Transactions on Intelligent Welding Manufacturing: Volume II No. 2 2018 [1st ed.] 978-981-13-3650-8, 978-981-13-3651-5

Table of contents :
Front Matter ....Pages i-xvi
Front Matter ....Pages 1-1
Thermal Behavior in Wire Arc Additive Manufacturing: Characteristics, Effects and Control (Bintao Wu, Zengxi Pan, Stephen van Duin, Huijun Li)....Pages 3-18
Front Matter ....Pages 19-19
Ultrasonic Welding of Polymer–Metal Hybrid Joints (Anwer Al-Obaidi, Candice Majewski)....Pages 21-38
Based on Multi-sensor of Roughness Set Model of Aluminum Alloy Pulsed GTAW Seam Forming Control Research (Jiyong Zhong, Yanling Xu, Huabin Chen, Na Lv, Shanben Chen)....Pages 39-57
Research on Adaptive Robust Control Algorithm for Delta Parallel Robots (Chendi Lu, Xingang Miao, Su Wang, Chenxi Zhang)....Pages 59-68
Weld Bead Penetration State Recognition in GTAW Process Based on a Human Auditory Perception Model (Yanfeng Gao, Qisheng Wang, Yanfeng Gong, Linran Huang)....Pages 69-84
Experimental Method of Mechanical Melting Point in A6N01-T5 Aluminum Alloy (Lichun Meng, Xiaohong Sun, Yongming Cheng, Gongxiang Zhao, Jijin Xu)....Pages 85-95
Mathematical Modeling and Workspace Analysis for Photographic Robot (Xuedong Li, Xingang Miao, Su Wang)....Pages 97-108
The Regulation of Laser-Arc Hybrid Welding Source on TC4 Ti Alloy to 304 Stainless Steel Joints with Interlayers (Hongyang Wang, Gang Song, Zhonglin Hou, Liming Liu)....Pages 109-120
A Machine Vision-Based Multifunctional Image Processing Platform (Baoming Li, Peiquan Xu)....Pages 121-130
The Formation and Control of Porosity During GMA Welding of Galvanized Steel (Yingming Wu, Chao Hu, Xizhang Chen)....Pages 131-141
Front Matter ....Pages 143-143
Optimization of SURF Algorithm for Image Matching of Parts (Hongyan Duan, Xiaoyu Zhang, Wensi He)....Pages 145-157
Research on Implicit Genetic Inverse Solution Algorithm for Eight-DOF Mechanical Arm of Photography Robot (Qi Dong, Xingang Miao, Su Wang, Xingai Peng)....Pages 159-167
Back Matter ....Pages 169-171

Citation preview

Transactions on Intelligent Welding Manufacturing Volume II No. 2 2018

Transactions on Intelligent Welding Manufacturing Editors-in-Chief Shanben Chen Shanghai Jiao Tong University PRC

Yuming Zhang University of Kentucky USA

Zhili Feng Oak Ridge National Laboratory USA

Honorary Editors G. Cook, USA K. L. Moore, USA Ji-Luan Pan, PRC

S. A. David, USA S. J. Na, KOR Lin Wu, PRC

Y. Hirata, JAP J. Norrish, AUS

T. Lienert, USA T. J. Tarn, USA

X. Q. Chen, NZL D. Hong, USA W. Zhou, SGP

D. Du, PRC X. D. Jiao, PRC

D. Fan, PRC I. Lopez-Juarez, MEX

Guest Editors H. P. Chen, USA J. C. Feng, PRC H. J. Li, AUS

Regional Editors Asia: L. X. Zhang, PRC America: Y. K. Liu, USA

Australia: Z. X. Pan, AUS Europe: S. Konovalov, RUS

Associate Editors Q. X. Cao, PRC B. H. Chang, PRC J. Chen, USA H. B. Chen, PRC S. J. Chen, PRC X. Z. Chen, PRC

A.-K. Christiansson, SWE Z. G. Li, PRC X. M. Hua, PRC

Y. Huang, USA S. Konovalov, RUS W. H. Li, PRC X. R. Li, USA Y. K. Liu, USA L. M. Liu, PRC H. Lu, PRC Z. Luo, PRC G. H. Ma, PRC

Pedro Neto, PRT G. Panoutsos, UK Z. X. Pan, AUS X. D. Peng, NL Y. Shi, PRC J. Wu, USA J. X. Xue, PRC L. J. Yang, PRC M. Wang, PRC

S. Wang, PRC X. W. Wang, PRC Z. Z. Wang, PRC G. J. Zhang, PRC H. Zhang, B, PRC H. Zhang, N, PRC L. X. Zhang, PRC W. J. Zhang, USA

S. L. Wang, PRC J. Xiao, PRC J. J. Xu, PRC Y. L. Xu, PRC C. Yu, PRC

H. W. Yu, PRC K. Zhang, PRC W. Z. Zhang, PRC Z. F. Zhang, PRC

Academic Assistant Editors J. Cao, PRC B. Chen, PRC Y. Luo, PRC N. Lv, PRC F. Li, PRC

S. B. Lin, PRC Y. Shao, USA Y. Tao, PRC J. J. Wang, PRC H. Y. Wang, PRC

Editorial Staff Executive Editor (Manuscript and Publication):

Dr. Yan Zhang, PRC

Responsible Editors (Academic and Technical):

Dr. Na Lv, PRC Dr. Jing Wu, USA

More information about this series at http://www.springer.com/series/15698

Shanben Chen Yuming Zhang Zhili Feng •

Editors

Transactions on Intelligent Welding Manufacturing Volume II No. 2 2018

123

Editors Shanben Chen Shanghai Jiao Tong University Shanghai, China

Zhili Feng Oak Ridge National Laboratory Oak Ridge, TN, USA

Yuming Zhang Department of Electrical and Computer Engineering University of Kentucky Lexington, KY, USA

ISSN 2520-8519 ISSN 2520-8527 (electronic) Transactions on Intelligent Welding Manufacturing ISBN 978-981-13-3650-8 ISBN 978-981-13-3651-5 (eBook) https://doi.org/10.1007/978-981-13-3651-5 Library of Congress Control Number: 2018963044 © Springer Nature Singapore Pte Ltd. 2019 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, 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. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Editorials

This issue of the Transactions on Intelligent Welding Manufacturing (TIWM) is selected in part from the high-quality contributions recommended by “2018 International Conference on Robotic Welding, Intelligence and Automation (RWIA’2018)” which provides a feature article and other 11 papers contributing to intelligent welding manufacturing through understanding and sensing and control of welding manufacturing processes. The featured article in this issue Chapter “Thermal Behavior in Wire Arc Additive Manufacturing: Characteristics, Effects and Control” is contributed by Bintao Wu, Zengxi Pan, Stephen van Duin, et al. from School of Mechanical, Materials, Mechatronic and Biomedical Engineering, University of Wollongong. This study provides a comprehensive overview of the thermal characteristics during a WAAM process and identifies the thermal behavior effects on the process stability, geometrical accuracy, and material properties of the deposited part. An innovative method for controlling thermal profiles during the build process is proposed and discussed, taking into account the requirement of the various alloys. The first paper of research papers, Chapter “Ultrasonic Welding of Polymer– Metal Hybrid Joints” is contributed by a joint research team from University of Sheffield and Mechanical Engineering Department, University of Wasit. This paper shows that the ultrasonic welding of ABS and Al6082-T6 has been achieved successfully. The second paper, Chapter “Based on Multi-sensor of Roughness Set Model of Aluminum Alloy Pulsed GTAW Seam Forming Control Research” is contributed by Jiyong Zhong, Yanling Xu, Huabin Chen, et al. from Intelligentized Robotic Welding Technology Laboratory (IRWTL), Shanghai Jiao Tong University. This paper combines visual sensing, arc sensing, and sound sensing to extract weld feature information in real time. Based on the rough set model, the multi-information fusion is proposed, and a prediction model of the weld backside width is proposed to realize the backside width control. And a fuzzy controller with genetic improvement is designed. The multi-information fusion prediction model based on roughness set is used to control the weld backside width in real time, and the control of the robot aluminum alloy GTAW weld forming is realized. v

