welding, including Gas Metal Arc welding (GMAW) and GTAW, there is no obvious difference in sound during different penetration states. Therefore, the key step of implementing penetration control based on sound is to extract effective sound features. Arc sound is induced from arc column that similar to a human throat, so a linear prediction coding model based on speech recognition technology is used to obtain welding sound features
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[6]. The total energy of arc sound signal, as a feature, has obvious correlation with welding subsidence [7] during GMAW. Little attention has been paid to GTAW including feature extraction from the welding arc sound [8]. In this paper, we employ linear prediction cepstral coefficients (LPCC) to extract arc sound features for penetration, and take ANN as classifier for recognition different penetration states.
2 Experimental Setup Figure 1 was a schematic diagram of the experimental setup for measuring and analyzing the welding arc sound. The system includes three sections. The first section is the GTA welding section, which consists of an invert AC current power supply, a torch, and an operating and cooling water supply. The second section is a computer controlling system for positioning the torch, moving the worktable, and adjusting the welding current. And the final section is the sound measurement and analysis unit, which includes a microphone, an amplifier, a data acquisition card, and an acquisition program based on VC++. The microphone is of the prepolarization free field type with a band width of 20 - 20000 Hz and a sensitivity of -26 ± 2 dB(50 mV/Pa). The sound signal was collected by the microphone, which was positioned away from welding pool about 300 mm, and acquired at a sampling frequency of 40 kHz. The sound pressure signal was recorded with voltage (V), which could be transferred to pressure (Pa) based on sensor sensitivity.
Fig. 1. Schematic diagram of welding arc acoustic signal detection
LF6 Aluminium alloy workpieces with 4 mm in thickness were welded. The standard welding conditions were defined as: welding current is 150A, welding speed is 3 mm/s, flow rate of shield gas is 15 L/min, arc length is 4mm, thickness of workpiece is 4mm, and microphone collection angle is 75 degree.
3 Model and Features of the Arc Sound Similar to the speech pipeline model, arc sound can also be described by the pipeline model of arc sound. In linear prediction (LP) analysis, the vocal tract transfer
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function is modelled by an all-pole filter with transfer function [9]. Thus a sound sample xˆt , can be predicted from the previous samples using p
xˆ t = ∑ a i xt −i i =1
(1)
where t is the time index, p is the prediction order, and ai are the predictor coefficients, In addition to predicting a systems output, LP can also be used to model the system itself.
Fig. 2. Arc sound pipeline model
Once obtained, the LP coefficients can be used by themselves or converted into various feature vectors such as the reflection coefficients or cepstral coefficients. Comparisons of these features have found that the linear prediction cepstrum coefficients give the best results for speaker recognition. The spectral envelope derived from the LPCC is much smoother than one from the LP coefficients and thus is more stable between utterances. The LPCC, cn, are obtained from the predictor coefficients through the recursive relationship
c1 = a1 n −1 ⎛ m⎞ c n = −a n − ∑ ⎜1 − ⎟a m c n− m n⎠ m =1 ⎝
1< n ≤ p
(2) (3) (4)
where cn and an are the nth-order cepstrum coefficient and linear prediction coefficient respectively and p is the prediction order.
4 Results The ANN was used in this work as a classifier, which had one input layer, one hidden layer and one output layer. The input layer consisted of above seven features. Information from the input layer was then processed in the course of one
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hidden layer, following output vector is computed in the output layer. The output layer had three nodes which represented three welding penetration states. A schematic description of the layers was given in Fig. 3.
Fig. 3. The structure of three layered neural network
The initial connection weights between two neurons and bias are randomly selected. The convergence values, i.e., the learning mean-squared errors (MSE), obtained for both training and generalization, are low enough to ensure good results of classification on the validation data. The process of classification is performed by using the features associated with the welding sound signals and the weld penetration classes to which these signals belong. In this work, three classes of weld penetration were defined, which included full-penetration, part-penetration and none-penetration. Welding pool image and the sound corresponding with this image were recorded synchronously. Accordingly, the features of welding sound in a period and penetration state at the same time were obtained by using above mentioned methods. The classifier was trained and tested by these 12 features that were chosen to be ideally representative of each class.
Fig. 4. Recognition result based on LPCC feature
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Training of the network is carried out using a standard back-propagation algorithm, and the network is trained for 1000 epochs, using 75% of the data set as training data, and the remaining 25% as the test and validation set. Test results were shown in Fig. 4, with accuracy 79%. Mistakes mostly existed between the two adjacent penetration states, which was hard to find in the full-penetration and none-penetration state.
5 Conclusions Welding sound had close relationship with pool state for GTAW. Thus, the arc sound signal is a potential and available resource for on-line monitoring and controlling welding quality. In this paper, sound pipeline model of welding arc was established, and a linear prediction all-pole was modeled transfer function. We take LPCC based on speech recognition methods to extract arc sound features, and employ an artificial neural network to classify the different penetration state, with 79% accuracy in test data. Acknowledgement. This work was supported by the National Natural Science Foundation of China No. 50575144 and Shanghai Natural Science Foundation No. 08ZR1409900.
References [1] Bastos, T.F., Calderon, L., Martin, J.M., Ceres, R.: Features ultrasonic sensors and arc welding - a noisy mix. Sensor Review 16, 26–32 (1996) [2] Manz, A.F.: Welding arc sounds. Welding Journal 5, 23–28 (1981) [3] Matteson, A., Morris, R., Tate, R.: Real-time GMAW quality classification using an artificial neural network with airborne acoustic signals as inputs. In: Proc. Int. Conf. Modelling and Control of Joining Proceses, Orlando, FL (1993) [4] Cudina, M., Prezelj, J., Polajnar, I.: Use of audible sound for on-line monitoring of gas metal arc welding process. Metalurgija 47, 81–85 (2008) [5] Tam, J., Huissoon, J.: Developing psycho-acoustic eperiments in gas metal arc welding. In: Proceedings of the IEEE International Conference on Mechatronics & Automation, Niagara Falls, Canada (2005) [6] Ma, Y., Ma, W., Min, Q., Jianhong, C.: Characteristics analyzing and parametric modeling of the arc sound in CO_2 GMAW for on-line quality monitoring. China welding 2, 6–13 (2006) [7] Shi, Y., Huang, J., Fan, D.: Relationship between arc sound and subsidence of weld bead in alminum alloy MIG welding process. Chinese Journal of Mechnical Engineering 43(10), 32–35 (2007) [8] Wang, J.F., Fenglin, L., Chen, S.B.: Analysis of arc sound characteristics for GTA welding. Sensor Review 29(3), 240–249 (2009) [9] Rabiner, L.: Fundamentals of Speech Recognition (1993) [10] Quatieri, T.F.: Speech Recognition Speech Signal Processing Discrete-Time (2004)
Investigation on Acoustic Signals for On-line Monitoring of Welding Na Lv and Shanben Chen Institute of Welding engineering, Shanghai Jiao Tong University, 200240, P.R. China e-mail:
[email protected]
Abstract. Aiming at on-line monitoring of welding quality based on the welding acoustics, a lot of works have been done in SJTU. This paper reviews the usefulness and source of the welding sound, the characteristics and processing of welding acoustics, also the modeling and pattern recognition of welding sound signals in GTAW welding. And all of these studies are valuable to achieve the on-line monitoring and controlling of the welding quality.
1 Introduction Welding is the most used and fundamental bonding method in manufacturing process in the metallic construction industry. And the automated welding is the developing trend of the traditional welding industry. On-line quality control in automated welding operations is an important factor contributing to higher productivity, lower costs and greater reliability of the welded components. But there are lots of uncontrollable factors like contamination and environmental conditions in welding process. Therefore, the real time monitoring and control is highly required to overcome the time-consuming and costly off-line inspection in modern automated welding environments. In order to observe welding phenomena and control welding condition, it is necessary to select and detect an information signal which express exactly the welding process. Current and voltage are considered to be the most reliable, simple and competitive information to reflect the real time state of the welding process. But these two factors are not sufficient enough. So visual inspection, artificial vision sensing, infrared sensing, etc, are also used in welding monitoring. These methods have their own advantages to others. However they still have some room to improve, such as, it is very difficult for them to grasp the dynamic welding characteristics, especially near the arc due to the high temperature, spatter formation, fumes, etc. The acoustic sensing may be useful to overcome these difficulties [1].
2 Usefulness of Welding Acoustic Signals Acoustic feedback plays an important role for expert welders when performing the welding process. In fact, most professional welders agree that welding sound T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 235–243. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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provides as much useful information concerning the process as vision does [2,3]. Joseph Tam designed and performed an experiment to show the importance of welding sound to the welders. The general experimental apparatus is schematically illustrated in Fig. 1. The microphone is attached to the welder’s helmet and powered by a custom built microphone amplification circuit that includes an antialiasing filter with a 12.5 kHz corner frequency and 60dB/decade roll-off [4].
Fig. 1. Schematic of experimental apparatus used in psycho-acoustic experiments
The results showed that time delay of the welding sound feedback could definitely affect the welding quality of welders. The ability of an expert welder to track changes in the welding process is directly affected by the phase lag in their situation awareness process. If the acoustic or even visual feedback received by the welder happens to be out of phase with their internal decision making cycle, instability could occur with any disturbance introduced. At 400 milliseconds delay, the welder’s acoustic response are seemed to be completely unstable and erratic. So the welding acoustic is a meaningful factor to achieve the on-line monitoring of welding process.
3 Source of Welding Acoustic Signals There are lots of factors could lead to the generation of welding sound, like the volume changing of burning arc, flowing of sheild gas, vibrating of the droplet transfer scouting welded pool and the noise of some welding apparatus. All in all, the welding sound induced from welding process includes acoustic emission and audible sound. Acoustic emission belongs to the elastic pressure wave which is produced by energy releasing in solid materials during deforming or fracturing processes with frequency over 20-20000Hz, is produced from arc in welding process. Generally speaking, most researcher are focusing on the audible sound in welding process [5]. The audible welding sound could be classified into two types according to the welding procedure. First one is for laser welding, plasma cutting and plasma welding, there are relatively obvious different sounds at the different stages of forming keyhole. Much attention has been paid to monitor laser and plasma. J.L. Liu
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Sound(V)
Voltage(V)
Current(A)
studied the relationship between the laser welding sound and the penertration of welded joints. The result showed that sound pressure and power spectrum welding sound power gradually weakened [6]. B.X. Hu et used the plasma welding sound and implemented the real time monitoring of welding quality [7]. Second one is for GTAW, CO2 and spot welding, there are no obvious characteristics like laser welding. But recently researchers started to pay more attention to the arc sound signals in these welding method. No matter which welding method is, the welding sound comes from the change of arc column energy. Y.Z. Ma analyzed the origin of arc sound. The results showed that acoustic wave of the arc is mainly related to the differential of arc power which reveals itself as “ringing” form and occurs in the stage of arc re-ignition at the end of short-circuit transition, and mainly is located on the frequency band of 4-8kHz. It is thought that the exciting source of arc sound generation is the variation of arc power. Time-varying tone channel is formed by the shielded gas and arc column. The combined action of sound excitation and tone channel system will generates the arc sound. Many researchers got the same conclusion. B. Chen compared the curve of welding sound to the current and voltage changes, and also the image, which showed in Fig 2. [8, 9]. J.F. Wang designed a series of experiments to prove that the welding sound source is a dipole model. Due to this special character of welding sound model, the microphone angle should be kept constant and >75 but less than 90.
Shown in Figure 2-17
Pulse peak Pulse base period period
Fig. 2. Arc voltage U(t) and s=in-step wave you shape of sound wave s(t)
The energy of arc sound is generated from the quick change of the plasma flow between anode and cathode during welding. And the movements of plasma flow leads to the spreading of arc sound curve. So we can conclude that:
PS = K ( I
dU dI +U ) dt dt
(1)
where PS stands for sound pressure, K stands for a constant value, U stands for arc voltage, I stands for the arc current.
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4 The Characteristic of Welding Sound There are a lot of factors which could affect the acoustic signals. Because the welding is a process accompanied by noise, flash, electromagnetism and high temperature, so the welding arc sound depended on the welding parameters. The study of relationship between welding sound and welding techniques had produced a lot of positive results. Yoshiaki ARATA analysis the effects of current waveforms on TIG welding arc sound, which indicated that the welding arc sound depended on the current waveforms, pulse frequency fp and pulse amplitude tp. An excellent frequency spectrum fEX agreed with fp in the triangular and sine wave current, but fEX changed to higher harmonic in the rectangular and sawtooth wave current [10]. Wen J.L implemented a series of experiments to analyze the connection of MIG welding arc sound, with the microphone, the protection gas, droplet transfer, welding spatter under different welding conditions. The aim was to achieve on-line identification, evaluation and monitoring of the welding quality [11]. And Fan Ding et al studied the effects of spatter loss to CO2 welding arc sound. They found that welding spatter loss was proportional to the arc sound energy produced at the end of short circuit transfer and the average arc sound energy produced during short circuit transfer [12]. In 2009, Wang J.F. of SJTU made a conclusion of the effects of welding parameters to welding sound, including gas flow, welding speed, arc length and collection angle during GMAW welding. The study showed that welding speed had less influence on sound pressure; welding sound increased with increasing of gas flow; and arc length had obviously influence on the welding sound, but when the arc length exceeded 5mm, SP was nearly a constant; the SP increased with of collection angle of the microphone. However it didn’t change a lot when the angle was larger than 60° [5].
Fig. 3. The comparison between welding sound and cosine function
Besides above reaserches, there are some other factors have been studied for some special welding. For example, researchers analyzed the relation between the nugget state and air-borne acoustic signals. The nugget states could be viewed as a kind of parameters that indicated the welding quality [13].
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5 The Processing and Modeling of the Welding Sound 5.1 Reducing Noise Because welding sound is easily disturbed by environmental noise during the welding process, the characteristics of acoustic signals are affected. Therefore, the first step of processing is to filter the noise. With the advancement in digital signal process, many new technologies have been developed in processing welding sound signals. The wavelet decomposition and the fast fixed-point algorithm of independent component analysis (ICA) were used to separate the noise from arc sound signals [14-15]. The experiment results showed that the burr of arc sound is effectively filtered out, the mutations of waveform are clearer and the noise ratio is obviously increased. The reduced noise provides a technical basis for extracting characteristics of arc signals and monitoring welding quality via these signals.
5.2 Processing of Welding Sound Signals The analyses of digital signals are usually divided into two parts: time domain analysis and frequence domain analysis. As the digital signal processing theory improves, lots of new techniques have been used in extracting the characteristic of welding sounds. Because of the special character of plasma arc welding and plasma arc cutting, the extracting characteristics of these methods are easily achieved by frequency spectrum [16-17]. However, for other welding methods, like GMAW, CO2, spot welding, a clear boundary for the penetration can not be found. So it will be hard to extract the characteristics of the welding arc sound. A lot of studies have been carried out for this topics. For spot welding independent component analysis (ICA) and multisensory array technology were used to research the acoustic emission signals. A multisensory array system with 8 microphones was used to measure the acoustic emission signals from spot welding synchronously. The results showed that the independent component analysis based on FastICA algorithm could clearly extract the characteristic signals of the spot welding from superimposed noises [18]. And the most famous way of processing acoustic signals is the wavelet packet frequency-shift algorithm, which is used to extract arc sound frequency-band energy that eliminated the frequency aliasing phenomenon caused by the interval sampling in the classical wavelet packet decomposition [19].
5.3 Modeling of Welding Sound After the extraction of characteristics, another problem is how to choose the proper parameters to diagnose the welding arc sound. The feature evaluation and selection methods were presented. Liu L.J chose the neural network to evaluate feature parameters. Because neural network satisfied the nonlinear mapping requirement for high-resolution information compression, the complex classification problem
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in welding penetration pattern recognition was transferred to feature processing stage [20]. Some researchers did experiments to achieve rapid recognition for droplet transfer in gas metal arc welding simply and effectively. Through obtaining welding arc sound signals during different droplet transfer, authors analyzed welding arc sound of different droplet transfer by ARMA bispectrum and extracting eigenvectore, then did pattern recognition to the obtained eigenvector by support vector machine [21]. Ma Yaozhou chose the LPC(linear prediction coding)for the parametrical modeling of sound signals, on the basis of frequency spectrum analysis of the arc sound signals under different shield gas fluxes and torch heights. The test results showed that the RBF networks and SVM classifiers with the prediction or reflection coefficients input vectors were all capable of the correct recognition of gas fluxes and torch heights to some extent, in which the classification exactitude rates of the models with the reflection coefficients input vectors were better than those with the prediction coefficents, the exactitude rates of the SVM classifiers clearly excelled those of the RBF networks models. The classification capability of the SVM classifiers was not debased quickly along with decrease of the training sample numbers [22]. Besides the time and frequency characteristics of the arc sound signals in welding are analyzed, the concept of tone channel and its equivalent electrical model were suggested in order to achieve the on-line monitoring. Some studies applied the radial basis function (RBF)neural networks for on-line pattern recognition of the gas-lack in welding, in which the input vectors of samples were constructed by the linear prediction coding(LPC) coefficients of sound signals. The tone channel can be equated with a time dependent coefficients digital filter, and can be represented parametrically with the LPC model of arc sound. The arc sound and its parametric model are valuable in on-line monitoring and controlling of welding quality [23].
Fig. 4. Fourier spectrum of the arc sound and estimated frequency response of the arc tone channel
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6 On-line Monitoring Most of the studies about the acoustic signal of welding process are concerning about the characteristic and processing of welding acoustics. And all of them provided technical basis for on-line monitoring of welding process via arc sound signals. But there are limited studies which have really achieved the automatic welding monitoring. Experts always like to pay more attention to plasma welding and laser welding, because their remarkable character of the penetration. Hu Baixi had implemented the on-line welding quality monitoring based on the welding sound. Besides, the system of on-line plasma cutting also had been achieved. To aim to ensure that the welding robot handlers can scout and control the welding critical state or utmost deviant instance in time, and to avoid intermit welding and welding accident, some experts designed Speech Synthesis warning card with limited vocabulary and the pronunciation synthesizing prompting card with limited vocabulary according to the principle of pipe arc welding robot. As a result, these two techniques can control effectively the welding process, reinforce the stability of pipe arc welding robot control system, and can improve the welding efficiency [24-25].
7 Conclusions The SJTU have been doing a lot of work about the on-line welding quality monitoring. And a lot of fundamental research has been implemented about the characteristics of welding sound, the processing of welding sound signals and the pattern recognition of the welding sound signal and welding penetration. All of these studies provide the technical basis for achieving the welding automation using welding sound. But the automatic welding can only be achieved in laser and plasma welding. The on-line GTAW welding monitoring still has some problems to solve. Therefore, to achieve the monitoring of GTAW welding quality using sound will be the research topic in the future.
,
Acknowledgement. This work is supported by the National Natural Science Foundation of China under the Grant No. 51075268 and Shanghai Sciences & Technology Committee under Grant No. 09JC1407100, P R China. The author wish to acknowledge the relative study works finished by Dr Liu Lijun, Dr Fan Ding, Dr Ma Yuezhou, Dr Wang Jifeng, Dr Chen Bo etc.
References [1] Pal, K., Bhattacharya, S., Pal, S.K.: Investigation on arc sound and metal transfer modes for on-line monitoring in pulsed gas metal arc welding. Journal of Materials Processing Technology 210, 1397–1410 (2010) [2] Matteson, A., Morris, R., Tate, R.: Real-Time GMAW Quality Classification using an Artificial Neural Network with Airborne Acoustic Signals as Inputs. In: Processing of the 12th International Conference on Offshore Mechanics and Arctic Engineering, IIIA, pp. 273–278 (1993)
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[3] Kralj, V.: Biocybernetic investigations of hand movements of human operator in hand welding. IIW/IIS Doc.212-140-68 (1968) [4] Tam, J., Huissoon, J.: Developing Psycho-Acoustic Experiments in Gas Metal Arc Welding. In: International Conference on Mechatronics & Automation Niagara Falls, Canada, pp. 1112–1117 (July 2005) [5] Wang, J.F., Chen, B., Chen, H.B., Chen, S.B.: Analysis of arc sound characteristics for gas tungsten argon welding. Sensor Review 29(3), 240–249 (2009) [6] Liu, J.L., Chen, Y.B., Xu, Q.H.: The relationship between laser welding sound and penetration. Transactions of the China Welding Institutinon 27(1), 72–80 (2006) [7] Hu, B.X., Huang, C.D., Long, A.L., Li, S.P.: The study of quality monitoring system based on the acoustic signal for all-position welding in pulse plasma weld. Welding 5, 17–20 (1980) [8] Ma, Y.Z., Jin, H., Liang, W.D., Zhang, P.X.: Signal characteristics of short-circuit GMAW arc sound and its formation mechanism. Journal of Gansu University of Technology 29(1), 11–14 (2003) [9] Chen, B.: Study on the Processing Method of Muti-sensor Information Fusion in Pulse GTAW. Shanghai Jiaotong University, Shanghai (2010) [10] Arata, Y., Futamata, K., Toh, T.: Investigation on welding arc sound (report III)Effects of current waveforms on TIG welding arc sound. Transactions of JWR 8(2), 33–38 (1979) [11] Wen, J.L.: MIG Welding Arc Sound Experimental Preliminary Study. Coal Mine Machinery 30(9), 99–100 (2009) [12] Fan, D., Ma, Y.Z., Pei, H.D., Chen, J.H., Shi, Y.: Investigation on correlation of welding arc sound with welding spatter. Journal of Gansu University of Technology 23(3), 1–5 (1997) [13] Luo, Z., Hu, S.S.: Application of time-frequency analysis to air-borne acoustic signals of aluminum alloys spot welding. Transctions of the China Welding Institution 25(1), 36–40 (2004) [14] Liu, L.J., Yu, Z.W., Lan, H., Gao, H.M.: Separating technology of arc sound noise based on ICA in MIG welding 31(4), 53–58 (2010) [15] Liu, L.J., Lan, H., Yu, Z.W., Zhou, B.T.: Reducing noise techniques of arc sound signal. Transactions of the China Welding Institution 30(12), 13–17 (2009) [16] Jun, L.L., Lan, H., Wen, J.L., Yu, Z.W.: Feature extraction of penetration arc sound in MIG welding via wavelet packet frequency-band energy. Transactions of the China Welding Institution 31(1), 45–50 (2010) [17] Xue, W., Nan yuan, Y.C., et al.: The develop of monitoring system in plasma arc cutting via acoustic. In: The 2nd Sensor Technology Conference in Yangtze River Delta, vol. 11 (2006) [18] Bu, X.Z., Shan, P., Luo, Z., Tang, X.X.: Characteristic extraction of acoustic signals emitted from resistance spot welding process based on independent component analysis. Transactions of the China Welding Institution 30(2), 41–45 (2009) [19] Liu, L.J., Lan, H., Wen, J.L., Yu, Z.W.: Feature extraction of penetration arc sound in MIG welding via wavelet packet frequency-band energy. Transactions of the China Welding Institution 31(1), 45–50 (2010) [20] Liu, L.J., Lan, H., Zheng, H.Y.: Feature evluation and selection of penetration arc sound signal based on neural network. Transactions of the China Welding Institution 31(3), 25–29 (2010)
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[21] Fan, D., Huang, J.K.: Droplet Transfer Pattern Recognition by SVM Based on Arc Acoustic ARMA Bispectrum Analysis. Journal of Shanghai Jiaotong University 42, 43–45 (2009) [22] Ma, Y.Z., Qu, M., Chen, J.H.: Pattern recognition of CO2-gas shield welding based on arc sound signal. Journal of Lanzhou University of Technology 32(4), 29–33 (2006) [23] Ma, Y.Z., Chen, J.H., Liang, W.D.: Parametric modeling of the arc sound GMAW for on-line quality monitoring. Chinese Journal of Mechanical Engineering 41(11), 109–114 (2005) [24] Zhan, X.H., Liu, L.J.: Design of Speech Synthesizing Warning Card in Welding Process. Journal Harbin Univ. SCI.&TECH. 9(4), 1–4 (2004) [25] Liu, L.J., Wu, L.: Pronunciation reappearance technology in remote arc welding robot. Electric Welding Machine 35(6), 18–21 (2005)
A Study on Applications of Multi-sensor Information Fusion in Pulsed-GTAW Bo Chen1,2 and Shanben Chen1 1
Institute of Welding engineering, Shanghai Jiao Tong University, 200240, P.R. China 2 School of Materials Science and Engineering, Harbin Institute of Technology at Weihai, 264209, P.R. China e-mail:
[email protected]
Abstract. To improve the prediction results of fusing multi-sensor information in pulsed GTAW, a new method for obtaining the basic probability assignment of DS evidence theory was proposed, the method was based on fuzzy membership functions and BP network. Experiment results showed that this method could improve the prediction accuracy than single BP network model, and so as to improve the prediction results of multi-sensor information fusion.
1 Introduction Welding process is a complex process accompanied by various disturbing factors, such as noise, intense arc, electromagnetism, etc. So it is difficult to obtain accurate information reflecting welding status exactly. Muti-sensor information fusion technology is a rising technology which uses information obtained by different sensors [1], it can give more accurate results than single sensor. In our previous research [2], D-S evidence theory was used as the information fusion technology to fuse the information obtained by arc sensor, visual sensor and sound sensor during pulsed GTAW. Experiment results revealed the advantages of multi-sensor information fusion, the prediction accuracy was more accurate than the prediction results of single sensor. However, the prediction results were still not very accurate. To improve the fusion result, there were two ways: one was to improve single sensor’s prediction accuracy; the other was to improve fusion technology, such as improving the D-S evidence theory or adopting other fusion methods. In this paper, we proposed a new way to obtain the BPA (basic probability assignments) of D-S evidence theory, this method used fuzzy membership function and self-organized network to obtain penetration status of the training data, and the obtained membership degree was used as BPA, after the training data’s membership degrees were obtained, they were used to construct the membership degree prediction model by BP network, and the prediction model were then used to obtain BPA of unknown data. Experiment results showed that the proposed method could obtain more accurate prediction results than merely BP network, so as to obtain more accurate fusion results by D-S evidence theory. T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 245–252. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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2 Experimental System The experiment system was shown in Fig 1, the experiment system included an arc sensor, a visual sensor and a sound sensor. The arc sensor used weld current and voltage sensor to obtain the weld current and voltage information; the visual sensor used a filter system to filter the intense arc light to obtain the weld pool image, which include the topside image and the backside image of the weld pool; and the sound sensor used a microphone and a signal conditioning to obtain the weld sound information during welding process.
Control Circuit Computer Mot or Driver
Condit ioner Protect ion Circuit Pulse COMPA 500
Control Circuit
CCD Camera
P
Mic Wire Feeder
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Filter Syst em Step Motor
Worktable
Fig. 1. The schematic diagram of the system
After the electronic, visual and sound information were obtained, different signal processing methods were used to extract the signal features [2, 3], and then the signal features could be fused by different information fusion method.
3 A Brief Introduction about D-S Evidence Theory D-S (Dempster-Shafer) evidence theory was an information fusion method based on statistics [4, 5], the basic concept of D-S evidence theory was the BPA, it was like the probability concept in the probability theory, but not the same. And D-S evidence theory provides the combination rules to combine the BPAs of different sensors, which can be used to fuse the information of different sensors. To fuse the information of different sensors by D-S evidence theory, the BPAs of different sensors should be first obtained. To data, there was still not any unified model to obtain the BPA. In our previous research [2], BP (back propagation) network was used as the tool to obtain the BPAs of arc, visual and sound sensor. However, from our further study, it was found the prediction results of this method were not very accurate. And if the training times of the BP network were increased to improve the prediction accuracy, the outputs of the network will be mostly discrete data such as 0 or 1, and it did not meet the requirements of BPA. So in this paper we considered to first express the penetration status as continuous value, and then model the input data and the continuous value.
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4 Obtaining Basic Probability Assignment by Kohonen and Fuzzy Membership Function In our previous research [2], it was found that although the fusion results were improved by D-S evidence theory compared with single sensor, the final accuracy was still not very high. To improve the accuracy, we developed a new method in this paper to obtain the BPA based on Gauss membership function and BP network. This method fist used Kohonen network and Gauss membership function to obtain the membership of penetration status, the membership degree was used as the BPA of D-S evidence theory, then BP neural network was used to obtain the membership prediction model of penetration status. After the membership prediction model was obtained, the model could be used to obtain the BPAs for different sensors. Experiment results showed that this method could obtain more accuracy prediction result than BP neural network, and therefore obtain better multi-sensor fusion results.
4.1 Membership Functions and Basic Probability Assignment The differences between class and regression are that the outputs of class problems are discrete value, and the outputs of regression problems are continuous value. Because the values of BPA are continuous values between 0 and 1, they can be obtained by regression method. So if the class results could be expressed by continuous value, then the continuous value could be trained by BP network or other regression method and a prediction model could be obtained. This paper used membership functions in fuzzy set theory to express the penetration status as continuous values, and the obtained membership degree was used as BPA. After the membership degrees of the training data were obtained, they were used to train a BP network to construct a membership prediction model for predicting the membership degree of unknown data. Because the membership degree was used as BPA, they could be fused by D-S evidence fusion rules. In fuzzy set theory, membership function represents the degree of truth as an extension of valuation. The membership degree μ A ( x ) quantifies the grade of membership of the element x to the fuzzy set A . The value 0 means that x is not a member of the fuzzy set; the value 1 means that x is fully a member of the fuzzy set. The values between 0 and 1 characterize fuzzy members, which belong to the fuzzy set only partially. From the definition of BPA it can be seen that it is a mapping to [0 1], and it reflects the correlation degree between two variables. From this aspect, it can be seen that it has much similarity with membership functions, so if the membership functions of penetration status could be obtained, they could be used as the BPA in D-S evidence theory. Currently the methods of obtaining membership functions are mostly based on manual experiences, obtaining membership functions from training data is also a
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difficult problem. In this paper we proposed a method to obtain the membership function by Kohonen network and BP network.
4.2 Membership Functions Representation of BPA Commonly used membership functions include piecewise linear function, Gauss function, s function and parabola function etc. This paper used Gauss function for its good smoothness and clear physical meanings. The Gauss expression used in this paper was
y=e
−
| x −c|2
α ⋅σ 2
(1)
where x was input feature vector, c was the center of feature vector, and α , σ were the parameters which determine the shape of Gauss function. After the membership function was determined, the next thing was to determine the parameters in Gauss function, where parameter c determined the function’s center, and c = 1 when x = c , it was usually estimated by mean value of samples, and σ was determined by sample variance. To accurately predict these parameters, this paper used Kohonen network to extract feature vectors which had obvious features with penetration status.
4.3 Extracting Training Data by Kohonen Network Kohonen network was proposed by Kohonen, it is an unsupervised, self-organized and self-learning network, and it can automatically extract the signal pattern features by self-organized learning. This paper used Kohonen network to extract the signals which can strongly reflect penetration status. To demonstrate clearly, the acquisition of visual sensor’s information was used as an example to introduce the acquisition of training data by Kohonen network. The network had two outputs which meant penetrated and incomplete penetrated statuses. To visual sensor, the input vectors were top weld pool’s length and width of current pulse and the two former pulses for considering the influence of heat accumulation. First, the network was trained by the visual sensor’s training data, after the network was trained, the network was used to predict the penetration status by the training data, and the network outputted the penetration status, the predicted penetration status was compared with the real penetration status, if the predicted result was right, it meant the training data had information with penetration status, and the training data was preserved, if not, the training data was abandoned. To arc sensor and sound sensor, the processes were the same, except that the input variables were replaced by the information acquired by arc sensor and visual sensor.
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4.4 Constructing Membership Prediction Model After the training data were treated, data had close relationship with penetration status were obtained, and they were then used to determine the parameters of Gauss membership functions, the parameters of Gauss function were determined by the following equations:
⎧ 1 n c xi = ∑ ⎪ n = 1 i ⎪⎪ ⎨σ =| A | ⎪ 2 ⎪α = − d max ⎪⎩ ln(0.5) ⋅ σ 2
(2)
where n was the number of training data obtained by Kohonen network, A was the covariance matrix of the matrix composed by the training data, and
2 d max was the
maximum value of | x − c | . After the membership functions were determined, the membership degree of the penetration data to penetration status and the membership degree of the incomplete penetration data to incomplete penetration status could be obtained. Fig 2 showed the membership function and the real backside width, it could be seen that the membership functions could reflect the changing trend of the backside width, and this proved the effectiveness of the proposed method. 2
Fig. 2. The relationship between membership and true backside width
After the membership degrees of every training data were obtained, BP network could be used to train the training data, where the input variables of the network were the information obtained by each sensor, and the output of the network was the membership degree, and the membership function prediction model between input signals and membership degree was obtained. The membership degree could be used as the BPA of D-S evidence theory. And the model could then be used to predict the BPAs of unknown data.
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5 Multi-sensor Information Fusion Based on D-S Evidence Theory After the BP membership prediction model was obtained, the model could be used to obtain BPA of unknown data, and then the obtained BPA could be fused by D-S evidence theory. The flowchart was shown in Fig 3.
Arc info
BP membership prediction model
BPA1
Visual info
BP membership prediction model
BPA2
Sound info
BP membership prediction model
BPA3
DS Combine
Fig. 3. Multi-sensor information fusion model based on membership prediction model and weighted D-S evidence fusion
First, the BP membership prediction model was used to process the signal features of different sensors, the outputs of the prediction model were membership degrees to penetration status, and they were used as the BPAs of different sensors; then the BPAs of different sensors were combined by D-S evidence theory and a fused BPA was obtained, then the fusion results could be used to predict the penetration status of current pulse, if the fusion result was less than 0.5, it meant current status was incomplete penetrated, else it meant the current status was penetrated.
6 Experimental Results In order to reveal the effectiveness of the proposed method, random experiment was done to obtain the information of different sensors [2], 982 data were totally obtained, and the obtained 982 data were separated ten times stochastically, 700 data were used to obtain the BP membership prediction model, and the rest of the 282 data were used to test the prediction result of the D-S evidence information fusion model. The comparison results are tabulated in Table 1. From Table 1 it could be seen that the average prediction accuracy of single sensor was about 80%, much high than the prediction results of BP network model [2], which was about 65%. And because the prediction accuracy of single sensor was improved, the fusion results by D-S evidence fusion were also improved.
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Table 1. Fusion results of D-S evidence theory
Single sensor
D-S evidence fusion result Arc and sound
Sound and visual
Arc and visual
Arc, sound and visual
Times
Arc
Sound
Visual
1
0.7928
0.745
0.7689
0.7968
0.7729
0.7689
0.8207
2
0.8189
0.8107
0.8477
0.8765
0.8395
0.8436
0.8848
3
0.7309
0.7229
0.7349
0.7309
0.7229
0.7269
0.787
4
0.7295
0.75
0.8525
0.8361
0.8074
0.7869
0.8525
5
0.7484
0.7407
0.7202
0.7695
0.7531
0.7654
0.8219
6
0.8107
0.8436
0.8148
0.823
0.8477
0.8765
0.8965
7
0.7746
0.7541
0.7541
0.7746
0.75
0.7623
0.8223
8
0.848
0.82
0.776
0.796
0.824
0.828
0.836
9
0.8153
0.7309
0.7831
0.8273
0.7952
0.8353
0.8313
10
0.9365
0.9383
0.9095
0.9342
0.9465
0.963
0.9506
Average
0.8006
0.7856
0.7962
0.8165
0.8059
0.8157
0.8504
7 Conclusions A new method for obtaining BPA of D-S evidence theory was proposed, this method used fuzzy membership function to express the BPA, and Kohonen network was used to extract data for constructing the parameters of the fuzzy membership functions. After the membership degree of every training data was obtained, the BPA prediction model was constructed by BP neural network to predict the BPA of unknown data. Experiment results showed that the proposed method could obtain more accurate prediction results than single BP network model, and so as to improve the prediction accuracy of the fusion results of D-S evidence theory. Acknowledgement. This work is supported by the National Natural Science Foundation of China under the Grant No. 51075268, and Shanghai Sciences & Technology Committee under Grant No. 09JC1407100, P.R. China.
References [1] Klein, L.A.: Sensor and Data Fusion Concepts and Applications: Society of PhotoOptical Instrumentation Engineers (SPIE) Bellingham, WA, USA (1999) [2] Chen, B., Wang, J., Chen, S.: Prediction of pulsed GTAW penetration status based on BP neural network and D-S evidence theory information fusion. International Journal of Advanced Manufacturing Technology 48(1-4), 83–94 (2010)
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[3] Chen, B., Wang, J., Chen, S.: A study on application of multi-sensor information fusion in pulsed GTAW. Industrial Robot 37(2), 168–176 (2010) [4] Dempster, A.P.: Upper and lower probabilities induced by a multivalued mapping. Annals of Mathematical Statistics AMS-38, 325–339 (1967) [5] Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)
Research on Image Process and Tracing of a Welding Robot G.H. Ma, L.Wang, G.Q. Liu, and M. Xiao Institute of Electromechanical Engineering, Nanchang Univeristy, Nanchang 330031, China e-mail:
[email protected]
Abstract. This paper designs a way to process image of crawling welding robot (CWR). With CCD sensors to capture man-made mark in interfering circumstance, we adopt and compare different image processing methods to extract useful information, including target location, edge extraction and centerline extraction. Beside this, we develop a controlling software with this image processing method and trace a man-made mark with the CWR. Result of tracing experiment shows that CWR can find and reach the mark precisely in welding circumstance.
1 Introductions With many merits, for example, precise sensor in danger and invisible circumstance, vision technology are used widely in many areas, including medical area, remote area and manufacturing area and so on. Therefore, vision technology and vision sensor technology become research hot in our welding auto manufacturing area, especially in welding seam-tracing area. But in welding robot area, there are three kind of sensor to tracing welding seam according to their sensors structure. One is arc sensor, the second is contact sensor and the last one is non-contact sensor [1-2]. Of these three kinds of sensors, arc sensor adopts variety of arc’s parameters as tracing information. This tracing sensor doesn’t need to add special sensor, it has best good real ability and flexibility and reachable ability. But this arc sensor has shortage that it is difficult to have a precise model between welding current variety and arc length. Another thing is difficult to check welding seam information when length of welding seam is short less 2-3 millimeter [3]. Contacting sensor adopts inching switch to judge polarity of deviation. Although this sensor has merit of simple structure and easy operation and no influence from welding soot and splash, it gets easily abrasion and distortion and so on. Ultrasonic sensor is a advanced welding seam tracing sensor with good real controlling ability, but its function is limited obviously by welding way and work piece size because ultrasonic sensor is need to stick close to work piece surface. Vision sensor is a new sensor for welding area, comparing with the three kind sensors, it has more information and flexibility and measure precise. Another obvious merit is that this vision sensor has strong power to resist influence of electromagnetism field and good adaptation ability in different welding situation [4]. T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 253–259. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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Although moving welding robots have the ability to weld large-sized work piece automatically at present, there is still a problem that this device cannot identify welding seam precisely. This identification influences welding quality directly. Therefore, it is an import thing of image process in large-size ship welding area with welding robots.
2 Hardware System CWR system adopts CCD sensor to capture image of man-made target and processes this image with designing way, and then calculates place between robot and man-made target. After this, the system gives information to left and right motors to run and reach the target with a designed path. The hardware layout of CWR system is listed as Fig. 1.
Fig. 1. Hardware layout
3 Image Pre-process Because it is difficult for CWR finds initial welding seam directly, in this paper we design a man-made T format target and place the target in the CCD area, then the CWR can find the target. The only thing for CWR is that the robot should find the initial pot of T target and reach the pot. T format target is listed as Fig. 2.
Fig. 2. Shape of target image
In order to identify man-made target from other interfering things, we design a pre-process way to filter other information. This pre-process way is different from image identification. We adopt an algorithm of two dimension median filter. In this algorithm we adopts sub-matrix of pixels from initial image and find equalizing value. In order to test this way, we add some interfering information whose format are not T format. Fig. 3 is listed as below. After this process, initial image can avoid inferring noise and breaking point of shade of gray greatly. Result of process is listed as Fig. 4.
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Fig. 3. Initial image
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Fig. 4. Two dimension median process
4 Image Information Extraction After pre-process, we need to have characteristic of target image by some way, so that CWR system can find information from other interfering images. According to this idea, we design image segmentation and characteristics extraction.
4.1 Image Initial Process In this step, we adopt fixed parameter threshold to segment target image. Because intensity of arc light is very high, we adopt K=255 as threshold value in our program. Fig. 5 is a processed image by this way.
Fig. 5. Threshold segmentation
From Fig. 5, processed image includes some highlight interfering things although this threshold segmentation way can find target. In order to solve this question, we use two rules to do it. One is that regarding one connecting area [5] in image as a whole and independent area when an area has a continuous edge. Secondly, after demarcating image, we remove little connecting area that has little square measure. In this way, we design program and process Fig. 5 and find target easily.
Fig. 6. Connecting area process
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4.2 Image Characteristics Extraction CWR system needs to find precise edge characteristics, and then this CWR can reach the right point. Classic edge extraction way takes into account change value of every pixel in some close fields. It adopts changing discipline of singleorder derivative or second derivative of close fields to check image edge [6]. That is to say we can find target edge if we can use some algorithms to check the change and show it in quantifying way. Some common arithmetic operators (OA) of image edge include Roberts’s OA and Prewitt OA and Kirsch OA and so on. In order to find the best algorithm for our image edge extraction, we develop four kinds of different programs to process Fig. 6 according to above arithmetic operator. Before using these programs, T format target image has been processed by pre-process and image segmentation and connecting process. There are four results listed as below in Fig. 7 according to the four programs.
(a) Roberts (Left) and Prewitt (Right) OA
(b) Kirsch (Left) and Canny (Right) OA Fig. 7. Edge extraction
From Fig. 7 we can see that Kirsch OA and Canny OA can give good results. Target image edge shows disconnection when processing with Robert OA. When adopting Prewit OA, target image edge shows disconnection and line width of edge becomes wider than others. Therefore we find Canny OA is a good way to process T format image in welding area. In order to simplify welding situation, we suppose that the welding torch moves along with centerline of the welding seam all the time in the whole welding process. According to target edge information and specific welding process, CWR system just needs to orient center point or centerline of welding seam in T target image when it traces welding seam. Here we design a program to process target image Fig. 7 (b) according to Hough transformation. Our aim is to get centerline and Fig. 8 is the result.
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Fig. 8. Hough transformation
Therefore we design a kind of way to process T format target image with inferring information in welding situation. The series of figs are listed as Fig. 9.
(a)Initial
(b)Median filter
(c)Threshold process
i
(d)Connect
(e)Canny process
(f)Hough
Fig. 9. Character process
5 Tracing Experiments Here we adopt the above image processing method and develop a program to control CWR system to trace man-made target. Fig. 10 is a controlling flow of CWR system. Fig. 11 shows captured images in serial tracing steps. Experiment shows that CWR can find and reach the required place. This experiment proves image process is a good way.
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Fig. 10. Controlling flow
(a) Start tracing
(b) Tracing target
(c) Tracing target
(d) End tracing
Fig. 11. Different steps in tracing
6 Conclusions In this paper a suitable way is designed to process man-made target image. This way contains pre-process and image segmentation and edge extraction. It can find the required line or point precisely and filter inferring information. Tracing experiment shows that this image process way is used well in welding situation. Acknowledgement. This research is funded by NSFC of China (No. 50705041).
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References [1] Wang, W., et al.: Development and Discuss of vision sensor technology in welding area. Welding Machine 32(5), 1–8 (2002) [2] Huang, S.: Welding seam tracing system and development in submerged-arc welding. Welding (1), 8–12 (2001) [3] Pan, J.: Modern arc controlling. Mechanic Industry Press, Beijing (2001) [4] Jiang, P., et al.: Research on voice sensor in welding seam tracing. Welding Machine 31(10), 9–12 (2001) [5] He, B., Ma, T., Wang, Y.: Visual C++ digital image process. People Post Press, Beijing (2001) [6] Jia, Y.: Machine Visual. Science Press, Beijing (2000)
Part III
Modeling and Intelligent Control of Welding Processing
Predictive Control of Weld Penetration in Pulsed Gas Metal Arc Welding Zhijiang Wang1,2, YuMing Zhang1,2, * and Lin Wu1 1
State Key Laboratory of Advanced Welding Production Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, P.R. China 2 Department of Electrical and Computer Engineering, University of Kentucky, Lexington, KY 40506, United States e-mail:
[email protected]
Abstract. Weld penetration control is one of the most difficulty problems in welding area. In this study, a simple method using arc voltage to derive the weld penetration is employed in pulsed gas metal arc welding (GMAW-P). And a predictive controller is designed to control the system output, the change in arc voltage during peak current period. Some welding experiments are conducted to validate the controller. Experiment results show that the change in arc voltage during peak current period can be controlled to a certain constant by the predictive controller.
1 Introduction Weld penetration plays a fundamental role in determining the mechanical strength of welds and its control is thus critical. However, weld penetration control is one of the most difficulty problems in the welding researches because: a) welding is a nonlinear and time-varying thermal process with large delay; b) the feedback signal which can well represent weld penetration is not easy to obtain. Depth of fusion and backside width are the most used parameters to represent the weld penetration, but both of them are not easy directly measured. Therefore, many methods have to be introduced to estimate it based on indirect measurements such as geometrical parameters of weld pool [1-4], temperature field [5], weld pool oscillation frequency [6], arc voltage [7], etc. To obtain indirect measurements, various techniques such as vision [1-4], ultrasonic [8], acoustic emission [9] and thermal [5] have been used. However, most of those efforts focused on gas tungsten arc welding (GTAW) which is less complex and much more stable in comparison with gas metal arc welding (GMAW). In the GMAW process, the bright arc and the more complex weld pool complicated by the metal transfer add additional difficulties to obtain indirect measurements needed to estimate the weld penetration. Weld penetration control for pulsed gas metal arc welding (GMAW-P) is studied in this work. An easily measureable feedback, the change in arc voltage during peak current period, is employed. After analyzing the system, a predictive controller is designed to control this system. *
Corresponding author.
T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 263–269. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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2 System Description This study concerns partial penetration application in which the depth of weld penetration is not directly visible. Our content study [10] shows that the change in arc voltage during peak current period ( ΔU ), which relates the change in the weld pool surface depth during the peak current period, can predict the depth of the weld penetration with an adequate accuracy in the GMAW-P process. Therefore, a constant weld penetration can be achieved if the change in arc voltage during peak current period is controlled to a certain constant in GMAW-P process. The change in arc voltage during peak current period is hence chosen to be the output of the system. In this study, the peak current and peak current period are set to constants, respectively 300 A and 8 ms, the change in arc voltage can only be controlled by the base current or base current time. Base current time ( tb ) is chosen to be the system input as it affects ΔU in a smoother way than the base current amperage. The hardware of the weld penetration control system is simple, as shown in Fig. 1.
Fig. 1. Hardware of the weld penetration control system
3 Step Experiments Step experiments with different magnitudes are conducted. The parameters are shown in Table 1, where Δt is the sample time, tb (1) is the input before step
and tb (2) represents the input after the step. Experiment No. 2 is taken as an example and given in Fig. 2. It can be seen that the system output is noisy and the system dynamics can not be easily identified in a traditional way. The static nonlinearity of the system is obtained from the steady-state values of the step experiments and is shown in Fig. 3. It can be fitted by two lines. The values for first section of 4 ms to 8 ms are small and change little in comparison with noise, hence it is of no significance to control it. Then the system input is limited to 8 ms to 14 ms.
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Table 1. Parameters for step experiments No.
Δt / s
tb (1) /ms
tb (2) /ms
1
0.1
4
10
2
0.1
6
10
3
0.1
8
10
4
0.1
10
12
5
0.1
10
14
3 Origin data Steady state value
ΔU/V
2.5 2 1.5 1 0.5
0
5
10
15
20
25
t /s
Fig. 2. Step experiment. tb steps from 6 ms to 10 ms.
4 Control Algorithm When the system input varies between 8 ms and 14 ms, the static nonlinearity is y = 0.2039 × u − 0.4490
(1)
So the system output y ∈ [1.1822,2.4056] . And denote ~ y = y − 1.1822, u~ = u − 8
(2)
3 Origin data Fitting
Δ U/V
2.5 2 1.5 1
4
6
8 10 tb/ms
12
Fig. 3. Static nonlinearity
14
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and ~ y ∈ [0,1.2234] . Then, ~ y (k ) = bu~ (k − 1)
(3)
where b = 0.2039 .
4.1 Pre-filter As the system is too noisy, a pre-filter is added to the system. The filter follows y (k ) = αy (k − 1) + (1 − α ) ~ y (k ) (4) f
f
Choosing different α produces different filtering results. When α = 0.8 , the filtering result is acceptable because it can effectively suppress the noise while the resultant control speed is still acceptable. And Equations 3 and 4 result in y f (k ) = αy f (k − 1) + b0u~ (k − 1) (5) where b0 = b(1 − α ) .
4.2 Predictive Control Algorithm The transfer function for system (5) is G( z)
1
b0 z 1
1
z
h(1) z
1
b0 z
1
h(2) z
b0 z 2
2
b0
h( j ) z
j 1
z
j
(6)
j
Hence, the system is a linear system which can be described using a finite impulse response (FIR) model if only first N items are taken into consideration. And the system can be expressed as y (k ) = h(1)u (k − 1) + h(2)u (k − 2) + L + h( N )u ( k − N )
(7)
b and α are known, so h( j ) ’s are as shown in Fig. 4. N is here chosen to be 15. 0.04
h(j)
0.03 0.02 0.01 0
0
10
20 j
30
Fig. 4. Impulse response
40
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Assume the control actions are kept unchanged after instant k , i.e., Δu (k + j ) = 0 (∀j > 0) . Predicting the output N -step-ahead yields: N
yˆ (k + N / k , Δu (k )) = y (k ) +
∑ h( j )(u (k − 1) − u (k − j )) + s( N )Δu(k )
(8)
j=2
N
where Δu (k ) = u (k ) − u (k − 1) and s( N ) =
∑ h(i) , then the control action Δu(k ) i =1
will be determined by yˆ (k + N / k , Δu (k )) = y *
(9)
Thus, N
Δu (k ) = ( y * − y (k ) −
∑ h( j)(u (k − 1) − u(k − j))) / s( N )
(10)
j =2
5 Results and Discussion 5.1 Simulation Take y * = 0.5 V as an example, the simulation result is shown in Fig. 5(a). A random noise signal is added to the system. The output for the system can be controlled to a certain range. If the reference value (RV) changes from 0.5 V to 1 V at the mid of the process, namely t / Δt = 150 , the corresponding result is shown in Fig. 5(b). From Fig. 5(b), it can be seen that: a) the controller can achieve desired level of the output; b) the controller can adjust smoothly with requirement. 10
6
Input/ms Output before filtering/V Output after filtering/V Reference value/V
10 Magnitute
Magnitute
12
Input/ms Output before filtering/V Output after filtering/V Reference value/V
8
4 2
8 6 4 2
0
0
0
100
200
300
0
100
t/ Δ t
(a)
200
t/ Δ t
(b)
Fig. 5. Simulation results for predictive control (a) Constant RV ( y * = 0.5 V)
(b) RV changes at t / Δt = 150
300
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5.2 Welding Experiments Welding experiments using predictive control algorithm above were conducted. Mild steel (C1018) plates were used as work-pieces. The welding torch stayed stationary and the work-pieces traveled with the welding tractor at a constant speed. The welding current pulsed from 120 A to 300 A and peak current period was 8 ms. The contact tip to work-piece distance was 12 mm. The electrode wire used was 1.2 mm mild steel wire ER70S-3 and wire feed speed was 5.08 m/min. Shielded gas was pure argon and gas feed rate was 18.9 liter/min. The experiment results are shown in Fig. 6. From Fig. 6(a) to Fig. 6(c), it can be seen that the change in arc voltage during peak current period can be controlled to a certain constant with little fluctuation, no matter the travel speed is changed or the thickness of the work-piece is changed. When the RV changes, the control system can be smoothly adjusted to a new reference as shown in Fig. 6(d). Output before filter/V Input/ms Reference/V Output after filter/V
Magnitude
8 6 4
8
Output before filter/V Input/ms Reference/V Output after filter/V
6 Magnitude
10
4 2
2
0
0 0
5
10
15 t /s
20
0
25
5
10
(a)
20
(b)
12
12
8 6 4 2
Output before filter/V Input/ms Reference/V Output after filter/V
10 Magnitude
Output before filter/V Input/ms Reference/V Output after filter/V
10 Magnitude
15
t/s
8 6 4 2
0
0
0
5
10
15 t/s
20
25
0
5
(c)
10
15 t /s
20
25
(d)
Fig. 6. Predictive control results (a) RV 1.5 V. Travel speed 0.36 m/min, plate thickness 6.35 mm, butt joint, square groove, root opening 1.5 mm, flat position. (b) RV 1.5 V. Travel speed 0.48 m/min, plate thickness 6.35 mm, butt joint, square groove, root opening: 1.5 mm, flat position. (c) RV 1.5 V. Travel speed 0.36 m/min, plate thickness 12.7 mm, bead on plate, flat position. (d) RV changes from 1.5 V to 2 V. Travel speed 0.36 m/min, plate thickness 6.35 mm, butt joint, square groove, root opening 1.5 mm, flat position.