vi

Editorials

The third paper, Chapter “Research on Adaptive Robust Control Algorithm for Delta Parallel Robots” is contributed by researchers from Beijing Key Laboratory of Robot Bionics and Function Research, Beijing Engineering Research Center of Monitoring for Construction Safety, Beijing University of Civil Engineering and Architecture. The adaptive control and robust control are designed in this paper to solve the uncertainty of control system. An adaptive robust control system is proposed in this paper using a delta parallel robot as an example. This paper also analyzes the joint position conditions of model reference adaptive control and adaptive robust control simulated in Simulink. The fourth selected paper, Chapter “Weld Bead Penetration State Recognition in GTAW Process Based on a Human Auditory Perception Model” is a contribution from School of Aeronautic Manufacturing Engineering, Nanchang Hangkong University. In this model, an auricle and middle-ear transformation function was adopted firstly to remove partial noise in the arc sound signals. Then through simulating the functions of human ear basement membrane, a gamma-tone frequency resolution algorithm was used to decompose the arc sound signals into 64 channels. At last, based on the short time energies of arc sound in these channels the feature vectors were built to identify the penetration states. The fifth paper is Chapter “Experimental Method of Mechanical Melting Point in A6N01-T5 Aluminum Alloy.” The authors are a joint research team from CRRC Qingdao Sifang Co., Ltd. and Key Laboratory of Shanghai Laser Manufacturing and Materials Modification, School of Materials Science and Engineering, Shanghai Jiao Tong University. In this paper, a novel experimental method is proposed to measure the mechanical melting point of A6N01-T5 aluminum alloy. The relationship between the residual stress and peak temperature is established. The sixth paper, Chapter “Mathematical Modeling and Workspace Analysis for Photographic Robot” is contributed from Beijing Engineering Research Center of Monitoring for Construction Safety, Beijing Key Laboratory of Robot Bionics and Function Research, Beijing University of Civil Engineering and Architecture. A new method is proposed in this paper to determine the membership degree of the photographic robot workspace. This paper uses the DH digital modeling method to establish the positive kinematics model of the photographic robot, solves the kinematics positive solution of photographic robot, and also utilizes the numerical analysis method to analyze the working space of the angle between the robot manipulator and guide rail, which will obtain the workspace, boundary point coordinates, and geometry to measure and divide membership of a complex workspace into several simple working subspaces, and then get the decision conditions of the comprehensive workspace. The seventh paper is Chapter “The Regulation of Laser-Arc Hybrid Welding Source on TC4 Ti Alloy to 304 Stainless Steel Joints with Interlayers,” in which laser-arc hybrid welding source is used to join TC4 Ti alloy to 304 stainless steel with Cu interlayer and adhesive. The influences of the welding source and the interlayers on the microstructures and intermetallic in the welding joint are analyzed elaborately.

Editorials

vii

The eighth paper, Chapter “A Machine Vision-Based Multifunctional Image Processing Platform” is contributed from College of Materials Engineering, Shanghai University of Engineering Science. This paper combines the open-source OpenCV2.4.9 computer vision library, Daheng ImgVision and its development kit, and VS2013 to achieve the docking. Based on MFC interface, modules such as “sub-pixel corner detection and processing,” “mouse center point extraction,” “measurement of plane and stereoscopic distance” are developed. Combined with the OpenCV library function, a series of algorithms are developed to implement the interface function. The ninth paper, Chapter “The Formation and Control of Porosity During GMA Welding of Galvanized Steel” is contributed by a group of researchers and engineers from School of Material Science and Engineering, Jiangsu University, and School of Mechanical and Electrical Engineering, Wenzhou University. GMA lap welding of 1.4-mm-thick galvanized steel DP780 was conducted in this investigation. Effects of different welding modes, heating inputs, and assembly conditions on the porosity in weld bead were examined by X-ray nondestructive detection. The experimental results reveal that the number of pores in the weld bead formed with double pulse mode is the minimum, compared to those formed under direct current, pulse, cold metal transfer, and pulse welding modes. The first paper of short papers, Chapter “Optimization of SURF Algorithm for Image Matching of Parts” is contributed from College of Mechanical and Electrical Engineering, Lanzhou University of Technology. In this paper, an improved SURF algorithm is proposed to increase matching pairs effectively and raise accuracy of matching. For the algorithm, two new type feature sets are added and 128-dimensional feature descriptor is established. The second paper of short papers, Chapter “Research on Implicit Genetic Inverse Solution Algorithm for Eight-DOF Mechanical Arm of Photography Robot” is contributed by a group of researchers and engineers from Beijing Key Laboratory of Robot Bionics and Function Research, Beijing University of Civil Engineering and Architecture, and China Petroleum Pipeline Engineering Corporation. In this paper, an implicit genetic algorithm is proposed to solve the inverse kinematics problem of a robot with redundant degree of freedom. I hope the publication of this issue of TIWM will show readers their new perspectives and developments in the field of intelligent welding research, as well as the topics related to the RWIA’2018 conference. Shanben Chen, Ph.D. TIWM Editor-in-chief

Contents

Part I

Feature Articles

Thermal Behavior in Wire Arc Additive Manufacturing: Characteristics, Effects and Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bintao Wu, Zengxi Pan, Stephen van Duin and Huijun Li Part II

3

Research Papers

Ultrasonic Welding of Polymer–Metal Hybrid Joints . . . . . . . . . . . . . . . Anwer Al-Obaidi and Candice Majewski

21

Based on Multi-sensor of Roughness Set Model of Aluminum Alloy Pulsed GTAW Seam Forming Control Research . . . . . . . . . . . . . . . . . . Jiyong Zhong, Yanling Xu, Huabin Chen, Na Lv and Shanben Chen

39

Research on Adaptive Robust Control Algorithm for Delta Parallel Robots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chendi Lu, Xingang Miao, Su Wang and Chenxi Zhang

59

Weld Bead Penetration State Recognition in GTAW Process Based on a Human Auditory Perception Model . . . . . . . . . . . . . . . . . . . Yanfeng Gao, Qisheng Wang, Yanfeng Gong and Linran Huang

69

Experimental Method of Mechanical Melting Point in A6N01-T5 Aluminum Alloy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lichun Meng, Xiaohong Sun, Yongming Cheng, Gongxiang Zhao and Jijin Xu Mathematical Modeling and Workspace Analysis for Photographic Robot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xuedong Li, Xingang Miao and Su Wang

85

97

The Regulation of Laser-Arc Hybrid Welding Source on TC4 Ti Alloy to 304 Stainless Steel Joints with Interlayers . . . . . . . . 109 Hongyang Wang, Gang Song, Zhonglin Hou and Liming Liu ix

x

Contents

A Machine Vision-Based Multifunctional Image Processing Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 Baoming Li and Peiquan Xu The Formation and Control of Porosity During GMA Welding of Galvanized Steel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 Yingming Wu, Chao Hu and Xizhang Chen Part III

Short Papers and Technical Notes

Optimization of SURF Algorithm for Image Matching of Parts . . . . . . . 145 Hongyan Duan, Xiaoyu Zhang and Wensi He Research on Implicit Genetic Inverse Solution Algorithm for Eight-DOF Mechanical Arm of Photography Robot . . . . . . . . . . . . . 159 Qi Dong, Xingang Miao, Su Wang and Xingai Peng Inmformation for Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171

Contributors

Anwer J. Al-Obaidi was born in Baghdad, Iraq, in 1984. He received his B.S. and M.S. degrees in mechanical engineering from the Nahrain University, Iraq, in 2005 and 2009, respectively. He got his Ph.D. degree in mechanical engineering from the University of Sheffield, UK, in 2018. Since May 2010, he joined the Department of Mechanical Engineering, University of Wasit, Iraq, as Lecturer. His current research interests include mechanical design, additive manufacturing, composite materials, and stress analysis. He is Associate Fellow of The Higher Education Academy in UK. He is Member of the Iraq Society for Engineering. Dr. Xizhang Chen is Professor at Wenzhou University, China. He leads the Materials Joining and Processing, 3D print Team, conducting both fundamental and applied R&D and technology innovations for diverse interdisciplinary subjects related to materials joining and allied materials manufacturing processes. He is also Adjunct Professor of Samara National Research University. With over 15 years R&D experience, he has proven record in developing and leading major R&D programs to advance materials joining and manufacturing science and technologies for automotive, nuclear energy, fossil energy, and defense applications.

xi

xii

Contributors

Qi Dong is Graduate Student of Beijing University of Civil Engineering and Architecture, in major of control science and engineering. Her main research field is robot control.

Hongyan Duan is Doctor from Handan, Hebei Province, mainly engaged in the study of mechanical structure strength and industrial robots. She presides over one project at the national, provincial, and municipal levels. She has won four provincial science and technology progress awards and teaching achievement awards. And she has also won four invention patents and published more than 20 papers (including ten SCI and EI articles).

Yanfeng Gao currently is Associate Professor, School of Aeronautic Manufacturing Engineering, Nanchang Hangkong University. He obtained his Master and Ph.D. degrees from Xi’an Jiaotong University and Nanchang University in 2003 and 2009, respectively. The major areas of his teaching and research interests are in the fields of automated welding, numerical control technology, etc. He has published more than 30 papers in journals and international conferences.

Contributors

xiii

Jiyong Zhong comes from Sichuan Province, studied materials science and engineering in Harbin Institute of Technology between 2003 and 2009, and obtained Ph.D. in Shanghai Jiao Tong University at 2017. In recent years, he works on robot automatic welding.

Baoming Li received his B.S. degree in materials processing engineering from Nanjing Institute of Technology in 2016. He will receive his M.S. degree in materials processing engineering from Shanghai University of Engineering Science in 2019. He did research on intelligent welding and welding metallurgy. His thesis is “A Machine Vision-Based Multifunctional Image Processing Platform”.

Xuedong Li is 25 years old and studying for a Master’s degree at Beijing University of Civil Engineering and Architecture. Studying at the School of Telecommunications College, the research direction is the research of the control system of the nursing robot for the aged. Tutor: Su Wang, Xingang Miao.

xiv

Contributors

Chendi Lu is 24 years old and studying for a Master’s degree at Beijing University of Civil Engineering and Architecture. Studying at the School of Telecommunications College, major is Control Science and Engineering. Tutor: Su Wang, Xingang Miao.

Lichun Meng was born in 1965. She obtained her Master’s degree and is Professor-level Senior Engineer. In present, she is mainly engaged in welding technology and prospective technology research of aluminum alloy car body in rail train. She has published more than 10 papers. email: [email protected]

Xingang Miao Ph.D., Senior Lecturer, Beijing University of Civil Engineering and Architecture, the main research field is welding robot, nursing robot, etc.

Contributors

xv

Zengxi Pan received his B.E. and M.E. degrees in machine design and mechatronics engineering at Tsinghua University, Beijing, China, in 1998 and 2000, and Ph.D. degree in robotics and control engineering from Stevens Institute of Technology Hoboken, NJ, USA, in 2005. He is currently Associate Professor at University of Wollongong. His research interests include industrial robotics, welding technology, and advanced manufacturing with innovation.

Dr. Hongyang Wang has been Associate Professor at Dalian University of Technology and Faculty Researcher in welding manufacture process since 2014. He finished his undergraduate courses in 2004 and Ph.D. degree in 2010, all in materials manufacture engineering. During his Ph.D. period, he worked on dissimilar metal welding process, such as Al and Mg alloys. He mainly studied laser and laser-arc welding of dissimilar metals before 2016. His areas of interest focus on the laser-arc hybrid welding light metals and hybrid bonding of metals and composites. He had published more than 30 journal papers, 20 of which have been indexed by SCI. Bintao Wu received his B.E. and M.E. degrees in shipbuilding and marine engineering at Harbin Engineering, China, in 2013 and 2015, and will get a Ph.D. degree in material science and engineering at University of Wollongong, Australia, in 2018. He has a lot of experiences on high-efficiency arc welding technology (DSAW, double electrode welding, etc.) and dissimilar metals joining (Al/Fe, Mg/Fe, and Al/Ti). Currently, he is working on wire arc additive manufacturing for large metal structures and functional structures using titanium, aluminum, and other functional materials.

xvi

Contributors

Yingming Wu 1990-, School of Material Science and Engineering, Jiangsu University, Zhenjiang, China. Master Student, mainly studied on welding porosity of galvanized dual phase steel welded joints. email: [email protected]

Jijin Xu graduated from Shanghai Jiao Tong University and obtained his Ph.D. in 2007. And then he began to work as Assistant Researcher in the School of Materials Science and Engineering, Shanghai Jiao Tong University. In 2009, he obtained a scholarship supported by FFCSA and began his postdoctoral research with Philippe GILLES in AREVA France. His main interests focus on the areas of welding mechanics and numerical simulation, the measurement and control of welding residual stresses, the integrity assessment of welded structure, and the control of welding quality. He has published more than 50 papers. Peiquan Xu is Professor in the Department of Materials Science and Engineering at Shanghai University of Engineering Science. He is Director on the Board of Shanghai Welding Society and Shanghai Welding Association. He received his B.S. in materials science and engineering from Dalian Railway Institute in 2002. In 2006, he received his Ph.D. in materials science and engineering from Shanghai Jiao Tong University. In 2011, he was Visiting Scholar in the Department of Mechanical and Aerospace Engineering at Hong Kong University of Science and Technology. In 2012, he was Postdoctoral Fellow in Mechanical Engineering at Utah State University.

Part I

Feature Articles

Thermal Behavior in Wire Arc Additive Manufacturing: Characteristics, Effects and Control Bintao Wu, Zengxi Pan, Stephen van Duin and Huijun Li

Abstract Wire arc additive manufacturing (WAAM) has attracted significant attention in the manufacturing industry due to its ability to economically produce largescale metal components with a relatively high buy-to-fly ratio. To date, a wide range of engineering materials has become associated with this process and application. As an electric arc and additive deposition have been combined, the complex heat transfer and thermal cycles cause several material processing challenges in WAAM. This study provides a comprehensive overview of the thermal characteristics during a WAAM process and identifies the thermal behavior effects on the process stability, geometrical accuracy and material properties of the deposited part. An innovative method for controlling thermal profiles during the build process is proposed and discussed, taken in to account the requirement of the various alloys. This paper concludes that the broad application of WAAM still presents many challenges, and these may need to be addressed in specific ways for different materials in order to achieve an operational system in an acceptable time frame. Highly accurate control of thermal profiles in deposition to produce defect-free and structurally sound produced parts still remains a crucial effort into the future. Keywords Wire arc additive manufacturing (WAAM) · Thermal profiles Process stability · Material properties · Active interpass cooling

1 Introduction The industrial sectors are continually searching for innovative manufacturing techniques for rapid product development with better buy-to-fly ratio. Over the past few years, additive manufacturing (AM) technologies have gained worldwide popularity in many fields due to their ability to produce cost-effective components with less B. Wu · Z. Pan (B) · S. van Duin · H. Li School of Mechanical, Materials, Mechatronic and Biomedical Engineering, University of Wollongong, Wollongong, NSW 2522, Australia e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 S. Chen et al. (eds.), Transactions on Intelligent Welding Manufacturing, Transactions on Intelligent Welding Manufacturing, https://doi.org/10.1007/978-981-13-3651-5_1

3

4

B. Wu et al.

Fig. 1 Schematic diagram of the heat dissipation modes, conduction (Qcond), convection (Qconv), radiation (Qrad): a at the beginning of WAAM; b during the build of a thin wall part; and c for a part with overlapping weld beads [5]

restrictions than traditional manufacturing methods. Wire arc additive manufacturing (WAAM) is one AM method which provides a promising alternative for producing large metal components for high-cost materials. It offers a number of potential benefits such as relatively low capital equipment, high deposition rate, high material utilization, and consequently, environmental friendliness [1]. Research efforts have developed the WAAM technique to become highly capable for a number of engineering materials, such as titanium alloy, aluminum alloy, nickel alloy and steel [2]. Uniquely, a WAAM system uses an electric arc for the fusion source, welding wire as feedstock material and typically an industrial robot for the delivery mechanism. Combing these creates a robotic WAAM system that allows industrial sectors to fabricate a target part in a layer by layer manner [3]. As the arc heat source is used, the WAAM-produced component experiences non-uniform thermal expansion and contraction under alternate re-heating and re-cooling cycles during deposition [4]. As the component geometry is built, there is a natural change in the mode of heat transfer; primarily with the conduction of heat through the base substrate for the surrounding atmosphere, followed by increasing amounts of radiation and convection as the geometry builds (see Fig. 1[5]). These thermal profiles can be uncertain, so the stability of the WAAM process, including arc and metal transfer behaviors, deposition defects, dimensional accuracy, microstructural evolution and material properties will be strongly affected. To prevent heat accumulation in an additively manufactured part, idle time is the most common method used to cool the deposit [6]. However, as in the case of adding an interlayer dwell period, this approach brings an identifiable reduction of productivity and consequently low manufacturing efficiency. An alternative approach to minimizing thermal effects involves the progressive reduction of thermal heat input using a water-cooled fixture [7]. However, as the convective heat flux to the surroundings increases, a constant cooling rate cannot be assured during the manufacturing process, as some heat is still dissipated through conduction. By implementing an optimized deposit strategy, including deposition pattern, direction and route, the

Thermal Behavior in Wire Arc …

5

heat transfer characteristics and the thermal cycles may also be changed to a desired thermal state. However, the variation in path planning may inadvertently result in different material properties due to the changes in welding start and stop position [8]. This research primarily contributes a detailed discussion on the thermal characteristics in a WAAM process and provides a comprehensive investigation of their effects on the process stability, geometrical accuracy and material properties. An innovative method for controlling the thermal profiles in the fabrication, and consequently improving the production quality, is proposed and discussed. This paper ends with a conclusion and future perspective in terms of thermal control as well as quality improvement in the WAAM process.