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To verify the effectiveness of the predictive control algorithm, PI control algorithm is implemented to compare with the predictive control algorithm. The PI control results show that the PI control performance is less satisfactory because (1) the fluctuation in PI control is larger; (2) adjustment time in PI control is longer.
6 Conclusions (1) The system, with the change in arc voltage during peak current period as output and base current time as input, is controllable and thus weld penetration can be controlled using the proposed system. (2) Predictive control is effective in real welding environment and can produce better control performance compared with PI control. (3) The weld penetration control method can be easily applied to manufacturing facilities as the feedback signal, the change in arc voltage during peak current period obtained from arc voltage signal, can be easily measured in manufacturing facilities. Acknowledgement. This work is funded by the National Science Foundation (USA) under grant CMMI-0726123 entitled “Measurement and Control of Dynamic Weld Pool Surface in Gas Metal Arc Welding”.
References [1] Kovacevic, R., Zhang, Y.M., Ruan, S.: Sensing and control of weld pool geometry for automated GTA welding. ASME Journal of Engineering for Industry 117(2), 210–222 (1995) [2] Song, H.S., Zhang, Y.M.: Measurement and analysis of three-dimensional specular gas tungsten arc weld pool surface. Welding Journal 87(4), 85–95 (2008) [3] Zhao, D.B.: Dynamic intelligent control for weld pool shape during pulsed GTAW with wire filler based on three-dimension visual sensing. Ph.D. dissertation, Harbin Institute of Technology (2000) (in Chinese) [4] Mnich, C., Al-Bayat, F., Debrunner, C., et al.: In situ weld pool measurement using stereovision. In: Proceedings of 2004, Japan-USA Symposium on Flexible Automation. ASME, Denver, Colorado (2004) [5] Chin, B.A., Madsen, N.H., Goodling, J.S.: Infrared thermography for sensing the arc welding process. Welding Journal 62, 227–234 (1983) [6] Madigan, R.B., Renwich, R.J., Farson, D.F., et al.: Computer-based control of fullpenetration TIG welding using pool oscillation sensing. In: Proceedings of first International Conference on Computer Technology in Welding, the Welding Institute, London (1986) [7] Wang, Q.L.: Real-time full-penetration control with arc sensor in the TIG welding of Al alloy. In: Proceedings of the International Conference on Joining/Welding 2000, IIW, The Netherland (1991) [8] Siores, E.: Development of a realtime ultrasonic sensing system for automated and robotic welding. Ph.D. dissertation. Brunel University (1988) [9] Groenwald, R.A., Mathieson, T.A., Kedzior, C.T., et al.: Acoustic emission weld monitor system – data acquisition and investigation, US Army Tank-Automotive Research and Development Command Report ADA085-518 (1979) [10] Wang, Z.J., Zhang, Y.M., Wu, L.: Measurement and estimation of weld pool surface depth and weld penetration in pulsed gas metal arc welding. Welding Journal 89(6), 117s–126s (2010)
Study on the MLD Modeling Method of Pulsed GTAW Process for Varied Welding Speed Hongbo Ma and Shanben Chen Intelligentized Robotic Welding Technology Laboratory, Shanghai Jiao Tong University, 200240, China e-mail:
[email protected]
Abstract. The purpose of this paper is to propose a MLD (mixed logical dynamical) model to predict the back bead width of pulsed GTAW (gas tungsten arc welding) process for varied welding speed. Variation of welding speed is considered as discrete input and the nonlinear welding process is approximated using piecewise linear models. An MLD model is then established for pulsed GTAW process with varied welding speed.
1 Introduction Arc welding is characterized as a multivariate, nonlinear, time varying process and has a strong coupling among its welding parameters [1]. In many practical applications, constant welding parameters can not acquire the desired quality and shape of the welding seam because of the uncertainties of the welding process. A good welder can overcome those uncertainties and make a good welding seam through adjusting welding parameters according to the visual information of the welding process. Therefore, sensing and modeling of the welding process are two important issues in automatic welding so that automatic welding device can simulate the operations of good welders. Vision sensing technology, especially of passive vision sensing, is similar to the observation behavior of welders. Therefore, passive vision sensing method is used to capture visual information of welding area. In order to simulate the understanding of the welding process like good welders, different models of the GTAW process are proposed [2, 3]. On the other hand, there are logical variables and knowledge rules during the operation of the welders, such as variation of the welding speed. Those logical variables and knowledge rules are not included in traditional modeling methods. Bemporad and Morari [4] presented a framework for modeling and controlling systems described by interdependent physical laws, logic rules, and operation constraints, denoted as mixed logical dynamical (MLD) systems. This modeling framework can easily combine the logic variables and knowledge rules, such as welding speed selectors. Welding speed is a key parameter that will influence the welding quality. A good welder sometimes changes the welding speed to obtain a sound weld seam. The effect of welding speed on GTA weld shape was studied and a higher welding speed decreases the heat input and temperature gradient on the pool surface, which weakens the Marangoni convection on the liquid surface [5]. The optimization of T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 271–278. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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robot welding speed was studied to maximize the speed while keeping complete joint penetration [6]. However, these studies cannot provide a model for real-time control of welding process when the welding speed changes in automatic welding. As a result, a kind of MLD model is proposed to describe the pulsed GTAW process with varied welding speed. This paper is organized as follows. In Section 2 a concise introduction of pulsed GTAW process is given. The framework of MLD model is introduced in section 3. Section 4 gives the modeling of pulsed GTAW process with varied welding speed in detail. Finally, some concluding remarks are made in Section 5.
2 Pulsed GTAW Process Pulsed GTAW is different from normal GTAW because it melts work piece using pulsed current and base current alternately. The weld pool is formed during pulsed current period and it is solidified during base current period. Finally, the weld seam is constituted by a lot of overlapping solider joints, shown in Fig. 1. Pulsed GTAW is commonly applied to thin plates of materials like aluminum, magnesium, aluminum alloy, magnesium alloy and so on, because these materials are easy to form high-melting-point oxide film on their surface. However, thermal deformation happens frequently for thin plates, such as misalignment. The heat input can be controlled by adjusting pulsed current, base current, pulsed current duration, base current duration, etc. Therefore, the size and quality of weld seam or the heat affected zone can be controlled. The experimental system of pulsed GTAW is shown in Fig. 1. The topside weld width W f , topside weld length L f and back bead width Wb are defined in Fig. 2. The weld pool image is captured by a CCD camera fixed behind the torch. and
Wf
L f can be computed from the image and they can be adjusted by changing
the welding parameters, such as pulsed current
I p . Normally, base current I b
stays constantly for a special welding process. Both
I b and I p are the outputs of
power supply and they can be controlled by the computer through D/A card.
Fig. 1. Experimental system diagram
Fig. 2. Weld pool parameters
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For pulsed GTAW process,
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I b could be lower than I p in order to maintain the
arc. At the duration of base current, the arc light is relative low so that clear weld pool image can be acquired, shown in Fig. 3. However, Wb can not be observed directly, as a result, in this study a kind of MLD model is proposed to predict
Wb
based on topside signals.
Fig. 3. Clear image of weld pool
3 Mixed Logical Dynamical Model Hybrid systems are dynamic systems that involve the interaction of continuous dynamics (modeled as differential or difference equations) and discrete dynamics (modeled by Petri Net or Automata). Hybrid systems have been a topic of intense research activity in recent years, primarily because of their potential importance in applications. Hybrid models are important to system analysis and control of hybrid systems. Bemporad et al. (1999) presented a framework for modeling and control of hybrid systems described by interdependent physical laws, logic rules, and operation constraints, denoted as mixed logical dynamical (MLD) systems. MLD systems generalize a wide set of models, among which there are linear hybrid systems, finite state machines, some classes of discrete event systems, constrained linear systems, and nonlinear systems whose nonlinearities can be expressed(or, at least, suitably approximated) by piecewise linear functions. In MLD modeling method, a binary variable is assigned to each statement. If and only if the statement is true, the value of the binary variable is true. For example, statement xi means “topside weld pool length is too short” for welding process. Thus, a binary variable
δi
is added to replace statement xi , expressed
as (1).
xi ≡ True ↔ δ i = 1
(1)
Combination of statements may be described with combination of binary variables, which can be suitably translated into linear inequalities involving binary variables δ i .
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Therefore, the system is modeled as an MLD through the following linear relations (2).
x(t + 1) = At x(t ) + B1t u (t ) + B2tδ (t ) + B3t z (t ) y (t ) = Ct x(t ) + D1t u (t ) + D2tδ (t ) + D3t z (t )
(2)
E2tδ (t ) + E3t z (t ) ≤ E1t u (t ) + E4t x(t ) + E5t Where x is a state vector of the system and contains continuous and binary variables. y and u are respectively output and input vectors of the system, which consist of continuous and discrete parts. The vectors
δ ∈ {0,1}
rl
and
z ∈ R rc
are auxiliary binary and continuous variables, respectively.
4 Modeling of Pulsed GTAW with Varied Welding Speed In this study, the model input and output of pulsed GTAW process are the pulsed current and back bead width respectively. Topside parameters are considered as the state variables of the model. The purpose of modeling pulsed GTAW process is tried to find a model to express the relation between the input, state and output of this process so that the back bead width can be evaluated by using this model. It is well known that the current topside and backside parameters are relative to those parameters of history times, shown in Fig. 4, where W f , L f , Wb at the time k+1 not only have relationship with I p ( k + 1) , but
W f , L f , Wb , I p at the times k and k-1 also influence the values of W f , L f , Wb at time k+1 because of the heat conduction. As a result, the model structure can be expressed as (3).
⎧W f ( k ) = f (W f ( k − 1), I p ( k ), I p ( k − 1)) ⎪ ⎪ L f ( k ) = f ( L f ( k − 1), I p ( k ), I p ( k − 1)) ⎨ ⎪Wb ( k ) = f (Wb ( k − 1), W f ( k ), W f ( k − 1), ⎪ L f ( k ), L f ( k − 1), I p ( k ), I p ( k − 1)) ⎩
(3)
W f , L f , Wb are pulsed current, topside width, topside length and back bead width respectively. k is the sampling time. In this model, only one hisWhere I p ,
torical sampling time is chosen.
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Fig. 4. The corresponding relations of welding parameters
In model (3), W f (k
− 1) and L f (k − 1) can be replaced by the measured val-
Wb (k − 1) only can be evaluated. The prediction error of Wb (k − 1) will be added to Wb (k ) . Thus, in our study the history values of ues from images, however
back bead width are not considered in model (3), and then model (3) is changed into (4).
⎧W f (k ) = f (W f (k − 1), I p ( k ), I p ( k − 1)) ⎪ ⎨ L f (k ) = f ( L f (k − 1), I p ( k ), I p (k − 1)) ⎪ ⎩Wb ( k ) = f (W f ( k ), W f (k − 1), L f (k ), L f ( k − 1), I p (k ), I p (k − 1))
(4)
When the welding speed changes, the dynamics of model (4) is changed. In order to reducing the complexity of the model, three coefficients k1k2 k3 are added to model (4), shown in (5). Here, a discrete input variable
ul1 can be introduced to
describe the welding speed change and they can be defined by (6).
⎧W f (k ) = k1 f (W f (k − 1), I p (k ), I p (k − 1)) ⎪ ⎨ L f (k ) = k2 f ( L f (k − 1), I p (k ), I p (k − 1)) ⎪ ⎩Wb (k ) = k3 f (W f (k ),W f (k − 1), L f (k ), L f (k − 1), I p (k ), I p (k − 1)) ⎧Vw = 16cm / min → ul1 = 0 ⎨ ⎩Vw = 24cm / min → ul1 = 1
(5)
(6)
xl1 and xl 2 are defined, where xl1 represents the situation Vw = 16cm / min and xl 2 represents the
According to the analysis above, two different discrete states
situation Vw = 24cm / min . These two discrete states are driven by the discrete input ul1 , shown in (7).
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⎧ xl1 =~ ul1 ⎨ ⎩ xl 2 = ul1
(7)
In order to analyze the nonlinear characteristics of pulsed GTAW process, two groups of random pulsed currents are designed to excite the dynamics of the process for two different welding speeds. According to the requirement of metal filling amount, the feed speed is changed according to different welding speeds. The welding conditions of random experiments are listed in Table 1. Table 1. Welding conditions of pulsed GTAW Work piece
Material:2mm LF6; size: 400*200mm2
Shielded gas
Pure Ar; flow rate: 15 L min-1
Base current
50A
Feed speed
Group1: 10mm/s Group2: 15mm/s
Weld speed
Group1: 16mm/min Group2: 24cm/min
Pulse frequency
2Hz
Linear ARX (auto-regressive with exogenous input) models are used to identify the relations between I p , W f , L f and Wb . Equation (8) shows the model for group 1. Equation (9) shows the model for group 2. Model (9) is based on equation (8), but the parameters k1k2 k3 are different, shown in Table 2.
⎧W f (k ) = 0.7842W f (k − 1) + 0.01667 I p ( k ) − 0.001801I p (k − 1) ⎪ ⎪ L f (k ) = 0.7041L f (k − 1) + 0.01842 I p (k ) − 0.004291I p (k − 1) ⎨ ⎪Wb (k ) = 0.0006559 I p ( k ) − 0.008156 I p ( k − 1) + 0.5848W f (k ) ⎪ + 0.147W f (k − 1) + 0.001552 L f (k ) − 0.0815L f (k − 1) ⎩
(8)
⎧W f (k ) = k1 (0.7842W f ( k − 1) + 0.01667 I p (k ) − 0.001801I p (k − 1)) ⎪ ⎪ L f (k ) = k2 (0.7041L f (k − 1) + 0.01842 I p (k ) − 0.004291I p (k − 1)) ⎨ ⎪Wb (k ) = k3 (0.0006559 I p (k ) − 0.008156 I p (k − 1) + 0.5848W f (k ) ⎪ + 0.147W f (k − 1) + 0.001552 L f (k ) − 0.0815 L f ( k − 1)) ⎩
(9)
Table 2. Model Parameters k1
k2
k3
0.96
0.93
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Benefiting from the software HYSDEL (hybrid system description language) [7], the MLD model for pulsed GTAW process with varied welding speed can be easily obtained, shown in (10). Due to the limited space, this paper does not give the numerical values of the MLD model (10).
⎡ xr (t + 1) ⎤ ⎡ Arr Arb ⎤ ⎡ xr (t ) ⎤ ⎡ B1rr B1rb ⎤ ⎡ur (t ) ⎤ ⎢ x (t + 1) ⎥ = ⎢ A ⎥+⎢ ⎥ ⎥⎢ ⎥⎢ ⎣ b ⎦ ⎣ br Abb ⎦ ⎣ xb (t ) ⎦ ⎣ B1br B1bb ⎦ ⎣ub (t ) ⎦ ⎡ B2 rb ⎤ ⎡ B3rr ⎤ d (t ) + ⎢ +⎢ ⎥ ⎥ z (t ) ⎣ B2bb ⎦ ⎣ B3rb ⎦ ⎡ yr (t ) ⎤ ⎡Crr Crb ⎤ ⎡ xr (t ) ⎤ ⎡ D1rr D1rb ⎤ ⎡ur (t ) ⎤ ⎥+⎢ ⎥ ⎢ y (t ) ⎥ = ⎢C ⎥⎢ ⎥⎢ ⎣ b ⎦ ⎣ br Cbb ⎦ ⎣ xb (t ) ⎦ ⎣ D1br D1bb ⎦ ⎣ub (t ) ⎦
(10)
⎡ D2 rb ⎤ ⎡ D3rr ⎤ +⎢ ⎥ d (t ) + ⎢ D ⎥ z (t ) D ⎣ 2bb ⎦ ⎣ 3br ⎦ ⎡ur (t ) ⎤ ⎡ xr (t ) ⎤ + E4 ⎢ E2 d (t ) + E3 z (t ) ≤ E1 ⎢ ⎥ ⎥ + E5 ⎣ub (t ) ⎦ ⎣ xb (t ) ⎦
5 Conclusions In this paper, a novel MLD modeling method is studied to describe the dynamics of pulsed GTAW process with varied welding speed. This study will make the modeling of welding process more easily and give a novel understanding of welding process from hybrid system. In future, more dynamics in welding process will be included by using the MLD modeling method and the predictive control of the welding process will be studied. Acknowledgement. This work is supported by the National Natural Science Foundation of China under the Grant No. 60874026, and Shanghai Sciences & Technology Committee under Grant No. 09JC1407100, P.R. China.
References [1] Chen, S.B., Lou, Y.J., Wu, L., et al.: Intelligent methodology for sensing, modeling and control of pulsed GTAW: Part1-Bead-on-plate welding. Welding Journal 79(6), 151–163 (2000) [2] Zhang, Y.M., Kovacevic, R., Li, L.: Adaptive control of full penetration gas tungsten arc welding. IEEE Transaction on Control Systems Technology 4(4), 394–403 (1996) [3] Li, W.H., Chen, S.B., Wang, B.: A variable precision rough set based modeling method for pulsed GTAW. The International Journal of Advanced Manufacturing Technology 36(11), 1072–1079 (2008) [4] Bemporad, A., Morari, M.: Control of systems integrating logic, dynamics, and constraints. Automatica 35(3), 407–428 (1999)
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[5] Lu, S., Fujii, H., Nogi, K.: Effects of CO2 shielding gas additions and welding speed on GTA weld shape. Journal of Materials Science 40(9-10), 2481–2485 (2005) [6] Ericsson, M., Nylen, P.: A look at the optimization of robot welding speed based on process modeling. Welding Journal 86(8), 238–244 (2007) [7] Torrisi, F.D., Bemporad, A.: HYSDEL-a tool for generating computational hybrid models for analysis and synthesis problems. IEEE Transactions on Control Systems Technology 12(2), 235–249 (2004)
Simulation of Decoupling Control of Pulsed MIG Welding for Aluminum Alloy Ding Fan, Jiankang Huang, Lihui Lu, and Yu Shi Lanzhou University of Technology, Lanzhou, China e-mail:
[email protected],
[email protected]
Abstract. Based on system identification of pulsed MIG welding process for aluminum alloy, due to strong coupling among the welding parameters, PID controller, fuzzy PID controller and neural network inverse controller were designed, the strong coupling multi-input and multi-output (MIMO) system was simulated using pulse current duty cycle and wire feed speed as control input, and wire extension and pool width as control output. The results show that the neural network inverse control provides faster response and better robustness, which lays a foundation for decoupling control of aluminum pulsed MIG welding.
1 Introduction With the wide use of aluminum alloy, higher requirements are put forward for the weld quality and welding production system. So accurate closed-loop control is required for aluminum alloy welding process. Pulsed MIG welding is one of the most common methods for aluminum alloy welding. So study of the control for pulsed MIG welding process for aluminum alloy is an important research direction. As we know, the MIG welding process for aluminum alloy has the characters, such as nonlinear, strong-coupling and time-varying. Only adjusting a certain welding parameter may cause the welding system to become instability. Therefore, one of the main difficulties in pulsed MIG welding process for aluminum alloy control is how to build the coupling model between the welding parameters and how to select decoupling control method. In [1, 2] multi-variable control model was established on the basis of selfadaptive control method in GMAW. Through the improvement of single variable control system in literature [3], weld pool width multivariable control for MIG welding for aluminum alloy was preliminarily realized. In view of the strong coupling effect between welding parameters in pulsed MIG welding process, MIMO coupling system, whose input were weld width and wire extension and output were wire feed speed and duty cycle, was put forward in this paper on the basis of the analysis of identification model. And diagonal matrix compensation decoupling control and neural network inverse object decoupling control were designed to do comparative simulation, then the decoupling control algorithm for pulsed MIG welding for aluminum alloy, which has ideal decoupling effect and quickly response, was obtained.
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2 The Mathematical Description and Analysis for the Model Pulsed MIG welding process for Aluminum alloy is a typical multi-input and multioutput system, and the input and output are coupled. One input affects multiple outputs and one output is affected by multiple inputs. This brings certain difficulties for control system design. Decoupling control is an effective mean to solve the problem of multivariable control. Compensation decoupling method realizes the decoupling control through designing the decoupling compensator based on the accurate model, which is widely used in the current industrial production [4, 5]. ⎡ yw ⎤ ⎡GW −I b ⎢ y ⎥ = ⎢G ⎣ L ⎦ ⎢⎣ L − Ib
GW −δ
GW −Vwire
GL −δ
GL −Vwire
⎡ 0.0170 −1.5705 s ⎢ 4.8241s + 1 e =⎢ ⎢ −0.1555 e −0.2294 s ⎢⎣ 0.3156 s + 1
⎡ Ib ⎤ GW −Vwelding ⎤ ⎢ δ ⎥ ⎥ ⎥⎢ GL −Vwelding ⎥ ⎢ Vwire ⎥ ⎦⎢ ⎥ ⎣⎢Vwelding ⎦⎥
0.1845 −1.1364 s e 5.0459 s + 1 −0.24 e−0.0253 s 0.0242 s + 1
0.96 e−0.4174 s 1.1691s + 1 0.9594 −0.4928s e 0.2663s + 1
−0.5160 −1.3487 s ⎤ ⎡ I b ⎤ e ⎥⎢ δ ⎥ 1.089 s + 1 ⎥ ⎥⎢ 0.21375 −0.5979 s ⎥ ⎢ Vwire ⎥ e ⎥⎦ ⎢⎢Vwelding ⎥⎥ 1.0214 s + 1 ⎣ ⎦
(1)
In the preceding research for pulsed MIG welding for aluminum alloy dynamic process, identification models of welding pool width, wire extension to duty cycle of pulse current, peak current and welding speed, wire feed speed were obtained [6, 7]. In eq. 1, yw is the welding pool width, yL is wire extension, I b is the base current,
δ
is duty cycle,
Vwire is wire feed speed, and Vwelding is welding speed.
According to the Niederlinski theorem [7], as to MIMO control system, the coupling degree could be determined by the relative gain.
⎡Y1 ( S ) ⎤ ⎡ k11G11 ( S ) k12G12 ( S ) ⎤ ⎡ X 1 ( S ) ⎤ ⎢Y ( S ) ⎥ = ⎢ k G ( S ) k G ( S ) ⎥ ⎢ X ( S ) ⎥ 22 22 ⎣ 2 ⎦ ⎣ 21 21 ⎦⎣ 2 ⎦
(2)
Through the analysis of eq. 1, and considering the actual welding process, variables δ and Vwire were selected to simultaneously control the welding pool width and wire extension of MIMO system. From the eq. 1 and eq. 2, double-input and double-output system model could be obtained, as follows: 0.96 −0.4174s ⎤ ⎡ 0.1845 −1.1364s e e ⎥⎡ δ ⎤ ⎡yw⎤ ⎢5.0459S +1 1.1691S +1 = ⎢ ⎥⎢ ⎥ ⎢y ⎥ ⎣ L ⎦ ⎢ −0.24 e−0.0253s 0.9594 e−0.4928s ⎥ ⎣Vwire ⎦ ⎢⎣0.0242S +1 ⎥⎦ 0.2663S +1
The gain matrix of the system is:
⎡0.1845 0.96 ⎤ ⎢ −0.24 0.9594 ⎥ ⎣ ⎦
(3)
Simulation of Decoupling Control of Pulsed MIG Welding for Aluminum Alloy
Coupling index:
D=
λ21λ12 = 1.6939 > 1 λ22 λ11
281
(4)
This means that the system is strong coupling. Figure1 is the square wave signal response in the controlled coupling system based on the model eq. 3. The square wave signal’s cycle is 20s. From the figure, we may find the coupling relation is strong. It also follows that the decoupling control is required by circumstances.
(a) The response of welding pool width
(b) The response of wire extension
Fig. 1. Coupling relation simulation in pulsed MIG welding process for aluminum alloy
3 Simulation of Decoupling Control 3.1 Compensation Decoupling Control Diagonal matrix decoupling system is one of the effective decoupling methods. Its principle is adding a matrix to the control system and making the generalized object matrix, which is the product of the added matrix and the object characteristic matrix, into a diagonal matrix. Thus realize the decoupling of the system. The diagonal matrix decoupling control system structure of double-input and double-output is shown in figure 2.
Fig. 2. Structure of the diagonal matrix decoupling system
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Decoupling compensation matrix is eq.8 ⎡ G11 ( S ) G12 ( S ) ⎤ ⎡ G p11 ( S ) G p12 ( S ) ⎤ ⎡ G11 ( S ) G12 ( S ) ⎤ ⎢ G ( S ) G ( S ) ⎥ ⎢G ( S ) G ( S ) ⎥ = ⎢G ( S ) G ( S ) ⎥ ⎣ 21 22 ⎦ ⎣⎢ p 21 p 22 22 ⎦ ⎦⎥ ⎣ 21
−1
⎡ G11 ( S ) 0 ⎤ ⎢0 G (S )⎥ = ⎣ 22 ⎦
⎡ G ( S ) G 22 ( S ) − G12 ( S ) G 22 ( S ) ⎤ 1 * ⎢ 11 ⎥ G11 ( S ) G 22 ( S ) − G12 ( S ) G 21 ( S ) ⎣ − G11 ( S ) G 21 ( S ) G11 ( S ) G 22 ( S ) ⎦
(5)
After simplifying, the diagonal matrix decoupling compensator is eq. 9 0.0551S 2 + 0.254S + 0.177 0.122 S 2 + 5.07 S + 17 G p12 = − 2 0.331S + 1.433S + 0.407 0.331S 2 + 1.433S + 0.407 2 0.137 S + 0.632 S + 0.44 0.005S 2 + 0.211S + 0.177 G p 21 = G p 22 = − 2 0.331S + 1.433S + 0.407 0.331S 2 + 1.433S + 0.407 G p11 =
、
、
(6)
、
When use a PI controller, K P1 =17 K i1 =1.5 K P 2 =1 K i 2 =2, the asynchronous pulse signal response is shown in figure 3, the cycle is 100s.
(a) Response of weld width
(b) Response of wire extension
Fig. 3. Decoupling simulation of diagonal matrix compensation PI controller
In figure 3, a diagonal matrix compensation decoupling control method with PI controller has some decoupling effect on pulsed MIG welding for aluminum alloy; the response of the system is well. But with the asynchronous signal, especially the changes of duty cycle have great influence on the wire extension, and cause sharp changes of the wire extension, shown as 3b.
2.2 Neural Network Object Inverse Model Decoupling Control Intelligent decoupling control is the hot spot in research field of control. It has unique superiority in solving nonlinear system and it can realize the precise online decoupling of linear and nonlinear systems, solves the problem of difficult to achieve precise decoupling with traditional decoupling method, for example compensation decoupling.
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Figure 4 is the designed system structure of the neural network object inverse model decoupling control for pulsed MIG welding for aluminum alloy. The structure of NN1 and NN2 are the same, but the input and output interface are transposition. Through transforming the weight matrix of NN1 into weight of NN2, the generalized object matrix can become a unit matrix, thus realize the full decoupling. NN1 adopts four-level neural network, its structure is 2×12×12×2 gradient descent method training network, and adopts off-line training and on-line update.
Fig. 4. Structure of neural network object inverse decoupling control system in welding
To get better simulation effect and performance, self-learning neural network PID controller was used to control the generalized object and realize the control of duty cycle of pulse current and wire feed speed. Nerve PID controller adopted incremental control method, and its structure is 3 × 12 × 2 . 1 .2
Out put y 1( t )
1 .0 0 .8 0 .6
r1 y1
0 .4 0 .2 0 .0 -0 .2 0
10
20
30
40
50
60
70
80
90
100
1 10
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T i me t / s 1 .2
Out put y 2( t )
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u2 y2
0 .2 0 .0 -0 .2 -0 .4 0
10
20
30
40
50
60
70
80
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T i me t / s
Fig. 5. Response curve of the neural network inverse-plant decoupling control system
Figure 5 shows that the coupling effect on duty cycle and wire feed speed to wire extension and welding pool width can be largely eliminated by using neural PID inverse model decoupling control, and respond quickly. Compared figure 3 with figure 5, it shows that the neural network object inverse model decoupling control system using neural network PID can eliminate the coupling relationship between each channel, and overcome the coupling effect on duty cycle to wire extension in diagonal matrix compensation decoupling control, and has good dynamic and steady performance. It can meet the practical control requirements of pulsed MIG welding for aluminum alloy.
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4 Conclusions Through the analysis of identification model for pulsed MIG welding for aluminum alloy, a MIMO system was determined, in which variable δ and Vwire were selected to control welding pool width and wire extension simultaneously, and then analyzed the coupling state of parameters. Decoupling control simulation on the determined coupling system, whose input were duty cycle and wire feed speed while output were welding pool width and wire extension, was carried out by separately adopting the diagonal matrix compensation decoupling control with a PI controller and a neural PID object inverse model decoupling control. The results show that the neural network object inverse model decoupling control system with the neural network PID controller can mostly eliminate the coupling relationship among each channel, and has good dynamic and stable performance and can meet the actual control requirements of the pulsed MIG welding for aluminum alloy. Acknowledegment. This research work is supported by National Nature Science Foundation of China (50675093).
References [1] Ozcelik, S., Moore, K.L., Naidu, S.D.: Application of MIMO Direct Adaptive Control to Gas Metal Arc Welding. In: Proceedings of the American Control Conference Ohiladelphia Rennsyvania, pp. 1762–1766 (1998) [2] Zhang, Y., Walcott, B.L., Wu, L.: Adaptive predictive decoupling control of full penetration process in GTAW. In: First IEEE Conference on Control Applications, pp. 938–943 (1992) [3] Shi, Y., Li, J., Fan, D., et al.: Vision-based control system for aluminum alloy MIG welding pool width. Transactions of the China Welding Institution 28(2), 9–12 (2007) [4] Ma, P., Yang, J., Cui, C., et al.: Current Situation and Development of Decoupling Control. Control Engineering of China (02), 97–100 (2005) [5] Tianyou, C.: Multivariable adaptive decoupling control and application. Science Press, Beijing (2001) [6] Shi, Y., Fan, D., Huang, A., et al.: Vision-based identification model of welding pool width dynamic respondence in aluminum alloy pulsed MIG process. Acta Metallrugica Sinica 41(09), 994–998 (2005) [7] Shi, Y., Huang, J., Fan, D., et al.: Vision-based identification model and extracting algorithm of wire extension in aluminum alloy MIG alloy welding process. Transactions of the China Welding Institution 28(08), 1–4 (2007)
Modeling and Decoupling Control Analysis for Consumable DE-GMAW Jiankang Huang, Yu Shi, Lihui Lu, Ming Zhu, Yuming Zhang, and Ding Fan Lanzhou University of Technology, Lanzhou, China e-mail:
[email protected],
[email protected]
Abstract. Aiming at the instability of dynamic welding arc and strong coupling among welding parameters, a mathematical model for consumable doubleelectrode gas metal arc welding (DE-GMAW) was established by the method of equivalent path and then dynamical analysis was carried out. On this basis, an approach was proposed to control the stability of the welding arc using the wire extensions of main and bypass torch as a control object. And then a comparative analysis of two different decoupling control schemes was made respectively using the bypass wire feed speed and the bypass current as input and using the wire feed speed of main and bypass as input. The results show that the mathematical model can reflect the process of consumable DE-GMAW well and the decoupling control scheme using the wire feed speed of main and bypass as inputs has better dynamic performance and robustness.
1 Introduction The description of welding arc in GMAW is more and more precise. On this basis, many methods for arc control were proposed. The literature [1] studied the influence of change of the conductive mouth’s contact point to metal transfer mode and arc length in pulsed MIG welding process. Taken into account the quality of the power source, the literature [2] studied the change of the genuine wire extension. Compared many arc models of GMAW, the arc models of DE-GMAW and laserarc welding were less reported because of insufficient understanding of the coupled arc. In this paper, the mathematical model for arc system in consumable DEGMAW was built by the method of equivalent current path, and it could reflect the dynamic changes of welding process perfectly. On the basis of analyzing the coupling relation of welding parameters, the effect of different control schemes was simulated and the best control scheme was determined.
2 Consumable DE-GMAW Consumable DE-GMAW is established on the basis of a conventional GMAW system by adding another GMAW torch as bypass. It can be seen that in Fig. 1
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that one GMAW torch is connected to the positive terminal of a CV power supply while another torch is connected to the negative terminal of a CC power supply. The current I flowing through the welding wire of main path (namely total welding current) divide into two parts in arc column zone. One part will flow back to the CC power supply, called bypass current Ibp while the other current will flow through the work-piece, called base metal current Ibm, shown as Fig. 2.
Fig. 1. Schematic diagram of consumable DE-GMAW[3]
Fig. 2. Current distribution
In this design, the current flowing through welding wire can be very large by increasing the bypass current, which is helpful to improve melting rate of welding wire and the deposition rate. According to the characters of DEGMAW, due to the bypass sharing part of current, so on the premise of ensuring large deposition rate, the heat input of base metal can be still maintained at a low level [3-4].
3 ARC Model of Consumable DE-GMAW As illustrated in Fig.3, coordinates ( xa , ya ) ( xb , yb ) ( xc , yc ) ( xd , yd ) ( x, y ) represent the points A, B, C, D, E. AC means the wire extension of bypass torch and BD means the wire extension of main torch. In the arc system, the current flows through the equivalent path DE,EC and EF. Beside the point E is the junction of the total equivalent current.
Fig. 3. Model diagram of consumable DE-GMAW
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There are several assumptions for the system model of DE-GMAW: 1). Assuming the CV and CC power supply for consumable DE-GMAW is ideal. 2). In the arc column area, assuming that the plasma has been formed, and form a good conductive channel. 3). The model was considered as two dimensions. 4). Assuming the current flowing through the welding arc obeys the minimal energy principle. 5). Assuming the work piece and point D is equipotent. The secular equation for welding arc in GMAW is also suitable for DE-GMAW. The expression of arc voltage varc is shown as equation [5].
varc = v + Ra I + Ea la
(1)
I is welding current; v and Ra ( Ω ) are constants. Ea (v/m) is potential gradient of arc column; la is length of arc. Where,
The voltage drop vls of wire extension in welding process is expressed as equation (2). vls = ρ ls (2) Where, ρ is density and ls is length of wire extension. So the output voltage of welding power source is expressed as equation (3).
U = varc + vls
(3)
The wire feed speed is invariable and the melting equation is:
k1I + k2ls I 2 = vm Where
(4)
k1 and k2 are coefficients; vm is melting rate.
Based on the above formulas, the constraint conditions of point E are shown as equation (5) (6).
⎧U − Rb lmain − U c − ( Ra I + Ea lde ) = U E ⎪ ⎨ Ra I bm + Ea le + U k = U E ⎪R I + E l + U + R l = U a ce k b ac E ⎩ a bp
(5)
lce + lde + le
(6)
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Where U is supply voltage; U C is anode voltage drop; U k is cathode voltage drop; U E is the potential of point E; Rb is coefficient. Try to solve the equations (4), (5) and (6). Because the coordinate of point E ( x, y ) and Ibm are unknown and constraint condition is given, the equations are difficult to solve. So the numerical calculation process as Fig. 4 is proposed.
Fig. 4. Flow chart of numerical calculation
4 Coupling Relation Analysis of Parameters in Arc System According to the built analytical model, dynamic system simulation was carried out with ideal CV and CC power supply by Matlab/simulink. Runge-Kutta algorithm was adopted and step is 0.001s. The simulation parameters are shown in the table 1. Table 1. Simulation parameters Symbol
Value
Unit
U0
15.7
V
Ra
0.022
Ω
Ea
128
V/m
k1
2.553e-4
m/(A s)
k2
4.623e-5
(A2 s)-1
·
·
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Wire feed speed of main torch is 0.25m/s while that of bypass torch is 0.10m/s. Initial wire extension of main torch is 0.015m while that of bypass torch is 0.010m. The main voltage Umain is 36V. The bypass current Ibp is 140A. The change of current and wire extension in initial state is shown as Fig. 5. 0.020
250
Wire extension Lex/m
I total
Current I/A
200 Ibp
150
100 Ibm
50
0
0.015
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0.005
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1000
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Time t/ms
Time t/ms
(a) Change of current
(b) Change of wire extension
Fig. 5. Parameters change with time in initial state
In order to test the correctness of the model, the simulation was carried out respectively under step of bypass current, step of wire extension of bypass torch and step of wire extension of main torch. Fig. 6 is the response of parameters under step of bypass current. From the Fig. 6(a), we can see when the bypass current decreased, the base metal current increased quickly and the total current increased a little. At the same time, the length of bypass wire extension increased quickly while the length of main wire extension decreased a little. 0.020
Current I/A
250
Wire extension Lex/m
300 Itotal
200 I bp
150 100
Ibm
50 0
0
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Time t/ms
(a) Change of current
1000
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0.010
Lbypass
0.005
0.000
0
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1000
Time t/ms
(b) Change of wire extension
Fig. 6. Response of parameters under step of bypass current
Fig. 7 is change of parameters under step of main wire feed speed. From Fig. 7(a) we can find the bypass current had no change, the total current decreased and the base metal current also decreased under step of main wire feed speed. Simultaneously, the length of main wire extension decreased while the bypass wire extension had almost no influence, as shown in Fig. 7(b).
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0.35
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0.30 WFS1
150 100
Ibp
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Ibm
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Itotal
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0.004
0.000
0
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800
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Time t/ms
(a) Change of current
(b) Change of wire extension
Fig. 7. Response of parameters under step of main wire feed speed
Fig. 8 is change of parameters under step of bypass wire feed speed. In Fig. 8, we can see that the step had little influence on current (the total and base metal current decreased a little) and main wire extension, but had great affect on bypass wire extension, as shown in Fig. 8(b).
Itotal
WFS2
0.20
Ibp
150
0.18 Ibm
100
0.16
50
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0
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0.12 1000
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0
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0.22
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250
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0.015
0.010
Lmain
0.005
0.000
0
200
400
600
800
1000
Time t/ms
(b) Change of wire extension
Fig. 8. Response of parameters under step of bypass wire feed speed
From Fig. 5, Fig. 6, Fig. 7 and Fig. 8 we can see that the simulation results based on built model is consistent with the results of consumable DE-GMAW step test in literature [6]. Through analyzing, we can find that the change of bypass parameters has great influence on arc system of consumable DE-GMAW and determines the stability of arc system.
5 Decoupling Control Simulation and Discussion To ensure the stability of consumable DE-GMAW process, the most direct method is to keep the wire extension of main and bypass invariant when other parameters change. The relatively simple method to control wire extension is to match suitable wire feed speed. Decoupling simulation for wire extension adopting feed forward compensation was carried out. Fig. 9 is the change of parameters under step of bypass current in decoupling control simulation.
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Current I/A
Wire feed speed v/(m/s)
Itotal
200
150 Ibp
100 Ibm
50
0
0.015
0.15
0
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250
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0.000 1000
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(a) Change of current
(b) wire feed speed and change of wire extension
Fig. 9. Change of parameters under step of bypass current in decoupling control process
As can be seen from Fig. 9b, the system quickly enters a new stable state under step of bypass current by adjusting wire feed speed of main and bypass simultaneously to control wire extension. Another scheme is through adjusting current and wire feed speed of bypass to control the stability of wire extension. In simulation of compensation decoupling control, adjusting the current and wire feed speed of bypass simultaneously will take a long time. So control analysis was made without step signal. Fig.10 is the change of parameters in decoupling control simulation. The bypass wire extension is almost unable to get stable in the simulation, as shown in Fig. 10(b),which is consistent with the actual welding test in literature. 0.016
150 Ibp
100 Ibm
50
0
0
200
400
600
800
Time t/ms
(a) Change of current
1000
0.10 Lmain
0.08
0.012
0.06 0.008 0.04
Lbypass
0.004
0.000
0.02
0
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Wire feed speed v/(m/s)
Itotal
200
Current I/A
Wire extension Lex/m
250
0.00 1000
Time t/ms t/ms
(b) Wire feed speed and change of wire extension
Fig. 10. Change of parameters in decoupling control process
Compared these two scheme, controlling the wire feed speed to stabilize the wire extension is better. For DE-GMAW, suitable sensing mode of wire extension is important. The method through welding voltage to sense wire extension is obvious inaccurate because the power supply for main torch is constant voltage. As to stabilizing the wire extension by adjusting current and wire feed speed of bypass, its response is slow and can’t meet the control requirement. Therefore, it requires more efficient controller and decoupling method, and further research is needed.
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6 Conclusions By comparing step simulation analysis with existing experiment results, it proved the correctness of the built mathematical model for consumable DE-GMAW. The model could reflect the actual welding process well and reveal the coupling relation of arc system. In consumable DE-GMAW, change of bypass parameters has great influence on stability of the coupling arc. Control simulation results show that adjusting wire feed speed of main and bypass simultaneously to stabilize wire extension can obtain a stable welding process, but further research is needed for sensing of wire extension. Acknowledgement. This research work is supported by National Nature Science Foundation of China (50805073).
References [1] Thomsen, J.S.: Advanced control methods for optimization of arc welding. Aalborg University (2005) [2] Li, C., Luo, Y., Du, C., et al.: Dynamic Model and Parametrical Control Based on the Genuine Length of Wire for Short-circuit CO_2 Arc Welding. China Mechanical Engineering 20(6), 1261–1264 (2009) [3] Li, K.H., Chen, J.S., Zhang, Y.M.: Double-Electrode GMAW Process and control. Welding Journal 86(8), 231–237 (2007) [4] Li, K., Zhang, Y.: Metal transfer in double-electrode gas metal arc welding. Journal of Manufacturing Science and Engineering 129(11), 991–999 (2007) [5] Choi, J.H., Lee, J.Y., Yoo, C.D.: Simulation of Dynamic Behavior in a GMAW System. Welding Journal 80(10), 293–254 (2001) [6] Li, K.: Double-electrode gas metal arc welding: the process, sensing, modeling and control. Kentucky, Lexington (2007)
The Structure Design and Kinematics Simulation for Rotating Arc Sensor of TIG Welding Based on Pro/E 3D Design Jianping Jia, Hongli Li, Wei Jin, and Shunping Yao Mechatronic Institute, Nanchang University, Nanchang, 330031, P.R. China e-mail:
[email protected]
Abstract. Considering the features and requirements of rotating arc seam tracking, a new type of sensor is presented in this paper. Through the application of Pro/E 3D modeling and mechanism kinematics simulation module, the structure design of a rotating arc sensor is optimized and some problems caused by Eccentic Mechanism such as vibration and interference were solved.
1 Introduction Kinematic problem can not be separated form Mechanical Product Design. In traditional 2D design, the similar product models are first produced based on required shape and size then checked in actual working environment, modified by result until can meet the requirements. These works waste much time, money and human resources. Pro/E can make the above work more easily with its mechanism kinematics simulation module and shorten the development cycle and cost. The simulation results not only can be shown in the form of animation, but also can be output by parameters, so you can learn whether there is interference between parts, how much the volume of intervention. In this paper through the application of Pro/E 3D modeling and mechanism kinematics simulation module, the structure design of rotating arc sensor is optimized and some problems caused by Eccentric Mechanism such as vibration and interference were solved.
2 Requirements Requirements of TIG sensor: compact size; the systems of cooling water and protective air without entanglement; rotating radius 0 ~ 3mm; light weight. Main movements are 1) Cone swing of Tungsten needle gripper. 2) Horizontal movement of eccentric Mechanism (when adjust the rotating radius).
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3 Basic Principle The TIG sensor is designed based on cone swing principle and driven by DC Torque Motor, Which is shown in Fig. 1. 1) Electric-air pole (for transmission protective air and electric ). 2) The hollow DC Torque Motor. 3) Eccentric mechanism. The electric-air pole is supported by a self-aligning bearing above through the hollow DC Torque Motor. There is no eccentricity for the above bearing. In the 3 place another self-aligning bearing and a slider body lie in the eccentric Mechanism. By adjusting the location of the slider body, Electric-air pole can be moved an angle. When the hollow DC Torque Motor works, eccentric Mechanism make bottomaligning bearing outer race drive Electric-air pole to move and cone swing movement is formed. The adjustment of rotating arc radius can be gained through change level location of slider body in the eccentric Mechanism. Fig. 1. Cone Swing Principle
4 Design Idea First all parts must be created in Parts creation mode, then skeleton model is established in assembly mode. All the parts of sensor housing were connected rigidly and the parts of core assembly were connected with proper Kinematic Pair. Next all the parts should be assembled together. The design idea is shown in Fig. 2. Rotating arc sensor
Sensor housing
Core assembly
Rotating eccentrics
Fig. 2. Design Idea
Balance body
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In this paper the simulation for core assembly is made. First the mass attribute, gravity, damping, force and torque must be defined. Second the servo motor is defined and then drive motor is simulated. Third the interference is checked according to the dynamic simulation results, and the interference is eliminated. Finally optimal parameters of the balanced block through analysis are given.
4.1 3D-Modeling of Important Parts In this paper only the eccentric Mechanism 3D-modeling is shown in the follow Fig3. The parts are mainly created by the Rotary and stretch. The eccentric Mechanism is composed of four parts (top-down) upper cover slider eccentric bushing under cover. Eccentricity value can be gained through adjusting set screws on the and promoting the slider movement in the slot of Max of Eccentric is set to 2mm.
;②
;③ ③
;④
①
①
4.2 Dynamic Balance of Rotating Parts For the sensor’s design, centre of mass of eccentric Mechanism and electric-air pole is not in rotating axis. When the arc sensor rotates with high speed, centrifugal inertia force generates directional cyclic changes with the rotating of rotary parts. Thus machine itself and its base will vibrate which cause the reduction of the sensor’s accuracy, reliability. For the above reasons a balance cover was needed to realign the mass distribution of rotating parts and fixed bearing on axial.
Fig. 3. The eccentric Mechanism
Because of the rotating parts with irregularly shape and different materials it is very difficult to make balancing calculate. In this paper Pro/E MECHANISM simulation was used to solve this problem.
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5 Dynamic Simulation Analysis 5.1 Dynamic Simulation Workflow Dynamic simulation workflow is shown in Fig. 4. The connections of core components are as follow: The motor and eccentric mechanism is rigid connection. The electric-air pole assembly is rigid connection. The motor and housing is pin connection. The self-aligning bearings are ball connections. The upper selfaligning bearings and electric-air pole is rigid connection. The lower self-aligning bearings and Electric-air pole is cylinder connection. Table 1 is the mass attributes of the components. Table 1. The mass attributes of the components component
material
density
Electric-air pole and balance block eccentric mechanism others
H62
8.43
45
7.8 1
Parts model
Establish connections
Assembly
Servo Motor
Set environment
Dynamic analysis
Analysis Model
Interference/measure/ curves Fig. 4. Dynamic Simulation Workflow
Get Results
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Force analyses are as follow: Only three contacts lie between rotary assembly and sensor housing: upper self-aligning bearings, two deep groove ball bearing. When rotation frequency and radius were certain, the force of upper self-aligning bearings is only related to the mass distribution of Electric-air pole. It has nothing to do with the size of balance block. Force analysis on two deep groove ball bearings is made in this paper.
5.2 Analysis Results When the playback of the dynamic simulation is shown, the institutional interference must be checked. When the maximum rotating radius is 3mm, electric-air pole had interference with gas mantle. When the parameters are adjusted and the electric pole length is up to 173mm, the interference does not occur. By sensitivity analysis, when the thickness of the balance block was up to 4.87mm, motor bearing force was the smallest. So the optimal parameter of balance block thickness should be designed 4.87mm. Results of the analysis were shown as follow Fig. 6.
Fig. 5. Simulation Results
6 Conclusions In this paper, Proe/E 3D modeling and mechanism kinematics simulation module was used to design the structural of a TIG rotating sensor. The results show that the design method prevented interference phenomena of parts, made a better solution to the movement body in force balance. It saved design cost, shortened design cycle and improved the reliability of the design. Acknowledgement. The authors wish to thank the financial support for this research from the Hi-tech Research and Development Program of China, No. 2007AA04Z242, and Tackle Key Problem Item of Jiangxi Province, China, No. 2007BG09200.
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References [1] Tong, H., Li, C.: Body motion simulation and dynamic analysis. The People’s Posts and Telecommunications Press, Beijing (2009) (in Chinese) [2] He, Q., Xu, Z.: Product Design and Dynamic Analysis. Mechanical Industry Press, Beijing (2004) (in Chinese) [3] Zhou, E., Xiao, Q.: Virtual Assembly and Kinematics of Gearbox Based on Pro/E Wildfire. Coal Mine Machinery 28(2), 78–80 (2007) [4] Ia, J., Zhang, H., Pan, J.: Development of New Type High Speed Rotating Scanning Arc Sensor Used in Arc Welding Robot. Journal of Nanchang University 22(3), 1–3 (2000)
Knowledge Model Building about a Motor Speed Regulation Fuzzy Control System Huimin Zhao1, Mingyan Ding2, Wu Deng1, Xiumei Li1, and Wen Li1 1 2
Software Technology Institute of Dalian Jiaotong University, Dalian, China Electronic Information School, Dalian Jiaotong University, Dalian, China
Abstract. As fuzzy rules can be properly and effectively extracted from the data even if the data are gained from testing and inconsistent information can be conveniently treated by the established fuzzy system model, the system properties can be objectively characterized by fuzzy systems. This paper discussed the descriptive method, consistency about knowledge model of fuzzy systems, drawn a concept and method of maximum consistency. Some systems, for example an Alternating Current Motor (AC motor), is difficult to devise an accurate mathematical model because of the existence of nonlinear and uncertainty factors. Using sampled data, this paper identified the speed model structure with the method introduced in this paper. The result shows that the structure of the speed model conforms to the impact factor of the actual motor speed, which indicates that describing the frequency conversion speed adjusting system of an AC motor using the knowledge model of fuzzy systems is effective.