2 The Heat Accumulation Effects in WAAM 2.1 The Effects of Heat Accumulation on Deposited Geometry Figure 2 shows the schematic diagram of width variation along the building height. In the first few layers, heat is readily conducted to the substrate, leading to a relatively fast cooling rate and narrow bead. As the wall is built upwards, the conductive thermal resistance to the substrate is significantly increased and consequently, an increasing amount of heat is dissipated to the surrounding atmosphere via the less effective convection and radiation. This leads to the slower heat dissipation condition of the molten pool and a wider bead at higher layers. As more layers are deposited with a fixed dwell time, the deposition width of the wall becomes constant as the heat input and dissipation reach a balance [9]. To reduce the effects of heat accumulation on bead geometry, the dwell period between each interlayer is usually controlled, in order to reach a specific interpass temperature [6]. When using this method, the deposition could be carried out on a surface of low enough temperature, which brings a constant weld pool size and consequently stable part geometry [5]. However, the accurate interpass temperature is difficult to control since it hard to measure temperature in-suit of the deposited layer [10]. That is to say, determination of an applicable interpass temperature value for depositing tends to be by resource-intensive trial and error, with limited systematic approaches proposed [11]. Furthermore, it is worth mentioning that if deposition of adjacent layers in addition to vertical layers is carried out during multilayer manufacturing, the thermal variation becomes more complex, offering less opportunity for steady state deposition to develop, as shown in Fig. 1c. The effectiveness of interpass temperature, in this case, seems much harder to achieve.

6

B. Wu et al.

Fig. 2 Schematic diagram of width variation along the building height

2.2 The Effects of Heat Accumulation on Process Stability In the WAAM process, the heat accumulation is usually utilized as a preheating heat source for the subsequent layer deposition. As the heat input increases, the metal vapor produced from the molten pool assists to increase the heat and radiation loss in low-temperature area of the arc, resulting in potential arc constriction [12]. Additionally, due to the considerable change in bead geometry, the distance between tungsten tip and molten pool increases, especially in the first few layers, which generates an obvious increase in arc length, as shown in Fig. 3. That is to say, the variation in arc shape occurs at different deposited layers by the combined effects of arc constriction and layer geometry; although it has no direct link with heat accumulation in the additive manufacturing process. Furthermore, this phenomenon strongly impacts the metal transfer behavior, which has a direct influence on the bead morphology and geometrical accuracy of the deposited metal. Taking the GTAW-based WAAM process as an example, the arc shape expands and then becomes stable along the built height. The metal transfer mode changes from uninterrupted bridging transfer that forms a smooth and consistent layer appearance, to the irregular free flight mode, which tends to produce humps and hollows on the surface [13], as shown in Fig. 3. It can also be noted that as the variation in molten pool geometry and arc shape occurs, the arc force acting on the metal droplet may change slightly during build-up, which results in a slight increase in metal transfer frequency [9]. Therefore, controlling the thermal state and suppressing heat accumulation occurrence offers greater improvement in-process stability for WAAM technology.

Thermal Behavior in Wire Arc …

7

Fig. 3 Arc shape and metal transfer in GTAW-based WAAM of Ti6Al4V [9]

2.3 The Effects of Heat Accumulation on Material Properties 2.3.1

Microstructure

Since differences appear in the thermal histories, or variation occurs in the thermal profile locally, it brings various phase transformation or microstructures forming along the build direction. Subsequently, this results in inhomogeneous material properties distributed in WAAM parts. Taking the WAAM-built Ti6Al4V wall as an example, due to the high cooling rate for the first few layers from direct contact with substrate, the martensite alpha composed of long orthogonally oriented martensitic plates was formed into lathlike matrix structures [14], as shown in Fig. 4a. In general, this lathlike alpha tends to grow in a long and narrow shape in the direction perpendicular to the liquid/solid interface driven by the maximum temperature gradient during the solidification process [15]. As further layers are deposited, more heat is accumulated in the wall and the process cooling rate continues to reduce so that a fully lamellar alpha morphology is preferentially formed, interwoven with basketweave structures (Fig. 4b, c) [16]. At the top layers, large colony alpha, which is decorated within prior beta grains and grain boundary alpha phase (Fig. 4d–f), are formed. With the increase of heat accumulation, the process temperature exceeds beta transus temperature Tβ (995 °C [17]) at the uppermost layers. In combination with a slow cooling rate, this results in the coarse colony alpha structures [18]. To minimize the effect of thermal variation on the microstructures, an alternative method involves the progressive reduction of the heat input for depositing [19]. Nonetheless,

8

B. Wu et al.

Fig. 4 Cross-sectional macrograph and micrographs of the WAAM-deposited Ti6Al4V wall

this method brings a reduction in productivity as it requires a decrease in wire feed speed and results in additional interlayer dwell time [20]. The WAAM-produced part often generates large columnar grains in its microstructure due to undergoing constant re-melting and re-solidification. Despite these columnar grains can assist to the high interpass temperature improvement, they often have lower mechanical strength and greater inferior corrosion resistance at regular operating temperatures compared to fine equiaxed microstructure [21]. Nonetheless, it is difficult to develop a fined equiaxed microstructure within the WAAM build, as grains tend to grow in preferred crystallographic orientation driven by the maximum temperature gradient, meaning the grains will be enlarged. The thermal gradient is an ineradicable fact in additive deposition; thereby the grain’s growth cannot be interrupted and as a result, provides conditions for development of heterogeneous mechanical properties and anisotropic corrosion properties which will be discussed in a later section.

2.3.2

Mechanical Properties

The asymmetrical mechanical properties present a material processing challenge due to the promotion of non-uniform microstructures containing large columnar grains. Usually the ultimate tensile strength of a WAAM-produced part is sensitive to the locations within the build, decreasing along the build height, which occurs as shown in Fig. 5, even though the various interpass temperatures were controlled throughout

Thermal Behavior in Wire Arc …

9

Fig. 5 Tensile strength profiles along the build direction of WAAM Ti6Al4V alloy

the build height [22]. As the thermal state varies with increasing deposition, the solid solution strengthening, including both grain boundary spacing and interstitial solute level, is weakened [23]. Moreover, the heat dissipation effect close to the substrate leads to the formation of parallel band structures in lower regions that are much stronger than convex bands exhibited in the upper deposition [24], as shown in Fig. 4. With the heat accumulation or interpass temperature increasing, more convex band structures will be developed, resulting in an increased part with greater inferior mechanical properties [22]. Due to the relatively low energy density of the electric arc which leads to low thermal gradient and low solidification rate, the heat sink effect of substrate can result in pronounced columnar grain growth aligned transverse to the weld direction. This grain growth mechanism also provides a development of anisotropic mechanical properties within the WAAM build, as shown in Table 1. Although such mechanical anisotropy exists, it can be observed from these summarized results that the mechanical properties of additively manufactured materials are comparable to cast or wrought material, demonstrating that WAAM is a promising alternative for manufacturing many metallic materials.

2.3.3

Corrosion Properties

In general, material properties are closely related to microstructure, and this is particularly the case for corrosion resistance. As discussed, for WAAM-produced part, a relatively slower cooling rate occurs in the build direction, contributing to the pronounced large and columnar grains growth with preferred crystallographic

Columnar β + martensite

α

Prior columnar β + Martensite

AF: as fabricated, HT: heat treated, AN: annealed, FC: furnace cooling a: in build direction, b: orthogonal to build direction

AF

/

AN (834 °C/2 h/FC)

PAW

/

HT (600 °C/2 h/FC)

AF

/

AF

Pulsed-PAW

Widmanstätten α + banded coarsened lamella α

AF

α

Widmanstätten α/β + Columnar β grains

803 ± 15a 950 ± 21b 861 ± 14a 892 ± 31b 891 ± 16a 915 ± 14b 856 ± 21a 893 ± 24b /

Lamellar structure

HT (834 °C/2 h/FC)

AF (600 °C/840 °C)

/

Lamellar structure

HT (600 °C/4 h/FC)

Plasma

/

α phase lamella basket weave structures

AF

877 ± 18.5b

909 ± 13.6b

/

/

Columnar prior β grains + Widmanstätten α/β

AF

GTAW

YS (MPa) 758 860

/ /

Cast Wrought

Microstructure

/ /

Condition

Process

968 ± 12.6b

988 ± 19.2b

929 ± 41a 965 ± 39b 939 ± 24a 1033 ± 32b 972 ± 41a 977 ± 14b 931 ± 19a 971 ± 28b 918 ± 17a 1033 ± 19b 937 ± 21a 963 ± 22b 976 ± 35a 981 ± 8b 931 ± 17a 962 ± 29b /

860 930

UTS (MPa)

11.5 ± 0.5b

7 ± 0.5b

9 ± 1.2a 9 ± 1b 16 ± 3a 7.8 ± 2.3b 12.5 ± 2.5a 6 ± 3b 21 ± 2a 14 ± 2b 14.8a 11.7b 16.5 ± 2.7a 7.8 ± 2b 11.6 ± 2.4a 6.6 ± 2.6b 20.4 ± 1.8a 13.5 ± 2b /

>8 >10

EL (%)

Lin et al. [30]

Lin et al. [29]

Martina et al. [28]

Brandl et al. [27]

Wang et al. [26]

Baufeld et al. [25]

Baufeld et al. [24]

ASTMF1108 ASTMF1472

Reported by

Table 1 Tensile properties of titanium alloy, depending on the microstructure generated by different WAAM process and in different post welding conditions

10 B. Wu et al.

Thermal Behavior in Wire Arc …

11

Fig. 6 Comparison in corrosion resistance of WAAM-fabricated Ti–6Al–4V and wrought base metal in 3.5% NaCl solution

orientation. Meanwhile, a comparatively faster cooling rate occurs in the welding direction, producing smaller grains. Because the anisotropy exists in microstructure, grain size, phase structure and orientation within the WAAM-produced part, the corrosion properties are also anisotropic. For example, as shown in Fig. 6, the electrochemical corrosion resistance of WAAM-fabricated Ti–6Al–4V has slightly inferior corrosion resistance in horizontal planes (welding direction) than vertical planes (building direction) due to its directional and non-uniform microstructures [31]. It needs to be noted that the microstructural uniformity also has a serious impact on the repeatability of electrochemical results for corrosion resistance. Even in adjacent regions of a WAAM-fabricated part, especially in the build direction, the examined microstructure has noticeable non-uniformities, producing significant fluctuations in the corrosion tests. The anisotropic microstructure resulting from the complex thermal history during deposition has a significant effect on the corrosion performance. Any future improvement to the process that aims to improve this aspect must address the mechanisms that produce microstructural anisotropy.

3 Active Interpass Cooling for Thermal Control in WAAM From the earlier discussion, the solidification microstructure, which is essentially dependent on the thermal history during build process, plays an important role in the material properties of a WAAM-produced part. If the thermal state during deposition is properly controlled, the desired homogenous and satisfactory microstructures will

12

B. Wu et al.

Fig. 7 Active interpass cooling configuration of equipment

result in the required mechanical properties with acceptable performance. However, due to the lack of an efficient approach for the accurate control of the thermal state, the achievement of satisfactory microstructure is still a challenge. Recently, our research group introduced active interpass cooling using compressed cryogenic CO2 into the WAAM process, attempting to provide a method to control the thermal state during manufacturing. A schematic of the setup adopted is shown in Fig. 7. The active interpass cooling method provides a modified thermal profile with significant reduction in the peak and amplitude values of interpass temperature, as shown in Fig. 8. Also, the rate of temperature change is significantly increased, contributing to a sharp removal of interlayer dwell time [32]. As a result, the manufacturing efficiency can be improved by at least 80% compared to WAAM without the cooling process (shown in Fig. 8). Because this process allows a constant interpass temperature that can be controlled, this potentially provides preheat for the proceeding layer [33]. Too high an interpass temperature can increase cross-sectional weld bead geometry variations as discussed in Sect. 2.2. This approach brings an effective strategy to avoid the geometric inconsistency, and as such improvements to geometric accuracy can be achieved. In addition, by controlling the thermal profile, the geometrical properties of the layer bead can be made more repeatable. Active interpass cooling is an effective method for minimizing surface oxidation in WAAM-produced reactive metals such as Ti6Al4V using localized gas shielding. By providing rapid flow of cooling gas, the surface temperature of deposited zone could be decreased immediately, avoiding the potential chemical reactions of targeted material with O2 from the surroundings. WAAM fabrication of highly reactive metals usually involves the use of a closed chamber containing an inert gas atmosphere. This imposes severe constraints on equipment operation and maximum component size, as well as the inability to introduce other production aids. As WAAM with active interpass cooling process operates in an open air, it offers significant cost savings in facilities and less limiting factors with regard to the built geometric features. Increasing the cooling rate within the weld pool through interpass cooling process provides an approach of achieving significant disruption in the columnar growth

Thermal Behavior in Wire Arc …

13

Fig. 8 Temperature profiles of the deposition: a without; b with forced interpass cooling

to develop equiaxed grain morphology. This means the nucleation rate within the microstructure could be increased, leading to improved material properties, as shown in Fig. 9. Moreover, as WAAM-produced part can experience various interpass temperature characteristics, active interpass cooling provides more extensive control over microstructural and phase development that can directly determine the solidification characteristic by setting the thermal boundary conditions for the next layer. An opportunity provided by active interpass cooling is a reduction in heat accumulation within the built part by accelerating the heat dissipated to surroundings, no matter what cooling gas flow rate or cooling time is used. Due to a reduction of heat accumulation, the thermal strain developing in the deposition is decreased, as expected, resulting in a low thermal distortion [34]. By reducing the interlayer interval, the accumulation of distortion can also be decreased [35]. That is to say, the active interpass cooling process can effectively reduce thermal distortion in the deposit and impart quality improvements to the final manufactured component, as shown in Fig. 10. Other benefits may include the removal of residual stress that can

14

B. Wu et al.

Fig. 9 Active interpass cooling effects on material properties

Fig. 10 Active interpass cooling effects on the distortion of WAAM Ti6Al4V deposit

induce solidification cracks and other corresponding defects in a WAAM deposit. However, these potential benefits are yet to be investigated. Based on the results presented in the previous section, the influence of in-process active interpass temperature on the thermal state, deposition geometry, distortion, surface oxidation, microstructure and mechanical properties is significant. The use of forced interpass cooling discernibly improves the material properties, which suggests that the overall material properties of build components, in this case, may be better controlled during the WAAM process, as shown in Fig. 11.

Thermal Behavior in Wire Arc …

15

Fig. 11 Flowchart primary and quality performance improvement by active interpass cooling in WAAM

Active cooling processes offer an ability to accelerate to the interpass temperature which may reduce the differences in thermal profile throughout the build. However, the decision regarding the selection of active interpass cooling processes depends on the metallurgy of metal. If the cooling timing is not controlled in an acceptable range, potential reactions may occur between targeted materials and cooling gas, especially for reactive metals. Additionally, high cooling rates during solidification in welding are generally detrimental to material properties. Therefore, to establish the capability of active interpass cooling for producing parts of a target material (list shown in Table 2), material characterization and cooling gas should be considered. Primary process considerations at the cooling stage include the cooling gas composition, cooling gas flow rate and cooling time.

4 Conclusions The WAAM technique has occupied a key position in the modernly advanced manufacturing sectors for the production of high-value parts. Especially in aerospace, WAAM has the capability of producing large valuable metal components, successfully replacing the more constrained conventional manufacturing processes. From

16

B. Wu et al.

Table 2 Metals typically used with WAAM process Applications Ti-based Al-based

Steel-based

Ni-based

Bimetal

Aerospace

[1]

[36]

–

[37]

[38]

Automotive

–

[39]

[40]

–

[41]

Marine

[42, 43]

–

[44]

–

–

Corrosion resistance

[45]

–

–

[46]

[47]

High temperature

[48]

–

–

[49]

[50]

Tools and molds

–

–

[51, 52]

–

–

the present analysis of the WAAM process, it is known that the control of thermal state directly influences the process stability and resultant material properties. The development of strategies or ancillary process to the thermal state control during manufacturing process is of prime importance. With the requirement of high quality WAAM part, the proposed technique using active interpass cooling to improve a part’s material properties and welding process efficiencies will see a wide application in the future years. Research and development of WAAM for metal components is interdisciplinary, integrating materials science and thermo-mechanical engineering. Due to the variety of requirements of different engineering materials and the varying scale of fabrication, many different WAAM system designs are expected to be developed that will be optimized for particular applications, rather than a single system that is capable of addressing all of the possible problems. Acknowledgements This research was carried out at the Welding Engineering Research Group, University of Wollongong. The authors would like to acknowledge the China Scholarship Council for their finical support (201506680056).