1 Foreword Fuzzy rules extracting from system input/output data will improve the objectivity effectively. There are always conflicting or inconsistent rules existing in the extracted initial fuzzy rules. And the incomplete data itself will result the inconsistency or incompleteness in the extracted rules as well as data noise. In order to solve these problem, this paper presents a method for the building of acknowledge model of fuzzy system [1-3]. First of all, this paper defined a knowledge model, and then defined the unitary relation and multiplex relation building on the knowledge model. This paper discussed the concept about categories and knowledge building on the knowledge model of fuzzy system yet, extended the most fundamental and important in rough set to knowledge model of fuzzy system, and gave the approximation in model and the measurement method [4-5]. After that, this paper discussed the consistency of knowledge models [6] of fuzzy system, present the concept about model consistency and the theorem about decomposition of maximum consistency. At last, taking the AC motor system as an example, on the basic of sampling data, with the method introduced in this paper, identified the structure of speed model, to verify the efficiency of the method raised in this paper. T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 299–306. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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2 Fuzzy System Model of Knowledge 2.1 Definitions for Model In the system S , which is constituted with n input points points
y1 ,
x1 ,
, x n and m output
, y m . After finite measurements (t) on the input/output of the system,
the input/output value sequence is got as the follows:
x11 , x 21 ,
x12 , x 22 ,
, x1n , , x 2n ,
y11 , y 21 ,
y12 , y 22 ,
, ,
x t1 ,
x t2 ,
,
y t1 ,
y t2 ,
,
x tn ,
after the fuzzy process to the Input / output value
y1m ; ⎫ y 2m ; ⎪⎪ ⎬ ⎪ y tm . ⎪⎭
(1)
xij and y ik ( i=1 ,…, t , j=1 ,…,
n , k=1 ,…, m ), formula (1) changed to (2):
~ A11 , ~ A21 ,
~ A12 , ~ A22 ,
, ,
~ A1n , ~ A2 n ,
~ B11 , ~ B11 ,
~ B12 , ~ B22 ,
~ At1 ,
~ At 2 ,
,
~ Atn ,
~ Bt1 ,
~ Bt 2 ,
~ , B1m ; ⎫ ⎪ ~ , B2 m ; ⎪ ⎬ ⎪ ~ , Btm . ⎪⎭
(2)
The formal definition of fuzzy system knowledge model can be given below: Definition 1. Let S be a fuzzy control system, a knowledge model KM for is defined as the following six member group KM L, X , Y , V X , VY , f
=〈
={ l ,…, l } is the label set of KM ; X = {x ,
〉
S
, x n } is the ~ input space of S ; Y = {y1 , , y m } is the output space of S ; V X { A1 ~ ~ ~ … AN }is the value space of input space X ; VY { B1 … BM }is the value space of output space Y ; f is the mutator from input/output space to their In which: L
,
1
1
t
= , ,
= ,
value space, also
f : L × ( X ∪ Y ) → (U X ∪ U Y )
=〈
(3)
〉
S be a fuzzy system, KM L, X , Y , V X ,VY , f is a knowledge model for S , Z = X ∪ Y , ϕ ≠ W ⊆ Z . For any z ∈ Z , specify Definition 2. Let
Knowledge Model Building about a Motor Speed Regulation Fuzzy Control System
zˆ
={< l , l i
>
j
li
、l
j
∈ L ∧ f (l i , z ) = f (l j , z ) }
301
(4)
zˆ is referred to as unitary relation on label set L among KM .
=〈
〉
L, X , Y , V X , VY , f be a knowledge model of fuzzy system, Z = X ∪ Y , ϕ ≠ W ⊆ Z . Specify that IND (W ) ∩{ zˆ z ∈ W } IND(W ) is referred to as multiplex relation about W on Label set L among KM . Definition 3. Let
KM
=
2.2 Categories and Knowledge in the Model
=〈
〉
KM L, X , Y , V X , VY , f be a knowledge model of fuzzy System, Z = X ∪ Y , ϕ ≠ W ⊆ Z , any equivalence class of L dependent zˆ is referred to as a single-point category in KM . Quotient set L zˆ is referred to as a single-point knowledge. For any l ∈ L , [l ] IND (W ) is referred to as a category about W in KM , and L IND (W ) is referred to as a knowledge Definition 4. Let
/
/
about
W in KM . Apparently: [l i ] IND (W )
= ∩[l ] z∈W
i Zˆ
.
2.3 The Approximation and Measurement to Category in Model
=〈
KM L, X , Y , V X , VY , f be a knowledge model of fuzzy System, Z = X ∪ Y , ϕ ≠ W ⊆ Z , if C is an union of some category about IND (W ) on L , then C is definable with IND (W ) , otherwise, C is indefinable with IND (W ) , or C can also be referred to as fuzzy about IND(W ) . There are two sets, LOW IND (W ) (C ) and UPIND (W ) (C ) , are defined Definition 5. Let
as bellows:
LOW IND (W ) (C ) UPIND (W ) (C )
={ l
={ l
l ∈ L ∧ [l ] IND (W ) ⊆ C },
(5)
l ∈ L ∧ [l ] IND (W ) ∩ C ≠ φ },
(6)
LOW IND (W ) (C ) is referred to as the lower approximation of IND(W ) of C ; UPIND (W ) (C ) is referred to as the upper approximation of IND(W ) of C .
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Therefore,
C is definable with IND(W ) , if and only if LOW IND (W ) (C )
=
UPIND (W ) (C ) .
=〈
〉
KM L, X , Y , V X , VY , f be a knowledge model of fuzzy System, Z = X ∪ Y , ϕ ≠ C ⊆ L , ϕ ≠ W ⊆ Z . The degree of definiteness of C about IND (W ) - was written as α IND (W ) (C ) , and a Equation is Definition 6. Let
defined as bellow:
α IND (W ) (C ) =
Card LOWIND (W ) (C )
(7)
Card UPIND (W ) (C )
2.4 Approximation and Measurement of Knowledge in the Model
=〈
〉 /
KM L, X , Y , V X , VY , f be a knowledge model of fuzzy System, Z = X ∪ Y , ϕ ≠ W ⊆ Z , L IND(Y ) { C1 , , C k }. The lower approximation of IND (W ) - of knowledge L IND (Y ) in KM is Definition 7. Let
defined as bellow:
= ∪ ( LOW
/
=
k
LOWIND (W ) ( L / IND(Y ))
IND (W )
(C i ) )
(8)
i =1
The upper approximation of defined as below:
IND(W ) - of knowledge L
= ∪ ( UP
/ IND(Y ) in KM
is
k
LOWIND (W ) ( L / IND(Y ))
IND (W )
(C i ) )
(9)
i =1
The degree of definiteness of id defined as below:
IND(W ) -of knowledge L
/ IND(Y ) in KM
k
α IND (W ) ( L / IND(Y ) =
∑ Card LOW i =1 k
(C i ) (10)
∑ Card UP i =1
IND (W )
IND (W )
(C i )
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3 The Consistency of the Model and the Decomposition of Maximum Consistency
=〈
〉
KM L, X , Y , V X , VY , f be a knowledge model of fuzzy System S , Z = X ∪ Y . for any l i ∈ L , L is label set, the symbol Definition 8. Let
f li means the sequence as below:
f (li , x1 ),
, f (li , x n ), f (li , y1 ),
, f (li , y m )
(11)
f li is referred to as the number l i rule. Symbol ( f li ↑ X ) and ( f li ↑ Y ) indicate the sequence as below:
f (l i , x1 ),
, f (li , x n ) and f (li , y1 ),
, f (li , y m )
(12)
And they are separately referred to as antecedent and consequent of rule (
=
=
li . If
f li ↑ X ) ( fl j ↑ X ) will ( f li ↑ Y ) ( f l j ↑ Y ), then the rule li is consis-
tent. If all the rules in KM are consistent, that means KM is consistent. It can be proved that if KM is consistent, then α IND ( X ) ( L / IND (Y ) = 1 .
=〈
〉
KM L, X , Y , V X ,VY , f be a knowledge model of fuzzy System S , L = L1 ∪ L0 . KM can be decomposed into two sub-model Propositions 1. Let
=〈 L , X , Y ,V
〉
=〈
〉
, VY , f , KM 0 L0 , X , Y , V X ,VY , f . So that α IND ( X ) ( L / IND (Y ) = 1 in KM 1 , and α IND ( X ) ( L / IND (Y ) = 0 in uniquely: KM 1
1
X
KM 0 . Theorem 1. (Maximal uniformity decomposition theorem) Let
〈 L, X , Y , V
=
KM
〉 be a random knowledge model of fuzzy System S . There must be a maximal sub-set L , it causes that the sub-model KM = 〈 L , X , Y ,V ,V , f 〉is uniformity. X
, VY , f
1
1
X
1
Y
4 The Building of Knowledge Model of AC Motor Speed Adjustment Fuzzy Control System During this study, we will verify the validity of algorithm by way of 300 group data acquired from AC motor speed adjustment system. The parameters are tabled as below:
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H. Zhao et al. Table 1. Parameters and Revolution (partial)
x1
x2
x3
x4
x5
x6
x7
y
0 4 12 … 44
2.52 2.27 2.45 … 3.73
2.51 2.85 2.52 … 3.08
0.98 1.37 1.43 … 3.28
2.52 2.5 2.5 … 2.98
2.5 2.52 2.33 … 3.78
1 1.46 1.87 … 1.64
1.01 0.99 1.54 … 1.84
Let the Form of the model of AC motor speed adjustment fuzzy control system be KM L, X , Y , V X , VY , f , input space is
=〈
〉
X = {x1 , x 2 , x3 , x 4 , x5 , x6 , x 7 } , output space is Y = {y} , in which x1 is frequency, x2 is the Instantaneous value of current 1, x3 is the Instantaneous value of current 2, x4 is the RMS current, x5 is the Instantaneous value of voltage 1, x6 is the Instantaneous value of voltage 2, x7 is the RMS voltage, y is the valid values of revolution. After the fuzzy processing to the acquired data, we got the table 2. Table 2. Fuzzy data table of input-output
No.
x1
x2
x3
x4
x5
x6
x7
y
1
~ A1
~ B4
~ B4
~ B2
~ C4
~ C4
~ C2
~ D3
…
…
…
…
…
…
…
…
…
300
~ A5
~ B5
~ B4
~ B4
~ C4
~ C5
~ C3
~ D5
With the foregoing method, by way of maximum consistency treatment and simplification to the fuzzy model get from table 2, the new consistency model can be obtained as table 3. Table 3. Initial consistent fuzzy model
L 1 … 92
y
x1 ~ A1
x2 ~ B4
x3 ~ B4
x4 ~ B2
x5 ~ C4
x6 ~ C4
x7 ~ C2
~ D3
…
…
…
…
…
…
…
…
~ A5
~ B3
~ B4
~ B4
~ C3
~ C5
~ C3
~ D5
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By way of the calculating of the approximation degree of knowledge of output space from input space, the effect degree of every input variant to output can be quantitative analyzed. First, we calculate the number of elements in the 8 multiplex relations IND( X ) , IND( X − {x1 }), , IND ( X − {x7 }) in the initial consistency model correspond to the lower approximation of result is shown in table 4.
L / IND(Y ) , the
Table 4. The statistical table of elements
No. of multiplex relation Number of elements
1
2
3
4
5
6
7
8
92
56
80
79
61
73
81
63
By means of the definition of consistency, the consistency point removed can be acquired as table 5.
α
Table 5. The table of approximate degree after removing
Symbol variant
of
Number of elements
with each input
xi
x1
x2
x3
x4
x5
x6
x7
0.61
0.87
0.86
0.66
0.79
0.88
0.68
From the result we can find that frequency, current RMS, voltage RMS have the most effective impact on the parameter speed. That proves that the identified structure of speed model have an agreement with the influential factor of motor speed in real system.
5 Conclusions This paper described a method in building a fuzzy system knowledge model. The definitions of the model, consistency of the model and the completeness, and the theorem about decomposition of maximum consistency and so on were also given. With the proposed method, the parameter speed of an AC Motor is identified, and the result shows that the method in building the model is effective. Acknowledgement. This paper is supported by National Natural Science Foundation of China (Grant NO. 60870009) and Innovative Teem Programs Foundation of Dalian Jiaotong University, China.
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References [1] Setnes, M.: Supervised fuzzy clustering for rule extraction. IEEE Trans. on Fuzzy Systems 8(4), 416–424 (2000) [2] Asharafa, S., Murty, M.N.: A rough fuzzy approach to web usage categorization. Fuzzy Sets and Systems 148(1), 119–129 (2004) [3] Çelikyılmaz, A., Türkşen, I.B.: Evolution of fuzzy system models: An overview and new directions. In: An, A., Stefanowski, J., Ramanna, S., Butz, C.J., Pedrycz, W., Wang, G. (eds.) RSFDGrC 2007. LNCS (LNAI), vol. 4482, pp. 119–126. Springer, Heidelberg (2007) [4] Pawlak, Z.: Rough sets. International Journal of Computer and Information Science 11(5), 341–356 (1982) [5] Li, D., Zhang, B., Yee, L.: On knowledge reduction in inconsistent decision information systems. Int’f Journal of Uncertainty. Fuzziness and Knowledge-Based Systems 12(5), 651–672 (2004) [6] Li, W.: Knowledge Model of Fuzzy System and Identification Approach, pp. 16–32. Harbin Institute of Technology (1999)
Research on Surface Recover of Aluminum Alloy PGTAW Pool Based on SFS Laiping Li1, Xueqin Yang1, Fengyan Zhang1, and Tao Lin2 1
Shanghai Spaceflight Precision Machinery Research Institute, Shanghai, 201600 2 Shanghai Jiaotong University, Shanghai, 200240 e-mail:
[email protected]
Abstract. Robot and intelligence is the tendency of welding technology. The key to intelligence is, in real-time, to acquire welding pool formation and adjust welding technology to assure the formation uniform. Shape from shading is the effective method to acquire the 3D formation of the welding pool. The reflectance map model of aluminum alloy pulse GTAW welding pool surface, resolution of reflectance map equation and calculation of the welding pool surface height is introduced. The calculation result can testify the characteristics of the welding pool.
1 Introduction The development of modern argon tungsten arc welding technology promotes the application of aluminum alloy welding construction. The conditional manual argon tungsten arc welding technology does not satisfy the high performance product, therefore, the automation and intelligence is the tendency of welding technology [1]. The shape and size of welding pool can indicate the penetration and backside formation. The skilled welder can observe the topside welding pool characteristic parameters, joint format, arc shape and droplet to forecast backside form and size and adjust the welding technology to assure welding quality stabilization. In general, it plays an important role in welding process control to acquire the shape information of welding pool. The passive visual sensing, which can acquire image information of welding zone by arc lighting or natural lighting without auxiliary light source, has widespread application foreground. The shape from shading method is to acquire the object shape information by image gray change [2]. The key to SFS is reflectance map model and reflectance map equation resolution. It needs single image to acquire the height and shape information by SFS, which can be satisfied in the welding pool visual sensing, thus the SFS method is a prospect method to recover welding pool surface. The reflectance map model of aluminum alloy pulse GTAW pool surface, preconditioning cognate gradient method of reflectance map equation, and surface height calculation of welding pool is introduced. T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 307–314. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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2 Welding Pool Image of Aluminum Alloy Pulsed GTAW Fig 1 is the topside welding pool image of aluminum alloy by the broad-pass visual sensing under the condition of table 1.
a) Concave welding pool
b) Convex welding pool
Fig. 1. Typical image of welding pool of aluminum alloy Table 1. Welding technology and imaging parameters of aluminum alloy pulsed GTAW Material
Pulse frequency
Tungsten diameter
Arc height
2A14T62 Size 400*150*2mm shuttle 1/1000s iris
1 Hz Welding velocity 16 cm/min Lens focus 10 mm Broad band filter pass 25%
2.0 mm Pulse current 120 A band filter wave length 590~710nm Lens diameter
5 mm Base current 30 A Imaging period 50 s Neutral filter pass
1/3 inch
10%
5
3 Reflectance Map Model of Welding Pool Surface 3.1 Spherical Light Source Model of Aluminum Alloy PGTAW The reflectance map equation setup the relation between reflectance map model and image gray. The key to SFS method is how to calculate the object surface height z from image gray under certain condition. The nearby point light source reflectance map model proposed by Lee and Kuo is based on the following assumption, such as point light source, hybrid reflectance surface, aperture imaging [3]. The imaging light source in the passive visual sensing is welding arc, whose principle is gas discharge. The light source characteristics of welding arc during GTAW is affected by material, electrode butt shape, welding parameters, arc height [4]. The incident light density of object surface point p of aluminum alloy GTAW is decided as following [5].
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(1) Where
C ( I c , λ , r , rs ) is spherical light source function,
(2)
3.2 Object Surface Reflectance Model The aluminum alloy pulsed GTAW surface reflectance is the common result of ideal diffused reflection, regular reflection, mutual reflection and ambient light.
(3)
f d (λ ) is diffused reflectance function, f s (η , λ , i, n, v, h) is regular reflectance function, f int er is mutual reflectance function, β d is diffused reflecWhere
tance factor,
βs
is regular reflectance factor,
β int er
is mutual reflectance factor,
F (i, h,η ) is Frenel item, G (i, n, v, h) is geometric attenuation factor, D(n, h, m) is micro plane normal distribution function.
3.3 Camera Characteristics The reflectance map model of aluminum alloy pulsed GTAW is as follows. (4)
Where R is the model function, d is lens diameter, f is lens focus, n z is the normal direction of x-o-y plane, v is the camera direction, g is the gain factor of CCD camera, b is CCD camera constant. Table 2 is the fixed parameters in pulse GTAW molten pool surface reflectance map.
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target size (mm × mm)
Gain factor
Constant
Image max size (pixel ×pixel)
Image size (pixel ×pixel)
7.95×6.45 Smooth factor
0.45 Initial weighted factor
0.9 Triangle length (pixel)
811×508 Initial criterion
128×128 Termination iteration criterion
0.3 x-o-y plane normal
1.0 shuttle
0.01 Welding symmetry
(0,0,1)
5
4 Arc attenuation factor 0.3
Frenel item
welding pool diffused reflectance function 95% welding pool mutual reflectance function 95%
welding pool regular reflectance factor 0.3 Seam diffused reflectance factor 1
0.4~0.5 welding pool mutual reflectance factor 0.2
iteration
pool
10-6 Neural filter luminousness
Axial symmetry
3.125%
welding pool roughness factor
welding pool diffused reflectance factor 0.7 Seam mutual reflectance factor
2.0 Seam regular reflectance factor
0
0
According to spectrum of aluminum, the characteristic spectrum is from 320 to 580nm and from 700nm to 800nm, the continuous spectrum from 580nm to 700nm.The range of aluminum alloy pulsed GTAW process broad band filter is from 580nm to 710nm, while the peak luminousness is 25%. The luminous of neutral filter is 10% [5].
4 Resolution of Reflectance Map Equation The other key to shape from shading is resolution of reflectance map equation. The reflectance map model is continuous nonlinear function, so reflectance map model must be discrete and linear to solve equation. The many-to-one relation between surface shape and image gray causes ill-conditioning problem, which can not be solved by single reflectance map equation. The smoothing constraint condition is introduced in the regularization method to cancel reflectance map equation ill-conditioning character. The minimal value method, which is transferred to functional formed by reflectance map equation and constraint condition, is a typical method to solve reflectance map equation. Because the functional extreme problem can by solved by linear equations set, key to shape from shading problem is to solve linear equations set [6].
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4.1 Reflectance Map Model Discretization and Linearization The reflectance map model must be firstly discretized and linearized to reduce complexity and increase calculation velocity. According to finite element theory, the reflectance map model can be described as the function of triangle climax height
Z i , Z j and Z l . (5)
Where
Lk is discrete.
Thus, the linearization reflectance map equation by the Taylor series expansion follows.
(6)
4.2 Reflectance Map Equation Resolution The minimal value of functional is the resolution of reflectance map equation. The key to minimal value method is constraint condition and resolution of functional extreme. The conventional constraint condition is luminance constraint, smoothness constraint, integral constraint, image gray gradient constraint, unit vector constraint, and so on. The functional extreme problem equals to resolution of linear equations set, thus the shape from shading problem is equivalent to resolution of linear equations set. The regularization method is effective to solve ill conditioning shape from shading problem and to increase robust, where the constraint condition is added to error cost function to improve the ill condition characteristics. The random smoothness surface comes from weighted sum of the thin plate and thin film model. (7)
es1 is thin plate constraint, es 2 is thin film constraint, Z is nodal height vector, B is the random smoothness constraint matrix, B = (1 − μ ) B1 + μB2 , B1
Where
is the match plate of thin plate smoothness matrix, B2 is the match plate of thin film smoothness matrix, μ is smoothness constraint factor, 0 ≤ μ ≤ 1 . When μ is zero, it means thin plate smoothness constraint and the
C 2 continuous curve
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surface. When μ equals to 1, it means thin film constraint and C curve continuous surface the following is new cost function combined luminance constraint and smoothness constraint.
(8)
Where
A = Ab + λ s B , Ab is symmetric stiffness matrix, b is load vector., λ s
is smoothness factor,
0 ≤ λs ≤ 1 . (9)
The precision and real-time of shape from shading method is decided by linear equations set, which is only resolved by numerical method. The preconditioning conjugate gradient method is an effective method to resolve the large sparse linear equations set. In shape from shading algorithm, the known characteristic condition, which is section absolute height, object edge in image and corresponding height, object surface symmetry, and so on, can improve the calculation precision.
5 Surface Height Calculation of Aluminum Alloy GTAW Welding Pool The experiential knowledge of welding pool, which is symmetric welding pool, zero edge, positive convex welding pool height, negative concave welding pool height, can improve the calculation result. Fig 2 and Fig 3 is the calculation result and the cross section value with vertical and parallel direction of concave and convex welding pool. The result shows that the calculation value of welding pool can display the characteristics of concave welding pool and convex welding pool.
a) Calculation result of concave welding pool b) Calculation of convex welding pool Fig. 2. Calculation result of aluminum alloy welding pool
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a) Section height along welding direction of concave welding pool b) Section height perpendicular to welding direction of concave welding pool c) Section height along welding direction of convex welding pool d) Section height perpendicular to welding direction of convex welding pool Fig. 3. Section height of aluminum alloy welding pool
6 Conclusions The reflectance map model of aluminum alloy pulsed GTAW welding pool is set up based on spherical light source model and hybrid reflectance model of a welding pool. The triangle plate element is to discrete and the Taylor series is to linearize the reflectance map model. The smoothness constraint condition is introduced to reduce the ill conditioning and preconditioning conjugate gradient to resolve the reflectance map equation. The height of convex welding pool and concave welding pool can be calculated and shows the shape characteristics. It is necessary for the shape from shading method to further improve. Acknowledgement. This work is supported by the Natural Science Foundation of Shanghai under Grand No. 08ZR1409500 and the Shanghai Sciences & Technology Committee under Grand QB1401500.
References [1] Chen, S.B., Lou, Y.J., Wu, L.: Intelligent Methodology for Sensing, Modeling and Control of Pulsed GTAW, Part I —Bead-on-plate Welding. Welding Journal 79(6), 151–163 (2000) [2] Horn, B.P.K.: Height and Gradient from Shading. International Journal of Computer Vision, 37–75 (1989)
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[3] Lee, K.M., Kuo, C.C.J.: Shape from Shading with a Linear Triangular Element Surface Model. IEEE Trans. Pattern Analysis and Machine Intelligence 15(8), 815–822 (1993) [4] Zhao, D.B., Chen, S.B., Wu, L.: Surface height and geometry parameters for describing shape of weld pool during pulsed GTAW. In: Proceedings of SPIE – The International Society for Optical Engineering, vol. 3833, pp. 91–98 (1999) [5] Li, L.P., Chen, S.B., Lin, T.: The Light Intensity Analysis of Passive Visual Sensing System in GTAW. The international Journal of Advanced Manufacturing Technology (2005) (SCI index source) [6] . Zhang R., Tsai P.S., Cryer J.E. etc., Shape from Shading: A Survey, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.21, No.8, 690-706, 1999.
Research on Fuzzy-Prediction-Control of GTAW Process Based on MLD Modeling Mingyan Ding1,2, Hongbo Ma1, and Shanben Chen1 1
Institute of Welding Engineering, Shanghai Jiao Tong University, Shanghai 200240, P.R. China 2 Electronic Information School, Dalian Jiaotong University, Dalian, 116028, P.R. China
Abstract. Aiming at hybrid property of GTAW nonlinear welding process, this paper introduces the modeling scheme of a hybrid system described by a MLD (Mixed Logical Dynamical). Regarding the MLD modeling as research methods, a MPC (Model-Prediction-Controller) is designed and the penalty matrix parameters of an FMPC (Fuzzy-Model-Prediction-Controller) are optimized by means of using FC (Fuzzy Control) idea. Simulation results demonstrate that the algorithm is effective and the scheme is able to stabilize the hybrid system on the desired output. Comparing to an MPC, the scheme of FMPC can increase the response of the control system and reduce the on-line computing of parameters. These studies can lay a foundation for the further research on MLD modeling and intelligence control in the welding process control system.
1 Introduction It is a new study that a MLD modeling idea is added to the welding process control system, linear models of a nonlinear system are integrated into a uniform framework. Modeling and control method based on MLD has many applications in chemical process and manufacturing process recently. For example, in Ref. [1], the authors made modeling based on MLD and optimization control in multiproduct batch processes. In Ref. [2], the paper discussed optimization control of hybrid systems with states and inputs constraints. In Ref. [3], it mainly researched on hybrid modeling and predictive control in a multi-tank system. To sum up, this idea can make full use of related knowledge of artificial intelligence, computational intelligence and applied logic etc. It is combined with information of welding industry process, so modeling and optimized control of the progress can be built up. In addition, the welding industry process has always constraint conditions and nonlinear properties. In reference [4], it indicated that the welding industry process could be approximated by linear models, so FMPC algorithm can be applied on the welding nonlinear system based on MLD modeling. T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 315–322. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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2 System Modeling The experiment system was shown as Fig 1. GTAW (Gas Tungsten Arc Welding) system was constituted by sensor system, welder equipment, signal conditioning and so on. It is necessary that front welding pool width W f and front welding pool length
L f of welding pool image are acquired. Back welding pool
width Wb based on MLD modeling is on-line calculated. This paper will primarily realize auto-detection and adjusting of welding pulse peak current I p or wire feed speed
V f through MFC algorithm.
Fig. 1. The actual picture of the GTAW system
2.1 MLD Model In reference [5], Bemporad and Morari proposed a modeling based on MLD and hybrid nature of the system was described by the modeling. Basic equation of the MLD modeling can be expressed as follows:
⎧ x(t + 1) = At x(t ) + B1t u (t ) + B2tδ (t ) + B3t z (t ) ⎪ ⎨ y (t ) = Ct x (t ) + D1t u (t ) + D2tδ (t ) + D3t z (t ) ⎪ E δ (t ) + E z (t ) ≤ E u (t ) + E x(t ) + E ⎩ 2t 3t 1t 4t 5t Where t ∈ Z ,
⎡ xc ⎤ ⎡y ⎤ n p x(t ) = ⎢ ⎥ ∈ R nc × {0,1} l ; y (t ) = ⎢ c ⎥ ∈ R pc × {0,1} l ; x y ⎣ l⎦ ⎣ l⎦ ⎡uc ⎤ m u (t ) = ⎢ ⎥ ∈ R mc × {0,1} l u ⎣ l⎦
(1)
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x(t ) is the state of the system, y (t ) is the output vector. u (t ) is command input,
uc and binary commands ul . δ (t) ∈{0,1}r
collecting both continuous commands
l
and z (t ) ∈ R rc represent respectively auxiliary logical and continuous variables. Eq. (1) includes mixed integer inequalities and constraint conditions.
E1, E2, E3, E4, E5 , each matrix has proper dimensions.
2.2 MLD Modeling on the GTAW Welding System The GTAW welding process control is related with continuous decision variables and discrete decision variables, that is to say, the system has properties of hybrid system. This paper makes W f and L f of the welding pool as continuous state variables, at the same time, I p or V f can be represented as continuous input variable. When back welding pool width Wb exceeding threshold, it will arouse logic input signal process.
δ ; Wb is made as continuous output variable for the controlling
First of all, the PWA (Piece-Wise-Affine) modeling of back welding pool width Wb , deriving from reference [5], can be set up. ⎧ A1Wb (t ) + B1 I p (t ) + F1W f (t ) + G1 L f (t )if δ 1 (t ) = 1 ⎪ Wb ( t + 1) = ⎨# ⎪ A W (t ) + B I (t ) + F W (t ) + G L (t )if δ (t ) = 1 n p n f n f n ⎩ n b
(2)
While δ i (t ) ∈ {0,1}, ∀ i = 1, " n are 0-1 variables satisfying the exclusive-or n
condition ⊕[δ i (t ) = 1] ; M = [M 1 , " M n ]' i =1
, m = [m , " m ] ; '
1
n
M j Δ max{max Ai jWb + Bi j I p + Fi jW f + Gi j L f } m j Δ min{max Ai jWb + Bi j I p + Fi jW f + Gi j L f } i =1,"n
i =1,"n
~
Eq. (3 5) can be expressed as MLD modeling, n
Wb (t + 1) = ∑ z i (t )
(3)
i =1
Δ
] z i (t ) = [ AW i b (t ) + Bi I p (t ) + FW i f (t ) + Gi L f (t ) ⋅ δ i (t )
(4)
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⎧ zi (t ) ≤ Mδ i (t ) ⎪ z (t ) ≥ mδ (t ) i s.t. ⎪ i ⎨ z (t ) ≤ A W (t ) + B I (t ) + F W (t ) + G L (t ) − m(1 − δ (t )) i b i p i f i f i ⎪ i ⎪ zi (t ) ≥ AiWb (t ) + Bi I p (t ) + FiW f (t ) + Gi L f (t ) − M (1 − δ i (t )) ⎩
(5)
Then the modeling structure is identified by least squares method and regression analysis. Datum of the MLD modeling were derived from reference [6], the PWA turn into the modeling based on MLD through HYSDEL language [7], namely, the modeling as Eq. (1) is set up. This part is not going to dwell upon it in detail due to limited space.
3 MPC Predictive control based on model makes MLD model as predictive model [11]. This part mainly considers the problem of optimized control and adopts MPC strategy, which possesses good robustness and has lower the need of model precision than PID strategy.
3.1 Predictive Control of MLD Systems Δ T −1
min J (I Tp−1, Lf (t),Wf (t))=∑ I p (k) − I pe T −1
{I p }
+ Lf (k | t) − Lfe
k=0
2 Q4
+ Wf (k | t) −Wfe
2 Q5
2 Q1
+ δ (k | t) −δe
2 Q2
+ z(k | t) − ze
2 Q3
(6)
+ Wb (k | t) −Wbe Q 2
6
⎧ L f (T | t ) = L fe ⎪ ⎪W f (T | t ) = W fe ⎪ L (k + 1 | t ) = A L (k | t ) + B I (k ) + B δ (k | t ) + B z (k | t ) 1 f 1 p 2 3 ⎪ f ~ ~ ~ ~ ⎪ ⎪W f ( k + 1 | t ) = A1 W f ( k | t ) + B 1 I p ( k ) + B 2 δ ( k | t ) + B 3 z ( k | t ) ⎪⎪W ( k | t ) = C L ( k | t ) + C W ( k | t ) + D I ( k ) + D δ ( k | t ) + D z ( k | t ) 1 f 2 f 1 p 2 3 ⎨ b ⎪ E 2 δ ( k | t ) + E 3 z ( k | t ) ≤ E 1 I p ( k ) + E 4W f ( k | t ) + E 5 L f ( k | t ) + E 6 ⎪ ⎪ I p min ( k ) ≤ I p ( k ) ≤ I p max ( k ) ⎪W ( k ) ≤ W b ( k ) ≤ W b max ( k ) ⎪ b min ⎪W f min ( k ) ≤ W f ( k ) ≤ W f max ( k ) ⎪ ⎪⎩ L f min ( k ) ≤ L f ( k ) ≤ L f max ( k )
(7)
where Q1 = Q1' > 0, Q2 = Q2' ≥ 0, Q3 = Q3' ≥ 0, Q4 = Q4' > 0, Q5 = Q5' > 0, Q6 = Q6' ≥ 0, Δ
L f (k | t ) = L f (t + k , L f (t ), I pk −1 )
(8)
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W f (k | t ), δ (k | t ), z (k | t ),Wb (k | t ) are similarly defined.
~
Eq. (6 8), deriving from reference [5], are shown as optimal control problem of predictive control based on MLD modeling systems above, let t be the current time. At every time t , the controller calculates a sequence of command inputs by means of on-line optimization procedure, accordingly, the scheme is able to stabilize the hybrid system on the desired output. At time t , only the first command input of the optimal sequence is actually applied to the system, and at time t + 1 , a previous sequence is substituted by a new sequence, so this way of receding horizon has not only the feature of feedback control, but also better conditions for developing intellectualized method of varying-domain optimization [8-9].
3.2 Fuzzy -MLD-PC As described above, the core of predictive control is receding horizon. So the whole algorithm can be regarded as the problem of performance optimization finally. Traditional MPC always adopted optimization methods which were linear and quadratic objective function. It could calculate optimal control law by means of minimizing objective function in control domain. Aiming to the welding control system, traditional method of MPC has bigger calculation. However,in some ways, fuzzy control can make output variable approaching optimal control law by means of selecting a set of parameters Qi ( i = 1,"5 ), etc. So this paper proposes a scheme on combining fuzzy control and MPC, which can absorb both advantages. It utilizes fuzzy control query table where parameters Qi have been off-line calculated, reducing PC-MLD calculation on on-line optimization.
Fig. 2. Structure of MLD-FPC
4 Stability of MLD Systems This paper mainly discusses constraint conditions of the single variable. In order to make sure of stability of MPC based on the MLD system and force terminal state to
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return to the balance point, Eq. (7) is added terminal equation constraints in the open-loop optimal control problem. In reference [10], it was shown that stability on MPC of terminal equation constraints based on Lyapunov theory is effective.
5 Simulation Based on Fig 3, the control scheme is consisted of two layers: parameters optimization layer and basic control layer. Control steps are as follows: (1). Parameters optimization layer: FC(off-line fuzzy query table of penalty matrix parameters Qi ) 1) At time t , given and feed-back value of back welding pool width Wb are denoted by Wbr (t ),Wbf (t ) . Eq.(9-5.2) are defined as error and error rate.
e(t ) = Wbr (t ) − Wbf (t ) ; ec(t ) = e(t ) − e(t − 1)
(9)
2) Then the error and error rate are normalized, setting universe e , ec as [-1,1]. 3) Fuzzification of variable e , ec . 4) Based on fuzzy rules (eg. if E=PM or PB and EC=PM or PS, then ΔQ1 = NB; ΔQ2 = ΔQ3 = ZE ; ΔQ4 = ΔQ5 = NB; ΔQ6 = ZE ), the fuzzy output Qi (k + 1) can be
offline queried by means of fuzzy rules table. With defuzzification, the precise output Qi (k + 1) are transmitted to optimization controller. (2) Basic control layer: Simulation parameters based on MPC: M=1(time-domain control), P=1(timedomain prediction), N=20(time-domain modeling). Region1: The given of back welding pool width Wbr (k ) as 4mm(region2: The given of back welding pool width Wbr (k ) as 6mm); Parameters Qi ( k + 1) are transmitted to Eq. (6), the optimal sequence I p (k ) can be deduced based on MIQP [12], and then the first command of the optimal sequence is applied to the system at t time. Based on receding horizon principle, the controller repeats the above steps.
Fig. 3. Simulation figure of FMPC and MPC
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In Fig. 5.1., welding pulse peak current I p creates a step signal transforming from 120A to126A (from region1 to region2), control method is MPC in region2. From this part, control effect of MPC has tiny overshooting number; Control method is FMPC in region1. Control effect of FMPC is shown that it has not only merit of the MPC but also better rapidity. Calculation of the penalty matrix parameters Qi gets simpler by means of off-line queried. From region2, the method of MPC has lower rapidity.
6 Conclusions This paper discusses the modeling and control of nonlinear welding system based on an FMPC. Through the above analysis, it can be seen that the FMPC has minimal overshoot, fast response and good stability. Contrasting the calculation of online optimization between FMPC and MPC, the FMPC can reduce it effectively. At the same time, the control characteristic is better for FMPC. Acknowledgement. This work is supported by the National Natural Science Foundation of China under the Grant No. 60874026, and Shanghai Sciences & Technology Committee under Grant No. 09JC1407100, P.R. China.
References [1] Potocnik, B., Bemporad, A., Torrisi, F.D., et al.: Hybrid modeling and optimal control of a multiproduct batch plant. Control Engineering Practice 12, 1127–1137 (2004) [2] Zhang, J., Li, P.: Optimal control of a class of hybrid systems with states and inputs constraints. Journal of Zhejiang University:Engineering Science 37(2), 139–143 (2003) (in Chinese) [3] Habibi, J., Moshiri, B., Sedigh, K.: Hybrid modeling an predictive control of a multitank system: a mixed logical dynamical approach. In: Proceedings of the 2005 International Conference on Computational Intelligence for Modeling, Control and Automation (2005) [4] Chen, S.B., Lou, Y.J., Wu, L., et al.: Intelligent methodology for sensing, modeling and control of pulsed GTAW. Part1: Bead-on-plate welding. Welding Journal 79(6), 151–163 (2000) [5] Bemporad, A., Morari, M.: Control of systems integrating logic, dynamics, and constraints. Automatica 35(3), 407–427 (1999) [6] Ma, H.: Research on mixed logical dynamical modeling method of robotic aluminium alloy pulsed TIG welding process based on vision sensing. Shanghai Jiao Tong Univsity, 96–102 (2010) [7] Torrisi, F.D., Bemporad, A.: HYSDEL-A Tool for generating computational hybrid models for analysis and synthesis problems. IEEE Transaction on Control System Technology(S1063-6536) 12(2), 235–249 (2004) [8] Clarke, D.W., Mohtadi, C., Tuffs, P.S.: Generalized predictive control, Part1 and Part2. Automatica 23(2), 137–160 (1987)
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[9] Abonyi, J., Nagy, L., Szeifert, F.: Fuzzy model-based predictive control by instantaneous linearization. Fuzzy Sets and Systerms 120, 109–122 (2001) [10] Michalska, H., Mayne, D.Q.: Robust receding horizon control of constrained nonlinear systems. IEEE Trans. on Automatic Control 38(11), 1623–1632 (1998) [11] Xi, Y.: Predictive Control. National Defence Industry Press, Beijing (1993) (in Chinese) [12] Lazimy, R.: Mixed-integer quadratic programming. Mathematical Programming 22, 332–349 (1982)
Application of Fuzzy Edge Detection in Weld Seam Tracking System Zhenyu Xiong1,2, Wen Wan2 , and Jiluan Pan1 1
Department of Mechanical Engineering, Tsinghua University, Beijing, 100084 2 School of Aeronautical Manufacturing Engineering, Nanchang Hangkong University, Nanchang, Jiangxi 330063 e-mail:
[email protected]
Abstract. Vision sensing is one of the most powerful non-contact sensing technologies used for the monitoring and automatic control of welding process. The effectiveness of image processing in vision sensing is the base for successful welding seam tracking. This paper introduces a welding seam tracking system which includes an arc welding robot, an image acquisition unit and an image processing system. An image processing algorithm based on fuzzy theory is presented in detail. Experimental results show that this method can properly detect the edges of welding seams.
1 Introduction With the rapid development of modern automation and artificial intelligence technologies, their application in welding has become a hot research topic. The technology of automatic weld seam tracking using visual sensor has drawn particular attention due to its effectiveness. Currently most weld seam tracking uses a laser as the initiative lamp-house because a laser can provide much energy and high luminance to the surface of workpieces with distortion (Ref. 1). It can distinguish the center position and characteristic information of free-formed weld seams after the images captured by a CCD camera are processed. Edge detecting is an important step in the image processing of vision weld seam tracking. There are some traditional methods for edge detection such as derivative operator, grads operator, Laplace operator and so on (Ref. 2). Some common edge detection technology are summarized and compared in references 3 and 4. Nowadays, technologies using fuzzy logic in image edge detection such as HMF, SRB, FC(Ref. 6), FIRE operator(Ref. 7), minimum fuzzy entropy criterion (Ref. 8) etc., are also in application. This paper develops a feasible method for edge detection and specially studies how to process the images that are captured by a CCD camera. The developed method is then tested in experiments. T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 323–330. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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2 Weld Seam Tracking System Weld seam image acquisition and processing is the most critical part of a visionbased weld seam tracking system. The schematic setup of the system is shown in fig. 1. After the CCD camera captures "—" form structure light sent out by the laser, it transmits the video signal to the image processing board. The DSP image processing board transforms the analog video signal into digital signal, and in the mean time performs image processing using a fuzzy logic and calculates the median deviation of weld torch and weld seam. The images acquired by the CCD camera and processed by the DSP board are displayed on the monitor in real time. Once the weld seam deviation is obtained, it is possible to calculate the variable to be controlled by applying the control rule. The control variable is then transmitted to the robot through the DSP board. Monitor
DSP Board
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Control cabinet
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Fig. 1. Arrangement of Weld seam tracking system
3 Edge Detection Principle 3.1 Edge Models Take a 3×3 window (as shown in fig. 2) from an M lines by N rows digital image as the analysis unit. Assume the pixel that needs to be processed is X, and the eight pixels adjacent to X are respectively Xi (i=1~8).
Application of Fuzzy Edge Detection in Weld Seam Tracking System
Suppose the direction of boundary line can only be one of the following: perpendicular edge, horizontal edge, 45 angle edge & negative 45angle edge. In the 3×3 window, an image points will have a sudden value change along the two opposite direction of boundary line. The patterns are graphically represented in fig. 3. (Pixels marked as Xs are boundary points).
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3.2 Fuzzification of Edge Detection In edge detection, whether a current pixel is a boundary pixel or not can be determined by the sudden change of the gray levels of those pixels around it. In the 3x3 window, suppose the Pi expresses the gray level of pixel Xi (i=1~8), then the difference of gray level value between pixel Xi and central pixel X is: Pi= P Pi (i=1 8). In order to decide whether X is the boundary pixel, six Pi are needed. For example, to detect the sudden change of gray level in horizontal direction (fig. 3(a), fig. 3(b)), P1 P2 P3 P5 P6 P7 must be known. If a gradient sudden change exists in the horizontal upward direction, i.e, P1, P2, and P3 are very low, while P5, P6, and P7 are very high, or P1, P2, and P3 are very high while P5, P6, and P7 are very low, pixel X can be determined to be a boundary pixel. Suppose black pixels in a processed image denote possible uniform regions and bright pixels denote possible object contours. Let big & little member functions be expressed as medium positive (MP) & medium negative (MN) respectively in the membership function, and y is the output variable. An operator can be designed by the following fuzzy reasoning: if a pixel belongs to a border region then make it
~
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white (WH), otherwise make it black (BL). A group of fuzzy rules for the detection of edge could be expressed as follows:
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if( P1 is MP) and ( P2 is MP) and ( P3 is MP) and ( P5 is MN) and ( P6 is MN) and ( P7 is MN) then ( y, WH);
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if( P2 is MP) and ( P3 is MP) and ( P4 is MP) and ( P6 is MN) and ( P7 is MN) and ( P8 is MN) then ( y, WH);
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if( P6 is MP) and ( P7 is MP) and ( P8 is MP) and ( P2 is MN) and ( P3 is MN) and ( P4 is MN) then ( y, WH);
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if( P3 is MP) and ( P4 is MP) and ( P5 is MP) and ( P7 is MN) and ( P8 is MN) and ( P1 is MN) then ( y, WH);
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if( P7 is MP) and ( P8 is MP) and ( P1 is MP) and ( P3 is MN) and ( P4 is MN) and ( P5 is MN) then ( y, WH);
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if( P4 is MP) and ( P5 is MP) and ( P6 is MP) and ( P8 is MN) and ( P1 is MN) and ( P2 is MN) then ( y, WH);
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if( P8 is MP) and ( P1 is MP) and ( P2 is MP) and ( P4 is MN) and ( P5 is MN) and ( P6 is MN) then ( y, WH).
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Where and denotes a fuzzy aggregation, white (WH) is a singleton centered on 255. If no border region is detected, the following action is performed: Else (y, BL), where black (BL) is singleton centered on 0. Among them, membership function of input variable Pi is shown in fig. 4, and membership function of output variable is shown in fig. 5.
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3.3 Parameter Choice of Membership Functions It is inevitable for the image signal to be intervened by all kinds of noise during the image acquisition and transmission. When a fuzzy logic is used for image edge detecting, parameter choice of input variable membership functions will have an important effect on the resultant image. Fig. 6 shows the Original image. When the membership function MP and MZ of input variable have small values shown as fig. 7 , the resultant image is as fig. 8. It can be seen that the resultant image includes much noise and the edge can not be decided.
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Fig. 7. Membership function of input variable
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Fig. 8. Edge image by fuzzy inference
When the membership function MP and MZ of input variable have big values (shown as fig. 9) the resultant image is shown as fig. 10. It can be seen that the image dandify more particular and the edge is detected improperly. Membership function MP
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Fig. 9. Membership function of input variable
Fig. 10. Edge image by fuzzy inference
Based on plenty of tests, the values of membership functions MP and MZ shown in fig. 11 have been chosen. The resultant image is shown as fig. 12. From this image, it can be seen that most noise has been eliminated and the particular is reserved, and the edge is detected properly.
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Fig. 12. Edge image by fuzzy inference
4 Conclusions Fuzzy logic has played a more and more important role in handling indefinite questions. Edge detection of digital images is one example of such indefinite questions. Fuzzification of edge detection described in this paper has simplified the characteristics of the image features and made the edge visible. However, this technology takes more time to process than traditional methods. High speed DSP will be necessary for image acquisition and to achieve real time image processing. Acknowledgement. The authors wish to acknowledge the financial support given by The S&T plan projects of Jiangxi Provincial Education Department.
References [1] Shibata, N., Hirai, A., Takano, Y., et al.: Development of groove recognition algorithm with visual sensor. Welding Research Abroad 46(6), 9–17 (2000) [2] Castleman, K.R.: Digital image processing. Publishing House of Electronics Industry, Beijing (2002) (in Chinese) [3] Sun, H., Zhou, H.X., Li, Z.H.: Study on edge detection technique in image processing. Computer Development and Application 15(10), 7–9 (2002) (in Chinese)
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[4] Zhou, X.M., Lan, S., Xu, Y.: Comparison of the edge detection algorithms in image processing. Modern Electric Power 17(3), 65–69 (2000) (in Chinese) [5] Tizhoosh, H.R.: Fast fuzzy edge detection. In: Fuzzy Information Processing Society. Proceedings NAFIPS 2002 Annual Meeting of the North American, pp. 239–242 (2002) [6] Russo, F.: FIRE operators for image processing. Fuzzy Sets and Systems 103, 265–275 (1999) [7] Ei-Khamy, S.E., Ghaleb, L., Ei-Yamany, N.A.: Fuzzy edge detection with minimum fuzzy entropy criterion. In: Electra Technical Conference MELECON 2002. Mediterranean, pp. 498–503 (2002)
Part IV
Welding Technics and Automations
Application and Research of Arc Welding Automation in Korea Suck-Joo Na Department of Mechanical Engineering, KAIST 335 Gwahangno, Yuseonggu, Daejeon, 305-701 Korea e-mail:
[email protected]
Abstract. This paper covers the research activities carried out in recent years by ALPA laboratory of KAIST for automation of gas metal arc welding processes, and also some industrial applications of welding process automation in Korea. This presentation will show the basic research results of various automation targets such as rotating arc, magnetic arc oscillation, pipe welding and groove welding with acute groove angles. It will also show some examples of welding automation systems implemented in Korean industry such as ship building and automobile industries.
1 Introduction With automation of the welding process a wide variety of sensors are used to detect the weld joints, to track the weld seams, and to monitor the welding phenomena. In particular, tactile, vision, and arc sensors are widely used for automatic weld seam tracking [1]. Among these sensors, the arc sensor is utilized in measuring the voltage and current of welding arcs and coaxially tracking the seams and monitoring the weld quality [2]. Through-arc sensing is widely used for automatic seam tracking because of its many advantages such as the possibilities for real-time control, no auxiliary parts around the welding torch, no need for maintenance and low cost. When using the arc as a sensor, however, it is generally necessary to weave or rotate the welding torch to stimulate intentionally differences in torch height. With conventional repetitive oscillation method the upper limit of the oscillation frequency is about 4-5Hz owing to mechanical restraints. The arc rotation and electromagnetic arc oscillation method enable high-speed rotation or oscillation of the arc over several tens of Hz [3-4]. Since the 1980s, the active vision sensor, which utilizes a CCD camera, laser beam and computer, has been effectively applied in tracking the weld line. The active vision sensor, based on optical triangulation, has a relatively high resolution and is classified into two types according to the beam characteristics: projected sheet of beam or scanned point beam [5]. Although the vision sensor with projected sheet of light is largely influenced by arc noise and the preprocessing time of the image is relatively long, it is widely used in automatic arc welding because T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 333–339. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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it is cheaper and has a simpler structure than that of the scanning beam. Therefore, much research has been conducted on the application of this type vision sensor with structured light for welding automation [6-8].
2 Welding Research Trend in Korea The statistical data were analyzed from the research papers published in the Journal of Korean Welding and Joining Society for the time period from 2000 to 2008. For some examples, Fig. 1 shows that the field of welding processes is studied more intensively, when compared to the mechanics and metallurgy. In average, more than 50 research papers are published annually. And in Fig. 2, it is shown that the number of research papers has gradually decreased in past years, but has been more or less stabilized in recent years.
Fig. 1. Number of papers in various
Fig. 2. Number of papers in welding area fields of welding published by KWJS published by KWJS
3 Automation of GMAW Process with Controlled Arc A magnetic field externally applied to the welding arc deflects the arc by the electromagnetic force (Lorentz force) in the plane normal to field lines. If an alternating field is applied to a GMA arc, then the arc can be oscillated in the position normal to the direction of welding as shown in Figure 3, and this has been used to improve the weld quality. Exp.(raw data) Exp.(low-pass filtered data)
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Magnetic arc oscillation changes arc length, which periodically changes the welding voltage and current. An alternating parallel magnetic field causes the arc to oscillate in position normal to the direction of welding, which takes effect like a mechanical weaving. When the welding arc is positioned at the center of groove, the current waveform becomes symmetric. If the welding arc deflects from the groove center, the current waveform changes to the asymmetrical shape. The welding current signals are simulated and compared with experimental ones as shown in Fig. 4. The simulation and experimental results show that the current waveform changes to asymmetrical shape in the V groove welding with 2mm offset distance. The mechanism of arc rotation includes a hollow-shaft motor, an eccentric tip. The electrode wire is deflected circularly by an eccentric tip that is rotated by the hollow shaft of rotating motor. This mechanism can be installed inside the electrode nozzle and connected directly to a conventional torch system.
(a) Fillet welding with centered torch
(b) Fillet welding with offset torch
Fig. 5. Geometry of fillet welding with rotating torch and the current signals
The welding torch is thought to be at centered position if its rotation axis is positioned at the angle bisector of the fillet structure. Otherwise, the welding torch is thought to be offset. Figs. 5(a) and 5(b) show the cross-section of the fillet welding and their corresponding current waveforms with a centered or offset rotating torch, respectively. The current waveform shows the symmetry at left and right half cycle when the torch is at the centered position (Fig. 5(a)). If the welding torch is deviated from the angle bisector of the fillet joint, the current becomes asymmetric (Fig. 5(b)). The difference between the average current values of the welding current of the left and right half cycle (I left − I right ) shows a linear relationship with the offset distance as shown in Fig. 5(b) and can be used for the torch offset detection during seam tracking. In order to achieve the full penetration of a thick plate, the root gap and groove angle must be adequately selected in the process of butt welding. The deposited metal decreases and productivity increases when the groove angle becomes smaller, but the preceding of the weld pool results in weld defects such as incomplete penetration and a lack of fusion at the groove face. For this reason, a wide range of groove shapes is tailored to different welding conditions.
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(a) Joint with 45o
(b) Current for 3 mm
(c) Current for 8 mm root gap
Fig. 6. Pulsed GMAW with acute groove angles
Fig. 6 shows the current waveforms obtained during torch weaving with a root gap of 3 mm and 8 mm at a groove angle of 45°. When compared with the case of large root gap of Fig. 6(b), the welding current decreases and increases rather gradually for the case of short root gap, Fig. 6(a). This is probably due to the fact that the transferred droplets flow into the root from the groove face to fill in the root gap as the welding proceeds, especially for the case of short root gap. For the case of large root case, this effect disappears, and the rapid change of welding current appears, Fig. 6(b). This effect of root filling by molten droplets decreases the average current value on root face as the root gap increases.
4 Automation and Monitoring with Vision Sensor A vision sensor was adopted firstly for seam tracking, and secondly for weld defect monitoring in pipe welding. Seam tracking image process algorithm was composed of image acquisition, median filter, threshold, thinning, Hough transform and 3D calibration. Maximum error between real coordinate and calculated coordinate was 0.23mm by using the calibration matrix. Monitoring process was used to find out the weld defect and also used to reconstruct the 3D surface profile. The purpose of process monitoring is to find the weld detect from bead shape. By comparing the result from monitoring process with ISO 5817 criterion, it could be easily applied to find out the weld defects. Fig. 7 shows the result of the bead shape monitoring. It seems that the bead profile from monitoring is very similar to the experimental bead shape. Maximum error between two bead shapes is approximately 0.2mm. Monitoring process takes100~150ms for 1 frame, because it requires more time than seam tracking due to more complicated algorithms.
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(a) Excessive convexity: B, Excessive symmetry fillet weld: D, Excessive throat thickness: B
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(b) Excessive convexity: B, Excessive symmetry fillet weld: C, Excessive throat thickness: B
Fig. 7. Comparison of experimental bead shape with the result from vision sensor
Fig. 8(a) shows the schematic diagram of automatic seam tracking system with a vision sensor developed for the corrugated membrane of LNG tanks and shipping containers. The apparatus comprised a robot which had two rectilinear coordinates, a torch rotation mechanism, a vision sensor composed of a CCD camera, a diode laser of 690 nm wavelength and a narrow band pass filter, and a PC for processing the image data and driving the robot.
(a) Schematic diagram
(c) Separation angle of 20o
(b) Cross section of LNG tank
(d) Separation angle of 30o
Fig. 8. Automatic seam tracking system with a vision sensor for corrugated membrane
Fig. 8(b) shows a typical corrugation shape which is prevalent in construction of LNG tanks. The shape of LNG tank corrugation consists of two linear parts and five circular arcs, and can be divided into seven segments from S1 to S7. Each
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segment of LNG tank corrugations could be expressed as the function of some parameters such as coordinates of the start point of line segment (or coordinates of the center point of circular arc segment), the length of line segment (or the radius of circular arc segment) and the angle of tangential line at the start and end point of segments. Fig. 8(c) and (d) show the simulated results for corrugated membrane of LNG tanks at the weld line, when the camera and laser diode of the vision sensor are located symmetrically about the vertical axis and have different separation angles. It is shown that the data deficiency occurred mainly at the corrugation corner and flat part of the corrugated sheet. The result also shows that the total separation angle between camera and laser beam of vision sensor largely affected its measuring efficiency for the corrugated sheets of LNG tanks.
5 Welding Automation in Industry Some examples of welding automation in Korean heavy and automobile industry will be shown in the oral presentation.
6 Conclusions This paper firstly showed the welding research trend in Korea by statistically analyzing the research papers published in the Journal of Korean Welding and Joining Society. And then some research results of automation and monitoring of GMAW processes were analyzed, which were developed at ALPA laboratory of KAIST. To improve the weld quality, the controlled arc welding system such as high speed arc oscillation and rotation method was developed and applied for various targets. For that purpose two main topics are investigated. The first one is about the automatic seam tracking sensor developed with a specially devised mechanism. These controlled arc welding systems ensure more accurate seam tracking with increased sensitivity and responsiveness and can be implemented for steel and Al alloy welding. And the second one is the improvement of their bead characteristics. Pulsed GMAW process was investigated for the V-groove joint with acute groove angles to decrease the amount of weld deposit and consequently to increase the welding speed. It was found that the arc sensor model requires detailed information of bead formation, especially in the case of small groove angle. A successful application of laser vision sensor was also demonstrated for automatic seam tracking and process monitoring in pipe welding. Finally some examples of welding process automation in Korean ship building and automobile industry are demonstrated. Acknowledgement. Support by the Brain Korea 21 project is gratefully acknowledged.
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References [1] Nomura, H.: Sensors and Control Systems in Arc Welding, pp. 1–7. Champman &Hall, London (1994) [2] Cook, G.E., Andersen, K., Fernadez, K.R., Sheperd, M.E., Wells Jr, A.M.: Electrical arc sensing for robot positioning control. Robotic Arc Welding IFS Publication, pp. 181–216 (1987) [3] Kim, C.H., Na, S.J.: A study of an arc sensor model for gas metal arc welding with rotating arc - Part 1: dynamic simulation of wire melting. PIME Part B 215(B9), 1271–1279 (2001) [4] Kang, Y.H., Na, S.J.: A study of the modeling of magnetic arc deflection and dynamic analysis of arc sensor. Welding Journal 81(1), 8/s–13/s (2002) [5] Yoo, W.S., Na, S.J.: A study on the vision sensor using scanning beam for welding process automation. Trans. of KSME 20-A(3), 891–900 (1996) [6] Yu, J.-Y., Na, S.-J.: A study on vision sensors for automatic welding of height-varying weldment, Part 2: Applications. J. of Mechatronics 8, 21–36 (1998) [7] Agapakis, J.E., Katz, J.M., Koifman, M., Epstein, G.N., Fridman, J.M., Eyring, D.O., Rutishauser, H.J.: Joint tracking and adaptive robotic welding using vision sensing of the weld joint geometry. Welding Journal 65(11), 33–41 (1986) [8] Richardson, R.W.: Robotic weld joint tracking systems - theory and implementation methods. Welding Journal 65(11), 43–51 (1986)
Offline Programming for a Complex Welding System Using DELMIA Automation Joseph Polden, Zengxi Pan, Nathan Larkin, Stephen Van Duin, and John Norrish Faculty of Engineering, University of Wollongong, Wollongong, NSW, 2530, Australia e-mail:
[email protected]
Abstract. This paper presents an offline programming (OLP) system for a complex robotic welding cell using DELMIA Automation. The goals of this research are aimed at investigating the feasibility of taking a commercially available robotic simulation package, DELMIA, and to use a Visual Basic Automation interface to reduce the programming time by creating automated ‘modules’ to carry out some of the tasks in the OLP process. The paper first investigates and presents the structure of OLP as a discreet method of individual steps. These steps are then evaluated for their potential as an automation candidate. The methods in which these steps are automated are then presented. A general analysis of the developed OLP system was carried out, providing a scope for future research and development.
1 Introduction A manufacturing facility in Australia has, over the last number of years, been tasked with handling the manufacture of an automobile hull. The vehicle hull is composed of steel plates, and is constructed in a monocoque type assembly with all of the various plates that make up the hull being welded together by the same way as a ship vessel. At the time of writing, the welding of the hull was being carried by manual processes alone; however an increase in future production demand has pushed the manufacturing facility to automate the welding processes on the hull via implementation of a complex robotic welding system. Due to the high number of seams to be welded and the complex geometry, which is inherent in the hull’s design, a specialized robotic cell was needed to maximize the number of external and internal seams that could be completed by the cell. This cell consists of two 6-DOF articulated welding robots. Each of these robots is then in-turn mounted on another, larger, 6-DOF ‘auxiliary’ robot and linear rail to create a form of homogenized 13-DOF robot, as shown in Figure 1. The hull itself is also mounted on a rotating trunnion to allow the welding robot access to areas such as the roof of the hull, or allow the weld robot better internal access through the opening such as the windscreen orifice. The robotic cell features appendages such as laser scanner and heat sensors for calibration purpose. T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 341–349. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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The robot-on-robot set up required a state-of-the-art communication system to ensure each robot was interfaced and could communicate correctly. The robotic cell was originally programmed via online jog-and-teach method. However the highly complex nature of the cell is a great hindrance to an efficient programming solution. A lot of time was invested in teaching the welding robot all the seam locations, as the extra degrees of freedom added by the auxiliary robot removed a lot of intuitiveness in manually jogging the robot to a specific target location without clashing with the vehicle hull; particularly when negotiating through complex internal geometry.
Fig. 1. A model of the homogenized 13-DOF robotic welding system, featuring two separate 6-DOF robot and a linear rail
The manufacturing company is now anticipating orders of other configurations of the vehicle, meaning that the robot cell will need to be reprogrammed to accommodate these various models of the vehicle. After the initial difficulties experienced when utilizing the online programming method; it was deemed necessary to explore options in which the robotic cell can be re-programmed for these new designs in a much more efficient manner. Researchers at the University of Wollongong proposed an offline programming approach as an alternative, hoping to create an automated programming system utilizing a simulation package widely used in industry today. At the outset of the project, a literature review of current OLP software was conducted [1]. The review indicated that OLP software mainly came from 3 sources; from generic robotics software producers, from robot manufacturers [2-5] and finally from research institutions that produce their own programming and simulation software, usually developed around existing CAD software such as AutoCAD or SolidWorks [6-9] or from scratch using OpenGL, VRML and Java technology [10-12]. To create an OLP software package for this complex welding cell, it was decided to utilize a commercially available generic robotic software package. This was chosen as a generic system can be much more flexible in its compatibility with various brands of system hardware and also features virtual reality, allowing the user to be fully immersed into the simulation environment. The DELMIA software package was chosen. Its current and widespread use in robot programming for industry and manufacturing processes, along with its Visual Basic
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programming interface, were major factors behind its selection. Details of the DELMIA robotics simulation software package are covered below, in section II.
2 DELMIA for Offline Programming and Automation DELMIA is a robotics and manufacturing based OLP package that is utilized widely within various respective manufacturing industries today. DELMIA utilizes a 3D simulation environment to test and optimize robot programs before implementation into real world applications. DELMIA features assorted ‘toolboxes’ that are available to provide a programmer with numerous functions which are useful to the various specific areas of robotic OLP, such as; robot target definition, reachability analysis, clash testing, path planning/augmentation etc. A user carrying out the programming of a robotic cell with DELMIA would follow the general steps for OLP highlighted in Figure 2.
Fig. 2. Block diagram of overall offline programming structure [1]
To aid in the programming of the complex welding cell in question, it is proposed that some of the steps in the above OLP process be automated. These areas related to 3 specific sections of the OLP process: The automatic extraction of seam data from CAD models, the automated generation of reachability and collision assessments and the automatic creation of the robot process with simulation. The proposed automation of these DELMIA functions was carried out with the Visual Basic interface that comes with DELMIA. DELMIA’s robotics related commands which were not accessible through the VBA functionality were controlled via the windows GUI automation program; AutoIT.
3 Automatic Seam Extraction Defining the weld seams to be carried out by the robotic cell is the first step in the OLP process. In DELMIA, these seams are defined by first loading a 3D CAD model of the work object to be welded. The programmer identifies a seam, and then defines it by individually allocating two separate tags at each end of the specific seam. The tag’s individual XYZ orientation angles are then augmented by the
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user to specify the correct approach angles for the manipulator/weld-torch. This process is not a difficult one, however when a high number of seams are to be defined by the user, it has proven to be a monotonous and time-consuming task. The VBA/DELMIA interface was identified as a tool, which can analyze the drawing features that make up the CAD model of the work object. A programming module was created to aid the programmer in efficiently defining these weld seams. The module assists by providing a ‘semi-automated’ method of defining the weld seam and then automatically snapping the tags to the seam. The semi automated approach reduces the time taken to define a seam by first prompting the user to select the edge they wish to be defined as a seam. The tags are then attached automatically to each end of the selected seam. The user is then prompted to click the two adjacent faces that make up the seam, this is done to define approach/orientation angles for the weld torch as it carries out the weld process, as seen in Figure 3 below. The semi-automated approach developed provides the programmer with a more effective interface to tag their work objects than standard methods available in DELMIA. This is due to the fact that standard methods for tagging in DELMIA require that the user first add two separate tags in the correct location to define the seam, and then they are able to orientate it for the correct approach angles. The semi-automated approach, however, already assumes you are searching for an edge to define as a seam; once the edge is selected by the user the tags are added and orientated automatically. This significantly reduces the amount of individual operations the user has to carry out in order to define a seam, hence cutting down the overall time required. Initial tests on tagging a vehicle hull indicate that the time taken to define each seam in the entire hull was more than halved when using the semi automated approach over standard methods currently available in DELMIA.
Fig. 3. The semi-auto hull tagging module defines the seam edges and approach/orientation angles for the weld torch
Once the seams have been defined by the user, the data relating to their position and orientation is automatically added to an excel spreadsheet for further use at a later stage of the OLP process.
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4 Path Planning Once the programmer has defined all of the seams within the vehicle that are to be welded they then move onto the next programming task, which relates to planning the motions required for the robotic cell to correctly carry out the welds. To undertake this task with the complex welding cell being used in this research, the programmer first has to program the auxiliary robot to carry the weld robot to a specific point in space which fulfils two criteria. Firstly, this point in space has to be close enough to the seam to ensure that the weld robot can reach its target. Secondly, specific orientations for the weld robot have to be selected so that it can carry out the welding of the seam without colliding with the work object. As each seam to weld has different locations and orientations in space; the programmer essentially has to find a new point in space that for fills the above criteria for each individual seam that is to be welded. Due to the high number of individual seams that feature on the vehicle in the complex welding cell, the repetitive nature of finding these points becomes monotonous and time consuming. However the repetitiveness of the task also highlighted that it is an ideal candidate for automation, meaning that a lot of time to program the cell will be saved if it were possible to control DELMIA in a way so that it can define automatically for the programmer these points in space, meaning that they don’t have to spend the time to find it 'manually'. The approach for automating this task was to address the two criteria mentioned above as separate modules. The first, which related to automatically finding points in space which fulfilled reachability criteria, was addressed before moving onto the second criteria of defining the correct motions and orientations for clash free motion when welding the seams. To automate the reachability assessments, a 3D array of potential positions for the auxiliary robot is defined by the programmer in an excel spreadsheet; the user can define the position of the array, the volume it occupies and the number of nodes or targets within the array. The inverse kinematics of the weld robot were mapped in Matlab, which utilized the previously defined excel data to calculate the reachability of the weld robot from each test node in the array to the previously defined weld seams within that area. These reachability results are fed back to the spreadsheet so that the programmer can see how many seams can be welded from each potential target in the 3D array of nodes. DELMIA is capable of carrying out reachability tests; however the VBA interface in DELMIA is restricted in its access to this class of commands, making automation of these commands with the VBA interface a non-functional pursuit. Matlab was used and the M-file created was able to automatically carry out these tests in a very fast and efficient manner. The results displayed to the programmer were offered in an intuitive yet detailed presentation. The programmer is able to see which nodes can provide the weld robot with reachability to a single seam, multiple seams (which are listed) or no seams at all. Data relating to where the node is in 3D Cartesian space is also displayed, along with the orientation (roll/pitch/yaw angles) of the node. After the reachability testing is concluded, the next 'module' is to automatically test whether the weld robot can move from the specific pre-defined nodes to the
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weld seam without clash. To automate these commands, the DELMIA program was interfaced with AutoIT, which is a program designed to control and operate with the windows GUI; meaning that instead of a human operator manually clicking through the DELMIA commands and testing clash, AutoIT was programmed to carry out the same operations. AutoIT was programmed to read the excel spreadsheet utilized in earlier sections of this research. It reads the calculated reachability data and then prompts DELMIA to move the position of the weld robot to each node previously deemed to have a positive reachability result. The robot is then commanded to move to a specified seam that has been deemed reachable at that particular node. As this motion is carried out, DELMIA monitors for clash. AutoIT cycles through each node and returns the clash results back to the excel spreadsheet. Each available robot configuration is also tested for clash, and the data relating to which specific configurations provide a clash free motion is also displayed in the excel spreadsheet of results. The result of these automated tests is essentially an array of targets for the auxiliary robot to carry the weld robot to. The robot programmer can be safe in the knowledge that these targets will firstly have at least one seam that is within reach of the weld robot from the particular array node. These nodes will also provide a clash free motion for the weld robot as it carries out its weld process on the seams. For optimisation, the programmer can easily check the created excel spreadsheet to select which nodes will be utilized in the final robot program. This is done easily and intuitively as they are provided with the data relating to the seams that are reachable from specific nodes; so they can easily select the fewest individual nodes required to carry out all seams within one particular area of the hull. This will effectively cut down the number of times the weld robot will have to be repositioned within the hull as it carries out its weld processes. Once the correct positioning for the weld robot and suitable configurations for clash free motion have been defined the robot tasks for each robot is created using this data. At the users request, the complete process is simulated from the beginning to verify that the procedure is carried out correctly and without clash. Once the process is verified, the Visual Basic interface exports the native robot programs to a folder on the computer desktop. If, for some reason, during the verification process an error such as clash or unreachability occurs then an error file is also exported with the program. This text file contains the nature of the error and the simulation time at which it occurred, making it easy for the programmer to trace through the simulation and fix any problems before exporting the program again.
5 Experimental Results The effectiveness of the created automation system was tested on a number of seams on the vehicle hull. Visual Basic was used to create a user interface, in which the programmer has access to the various buttons and controls that manage the operation of the OLP automation system created. The first automation control the user utilizes in the OLP process is the automated generation of the seam targets for the welding robot. The proposed seams to be created were located in an
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internal section at the rear of the hull in order to fully test the capabilities of the automation system. The user first enters a desired seam name into an input box on the Visual Basic user control panel, and then clicks the ‘Create Tags’ button. A blank model of the vehicle hull in a new DELMIA window with all of its drawing features exposed to the user is then opened. A pop up window prompts the user to select the edge between two plates that will form the first seam that is to be defined. The user is then prompted to click on the incident faces to this selected edge. Once the faces have been selected the blank model of the vehicle hull is closed and two tags are placed at each end of the previously selected edge on the hull in the complex welding cell. The previously selected faces automatically orientate the seam tags with the appropriate approach angles for the weld torch, which includes any push/pull angle if required in a corner. These tags are then automatically renamed to the previously defined seam name and are saved into a specific ‘semi-auto’ tag group in DELMIA. The seam is then fully defined and the user can now move on to repeat the process as many times as they want. Three seams were defined inside the rear section of the hull using the above method. The ‘export seam data to excel’ button on the user control panel was used to export all data relating to the seam tags locations/orientations to a specific Excel spreadsheet. A list of seams in the excel sheet was created, and the new tags imported are added to the end of this list. This data is used during the later stages of the OLP automation process. Another button on this tab has the capability of importing this list of tags back into the DELMIA environment if required, allowing the user to make modifications to the tag’s location/orientation in the excel environment and then import these modified tags back into the simulation model. Once the Seams have been defined the user then clicks on the ‘Path Planning’ tab on the user interface to expose the next set of automation controls. Before beginning the path planning automation functions, a matrix of targets for the Auxiliary Robot to place the Weld Robot has to be defined about the rear of the Hull. This is done in the same spreadsheet as the stored seam tag data. The user defines this matrix by specifying in a specific section of the excel sheet the upper left and lower right hand corners of the desired positioning matrix and also entering the desired number of nodes in the matrix. These coordinates are found using the DELMIA simulation model and the DELMIA compass tool. The user selects which robot programs they would like to generate as a result of these automated tests, in this instance the button to generate both the weld robot and auxiliary robot programs was selected. The programmer can also check the ‘Validate with Simulation’ box, which runs a final validation check on reach and clash at the end of the OLP process. All that remains for the user to do is enter the name of the weld seam they wish to create the programs for and utilize the ‘Generate Code’ button on the user interface to initialize the Matlab, Visual Basic and AutoIT automation components listed in section IV. After validating the process with simulation from start to finish the final output is two separate robot programs. The first program commands the auxiliary robot to move the weld robot to a suitable position close to the weld seams. The second robot program commands the weld robot to move from this base position to carry
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out the seam weld without clashing with the vehicle hull. If desired the user can at this stage make ‘manual’ adjustments to the robot task within the simulation environment by using traditional DELMIA commands and then export the modified robot programs again. Once the first seam has been completely programmed, the operator then moves on to obtaining the robot programs for the remaining two seams at the rear section of the hull. This is done by inputting the next remaining seam name into the user control panel and then clicking the ‘Generate Program’ button again. Once the program has finished obtaining the code for these seams, the final seam at the rear of the hull was addressed in the same fashion. The time to obtain the programs to correctly weld these three seams took the automation module approximately 5 minutes from start to finish. This result provides a significant improvement over using traditional ‘online’ jogging methods currently employed by the manufacturing facility.
6 Conclusion and Future Work This research has shown that it is possible to modify currently available simulation software to automate some of the steps in the OLP process. The developed modules provide automation functionality to both the tag generation and trajectory planning stages of the OLP process, giving an overall improvement to the time taken for a programmer to produce code for a robotized welding cell. Whilst the created system is able to provide a level of success in delivering a working package, there are a number of noted issues, which have a negative impact on its operation. The main issue negatively affecting the process relates to the level of functionality that is exposed in the DELMIA/VBA interface. DELMIA, in its current development state, provides access to the majority of its functions via the VBA programming interface. However, access to DELMIA’s robotics related functions was minimal. This resulted in having to use indirect methods, such as accessing the commands through DELMIA’s GUI rather than getting direct access to use certain functions or commands. Examples of this inaccessibility include restricted access to the reachability and clash checking commands. Whilst the overall speed is a significant improvement over ‘manual’ methods of offline programming, there exists room for improvement in the developed OLP automation package. To overcome this, work has begun on creating new OLP modules in Matlab to replace some of the tasks in the OLP process, removing the reliance on the slower VBA/DELMIA interface in much the same way Matlab was implemented in section IV to carry out the reachability assessments of the weld robot. The main goal behind this is to improve the efficiency of these tasks, whilst also improving the reliability of the program by moving the role of DELMIA/VBA towards being just a tool for simulation. Acknowledgement. This work is funded by the Australian Defence Materials Technology Centre (DMTC).
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References [1] Pan, Z., Polden, J., Larkin, N., Van Duin; Norrish, J.: Recent Progress on Programming Methods for Industrial Robots. In: ISR/ROBOTIK, Munich, Germany, June 7-9 (2010) [2] Brown, R.G.: Driving digital manufacturing to reality. In: Proceedings of 2000 Winter Simulatin Conference, December 10-13, vol. 1, pp. 224–228 (2000) [3] Bruccoleri, M., D’Onofrio, C., La Commare, U.: Off-line Programming and simulation for automatic robot control software generation. In: 5th International Conference on Industrial Informatics, June 23-27, vol. 1, pp. 491–496 (2007) [4] Dong, W., Li, H., Teng, X.: Off-line programming of Spot-weld Robot for Car-body in White Based on Robcad. In: International Conference on Mechatronics and Automation, ICMA 2007, August 5-8, pp. 763–768 (2007) [5] Lee, D.M.A., Elmaraghy, W.H.: OBOSIM: a CAD-based off-line programming and analysis system for robotic manipulators. Computer-Aided Engineering Journal (October 1990) [6] Pries, J.N., Godinho, T., Ferreira, P.: CAD interface for automatic robotic welding programming. Industrial Robot: An International Journal 31(1), 71–76 (2004) [7] Mitsi, S., et al.: Off-line programming of an industrial robot for manufacturing. International Journal of Advanced Manufacturing Technology 26, 262–267 (2005) [8] Soron, M., Kalaykov, I.: Generation of continuous tool paths based on CAD models for Friction Stir Welding in 3D. In: Mediterranean Conference on Control & Automation, MED 2007, June 27-29, pp. 1–5 (2007) [9] Yang, Y., Chen, X., Ling, C., Kang, B.: A Robot Simulation System Basing on AutoLisp. In: 2nd International Conference on Industrial Electronics and Applications, ICIEA 2007, May 23-25, pp. 2154–2156 (2007) [10] Dai, W., Kampker, M.: PIN-a PC-based robot simulation and offline programming system using macro programming techniques. In: The 25th Annual Conference of the Industrial Electronics Society, November 29 –December 3, vol. 1, pp. 442–446 (1999) [11] Jaramillo-Botero, A., Matta-Gomez, A., Correa-Caicedo, J.F., Perea-Castro, W.: ROBOMOSP. IEEE Robotics & Automation Magazine 13(4), 62–73 (2006) [12] Kim, C.-S., Hong, K.-S., Han, H.Y.-S., Kim, S.-H., Kwon, S.-C.: PC-based off-line programming using VRML for welding robots in shipbuilding. In: IEEE Conference on Robotics, Automation and Mechatronics, December 1-3, vol. 2, pp. 949–954 (2004)
Robot Path Planning in Multi-pass Weaving Welding for Thick Plates Huajun Zhang1,2,3,*, Hanzhong Lu2, Chunbo Cai3, and Shanben Chen2 1
School of material science and technology, Shanghai Jiao Tong University, Shanghai 200240, China 2 Shanghai Zhenhua Heavy Industries Co., Ltd., Shanghai 200125, China 3 School of material science and technology, Harbin University of Science and technology, Harbin 150040, China e-mail:
[email protected]
Abstract. Multi-pass weaving welding is usually used in thick plates. Automatic path layout of multi-pass weaving welding is a key technology to realize robot automatic welding for thick plates. In this study, a user self-define path layout model is developed to realize teach offline programming for multi-pass weaving welding. Users can set the welding parameters of every pass, the number of layers and passes, and welding sequence according to the real welding technology. The developed system can produce the position, pose of welding torch and oscillation displacement of total passes automatically. The multi-pass welding shape is well and meets the actual production requirement through robot multi-pass weaving welding experiments for single V-groove thick plates.
1 Introduction Large thick plate structures are widely used in shipbuilding, high pressure vessel and heavy-duty machinery and so on [1]. At present, these thick plate structures adopt manual and semi-automatic arc welding. It is very urgent to realize robot automatic welding. Multi-pass weaving welding is usually used in thick plate. Automatic path layout of multi-pass weaving welding is a key technology to realize robot automatic welding for thick plate. For thin and medium thick plate, robot teach online is adopted usually. But for large thick plate, It is difficulty for this way of teach online to realize robot welding [2-3]. At present, off-line programming is used to layout the multi-pass welding path. In 1987, M. Masaharu adopted equal area method to arrange the multi-pass welding path through research to the effect of welding parameters to welds fillet [4]. L.X. Sun and K. Li adopted this method also [5-6]. In 2005, X. H. Tang developed offline simulation system of multi-pass basis on three-dimensional graphics [7]. Recently H.J. Zhang designed a self-defining layout model for multi-pass welding basis on visual graphics [8]. Seen from above researches, path layout with *
Corresponding author.
T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 351–359. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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multi-pass weaving welding was not reported. Therefore, it is very significant for multi-pass weaving welding to carry out intensive study so as to meet the production requirements. It is well known that on-line teach of multi-pass weaving welding is slow and difficulty. Therefore, off-line teach module based on graph is developed. A selfdefine mathematic model of path layout is deduced. Users can design layer number, pass number and welding parameter of multi-pass welding according to welding technology. Path parameters such as position, attitude, weaving amplitude of weld torch and so on can be generated automatically. So the off-line teach programs of robot for multi-pass welding are obtained quickly and accurately, which will raise the programmable efficiency of robot for multi-pass welding of large thick plate.
2 Establishment of Offline Simulation System In this study, Visual C++ software is used to develop the offline simulation system in SOLIDWORKS software interface. Offline programming procedure of multipass weaving welding is shown in Fig.1. The whole process is as follows: three dimension modeling, feature modeling, multi-pass path layout, the producing of path parameters (position, pose and oscillation displacement), selecting of reference path, producing of multi-pass path and finally the producing of robot program. In the path layout step, Users can set the welding parameters of every pass, the number of layers and passes, and welding sequence according to the real welding technology. Finally, the system calculates the motion parameters of every pass including weaving amplitude, transverse deviation, height deviation and pose angle of torch relative to the reference path automatically.
Fig. 1. Path layout procedure of multi-pass welding
2.1 Relationship of Weld Shape and Welding Parameter In order to obtain the weld shape under different welding parameters, the relationship of rate of feed and welding current is measured. The feed speed v1 is expressed with welding current I as follows.
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υ1 = ϕ ( I )
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(1)
When the dimension of weld wire D is constant, the weld cross-sectional area is determined by the feed speed v1 and welding speed v2. The cross-sectional area of single pass S is as follows.
S=
πυ1 D 2 2υ2
(2)
According to the formula (1) and (2), the final filling area S is expressed by formula (3).
S=
π D 2ϕ ( I ) 2υ2
(3)
2.2 Self-defining Multi-pass Weaving Welding Layout Model At present, most researchers adopted equal area or equal height method to layout multi-pass welding path of robot [5-6]. But in many cases, welding condition is not unstable such as welding parameters, layer and pass number, welding sequence of every pass and so on. Therefore users can set the welding parameters of every pass, the number of layers and passes, and welding sequence according to the real welding technology. Firstly, Users only select the reference path in visual simulation graph. The system can automatically produce the other paths according to the reference path. Fig. 2 shows the reference plane and reference line. The reference line is the angle bisector of both sides. Then multi-pass paths are produced through setting interpolation points, duplicating, translating and pose adjusting. So adjustment parameters of every pass such as Z direction position deviation ΔZ i j , Y direction position deviation ΔYi j and pose angle deviation
Δθ ij should be obtained.
Fig. 2. Reference path selection
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2.3 Position and Attitude of Welding Torch Section shape of multi-pass welding seam can be simplified into trapezium A and parallelogram B as shown in Fig. 3 [8]. The dimension of work piece is thickness H, root gap g, groove angle β. If total layers are n, then there are mi passes in layer i. L Q M PL . When the welding current and welding speed of layer i pass j is inputted by user, the area of this pass 6LM is shown in formula (3).
Fig. 3. Section shape of multi-pass welding seam
Δhi of layer i relative to the reference path is expressed by formula (4). The thickness hi of layer According to the geometrical relationship, the height deviation
i is shown by formula (5).
β
Δhi =
i
mi
g 2 + 4tg ( ) ⋅ ∑∑ S k ,l − g 2 k l
β
(4)
2tg ( ) 2 i −1
hi = Δhi − ∑ hk
(5)
k =0
As for trapezium A, the position and pose of torch is easy to calculate. The point F is the midpoint of the line segment MN as shown in Fig. 4a. The pose angle is zone [8]. When mi=1 and j=mi, the welding seam shape is belong to trapezium A or triangle (g=0). The line segment AG can be calculated as follows.
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(a) trapezium A
(b) parallelogram B Fig. 4. Position and pose of torch
S β AG = hi tg ( ) + i 2 mi hi
(6)
The position and pose deviation relative to reference path is as follows.
Δy =
( mi − 1) Si
(7)
2mi hi
Δz = Δhi
(8)
Δθ = 0
(9)
But for parallelogram B, the position and pose of torch is difficulty to determine. When mi>1 and 0<j<mi, the welding seam shape is belong to parallelogram B. Seen from the fig. 4b, the line segment GB AB BC AC can be calculated according to the geometric relationship. In order to obtain the parallelogram B, intersection point F with perpendicular bisector of line segment AC and line segment AD is the position of the torch of this pass. By ΔAEF ≈ ΔAGC Then the transverse deviation y as follows.
、 、 、
,
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β ⎛ ⎞ ⎜ 2 Si ctg ( 2 ) β ⎟ 2 + hi tg ( ) ⎟ + ( hi ) ⎜ 2 ⎟ ⎜ mi hi ⎝ ⎠ y= 2
(10)
β
4 Si ctg ( ) 2 + 2h tg ( β ) i mi hi 2
The position and pose deviation relative to reference path is as follows. β ⎛ ⎞ ⎜ 2Si ctg ( 2 ) β ⎟ 2 + ( ) h tg ⎜ ⎟ + ( hi ) i 2 m h i i ⎜ ⎟ g ⎠ − −⎝ β 2 4Si ctg ( ) 2 + 2h tg ( β ) i 2 mi hi 2
Δy =
i
mi
k
l
∑∑ S 2Δhi
k ,l
Δz = Δhi Δθ = arctg (
β
hi
(11)
(12)
)
2 Si ctg ( ) 2 + h tg ( β ) i 2 mi hi
(13)
Then the position and pose of the layer i pass j is as follows. j
yi , j = yi ,0 +
∑S k =0
i ,k
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hi
zi , j = z0,0 + Δhi
(15)
2.4 Oscillation Displacement Determination As for trapezium A or triangle (g=0), the weaving width is as follows.
W=
S 1 β hi tg ( ) + i − δ 2 2 2mi hi
(16)
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Similarly, the weaving width of parallelogram B is expressed as follows. ⎛ ⎜ AC 1⎜ −δ = W= 2 2⎜ ⎜ ⎝
2 ⎞ β ⎛ ⎞ ⎟ ⎜ 2Si ctg ( 2 ) β ⎟ 2 ⎟ + tg ( )hi ⎟ + ( hi ) − δ ⎜ ⎟ 2 ⎟ ⎜ mi hi ⎟ ⎝ ⎠ ⎠
(17)
Where δ is the correction factor of weaving width as shown in Fig. 5. Transformation matrix of position and pose of robot motion is expressed as follows. Sph( Δθ , ΔYij , ΔZ ij ) = Rot ( x, Δθ )Trans (0, ΔYij , ΔZ ij ) 0 0 ⎡1 ⎢0 cos(Δθ ) − sin(Δθ ) =⎢ ⎢0 sin(Δθ ) cos(Δθ ) ⎢ 0 0 ⎣0
0 ⎤ ⎡1 0 ⎥ ⎢0 ⎥⎢ 0 ⎥ ⎢0 ⎥⎢ 1 ⎦ ⎣0
(a) trapezium A
0 0 0 ⎤ 1 0 ΔYij ⎥ ⎥ 0 1 ΔZ ij ⎥ ⎥ 0 0 1 ⎦
(18)
(b) parallelogram B
Fig. 5. Weaving width of welding torch
3 Multi-pass Welding Experiments Welding condition is V-groove, thickness 25mm, groove angle β=60°, root gap 3mm, welding sequence, 5 layers, 8 passes in total as shown Fig. 6. Welding current and speed of every layer is different as shown in Table1. Welding method is Gas Metal Arc Welding (GMAW) with Ar (98%)+O2 (2%) gas. Welding wire dimension is 1mm. Welding robot is KUKA KR16 robot. Fig. 6 shows that the arrows are position and pose of every pass torch such as P11, P21,…,P52. The correction factor δ is equal to 2mm. The transverse and height position deviation, pose angle deviation and weaving width are shown in Table1. Macro metallographic sections of multi-pass weld by robot are shown in Fig. 7. Weld shape and sidewall fusion are well, which illustrate the path layout model is correct and meet the actual requirement of multi-pass welding production for large thick plate.
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Fig. 6. Welding Sequence and Layout results Table 1. Path layout parameters
Fig. 7. Multi-pass welding metallographic sections
4 Conclusions i: An off-line programming system for multi-pass weaving welding of thick plate arc welding robot is developed. ii: A user self-define path layout model is established for multi-pass weaving welding. Users can set the welding parameters of every pass, the number of layers and passes, and welding sequence according to the real welding technology. The developed system can produce the position, pose of welding torch and oscillation displacement of total passes automatically.
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iii: The multi-pass welding shape is well and meets the actual production requirement through robot multi-pass weaving welding experiment for single Vgroove thick plate. Acknowledgement. This research is supported by National Postdoctoral Foundation of China (No. 20090460616), by Harbin Creative Talent Science and Technology Foundation of China (No. 2010RFQXG005), by Heilongjiang Province Education Foundation (No. 20100503066) and by Shanghai Sciences & Technology Committee under Grant (No. 09JC1407100).
References [1] Shan, S., Zhou, C.L.: Robot application in multi-pass welding. Manufacturing Automation 1, 12–14 (2005) (in Chinese) [2] Qin, G.L.: Automatic arranged system based on artificial neural networks prediction for submerged arc weld. Master Thesis of Harbin Institute of Technology 7, 7–8 (2000) (in Chinese) [3] Wu, J., Smith, J.S., Lucas, J.: Weld Bead Placement System for Multipass welding. IEEE Proc.Sci. Mesa. Technology 143(2), 85–90 (1996) [4] Masaharu, M., Seigo, H., Megumi, O.: Development of a multi-pass welding program for arc welding robots and its application to heavy electrical section pieces. Transaction of the Japan Welding Society 24(1), 16–21 (1993) [5] Li, K., Dai, S.J., Sun, L.X., et al.: Filling Strategy of Multi-pass Welding. Transactions of the China Welding Institution 22(2), 46–48 (2001) (in Chinese) [6] Sun, L.X.: Weld arrangement and tracking system of multi-pass welding based on structured light. Doctor Thesis of Harbin Institute of Technology, 85– 90 (1999) (in Chinese) [7] Tang, X.H., Drews, P.: 3D-visualized offline-programming and simulation system for industrial robots. Transactions of the China Welding Institution 26(2), 64–68 (2005) (in Chinese) [8] Zhang, H.J., Zhang, G.J., Cai, C.B., et al.: Self-defining path layout strategy by thick plate arc welding robot. Transactions of the China Welding Institution 30(3), 61–64 (2009) (in Chinese)
Influence of the Bar Shape on the Welding Quality of Friction Hydro Pillar Processing Xiangdong Jiao, Hui Gao, Hongwei Zhan, Canfeng Zhou, Jiaqing Chen, and Yanqing Zhang Beijing Institute of Petrochemical Technology, China e-mail: {jiaoxiangdong,gaohuiadam}@bipt.edu.cn
Abstract. Traditional underwater arc welding methods, for example, TIG and MIG, are generally expensive and complex to implement under water. Besides, the welding quality decreases significantly with the increase of depth of water. So a new-style solid phase welding method called friction stitch welding (FSW) is introduced, since the welding quality of which is almost insensitive to water. This article studies the influence of the bar shape on the welding quality. In the experiments, three typical kinds of shape combinations are adopted. Argon is used as protecting gas, and the flux of which is 30L/min. The feed speed of the spindle is 0.5 mm/s, the rotation speed is 4000r/min. Through the comparison of the macro features and microstructure of the welded joints, the influence of the bar shape on the welding quality is analyzed, and static stress distribution along with the influence on welding temperature by different shapes are investigated. The results indicate that the welding quality obtained by using straight bars with round head is much better than the other shape combinations, the welding defects are reduced and the welded material is much finer.
1 Introduction It can be observed in the past few years that with the continuous exploitation of offshore oil-gas field, the exploitation of marine oil has been transferred from the offshore to the deep-sea, and the repairs of underwater structures has become even more important with the increase of theirs service life. Underwater hyperbaric dry welding is the usual method for underwater welding repairs, for it is endowed with a lot of merits such as the excellent welded joints, which can compete the joints acquired on ground [1]. However, the hyperbaric chamber not only costs a lot, but the stability of welding arc decreases a lot with the increase of water depths [2], so it’s restricted to be widely used for deepwater welding repair. As a potent complementary method to the underwater hyperbaric dry welding, friction stitch welding(FSW) was invented by the welding institute(TWI) at 1992, which is a late model solid phase welding method and is based on offshore platform as well as seabed pipeline maintaining [3]. Compared with the arc welding at several occasions, the FSW welded joints have many outstanding advantages, such as the exceptional joints, high-efficiency, low material consumption and non-pollution etc. FSW has proved to be one of the most promising welding methods, especially at T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 361–367. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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the deepwater welding field. Above all, the friction welding is not sensitive to water, and the welding parameters change a little under the situation of different water depths [4], which is beyond the reach of comparison to the arc welding. Therefore, FSW has become a popular research field on the underwater repairs for steel structure, and this technology has wined a lot of industrial application in many developed countries. Over the past ten years, some countries such as England, Germany, Japan, America and Brazil, etc has focused on the research work of the friction stitch welding process successively [5]. In China, this new-style friction welding technology typified by friction stir welding was just launched at the beginning of the new century. Moreover, the research work of both the equipment and the welding process of friction stitch welding is nearly in a state of blankness. Therefore, the research on the friction hydro pillar processing (FHPP) welding process of FSW is pressing and of extreme importance to the development of FSW technology in the country. It’s expected to provide some technical storage for the repairs of seabed pipeline, offshore platform and some other underwater projects, such as ocean transportation, port, warship, national defense. The purpose of this investigation is to get the effect of the shape of the bar and hole combinations on the welding quality. The welded joints of the same welding parameters but different shapes combination are obtained by a series of experiments. Besides, the macro features and micro structures of the welded joints, the static stress distribution by different shape combinations along with their effects on the welding temperature are analyzed and discussed, which can lay a foundation for the further investigation.
2 Experimental Procedures and Method The base metal used for experiment is 2024-T4 aluminum alloy, which has been subjected the course of solution heat treatment and natural aging. In order to investigate the effect of single influence factor, namely shapes of friction interface, on the welding quality, thus three kinds of shape combinations are designed before the FHPP experiments, which are flat-bottomed, taper-bottomed and roundbottomed shape, and the schematic diagram is shown in fig. 1. The diameter of all the bar head and plate hole is 14mm and 16mm each, and the hole-depth is 20mm. Meanwhile, argon is adopted to prevent the oxidization of aluminum alloy, the flow of which is 30L/min. Moreover, the welding speed and rotation speed are set as 30mm/min and 4000rpm respectively. Welding spindle is the most important component of the equipment of the experimental system, the rated axial working pressure and torque of which are 20kN and 50N·m respectively, and the maximum rotation speed of hydraulic motor is 5000r/min. PLC is set as the main control unit of the electrical control system, the human-computer interface of which is in the hands of touch screen. In order to collect and record the process parameters synchronously during the welding process, thus a set of special data acquisition system directed at FSW welding process experiments is devised. The whole data acquisition system is composed of
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temperature sensor, displacement transducer, pressure sensor, and laser rotation speed sensor. During the welding, all the experimental data measured by the sensors are gathered by S7-200PLC, then the data are subjected to a string of processing via signal conversion module of a data collector (WAVEBOOK/516E), and the processing results can be transmitted to the computer through Ethernet, shown in fig. 2. Therefore, this data acquisition system realizes the gathering and realtime surveying of the whole experimental data.
Fig. 1. Schematic diagram of FHPP
Fig. 2. Constitution of the whole data acquisition system
3 Results and Analyses 3.1 General Features of the Welded Joints In the FHPP experiments, three kinds of shape combinations are applied, and the typically welding joints are shown in Fig. 3. It can be seen from the figures that the material compactness at filled welding region through flat-bottomed combination is apparently lower than that through the other two combinations, and the welded joint quality shown in Fig. 3c is particularly well. A substantial number of
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FHPP comparison experiments indicate that the welded joints quality through round-bottomed combinations is almost satisfactory with the same welding parameters: namely, equal flux of protecting gas, equal rotation speed and feed speed of welding spindle. The round-bottomed combination also brings the highest probability of welding success. It is probably because that the alloy material at the welding line can achieve a state of super plasticity more easily with the improving of shape combinations, and the flowability of welded material can be enhanced by the action of welding pressure, which can also make the padding of welded material more compact and sufficient, and the welding defects decreased subsequently.
(a)
(b) (c) (a) Flat-bottomed combination (b) Taper-bottomed combination (c) Round-bottomed combination
Fig. 3. Microstructure of the joints through three shape combinations
3.2 Microstructure Characterization The welding quality acquired from FHPP experiments presents a trend that it raises with the increase of contact area of the frictional interface, shown in fig. 4. It’s properly due to the reason that frictional resistance will increase when the bar and plate begin to rub with the increase of contact area, which can lead to an increase of friction period and generate more heating power. With regard to the FHPP welding, the bottom corner of joint surface is called as “cold area”, in which defects often occur with high probability. Because the initial contact interface of the filled area is the threshold of thermal source among the whole welding process, the welding temperature at the beginning is relatively low. In addition, the welding pressure is inadequate so that the superplastic material can not fill the weld compactly, and the extent of superplasticity as well as the flowability of welding material is insufficient. In order to decrease or eliminate welding defects of the “cold area”, the following two methods can be resorted to, one is to preheat the bar and plate, which can elevate the temperature of the “cold area” to a great extent; the other is to increase the welding pressure or feed speed in a proper range, which can facilitate the diminishing the welded defects and also make the joining between filled material and the parent metal more compactly.
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Fig. 4. Microstructure of the weld zones
3.3 Static Stress Distribution of Different Shapes Finite element analysis is also conducted in this paper to investigate the stress situation for different shapes of the bar, shown in fig. 5. The results indicate that the stress distribution of the flat-bottomed and taper-bottomed bar is almost equivalent under the same axial pressure; however, the stress distribution at the transitional region of the round-bottomed bar is much higher than that of the others. From the above saying, a conclusion could be drew that the higher stress distribution will contribute to the deformation and moving of the super-plastic material on the condition of equivalent welding temperature, which is crucial especially in the initial phase of welding process. Furthermore, the round-bottomed interface can facilitate the up-climbing and transferring of the welded material, bringing with a significant decrease of welding defects that often occur at the beginning of the welding.
Fig. 5. Static stress distribution by different shape combinations
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3.4 Influence of Different Shapes on the Welding Temperature What’s shown in fig. 6a is a simplified model for the application of thermocouples in the FHPP experiments. What’s shown in fig. 6b reflects the temperature history measured by thermocouples through flat-bottomed and round-bottomed combinations. P1 p2 p3 p4 of fig. 6b stand for the flat-bottomed specimen, and q1 q2 q3 q4 stand for the round-bottomed specimen. Seen from the temperature curves in fig. 6b, welding temperature of the corresponding measuring points by round-bottomed combinations is higher than that by the flat-bottomed combinations on the whole. This is because that the round-bottomed interface can bring the more heat input, especially at the beginning of welding. At the same time, the cooling rate of weld material will mount up and the heat affected zone (HAZ) can be diminish a lot, resulting in a quick solidification of welded material and a proper increase of the hardness [6-8], thereby the mechanical properties of welded joints will be improved from another point of view [9]. Aside from what mentioned above, the temperature history curve in fig. 6b also suggests that the welding heat source is moving up with the incessant infilling of welding material, and the subjacent material begin to cool and solidify at the same time, which just coincides with the actual FHPP welding process.
、 、 、 、 、 、
(a) Temperature measuring model
(b) Temperature curves Fig. 6. Temperature history curve of each measuring point
4 Conclusions (1) The welded joints feature of round-bottomed combinations is the finest under the conditions of equal welding parameters, and it brings with the highest
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probability of welding success. The welded joints feature of round-bottomed combinations is proved to be a perfect shape combination. (2) Stress distribution at the transitional area of the round-bottomed bar is clearly higher than the others, which can increase the warming up time at the beginning of welding and also facilitate the up-climbing and transferring of the welded material. (3) Initial contact surface of the filled area is the foremost thermal source during the welding, and the welding temperature over there is relatively low. The temperature measure indicates that more heat input can be produced by the roundbottomed combination, which can bring higher cooling rate and make better comprehensive properties of welded joints finally. Acknowledgement. This project is supported by National Hi-tech Research and Development Program of China (863 Program, Grant No. 2006AA09Z329), National Natural Science Foundation of China (Grant No. 40776054), Funding Project for Academic Human Resources Development in Institutions of Higher Learning Under the Jurisdiction of Beijing Municipality (Grant No. PHR201007134 and PHR20090519).
References [1] Chen, J.Q., Jiao, X.D., Zhou, C.F.: Friction stitch welding—a new kind of material forming technology. Transactions of the China Welding Institution 28(9), 118–112 (2007) (in Chinese) [2] Jiao, X.D., Zhou, C.F., Chen, J.Q.: Challenges and counter measures of offshore engineering joining technology in 21st century. Electric Welding Machine 37(6), 75–80 (2007) (in Chinese) [3] Meyer, A.: Friction hydro pillar processing bonding mechanism and properties. GKSSForschungszentrum Geesthacht GmbH, Geesthacht (2003) [4] Ellis, C.R.G.: Continuous Drive Friction Welding of Mild Steel. Welding Journal, 183s–197s (April 1972) [5] Faes, K., Dhooge, A., De Baets, P.: Parameter optimisation for automatic pipeline girth welding using a new friction welding method. Materials and Design 30, 581–589 (2009) [6] Xu, L.H., Tiah, Z.L., Zhang, X.M., et al.: Metal Inert Gas Welding of 2519-T87 High Strength Aluminum Alloy. Chinese Journal of Mechanical Engineering 20(4), 32–35 (2007) [7] Wu, Z.S., Liu, C.R., Wang, J.H.: Microstructure and properties of deep crogenic treatment electrodes for spot welding hot dip galvanized steel. Chinese Journal of Mechanical Engineering 18(1), 17–20 (2005) [8] Wang, W.X., Huo, L.X., Zhang, Y.F., et al.: Solid-State Phase Transformation of Welded Metal in Control of Welding Distortion Process. Chinese Journal of Mechanical Engineering 18(1), 64–67 (2005) [9] Wang, X.J., Niu, Y., Zhang, J.: Study on Fatigue Properties of Aluminum Alloy 2024 Joint by Friction Stir Welding. Casting Forging Welding 37(15), 4–6 (2008) (in Chinese)
Interference Analysis of Infrared Temperature Measurement in Hybrid Welding Jifeng Wang, Houde Yu, Yaozhou Qian, and Rongzun Yang Shanghai Institute of Special Equipment Inspection & Technical Research, Shanghai 200062 e-mail:
[email protected]
Abstact. Welding is an essential thermal processing and a thermal conducting, so infrared sensing is a natural choice for weld process monitoring to measure and control temperature. However, the infrared temperature is interfered because of radiation reflection during applying IR sensing to the welding. The objective of this research is to measure the top-face temperature distribution in hybrid laserTIG welding process. The results of experiments show that this high light zone interference which caused by special direction specular reflection of arc light, ceramic nozzle, electrode and laser nozzle are transferred out of welding seam when the IR thermography is placed perpendicular to the welding seam. The interference in welds surface that caused by specular reflection in other places was little and was further reduced through the decreasing viewing angle. Finally, the IR thermography was calibrated by using thermocouple and IR images were obtained. The result shows that temperature data captured by the IR termography is reliable using this method in hybrid laser-TIG welding of AZ31B.