References 1. Williams SW, Martina F, Addison AC et al (2016) Wire + arc additive manufacturing. Mater Sci Technol 32(7):641–647 2. Herzog D, Seyda V, Wycisk E et al (2016) Additive manufacturing of metals. Acta Mater 117:371–392 3. Ding D, Pan Z, Cuiuri D et al (2015) Wire-feed additive manufacturing of metal components: technologies, developments and future interests. Int J Adv Manuf Technol 81(1–4):465–481 4. Collins PC, Brice DA, Samimi P et al (2016) Microstructural control of additively manufactured metallic materials. Annu Rev Mater Res 46(1):63–91 5. Cunningham CR, Flynn JM, Shokrani A et al (2018) Invited review article: strategies and processes for high quality wire arc additive manufacturing. Add Manuf 22:672–686 6. Montevecchi F, Venturini G, Grossi N et al (2018) Idle time selection for wire-arc additive manufacturing: a finite element-based technique. Add Manuf 21:479–486 7. Ding J, Colegrove P, Mehnen J et al (2014) A computationally efficient finite element model of wire and arc additive manufacture. Int J Adv Manuf Technol 70(1):227–236

Thermal Behavior in Wire Arc …

17

8. ASTM B265-06 (2006) Sheet, and plate. ASTM International, West Conshohocken, PA. www. astm.org 9. Wu B, Ding D, Pan Z et al (2017) Effects of heat accumulation on the arc characteristics and metal transfer behavior in Wire Arc Additive Manufacturing of Ti6Al4V. J Mater Process Technol 250:304–312 10. Ríos S, Colegrove PA, Martina F et al (2018) Analytical process model for wire + arc additive manufacturing. Add Manuf 21:651–657 11. Geng H, Li J, Xiong J et al (2017) Optimization of interpass temperature and heat input for wire and arc additive manufacturing 5A06 aluminium alloy. Sci Technol Weld Joining 22(6):472–483 12. Zhou X, Zhang H, Wang G et al (2016) Three-dimensional numerical simulation of arc and metal transport in arc welding based additive manufacturing. Int J Heat Mass Transf 103:521–537 13. Geng H, Li J, Xiong J et al (2017) Optimization of wire feed for GTAW based additive manufacturing. J Mater Process Technol 243:40–47 14. Ahmed T, Rack HJ (1998) Phase transformations during cooling in α + β titanium alloys. Mater Sci Eng, A 243(1–2):206–211 15. Brandl E, Schoberth A, Leyens C (2012) Morphology, microstructure, and hardness of titanium (Ti-6Al-4V) blocks deposited by wire-feed additive layer manufacturing (ALM). Mater Sci Eng, A 532:295–307 16. Semiatin SL, Knisley SL, Fagin PN et al (2003) Microstructure evolution during alpha-beta heat treatment of Ti-6Al-4V. Metall Mater Trans A 34(10):2377–2386 17. Peters M, Hemptenmacher J, Kumpfert J et al (2005) Structure and properties of Titanium and Titanium alloys, Titanium and Titanium alloys. Wiley-VCH Verlag GmbH & Co., pp 1–36 18. Welsch G, Boyer R, Collings EW (1993) Materials properties handbook: Titanium alloys. ASM International 19. Cooper DE (2016) The high deposition rate additive manufacture of nickel superalloys and metal matrix composites. Dissertation, University of Warwick 20. Li F, Chen S, Shi J et al (2018) Thermoelectric cooling-aided bead geometry regulation in wire and arc-based additive manufacturing of thin-walled structures. Appl Sci 8(2):207 21. Murr LE (2015) Examples of directional crystal structures: gas-turbine component applications in superalloys, handbook of materials structures, properties, processing and performance. Springer, pp 375–401 22. Fei Z, Pan Z, Cuiuri D et al (2018) Investigation into the viability of K-TIG for joining armour grade quenched and tempered steel. J Manuf Processes 32:482–493 23. Lütjering G (1998) Influence of processing on microstructure and mechanical properties of (α + β) titanium alloys. Mater Sci Eng, A 243(1–2):32–45 24. Baufeld B, van der Biest O, Gault R (2010) Additive manufacturing of Ti–6Al–4V components by shaped metal deposition: microstructure and mechanical properties. Mater Des 31(Sup 1):S106–S111 25. Baufeld B, Brandl E, van der Biest O (2011) Wire based additive layer manufacturing: comparison of microstructure and mechanical properties of Ti–6Al–4V components fabricated by laser-beam deposition and shaped metal deposition. J Mater Process Technol 211(6):1146–1158 26. Wang F, Williams S, Colegrove P et al (2013) Microstructure and mechanical properties of wire and arc additive manufactured Ti-6Al-4V, metall. Mater Trans A 44(2):968–977 27. Brandl E, Baufeld B, Leyens C et al (2010) Additive manufactured Ti-6Al-4V using welding wire: comparison of laser and arc beam deposition and evaluation with respect to aerospace material specifications. Phys Procedia 5:595–606 28. Martina F, Mehnen J, Williams SW et al (2012) Investigation of the benefits of plasma deposition for the additive layer manufacture of Ti–6Al–4 V. J Mater Process Technol 212(6):1377–1386 29. Lin JJ, Lv YH, Liu YX et al (2016) Microstructural evolution and mechanical properties of Ti-6Al-4V wall deposited by pulsed plasma arc additive manufacturing. Mater Des 102:30–40 30. Lin J, Lv Y, Liu Y et al (2017) Microstructural evolution and mechanical property of Ti-6Al-4V wall deposited by continuous plasma arc additive manufacturing without post heat treatment. J Mech Behav Biomed Mater 69:19–29

18

B. Wu et al.

31. Wu B, Pan Z, Li S et al (2018) The anisotropic corrosion behaviour of wire arc additive manufactured Ti-6Al-4V alloy in 3.5% NaCl solution. Corros Sci 137:176–183 32. Queguineur A, Rückert G, Cortial F et al (2018) Evaluation of wire arc additive manufacturing for large-sized components in naval applications. Weld World 62(2):259–266 33. Xiong J, Lei Y, Li R (2017) Finite element analysis and experimental validation of thermal behavior for thin-walled parts in GMAW-based additive manufacturing with various substrate preheating temperatures. Appl Therm Eng 126:43–52 34. Mukherjee T, Zuback J, De A et al (2016) Printability of alloys for additive manufacturing. Sci Rep 6:19717 35. Denlinger ER, Heigel JC, Michaleris P et al (2015) Effect of inter-layer dwell time on distortion and residual stress in additive manufacturing of titanium and nickel alloys. J Mater Process Technol 215:123–131 36. Gu J, Ding J, Williams SW et al (2016) The strengthening effect of inter-layer cold working and post-deposition heat treatment on the additively manufactured Al–6.3Cu alloy. Mater Sci Eng, A 651:18–26 37. Uriondo A, Esperon-Miguez M, Perinpanayagam S (2015) The present and future of additive manufacturing in the aerospace sector: a review of important aspects. J Aerosp Eng 229(11):2132–2147 38. Sing SL, An J, Yeong WY et al (2016) Laser and electron-beam powder-bed additive manufacturing of metallic implants: a review on processes, materials and designs. J Orthop Res 34(3):369–385 39. Murr LE, Gaytan S, Ceylan A et al (2010) Characterization of titanium aluminide alloy components fabricated by additive manufacturing using electron beam melting. Acta Mater 58(5):1887–1894 40. Guo N, Leu MC (2013) Additive manufacturing: technology, applications and research needs. Front Mech Eng 8(3):215–243 41. Kainer KU (2006) Metal matrix composites: custom-made materials for automotive and aerospace engineering. Wiley 42. Leyens C, Peters M (2003) Titanium and titanium alloys: fundamentals and applications. Wiley 43. Kim TB, Yue S, Zhang Z et al (2014) Additive manufactured porous titanium structures: through-process quantification of pore and strut networks. J Mater Process Technol 214(11):2706–2715 44. Wang R, Beck FH (1983) New stainless steel without nickel or chromium for marine applications. Met Prog 123(4):72 45. Aziz-Kerrzo M, Conroy KG, Fenelon AM et al (2001) Electrochemical studies on the stability and corrosion resistance of titanium-based implant materials. Biomaterials 22(12):1531–1539 46. Lu G, Zangari G (2002) Corrosion resistance of ternary Ni-P based alloys in sulfuric acid solutions. Electrochim Acta 47(18):2969–2979 47. Stoloff N, Liu C, Deevi S (2000) Emerging applications of intermetallics. Intermetallics 8(9):1313–1320 48. Varghese OK, Gong D, Paulose M et al (2003) Crystallization and high-temperature structural stability of titanium oxide nanotube arrays. J Mater Res 18(1):156–165 49. Bewlay B, Jackson M, Subramanian P et al (2003) A review of very-high-temperature Nbsilicide-based composites. Metall Mater Trans A 34(10):2043–2052 50. Jackson M, Bewlay B, Rowe R et al (1996) High-temperature refractory metal-intermetallic composites. JOM 48(1):39–44 51. Bissacco G, Hansen HN, de Chiffre L (2005) Micromilling of hardened tool steel for mould making applications. J Mater Process Technol 167(2):201–207 52. Yan L (2013) Wire and arc addictive manufacture (WAAM) reusable tooling investigation