1 Introduction Welding temperature field is important because of subsequent reasons. Firstly, the microstructure and the mechanical properties strongly depend on the cooling rate, and the temperature field can supply this information. Secondly, the welding temperature field is affected by welding parameters, so it can lead us to understanding and control welding process further. Finally, welding temperature field is also used in a number of simulation models, to improve the correction of simulation and prediction. A lot of research works have done to develop temperature measuring in welding processes. At present, temperature measuring methods are divided into two kinds: contact and noncontact. The advantage of infrared thermography is that the sensor is not necessary to directly contact with the products when welding and that this kind method has very quickly response. However, the major difficulty of IR measuring is the interference from interference source such as: arc light, hot tungsten electrode and so on. The interference is more serious for materials such as aluminium alloy and magnesium alloy because of high reflectivity. Most of the wavelength of arc radiation is between 0.34 and 1.8μm. The weld radiation is greater than the arc energy above 2μm of wavelength [1]. A. Bichnell T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 369–374. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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[2]gained progressive improvement in image quality through high-pass and band pass filters, in fact within his spectrum pass band, arc radiation still could not be ignored. D.Farson [3] used filtering and shielding cup to minish arc and electrode interference from the infrared thermal radiation in TIG welding of AISI 1250 sheet. But the shielding cup soon was heated and changed into new interference resource. P.W. Ramsey [4] measured radiation interference from arc in different viewing angle and location of radiometer, however, this method can not avoided the interference from high light zone. In addition, W.E. Lukens [5] could control weld metal cooling rate ignoring the interference from arc and electrode. Many studies [6-10] performed at auburn University, concerning the viability of point infrared detectors for GTA welding process control. Hybrid laser-TIG welding combined laser welding with high speed and productivity and the traditional TIG welding. Many of research results are continuously increasing recently [11], but there is not any report which involved with measuring temperature field using IR sensor.
2 Experimental Setup The workpiece used in this study is AZ31B magnesium alloy plate with 300×100×2.5mm. The surface of the plates were chemically cleaned using acetone and scratched by steel brush before welding to maintain consistent surface condition. The welding conditions were showed in Table 1, setup was shown in Fig.1. The infrared thermography in this experiment is sensitive in the spectral range of 8 to 12 μ m. The emissivity is various in different directions β angle. Maximal value of emissivity of nonmetal can be obtained in its normal direction, the viewing angle β was fixed as 45 degree; and the optical axis of infrared thermography is perpendicular to weld seam. Table 1. Welding Conditions
Welding Speed Shielding Gas Welding Current Electrode Pulse Laser
400mm/min Argon, 5L/min AC-70A 3.2mm Average 300w
Fig. 1. Hybrid laser-TIG welding process
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3 Interference Analysis 3.1 Arc Llight Interference In this experiment, TIG torch located above A plate and 9mm distant to A verge. B plate is placed parallelly to A with 1mm clearance, as shown in Fig. 3a. The infrared thermography catch IR image after arc has ignited for 3 seconds when arc has reached stabilization. L01 line scaned arc temperature profile, L02 line scaned arc inverted temperature profile. The temperature in L01 is higher than L02 (Fig. 3b). Arc dimension approachs cermet nozzle diameter, radiation interference I area is little in n direction mostly casused by arc diffuse reflection; however, raG diation interference area is long in m direction due to arc specular reflection. G Thus, the interference zone is larger in m direction (point to the IR position), G I while it is small in n direction (perpendicular to m , namely welding seam) and the interference intensity is little, either.
Fig. 2a. Arc IR map
Fig. 2b. Scan-line
3.2 TIG Ceramic Nozzle Interference Experiment condition is same to section 3.1, but worktable moved. IR images were captured during welding current abruptly decreased in order to minish arc radiation interference. These images are respectively selected in halfway (Fig. 3a). Because plate B is not heated by the arc, its surface interference is almost from
Fig. 3a. IR map in half of welding process
Fig. 3b. Scan-line
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ceramic nozzle radiation, while from arc radiation is small (position scan line L01 and L02). Fig. 3b showed that the interferential temperature of ceramic nozzle inverted image which caused by specular reflection increased ceaselessly because continuously heated, but the interference outside of inverted image is little. Fortunately, welding seam will be not interfered by this inverted image if IR thermography is placed like Fig. 1.
3.3 Laser Nozzle Iinterference To analyse the radiation interference from laer nozzle, the IR thermography is placed behind the weld pool. Fig. 4 showed one of IR image of laser-TIG hybrid welding. It can be found that there are two interference zones in temperature field. One was the complex interference zone including tungsten, TIG ceramic nozzle and arc light. The other was laser nozzle interference zone (laser nozzle inverted image caused by specular reflection). This interference can be wiped off (Fig. 5b) if the IR thermography is placed like Fig.1.
Fig. 4. IR map of laser nozzle interference
3.4 The Method of Removing Interferences How to remove the interferences always puzzled the researchers in this field. In traditional experiment, the temperature image was captured paralleling the direction of welding seam, and IR device was placed behind welding pool as shown in Fig. 5a, and the temperature field measured was interfered badly by something
Fig. 5a. IR behind welding pool
Fig. 5b. IR perpendicularly welding seam
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mentioned above. In this paper, the IR thermography is placed perpendicularly to welding seam (Fig. 1). Those interferences that caused by specular reflection was transferred out of welding seam as shown in Fig5b. So when measuring top-weld temperature filed, the errors cased by these interference facts became small.
4 Results 4.1 Calibration On the base of the anterior results, the validation system of IR measuring was designed as shown in Fig. 6. The location of the sensing point was showed in the Fig. 6. The thermocouple is attached at sensing point, its signal is transmited on the screen where temperature is displayed; At the same time, IR thermography also catch the sensing point through itself point measurement model, and this temperature value is also displayed on the screen in real-time. These two temperature values are accord, when emissivity is 0.18. Further study showed that IR temperature distribution data was reliable in the zone of x>10 mm and y>6 mm.
Fig. 6. Principle of the IR temperature measuring system
4.2 Temperature Measurement in Hybrid Welding Bead-on-plate hybrid laser-TIG welding of AZ31B magnesium alloys was investigated. IR images(Fig. 7) were obtained during welding process. Scan line showed that the temperature appeared peak in welding centerline and flat step in both sides of centerline.
Fig. 7a. IR map in hybrid welding
Fig. 7b. Scan-line in hybrid welding
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5 Conclusions Based on the experiments described above, it was concluded that the radiation from arc light, ceramic nozzle, electrode and laser nozzle significantly interfered measurement temperature at a location on the base metal. The high light zone interference which caused by special direction specular reflection is primary. The IR thermography is placed perpendicularly to the welding seam. These high light interference that caused by specular reflection of arc light, ceramic nozzle, electrode and laser nozzle were transferred out of welding seam. The interference that caused by specular reflection in other places was little and was further reduced through the decreasing viewing angle. The experiments verified that this method practically reduces interference from the infrared thermal measurements. Finally, an experimental calibration of the infrared measurements based on thermocouple measurements was performed at a location of 10 mm behind and 6mm to the side of the weld seam.
References [1] American Welding Society: Welding handbook, 7th edn., Miami (1976) [2] Bicknell, A.: Infrared sensor for top face monitoring of weld pools. Meas. Sci. Technol. 5, 371–378 (1994) [3] Farson, D.: Infrared measurement of base metal temperature in gas tungsten arcwelding. Welding Research Supplement, 396–401 (September 1998) [4] Ramsey, P.W.: Infrared temperature sensing systems for automatic fusion welding. Welding Research Supplement, 89–96 (April 2000) [5] Lukens, W.E.: Infrared temperature sensing of cooling rates for arc welding control. Welding Journal, 27–33 (January 1982) [6] Chin, B.A.: Infrared thermography for sensing the arc welding process. Welding Research Supplement, 227–234 (September 1983) [7] Fan, H.: Low-cost infrared sensing system for monitoring the welding process in the presence of plate inclination angle. J. Materials processing Tech. 140, 668–675 (2003) [8] Nagarajan, S.: Infrared sensing of adaptive arc welding. Welding Research Supplement, 462–466 (November 1989) [9] Sundaram: Control of the welding process using infrared sensors. IEEE Transactions on Robotics 8, 86–93 (1992) [10] Banerjee, P.: Infrared sensing for on-line weld geometry monitoring and control. J. Engineering for Industy, 223–230 (August 1995) [11] Seyffartn, P.: Laser-Arc processes and their Applications in Welding and Material Treatment. Taylor & Francis, Taylor (2002)
Preliminary Investigation on Embedding FBG Fibre within AA6061 Matrices by Ultrasonic Welding Zhengqiang Zhu, Yifu Zhang, Chun Zeng, and Zhilin Xiong School of mechatronics engineering, Nanchang University, Nanchang, 330031, China e-mail:
[email protected]
Abstract. Ultrasonic welding can be used to join plastic and metal through highfrequency (more than 20 kHz) acoustic vibrations. As the process is low temperature and low pressure, it is easy to embed fragile sensors such as FBG fibre within metal matrices. In this research, the optimal parameters are obtained by experiments of tensile and nanoindentation hardness firstly. Then the microstructures are investigated in this paper to check the welding effect. The sensor effect of FBG fibre is also investigated. These experimental results clearly demonstrate that the strength and hardness of metal matrices will increase under ultrasonic welding. In addition, the temperature sensitivity of the nickel-coat FBG was improved after welding. From the experimental results, it is completely feasible to embed FBG fibre within AA6061 matrices by ultrasonic welding. Therefore, the 3-D smart metal structure can be made by the ultrasonic metal welding.
1 Introduction Ultrasonic metal welding (USMW) is used to joined homogenous and dissimilar metal foils by applying pressure and ultrasonic vibration [1-3]. Now, USMW is widely used in microelectronics and other industrial areas [4-5]. Ultrasonic welder includes transducer, sonotrode, pneumatic pump and booster. The welder’s diagram is shown in Fig.1. The high frequency relative motion between the parts forms a solid-state weld through progressive shearing and plastic deformation. During the process, the oxides and contaminants are dispersed and a pure metal contact between the adjacent surfaces are performed.
Fig. 1. The diagram of USMW T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 375–381. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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The main advantages of USMW include absence of liquid-solid transformations, low energy consumption, low pressure, low temperature which allows embedding of electronics, such as sensors and actuators. FBG sensors are widely used for measuring strain and temperature with the advantages including considerably improved accuracy, sensitivity, and immunity to electromagnetic interference, lightweight, etc. So FBG can be used to create smart materials that can operate in harsh environments—such as underwater—where conventional sensors cannot work. In this research, FBG fibre is embedded in AA6061 matrices under ultrasonic welding. Firstly, by experiments, the optimal parameters of USMW are obtained. Then the microstructures are investigated in this paper to check the welding effect. The sensor effect of FBG fibre is also investigated.
2 Process Conditions and Experiments 2.1 Test Materials The material studied is aluminium alloy 6061 in the form of foil with 0.4 mm thickness. Table 1 lists the composition and mechanical properties of this material. The surface roughness of the 6061 foil is about 0.4 µm (Ra). AA6061 has good weldability and formability and has long been used in aerospace, structural, transport and construction applications. FBG fibre used in this experiment has a cladding diameter of 125 μm and the central wavelength is 1540 nm at 30 .
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Table 1. Composition and mechanical properties of aluminium alloy 6061 foils Composition Al-1.0Mg-0.6Si-0.7Fe-0.3Cu0.2Cr-0.15Mn-0.25Zn-0.15Ti
Tensile strength (MPa) 113-117
Yield strength (MPa) 45-50
Elongation at break (%) 10-10.5
2.2 Test Procedure The metal ultrasonic welding machine is shown in fig. 2,rated at 3.2 kW and frequency f=20 kHz, with a 125-mm beam tool-steel sonotrode, ending at 15×15mm-square tip with sand grinding surface. Firstly, the optimal welding parameters are obtained by experiments. In these experiments, the amplitude and frequency is fixed, the welding time is changing. Then tear tests and nanoindentation hardness tests are performed to obtain the tearing force data and the nanoindentation hardness data to confirm the optimal welding parameters. Then, under this parameter, bare FBG fibre is embedded
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within AA6061 matrices. Then the micro-photos of both the monolithic welding sample and FBG fibre embedded sample are obtained. Also the temperature sensitivity of the nickel-coat FBG fibre is measured after embedded within metal.
Fig. 2. The metal ultrasonic welding machine
3 Results and Discussion The fixed amplitude is about 30 μm, welding current is 12 A, and the pressure is also certain which is effected by pneumatic cylinders. When welding time is 230 ms, the welding effect is the optimal with the tearing force of 128.5 N. The longer of the welding time, the higher of the temperature, and the better of the welding effect under the fixed pressure. Fig. 3 shows the welding effect of the sample with 230 ms welding time. With the welding time increases, the tearing force tends to increase linearly firstly then basically unchanged. However, with the increase of welding time, the temperature is rising and the specimen edges will occur melting, which will affect welding effect. Therefore, welding time cannot increase unlimitedly. Fig. 4 is the diagram between welding time and the tearing force (tensile speed=15mm/min). Each experiment, the welding current is constant but the voltage will be different. Fig. 5 is the relationship between welding time and the nanoindentation hardness (maximum load=10 gf, dwell time=15 s) of around welding interface. Fig. 5 shows the increasing tendency of the hardness in the vicinity of the weld interface. The results show that after ultrasonic welding the hardness of all matrices increases, compared with that of the original aluminum foils, especially at the region around the weld interface. It is also found that there is no obvious influence with respect welding time. Because plastic deformation causes dislocation movement, and dislocations stress fields interact with each other, the existence of one dislocation will hinder the movement of the others. When a material is plastically deformed, dislocations become denser and less mobile and, as a result, the material will present higher strength and hardness.
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Fig. 6 is the microstructure photos of the samples with welding time 200 ms and 230 ms. From (a), there exists a clearly welding interface. While from (b) there is no clear welding line. Both foils are exactly boned, which proves once again that the welding effect of time 230 ms is optimal.
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Surface treatment for the FBG fibre is necessary before embedded within AA6061 matrices. Firstly, the layer of plastic jacket must be removed. The part of fibre needs to soak in the acetone for about 10 minutes. Then take it out and use the cotton to dry it. And then the plastic skin is stripped by needle nose pliers. After that, the bare part is placed in the middle of the two experimental foils. Fig. 7 is the microscopic photos of the samples embedded FBG fibre with different welding time. Fig. 7(a) shows that when the welding time is 180 ms, a small part of the both foils are welded. Around the fibre, there is much gap without metal. While when welding time is 230 ms, Fig. 7(b) and Fig. 8 shows that both foils are full bonded. And there is no gap around the FBG fibre. The fibre was not deformed during the process, retaining its circular external geometry. The center of the fibre was found to be located at the interface between top and bottom AA6061 foils. Metal foils in the vicinity of the fibre were found to bond well, in particular. The cavity created by the placement of a FBG fibre between two foils was fully filled by the matrix metal, indicating significant plastic deformation of the matrix material during ultrasonic welding processing.
(a) Welding time 180 ms
(b) Welding time 230 ms
Fig. 7. Microscopic photos of the samples embedded fibre with different welding time
Fig. 8. SEM image of fibre embedded between Al foils with welding time 230 ms
For prepare to embed the FBG in metal by ultrasonic welding successfully. First the chemical plating and electroplating process for nickel coating of FBG. The diameter of the nickel-clad FBG is 140.3 μm. Fig. 9 is the thermal responses after chemical plating and electroplating experiment. Fig. 9 shows that the best
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linear fit curves give an average temperature sensitivity of 15 pm/ . Fig. 10 shows that when the welding time is 100 ms, the nickel-coat FBG embedded in 6061 Al matrices by ultrasonic welding. Temperature sensitivity was measured, and the result shows that the best linear fit curves given an average temperature sensitivity of 17.9 pm/ . After welding, it is clear that this enhanced apparent temperature sensitivity due to the combination effect on thermal expansion of aluminium, nickel and silica glass material. The thermal expansion coefficients of aluminium, nickel and silica glass are 23.4e-6/ , 14.2e-6/ and 0.55e-6/ . Enhanced temperature sensitivity could be an advantage for decoupling temperature cross-sensitivity, and offering better measurement accuracy.
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4 Conclusions During USMW, the optimal welding parameters are found. When the welding time is 230ms with exact frequency of 20 kHz, and the amplitude 30 μm, the welding effect is perfect. Use these parameters to embed the FBG fibre within the
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metal AA6061 matrices. It is found after welding the fibre is intact. That means we can embed FBG fibre within AA6061 matrices. In addition, the nickel-coat FBG embedded in 6061 Al matrices under ultrasonic welding, the temperature sensitivity was improved obviously. Therefore, the 3-D smart metal structure can be made by USMW which is the authors’ main research direction for future. Acknowledgement. The authors wish to thank the financial support for this research from the National Natural Science Foundation of China (Grant No. 50865007); the Raise Object Project Grant of Jiangxi Province for Young Scientists (Jinggang Star); the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry; 2010 Annual Key Science and Technology projects of Jiangxi Provincial Department of Education (GJJ10013).
References [1] Jones, J.B., Meyer, F.R.: Ultrasonic welding of structural aluminium alloys. Welding Journal 37(3), 81–92 (1958) [2] White, D.R.: Ultrasonic consolidation of aluminium tooling. Advanced Materials Processing 161(1), 64–65 (2003) [3] O’Brien, R.L.: Welding Handbook, Welding Processes, 8th edn., vol. 2, pp. 783–812. American Welding Society, Miami (1991) [4] Harman, G.G., Albers, J.: Ultrasonic welding mechanism as applied to aluminium-wire and gold-wire bongding in microelectronics. IEEE Trans. Parts, Hybrids Packaging 13(4), 406–412 (1997) [5] Aro, H., Kallioniemi, H., Aho, A.J.: Ultrasonic welding of experimental osteotomies. Acta Orthop. Scand 51(4), 703 (1980)
Thermal Process Analysis in Welding Prototyping of Metal Structures Jian-ning Xu, Hua Zhang, Ronghua Hu, and Yulong Li School of mechatronics engineering, Nanchang University, Jiangxi province, 330031, P.R. China e-mail:
[email protected]
Abstract. Using welding prototyping method to process metal parts can organize compact tissue and controllable dimensions at a lower cost. Welding is a transient heating and rapid cooling process. The changes of welding temperature field have an important impact on the quality of metal parts. In this paper, ANSYS is used to analyze thermal process of welding prototyping, to obtain welding temperature field and residual stress changes of metal structure in welding prototyping process. It is also used to analyze the impact of welding temperature field changes on the metal structure.
1 Introduction The processed metal parts based on TIG welding rapid prototyping method are composed of whole weld. Its high density is able to meet the strength and performance requirements of metal parts. Using TIG arc as a heat source and using ordinary wires as welding materials, greatly reduces the costs of welding rapid prototyping technology [1-3]. In the welding process, welding temperature field has a significant effect on the microstructure and mechanical properties of the welded metal parts, using numerical simulation and experimental method to research the thermal process of welding metal parts forming, can effectively reduce the trials number, and provide theoretical basis for the control of welding prototyping process. In this paper, use ANSYS finite element software as a tool, to prepare APDL procedures, study welding prototyping thermal process of metal structure and achieve effective prediction for temperature field of welding prototyping process.
2 Mathematical Model Welding is a transient heating and rapid cooling process. In this paper, first carry on three-dimensional transient thermal analysis, gain temperature change in welding process; and then use the thermal analysis results as the payload to carry out three-dimensional transient stress analysis. T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 383–390. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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2.1 Thermal Analysis Because welding rapid prototyping is a heating process, heat conductions play a key role in the whole process. When the heat source moving, the temperature of the entire weldment changes rapidly along with time and space, the thermal physical properties are also dramatic changing with temperature, while there still exist in the latent heat phenomenon of melting and phase transition [4, 5]. Therefore, welding temperature field analysis belongs to a typical non-linear transient heat conduction problem. The control equation of non-linear transient heat conduction problem is [6]: ρc
∂T ∂ ⎛ ∂T ⎞ ∂ ⎛ ∂T ⎞ ∂ ⎛ ∂T ⎞ ⎟ + ⎜λ = ⎜λ ⎟ + ⎜λ ⎟+Q ∂t ∂x ⎝ ∂x ⎠ ∂y ⎜⎝ ∂y ⎟⎠ ∂z ⎝ ∂z ⎠
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ρ c and λ for respectively material density, specific heat and thermal conductivity, which are temperature-related functions; T for the temperature field distribution function; t for heat transfer time; Q as the internal heat source intensity. Encountered heat flow and heat transfer boundary conditions in welding rapid manufacturing. Thinking about the heat source model selecting directly impacts on the calculation accuracy of transient welding temperature field, when TIG welding, commonly use Gaussian heat source model, the heat flux distribution as follows:
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r2 ) r2
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Where, r: the distance from the heat source center; r :arc effective heating radius; q m :Maximum heat flux ratio. In the welding rapid prototyping, can use the above-mentioned heat sources and continuously move along the welding direction. 2.2 Stress Analysis Using the junction temperature obtained from the thermal analysis as the body loads and exertingits on the structural stress analysis, the internal stress and strain field distribution of weldment change along with the welding heat source movement and is a transient problem, the analysis unit will also experience changes of elastic and plastic state. With the dramatic changes in temperature, constitutive relationship between stress and strain shows up non-linear, generally believe that welding distortion belongs to small deformation field, so need to adopt an incremental thermal elastic-plastic finite element theory to analyze. The established finite element formulation as follows [7, 8]:
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Where, [K]:elastic-plastic stiffness matrix; {du}i :The displacement increments
obtained from the ith load; {dF }i :The ith load increment vector.
3 The Physical Model and Mesh Division The main feature of three-dimensional model is to use mobile heat source, cell life and death technology, the temperature-dependent material properties, different thermal coefficients of the different surface (such as near the pool area and away from the pool area, etc.). Apply ANSYS programming language (APDL) to establish mobile heat source and the corresponding boundary conditions. The cell types of thermal analysis and stress analysis are of 8-node body element SOLID70 and SOLID45. The grid of thermal analysis and stress analysis are familiar, shown in Figure 1.
Fig. 1. Finite element mesh for calculation
4 The Results and Discussion 4.1 Temperature Field Distribution Figure 2 is temperature distribution of the first layer of wall in both cases of the welding direction using Z-shaped (the former weld’s arc-extinction point is the post weld’s arcing point) and same-direction (arc-extinction point and arcing point of the two weld put out the same in the horizontal direction), as can be seen from the figure, in the welding process, both ends temperature of weld is higher than the intermediate temperature, it is because heat accumulation is more prone to occur at both ends of the weld. At the same time, when the welding torch taking a different direction, metal temperature distribution obtained form weld prototyping is not the same, both ends temperature and wall average temperature obtained from Zshaped weld is significantly higher than the same direction weld. Because of in the Z-shaped welding, arc starting point coincides with the extinction point, the temperature accumulated more intense than in the same direction welding. When using the same direction weld, arc-extinction point and arcing point has a longer
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cooling time. For example, the 10th layer average temperature by using the same direction method is 1874.52 , while by using Z-shaped method is 1973.283 . In order to reduce the impact of heat accumulation, using the same direction processing should be more appropriate, Figure 3 is the temperature distribution using Z-shaped processing and the same direction processing in 100S. Using Z-shaped processing, the maximum temperature is 2807 , which higher than the maximum temperature of 2577 by using the same direction processing.
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Fig. 3. The temperature distribution cloud of structure in surfacing to 100s
4.2 Stress Analysis The welding temperature field distributions directly affect the residual stress distribution of structure. Welding residual stress is caused by conventional welding techniques and this defect is almost impossible to avoid, the harm is very great, greatly affect the anti-fatigue, brittle fracture, stress corrosion damage and buckling strength, dimensional stability of welded structure. Therefore, to minimize or eliminate the welding residual stress, is major practical problems. And the structure parts based on welding rapid manufacturing is composed all from the weld, its residual stress distribution will be more complicated, because the follow-up
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welding layer inevitably produces heat aging effect on the front welding layer, and has greater impact on the final welding temperature field and the residual stress field formation. Figure 4 is the Mises stress distribution of 8th-layer when cooling 300s after welding, can see all are tensile stress, and stress at both ends is larger, Z-type welding residual stress is higher than the same direction welding.
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Although, the residual stress in the same direction welding is lower than Zwelding, but in the end, the Z-residual stress in the same direction welding is higher than Z-welding, shown in Figure5, while the larger compressive stress have occurred in the last layer end. The σz distribution of Z-welded end-point show wave-like distribution and are all the tensile stress. Because Z-tensile stress is the main reason for arising lamellar tearing in welded structure, so the lamellar tearing most likely to occur at the beginning layer in same direction welding, while at the other layers in Z-welding. The best solution is to preheat substrate before welding and heat-treating the entire structure after welding. 600
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5 The Effects of Welding Temperature Field on Weld Metal Using the norms of welding current 120A, wire feed speed 100cm/min, welding speed 150mm/min to process single-channel multi-layer welding, obtains wall
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ⅠⅡⅢ
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Based on the thermal process simulation of wall welding in the section, we can see that following weld bead will generate heat treatment effect on the former weld in the course of the wall surfacing, create a kind of alternate thermal cycling role. That is, the martensite, bainite of single-channel single-weld can be transformed into tempered structure after heat treatment, tempering structure occur re-crystallization and forming equiaxed organizations similar to normalized organization. Therefore, the last 2 ~ 3 layer seam of wall is martensite (thickness is about 3 ~ 5mm), while the interior is the equiaxed organization similar to normalizing organization. The organization of single-channel single-weld is smaller because of its faster cooling rate, while the wall space for heat dissipation is relatively limited, and the front-seam has the residual heat, so result in the slower cooling rate, therefore the organization of its outer layer is relatively thick. As can be seen in the weld forming process, due to the heat treatment role of following weld, makes the internal organization grain of the molding pieces reconstruction and refinement improves the mechanical properties of the material.
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1. The welding organization is ferrite and a small amount of pearlite. Multilayer welding can refine grains and improve the organization and obtain a smooth surface and forming beautiful parts. These are mainly due to the heat-treatment effects of after layer to previous layer. 2. Middle weld is softened and lead to lower hardness because of drawing effects of after-weld, hardness of the top weld is the highest, close to the true hardness of welding materials. 3. Establishing a three-dimensional heat-machine coupled model based on welding rapid manufacturing simple wall, which effectively simulated the temperature gradient and the resulting residual stress in rapid manufacturing process. 4. In welding rapid manufacturing technology, path planning is equally important. For example, the temperature field and residual stress distribution of Zwelding and the same direction-soldering are not the same. 5. As a whole, welding temperature field and residual stress produced from the same direction-soldering are smaller than those of the Z-welding. However, σz of start welding position in the same direction must be high, otherwise it easily leads to the fracture of walls and base plates. Z-weld shows a fluctuated high z-direction tensile stress at the start that is likely to cause lamellar tearing. So in the welding rapid manufacturing, it is recommended to preheat substrate before welding and heat-treat the entire structure after welding to reduce the residual stress of structure and improve the performance of the structure. Acknowledgement. Thanks to the reviewers for the valuable comments. The work is supported by Special Prophase Project on Basic Research of The National Department of Science and Technology. No. 2005CCA04300.
References [1] Jandric, Z.,Kmecko, I .S., Kovacevic, R.: Dynamic modeling of GTAW for rapid prototyping with welding-based deposition. Technical Paper - Society of Manufacturing Engineers. AD, AD02-242 (2002) [2] Wu, Y., Kovacevic, R.: Mechanically assisted droplet transfer process in gas metal arc welding. Proceedings of the Institution of Mechanical Engineers, Part B, Journal of Engineering Manufacture 216(4), 555–564 (2002) [3] Zhang, Y.M., Li, P., Chen, Y., Male, A.T.: Automated system for welding-based rapid prototyping. Mechatronics 12(1), 37–53 (2002) [4] Lu, F., Yao, S., Lou, S., Li, Y.: Modeling and finite element analysis on GTAW arc and weld pool. Computational Materials Science 29(3), 371–378 (2004) [5] Chu, S.C., Lian, S.S.: Numerical analysis of temperature distribution of plasma arc with molten pool in plasma arc melting. Computational Materials Science 30(3-4), 441–447 (2004)
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[6] Zhang, H.W., Zhang, Z., Chen, J.T.: 3D modeling of material flow in friction stir welding under different process parameters. Journal of Materials Processing Technology 183(1), 62–70 (2007) [7] Sunar, M., Yilbas, B.S., Boran, K.: Thermal and stress analysis of a sheet metal in welding. Journal of Materials Processing Technology 172(1), 123–129 (2006) [8] Mahapatra, M.M., Datta, G.L., Pradhan, B., Mandal, N.R.: Three-dimensional finite element analysis to predict the effects of SAW process parameters on temperature distribution and angular distortions in single-pass butt joints with top and bottom reinforcements. International Journal of Pressure Vessels and Piping 83(10), 721–729 (2006)
Study on Sub-sea Pipelines Hyperbaric Welding Repair under High Air Pressures Canfeng Zhou1, Xiangdong Jiao1, Long Xue1, Jiaqing Chen1, and Xiaoming Fang2 1 2
Beijing Institute of Petrochemical Technology Offshore Oil Engineering Co, Ltd. e-mail:
[email protected]
Abstract. Most Chinese sub-sea pipelines are buried in Bohai Sea less than 60m water depth, where air diving are widely used For the application of offshore pipelines repair, the hyperbaric TIG welding process under high air pressures has been successfully developed. Firstly, the hyperbaric welding test chamber is designed and constructed in laboratory, and the welding machine is manufactured. Then, special welding experiments are carried out based on 16Mn steel plates and pipes under 1-7bar air pressures, and high weld quality is obtained. Lastly, the sea-trial of the welding machine and the welding procedure is carried out in Bohai Sea in China, and a perfect all-position 5G girth weld of 16Mn steel pipe is achieved.
1 Introduction Because high quality joint can be obtained, hyperbaric welding is often used in offshore structures repair. The effects of pressure on electrical performance and weld bead geometry at pressures up to 250bar, equivalent to water depths of 2500m (8,200ft), are investigated, and overall process stability of GMAW is shown in Ref.1. Also under the same high pressure acceptable butt joints including positional linear welds and orbital welds on API 5L X65, API 5L X70 and supermartensitic pipeline steels are produced [2], and a fillet welded sleeve on API 5L X65 pipeline are also obtained successfully [3], which can be of great application value in driverless underwater repair system. In fact, in order to develop driverless underwater repair system applied in tie-in and hot tapping needed in many cases, such as the Langeled pipeline in the North Sea, the study of hyperbaric welding has been carried out continually during the past few years. Hyperbaric GMAW experiments based on X65 steel with low alloyed steel and Inconel 625 wires are carried out under the pressure of 12-35 bars, and all the welds show excellent mechanical properties [4]. In addition, hyperbaric GTAW of X70. Pipeline are also investigated, and the overall properties of the produced weld are exceptional [5]. As is known to all, the circumstances of Bohai Sea differ a lot with that of the North Sea, and the water depth is below 60m, air diving is usually used in underwater repair. In the research of the sub-sea pipeline hyperbaric repair programme, for the sake of flexibility and economical efficiency of the repair operation, T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 391–397. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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compressed air is selected as chamber gases to drive water. However, two problems lie in the selection above, one is that the flammability of objects increases greatly, the other is that the weld pool protection is more difficult [6]. In addition, some other tough problems also must be solved, such as misalignment, arc viewing, and operation safety. The present investigation concerns hyperbaric GTAW process based on 16Mn steel pipe under high air pressures. The hyperbaric welding test chamber is built, the orbital welding machine is manufactured, and successful experiments are carried out.
2 Hyperbaric Welding Equipments 2.1 The Hyperbaric Welding Chamber The inner space of the hyperbaric welding chamber (Fig. 1) is a room of 4.5m ×3.5m×3m, in which 3 divers can carry out tasks in sub-sea of 60m water depth and 2 km flow current. Tools including the welding head are stored in waterproof containers on the chamber sides. 2 manipulators are installed to hang up pipeline from the sea bed, and can move up and down. The chamber can be adjusted along three directions with hydro-cylinders integrated on the frame structure, and each side with an inverted “U” opening to accommodate the pipeline. When the chamber is set in right place and the pipeline is lifted by manipulators, two half rings with rubbers is driven by hydro-cylinders to seal the circle part of the inverted “U” opening. When compressed gases are charged into the chamber, water is driven out from the floor gradually, and a dry hyperbaric space is built.
Fig. 1. The hyperbaric welding chamber
2.2 The Hyperbaric Welding Test Chamber and Pipe Welding Machine In order to develop welding process specifications under hyperbaric conditions in laboratory, the hyperbaric welding test chamber is built, and the pipe welding machine is manufactured (Fig.2). The experiment system is mainly composed of the test chamber, the gases storage tank, the automatic GTAW welding machine, monitoring device and an air compressor. Hyperbaric welding test is carried out
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by the welding head in the test chamber, a special designed horizontal pressure vessel with inner diameter of 1600mm and a cover are driven by hydro-cylinders, and the welding process is monitored by CCD cameras. Thanks to such a large space, not only 5G girth weld of pipe, but any positional weld of plate can be produced in the chamber. The pipe welding machine in the test chamber is installed on the guiding rail, which is self-propelled and handled by the welders using the remote control box outside the chamber. The three motor-driven axes of the welding bug can move along the guiding rail, oscillate in the weld crosswise direction, and torch up and down. During the welding process, images of arc and pool are trapped by CCD cameras, and can be displayed on the TV screen outside the chamber synchronously.
Fig. 2. The test chamber and pipe welding machine
The tungsten electrode movement is controlled by a DC servo drive system shown as Fig.3. According to the deviation between the arc voltage from the sensor and the set voltage from the remote control box, PLC (Programmable Logic Controller) outputs corresponding regulating voltage to the servo drive system. Driven by the servo motor, the tungsten electrode is moved up and down till the arc length equals to the set value of the control box. None other than the setting arc length, the welding process of the pipeline repair can be carried out perfectly although there is out-of roundness or misalignment to some extent. In addition, compared to power source output voltage, arc length can be easily maintained constant in spite of the complexity of chamber gases and cable, which can bring great flexibility for pipeline repair. U1 Arc voltage
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In fact, arc voltage feedback is also the basement of touch striking arc ignition. When the welding power source is just started, U1 equals to open-circuit voltage of the power source. When the operator presses the welding button on the control box, the electrode moves down and contacts with the work piece, U1 instantly changes from open-circuit voltage to a value about 0, then the electrode moves up and the arc is activated.
3 GTAW Arc under High Air Pressures 3.1 Testing Conditions Under high air pressures varying between 1 and 7 bars, welding tests of 16Mn steel plates at horizontal position are carried out to study arc behavior and static characteristics. Around the non-consumable tungsten electrode, there is a ceramic shroud through which higher argon is passed to create an inert atmosphere to protect the electrode from high atmospheric contamination. Filler wire is selected as AWS5.18 ER70S-6 with a diameter of 0.8 mm.
3.2 Arc Behavior and Static Characteristics A high speed camera housed in a special designed pressure proof enclosure is applied to obtain arc image under high air pressures. Typical observations at 1-7 bars are shown in Fig. 4. Because particle density increases in proportion to the ambient pressure, energy losses from the outer regions of the arc increase gives rise to arc cross-section contraction, and arc brightness increase. Undoubtedly, successive frames of arc image from the high speed camera indicate that GTAW process is stable enough under high air pressures at 7 bars.
(a) 0 .1 M P a
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Fig. 4. Arc images at high air pressures 1-7 bars
When the arc length equals to 5.5mm, arc static characteristic curves at 1-7 bars are shown as Fig. 5, and several conclusions can be drawn thereby:
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Similar to other gas shielded arc welding processes, Arc voltage increases when welding current increases from a rather large value about 50 A. Similar to hyperbaric welding process under Argon ambient conditions, arc voltage increases about 5-10 V when air pressure increase 1 Mpa. Compared to Argon ambient conditions, arc voltage at the same pressure and the same welding current is higher about 1 V, which can be explained from more arc energy losses caused by air.
Therefore, the same welding power source and similar parameters under high Argon ambient conditions can be applied in high air pressures. 0 .1 M P a 0 .2 M P a 0 .3 M P a 0 .4 M P a 0 .5 M P a 0 .6 M P a 0 .7 M P a
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4 Girth Weld under High Air Pressures On the basement of linear weld experiment, butt welds of pipe in 5G at 1-7 bar air pressures were produced, shown in Fig. 6. The welds are completed in multiple passes, including the root pass welded by pulsed current, and other passes welded by constant current about 140-160 A. Compared to welding in atmosphere, in order to realize perfect protection for the arc and weld pool, the pressure and flow of shielding gas is much higher. For example, when ambient pressure is 7 bars, Argon flow is about 50 L/min. All welds are subjected to mechanical testing and meet the requirements of AWS D3.6M:1999.
Fig. 6. Butt welds of pipe in 5G at 1-7 bar
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5 Sea-Trial of The Welding Repair System Sea-trial of the welding repair system is carried out in Bohai Sea in China. After the chamber installation, pipeline alignment, water draining by compressed air from board, pipeline cutting and pipe ends sealing, a 5G girth weld of sub-sea pipe is produced, and the welding process is controlled remotely by the welder on the surface after the welding head had been installed on the pipe by the diver in the chamber. A series of photos of sea-trial are shown as below.
Fig. 7. The hyperbaric welding chamber installation
Fig. 8. Welding head installation in the chamber
Fig. 9. The welding process controlled remotely
Fig. 10. Arc image under high air pressures
Fig. 11. Girth weld of sub-sea pipe made in the sea-trial
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6 Conclusions The research reported here has confirmed that GTAW welding process is capable of producing good quality welds even at high air pressures. Parameters and machines have been developed for positional and orbital welding operations. On the basis of this work, it may be concluded that: z z z z
On the condition of fire-resistant ability and perfect weld pool protection can be assured in the chamber, GTAW also can be realized under high air pressures. Arc voltage feedback plays a key role in GTAW repair of sub-sea pipelines, including arc ignition, arc length control, and flexibility of repair operation. Under high air pressures, arc is stable, and static characteristic curves are similar to hyperbaric welding process under Argon ambient conditions. Under high air pressures, acceptable linear welds and pipe butt welds can be produced.
Acknowledgement. The authors would like to thank current and former sponsors of the Underwater Hyperbaric Welding Research programmers: Ministry of Science and Technology of the People’s Republic of China, National Natural Science Foundation of China, and Offshore Oil Engineering Co., Ltd.
References [1] Hart, P., Richardson, I.M., Nixon, J.H.: The Effects of Pressure on Electrical Performance and Weld Bead Geometry Inhigh Pressure GMA Welding. Welding in the World 45(11/12), 29–37 (2001) [2] Richardson, I.M., Woodward, N.J., Billingham, J.: Deepwater Welding for Installation and Repair – A Viable Technology. In: 12th Int. Offshore and Polar Eng. Conf. ISOPE, Kitakyushu, Japan, vol. 4, pp. 295–302 (2002) [3] Woodward, N.J., Yapp, D., Blackman, S.: Diverless Underwater GMA Welding for Pipeline Repair Using a Fillet Welding Sleeve. In: ASME 5th International Pipeline Conference (IPC), Calgary, Alberta, Canada, vol. 2, pp. 1475–1484 (2004) [4] Akselsen, O.M., Fostervoll, H., Ahlen, C.H.: Hyperbaric Gas Metal Arc Welding of API X65 Pipeline Steel at 12, 25 and 35 Bar. In: 18th Int Offshore and Polar Eng. Conf. ISOPE, Vancouver, BC, Canada, pp. 246–253 (September 2008) [5] Akselsen, O.M., Fostervoll, H., Hårsvær, A.: Weld Metal Mechanical Properties in Hyperbaric GTAW of X70 Pipeline. International Journal of Offshore and Polar Engineering 16(3), 233–240 (2006) [6] Nixon, J.H.: Underwater repair Technology. Woodhead Publishing Ltd (2000) ISBN: 185573-2394
Part V
Special Robot Technology and Systems
The Mechanism Design of a Wheeled Climbing Welding Robot with Passing Obstacles Capability Minghui Wu1,2, Xiaofei Gao2, Z. Fu2, Yanzheng Zhao2, and Shanben Chen3 1
School of Mechanical Engineering, Xiangtan University, Xiangtan 411105, P.R. China 2 State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, P.R. China 3 Welding Engineering Institute, Shanghai Jiao Tong University, Shanghai 200240, P.R. China
Abstract. The paper describes the mechanical design of a new wheeled climbing robot which is planned to weld and inspect large scale non-structure equipment. The robot includes a 5-DOF manipulator and three pairs of novel wheel-mobile obstacle-passing units. The magnetic adhesion mechanism is a style of structure magnet which is mounted under the chassis of the robot and has a large payload. The lifting mechanism can pull the wheeled unit off the wall surface and adjust the gap between the magnet and the surface to change the adhesion force, using only one actuator. The robot with the novel mechanism has the advantages of having small number of motors, good obstacle-passing and large payload capabilities. While climbing vertical surface, the experiment shows that the robot can pass over a 70mm high obstacle and can carry 50KG payload.
1 Introduction In the process of manufacture and maintenance of large scale non-structure equipment, such as ships, oil storage tanks, nuclear electricity equipment and other steel fabrications (Fig 1), welding and inspecting are the major works. These works have bad work environment and great danger. Using mobile robots to implement these works is an attractive alternative to conventional methods. In the past half century, it has become a hot topic and many researchers have launched plenty of works and manufactured different types of climbing robots which are used in the manufacturing and daily maintenance of large equipments. The robot aimed at welding and inspecting is required to climb on vertical wall and carry with some sensors and attachment equipment, it requires the robot to have a high mobility and large payload capabilities. But unfortunately, these usually lead to that the robot has a complex structure and large weight. It is a challenge to develop a climbing welding robot which has satisfying mobile and payload capabilities but light weight and simple structure. For wall climbing robot, the locomotion T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 401–409. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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mechanism and adhesion mechanism are the keys. Many researchers have done much work on this field and some new climbing welding robots have been developed. Track and wheel are the two most common locomotion mechanisms of climbing robot. As mentioned in Ref [1], a four-wheel mobile welding robot developed for welding cabin grid component of ship. The robot has a 2-DOF cross manipulator and can adjust the welding torch to trace the welding seam precisely. But the robot has no adsorption mechanism. Considering most of the welding robots work in the ferromagnetic environment and permanent magnet adsorption has outstanding performance, permanent magnetic adsorption wheeled mechanism and tracked mechanism are adopted for robot. For example, Fabien Tache developed a magnet-wheel climbing robot for inspecting complex shaped pipe [2]. While magnetic wheel climbing robots have small contacting area between wheels and ground, it leads to low magnetic efficiency. On the contrary, the magnetic tracked robots have big contacting area and adhesion force. The magnet-track climbing robots are paid enough attention [3]. In order to overcome these disadvantages, a non-contact magnet adhesion mechanism called as structure magnet was developed for wall climbing robot [4-5] and which is mounted under the chassis. From the paper of Ref [6], we can calculate the value of the magnetic energy density (MED) of magnetic wheel is about 22.5, while the value of the structure magnet is approximately 52. The paper of Ref [5] proposed a welding robot with the type adhesion mechanism and its payload capability can reach as high as 100 kg.
Fig. 1. Ship and oil tank welding scene
These robots, as noted above, don’t have mostly passing obstacles capability and can’t change the magnetic force. These disadvantages restrict the robot’s practical use. To overcome them, some robots are equipped with special mechanisms within their structure. For example, a novel solution to a mobile climbing robot with magnetic wheels was described in Ref [7], the robot has 4 pairs of magnetic wheels and the forward and backward pairs can be moved up and down by linear actuators in order to pass obstacles. Another robot of “Pipe-Surface Inspection Robot” [8] which has 3 pairs of magnetic wheels can traverse flanges along the outside of piping. Although the two robots have passing obstacles ability, the former one needs 8 wheels to surmount obstacles and the robot of Suzuki needs 12 actuators for force-decreasing and wheel-lifting. This leads to a complicated structure.
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2 Objectives and Challenge The paper proposed the project that the All-position Autonomic Welding (APAW) robot is planned to develop and the robot is aimed at welding and inspecting large scale non-structure equipment, such as ships, large storage tank and other steel fabrications. While in process of welding and inspecting, the APAW robot often need to climb on vertical walls, pass obstacles. At the same time, the APAW robot need carry some heavy equipment, such as power equipment, power cables, welding torch, control unit, sensors and others. So the robot should have high mobility and payload abilities. Although some literatures show some wheel climbing robots without additional mechanisms can surmount simple obstacles [9]. But for more difficult obstacles, these robots can do nothing. As we said in section 1, some robots use active elements or extra mechanisms to lift up the wheels [7-8], but these robots require too many wheels or actuators which makes the robot huge, expensive and difficult to control. Some examples of the real work environment for manufacturing ship and storage tank are shown in Fig 1. Considering most of the welding climbing robots work in the ferromagnetic environment and permanent magnet adsorption has outstanding performance, for a welding climbing robot, the magnet is a good select. As mentioned previously, there are a great number of robots with magnet adhesion have been developed by researchers in different organizations. There are some problems in current welding robots need to solve: 1). Magnet-wheel robot and magnet-track robot have its own advantages and disadvantages. It is a challenge to design robot which has both high mobility and payload capabilities. 2). Most climbing welding robot with passing obstacles capability have complicated structure and too much motors.
3 Concept Structure of the Robot On the basis of analyzing the existent wall climbing robots and mobile welding robots, we take the advantages of these robots and design a novel adhesion mobile mechanism for the robot. The robot is composed of a 5-DOF manipulator and three identical such mechanism. The concept structure of the robot is shown Fig 2. Each adhesion mobile mechanism includes a wheel unit, a structure magnet and a lifting mechanism (Fig 3). The wheel unit includes two driving wheels and an axle. The turning of robot is realized by controlling the speed and direction of both wheels with differential steering. The structure magnet, which is mounted under the chassis of the robot, is composed of yoke and permanent magnet. The lifting mechanism includes a spindle, two trapezoid nuts and two trails. The screw-pitch of the two trapezoid nuts is different, one is mounted on adhesion unit and the other is mounted on axle. The spindle is driven by an actuator through a timing belt and making the wheel unit and structure magnet move up and down together. When the robot meets an obstacle the three pairs of adhesion mobile mechanism are pulled off the wall in turn, by this way the robot can climb over obstacles. At the same time the lifting mechanism can move the structure magnet up and down relative to the wheel unit and this leads to the air gap changing between the
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structure magnet and wall. By this way the magnetic adhesion force is adjusted by changing the air gap. The robot with the novel mechanism has large payload and passing obstacles capabilities.
Fig. 2. The concept structure of the robot
Fig. 3. Sketch of adhesion mobile mechanism
4 The Mechanism Design of the Robot We develop a prototype base on the concept structure of the robot which is composed of a 5-DOF manipulator and three identical adhesion mobile mechanisms (Fig 4). The CAD-model of adhesion mobile mechanism is shown in Fig 5. There are fourteen suits of actuator and controller for the robot all together, 5 for manipulator, 6 for driving wheels and 3 for lifting mechanism. The outline dimension of the robot is about 680 mm in length, 440 mm in width, and 280 mm in height. The height of robot can change from 220 mm to 300 mm by the lifting mechanism. To save weight, most parts of robot are made of aluminum alloy. Adding the manipulator the total weight of the robot is about 50 kg.
Fig. 4. The structure of the robot
4.1 The Design of the Manipulator To achieve the accurate control of welding torch optimal orientation and dynamic process of welding is the key to realize the high quality robot weld. In the process of manufacture and maintenance of large scale non-structure equipment, there are some complicated welds, such as fillet welds and curving welds. While moving
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and passing over obstacles, it is necessary for the robot that to accomplish the coordination control of the end effector of manipulator. Theoretically, a 6-DOF manipulator can point to any position with any orientation in its workspace. In the process of welding, it is not necessary for the DOF that the weld torch revolves about the axis of welding wires, so an articulated 5-DOF manipulator meets the requirement of welding complicated welds in non-structure environment. The photo of the manipulator is shown as Fig 5. The wrist of robot has two joint whose axes are orthogonal. Velocity and position control accuracy of manipulator mobile has special requirement, so DC servomotors and harmonic reducers are selected to drive wheels of the robot. The length L1of arm 1 is 150 mm (the distance between Z1and Z2). The lengths of other three arms are 180 mm, 250 mm, 80 mm respectively. Load capability of the manipulator is 30 N.
4.2 The Design of the Wheeled and Lifting Mechanism The wheel unit includes two driving wheels, an axle and two actuators with planetary gearboxes (Fig 5). The wheel’s diameter is R=135 mm and covered with a thick layer of polyurethane rubber to increase the friction, and each wheel is driven by an actuator separately. The turning of robot is realized by controlling the speed and direction of both wheels with differential steering. Actuators mounted inside axle drive wheels through another gear reducer (i=2) which is fixed inside the wheel (Fig 5). We select a DC Servomotor with a four stages planetary gearbox (i=304). The normal output torque is 18Nm and the output power is 78W. With the normal output speed after the planetary gearbox being 19 rpm, we can calculate the motion speed is (19/2)*pi*R=4 m/min. This value is fast enough for robot to weld and inspect.
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(a) Adhesion mobile mechanism; (b) Partial view. Fig. 5. CAD-model of the adhesion mobile mechanism
The structure of lifting mechanism is shown in Fig 5. The lifting mechanism includes two trapezoid screw nuts, a trapezoid screw spindle, an actuator, a timing belt and two linear motion guides. The two nuts have different pitches, the one with 3 mm pitch is mounted on the yoke of absorption unit and the other with 5
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mm pitch is fixed on the axle of wheel-unit. The trapezoid screw spindle has two segments of screw which fits the two nuts respectively and forms kinematic pairs. When the spindle is driven by the actuator through the timing belt and pulleys the wheel-unit and absorption unit move up and down along the linear motion guides. The wheel-unit can be lifted 70 mm high, while the gap width between the adhesion unit and surface can be changed from 2 mm to 20 mm (Fig 6). In order to reduce tension force between spindle and nuts, four springs are installed between adsorption unit and axle around the guide bars to produce internal balanced force. The coefficient of spring elasticity is k=50 N/mm. By this means, the frication force is reduced between the spindle and the nuts.
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(a) The structure magnet in highest position; (b) The structure magnet in lowest position Fig. 6. Photos of moving up and down magnetic adhesion unit
4.3 Optimal Design of the Adhesion Unit On the basis of analyzing the existent adhesion mechanisms of wall climbing robot, we take the advantages of the track and wheel adhesion mobile mechanism and select the type of structure magnet as the magnetic adhesion unit of the robot. The robot has three adhesion units in all, and each consists of a piece of yoke and many pieces of permanent magnet (Fig 7). We select sintered NdFeB (N45SH) as adhesion unit’s material; the yoke material is electric pure iron DT which has high magnetic permeability and saturation magnetic induction. Suppose that vertical surface is Q235 steel board and its thickness is 10 mm. The adhesion force and weight are the most important performance parameters of adhesion unit. The optimization target is to get as large force as possible with the least possible weight of adhesion unit. Magnets, air gap and adsorption surface form a complex magnetic circuit, and the parameters of thickness, width, yoke iron thickness and air gap thickness have large influence on the adsorption force. Optimal design of the structure parameters of magnets has great significance. Traditional models always lead to large errors, but the finite element methods (FEM) is an effective way to solve these problems. In this paper, the finite element software Ansoft Maxwell V10 is used to optimize the structure parameters of magnet adhesion unit. Based on the optimal result, we made a sample of magnetic
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adhesion unit (Fig 7). The width of permanent magnet 1 and permanent magnet 2 are respectively 25 mm and 50 mm; the thickness of yoke and permanent magnets are respectively 9mm and 12mm;the distance of space between two permanent magnets is 11 mm. The size of each magnetic adhesion unit is 244×120×21 mm3 and the total weight of one unit (including permanent magnets and yoke) is Gm=4.33 kg. We tested the adhesion force of the magnetic adhesion unit in different heights of air gap. The result of test and simulation are shown in Fig 9.
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5 Experiment of Passing Obstacles We designed an experiment plant on which is used to simulate the manufacturing environment of large non-structure equipment. Some sensors, such as distance and visual sensors are installed on the robot to recognize obstacle and measure its distance, width and height and design a real-time controller to control robot to pass over obstacle. In experiment, the robot moved up and down on vertical surface and overcome a 70 mm high obstacle. The operations and the sequence of overcoming obstacles are shown in Fig 9.
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(a) The photo of experiment plant; (b) The robot climbing on vertical surface; (c) The robot passing obstacle Fig. 9. Photos of the robot overcoming the obstacle on vertical surface
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6 Conclusion and Future Work In this paper, a climbing welding robot with a novel lifting mechanism and adhesion mechanism is proposed. The lifting mechanism can pull the wheel unit off the wall and change the magnetic adhesion force to help the robot pass obstacles, using only one motor. The adhesion mechanism is a structure magnet which is mounted under the robot’s chassis and has a bigger adhesion force than the magnetic wheel. The new design not only reduces the number of motors but also makes the robot with better mobility and payload capabilities. The experiment shown that the climbing welding robot works well and has a large payload capability (≥50 kg) and can pass 70mm high obstacles on a vertical surface. Now, although experiments have proved that the new mechanism works well, the robot system needs to be perfected and motion control need to be further studied. The ongoing work also stresses on integrating sensors and the necessary electronics for controlling the robot. The robot is planned to be used in welding and inspecting some large equipments. In future research, we plan to develop an industrial version intelligent welding robot which can navigate, pass obstacles and seek seam automatically in an unknown environment. Acknowledgement. The support of the national High Technology Research and Development Program of China, through the Ministry of Science and Technology of China Grant No. 2009AA04Z221, is gratefully acknowledged.
References [1] Yang Bae, J., Byoung Oh, K., et al.: Seam tracking and welding speed control of mobile robot for lattice type welding. In: Proceedings of ISIE 2001 IEEE International Symposium on Industrial Electronics, June 12-16. IEEE, Piscataway (2001) [2] Tache, F., Fischer, W., et al.: Compact magnetic wheeled robot with high mobility for inspecting complex shaped pipe structures. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, October 29 -November 2. IEEE, Piscataway (2007) [3] Gao, X., Xu, D., et al.: Multifunctional robot to maintain boiler water-cooling tubes. Robotica 27(6), 941–948 (2009) [4] Gui, Z., Chen, Q., et al.: Wall climbing robot employing multibody flexible permanent magnetic adhesion system. Chinese Journal of Mechanical Engineering 44, 177–182 (2008) [5] Kitai, S., Tsuru, K., Hirose, S.: The Proposal of Swarm Type Wall Climbing Robot System Anchor Climber. In: Proc. IROS, pp. 3999–4004 (2005) [6] Fischer, W., Caprari, G., et al.: Foldable magnetic wheeled climbing robot for the inspection of gas turbines and similar environments with very narrow access holes. Industrial Robot 37(3), 244–249 (2010)
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[7] Bridge, B., Leon Rodriguez, H.E., et al.: Field trials of a cell of climbing cooperating robots for fast and flexible manufacturing of large scale engineering structures. In: Proc. of The 12th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR (2009) [8] Suzuki, M., Yukawa, T., et al.: Mechanisms of Autonomous Pipe-Surface Inspection Robot with Magnetic Elements. In: 2006 IEEE International Conference on Systems, Man, and Cybernetics (2006) [9] Gui, Z., Chen, Q., et al.: Optimization of permanent-magnetic adhesion device for wall-climbing robot. Diangong Jishu Xuebao/Transactions of China Electrotechnical Society 21(11), 40–46 (2006)
Anytime Ant System for Manipulator Path Planning D. Wang1, N.M. Kwok1, G. Fang2, and Q.P. Ha2 1
School of Mechanical & Manufacturing Engineering, University of New South Wales, Australia e-mail:
[email protected],
[email protected] 2 The School of Electrical, Mechanical and Mechatronic Systems, University of Technology, Sydney, Australia e-mail:
[email protected]
Abstract. An efficient algorithm for manipulator path planning is presented in this paper. Because of the complexity of the problem nature, it frequently takes a long time for the planner to find an optimal path. This drawback may hinder a robotic manipulator system from many real-time applications. In this research work, the concept of anytime algorithm is integrated into a novel swarm intelligence method, the Ant System with Negative Feedback (ASNF). With the proposed Anytime Ant System (AAS), a planner is able to find a suboptimal solution quickly, then improve the quality of this solution while time allows. Simulations based on a two-link manipulator have been carried out to demonstrate the feasibility and effectiveness of the proposed approach.
1 Introduction This research studies the problem of manipulator motion planning, which can be formulated as finding a series of feasible poses or joint angles connecting the initial configuration and the desired goal configuration. Researchers have invested heavy efforts on this topic and many insightful approaches have been presented [1]. In the potential field method, a manipulator is designed to be repelled by obstacles and attracted by its goal [2]. The potential field method and its variants are mathematically simple and fast but may suffer from being trapped in local minima [3-5]. Approaches based on the octree model avoid the time-consuming process of generating configuration space and find collision free paths by searching grid points directly in the Euclidean space [6, 7]. These algorithms are fast but may fail to find a feasible solution even if it exists. Heuristic algorithms, such as the Probabilistic Roadmap method (PRM) [8] and Rapidly-exploring Random Tree (RRT) method [9], have been developed to enhance the search process in configuration spaces. Swarm intelligence methods, such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) have also been successfully applied in the manipulator motion planning and collision avoidance problems [10-12]. T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 411–420. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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Although some aforementioned algorithms are able to produce solutions with good quality, they can take an extended period of time to run, which is not always practical in real applications. The class of Anytime Algorithms is algorithms that trade execution time for quality of results [13-15]. Anytime algorithms are able to return a suboptimal solution, whose quality depends on the amount of computation they were able to perform. The answer generated by anytime algorithms is an approximation of the correct answer and they are improved progressively over time. In addition, from observing the success of many natural species, several novel algorithms had been developed for robotic manipulator planning. Inspired by the experimental studies on ant foraging behavior under crowded conditions, an Ant System with Negative Feedback approach (ASNF) has been present in [16]. ASNF differs from existing ACO algorithms by introducing a new pheromone, the crowded pheromone, into the algorithmic framework. The crowded pheromone is determined by an individual’s evaluation on current solutions and acts as negative feedback in the framework. The interplay of crowded pheromone and path seeking pheromone forms a self-adaptive system for best total performance. In this paper, the ASNF is applied in the manipulator path planning and collision avoidance. The presented Anytime Ant System (AAS) integrates the concept of anytime computing into ASNF. AAS is designed to be able to generate an initial solution quickly and improve its quality over subsequent iterations. In section 2, we will give a brief introduction on the ASNF. Section 3 introduces the Anytime Ant System approach in detail. Section 4 tests the presented approach with a 2-link manipulator case. Conclusions and future work are given in Section.
2 Ant System with Negative Feedback 2.1 Ant Colony Optimization The Ant Colony Optimization algorithm (ACO) is inspired by observing real ants’ forging behavior. It was found that ants deposit chemicals called pheromone on the ground when they walk between a food source and their nest. The initial path arises from the random exploration of ants. Once a feasible path has been found, pheromone will be deposited on the path by the returning ants. Since ants prefer to search for food resource following the paths with higher pheromone concentration, the difference of pheromone intensity is amplified when a path has been taken by more ants. Through this positive feedback mechanism, ants are capable of transporting food along the shortest path between food sources and nests [17]. The first ACO algorithm was proposed by Dorigo and colleagues in the 1990s and is known as the Ant System (AS) [18]. Several modifications have been proposed to improve the performance of the basic AS algorithm. In the Ant Colony System (ACS), each ant performs a local pheromone update in addition to the global pheromone update. This decreases the pheromone concentration on the traversed edges and encourages subsequent ants to choose other edges [19]. In the MAX-MIN Ant System (MMAS),only the ant with current best solution updates
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the pheromone values [20]. The pheromone values in MAX-MIN Ant System are bounded. For a comprehensive review of ACO algorithms, refer to [21].
2.2 Ant System with Negative Feedback The Ant System with Negative Feedback (ASNF) has been proposed in [16]. The ASNF is inspired by the experimental studies on ant foraging behavior under crowded conditions [22, 23]. Two types of diamond-shaped bridges used in their experiments are shown in Fig. 1. The first bridge has two branches with equal lengths but different widths. The second bridge has two branches and also with different lengths and widths. Experiments have shown that a wider branch is always preferred by ants in highly crowded conditions, even if it is longer. The reasons underlying this phenomenon are: 1) The difference in travel duration between the two branches of the bridges because of that congestion on the narrow branch results in a longer travel duration. 2) The “pushing” actions between ants making head-on encounters at the entrance of the two branches of the bridges, as shown in Fig. 2. It was found that overcrowding on a narrow branch tends to result an increase in the travel time and a number of “pushing” action [23]. In a “pushing” scene, when two ants meet at the entrance of branches, the ant from a crowded path diverts another ant to a path with better traffic condition (Fig. 2). As a result, ants in the experiments choose the wider path which minimizes their travel time instead of path length. In a word, the path selection is a trade-off between travel time and travel cost. In a foraging process, ants prefer to choose paths with higher pheromone concentration. The pheromone on certain path acts as a positive feedback, through which a shortest path to food source can be established [17]. In the experiments described above, congestion occurs on the narrow passage, which slows down the progression of the ants and leads to a decrease in food transportation efficiency. The “pushing” behavior between ants now plays the role of negative feedback, which diverts ants to other paths. The interplay between pheromone accumulation and “pushing” behavior forms a self-adaptive foraging strategy. Inspired by findings aforementioned, the Ant System with Negative Feedback (ASNF) has been presented in [16]. Unlike other existing ant based approaches, two types of pheromone are used in ASNF. The first one is called “Path Seeking Pheromone” (PSP), which works the same way as pheromone in traditional ACO algorithms. The PSP is deposited by ants on their ways to attract other ants. The second pheromone is called “Crowded Pheromone” (CP), which denotes the degree of crowdedness of an ant's current path. The meaning of CP should vary with different problems. For example, it is defined as a function of the number of “collisions” between ants. A “collision” is defined to be a situation that an ant is in another ant's vicinity. When an ant is on its way back to the nest, it counts the number of “collisions”. If the collision number exceeds a threshold value, the ant deposits an amount of CP on its path. The methodology of ASNF is detailed below.
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In the initialization phase, ants are positioned at their starting points. At each iteration, an ant senses the pheromone concentration at its neighbouring locations and determines its next position in a probabilistic manner. Consider a grid map shown in Fig. 3, in which an ant is positioned at P. The possible next position will be selected from its eight neighboring grids denote by Pi , i = 1,2, … 8 . The distances from P to Pi are denoted by d PPi .
For ant k, the transfer probability (TP) from point P to Pi is given by: ⎧ [τ Pi ]α [η PPi ]β (1 + [ϕ Pi ]γ ) ⎪ , if Pl ∈ N ( s k ) α β γ TPi k = ⎨ ∑ + ϕ ( 1 [ ] ) k [τ P ] [η PP ] Pi P ∈N (s ) i i ⎪ l 0 otherwises ⎩
Fig. 1. Bridges in Dussutour et al’s Ant Experiments [23]
Fig. 2. Ant Pushing under Crowded Conditions [22]
Fig. 3. P and Eight Neighboring Points
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where τ Pi is the PSP concentration at point Pi . η PPi is the heuristic value representing the cost of choosing point Pi , which can be set as 1 d PPi , d PPi is the dis-
tance between point P and Pi ). ϕ Pi is the CP concentration at the point Pi . α, β and γ are parameters indicating the relative importance of the path seeking pheromone, the heuristic value and the crowded pheromone, respectively. N ( s k ) is the set of feasible neighboring points Pl . In each iteration, the path seeking pheromone concentration of each point P is updated as follows: m
τ P ← [(1 − ρ1 ) ⋅ τ P + ∑ Δτ Pk ]ττ max min k =1
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⎧⎪Q + Q2C Pk , if ant k goes to nest via P Δτ Pk = ⎨ 1 ⎪⎩ 0, otherwises
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ϕ P ← [(1 − ρ 2 ) ⋅ ϕ P + ∑ Δϕ Pk ]ϕϕ max min k =1
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where ρ 2 is the CP evaporation rate. ϕ max and ϕ min are the upper and lower bounds of CP, respectively. Δϕ P represents the quantity of crowded pheromone laid on point P by ant k. Δϕ P is defined as: k
⎧⎪Q ⋅ (C nk − Q4 ), if Cn ≥ Q4 Δϕ Pk = ⎨ 3 ⎪⎩ otherwises 0,
(5)
k where Q3 is a constant. Cn is the number of collisions recorded by ant k on point P to nest. Q4 is a threshold parameter. An ant will start to deposit crowded phek romone on its path when Cn ≥ Q4 .
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ASNF differs from other ACO algorithms by introducing a new pheromone, the crowded pheromone, into the algorithmic framework. The crowded pheromone is determined by individual's evaluation on current solutions and acts as negative feedback in the framework. The interplay of crowded pheromone and path seeking pheromone forms a self-adaptive system for best overall performance.
3 Anytime Ant System for Manipulator Path Planning In this section, we introduce the proposed Anytime Ant System approach. As other swarm intelligence methods, it may take a long time for the ASNF to find the best solution. The concept of anytime algorithm is adopted to find a suboptimal solution quickly. At any time the algorithm maintains the best solution found so far can be returned and will be improved continuously as iteration continues. Figure 4 gives the flow chart of the proposed approach. Firstly, a configuration space is generated for a given task. The configuration space is then decomposed into grids at a certain precision level. The manipulator path planning and collision avoidance problem is hence converted into a grid search problem. The ASNF is then adopted to search for feasible solutions. Please note that the search process will be greatly accelerated when the grid cells are large. However, the planner may fail to find a solution because of the rough approximation of search space. If a feasible solution cannot be found in a certain time or number of iterations, the planner will re-decompose the configuration space into smaller grids. In ASNF, each grid contains a certain amount of pheromone, i.e. PSP and CP. When a grid is divided into smaller grids, its pheromone intensities are inherited by all descendants. Every ant in ASNF keeps a memory of the current best solution found by it. Therefore, the search process can be interrupted and restarted at any time point. Once a suboptimal solution is found, it will be evaluated. This path will be executed if it meets the lowest requirement. Otherwise, ASNF continues to run until a better solution is found. The selected path will be improved continuously while a planned path is executed. The quality of real path depends on assigned computation time.
4 Case Studies The simulations have been carried out in a 10m×10m area as shown in Fig. 5. A 2link RR manipulator is supposed to move from its initial configuration to the goal configuration while avoids collision with two obstacles. The first link is 2.5 meters long and 0.3 meters wide. The second link is 1.5 meters long and 0.3 meters wide. The pose of each link is represented by the angles between links and the X coordinate.
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Fig. 4. AAS Flowchart
The initial pose of the manipulator is shown in Fig. 5, where the joint angles from link 1 and link 2 to X coordinate are 340 degrees and 320 degrees, respectively. The manipulator’s initial pose and desired pose are shown in red polygons and black polygons, respectively. The base of this manipulator locates at (5, 5).The position of its end effector is at (8.498, 3.181) and is supposed to reach goal (5, 8). The configuration space is shown in Fig. 6. At the goal configuration, the joint angles from link 1 and link 2 to X coordinate are 120 degrees and 34 degrees, respectively. The ASNF parameters used in the simulations are set as: m = 50 , α = 1 , β = 1 , γ = 0.5 , ρ1 = 0.01 , ρ 2 = 0.01 , Q1 = 3 , Q2 = 0.5 , Q3 = 1 , Q4 = 5 , τ max = 50 , τ min = 0 , ϕ max = 10 , ϕ min = 0 . The evaluation function is set to be a function of the path length in configuration space, such as f (cost) < k ⋅ d q start q goal
(6)
where d q start q goal is the straight-line distance between q start (340 ,320 ) and q goal (210 ,124 ) , which is equal to 161.6 in this case. k is a positive constant with k > 1 .
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Fig. 5. Simulation Environment
Fig. 6. Configuration Space
In the initialization process, the configuration space is divided into a grid map at units of 10 degrees, as shown in Fig. 7. Note that since the unit of coordinates is 10 degrees, the start pose and goal pose are now q start (34,32) and q goal (21,12) , the distance between the two points is 23.854. We set k = 2.5 , then the threshold given by evaluation function (6) is modified to f (cost) < k ⋅ d q start q goal = 2.5 * 23.854 = 59.635
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The simulations have been carried out on a laptop with Inter Celeron 2.2GHz and 2G RAM, running Matlab 2009b in Windows 7 environment. It takes the planner ' 4.396 seconds to find the first solution (87 iterations). In Fig. 7, the unit of θ1 and
θ 2' is 10 degrees. The planned path is given by the polyline connecting q start and q goal . The total length of the resultant path is 105.225, which does not satisfy the evaluation function. The iteration continues and more solutions are obtained. Figure 8 shows a solution found in iteration 548. It takes the planner 46.747 seconds to compute this path. The length of this path is 59.355, which is less than the threshold in (7). This path is considered acceptable, so the manipulator begins to move along this path. The AAS is able to improve the resultant path during task execution. The quality of the final path depends on assigned computation time. Our simulation is stated as below: when the manipulator reaches pose (30, 23), the task execution is paused. The configuration space is decomposed at unit of 5 degrees. Note that q start is now at (68, 64) and q goal is at (42, 25). The planner continues to run and a near optimal path is show in Fig. 9. The path length of the whole path is 76.355.
5 Conclusions and Future Work This paper has presented an anytime algorithm for manipulator path planning. With the proposed Anytime Ant System, a planner is able to search a collisionfree path in the configuration space. The main merit of this approach is that an initial suboptimal solution can be found very quickly, which makes it suitable for
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real-time applications. This approach is resolution complete. The quality of the resultant paths can be improved as iteration continues. Simulations with a two link RR manipulator have been carried out to demonstrate the feasibility and effectiveness of proposed approach. In this research work, a solution’s quality is evaluated by a function of path length in the configuration space. Future work will include taking other factors into consideration, such as minimizing overall task execution time and allowing sufficient clearance to obstacles. We are also considering extending this approach to redundant manipulators with higher degrees of freedom.
Fig. 7. A Path found at Iteration 87
Fig. 8 A Path found at Iteration 548
Fig. 9. The Final Path
References [1] Latombe, J.-C.: Robot motion planning. Kluwer Academic Publishers, Boston (1991) [2] Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. International Journal of Robotics Research 5, 90–98 (1986) [3] Brock, O., Khatib, O.: Real-time re-planning in high-dimensional configuration spaces using sets of homotopic paths. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 550–555 (2000) [4] Chotiprayanakul, P., Wang, D., Kwok, N.M., Liu, D.K.: A haptic based human robot inter-action approach for robotic grit blasting. In: Proceedings of the 25th International Symposium on Automation and Robotics in Construction (ISARC 2008), Vilnius, Lithuania, June 26-29, pp. 148–154 (2008) [5] Chotiprayanakul, P., Liu, D.K., Wang, D., Dissanayake, G.: Collision-free trajectory plan-ning for manipulators using virtual force based approach. In: Proceedings of the International Conference on Engineering, Applied Sciences, and Technology (ICEAST 2007), Swissôtel Le Concorde, Bangkok, Thailand, November 21-23 (2007) [6] Wu, L., Hori, Y.: Real-time collision-free path planning for robot manipulator based on octree model. In: Proceedings of the 9th IEEE International Workshop on Advanced Motion Control, pp. 284–288 (2006) [7] Hamada, K., Hori, Y.: Octree-based approach to real-time collision-free path planning for robot manipulator. In: Proceedings of the 4th International Workshop on Advanced Motion Control, March 18-21, vol. 2, pp. 705–710 (1996)
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[8] Kavraki, L.E., Svestka, P., Latombe, J.-C., Overmars, M.H.: Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Transactions on Robotics and Automation 12(4), 566–580 (1996) [9] LaValle, S.M., Kuffner, J.J.: Rapidly-exploring random trees: Progress and prospects. In: Donald, B.R., Lynch, K.M., Rus, D. (eds.) Algorithmic and Computational Robotics: New Directions, pp. 293–308. A K Peters, Wellesley (2001) [10] Tewolde, G.S., Sheng, W.: Robot Path Integration in Manufacturing Processes: Genetic Al-gorithm Versus Ant Colony Optimization. IEEE Transactions on Systems, Man and Cyber-netics, Part A: Systems and Humans 38(2), 278–287 (2008) [11] Wen, Z., Luo, J., Li, Z.: On the global optimum motion planning for dynamic coupling robotic manipulators using particle swarm optimization technique. In: Proceedings of the IEEE International Conference on Robotics and Biomimetics (ROBIO), December19-23, pp. 2177–2182 (2009) [12] Li, H.-X., Shi, Y.-H., Wang, G.-R., Zheng, X.-X.: Automatic Teaching of Welding Robot for 3-Dimensional Seam Based on Ant Colony Optimization Algorithm. In: Proceedings of the Second International Conference on Intelligent Computation Technology and Automation (ICICTA 2009), October 10-11, vol. 3, pp. 398–402 (2009) [13] Zilberstein, S.: Using Anytime Algorithms in Intelligent Systems. AI Magazine 17(3), 73–83 (1996) [14] Ferguson, D., Stentz, A.: Anytime RRTs. In: Proceedings of the 2006 IEEE/RSJ Interna-tional Conference on Intelligent Robots and Systems (IROS 2006), pp. 5369–5375 (October 2006) [15] Likhachev, M., Gordon, G., Thrun, S.: ARA*: Anytime A* with provable bounds on sub-optimality. In: Advances in Neural Information Processing Systems. MIT Press, Cambridge (2003) [16] Wang, D., Kwok, N.M.: Ant System with Negative Feedback for Bushfire Fighting with Swarm Robots. Submitted to 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (2010) [17] Deneubourg, J.-L., Aron, S., Goss, S., Pasteels, J.-M.: The Self-organizing Exploratory Pattern of the Argentine ant. Journal of Insect Behavior 3, 159–168 (1990) [18] Dorigo, M., Maniezzo, V., Colorni, A.: Ant System: Optimization by a Colony of Cooperating Agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B 26(1), 29–41 (1996) [19] Dorigo, M., Gambardella, L.M.: Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem. IEEE Transactions on Evolutionary Computation 1(1), 53–66 (1997) [20] Stzle, T., Hoos, H.H.: MAX-MIN Ant System. Future Generation Computer Systems 16(8), 889–914 (2000) [21] Dorigo, M., Birattari, M., Stzle, T.: Ant Colony Optimization. IEEE Computational Intelligence Magazine 1(4), 28–39 (2006) [22] Dussutour, A., Fourcassi, V., Helbing, D., Deneubourg, J.-L.: Optimal Traffic Organization in Ants under Crowded Condition. Nature 428, 70–73 (2004) [23] Dussutour, A., Nicolis, S.C., Deneubourg, J.-L., Four-cassi, V.: Collective Decision in Ants under Crowded Conditions. Behavioral Ecology and Sociobiology 61, 17–30 (2006)
Path Planning and Computer Simulation of a Mobile Welding Robot Tao Zhang and Shanben Chen Welding Engineering Institute, Shanghai Jiao Tong University, Shanghai 200240, P.R. China e-mail:
[email protected]
Abstract. For a mobile welding robot system, we calculate its kinematic and inverse kinematic model based on the D-H method. This paper uses the method of interpolation to do path planning of a mobile welding robot with a 5-DOF arm, and adopt the method to make various welding path simulation. The trajectory planning is a constrained and non-liner implication problem. The modeling and simulation is processed under the environment of MATLAB. When the vehicle is walking across obstacles, it should do the welding at the same time. We must ensure the continuity of the welding process. The whole process is discussed in the paper.
1 Introduction Trajectory of the robot arm movement is the displacement, velocity and acceleration of robot in the process of motion. Trajectory planning is to calculate the expected trajectory based on tasks. Robot trajectory planning is a bottom plan, which basically does not involve artificial intelligence, but it is a discussion about robot trajectory planning and trajectory generation in the joint space and Cartesian space based on the robot kinematics and dynamics. Trajectory planning for a robot manipulator is a complicated problem, involving several aspects such as modeling obstacles, handling sensor information, considering robot dynamics, searching for collision-free paths and avoiding singular configurations of the robot [1]. A formal definition of trajectory planning can be found in Singh and Leu. Path planning is a closely related issue of trajectory planning [2]. Reynier etal. [3] discussed the optimal robot positioning problem among obstacles. Path planning at the kinematics level is very useful and can be further extended to include dynamic functions, since robot manipulators are essentially dynamic systems. Wang and Hamam [4] also developed an algorithm for solving the trajectory-planning problem with collision detection and avoidance. As the method of Cartesian space has many shortcomings, the way for the joint space is widely used. Commonly used method is to show the current teaching point, which takes some of the key points in the trajectory of the robot, to remember corresponding joint coordinates of these points, and then it does some interpolation between two key points. Clearly, in order to make the end of actuator along the required trajectory strictly, we must take enough points. In practical applications it often needs to operate the robot along the numerous, complex spatial trajectory. If each trajectory must be conducted, the workload will be very large. T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 421–428. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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Robot trajectory is generally circular or linear. This paper will use the method of interpolation to do path planning of mobile welding robot with a 5-DOF arm. And the welding process when the vehicle is walking across obstacles has been discussed.
2 Robot Kinematic Modeling 2.1 Mathematical Model Fig 1 shows the mobile welding robot system. The welding robot manipulators in the system have five rotational joints. Before establishing the mathematical model, we first construct the connecting rod of the coordinates properly in accordance with the Denavit-Hartenberg rules, and then we can derive robot kinematics equation. The D-H Parameter table is depicted in Table 1.
Fig. 1. The mobile welding robot system Table 1. D-H Parameter
i 1 2 3 4 5
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di: The distance of two public vertical line along the axis of rod θi: Intersection angle of two public vertical line in the surface Perpendicular to the axis of rod i ai: The distance of two axis of rod along the public vertical line αi: Intersection angle of two axis of rod in the surface Perpendicular to ai The robot kinematics equation is: ⎡ r11 ⎢r 0 0 1 2 3 4 ⎢ 21 5T = 1T 2T 3T 4T 5T = ⎢ r31 ⎢ ⎣0
r12 r22 r32 0
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px ⎤ p y ⎥⎥ pz ⎥ ⎥ 1⎦
(1)
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where: ⎧r11 = c1 c234 c5 − s1 s5 ⎪r = s c c + c s 1 234 5 1 5 ⎪ 21 ⎪r31 = s 234 c5 ⎪ ⎪r12 = −c1c234 s5 − s1c5 ⎪r = − s c s + c c 1 234 5 1 5 ⎪ 22 ⎪r32 = − s 234 s5 ⎨ ⎪r13 = −c1 s 234 ⎪r23 = − s1 s 234 ⎪ ⎪r33 = c234 ⎪p = c c a + c c a + c a 1 23 3 1 2 2 1 1 ⎪ x ⎪ p y = s1c23 a3 + s1c 2 a 2 + s1 a1 ⎪ ⎩ p z = s 23 a3 + s 2 a 2
(2)
2.2 The Method of Choosing Geometric Structure Geometric modeling is the basis for graphical simulation modeling. The accuracy and intuitiveness of modeling impact the reliability of the results and display directly. The robot is much rod linkage with comparatively complicated shape. To export robotic external form on the computer in artwork way, the robotic geometric actual form should be appropriated, and be translated into information which computer can receive. In this system, two physical factors have been used: cylinder and cuboids.
2.3 Inverse Kinematics Problem The robot inverse kinematics problem is to find the joint angle with known position and orientation of the robot. For a given robot, the solvability of the robot is whether its analytic form of inverse kinematics can be calculated. Through computing, concrete results can be obtained as follows: py ⎧ ⎪θ 1 = 180° ± φ = 180° ± arctan px ⎪ ⎪ c1 p x + s1 p y − a1 ⎪θ 2 = 180° − arctan pz ⎪ ⎪ 2 c p s p a ( ) + − + p z2 + a 22 − a 32 1 y 1 ⎪− arcsin 1 x ⎪ 2a 2 p z2 + (c1 p x + s1 p y − a1 ) 2 ⎪ ⎪ c1 p x + s1 p y − a1 ⎪θ 23 = 180° − arctan pz ⎨ ⎪ 2 2 2 2 ⎪ arcsin (c1 p x + s1 p y − a1 ) + p z + a 3 − a 2 ⎪− 2 2 2 a 3 p z + (c1 p x + s1 p y − a1 ) ⎪ ⎪(θ = θ − θ ) 23 2 ⎪ 3 c1c 23 r13 + s1 s 23 r23 + s 23 r33 ⎪ ⎪θ 4 = arctan c s r + s s r − c r 1 23 13 1 23 23 23 33 ⎪ ⎪ s1 r11 − c1 r21 θ arctan = ⎪ 5 s1 r12 − c1 r22 ⎩
(3)
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We have obtained five values of joint angle which decide the position and orientation of robot. Once the position and orientation of robot have been known, the value of each joint angle can be obtained with the results.
3 Trajectory Planning Method Robot trajectory planning is to design the law of motion of each joint based on the task robots must complete.
, T ,…, T ,can be calculated by inverse kinematics after inputting each route counts P ,P ,…,P , In this article, corresponding transformation matrix
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and then the value of each joint angle can be got. The shape of the robot is described on the computer. There are two kinds of trajectory planning programs: point-to-point motion (PTP) and continuous-path motion (CP).The cylindrical coordinate robot in this article, has used PTP and CP trajectory planning. The movement from point A to point B between which there is no middle point, which does not have been set requirements for the movement path, can be called PTP trajectory planning. In this article, we focus on the CP trajectory planning.
3.1 CP Trajectory Planning For continuous path movement, it must not only stipulate start and end points of the manipulator, but also specify several intermediate points between two points (path points).The movement must be along a specific path (path constraint). With ensuring the same attitude towards the welding, we simulate the entire robot arm, welding torch and the path by matlab. Here are the steps and simulation results of CP trajectory planning. Figure 2 shows with the welding torch's movement along the arc, the robot arm moves from the initial location to the final position. The trajectory planning ideas and processes are shown in Figure 3. Figure 4~6 shows projection of the robot arm in various planes, through the entire welding process.
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Start simulation XOZ Planar Projection 150
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3.2 PTP Trajectory Planning With Obstacles When the vehicle meets obstacles, it needs to cross the obstacles. In order to ensure the continuity of welding when crossing the obstacles, the speed of the body and welding torch must be controlled. There are seven processes from the start to complete crossing the obstacles: front wheel lift, walk, front wheel down and middle wheel lift, walk, middle wheel down and rear wheel lift, walk, rear wheel lift. The process is shown in Figure 7. Abscissa represents unit of time, while vertical axis represents unit of distance when the body is moving forward. We assume that the ratio of the vehicle speed and the welding torch speed is 1:0.782. Figure 8 shows that with the welding torch's movement along the line, while the vehicle meets obstacles, the arm moves from the initial location to the final position. The twisted state of the robot arm can be seen from the figure. More simulation results show that, he greater the ratio of the vehicle speed and the welding torch speed, the distortion of the robot arm is more serious. It is likely to cause arm damage. Therefore, the right speed ratio must be select in the experiment. 80 70 60 50 40 30 20 10 0
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Fig. 8. The Change of Robot Arm's Shape When Welding Across Obstacles
4 Conclusions The trajectory planning method in this article has interpolated space position of the actuator of the welding torch. And then through kinematics and inverse kinematics, we calculate the change of 5 joint angles of the robot arm, and determine some changes in the arm shape. This method is more intuitive, which has a certain reference value for trajectory planning of robots. The robot trajectory planning with obstacles has a certain novelty. Acknowledgement. This work is supported by National 863 plan of China under Grant No. 2009AAA042221, and Shanghai Sciences & Technology Committee under Grant No. 09JC1407100, P.R. China.
References [1] Khoukhi, A., Hamam, Y.: Optimal time-energy trajectory planning for robot with hard constraints. Control-Theory and Advanced Technology 6, 417–439 (1990) [2] Singh, S.K., Leu, M.C.: Manipulator motion planning in the presence of obstacles and dynamic constraints. International Journal of Robotics Research 10, 171–187 (1991) [3] Reynier, F., Chedmail, P., Wenger, P.: Optimal positioning of robots: feasibility of continuous trajectories among obstacles. In: Proceedings of the 1992 IEEE International Conference on Systems, Man and Cybernetics, vol. 1, pp. 189–194 (1992) [4] Wang, D.M., Hamam, Y.: Optimal trajectory planning of manipulators with collision detection and avoidance. International Journal of Robotics Research 11, 460–468 (1992) [5] Craig, J.J.: Introduction to Robotics: Mechanics and Control, 3rd edn. [6] Liu, C.L., Zhang, K., Fu, Z., Cao, Q.X., Yin, Y.H.: Trajectory Planning and Simulation of Welding Robot for IVECO Automobile Crossbeam. Robot (October 2001) [7] Ren, J.Y., Sun, H.X.: A Novel Method of Trajectory Planning in Cartesian Space. Robot (May 2002) [8] Liu, X., Fang, H.-r.: The kinematics analysis and trajectory planning of serial cutting robot. Manufacturing Automation (March 2008) [9] Leng, D.Y., Chen, M.: Robot Trajectory Planning Using Simulation. Robotics & Computer-Integrated Manufacturing (1997)
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[10] Hu, H., Brady, M., Probert, P.: Trajectory Planning and Optimal Tracking for an Industrial Mobile Robot. Mobile Robots (1993) [11] Su, J., Feng, C.: A Trajectory Planning Method For A Robot To Grasp A Moving Target. ROBOT (1994) [12] Yang, G.: The Study of Time Optimal Trajectory Planning for Robot Manipulators. China Mechanical Engineering (2002-20) [13] Wang, X.-l., Hou, Y.-b., Wang, T.: Research of Trajectory Planning of Industrial Robot Based on VC++. Industry and Mine Automation (2009)
The Control System Design of a Climbing Welding Robot Based on CAN Bus Xiaofei Gao1, Minghui Wu1, Z. Fu1, Yanzheng Zhao1, and Shanben Chen2 1
School of Mechanical Engineering, Shanghai Jiao Tong University, State Key Laboratory of Mechanical System and Vibration, Shanghai 200240, P.R. China 2 Welding Engineering Institute, Shanghai Jiao Tong University, Shanghai 200240, P.R. China
Abstract. A control system of a climbing welding robot based on CAN bus is proposed and designed in this paper. The robot is used for autonomous welding in the manufacturing and maintenance of large non-structure equipment. Under the working circumstance, it is suitable to establish the robot control system based on CAN bus. Compared with a traditional system, CAN bus has the advantage of high anti-interfering, high stability and high transfer rate. The 1st section is the introduction of the system; the 2nd section is a brief description of the robot’s structure. The 3rd section is the detailed design of the control system. The control system consists of three main parts: the establishment of a CAN bus, controlling of servo motors and receiving signals of sensors. The analyses of information, calculation of kinematic and overall control are all carried out in a PC. And the 4th section is the conclusion.
1 Introduction In the manufacturing and maintenance of large non-structure equipment such as ships, oil tanks and nuclear electricity equipment, welding works are often applied. Using robots to accomplish these works would greatly improve workers’ labor environment, working quality and efficiency. The designed welding robot has many modules such as motor control modules, sensor modules and others. Hence the data transfer and overall control of the robot are quite complicated. CAN or CAN bus (Controller–area network) is a vehicle bus standard designed to allow microcontrollers and devices to communicate with each other within a vehicle without a host computer. CAN is a message based protocol, designed specifically for automotive applications but now also used in other areas such as industrial automation. CAN is a multi-master broadcast serial bus. The devices that are connected by a CAN network are typically sensors, actuators, and other control devices. These devices are not connected directly to the bus, but through a host processor and a CAN controller. The transfer rate can reach 1Mbps under the distance of 40m. Reducing bit rate can increasing the transfer distance [1]. CAN bus has two signal wires CAN-high and CAN–low. This bus structure allows easy extension for extra nodes. New nodes wouldn’t affect existing nodes and single node’s error won’t affect others neither. T.-J. Tarn et al. (Eds): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 429–434. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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Traditional servo system control structure is made up with the upper computer, motion control card and servo-driver. Output signal from the motion control card will control the motor’s operation after driven by servo-driver [2]. Compared with traditional system, CAN bus has the advantage of high anti-interfering, high stability and high transfer rate, and also it has a simpler structure. Considering the advantage of CAN bus, it’s very suitable to establish the control system of a multimotor climbing welding robot based on CAN [3]. CAN bus have been used in some of industry robots and other robots already. Due to the high stability, some military robots also use CAN [4].CAN bus is very suitable for mobile robots [5,6] because it only requires two wires for data transfer. CAN bus also provide the good expandability for sensors and other devices [7].
2 Climbing Welding Robot Structure The climbing welding robot uses the adhesion method of permanent magnets. The magnets exert force on ferromagnetic walls and the adhesion force produces the fraction between the wheel and the wall. Hence the robot can climb on vertical walls. The mechanical structure of the climbing welding robot mainly consists of: mobile mechanism, magnetic adhesion mechanism, elevation mechanism, robot body and robot arm. The magnets are fixed on the frame between wheels, each group of wheels and magnets can be elevated by screw. On the same time, the air gap between magnets and wall surface can be adjusted to change the adhesion force. The robot arm has a DOF of 5 to control the torch.
Fig. 1. Robot structure 3D model
To accomplish autonomous welding, we should obtain enough environment information. So we adopt sensors and CCD cameras to collect information of the robots and the obstacles.
3 Control System Design The welding robot (Fig2) is used for autonomous welding in the manufacturing and maintenance of large non-structure equipment. Compared with traditional
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Fig. 2. Welding robot prototype (without sensors)
system, CAN bus has the advantage of high anti-interfering, high stability and high transfer rate. So the control system of the robot is mainly based on CAN. Fig3 is a schematic of our control system. The whole control system mainly has three parts: the establishing of CAN bus, controlling of servo motors and receiving signals of sensors. The analyses of information, calculation of kinematic and overall control are all proceeding in the PC.
Fig. 3. Control System Structure
3.1 Hardware The hardware of the control system consists of three main parts:PC, motors with servo controller, sensors and CCD cameras. CAN bus is adopted to accomplish data transfer between each part. We adopt IXATT PC/CAN interface USB-to-CAN compact to connect PC and the CAN bus. The CAN-controller of the USB-to-CAN compact is Philips SJA100 and connects to the PC through USB2.0. The servo controller is Accelnet Micro Panel (Copley Control Corp.) The CANopen distributed control architecture is supported by the panel. It can work as a CAN node operating under the CANopen protocol. The sensors fixed on robot are used to conduct preliminary perception and positioning of obstacles in welding environment and assist the robot avoiding or surmounting these obstacles. The sensors we used include ultrasonic sensors and
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infrared sensors. The infrared sensors include infrared measuring sensor and infrared approaching switch. The signal of the sensors is collected by the dsPIC30F chip (Microchip Corp.) and transferred to the PC by CAN bus.
3.2 CAN Network CAN bus is a multi-master serial bus and any node can be set as the main node [8]. In our control system, we set the node 1 (the IXATT USB-to-CAN compact) which connected with the PC as the main node and other as the bottom node. The PC communicates with the CPU in USB-to-CAN compact through USB port, and the CPU communicate with the CAN transceiver to accomplish data’s sending and receiving on CAN bus. The bottom nodes have different function while connected with CAN bus at the same time and controlled by the PC and main node (shown in Fig4).
Fig. 4. Sketch of control system structure based on can
According to the information amount and welding work radius we set the CAN bit rate at 250Kbps (or 500Kbps if welding work need higher precision, but the working radius is limited to 100m). After experiment, we find robot’s performance is stable and reliable under this bit rate.
3.3 Robot Motion Control The robot motion mechanism can be divided into three obstacle-passing moving mechanisms and a 5-DOF robot arm. Single obstacle-passing moving mechanism has three servo motors, two for wheels and one for elevation. The 5-DOF arm has 5 motors, one for each joint. So the whole robot has 14 DC servo motors. We use Accelent Micro Panel servo controller (produced by Copley Control Corp) to control the motors. Each controller controls one motor and collects the pulse signal of encoder. Each controller has 12 GPIO (General Purpose Input Output) which can be connected to position limit photoelectric switches or motor brakes or other devices to conduct some extra logical control if needed. The control loop of single servo motor is shown in Fig 5.
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Fig. 5. Control loop of a single servo motor
The inverse dynamic of the motion mechanism is calculated by the PC. PC receives the environment information from the CCD cameras and sensors to calculate target welding joint’s position information. PC also receives the motors’ information through servo controllers to calculate current situation of the robot. Combining both the robot’s and welding joint’s information the PC can derive the motion trace for each motor to accomplish the welding work and the send control commands to servo controllers through CAN bus.
3.4 Multi-sensor Network Design The sensors fixed on robot are used to conduct preliminary perception and positioning of obstacles in welding environment and assist the robot avoiding or surmounting these obstacles. The sensors we used include ultrasonic sensors and infrared sensors. The infrared sensors include infrared measuring sensor and infrared approaching switch. The ultrasonic sensors are used to exert detection of obstacles at long distance. The sensors we select are the 600 series sensor (Beijing Northking Co.) and URM37V3.1 ultrasonic module. The 600 series sensors have a range from 15cm to 10m which is used for preliminary detection; URM37 module has a range from 4cm to 5m and is used for nearer detection. In addition, URM37 can be connected to a steering motor and exert scanning detection. The ultrasonic sensors have significant errors when detecting close objects, so we use infrared sensors additionally. The type is GP2D12 produced by SHARP. We use Microchip’s dsPIC30F5015 chip to collect the sensors’ signals. The chip can perform as a CAN node and connect to PC through CAN bus. The chip collects sensors information cyclically. After collecting sensors feedback information, it calculates and processes the data preliminarily and transfers the results to PC through CAN bus. The PC further analyzes and processes the data and controls the robot’s motion. A sensor module is shown in Fig 6.
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Fig. 6. Sensor module
4 Conclusions The designed control system based on CAN bus can guarantee the information transfer among different modules of the robot and the coordination between information collection and motion control. Under this control system, the robot can accomplish simple autonomous welding work. The CAN bus also provides a good extensibility of the system. New function modules can be added to the system through CAN bus easily. Acknowledgement. The support of the national High Technology Research and Development Program of China, through the Ministry of Science and Technology of China Grant No. 2009AA04Z221, is gratefully acknowledged.
References [1] Liu, D., Deng, J., Jia, P., He, T.: Pile Robot System Based On CAN Bus Control Servo. Manufacturing Technology & Machine Tool 5, 136–137 (2009) [2] Cheng, X., Zhou, Y., Yang, L.: The Research on Networked Servo Control System Based on CAN Bus. In: 2010 Second International Workshop on Education Technology and Computer Science (2010) [3] Zhou, Y., Wang, Z., Li, J.: Design of the Control System of Tightening Machine Based on CAN Bus. In: 2009 International Conference on Intelligent Human-Machine Systems and Cybernetics (2009) [4] Fang, W., Kong, F.: Control System of EOD Robot Based on CAN. Field Bus Control Technology 32(2), 52–53 (2010) [5] Wu, X., Li, B., Yang, T.: Design of CAN Bus System for Robot Car. Journal Of Wut (Information & Management Engineering 28(6), 34–36 (2006) [6] Yao, Z., Dai, X.: Application of CAN- Bus for mobile robots. Journal of Qiqihar University 25(6), 16–19 (2009) [7] Chen, X., Xu, H., Zhang, X., Wang, L., Yang, C.: Design of mobile robot sensor network based on CAN bus. Journal of China University of Metrology 20(3) (September 2009) [8] Yang, K., Li, S., Lu, G., Tian, H.: Design of Control System of Wheel - Leg Robot based on CAN. Small & Special Electrical Machines 4, 28–30 (2009)
An Implementation of Seamless Human-Robot Interaction for Pipeline Welding Telerobotics Na Dong, Haichao Li, Hongming Gao, and Lin Wu State Key Laboratory of Advanced Welding Production Technology, Harbin Institute of Technology, Harbin, China e-mail:
[email protected]
Abstract. Although welding is an important technology for nuclear pipeline maintenance, it cannot be performed effectively up to date because of the robot can not be fully autonomous. This paper presents a human-robot cooperation telerobotics system for pipeline welding in a nuclear environment. The implementation of humanrobot welding systems can be extremely challenging when the robot is not directly controlled by the human. Depending on the task, the interaction mode is composed of continuous manual, semi-autonomous or autonomous. To address the Human-Robot Interaction (HRI) issues in such a system, a concept of seamless HRI is introduced. The main idea seamless HRI is to design a telerobotics welding system that allows a shift from manual to autonomous operation, dynamically, via different human-robot roles and relationships. These roles are Master-Slave, Supervisor-Subordinate, PeerPeer, Teacher-Learner and Full Autonomous mode by the robot. The theoretical foundations for seamless HRI are introduced. An implementation of the concept is presented and experimental results show the system get through the pipeline welding.
1 Introduction Telerobotic system plays an important role in repairing the nuclear pipe [1], because the nuclear radiation pollution caused by the chemical substances makes the maintenances by human directly difficult. Although, autonomous robots that have the anthropomorphic dimensions, mimic human-like behaviors, and include human-like reasoning are known as humanoid robots and work in this area has been ongoing for over a decade[2-6], the intelligence of robot is not so perfect to replace the human. So, the human-robot interaction is an effective way to meet the remote task. HRI is a field of study dedicated to understanding, designing, and evaluating robotic systems for use by or with humans. In telerobotic welding system, the operator in safety environment manipulates the telerobot which is in dangerous environment to execute the welding task[7]. Human can play different roles in the possible relationships among the human, the robot, and the task. There are five roles, show as Fig.1.Supervisory is that the human monitors the robot and changes the plan when necessary; Operator is that the human directly interacts with the robot to change its behaviors; Mechanic is that the human physically intervenes with the robot to change its capabilities through modifying hardware or software; Peer is that the human works with the T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 435–442. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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Fig. 1. The relationship between human and robot in teleoperation system
robot to perform a task, Usually a peer interaction between human and robot occurs at high level of behavior; Bystander is that the human is not directly involved in controlling the robot, but effect how the robot accomplishes the task [8].For example, humans in the same building can effect how a robot navigates the environment by blocking the robot or opening or closing a door. The key is how to envolute the teleoperation mode orienting to the pipeline remote welding. In this paper, a new and innovative human-machine interaction method is required to envoluate the operating mode, plan the pipeling maintenance task and accomplish the remote pipeline welding.
2 Task Planning Fig. 2 shows the pipeline remote welding process which is a new pipe replaced the damage one. A new pipe and an original pipe are assembled using welding processes. The maintenance process contains the detection, cutting, assembling and welding. First, a crack is checked out, then the cutting range is determined and the new pipe length is selected. Second, the spoiled pipe is cut down and the new one is placed to the waiting position gripped by the micro robot. Third, the first weld is done and the macro manipulator takes the micro robot away from the pipe. Fourth, the micro robot is assembled on the pipe again and the second pipe groove must then be welded.
Fig. 2. Scheme of nuclear environment pipeline maintenance
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Task planning will give the basic method of human-robot interaction. For the task T , the execute process is h = {α1 , α 2 ,..., α n } . α i is one action of h .The anticipation aim of the task planning is g pre and the task characteristic aggregate is characterT analyzing by Analyse(T ) .The environment is KnowledgeE observed by Observe( E ) .The process is
Analyse(T ) → characterT ∧ Observe( E ) → KnowledgeE
(1)
Analyzing the task characteristic characterT and the environment KnowledgeE by
Analyse(characterT , KnowledgeE ) , the solving aggregate is N = { A1 , A2 ,..., Am } . Denoting by Think ( h, g pre ) , the only way to solve the problem is the action
h .And, Think Ai (h, g pre ) , the solving aggregate of Ai will achieve the anticipation task by cooperation. For the complicated task TS , according to the breaking down methods of Decompose(TS ) → {TS1 , TS2 ,..., TSn } , the subtask is
{TS1 , TS2 ,..., TSn } . As to any subtask Ti , the corresponding solving is got by matching TS1 6 A1 , TS2 6 A2 , … , TSn 6 Am . After the changing of working environment, task planning will carry out the action of Modify ( KnowledgeE , KnowledgeE ' ) → KnowledgeEn by
Analyse(characterT , KnowledgeE ) , and then, KnowledgeEn adjusts the task analysis. There are two situations. One is the changing is quite big, anther is the changing is bot big. For the first one, new task will generate, and, for the second, the original task will be modefied by Adjust (TS ) .The new subtask aggregate is
{TS'1 , TS'2 ,..., TS'n } , and the new matching T ' 6 A' S1
1
,T
'
S2
6 A2'
,…, T
'
Sn
6 Am' .
As to the pipeline remote welding task, the subtask is got by this method. For normal environment, the subtask is
:Starting the pipeline remote welding system; :Virtual environment modelling of nuclear pipeline; T :Telerobot Moving to the top of the all position welding device; T :Telerobot joining with the all position welding device; T :Telerobot breaking the all position welding device away from tool shaft; T :The all position welding device clamping the new pipe; T :The welding device getting to the top of pipe assembling; T1
T2 3
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:Telerobot assembling the new pipe; :Adjusting the micro device, centered the welding torch and the groove; T :The first all position weld; T :Telerobot breaking the all position welding device away from pipeline; T : Telerobot taking the all position welding device assembling with the T8 T9
10 11 12
pipeline again;
T13 T14 T15 T16 T17 T18 T19
:Adjusting the micro device, centered the welding torch and the groove; :The second all position weld; :Telerobot breaking the all position welding device away from pipeline; :Telerobot taking the all position welding device near the tool shaft; :Telerobot taking the all position welding device back to the tool shaft; :Telerobot breaking away from the all position welding device; :Telerobot back to the basic position.
From the task planning, we get the subtask of pipeline remote welding. And, according to the subtask, corresponding teleoperation method is planning. It is Master-Slave, Supervisor-Subordinate, Peer-Peer, Teacher-Learner and Full Autonomous mode, etc. This paper will not introduce the detail of the control method because that it is the mature teleoperation control mode.