Part II

Research Papers

Ultrasonic Welding of Polymer–Metal Hybrid Joints Anwer Al-Obaidi and Candice Majewski

Abstract Joining of lightweight dissimilar materials becomes increasingly important, especially for structural applications and transportation industries to reduce the weight and thus decrease the fuel consumption and CO2 emissions. Previously, the joining of lightweight materials (metals and polymers) has been performed using mechanical fastenings, such as screws, bolts, and rivets, or adhesion techniques. However, the disadvantages of these mechanical methods are considerable stress concentration around the fastener hole, the potential in the corrosion problems, and potential in fatigue cracking in metallic materials. Ultrasonic welding is particularly suitable for applications with rapid process and high process reliability requirements. The quality, strength, and energy-saving technology also characterize ultrasonic welding. However, no research has been reported on joining lightweight dissimilar materials of thermoplastic polymers and metals using ultrasonic spot welding yet. Amorphous thermoplastic polymer (ABS-750SW) and aluminium alloy (Al6082-T6) are common engineering materials for manufacturing of hybrid structure and components for engineering applications. Our research shows that the ultrasonic welding of ABS and Al6082-T6 has been achieved successfully. The maximum lap shear strength obtained is 2.312 MPa (1.156 KN shear force). Keywords Ultrasonic welding · Amorphous polymer · Aluminium alloy Hybrid joints

A. Al-Obaidi (B) · C. Majewski (B) Mechanical Engineering Department, University of Sheffield, Sheffield S10 2TN, UK e-mail: [email protected] C. Majewski e-mail: [email protected] A. Al-Obaidi Mechanical Engineering Department, University of Wasit, Wasit, Iraq © Springer Nature Singapore Pte Ltd. 2019 S. Chen et al. (eds.), Transactions on Intelligent Welding Manufacturing, Transactions on Intelligent Welding Manufacturing, https://doi.org/10.1007/978-981-13-3651-5_2

21

22

A. Al-Obaidi and C. Majewski

1 Introduction The continuous developments in today’s world, especially in industrial, manufacturing, and related industries, lead to using functional structures made from different materials such as metal and polymer. These materials have been connected by many joining types. The welding process is one of the most famous of these joining types that has played a crucial role [1]. Welding can be defined as a fabrication process that can be used to join materials together. Generally, welding process produces permanent joints between the welded parts. There are many types of energy sources can be used for the welding process such as an electric arc, a laser, a gas flame, friction, an electron beam, and ultrasound [2]. Therefore, the welding process can be divided into two major groups: ➀ fusion welding and ➁ solid-state welding. The industry demands of improved properties of joints, combined with energy saving and reduction in cost, which make the importance of the solid-state welding has increased. The traditional types of solid-state welding are friction welding, friction stir welding, and ultrasonic welding. Although friction stir welding and friction welding are close competitors to ultrasonic welding and categorize as the friction welding type in the joining of materials, ultrasonic welding offers the benefits of being very fast, clean, simple, easily automated, and ability to weld dissimilar materials [3, 4].

1.1 Ultrasonic Welding of Similar Materials There are several investigations have been done on the joining of similar materials (polymer–polymer) or (metal–metal) using an ultrasonic welding technique and practiced for many years. ABS (amorphous polymer) and PP (semi-crystalline polymer) were welded ultrasonically by [3]. The author adopted general linear model (GLM) statistical analysis to find the effect of using welding parameters on the lap shear joint strength. The findings of this work (LSS) were 17 MPa (about 34% of the base material) and 6 MPa (about 14.6%) for ABS and PP, respectively. This work observed that LSS of ABS was higher than LSS of PP at the same welding parameters because the softening temperature of ABS (103 °C) is lower than the melting point of PP (170 °C) and that meant ABS material began melting and flowing quicker than PP material. Thus, a stronger joint strength was achieved. Amorphous thermoplastics, such as ABS, PS, are more easily to weld ultrasonically than semi-crystalline polymers, and energy-efficient; thereby, it is preferred for ultrasonic welding [5]. These properties of amorphous thermoplastics result from random molecular arrangements of the amorphous and its wide softening temperature range. These characteristics allow polymer materials to flow easily, and gradually thereby, the premature solidifications are avoided [6]. Whereas semi-crystalline polymers are difficult to weld by USW, they tend to absorb the oscillation energy before

Ultrasonic Welding of Polymer–Metal Hybrid Joints

23

passing through the welding zone and they have a sharper fusing temperature. Thus, semi-crystalline materials need more power to weld them [7]. The ultrasonic welding of metals is a solid-state type; thereby, metals do not reach the melting point and its energy proportional to the shear force that results due to the horizontal oscillation [8, 9]. Hence, the diffusion process in the welding area is the basis of welding process in metals [10]. The ultrasonic metal welding was invented by accident during efforts to improve the grain structure of traditional spot welds by using ultrasonic. It was discovered that welds could sometimes be occurred through the vibration of the electrode without flowing the welding current [9]. The primary keys of metals weldability are Young’s modulus, hardness, yield strength and thermal conductivity that improve the atom diffusions between the contact surfaces. Therefore, the soft metals, such as aluminium and copper, are easier to weld than hard metals like nickel [11]. The ultrasonic bonding of Al–Al was investigated by [12]. Taguchi method was used in this research to find the optimum parameters that affecting to achieve a maximum joint strength using a minimum number of tests. This approach is very useful to consume both time and the cost because the traditional methods are using a huge number of experiments. The results showed that the best values of parameters to get a weld Al–Al (3.29 MPa) were 2.5 s (welding time), 2.5 bars (welding pressure), and 45 µm (amplitude). Additionally, the interface temperature during welding Al–Al varied between 90 and 120 °C. The comparison between Al–Al joints and the Cu–Cu joint was investigated by the previous work [13]. This study summarized that the joint strength of Al–Al is more than around 15% higher than the joint strength of Cu–Cu at the same welding parameters. The reasons for these findings are the aluminium materials which can be easily deformed plastically during the USW process and that resulted due to the aluminium is a softer metal than copper.

1.2 Ultrasonic Welding of Dissimilar Materials There are very limited investigations on ultrasonic welding of dissimilar materials (metal–polymer). The common challenges of this welding are the different properties of dissimilar materials such as the melting point, modulus of elasticity, hardness, thermal conductivity, and crystallization structure form (e.g. amorphous, semicrystalline, and crystalline structures). The welding between aluminium alloy (AA2024) and carbon fibre-reinforced polymer (CFRP) was investigated by Ref. [14]. This investigation was applied by using ultrasonic metal welding (USMW) that means the vibration direction parallel to the welding surface. The reason of using USMW is the polymer matrix of the CFRP which plasticized and displaced out of the welding region by the transversal vibration, and thus, the contact between AA2024 and the fibres of the CFRP is formed. Therefore, the load transmits directly from the metal into the fibres. Whereas

24

A. Al-Obaidi and C. Majewski

ultrasonic plastic welding (USPW) forms a joint between the polymer matrix and metal, the mechanical load cannot transmit directly from metal into fibre, and the USPW method causes damage at the fibres because the direction of oscillation is perpendicular to the bonding surface. AA5754 and AA1050 alloys were welded ultrasonically with CFRP with PA66 matrix (polyamide 66) by Ref. [8]. The results summarized that the tensile shear strength of AA5754-CF-PA66 joint (about 32.5 MPa) was stronger than AA1050-CFPA66 joint strength (about 24.2 MPa) due to the monotonic properties differ between AA5754 and AA1050, and thus, many small cracks were observed on AA1050 surface when the oscillation amplitude higher than 34 µm, while the best joint strength was achieved at 40 µm for AA5754. Therefore, it can be concluded that most of the works were conducted on metal–metal or polymer–polymer joining, and there are very limited attempts which were carried out on metal–thermoplastic composites using ultrasonic welding. However, joining dissimilar materials of metal–polymer using ultrasonic welding has not been investigated so far. This work aims to develop a deeper and comprehensive understanding of the novel ultrasonic welding between polymer–metal joint and give a comprehensive investigation about the welding parameters to allow optimization of the lap shear strength (LSS).