3 Seamless HRI Algorithm The seamless HRI Algorithm focuses on the contribution of different teleoperation control mode. The algorithm takes the fuzzy aggregate to solve the problem. Define the fuzzy aggregate as: Settings the domain of discourse is U , then, any mapping μ A of U to closed interval makes sure a fuzzy subset A
μ A : U → [ 0,1]
(2)
μ → μ A (μ )
(3)
Where, μ A is the membership function of fuzzy subset and μ A ( μ ) is the degree of membership about μ to A .Then, the fuzzy aggregate is
A=
A( μ1 )
μ1
+
A( μ2 )
μ2
+ ... +
A( μn )
μn
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And, μi
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is the corresponding relationship between the element μi of domain of
discourse and the degree of membership A( μi ) of μi .The fuzzy subset conforms to human intelligence because of indefinite borderline. It is the important method to achieve the human robot interaction. The fuzzy human language should be stored and computed by translating to measurable information. Mood operator is used to quantify the human language. The definition is
( H λ A)( μ ) [ A( μ ) ]
λ
(5)
H λ is mood operator. When λ > 1 , it is centralization operator and it enhances
the mood of affirmation degree. When λ < 1 , it is desultoriness operator and it weaken the mood. Using in evaluate the contribution, the algorithm is: Set Yi = ⎡⎣ηi1 ,ηi2 ,...,ηir ⎤⎦ j
is the contribution degree of subset Ai .Where,
j ≤ r ) is contribution capability of Ai in the j dimensional. First, setting a evaluating aggregate perfect, good, normal, not good bad , and then, the evaluating weight is denoting by the matrix of V = [ v1 , v2 , v3 , v4 , v5 ] . Third, ηi j = d i
bi j ,
(1 ≤
λ
{
, }
setting up the judging matrix Ei according to the experience of operator and the expression of the working history.
Her ⎡ e11 ⎢e ability 2 : Ei = ⎢ 21 ⎢# # ⎢ abilityr : ⎣ er1 ability1:
H e12 e22
M e13 e23
L e14 e24
er 2
er 3
er 4
Ler e15 ⎤ e25 ⎥⎥ ⎥ ⎥ er 5 ⎦
(6)
The contribution degree of Ai is computed by Ei and V , which is
Yi = Ei × V T .Then, the ability vector of
Ai
is got by Yi , which is
Di = ⎡⎣ d , d ," , d ⎤⎦ , di j = bi j × ηi j , (1 ≤ j ≤ r ) .The weight matrix denotes by 1 i
2 i
r i
W = [ω1 , ω2 ," , ωr ] . And, the whole ability contribution degree of Ai is Di′ = Di × W T . Ai is evaluated by u ( Ai ) =
Di′ V (N ) ∑ Di′
Ai ∈N
(7)
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For the master-slave teleoperation controlling, we used the algorithm to evaluate the master Am . There are three kinds of Am , which are teleoperation handle F, teaching box M and space mouse K. The cost of master contribution is computed by the algorithm. The results provide the foundation of the master device choice. Setting the master-slave teleoperation is S = { Ar , Av , Am } , and the utilization is
V ( s ) = 4 .The ability vector is Am =< 3,3,3 > , and then, the judging matrix Em is Her
H
M
L
Ler
F: ⎡ 0.6 0.2 0.2 0.0 0.0⎤ M : Ei = ⎢⎢ 0.1 0.4 0.2 0.3 0.0 ⎥⎥ ⎢⎣ 0.0 0.0 0.2 0.2 0.6 ⎥⎦ K: Setting
VM = [1,0.75, 0.5,0.25, 0]
,
and
the
contribution
(8)
degree
is
E1 × V = [ 0.850 0.575 0.600] . The dimension ability of Am is T
T
F : d m1 = bm1 × ηm1 = 3 × 0.850 = 2.55
(9)
M : d m2 = bm2 × η m2 = 3 × 0.575 = 1.725
(10)
K : d m3 = bm3 × η m3 = 3 × 0.600 = 1.8
(11)
According to the cost, the highest one will be chosen to be the master automatically. The others will be the standby in the master-slave teleoperation. For other kinds of choice, the similar process is carried out.
4 Experiment and Results The experiment was carried out to weld the pipeline according to the remote pipeline maintenance plan. The whole working process is show as Fig.3. By the human-robot interaction, master-slave, Supervisor-Subordinate, Peer-Peer, and Teacher-Learner are evaluated by the seamless changing algorithm. The humanrobot interface provide the evaluate dialog of human. The information of executing is presented to human in the interface. At last, the remote pipeline welding is accomplished using the human-robot interaction method.
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Fig. 3. Process of remote pipeline welding
5 Conclusions An innovative HRI telerobotic welding system is developed for nuclear pipeline maintenance. The interaction between the human and the robot is concerned about the task planning and contribution evolution. According the evolution, the teleoperation controlling mode is chosen automatically. The algorithm of human– robot changing is given out. Finally, pipeline welding experiments are performed, and the results demonstrate that the HRI system performing and the installing process can be performed effectively.
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Acknowledgement. This work was supported by contract (NCET-07-0233) from New Century Excellent Talents Support Program and by the National Natural Science Foundation of China (NSFC) under the Grant number 50905043. The authors also greatly appreciate the editor and the referees for their thoughtful comments which led to a substantial improvement on the presentation of this paper.
References [1] Pegman, G.: Robotics and the nuclear industry. Industry Robot 33, 160–161 (2006) [2] Ambrose, R.O., Aldridge, H., Askew, R.S., et al.: Robonaut: NASA’s space humanoid. IEEE Intelligent Systems and Thrie Applications 15, 57–63 (2000) [3] Breazeal, C.: Emotion and Social humanoid robots. International Journal of HumanComputer Studies 59, 119–155 (2003) [4] Shimada, M., Minato, T., Itakur, S., Ishiguro, H.: Development of androids for studying human-robot interaction. In: Proceeding of the 6th IEEE-RAS International Conference on Humanoid Robots (2006) [5] Breazeal, C.: Toward sociable robots. Robotics and Autonomous Systems 42, 167–175 (2003) [6] Chen, J.R.: Constructing task-level assembly stratrgies in robot programming by demonstration. International Journal of Robotics Research 24, 1073–1085 (2005) [7] Billard, A., Dautenhahn, K.: Experiments in social robotics: Grounding and use of communication in automous agents. Adaptive Behavior 7, 415–438 (1999) [8] Wang, J.: Human Control of Cooperating Robots, pp. 2–10. University of Pittsburgh (2007)
Design and Experiment of a Novel Portable All-Position Welding Robot Bin Du, Jing Zhao, and Yu Liu College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing 100124, P.R. China e-mail:
[email protected]
Abstract. This research develops a portable all-position welding robot for the welding of intersected pipes. A complete procedure is adopted to conduct the design. The task and motion of the robot are analyzed. Based on that, three representative types of the robot are chosen in the type synthesis of the mechanism. Two new indices proposed to evaluate required properties of the robot, along with the traditional dexterity index, are chosen to be the criteria in the dimension synthesis of the mechanism. Through the optimization by genetic algorithm, the best robot in type and dimension is determined after comparison on their performances. Finally, the prototype is developed. The welding experiment result shows that the welding robot works stably, and the quality of weld seam is acceptable simultaneously. It has great practical value.
1 Introduction Since 1980s, with the fast development of both the robotic technology and welding technology, the use of robots in welding industry has grown greatly, and its application has been seen as a typical example of the industrial automation. All-position welding robot is a robotic system that can accomplish 360-degrees welding task entirely by its own, without the help of other auxiliaries such as positioning machines. Comparing to the traditional ones, all-position welding robot is more convenient for operating and easier to be miniaturized. It has become one of the new frontier subjects in welding automations. One of the main applications for all-position welding robots is the welding of pipes intersecting, as showed in Fig.1. In the welding of large boilers and flange plates, the weld seam is a saddle-like curve formed by two cylinders. It is a kind of space curve, which is far more complicated than lines or circles and require constant adjustments of the pose of the welding-gun. And also for the reason of the complex and harsh working environment, it is nearly impossible to employ positioning machines to help the robot to complete 360-degrees welding. Actually, in practices, manual welding is usually adopted by workers in such cases, which is proved to be low efficient, high labor intensive and especially difficult to guarantee welding quality. In recent years, the application of gas shielded arc welding in all-position welding technique has matured and laid a foundation for the further research of the all-position welding robots [1, 2]. T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 443–450. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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Some researchers have designed a right-angle cylindrical coordinate robot to implement the all position welding [3]. This robot has 5 DOFs and uses two prismatic joints to locate the position of the wrist origin. It has a simple structure to achieve easy control and high accuracy. But all of them didn’t consider the requirements of compactness and the interference issue. In this paper, the design of an all position robot will be comprehensively addressed and comparisons between different types of robots will be conducted based on indices. Apply optimal design on the drive layout of the welding robot for its center of gravity is as much as possible in the axis of branch pipe. In this way the welding robot working stability could be improved greatly. Eventually, the welding experiment result shows that the welding robot could be anchored firmly on the branch pipe, and revolving axis of the waist joint agrees well with that of branch pipe. The welding robot works stably, and the quality of weld seam is acceptable.
Table 1. Requirements and motion analysis Requirements of the welding robot Dexterity
Compactness
No interference
Motion analysis
Fig. 1. Intersected pipes
Swivel
Position
Orientation
1-DOF
2-DOF
2-DOF
2 Preliminaries 2.1 Task Analysis The welding part is made of two pipes with different radius, as illustrated in Fig.1. The weld seam is a saddle-like space curve formed by two cylinders. It is obvious that at different point of the curve, the robot must have different figure to enable its end-effector to point at its aim with good accuracy and pose. So the robot must have a good dexterity during the whole welding process. In real practice, the working condition is usually a big pipe inserted with many different sizes of small pipes, and all the welding is conducted simultaneously. So the robot must have a small size to avoid interferences with each other during the welding process. And also because the robots are often carried by welders and installed on the top pipes by fixtures, they must be portable and compact. Over all, the welding robot must meet three requirements shown in Table.1.
2.2 Motion Analysis The motion of the robot during this welding process can be divided into three categories. The first kind of motions is the swivel around the central axis of the top
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pipe. The robot is usually installed on the top pipe and concentric with its axis. As an obvious result, this motion only requires 1-DOF. The second kind of motions is the positioning of the end-effector. Because the concentric arrangement in the first motion, the location of the end-effector is actually within a plane which is rotated around the central axis of the top pipe. This motion requires 2-DOF.The third kind of motions is the adjustment of the pose of the end-effector. To determine the orientation of a vector in space, it must take 3-DOF. But for this task, because the presence of a symmetry axis, it requires only 2-DOF for the orientation. In all, the motion of the robot requires 5-DOF, as shown in Table. 1.
2.3 Construction of Performance Index Definition 1. (Compactness Index c) Index c is constructed to evaluate the compactness of the robot:
c=
Vr Vp
(1)
Where V p is the volume of the small pipe of which the bottom is sealed by lowest point of the intersected curve, as shown in Fig.2. V r is the volume of the body enveloped by all outlines of the robotic configurations at different moment of the welding process, as shown in Fig.3. The value of index c is always bigger than 1. The less the index c is, the better the compactness of the robot is. Definition 2. (Interference Index i) Index i is constructed to evaluate the robot’s ability to avoid interferences:
i=
Lw Lt
(2)
Lt is the maximum distance between the wrist joint and the central axis of the small pipe in welding process, which is uniquely determined only by task. L w
where
is the maximum distance between the outmost joint and the central axis of the small pipe in welding process, as shown in Fig.4. The value of index i is always bigger or equal to 1.The less the index i is, the better the ability of the robot has to avoid interference. Definition 3. (Dexterity Index ρ ) A lot of former researches have been done and a lot of indices have been constructed to evaluate the dexterity of robot [4]. Index ρ is constructed to evaluate the dexterity of robot. The condition number ρ [5]:
ρ=
σ1 σr
(3)
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It is worth noting that the condition number is dimensionless only when the Jacobian represents single position mapping or single orientation mapping. However, when both the position and the orientation of the end-effector are included in the kinematic equations, it can be readily seen that the condition number of the Jacobian matrix will not be invariant under a scaling of the dimensions of the robot [6]. As for this welding robot, its Jacobian matrix J = R5×5 is:
⎡J ⎤ J =⎢ v⎥ ⎣ Jr ⎦ where
(4)
J v is the mapping relation between the positional velocity of end-effector
and the joint velocity, and J r is the mapping relation between the orientation velocity of end-effector and the joint velocity. Some methods have been developed to solve this problem including new ways to construct Jacobian mapping and adapted indices. Here, to simplify the computation, the new condition number is obtained as the mean value of the condition numbers of J v and J r , which both are scale-independent:
w = 0.5( ρv + ρr )
(5)
In computation and programming, all the condition number at each point on the trajectory will be added together, and the average value is taken to represent the general dexterity of the robot during the whole welding process. Definition 4. (Application Range of Tasks and the Total Index p) For a specific application range of tasks, the robot should have a generally good performance over those indices. To take adaptability into consideration, we define the total index p as follow. The total index p is the average of the sum of the modified indices: n 1 1 1 p = (1 / n) ∑ ( f1 + f 2 + f 3 ) t =1 c i w
where n represents the number of possible radius combinations,
(6)
f1 , f 2 , f3 are
3
weighting factors and
∑ fi = 1.
i =1
In Eq.(6), reciprocals of indices are added together. There are two reasons for this modification. First, after taken reciprocal, all the values of the three indices are normalized into the range of 0 and 1, where 1 is the best and 0 is the worst. Second, in computation and programming, if a given figure of the robot cannot finish the task or interferes with pipes it is working on, the value of its index can be taken as 0, which is the worst value.
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Fig. 2. Definition of V p
Fig. 3. Definition of V r
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Fig. 4. Definition of index i
3 Type Synthesis of Mechanism With the result in Table.1, the welding tasks performed by robot can be divided into three categories of motions. Correspondingly, the structure of the robot can also be categorized as three different parts. The first part of structure is a rotational joint that can accomplish the 1-DOF rotational motion. The second part is a two joints serial structure that is adopted to complete the 2-DOF positioning of wrist origin. The third part is a wrist structure, containing two rotational-joints, and can be used to realize the 2-DOF pose adjusting. The result is shown in Table.2. The first part and the third part structures can be uniquely determined. However the second part, with combinations of different joint-types and different offset arrangements, has many different possible structures. With different joint-types, there are rotational-rotational, prismatic-prismatic and prismatic-rotational three different combinations. And within each combination, there are different possible structures with different offset arrangements. Table 2. Structures for different motions 1-DOF Rotation
2-DOf Positioning
2-DOF pose adjusting
Taking the prismatic-rotational combination as example, there are several different structures as shown in Fig.5.Some of those structures have innate disadvantages. Like the first two structures in Fig.5, it’s very easy for them to have interferences with the edge of the small pipe that is beneath them. After a round of brief examination, three structures are chosen as the final candidates. They are shown in Fig.6. The first is a R-R type all rotating joint robot, which is very common in industrial application. The second is a P-P type cylindrical coordinated robot with fixed right-angle structure. The third is a P-R type robot that is the combination of the first two types.
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Fig. 6. The final three structures
Fig. 5. Different structures for P-R type robot
4 Dimension Synthesis of Mechanism Size synthesis of Mechanism is a procedure to determine the geometric and kinematic parameters of the robot on the basis of type synthesis. It is a process of optimization with the objective to maximize the total index p. After experiments, the weighting factors are chosen as f1 = 1 / 3, f 2 = 1 / 3, f 3 = 1 / 3 , to make the three indices have same impact on the final total index p. The height of the robot h and the length of the welding tool t are fixed as h=80, t=60. The big pipe’s length is 550mm and radius is 135mm, the small pipes’ length is 130mm and radius is 50mm, 60mm, 75mm respectively. A continuous genetic algorithm (GA) is adopted for this optimization, for the reason that GA doesn’t require analytical expression and derivative information of the cost function. The initial population is randomly selected with the size of 20. The simulation of the welding process implemented by the each optimal joint type is shown in Fig.7, 8, 9 respectively. The result of the optimization is shown in Table.3. Table 3. Result of optimization Robot joint type
Optimization variables
R-R P-P R-P
a2
d4
a2 , d 4
105
185
—
—
—
d4
—
188
Fig. 7. R-R robot
Result p c
w
i
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3.33
1
0.39
6.67
33.33
1
0.242
8.47
12.5
1.894
0.49
Fig. 8. R-R robot
Fig. 9. R-R robot
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From the synthesis result in Table.3, the comparisons of those three types of robots are made. The R-R type robot has a better performance on all the four indices than other two types. The P-R type robot is the worst one, which combines all the disadvantages of the other two. The P-P type robot has a medium performance on indices but, with its special structure, is more easily to be fabricated and controlled.
5 Motor Layout Optimization Design In the design process, make the center of gravity of welding robot as much as possible in the axis of branch pipe to ensure that the welding robot works smoothly, achieves high accuracy. The welding robot will be held well during the process of fixing even if for different branch pipes on the base. The motor and welding gun of it are bilateral symmetry layout. Shoulder joint, elbow joint and wrist joint are driven by band. On the assumption that the coordinate origin is the intersection of two pipe axes, and the center of gravity coordinate of the welding robot is (-0.24, 337.27, -3.01) in this reference frame, and which is only 3.02mm away from the axis of the branch pipe. As shown in Figure 10, it satisfies the design needs basically. The waist joint rotates when the welding robot works. The output shaft of the waist joints motor is downwards, and is fixed to the base by the pin. It outputs joint torque through the rotating cylinder shell of the motor. This can effectively prevent the cable from winding during the waist joint rotating.
Fig. 10. Wrist drive layout
Fig. 11. Virtual prototype
Fig. 12. Prototype of welding robot
6 Virtual Prototype Simulations and Experiment Based on the analysis above, we employ ADAMS to analyze the kinematics and dynamics of the virtual robot system under the consideration of the effect of gravity, as shown in Figure 11.The joint angle within a cycle is configured respectively in ADAMS, and the simulation time is 120 seconds. Then measure joint torque respectively in ADAMS. Subsequently we choose motors according to the joint torques respectively. The constructional robot by the design above is shown in Figure
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12.We use this welding robot to weld the pipe intersection which is a saddle-like curve formed by two cylinders, where the thickness of central pipe and branch pipe is both 10mm, and diameter of them is 220mm and 120mm respectively. Eventually, the welding experiment result shows that the welding robot could be anchored firmly on the branch pipe, and revolving axis of the waist joint agrees well with that of branch pipe. The welding robot work stably, and the quality of weld seam is acceptable.
7 Conclusions A new portable all-position welding robot for the intersecting line is described in this paper. Unlike the available all-position welding robot, those are only applicable in the spacious working environment. The proposed welding robot can be used in the compact working environment. A feature of the paper is that the whole design process is conducted by the modern design methods, which consist in task and motion analysis, two new indices construction to evaluate requirement property of the robot, type synthesis, dimension synthesis, prototype simulation and experiment. The welding experiment result shows that the welding robot has a great value in use. Acknowledgement. The supports of this research by Scientific Research Key Program of Beijing Municipal Commission of Education (KZ200910005003) and National Natural Science Foundation of China (No. 50775002) are greatly appreciated.
References [1] Herty, M., Seaid, M.: Simulation of transient gas flow at pipe-to-pipe intersections. International Journal for Numerical Methods in Fluids 56(5), 485–506 (2008) [2] Yan, B., Yan, G.: Design of weld inspection system for intersected pipe based on redundant manipulator. Optics and Precision Engineering 12(4), 420–425 (2004) [3] Li, X., Wang, S.: Kinematic analysis and simulation of saddle-back coping welding robot. Journal of Beijing University of Aeronautics and Astronautics 34(8), 964–968 (2008) [4] Zhao, J., Li, Q.: On the joint velocity jump for redundant robots in the presence of locked-joint failures. Journal of Mechanical Design 130(10), 102305-1–102305-7 (2008) [5] Salisbury, J.K., Craig, J.J.: Articulated Hands: Force Control and Kinematic Issues. The International Journal of Robotics Research 1(1), 4–17 (1982) [6] Gosselin, C.M.: Dexterity indices for planar and spatial robotic manipulators. In: Proceedings 1990 IEEE International Conference on Robotics and Automation (Cat. No.90CH2876-1), May 13-18. IEEE Comput. Soc. Press, Los Alamitos (1990)
The Power and Propulsion of Medical Microrobots Xueqin Lv1, Rongfu Qiu1, Gang Liu1, and Yixiong Wu2 1
Department of Information and Control Engineering, Shanghai University of Electric Power, Shanghai 200090, China e-mail:
[email protected] 2 School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Abstract. This paper provides a detailed insight into the present-day state of medical microrobot technology and development on power and propulsion. Firstly,some of the critical aspects of medical microrobots design are considered and then the characteristic and application of the external field and other sources driving technology used in microrobots are introduced; finally some correlative technology problems and perspective in future are analyzed. This paper shows that medical microrobot developments are at an early stage and involve a disparate family of technologies and disciplines, such as MEMS, nanotechnology, biomimetics and hydrodynamics, and the critical design issues include power sources, propulsion and location, and many different schemes have been proposed.
1 Introduction The disease of gastrointestinal (G.I.) bleeding is very popular at present. The key of diagnosing or treating GI bleeding successfully is to know where and why is the bleeding. The traditional methods may lead high misdiagnosis, and frequent exposal under X radial is harmful to patients, such as endoscope checking, helical CT, barium meal and so on [1]. A miniature robot system is presented for checking hemorrhagic spot and stopping bleeding. Medical microrobots can be thought of as miniaturized, selfpowered, mobile devices with characteristic dimensions in the sub-mm region and aimed at in vivo applications. It is anticipated that they will travel through blood vessels, the spinal canal, the urethra or the alimentary system to undertake a range of clinical tasks. In fact, more and more researchers are realizing that wireless drive to microrobot is the key point to enhance its feasibility and reliability. This paper will discuss the development of the power supply and other relevant issues of the medical microrobots [2-3].
2 Issues Facing the Medical Microrobots The development of medical microrobots is still at an early stage and poses an enormous, multi-disciplinary challenge. It involves a wide and diverse range of T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 451–459. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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technologies and disciplines, such as silicon microtechnology and MEMS, nanotechnology and NEMS, micro-electronics, bioengineering, materials science, electromagnetism and hydrodynamics. Consequently, successful research programs are likely to necessitate collaboration between groups with differing but complementary fields of expertise. In addition to imparting the required functionality to the robot, other critical considerations include:(1) power – how to provide power to the robot, (2) propulsion – how to move it around the body, (3) steering and location – how to control it and monitor its position, (4) Insertion and removal – how to introduce it into the body and remove of it when its job is done. Power is clearly a vital issue and a number of radically different schemes have been proposed. This fall into two broad categories: on-board or external. One of the most topical on-board concepts draws heavily on the field of energy “harvesting” or “scavenging”, which is presently attracting great interest from the sensing community. Many such systems are based on the conversion of mechanical energy into electrical power and an example, which has potential in the microrobot context, is the nanogenerator developed by a group at the Georgia Institute of Technology. This relies on the well known piezoelectric effect which is exploited through the use of an array of around 500 vertically-aligned zinc oxide (ZnO) nanowires. These are piezoelectric and respond to an external force by generating a voltage which should be sufficient to power in vivo medical devices. Some examples of devices using external and other power sources are considered later in this paper.
3 The Power and Propulsion of Medical Microrobots 3.1 Microrobot Driven by GMA The mechanism of the motion with giant magnetostrictive alloy (GMA) is that the magnetic energy is converted into mechanical vibration energy through the action of piezomagnetism and magneto mechanical couple of its micro GMA when timevarying oscillating magnetic field with different frequency applied through outside pipe. Thereafter robot moves forward by transferring axial vibration of GMA into radial vibration by installing elastic legs with different stiffness between upside and downside. Toshio Fukuda et al [4] put forward an in-pipe micro mobile robot which powered by a GMA actuator and do not require any power supply cables. This cableless micro mobile robot can be controlled by the magnetic fields supplied by the outer side. The micro model of the robot has sixteen legs, where the robot is 6 mm in diameter for the one way motion. The GMA is installed into the robot as shown in Fig. 1 and has a magnetic flux perpendicular with the pipe. The tip of the legs has an opposite inclination to the direction of the movement like the macro model and it is pressed against the inner wall of the pipe. The robot can move by vibrating the legs like a macro model.
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Fig. 1. Structure of micro model
3.2 Microrobot Driven by Piezoelectric Element Fig. 2 shows a basic motion principle of the piezo impact drive mechanism [5]. The motion mechanism consists of three components: the main body, the actuator and the inertial (counter) weight. The main body is laid down on the guiding surface with only the friction acting between the surfaces. On the one end of the main body an actuator is attached. The weight does not touch the surface. The processes of the motion are described as: (a) The cycle starts with the actuator in extended state; (b) The actuator makes slow contraction so that the inertial force caused by the contraction should not exceed the static friction. The main body keeps the position; (c) At the end of contraction process, a sudden stop of the motion is made to small move the main body, and then; (d) a rapid expansion of the actuator causes impulsive inertial force, which results in the step-like motion of the main body. Making slow extension and rapid contraction can carry out motion toward the other direction. The motion amplitude of the actuator can control the step size of the motion. Repeating those process through (a)-(d) a long distance motion is made possible.
Fig. 2. Operation principle: moving toward left
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Fig. 3. Structure of locomotion platform composed of four piezo elements and two U-shape magnet legs
For this decade, such micro-handling and nano-manipulation techniques have been developed. One precise small robot with the size of 30 mm in cubic is shown in Fig. 3 [6]. Here two U shape electromagnets are set up diagonally and these magnets are connected by four stack-type piezo elements each other. When synchronizing these actuators each other, then this mechanism can walk step by step like an inch-worm.
3.3 Microrobot Driven by Cell Battery Toshiaki Yamaguchi et al [7] made a miniature robot system which has two parts. The main part is a micro robot which works in human’s G.I. tract like a capsule. The subsidiary part is a receiver which works outside of body and receive signal from the microrobot. The structure of micro robot is depicted in Fig. 4. The miniature robot is very significant to be improved as an advanced medical instrument for gastrointestinal bleeding, particularly for bleeding in small intestine. But the micro robot is supplied by high-energy cell battery, how to reduce the power consumption is very important.
Fig. 4. Structure of inner micro robot
3.4 Microrobot Driven by Magnetic Field Wireless microrobots controlled by magnetic field are used in a wide range of biomedical application. Especially microsurgery in blood vessels is expected to become an increasingly popular medical practice. In the meantime, being
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characterized with high reliability and safety, it could reach deep cavern by the medium of body fluid organism with flexibility, thus the microrobot for biomedical application, as a new important approach on therapy in term of interposition, has a widespread prospect in the field of medical engineering.
Fig. 5. The structure of the spiral
Qinxue Pan et al [8] presented a novel spiral type of the microrobot in a pipe which have a small scale, can be propelled by low voltage, and has a quick response. And also, for the advantage of this kind design of the microrobot, it can not only move smoothly in the human body but also it can remove the obstructing in intestine or blood vessel using the particular structure. The structure of the proposed spiral type microrobot (Swimmer-1) is shown in Fig. 5. In order to rotate the microrobot in the magnetic field, they set four permanent magnets on the body, which are set in the top, bottom, front and back of the body, respectively, and not in the same line in order to generate torsion motion. The body in spiral structure is made by rubber. Consider the accuracy control of microrobot; they have improved the swimmer-1 which increased the balance, control stability and even propulsive force. The improved microrobot (swimmer-2) has been developed, as shown in Fig. 6. When current flows through the solenoid, a magnetic field is generated. When an alternate magnetic field parallel to the direction of advance is applied, movement due to an impelling force arising from a permanent magnet rotates and vibrates the connected fin. The motion mechanism of the tail is shown in Fig. 7.
Fig. 6. The conceptual design of the improved microrobot
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Fig. 7. Motion mechanism of the tail
3.5 Microrobot Driven by Biotic Energy Medical nano robots are a very promising research direction for creating efficient mechanisms for medication delivery and for directly fighting disease, i.e., nano robots working along with natural white cells to fight viruses inside the human body. Medical nano robots can help us eliminate the problem of antibiotic resistant viruses once and for all and it is certainly one application of robotics. According to a CBC article, Researchers at Cornell University are trying to find out how to use the same mechanism that powers human sperm cells in medical nano robots. By deconstructing the stages in the biological pathway sperm cells use to generate their relentless energy, researchers at the Cornell University College of Veterinary Medicine in the United States hope to recreate that process in an artificial device. Powerful, albeit microscopic, sperm cells use a kind of dualengine system to generate their energy. Organelles in a sperm cell's midsection provide one part of its battery power, while a second process occurring in the long, spindly tail gives it an additional boost. Researchers have attached three of the 10 enzymes needed to create this glycolytic pathway to nickel ions on a tiny manufactured chip. Their goal now is to attach the remaining seven enzymes, in effect creating nano-robots fuelled by sperm power.
3.6 Microrobot Driven by PDMS Microbial Fuel Cell Chin-Pang-Billy Siu et al [9] have recently find that people of blood glucose for energy in the yeast cells that one day people will be driven into the body of the electronic devices, such as pacemakers. This vivid self-generated power, periodic replacement of batteries will replace conventional surgery. Since the University of British Columbia, Canada will be a team of scientists at yeast encapsulation soft capsules, has been developed to such a micro fuel cell micro-organisms, can be applied to the treatment of paralysis of the spinal intracellular microelectrode such electronic devices. Engaged in the development of these yeast fuel cell scientists, said spine microelectrodes implanted in the spine necessary therefore very difficult to replace batteries.
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Fig. 8. Operating principle of the PDMS MFC
The new fuel cell component from saccharomyces cerevisiae, the yeast sealed by poly dimethyl siloxane (PDMS) material in the capsule, together constitute the fuel cell. The currently developed fuel cells are Samples micron level, with an area of 15 square microns, 1.4 microns thick. Fig. 8 shows the operating principle of a PDMS MFC. When yeast break down glucose, the methylene blue from the yeast cells where the "theft" electronics, electronic delivery of this will be followed by the other side of yeast cells, resulting in a weak current. On the cathode, from yeast cells combine hydrogen and oxygen produced water. To increase the size of the electrode to increase the fuel cell power output, the group using silicon etching techniques to create "mini-poles", this covers an area of about 40 square microns, 8 microns high. Tested, the yeast can produce fuel-cell around 40 nava electricity, compared to quartz watches generally can produce electron micro-watt power class. Therefore, if the use of the energy storage capacitors, and the yeast will be able to fuel cells some device drivers. This yeast will be genetically modified so that it will have greater power output capability. However, this goal faces many challenges. For instance, let the healthy growth of yeast cells, and its waste cases no damage was cleaned up, so that discharges of harmful substances to human blood.
4 Other Relevant Issues Whilst most of the above examples address a limited range of specific technological issues rather than aiming to develop commercial products, September 2006 saw the launch of a large, highly multi-disciplinary and more ambitious programme: the € € 9.5 million European versatile endoscopic capsule for gastrointestinal tumor recognition and therapy (VECTOR) project. Funded under the European Commission’s Sixth Framework Programme and led by novineon Healthcare Technology Partners GmbH, this four year project involves 18 organisations from nine European countries, together with the Intelligent Microsystem Centre at the Korean Institute of Science and Technology. The aim is to exploit the latest developments in MEMS and nanotechnology to fabricate a miniaturized robotic “pill” for advanced cancer diagnostics and therapy in the human digestive tract [3, 10-13].
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Fig. 9. Schematic of the VECTOR endoscopic robot
This application reflects that these cancers can be treated if detected early but only a fraction of patients make use of screening endoscopy since current procedures are associated with notable discomfort and pain. Thus, novel, painless endoscopic devices are needed to increase screening rates. Perhaps, more of a mini-robot than a true microrobot, the VECTOR device will take the form of an actively controllable, miniature capsule endoscope, equipped with a vision system and operating instruments which could not only detect stomach and bowel cancer in the early stages but also treat it in situ. This will be made possible by equipping the VECTOR capsules with miniaturised grippers and operating instruments to remove or destroy diseased tissues (Fig. 9) [3].
5 Conclusions In the future, the design and energy supply of the wireless microrobot will be optimized. Design and use an electromagnetism sensor for detecting the location of the microrobot in pipe and accuracy control in speed and position of the microrobot must be realized. The application of in-pipe inspections, operation in microenvironment and microsurgery of blood vessels will be useful in industrial and medical field. These examples illustrate the present state of the medical microrobot technology. It is evident that no single approach has emerged which will resolve critical design aspects such as power or propulsion, which will almost certainly be governed by the application. Opinions are divided as to when medical microrobots will become commercially available and vary from five to ten years and, of course, they will need to undergo extensive testing and approvals prior to being deployed on a routine basis. Nevertheless, research is gaining momentum and what appeared to be little more than mere speculation a decade ago is now poised to become a reality. Acknowledgement. This work was supported by Leading Academic Discipline Project of Shanghai Municipal Education Commission, Project Number: J51301.
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References [1] Yan, G.-z., Shi, J., Fang, Y.: Interventional Miniature Robot for Diagnosing and Treating G. I. Bleeding. In: The First IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, February 20-22, pp. 1020–1023 (2006) [2] Oleynikova, D., Rentschlera, M., Hadzialicb, A., et al.: In vivo camera robots provide improved vision for laparoscopic surgery. International Congress Series, vol. 1268, pp. 787–792 (2004) [3] Bogue, R.: The development of medical microrobots: a review of progress. Industrial Robot: An International Journal 35(4), 294–299 (2008) [4] Fukuda, T., Hosokai, H., Ohyama, H.: Giant magnetostrictive alloy applications to micro mobile robot as a micro actuator without power supper cables. In: Proceding of 1990 IEEE Conference on Micro Electro Mechanical Systems, pp. 210–215 (1990) [5] http://www.aml.t.u-tokyo.ac.jp/research/pidm/pidm_e.html [6] Fuchiwaki, O., Tobe, N., Aoyama, H., Misaki, D., Usuda, T.: Automatic microindentation and inspection system by piezo driven micro robot with multiple inner sensors. In: Proceedings of the IEEE International Conference on Mechatronics & Automation Niagara Falls, Canada, pp. 83–88 (July 2005) [7] Yamaguchi, T., Kagawa, Y., Hayashi, I., Iwatsuki, N., Morikawa, K., Nakamura, K.: Screw principle microrobot passing steps in a small pipe. In: International Symposium on Micro Mechatronics and Human Science, pp. 149–152 (1999) [8] Pan, Q., Guo, S., Li, D.: Mechanism and Control of a Spiral Type of Microrobot in Pipe. In: Proceedings of the 2008 IEEE International Conference on Robotics and Biomimetics, Bangkok, Thailand, pp. 21–26 (February 2009) [9] Siu, C.-P.-B.: Student Member, and Mu Chiao, A Microfabricated PDMS Microbial Fuel Cell. Journal of Microelectromechanical Systems 17(6), 1329–1341 (2008) [10] Itoh, A.: Motion control of protozoa for bio MEMS. IEEE/ASME Trans. Mechatronics 5(2), 181–188 (2000) [11] Ogawaa, N., Okua, H., Hashimotob, K., Ishikawa, M.: A physical model for galvanotaxis of paramecium cell. Journal of Theoretical Biology 242, 314–328 (2006) [12] Ogawa, N., Oku, H., Hashimoto, K., Ishikawa, M.: Trajectory planning of motile cell for microrobotic applications. Robotics and Mechatronics 19(2), 190–197 (2007) [13] Davies, A., Ogawa, N., Oku, H., Hashimoto, K., Ishikawa, M.: Visualization and estimation of contact stimuli using living microorganisms. In: Proc. 2006 IEEE Int. Conf. Robotics & Biomimetics (ROBIO 2006), pp. 445–450 (December 2006)
Mechanical Design and Analysis of an Articulated-Tracked Robot for Pipe Inspection Z.Y. Chen, G.Z. Yan, Z.W. Wang, and K.D. Wang Institute of Precision Engineering and Intelligent Micro system, Shanghai Jiao Tong University (SJTU), Shanghai 200240, P.R. China
e-mail:
[email protected]
Abstract. In order to execute the task of inspecting welding defects, spray painting and corrosive status inside a pipe automatically, an articulated-tracked robot was designed to replace the manual labor. In this paper, authors describe the mechanical design and analysis of an articulated-tracked robot which has four swing arms and six tracks. The actuation mechanism and transmission mechanism are described in detail. To guarantee the operation of this robot, the mutative bending moment acting on the main shaft is calculated and described through force diagram and algebraic expressions. In addition, the dimensions of the robot are determined according to the maximum moment acting on the dangerous crosssections.
1 Introduction The pipe robot is a special kind of robot which can get access into the industrial pipes and execute work such as inspection, welding and spray painting inside the pipe instead of human. In recent years, several kinds of actuation solution for the pipe robot have been developed. The locomotion strategies mainly base on the tracks, wheels or the legs [1,5]. The robot characterized in this paper is used to accomplish the mission of detecting special underground industrial pipes which are damaged in the earthquake. The condition inside the pipe is unclear. So it is not a wise choice of detecting it by people directly. In this condition, the author designed a four degree of freedom actuation system which includes four swing arms and six tracks, allowing more capacity of obstacle-negotiation. Hence, it is called the articulated-tracked robot.
2 Pipe Inspection Robot System The process of the robot system in pipe inspection is described In Fig.1. During the process, the video is sent to the ground control center by a camera on the top of the robot through an electric cable. The operator can control the locomotion of the robot inside the pipe through the center control computer in the control center, including moving forward and backward, turning around or step-climbing. The structure of the robot system consists of three main parts: T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 461–467. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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1) Inner pipe device includes the locomotion system, the camera, the light device; 2) Outer control center includes a computer provided with suitable control, image collecting software and power supply for the locomotion system; 3) Connection device includes several electric cables for communication and power supply.
Fig. 1. Process diagram for the pipe-inspection robotic system
3 Locomotion Analysis for Obstacle-Negotiation The articulated-tracked mechanism has advantages in adapting to the uneven surface and in obstacle-negotiation [2,3]. The two front swing arms and the two rear ones have the same mechanical structures and installation. They can all rotate synchronously. The step-climbing process of the robot is displayed in Fig.2. By changing the swing arms with a certain angle the performance of the tracked robot for step-climbing is dramatically improved [4].
Fig. 2. Obstacle-negotiation process of the articulated-tracked robot
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4 Actuation System Description The proposed solution is illustrated in Fig.3. This robot device needs four motors, four swing arms and six tracks. The whole device can be separated into two parts, one is the moving device and the other is the device of swing arms.
Fig. 3. The mechanism of the articulated-tracked robot
For the moving device shows in Fig.3(a),each of the two motors on the bottom can control the speed of the main track on one side. The output rotation of each motor is transmitted to the tracks by a pair of cone gears and the main shaft. One end of the main shaft is connected with the active wheels by the shaft keys while the other end is connect with the inactive wheels through bearings. For the swing arms device shown in Fig.3(b), the other two motors on the top could change the angle position of the swing arms. The two front swing arms are connected together and are driven by one motor. The two back swing arms are the same. Two of the gears on the bottom are used to transmit the output rotation and are connected with the main shaft through bearings. So its movement will not be affected by the rotation of the main shaft. In addition, some extra wheels were added to support or change the tightness of the tracks. Finally, a special metal casing was made for the whole actuation system to prevent it from water or dust as show in Fig.3(c)(d).
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5 Actuation System Dimensioning 5.1 Robot Motivation Requirement and Motor Selection The requirement of motivation depends on the fact that the tractive force overcome the resistance force
FZT can
FW during the movement. (Fig.4)
Fig. 4. The motivation force diagram of the actuate system
FW includes the internal resistance FWi , the frictional resistance FWf and the drag resistance FWd . The FWi depends on the weight of the The resistance force
whole actuate system, or the degree of the tightness of the caterpillars. The quantity is up to 3-8% of the whole actuate system’s gravity. The FWd depends on the weight of the electric cable and the frictional coefficient of the surface in the pipe. Due to the knowledge of the condition and mechanism we design, the value of the resistance force can be found by using the following parameters:The weight of the actuate system is 55 kg, the frictional coefficient is 0.2. The electric cable is 100 meter long and 20kg in weight. So the resistance force can be calculated as:
FW = FWi + FWf + FWd = 194 N
(1)
To overcome the resistance force, the robot needs suitable motors which can provide enough tractive force FZT [6,7]. Considering the following parameters of the maxon DC motor:the rated torque is 177 Nmm; rated revolution speed n0 =4920r/min;the value of reduction is 124;the revolution speed n1 =40r/min for the main wheels; and 0.91 for the efficiency considering the power reduced by the transmission mechanism. The tractive force can be calculated through formulae:
Tn0 ×ηT = 165w 9550
(2)
PT ≈ 656N 2π Rn1 60
(3)
PT = P0ηT = 2 ×
FZT = PT ν T =
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The tractive force
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FZT we deduced can overcome the resistance force FW . In
this case, we select this maxon DC motor to actuate the robot.
5.2 Mechanical Dimensioning According to the mechanism of the actuation system described above, one of the key components of the robot is the main shaft. Its function includes connection between the wheels, supporting the swing arms, also transmit the torque from the motor to the main wheels. During the transmission of the tracks, the load acting on the shaft may cause the main shaft flexuous. In that case, the quantity of the load should be calculated in order to determine the dimension and material of the shaft. The tractive force FZT is generated by two motors. Each motor provides 83W of power. And the load act on each shaft is:
FZ =
1 FZT = 328 N . 2
(4)
Fig 5 shows the structure of the main shaft and the related parts. Each main shaft drives two parallel tracks. Hence, the force acting on the shaft was uniformly distributed. As show in the Fig.5, one side of the main shaft is considered as cantilever beam with one end point A fixed in the frame. To prevent the shaft from flexure, the maximum moment acting on the shaft should be calculated.
Fig. 5. Actuator structure
Fig. 6. Shaft force diagram in two directions
Fig. 7. Shaft force diagram in horizontal direction
Actually, the swing angle α shows in Fig.4 is changeable. In this case, the force must be calculated in two directions, includes the horizontal and vertical direction.
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Fig.6(a) depicts the force the
Fh in the horizontal direction while the Fig.6(b) depicts
Fv in vertical direction. Uniformly distributed load q: q=
Fz = 328 / 40 = 8.2 (N/mm) l
And the force between B and C is changed with the angle
(5)
α
. The angle
α
ranges in [ −90 ,90 ] . According to this force diagram, the moment act on the each point of the shaft can be obtained. Actually the moment is a function of moment M (α ) . We present the expression of the moment of the most dangerous point D and E in two directions: o
o
⎧ M Dv = 400q sin α ⎪ M = 1000q ⎪ Dh ⎨ ⎪ M Ev = 800q sin α ⎪⎩ M Eh = 800q + 400q cos α
(6)
The resultant bending moment can be obtained:
⎧ M = M 2 + M 2 = (1000q) 2 + (400q sin α ) 2 Dh Dv ⎪ D ⎨ ⎪⎩ M E = M Eh 2 + M Ev 2 = (800q sin α ) 2 + (800q + 400q cos α ) 2 According to expressions of
(7)
M D M E , the maximum moment acting on the two
points are:
M E max = 9840 Nmm ( α = 0o horizontal swing arm) M D max = 8616.26 Nmm ( α = 90o vertical swing arm) The calculation process of the maximum moment
M E max is shown in the Fig.7, as
the swing arm is horizontal. The force diagram of the main shaft is shown in Fig.7(a). Fig.7(b) is the shearing force diagram while the Fig.7(c) is the moment force diagram. As a result, the maximum moment acting on the point E (the end of the shaft) is M max = 9840 Nmm . After the study of the bending strength of several materials, the structural alloy steel is chosen as the material of the shaft.
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d min = As
3
M E max = 10.98mm 0.1[σ −1b ]
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(8)
[σ −1b ] depends on the material, the diameter of the shaft should be no less
than 10.98mm.
6 Conclusions Based on the mechanism of the articulated-tracked robot, the authors analyzed the requirement and selected the right motors. In order to enhance the reliability and prevent the components from overload, the varied bending moment acting on the main shaft is described through force diagram and algebraic expressions. Finally, the dimension of the main shaft is determined according to the maximum moment acting on the dangerous sections.
References [1] Kawaguchi, Y., Yoshida, I., Kurmatani, H., Kikuta, T.: Development of an In-pipe Inspection Robot for Iron Pipes. Journal of Robotics Society of Japan 14(1), 137–143 (1996) [2] Li, Y.W., Ge, S.R., Fang, H.F.: Effects of the Fiber Releasing on Step-climbing Performance of the Articulated Tracks Robots. In: Proceedings of the 2009 IEEE International Conference on Robotics and Biomimetics, Guilin, China, December 19 -23 (2009) [3] Chen, C., Trivedi, M.M.: Reactive Locomotion Control of Articulated-Tracked Mobile Robots for Obstacle Negotiation. In: Proceedings of the 1993 IEEE/RSJ International Conference on Intelligent Robots and Systems, Yokohama, Japan (1993) [4] Chen, Z.Y.: Research on pipeline in-service inspection robot system. Bachelor’s Thesis of Shanghai Jiao Tong University (July 2009) [5] Quirini, M., Menciassi, A., Scapellato, S., Stefanini, C., Dario, P.: Design and Fabrication of a Motor Legged Capsule for the Active Exploration of the Gastrointestinal Tract. IEEE/ASME Transaction on Mechatronic 13(2) (2008) [6] Liu, J., Wang, Y., Ma, S., Li, B.: Analysis of stairs-climbing ability for a tracked reconfigure-able modular robot. In: Proceedings of the IEEE International Workshop on Safety security and Rescue Robotics, Kobe, Kobe, pp. 36–41 (2005) [7] Kim, C., Yun, S., Park, K.: Sensing system design and torque analysis of a haptic operated climbing robot. In: Proceedings of the IEEE /RSJ International Conference on Intelligent Robots and Systems, Piscataway, NJ, USA, pp. 1845–1848 (2004)
Part VI
Intelligent Control and Its Applications in Engineering
A Construction Method of Rational Approximation Model for Fractional Calculus Operators in Frequency Domain Wen Li, Guanghai Zheng, Bing Nie, Huimin Zhao, and Ming Huang Software Technology Institute of Dalian Jiaotong University, Dalian, China
Abstract. The controller model containing fractional calculus operators is an irrational function, therefore, to research the rational approximate of fractional calculus operators is an important content for analysis and realization of fractional-order controllers. In this paper, a construction method of fractional order operator was proposed in the frequency domain. Research shows that the rational approximation model constructed by the method can approximate fractional order operator effectively in a given frequency bandwidth.
1 Introduction The discretization of the fractional order differentiator and integrator have been developed during the last 10 years [1-5], most researches are on direct discretization methods. Their works showed how to approximate the fractional order operator by a rational discrete-time models, which can be achieved by using various approximation methods [2-4], such as direct method, indirect method and so on. In general, the direct discretization method based on discrete-time models, first fractional order operators s ±α , 0 < α < 1, α ∈ R are expressed by so-called generating
function s = ω ( z −1 ) [8-9]. There are three of the most commonly used discretization schemes, namely the trapezoidal (Tustin) rule, the backward difference (Euler) rule, and the more recently introduced Al-Alaoui operator, which is obtained by the stable inversion of the weighted sum of the Tustin integration rule and the Euler integration rule [10]. The approximation performance is test by comparing frequency characteristics of approximation model with that of desired fractional order operator in frequency domain. In fact, generating function transforms fractional order operator s ±α to discrete-time z domain, the transformed operator is an irrational z-function. The irrational z-function is expanded by Power Series Expansion, Continued Fraction Expansion or other expansion method. The expansion obtained is a discrete-time model G ( z −1 ) [3-4], which is the approximation of fractional order operator. In more recent articles, a direct discretization method based on Rational Chebyshev Approximation (RCA) is proposed and good approximating performance is achieved, because the RCA is a useful tool to find good rational approximation to a given function f (x) in a given interval [a, b][2]. T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 471–478. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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Some indirect discretization methods are also discussed [1,5,11]. The literature [5] is a representative research paper and gives an in-depth discussion on frequency-band complex noninteger differentiator. In paper [11], a new method is proposed for multiple fractional order approximation. The basic idea is to reduce the multiple-fractional order system to a simple order fractional one and apply the singularity function method to approximate it by rational transfer functions of alternate pole-zero pairs. However there are not a lot of papers about indirect discretization methods. Researches show that the type of generating function and expansion method affect approximating performance for direct discretization methods, and some methods are complex to get discrete-time approximation models [6-7]. In addition, approximation performance of the obtained discrete-time model usually needs to be test by log-magnitude characteristic and phases-angle characteristic. It is therefore most necessary and feasible for us to research constructing rational approximation model of fractional order operator in frequency domain [1,5-6]. The aim of this paper is to find a convenient and practical method of constructing rational model to approximate fractional order operator in frequency domain, the approximation precision can be selected in advance. The paper is organized as follows. First, the construction method is introduced in detail, and then the construction algorithm is presented. Next, the validity of the construction method is proved by some demonstrating examples. Finally, some conclusions are given.
2 The Construction Method of Rational Approximation Model in Frequency In order to make the rational approximation method proposed in this paper convenient and practical in frequency domain, the concept of minimum phase system is applied in constructing method of rational approximation model. That is, if only all first-order m-zeros and first-order n-poles of the rational approximation model to be constructed are negative real number, then consistency between phase frequency characteristic and magnitude frequency characteristic will be satisfied. Therefore the research focus is on the discussion of how to construct logmagnitude characteristic of rational approximation model, the construction of phase frequency characteristic is ignored.
2.1 Construction Method Suppose that a transfer function is composed of fractional order integrator and proportion coefficient, namely
G0 ( s ) =
k0 , k 0 = ω cα , 0< α <1, α s
(1)
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k 0 =1, the G0 ( s) is a pure fractional order integral operator. Also suppose the frequency bandwidth of practical interest is [ ω a , ω b ], where there exists ω a < ω c < ω b . First according to Bode diagram plotting approach, let s = jω , the logwhen
magnitude characteristic of Eq.(1), denoted by L0, is given in Fig.1. Next to draw two parallel lines L1 and L2 with the line L0, the difference ε(dB) between straight lines is called the maximum approximation error, as shown in Fig.1. The lines L1 and L2 are called auxiliary line, their transform functions can be expressed by
G1 ( s ) =
k1 , sα
G2 (s) =
(2)
k2 . sα
(3)
The log-magnitude expressions of lines L0, L1 and L2 are given as below:
L0 (ω ) = 20 lg k 0 − 20 lg ω α ,
(4)
L1 (ω ) = 20 lg k1 − 20 lg ω α ,
(5)
L2 (ω ) = 20 lg k 2 − 20 lg ω α
(6)
Slopes of three log-magnitude curves are all -20 α dB/decade and formulas of computing coefficients k1 and k 2 can be deduced from eqns. (4), (5) and (6): ε
k1 = k 0 × 10 , 20
k2 =
k0 ε
10
.
20
Fig. 1. Log-magnitude curves of L0, L1 and L2
(7) (8)
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The basics idea of constructing rational approximation model is based on the fact that the transfer function of a non-minimum phase system can be drawn according to its log-magnitude asymptote directly by control theory. Therefore we can make use of log-magnitude asymptote to construct an integer order rational function to approximate a fractional order operator. We call the integer order rational function as rational approximation model. Between straight-line L1 and straight-line L2, the straight-line L0 is approximated by a polygonal-line which is made up of straight-lines with slope -20dB/decade or +20dB/decade and 0dB/decade as shown in Fig.1. It is polygonal-line that is log-magnitude asymptote of the rational approximation model expected. The transfer function of the model can be written easily from the asymptote constructed. Taking equation (1) for example, the procedure of constructing rational approximation model is as follows: Step 1. Drawing a straight line having a slope -20dB/decade, the start-point is on auxiliary line L1 at ω = ω a , denoted by ω1 , and the end-point is on auxiliary line L2, denoted by ω 1 . '
The straight line can be described by transfer function quency ω1 its log-magnitude is
20 lg h1 − 20 lg ω1 = L1 (ω1 ) . From Eq. (9), constant
h1 and at fres
(9)
h1 can be computed by h1 = k1ω11−α .
At ω '1 , there is equation as follows:
20 lg h1 − 20 lg ω '1 = L2 (ω '1 )
(10)
From Eq. (10), the frequency ω 1 can be determined by '
1
h ω '1 = ( 1 ) 1−α . k2 Step 2. Drawing a horizontal-line from the point on auxiliary line, L2 where fre-
quency ω = ω 1 , to the point of intersection with auxiliary line L1, corresponding frequency of the point is ω 2 . At frequency ω 2 , there is following equation: '
L1 (ω 2 ) = L2 (ω '1 ) .
(11)
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From Eq. (11), the frequency ω 2 is calculated by
⎛k ω 2 = ⎜⎜ 1 ⎝ k2
1
⎞α ⎟⎟ ω '1 . ⎠
Frequencies such as ω 1 , ω 2 , are corner frequency. '
Step 3. Repeat steps 1 and 2, the i-th straight line with a slope of -20dB/decade
can be represented by
hi , and following formula can be deduced as below: s hi = k1ω i1−α .
(12)
The formulae to determine i-th corner frequency iliary line L2 and the i+1-th corner frequency can be deduced as below:
⎛h ω ' i = ⎜⎜ i ⎝ k2
ω 'i
ωi +1
(i-th real zero - ω ' i ) for aux-
(i+1-th real poles - ω i +1 ) also
1
⎞ 1−α ⎟⎟ , ⎠
(13)
1
ω i +1 Remark: each
ωi'
and
ω i +1
⎛ k ⎞α = ⎜⎜ 1 ⎟⎟ ω i' ⎝ k2 ⎠
determined should be compared with
(14)
ωb
.If
ω ≥ ωb or ωi +1 ≥ ωb , then the procedure is finished. ' i
Suppose m real zeros and n real poles are determined, a transfer function of minimum-phase type can be written from Fig.1 as follows:
⎛ s ⎞ ⎛ s ⎞ ⎜⎜ + 1⎟⎟ "⎜⎜ + 1⎟⎟ ⎝ ω '1 ⎠ ⎝ ω 'i ⎠ R(s) = K ⎞ ⎛ s ⎞ ⎛ s ⎜⎜ + 1⎟⎟ "⎜ + 1⎟ ⎜ ⎟ ⎝ ω1 ⎠ ⎝ωj ⎠ where
K=
k1
ω aα
(15)
. Expanding equation (15), the rational approximation model
constructed is given as below:
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R( s) =
bi s m + bi −1 s m −1 + " + b1 s + b0 , n −m ≤1 a j s n + a j −1 s n −1 + " + a1 s + 1
(16)
From the angle of control theory to see the Eq. (16), the rational approximation model is made up of m one-order differential taches and n inertia taches. Because inequality n − m ≤ 1 , maximum lag angle is no more than
π
2
.
2.2 Error Analysis According the construction method of rational approximation model R ( s ) , it is obvious that the maximum error between the polygonal line La and straight line L0 shown in Fig.1 is the given approximation error ε(dB) in the given frequency band [ ω a , ω b ]. It is important to note that the polygonal line La is the logmagnitude asymptote of model R ( s ) . The actual log-magnitude curve of the model R ( s ) will be more close to the straight line L0 at corner frequencies with the maximum approximation error decreasing. That is to say, the maximum error between the log-magnitude curve of Eq. (15) and the one of Eq. (2) is always less than the given maximum approximation error ε(dB).
3 Frequency Characteristic Analysis for an Example First let fractional order function is
G0 ( s ) =
k0 , k 0 = 1 , α = 0 .5 ; ω c = 1 sα
(17)
Frequency bandwidth given is [ ω a , ω b ]=[0.01,100] and maximum approximation
errors are ε = 2dB, 4dB and 6dB respectively. Next, we apply the construction method proposed in this paper to the fractional order function to be approximated, and make the characteristic analysis of approximation model obtained. Following three representations are of the approximation models acquired by MATLAB programming:
R ε = 2 dB ( s ) =
12 .59 s 5 + 595 .5 s 4 + 3852 s 3 + 3852 s 2 + 595 .5 s + 12 .59 s 6 + 118 .8 s 5 + 1932 s 4 + 4869 s 3 + 1932 s 2 + 118 .8 s + 1
Rε = 4 dB (s ) =
0.0631s 3 + 6.472s 2 + 16.26s + 1 s 3 + 16.26s 2 + 6.472s + 0.0631
(18)
(19)
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Rε =6 dB ( s ) =
477
50.12s 2 + 2003s + 316.2 s + 633.5s 2 + 1591s + 15.85
(20)
3
The upper portion of Fig.2 shows four log-magnitude curves, thereinto, there are of three approximation models and one is of the ideal model Eq. (17); the lower portion of Fig.2 shows three error curves between approximation models and ideal model. It can be found from Fig.2 that the less the approximation error ε is, the better the approximating performance of approximation model obtained, but the approximation model order increases with the decrease of maximum approximation error ε . Also it can be found that, in the frequency bandwidth given, the actual approximation errors are less than the maximum approximation errors correspondingly. In our case, orders of three approximation models are 5,3,3 when maximum approximation error ε = 2(dB), 4(dB), 6(dB) respectively. Obviously, log-magnitude characteristic of Eq. (19) is better than the one of Eq. (20) from Fig.2. Considering approximation model complexity and approximation performance synthetically, selecting approximation model Eq. (19) with ε = 4dB is more suitable for the Eq. (17). Similarly,Fig.3 shows the phase angle curves and error curves and consistent analysis results with that of Fig.2.
Fig. 2. Log-magnitude curves and error curves with different
Fig. 3. Phase angle curves and error curves with different
ε
ε
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4 Conclusions The method proposed in this paper is convenient and effective to construct a rational approximation model with a maximum approximation error ε for a given fractional order operator in the frequency bandwidth of practical interest. The model obtained by the method is a minimum-phase system from the control theory point of view, all zeros and poles of the model are all negative real numbers, so the model constructed by the method is convergent. The research shows that the rational approximation model constructed by the method can achieve satisfying approximation performance in a given frequency bandwidth. Acknowledgement. This paper is supported by National Natural Science Foundation (Grant No. 60870009 and Innovative Teem Programs Foundation of Dalian Jiaotong University).
References [1] Hamdaoui, K., Charef, A.: A new discretization method for fractional order differentiators via the bilinear transformation. In: Proc. of the 2007 15th Intl. Conf. on Digital Signal Processing (DSP 2007), pp. 280–283 (2007) [2] Romero, M., de Madrid, A.P., et al.: Discretization of the fractional order differentiator / integrator by the rational Chebyshev approximation. In: Proc. of Control 2006 7th Portuguese Conference on Automatic Control Institute Superior Tecnico, Lisboa, Portugal, September 11-13 (2006) [3] Chen, Y.Q., Vinagre, B.M., et al.: A new discretization method for fractional order differentiators via continued fraction expansion. In: Proc. of 2003 ASME Design Engineering Technical conferences, Chicago, Illinois, USA, September 2-6, pp. 1–9 (2003) [4] Chen, Y.Q., Vinagre, B.M.: A new IIR-type digital fractional order differentiator. Signal Processing 83, 2359–2365 (2003) [5] Oustaloup, A., Levron, F., Nanot, F., Mathieu, B.: Frequency band complex noninteger differentiator: characterization and synthesis. IEEE Trans. Circuits systems I:Fundam. Theory Appl. 47(1), 25–40 (2000) [6] Bai, J.: Research on discretization method of fractional order model. Dalian Jiaotong University, Dalian (2009) [7] Fan, Y.: Research on Fractional Order Disturbance Observer. Dalian Jiaotong University, Dalian (2007) [8] Chen, Y.Q., Moore, K.L.: Discretization schemes for frational-order differentiators and integrators. IEEE Transactions on Circuits and Systems-I: Fundamental Theory and Applications 49(3), 363–367 (2002) [9] Vinagre, V., Podlubny, I., Hernandez, A., Feliu, V.: Some approximations of fractional order operators used in control theory and applications. Fractional Calculus and Applied analysis 3(3), 231–248 (2000) [10] Al-Alaoui, M.A.: Novel digital integrator and differentiator. Electronics Letters 29(4), 376–378 (1993) [11] Santouh, Z., Dharef, A., Assabaa, M.: Approximation of multiple fractional order systems. Arab Research Institute in Sciences & Engineering 3(4), 155–161 (2007)
Research of Direct Discretization Method of Fractional Order Differentiator/Integrator Based on Rational Function Approximation Bing Nie, Wen Li, Haibo Ma, Deguang Wang, and Xu Liang Software Technology Institute of Dalian Jiaotong University, Dalian, China
Abstract. The discretization method of the fractional order differentiator/integrator is an important theoretical basis of fractional control system. This paper proposes a rational Remez approximation algorithm based on direct discretization of the fractional order differentiator/integrator. In the paper, implementation of the Remez approximation algorithm and evaluation process of algorithm using system simulation and sample dispersion are discussed in detail. It is shown how, for a given order of the transfer function, Remez approximation algorithm is good for the direct discretization of the fractional order differentiator/integrator, uses which to achieve accurate approximation.
1 Introduction The theory of the fractional order differentiator/integrator [1] is used widely in control system, rational function approximation and discretization of the fractional order differentiator/integrator are important tools for researching of fractional order control system. The discretization of the fractional order differentiator/integrator can be implemented by two means: indirect or direct. In this paper, Remez approximation algorithm is applied to rational function approximation of the fractional order differentiator/integrator using direct discretization method. The theory of Remez approximation algorithm and implementation steps of Remez approximation algorithm are discussed in detail. This paper also discusses the testing method and validation result of Remez approximation algorithm by system simulation and sample dispersion method. Results show that Remez approximation algorithm can achieve more accurate approximation result than classic methods in both time domain and frequency domain.
2 Origin of the Best Approximation Problem In the researching of approximation problem of Chebychev, n is fixed, and the collection of polynomial who’s power less than or equal to n is H n ⊂ C[a, b] .
H n = spsn{1, x,", x n } , 1, x, ", x n are set of linear independent function group in a T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 479–485. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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given interval [a,b], those are sets of radicals in H n . The element Pn (x ) in H n is represented as: Pn ( x) = a0 + a1 x + " + a n x n , where a0 , a1 , " , an are any real number. If
Pn* ( x) can be solved in H n , which to approximate f ( x) ∈ C[a, b] , and approximation error satisfies the following condition:
max f ( x ) − Pn* ( x ) = min max f ( x) − Pn ( x ) Pn ∈H n a ≤ x ≤b
a ≤ x ≤b
(1)
This is the best approximation problem or Chebyshev problem [2]. Usually, Chebychev approximation algorithm is used to approximate rational polynomial expression. For a given function F (x) , it can be represented N
as F ( x ) = ∑ c k Tk ( x ) by using Chebyshev polynomial expression. Rational k =0
n
function R ( x ) =
∑p T k
k
( x)
k
( x)
k =0 m
∑q T k
is used to approximate F(x), where r = n + m, q0 = 1 .
k =0
But coefficient pk, qk of the rational function R(x) aren’t unique, they don’t satisfy requirement of uniqueness of rational approximation, so the best rational approximation should be researched to solve this problem.
3 Rational Remez Approximation Algorithm Suppose using the rational fraction:
R m* , n ( x ) =
p 0* + p1* x + p 2* x 2 + " + p m* x m q 0* + q1* x + q 2* x 2 + " + q n* x n
(2)
In Rm ,n ( x) to approximate F ( x ) ∈ C [a, b] . Let constant term q0* of denominator in (1) is equal to 1, so it can be implemented by a method that multiplying both numerator and denominator by a non-zero constant at the same time. Suppose m+n+2 crossing points are represented as x k* , k = 1,2,", m + n + 2 :
a = x1* < x2* < " < xm* + n+1 < xm* +n+2 = b
(3)
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From the best rational approximation theorem, we can get:
p 0* + p1* x k* + p 2* x k* + " + p m* ( x k* ) m − F ( x k* ) = (−1) k μ * , * * * * * * * n q 0 + q1 x k + q 2 x k + " + q n ( x k )
(4)
Where μ * = max Rm* , n ( x ) − F ( x) . a≤ x≤b
[
]
p 0* + p1* x k* + p 2* x k* + " + p m* ( x k* ) m + q1* x k* − F ( x k* ) − (−1) k μ * + q 2* ( x k* ) 2
[
]
[
]
So: − F ( x * ) − (−1) k μ * + " + q * ( x * ) n − F ( x * ) − (−1) k μ * − (−1) k μ * = F ( x * ) k n k k k
k = 1,2, " , m + n + 2 If the crossing points x k* , k = 1,2, ", m + n + 2 are known, and nonlinear systems of equations can be solved, the coefficient of Rm* ,n ( x ) can be obtained, then Rm* ,n ( x ) can be solved. Remez algorithm gets the coefficients of Rm* ,n ( x ) using below iterated algorithm: Step1.
Choosing
the
initial
value
x k , k = 1,2, ", m + n + 2
,
let
a = x1 < x 2 < " < x m+ n + 2 = b . In general, select extreme point of Chebyshev polynomial as the initial point:
1 (b − a ) cos (m + n + 2 − k )π + 1 (b + a ), 2 2 m + n +1 k = 1,2," , m + n + 2. xk =
(5)
Where x1 = a, x m+ n= 2 = b. Step2. Solving m+n+2 nonlinear systems of equations as follows:
[
p0 + p1xk + " + pm xk + q1xk − F ( xk ) − (−1)k μ m
[
]
[
]
]
+ q2 xk − F ( xk ) − (−1)k μ + " + qn xk − F ( xk ) − (−1)k μ − (−1)k μ = F ( xk ) 2
n
(6)
k = 1,2, ", m + n + 2 which can solve μ and coefficient pi , q j of the following rational fraction. The rational fraction is: Rm ,n ( x ) = Step3.
p0 + p1 x + p 2 x 2 + " + p m x m , q0 = 1 q0 + q1 x + q 2 x 2 + " + q n x n
Solving the extreme points of
the absolute error
function
Rm,n ( x) − F ( x) in the interval [a, b] . For simplicity, suppose it has m+n+2 extreme
points in the interval [a, b ] , those are y k , k = 1,2," , m + n + 2 :
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a = y1 < y 2 < " < ym+n+2 = b Step4. Using y k instead of x k , k = 1,2, ", m + n + 2 , repeat step 2 and subsequent steps.
4 Remez Approximation Algorithm Validations Literature [7] shows that Continued Fraction Expansion (CFE) is more accurate than other rational approximation method in terms of expanding operator. In this section, we will illustrate the rational approximation of the fractional integrator of order 0.5, s −0.5 , using CFE and Remez. This research will be carried out with three different generating functions, and compare the characteristic of approximation from the time domain, frequency domain and sample dispersion. Three different generating functions [3-5] are: Euler generating function: ω B ( z −1 ) =
1 (1 − z −1 ) T
(7)
2 1 − z −1 T 1 + z −1
(8)
Tustin generating function: ωT ( z −1 ) =
Al-Alaoui generating function: ω A ( z −1 ) =
8 1 − z −1 T 7 + z −1
(9)
Remez will be compared with CFE using third order approximations, n = m = 3 , with sampling period T=0.1 seconds, Remez is in the interval [-0.999,0.999] and CFE is in the interval [-1,1]. The obtained transfer functions are as follows: Euler rule:
G E _ Re m ( z −1 ) =
G E _ CFE ( z −1 ) =
0.311815 − 0.709425z-1 + 0.495690z-2 − 0.098043z-3 (10) 1 − 2.765800z-1 + 2.534745 z -2 − 0.768944z -3
0.316227 − 0.395284z -1 + 0.118585z -2 − 0.004941z -3 1 − 1.750000z -1 + 0.875000z -2 − 0.109375z -3
(11)
Tustin rule:
GT _ Re m ( z −1 ) =
0.21874 − 0.22811z-1 − 0.15383z-2 + 0.16336z-3 1 − 2.31143z-1 + 1.63621z -2 − 0.32478z-3
(12)
GT _ CFE ( z −1 ) =
0.22361 + 0.11180z -1 − 0.11180z -2 − 0.02795z -3 1 − 0.50000z -1 − 0.50000z -2 + 0.12500z -3
(13)
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Al-Alaoui rule:
G A _ Re m ( z −1 ) =
0.29274 − 0.60796z-1 + 0.36036z-2 − 0.04504z-3 1 − 2.65670z-1 + 2.32061z -2 − 0.66391z-3
(13)
G A _ CFE ( z −1 ) =
0.29580 − 0.29580z -1 + 0.042257z-2 + 0.00604z -3 1 − 1.57143z -1 + 0.63265z -2 − 0.03790z -3
(14)
Figure 1,2,3 illustrate the obtained results in time domain. It can be observed in the figure 1,2,3 that approximation result is very accurate using Remez. The step response Compared with the ideal continuous fractional order systems is quite similar. The approximation result of Remez is much better than CFE. Figure 4,5,6 are the corresponding bode diagrams of three generating functions’ transfer functions. It can be observed in these figures that Remez approximation is much better than CFE at low frequencies, and they are quite similar at high frequencies, so Remez improves characteristic of approximation at low frequencies. It has an accurate result in low order.
Fig. 1. Step response using the Euler generating function
Fig. 2. Step response using the Tustin generating function
Fig. 3. Step response using the Al-Alaoui generating function
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Fig. 4. Bode diagram using the Euler generating function
Fig. 5. Bode diagram using the Tustin generating function
Fig. 6. Bode diagram using the Al-Alaoui generating function
Sample dispersion is another approach to evaluate approximation’s result. Sample dispersion is a value used in probability distribution to reflect the deviation rate of actual value to theoretical value. For the same theoretical value, the more sample dispersion, the more error. The smaller sample dispersion, the smaller error. We have illustrated the rational approximation of the fractional integrator of order 0.5 by using three different generating functions of CFE and Remez, then we have six transfer functions (formula 5-10). The lag angle of phase-frequency
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characteristic of fractional integrator of order 0.5 is 45 degrees. Six sample points which are selected from whole band rang are used for both of approximation algorithms. Comparing sampling lag angles with 45degrees, we get a table of sample dispersion (see table 1). It is observed that sample dispersions of Remez are smaller than CFE through sampling from low frequency to high frequency, so the error is smaller than CFE. It shows that degrees of fitting of Remez’s approximation characteristic are better than CFE. Table 1. Sample dispersions of lag angles
Remez CFE
Eule 59º 86º
Tustin 48º 86º
Al-Alaoui 59º 86º
5 Conclusions This paper introduced the Remez algorithm which was applied to fractional order differentiator/integrator with direct discretization method. It shows that approximation result is accurate than CFE. Simulation result shows that the step response of Remez is quite similar to the ideal continuous fractional order systems in the time domain. In the frequency domain, Remez is much better at low frequencies than CFE, at high frequencies they are quite similar, so Remez improved characteristic of approximation at low frequencies. Sample dispersions of Remez are smaller than CFE, degree of fitting of Remez approximation characteristics is good regarding to the ideal continuous fractional order systems. In conclusion, Remez algorithm provides an effective method for direct discretization of fractional order differentiator/integrator. Acknowledgement. This paper is supported by National Natural Science Foundation of China (Grant NO. 60870009) and Innovative Teem Programs Foundation of Dalian Jiaotong University, China.
References [1] Oldham, K.B., Spanier, J.: Fractional Calculus: Theory and Applications, Differentiation and Integration to Arbitrary Order. Academic Press, New York (1974) [2] Li, Q., Wang, N., Yi, D.: Numerical Analysis, pp. 58–60. HuaZhong University of Science&Technology Press, Wuhan (1986) [3] Li, Y.: Fractional-Order Calculus Filter Theory, Application and Fractioanal-Order System Identification. Nanjing University of Aeronautics and Astronautics, Nanjing (2007) [4] Zhang, B.: The Study on the Control System of Aerodynamic Missile Based on Fractional Calculus. Nanjing University of Science and Technology, Nanjing (2005) [5] Fan, Y.: Research on Fractional Order Disturbance Observer. Dalian Jiaotong University, Dalian (2007)
Blind Source Separation of Vibration Signal of Electric Motor Velocity Modulation System Xiumei Li1, Wen Li1, Yannan Sun2 and Guanghai Zheng1 1 2
Software Institute, Dalian Jiaotong University School of Electrics and Information Engineering, Dalian Jiaotong University Dalian 116028, China
Abstract. Blind signal separation technique is the new one for array processing and data analysis. The research object of the paper is the complex and aliasing vibration signal measured from the AC electric motor velocity modulation system. Fast ICA algorithm is adapted to separate the vibration signals. Major source signals have been successfully and clearly separated from the complex and aliasing measured vibration signals. Then the characteristics of each source vibration signals can be effectively extracted in different frequency by analyzing source signals. The results are helpful for the later data analysis.
1 Introduction In order to suppress validly vibration of electric motor velocity modulation system, and provide necessary information for vibration suppression algorithm, we measured and analyzed the signal of electric motor velocity modulation system. Then the number of vibration sources in the system is estimated preliminarily, and the distribution of the vibration sources in high-frequency and low-frequency are obtained. This necessary information was important for designing algorithm to suppress the vibration of the system. In 1986 Heralt and Jutten proposed blind source separation method [1], using it to separate multi-aliasing signals, and found that the method isn’t affected by overlap of the time and the spectrum of source signals, and the feature information of the weak source signal will not be lost [2]. Hy var inen has derived a fixedpoint algorithm (Fixed-Point Algorithms) [3-4] from the entropy optimization method. The algorithm is a fast, robust off-line algorithm, also known as the fast in dependent component analysis (Fast Independent Component Analysis, Fast ICA) algorithm. Fast ICA algorithm is used to separate source signals from the observed data. In the paper, Fast ICA algorithm of blind source separation method is applied to analyze source vibration of motor velocity modulation system. Firstly, introduce the basic theory of blind source separation and Fast ICA algorithm. Then Fast ICA is applied to separate main vibration signals from the measured vibration signals. The separated results indicate that most of the source signals can be clearly separated from mixed signals by Fast ICA algorithm. Some characteristics of T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 487–495. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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the source signal can be initially identified through the analysis source signal in different frequency.
2 Blind Source Separation Principle and Fast ICA Algorithm 2.1 The Principle of Blind Source Separation When the source is unknown, Blind Source Separation is used to estimate the number of the source signals by separating the measured signals. Assume that N statistical independent source signals which mixed by linear instantaneous, they are received by M sensors. And then each observation signal is a linear combination of the N signals. Linear time-invariant instantaneous mixtures can be denoted by the vector form, as follow:
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Where: x (t ) = [ x1 (t ), x2 (t ),......, x m (t )] , s (t ) = [ s1 (t ), s 2 (t ),......, s n (t )] , A ∈ R M × N is a mixture matrix, a ji is its element. The essence of blind source separation is to compute the inverse matrix of A, that is separating the source signals s(t) from the observed signals x(t).
2.2 Fast ICA Algorithm The independent component analysis of n-source signals is transformed to a noptimization problem solving by Fast ICA algorithm, that is, the source signals are separated one by one. During the solution process, Fast ICA algorithm shows a fast convergence speed and good stability. According to the central limit theorem, a random vector which is composed of a number of independent random variables, it must obey the near Gaussian distribution if the independent random variables have a finite mean and variance, regardless of the random variables follow any distribution. The theory has been applied to the ICA. During the separation process, using non-Gaussian of the separation signals to monitor the independence among the separated signals. When non-Gaussian value of the separation signal y(t) reaches the maximum, the observation signal x(t) has been basically separated. Base on the information theory, a random variable that follows Gaussian distribution, it has the largest entropy among the random variables which have same variance. The stronger the nonGaussian nature is, the smaller the information entropy is. For the random variable x, whose probability density function is denoted by p(x), the negative entropy is defined as:
J ( x) = H G ( x) − H ( x) = D ( p ( x ) p G ( x ))
(2)
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Only if the variable x has a Gaussian distribution, J ( x) = 0 .The stronger the nonGaussian nature of x is, the larger the value of J (x) is. However, before calculating the negative entropy we must estimate probability density of the random variable, While the probability density is not easy to estimate, so the literature [5] presented an approximate formula of non-Gaussian metric, namely:
N (wT x)
∝ {E[G( y)] − E[G( y
)]}
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nal. If y is the estimated vibration source signal, we can get yi (t ) = si (t ) = Wxi (t ) according eq. (1).Here W matrix is the inverse matrix of A. Suppose w is a vector of the separation matrix W.x is the data that has been dealt with, and it has zero mean value and unit covariance. Then get:
N ( wT x)
yi = wT x .Put it into the eq.(3) and we can
∝ {E[G(w x] − E[G( y
)]}
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The negative entropy of yi = wi x can get the maximum when wi get the optimal value, then yi is an estimated source signal. That is to say yi has a maximum T
T
of negative entropy is equivalent to E[G ( wi x)] get a maximum value. According T
to Kuhn-Tucker [5] conditions, if E[G ( wi x)] obtains the optimal value, there has the following formula:
E{xg ( w0 x )} − βw0 = 0 T
(5)
Here g (⋅) is the derivative of G (⋅) , β = E{w0 xg ( w0 x )} , where w0 is the optiT
mum value of
T
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F (wi ) = E{xg (wiT x)} − βwi , then finding the optimal value of wi is equivalent to seek the minimum value of F ( wi ) . In order to get the minimum value, Jacobian matrix of F ( wi ) is used, JF ( wi ) is first calculated.
JF ( wi ) = E{ xx T g ' ( wi x )} − β I T
(6)
After the observed signal has been dealt with to white one, there is the following formula:
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E{xx T g ' ( wi x)} ≈ E[ xx T ]E[ g ' ( wT x )] = E[ g ' ( wT x)]I T
(7)
Because the inverse of a diagonal matrix is very easily to get, The Jacobian matrix is translated into a diagonal matrix. Here use Newton iterative method to seek w0. If the current evaluation wi is instead of w0, the approximate Newton iteration formula can be obtained:
wi (k + 1) = wi (k ) − [ E{xg (w(k ) i x)} − βw(k ) i ] /[ E{g ' (wi (k )T x)} − β ] T
wi (k + 1) = wi (k + 1) / wi (k + 1)
(8)
In order to increase the stability of the iteration operation, wi is normalized, when
wi gets to the optimal value, according to eq. (5) we get: β = E {wiT xg ( wiT x )}. ' T To simplify eq. (8), multiply the eq.(8) by E{g ( wi x)}− β on both sides. Following formula is got:
{ wi + = wi E{xg ' ( wi T x)} − E{xg ( wi T x)} wi+ = wi+ / wi+
(9)
According to the above, the Fast ICA algorithm steps are as follows: Step1: Make the mean of observation signals be zero. T Step2: Deal with the observation signal to make E[ xx ] = I . Step3: Determine the number of independent components n, and set p = 1. Step4: Select the initial wp to make w p = 1 . T T Step5: According to wi (k + 1) = wi (k ) E{xg ' ( wi (k ) x)} − E{xg ( wi (k ) x)} , To calculate wp. Step6: Make orthogonal treatment: p
wi ( k + 1) = wi (k ) − ∑ ( wi (k + 1)T wi (k )) wi (k ) j =1
Step7: Normalize: wi (k + 1) = wi (k + 1) / wi (k + 1) Step8: If wp(k) is approach to wp (k +1), that is, wp is convergence, iteration stop. Otherwise turn to step 5 until wp is convergence. Step9: Let p=p+1. If p≤n, go to step 4. Otherwise the algorithm is over. Then all components of the s(t) have been isolated.
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3 Separating Source Vibration Signal of Motor Velocity Modulation System 3.1
Collecting Data of Vibration Signal of Motor Velocity Modulation System
Motor velocity modulation experimental system consists of motor-generator sets, the frequency-converter to adjust motor speeding modules, data acquisition unit. The electric motors and generators are connected through the coaxial, they are fixed on the same pedestal. In order to collect more information of the vibration source in the motor velocity modulation system, acceleration sensors are respectively put on the motor horizontal, vertical direction, the base installation. Those four acceleration sensors collect vibration signals of motor velocity modulation system in the different directions. In the experiment, the FM’s frequency of the velocity modulation system is 25Hz.While the motor rotational speed is 1406 rpm, the data sampling frequency is 1652Hz, the data length is 1652. 4-way vibration signals collected from the time domain are shown in Fig. 1.And the power spectrum of the vibration signals in frequency domain is shown in Fig. 2.
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Fig. 2. Power spectrum of the measured signal
3.2 Isolation and Analysis of the Vibration Source Signals Measured vibration signals from the motor velocity modulation system are with noise, while the Fast ICA algorithm is based on instantaneous-aliasing and noisefree model. In order to make the signal as much as possible to meet the conditions of blind source separation algorithm, measured vibration signals must be preprocessed such as to mean, whitening treatment before using Fast ICA algorithm to separate the signals. Because the acquisition aliased-vibration signals are more than the vibration source signals in fact, here principal component, namely PCA, which is used to reduce the dimension of measured data. 95.9521% of the nonzero characteristic value is retained during the reducing dimension process.
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Fast ICA algorithm is adopted to separate gathered mixed-signal in Fig.1. In the separation algorithm, the 1652 sample points, G ( y ) = y 4 / 4 .Iterative method is used to separate the measured signals. Three independent components are isolated by using the Matlab7, of which IC1 is convergence at 27th step, IC2 is convergence in step 8 and IC3 is convergence in step 2. The time-domain diagrams of the separated independent components are shown in Fig. 3.Comparing the Fig 1, there is no significant difference in the time domain, which is a clear characteristic of the blind source separation when the separated signals are non-Gaussian nature and the complex vibration signals. In order to facilitate the spectral analysis of the vibration signals before and after separation, and observe the different characteristics of the vibration signals before and after separation in the frequency domain, Fig.4 is the power spectra of Fig. 3.In the power spectrum high-frequency and low-frequency parts are be separated clearly. The aliasing phenomenon of generator and motor vibration signals have been clearly eliminated in Fig.4.Graph G1of Fig.4 contains mainly low-frequency part of the separated signals, fundamental frequency and two multiplier components of the power grid frequency are shown in graph G1, such as the peak appears near to 50Hz and 100Hz, Because the grid frequency is 50Hz, the vibrations in the power grid frequency are unilateral magnetic pull sound vibration, and vibrations of twice the grid frequency are the pulse of pole radial magnetic pull noise vibrations. The high-frequency parts of the source signals are isolated in the graph G3.The vibration signals are mainly concentrated in the 626HZ ~ 696Hz and 754Hz ~ 803Hz frequency band. The most intermediate frequency components are in graph G2. There are some low-frequency and high-frequency components, which can not be explained. And it demand to be further analyzed and studied.
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4 Conclusions In this paper, the fixed-point algorithm (Fast ICA algorithm) of the blind source separation is adopted to separate the measured vibration signal of the motor velocity modulation system. The three major source signals are obtained, namely: the low-frequency source signal, the intermediate frequency source signal and the high-frequency source signal. The separation results demonstrate that the measured signals can be well separated into the high-frequency and low frequency signal parts by this algorithm. The independence feature of each separated source signal is not very clear in the time domain. And its separation efficiency can not been easily seen in the time domain. So each separated signal in the time domain is transformed into the frequency domain, then its independence characteristics is clearly reflected. The experiment and separation results show that the blind source separation method is valid to analyze vibration signals of motor velocity modulation system. At the same time, the separation results provide the basic information for designing vibration suppression algorithm.
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References [1] Herault, J., Jutten, C.: Space or time adaptive signal processing by neural network models. Network for Computing. In: Proceedings of AIP Conference, pp. 206–211. New York (1986) [2] Comon, P.: Independent Component Analysis, A New Concept. Signal Processing 36, 287–314 (1994) [3] Hyvärinen, A.: Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. On Neural Networks 10(3), 626–634 (1999) [4] Hyvärinen, A., Oja, E.: Independent component analysis: algorithms and applications. Neural Networks 13, 411–430 (2000) [5] Luenberger, D.G.: Optimization by Vector Space Methods. John Wiley and Sons, New York (1969)
A Survey on Artificial Intelligence Algorithm for Distribution Network Reconfiguration Rongfu Qiu, Xueqin Lv and Shuguo Chen The Department of Information and Control Engineering, Shanghai University of Electric Power ShangHai 200090, China e-mail:
[email protected]
Abstract. Distribution network reconfiguration (DNRC) in distribution systems has to solve the complex combinatorial optimization problem with the purpose of reducing loss, balancing load and improving the quality of voltage in the system. DNRC is realized by changing the status of sectionalizing switches. The achievements and prospect of artificial intelligence algorithm for DNRC are introduced. The early classic methods for DNRC are genetic algorithm, fuzzy control algorithm and artificial neural network algorithm. But these methods have shortcomings in DNRC to some extent. The genetic algorithm costs time and its global optimization ability is poor; Fuzzy control algorithm will act upon combination with other technologies; The accuracy of neural network algorithm depends on the level of training samples and its computation is complex resulting in poor control in real-time. In order to simplify DNRC algorithms, improve computation efficiency, and optimize solution feasibility, the improved genetic algorithm, fuzzy control algorithm and artificial neural network are well presented in this paper. It is indicated that the research should be broader and deeper to fit the requirements of application and it is essential to probe into the related problems with the search method such as load forecasting and flow calculation. It is necessary to make full use of artificial intelligence to speed up the theoretical research and application of DNRC.
1 Introduction The power electric system is composed of five phases. They are: power generation, power transformation, power transmission, power distribution and power consumption. The power distribution network, which is the most extensive part of electric power systems, connected the transmission network and load network. With the lower power class, power distribution networks produce lots of reactive loss accounting for 60% as per the survey status. The distribution network reconfiguration serves for finding radical operating structure minimize the power loss of distribution network under the normal operation conditions. In fact, the distribution network reconfiguration can be viewed as a problem of determining an optimal tree of the given graph. DNRC can solve the complex combinatorial optimization problem and it can reduce loss, balance load and improve the quality of voltage in the system. T.-J. Tarn et al. (Eds.): Robotic Welding, Intelligence and Automation, LNEE 88, pp. 497–504. springerlink.com © Springer-Verlag Berlin Heidelberg 2011
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The research in DNRC is now on its way of diverging into various categories. The optimal distribution network reconfiguration technique was first used by Merlin and Back in 1975, but it can not handle the complex large-scale power systems. Optimal Flow Pattern and Branch Exchange Method are the two main heuristic and stochastic optimization techniques. OFP must do a power flow calculate when the state of switch has to be changed. So it may take long time computation, However, the results of OFP has nothing to do with the initial network state and relatively easy to converge to the optimal solution. DENG You-man and ZHANG Bo-ming of Tsinghua University, et al. [1] made the improvement of distribution network reconfiguration optimal flow pattern algorithm is fast and effective, but there are much idealistic assumptions, as a result, the result are computing large deviations. The disadvantage of BEM is that multi-step of calculation, low efficiency, the calculation results are depended on the initial network structure, and it may easily converge to local optimal solution. BI Peng-xiang, LIU-Jian of Xi'an Jiaotong University [2] made adjustments based on an ideal topology branch-exchange method, it is to be proved that the method can avoid non-meaningful topology changes and improve efficiency. In recent years, nearly global optimization techniques (such as the simulated annealing algorithm [3], genetic algorithm, tabu search algorithms [4],etc.) has attracted wide attention, because they can solve many engineering problems. With the development of artificial intelligence algorithm, new algorithms are applied to the electric power system. Such as improved genetic algorithm, fuzzy control algorithm and artificial neural network, which is hybrid algorithm. In the paper, we introduced the mathematics model of distribution networks reconfiguration first, which is the foundation of DNRC algorithm. Then,we discuss the development of the solutions to DNRC. We discuss it from the early conventional analytical methods to heuristic and stochastic optimization techniques, and then to the recent artificial intelligence technology. At present, the hybrid algorithm has become an heated issue. So we discuss the advantages of the new hybrid algorithms in the distribution networks. At last, we may get an expectation of development of the DNRC.
2 The Mathematical Model of DNRC Every DNRC algorithm must rely on the mathematical model, different mathematical model of DNRC can construct different algorithm and it is important to the algorithm of efficiency. We can find mathematical model of DNRC based on the aims of DNRC. While the main purpose of DNRC is to find a radial operating structure that minimizes the system power loss while satisfying operating constraints. Thus, the following model can represent the DNRC problem.
min ΔP
LOSS
2
2
i
i
P +Q ) = ∑k r ( U i ∈N N
i =1
i
i
2
i
(1)
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Such that Real power constraint:
Ki/Pi/≤Pimax
(2)
Reactive power constraint:
Ki/Qi/≤Qimax
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Voltage constraint :
Uimin≤Ui≤Uimax
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Real power balance:
Gi(P,K)=0
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Reactive power balance
Gi(Q,K)=0
(6)
Voltage balance:
Gi(U,K)=0
(7)
Radial operating structure constraint: H(k)=0
(8)
Where: Pi: the real power in branch i;Qi:the reactive power in branch i;ri: the resistance of branch i;Ui: the node voltage at node i;Ki: represents the topological status of the branches. Ki=1 if the branch i is closed, and Ki=0 if the branch i is open: l;N: the set of nodes; i: the set of branches; Subscripts ‘min’ and ‘max’ represent the lower and upper bounds of the constraint. Nowadays, we have developed other mathematical models of DNRC, paper [5] present the mathematic model of DNRC which aims to balance load, the proposed method can improve the stability and reliability of distribution network. LI Haifong [6] considered the minimum frequency of changing switch as the goal of DNRC. We found that there is a new development that researchers use the multiobjective as the mathematical model of DNRC while they can meet many energy requests in the modern power system. We conclude the mathematical models of DNRC in tab 1. Table 1. Mathematical models of DNRC Mathematical Model 2
•
N
min LBI = ∑ Li ( I / I i , R ) i i =1 m
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minF'(Y, Z) = ∑(1− y) + ∑zj i
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min p = ∑ F i (U i −1 , ti)
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max f = ∫ C (t )ΔP(t ) dt − K (T ) S
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minf = λ1∑VBIm /VBI0 +λ2 ∑kn Inrn / PLoss0, 2
Multiobjective of VBI and minimum loss
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3 Fuzzy Control Algorithm The fuzzy set theory has developed very rapidly since LA Zadeh put forward the concept of fuzzy sets theory in 1965.It has been got widely application in the power system. Fuzzy control is one of most important application in the application of fuzzy set theory, which is mainly imitate human control experience without the need for accurate control of the object model. So fuzzy controller can achieve some of the human intelligence, and thus is also a kind of intelligent control. However, the application of fuzzy theory must rely on other technologies, therefore it has certain limitations. Debapriya Das [7] presents an algorithm for network reconfiguration based on the fuzzy and heuristic rules multiobjective approach. From the fig.1, we can clearly find that the proposed algorithm can balance the feeder current after reconfiguration. It also can minimize the number of tie-switch operations and, hence, the search space is reduced and computational time is less.
Fig. 1. Feeders current before and after reconfiguration
If the fuzzy control algorithm can combine with heuristic and stochastic optimization techniques, it can make up the disadvantage of fuzzy control algorithm’s long computational and bad ability of convergent. Thereby it can be enhanced the value of its practical application in DNRC.
4 Artificial Neural Network Algorithm Artificial neural network (ANN) simulates the basic characteristics of the human brain, non-linear relationship between input and output through the training sample is stored in the weights of neurons [8]. Therefore, ANN can reflect the non-linear relationship between the distribution network load mode (each node load combination) and the optimal structure of the distribution network. However, the accuracy of reconfiguration depends on the provision of training samples, and requires a lot of time to complete the network training. It needs to be re-trained when the power distribution system network structure and the operation mode are changed. What’s worse, it has the disadvantages of poor convenience and difficult application. If the ANN algorithm has not studied the complexity of network reconfiguration, the
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optimal result will be far from ideal. Jin Li-cheng [9] proposed a network reconfiguration model based on a CMAC neural network, show in fig.2, distribution system load model(X) is regarded as input, while the state of switch under minimizing the power loss(F(X)) as output. By mapping the complex nonlinear relationship between the inputs and outputs and using the generalization capability using CMAC neural network to map the input-output relationship. The relationship has been established, it is easy to get the output from the input. The results show that it is suitable for large-scale distribution power network.
Fig. 2. Structure of CMAC neural network
DNRC algorithms based on ANN don’t need the step of power flow calculation. So it can quickly arrive at the results, but its accuracy depends on the sample. The problem is what gaining the full sample is very difficult, and requiring longer training time. So there is a development that DNRC algorithms are mixed heuristic and stochastic optimization techniques. Heuristic and stochastic optimization techniques can find optimization value from another way.
5 Genetic Algorithm Now genetic algorithm is widely researched in the electric power system. The branch of switch status (0 or 1) can be directly transformed into coded chromosome by genetic algorithm(GA).Distribution network reconfiguration based on GA is to find radical operating structure meeting the objective function by simulating the biological evolution of reproduction/crossover/mutation operation. The methods try to change the state of every switch. GA is easy to converge to the optimal solution. Therefore, genetic algorithm is convenient and very effective in the application of distribution network reconfiguration [10]. But calculation of GA needs many steps, such as evolution of reproduction, crossover, mutation operation, so it accounts long time. If the mutation is not well controlled, the results are easy to precocity, that is to say it easy to converge to local solution. Fig. 3 is the flowchart of working principle of is improved GA [11]. Real valued genes and an adaptive mutation rate is used in the modified GA. The improved GA can solve the problem of computationally intensive. Shanghai Jiao Tong University, O Yang-wu proposed a new
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improved GA with the strategy of searching random spanning trees in distribution network reconfiguration. The real coding is adopted to produce random sequences and available structures of distribution network are then obtained using graph theory, which can guarantee the continuity of the GA evolution. The results proved that the improved GA is effectiveness and simplicity. But the actual distribution network is more complex, it has a certain lack of solving practical problems.
Fig. 3. A flowchart of working principle of the modified genetic algorithm
GA can not ensure the radial structure of network in distribution net-work reconfiguration and it brings a large number of unfeasible solutions. Though some improved GA methods were proposed, but it has not been completely solved due to their complicated operations and a large amount of calculations. After analyzing the advantages and disadvantages of GA, we can find that in order to ensure the population find the optimal solution from different direction and avoid premature GA must maintain the population diversity. So we propose the GA algorithm should combine with the other artificial intelligence algorithm, the hybrid GA algorithm is more effective than the single GA algorithm, such hybrid particle swarm optimal [12], hybrid fuzzy algorithm [13]. In addition, we pay attention to the new intelligence algorithm, such as fish swarm algorithm [14], improved plant growth simulation algorithm [15].
6 Conclusions Along with the advance of artificial intelligence technologies, fuzzy control, neural networks, genetic algorithms and other classical intelligence technology have been effectively utilized in network distribution which is a large-scale complex combinatorial optimization system. Fuzzy control algorithm does not require a precise mathematical model, so that is conducive to multi-objective distribution network reconfiguration. But it takes a lot of computing time which is a deficiency in engineering. In contrast, neural network algorithm doesn’t require a lot of computational power, so it is relatively easy to implement. However, it requires a complete sample and training. It is difficult to know the real-time data of network
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distribution, because the automation of electric power system isn’t fully achieved, therefore, the development of neural networks algorithm is limited in DNRC. Genetic algorithm is a mature method and can find the optimal value within the global scope. Now it is becoming the most valuable of an DNRC’s algorithm. However, DNRC is affected by the impact of load forecasting and flow calculation. The accrue of load forecasting is the research should be broader and deeper to fit the requirements of application and it is important to study the related problems with the search method such as load forecasting and flow calculation. It is necessary to make full use of artificial intelligence to speed up the theoretical research and application of DNRC. Acknowledgement. This work was supported by Leading Academic Discipline Project of Shanghai Municipal Education Commission, Project Number: J51301.
References [1] Deng, Y., Zhang, B., Xiang, N.: An Improved Optimal Flow Pattern Algorithm for Distribution Network Reconfiguration. Power System Technology 19(7), 47–50 (1995) [2] Bi, P.-x., Liu, J., Zhang, W.-y.,et al.: A Refined Branch-Exchange Algorithm for Distribution Networks Reconfiguration. In: Proceedings of the CSEE, vol. 21(8), pp. 98–103 (2001) [3] Chen, X., Cheng, H.-z.: Application of Simulated Annealing Particle Swarm Optimization Algorithm in Reconfiguration of Distribution Networks. High Voltage Engineering, 148–153 (2008) [4] Zuo, F., Zhuo, J.-q.: Application of TS Algorithm in Reconfiguration of Distribution Networks. In: Proceedings of the CSU-EPSA, pp. 66–69 (2004) [5] Papadopoulos, P.M., Peponis, G.J., Boulaxis, N.G.: Heuristic methods for the optimization of MV distribution networks operation and planning. In: Proceedings of 14th International Conference and Exhibition on Electricity Distribution, pp. 911–915 (1997) [6] Li, H.-f., Zhang, Y.: Study on The Algorithm for Service Restoration Reconfiguration in Distribution Networks. Electric and Power System 8, 34–37 (2001) [7] Das, D.: A Fuzzy Multiobjective Approach for Network Reconfiguration of Distribution Systems. IEEE Transactions On Power Delivery 21(1), 202–209 (2006) [8] Sha, Z.-c., Dong, S.: Study on Artificial Intelligence Methods for Distribution Network Reconfiguration. Shang Dong Dian liJishu 162(2), 47–52 (2008) [9] Jin, L.-c., Qiu, J.-j.: Model of Distribution Power network Reconfiguration Based on CMAC Neural Network. Journal of Zhejiang University 35(6), 784–788 (2004) [10] Danhong, Z., Jie, W., Dejian, K., et al.: Enhanced genetic algorithm in electric network planning. Intelligent Control and Automation. In: Proceeding of the 3rd World Congress, vol. 1(6), pp. 53–57 (2000) [11] Radha, B., King, R.T.F.A., et al.: A Modified Genetic Algorithm for Optimal Electrical Distribution Network Reconfiguration. IEEE, Los Alamitos (2008) [12] Niu, D.-x., Gu, X.-h.: Multi-objective Optimization of Distribution Network Reconfiguration Based on Hybrid Genetic Particle Swarm Optimization Algorithm. North China Electirc Power 9(1), 1–7 (2007)
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[13] Liu, L., Chen, X.-y.: Reconfiguration of Distribution Networks Based on Fuzzy Genetic Algorithms. In: Proceedings of the CSEE, vol. 20(2), pp. 66–69 (2000) [14] Zhang, Q.-l., Cheng, X.-r., Wang, Z.-h.: Application of Artificial Fish Swam Algorithm to Power Distribution System Reconfiguration. East Chian Electric Power 36(5), 31–34 (2008) [15] Yong-Zhe, Y., Jia-dong, H.: Reconfiguration of Distribution Network Based on Improved Plant Growth Simulation Algorithm. In: 2009 Second International Conference on Intelligent Computation Technology and Automation, pp. 387–391 (2009)
Author Index
Cai, Chunbo 87, 351 Chen, Bo 245 Chen, Hongtang 81 Chen, Huabin 115, 193 Chen, Huanming 73 Chen, Jiaqing 361, 391 Chen, S.B. 3 Chen, Shanben 41, 107, 115, 123, 145, 185, 193, 203, 211, 219, 235, 245, 271, 315, 351, 401, 421, 429 Chen, Shuguo 497 Chen, Xizhang 185 Chen, Z.Y. 461 Deng, Wu 299 Ding, Mingyan 299, 315 Dinham, Mitchell 23 Dong, Na 435 Du, Bin 443 Du, B.Z. 97 Duan, Ye 169 Fan, Ding 153, 279, 285 Fan, Jiawei 153 Fang, G. 411 Fang, Gu 23 Fang, Xiaoming 391 Fu, Z. 401, 429 Gaede, Rainer 15 Gao, Hongming 49, 81, 435 Gao, Hui 361 Gao, Xiaofei 401, 429
Ha, Q.P. 411 Hu, Ronghua 383 Huang, Jiankang 153, 279, 285 Huang, Ming 471 Huang, Xixia 169 Huang, Yichang 229 Jia, Jianping 293 Jiang, F.R. 179 Jiao, Xiangdong 361, 391 Jin, Wei 293 Kong, Meng 41 Kong, Xiangfeng 145 Kwok, N.M. 411 Larkin, Nathan 341 Li, Bowen 57 Li, Haichao 81, 435 Li, Hongli 293 Li, Laiping 307 Li, Wen 299, 471, 479, 487 Li, Wenhang 211 Li, Xiaohui 161 Li, Xiumei 299, 487 Li, Yulong 383 Liang, Xu 479 Lin, Tao 41, 193, 307 Liu, D.H. 97 Liu, G.Q. 253 Liu, Gang 451 Liu, Yu 443 Lu, Hanzhong 351 Lu, Lihui 153, 279, 285
506
Author Index
Lv, Na 235 Lv, Xueqin 451, 497 Ma, G.H. 97, 253 Ma, Gouhong 179 Ma, Haibo 479 Ma, Hongbo 123, 271, 315 Meng, Yichen 73 Miao, Xingang 33, 161 Micallef, Kevin 23 Na, Suck-Joo 333 Nie, Bing 471, 479 Norrish, John 341 Pan, Jiluan 323 Pan, Song 229 Pan, Zengxi 341 Peng, Xingai 33 Polden, Joseph 341 Qian, Yaozhou 369 Qin, J. 179 Qiu, Rongfu 451, 497 Seyffarth, Peter 15 Shen, Daming 87 Shi, Fanhuai 169 Shi, Yu 153, 279, 285 Sun, Yannan 487 Tan, Shengqi Tao, Xinghua
57 65
Van Duin, Stephen
341
Wan, Benting 161 Wan, Wen 323 Wang, D. 411 Wang, Deguang 479 Wang, H.B. 97, 179 Wang, Jifeng 203, 229, 369 Wang, K.D. 461 Wang, L. 253 Wang, Su 33, 161 Wang, Wenyi 211 Wang, Xiaofeng 73 Wang, Z.W. 461 Wang, Zhijiang 263 Wei, Shanchun 41
Wu, Lin 49, 81, 263, 435 Wu, Minghui 401, 429 Wu, Yixiong 451 Xiao, M. 253 Xiao, Zengwen 139 Xiong, Zhenyu 323 Xiong, Zhilin 375 Xu, Jian-ning 383 Xu, Lili 65, 131 Xu, Yanling 145 Xue, Houlu 185 Xue, Long 131, 391 Yan, G.Z. 461 Yang, Bo 229 Yang, Chengdong 107 Yang, Rongzun 369 Yang, Xueqin 307 Yang, Yuan 33 Yao, Shunping 293 Ye, Zhen 203 Yin, Ziqiang 49 Yu, Houde 369 Yu, Huanwei 219 Zeng, Chun 375 Zhan, Hongwei 361 Zhang, Fengyan 307 Zhang, Guangjun 49, 81, 87 Zhang, Hua 383 Zhang, Huajun 87, 351 Zhang, Tao 421 Zhang, Wenzeng 57 Zhang, Yanqing 361 Zhang, Yifu 375 Zhang, YuMing 263 Zhang, Yuming 285 Zhao, Huihui 49 Zhao, Huimin 299, 471 Zhao, Jing 443 Zhao, Yanzheng 401, 429 Zheng, Guanghai 471, 487 Zhong, Jiyong 115 Zhou, Canfeng 361, 391 Zhu, Hongwu 65 Zhu, Ming 285 Zhu, Zhengqiang 375 Zou, Yong 131 Zuo, Yantian 229