2 Methodology and Experimental Work 2.1 Material Selection The hybrid joints are very necessary for the hybrid structures for implementing car bodies and other engineering applications [15]. In addition, all transportation industries have the benefits from the mass reduction to produce vehicles which have more fuel efficient and thus reduce in CO2 emission [16, 17]. This reduction in the total mass depends on the selected materials that should have higher strength, less dense, and well-engineered materials. Both plastics and metals can be able to provide the desired properties for a given application, such as reduce weight, high strength, and low cost. Hence, the combination issues of these dissimilar lightweight materials in industries, engineering applications, or hybrid structures have become unavoidable. Hence, the materials that were used in this work are acrylonitrile butadiene styrene ABS-750SW that was supplied from Korea Kumho Petrochemical Co., Ltd. and Al6082-T6 which supplied from Wilsons Metals Co. The physical and chemical properties of ABS-750SW and Al6082-T6 are shown in Table 1. Both ABS and Al alloys shown to weld effectively using ultrasonic process, as observed in the literatures. According to [14], the weld of amorphous and semicrystalline thermoplastic polymers occurs between glass transition temperature and

Ultrasonic Welding of Polymer–Metal Hybrid Joints

25

Table 1 Physical and chemical properties of ABS-750SW and Al6082-T6 Items Al6082-T6 ABS-750SW Density (g/cm3 )

2.69–2.7

1.06

Modulus of elasticity (GPa)

69

1.9

Elongation %

12

20

Poisson’s ratio Shear modulus (GPa)

0.33 34

0.41 0.95

Tensile strength, ultimate (MPa)

295

40

Thermal conductivity (W/m K)

154–188

0.23

Melting temperature (o C)

555

N/A

/

105

Glass transition temperature

(o C)

the melting temperature. Therefore, ABS can be able to weld at 105 °C (T g of ABS is about 105 °C), while the interface temperature during welding of Al alloy varied between 85 and 125 °C [18]. Therefore, the desired temperature, which considered a significant factor to success the welding, can be achieved for this welding. Furthermore, the energy director for ABS can be used to concentrate the energy in the welding zone to support and strengthen the joint. In addition, ABS and Al alloy can be able to provide particular desired properties for a given application, such as reduce weight, high strength, and low cost. The combination issues of these dissimilar lightweight material industries, engineering applications, or hybrid structures have become unavoidable. In the summative evaluation, ABS and Al6082 joints can also have potential applications in the future and the current applications in electronic and transportation industries.

2.2 Sample Preparation Sample Dimensions In the absence of the standard dimensions of ultrasonic welding of lightweight dissimilar materials (metal–polymer), the samples were performed to a rectangular shape because this shape is suitable for lap shear testing. Based on a previous work [3], the sample dimensions are shown in Table 2.

Table 2 Sample dimensions Materials Width (mm) ABS-750SW Al6082-T6

22 22

Length (mm)

Thickness (mm)

59 59

2 1, 1.5 and 2

26

A. Al-Obaidi and C. Majewski

Polymer Samples The injection moulding technique is applied to produce ABS-750SW samples based on the dimensions that will mention later. TRAVIN Mini Moulder (TP1) device is adopted during the process of polymer sample production. In this technique, the procedure of preparation ABS samples, and design and manufacturing the mould followed the British Standard EN ISO 294-1:1998. This technique depends on melting the polymer inside the device’s cylinder and presses the molten polymer into the mould. Metal Samples Al6082-T6 samples were cut by wire EDM at the workshop of the University of Sheffield based on the dimensions that were shown in Table 2. Then, the Al6082 samples were treated. The importance of pre-treatment depends on modifying the surface of aluminium alloy to become a formatted to bond with the polymer. Al6082T6 samples were pretreated according to ASTM D3933-98 (Reapproved 2010), and this pre-treatment used to join metal to polymer by adhesion technique. The pretreatment produced a rough and porous surface and thus interlocked with polymer surface.

2.3 Experimental Setup The ultrasonic welding machine that used in this work is shown in Fig. 1. The USW system is comprised of a US generator (Telsonic Ultrasonics—Model SG-22-50003), a US transducer or convertor (Telsonic Ultrasonics—Model SE 2050 A), a booster (Telsonic Ultrasonics—Model 1.5QC), a welding horn or sonotrode (circular end tip 39 mm diameter, made from titanium), moving anvil, air compressor (Bambi—Model MD 150/500) with pneumatics circuit, and fixing tools. The power is supplied to the transducer by the US generator and this electrical signal is converted into mechanical vibration by the US transducer. The operating frequency is 20 kHz, and the amplitude is between 14 and 21 µm. This range of vibration amplitude was achieved and amplified by the booster. Then, the vibration was taken to the samples by the sonotrode. The air compressor moves up and down the anvil, and thus, the welding force (static force) can be measured by this movement. This compressor supplies a static force between 450 and 2350 N. This force can be controlled by the pressure regulator, and thus, the pressure sensor in the programmable logic controller (PLC) displays the values. There is a table to convert the values that are shown in the PLC into force meter in Newton. While the control of welding time and holding time was computerized control by using the Crouzet Millenium III Software, the PC is connected to the PLC by a specific cable. The starting button of welding is on the PLC, and when pressed it, the ultrasonic welding was operated at selected welding force, welding time, and hold time. Welding Parameters Prior to starting main experiments, it was important to assess the range of parameters

Ultrasonic Welding of Polymer–Metal Hybrid Joints

27

Fig. 1 Ultrasonic welding machines Table 3 Welding parameters Parameter

Unit

Initial range

Parameter interval

Vibration frequency

kHz

20

Constant

Vibration amplitude

µm

16.8–21

2.1

Welding time

s

1–2

0.5

Welding force

N

850–1050

100

Hold time Energy director (polymer)

s /

1 Constant Triangular, semicircular, / and rectangular shapes

Sample position

/

Upper sample is metal

/

Upper sample thickness

mm

1–2

0.5

suitable and eliminate any not important once. The determination of best range of levels of welding parameters is essential for good welding. If the levels of these parameters are too low, it is possible to get joint or called under-weld between samples generally because of insufficient energy into the part, or insufficient one of welding parameters such as welding time or vibration amplitude, while too much energy or too much one of welding parameters can lead to over-welding. In the absence of standard levels of welding parameters and no information in the literature about the range of levels to weld polymer and metal ultrasonically, the range of levels of welding parameters that will use in the current research was determined experimentally. In some cases, the current machine was only capable of some settings, but they fit with literature. The welding parameters were selected, and the range of levels was selected, as illustrated in Table 3.

28

A. Al-Obaidi and C. Majewski

Fig. 2 Energy director shapes

The dimensions of energy director shapes are shown in Fig. 2. In this study, the Tinius Olsen H5KS device was adopted to test weld specimens with laser extensometer. This device consists of a computer controlled for data logging, acquisition, and calculations, load cells (5 kN), dumbbell and roller grips. The tested specimen is physically fastened to the load cell by grips for the tension testing. The tests were carried out at ambient temperature with cross-head speed of 2 mm/min.

2.4 Design of Experiment Design of experiment is a mathematical technique for analysing any case that involves a response that changes as a function of one or more independent variables [19]. DOE was adopted to collect experimental data and/or analyse it. Thus, DOE can be used in many branches of scientific study, especially in manufacturing, engineering, economics, biology, marketing, and so on. DOE can provide the answer to particular questions about the behaviour of a system if it used correctly and so applying the optimum number of experimental runs (observations) [20]. Any process model has input variables or called parameters that find how the process runs and output or called response that generated by the process. Therefore, determining the effects of inputs on the outputs is the purpose of an experiment. An interaction occurs when the response of one variable differs at different levels of another variable. The interaction is possible to happen when a process includes two or more parameters. In addition, it can occur between two or more parameters, but higher-order interactions such as three parameters or more are usually assumed to be insignificant as a safe assumption [20]. There are many types of experimental designs such as full factorial design (FFD), Taguchi approach, and response surface methodology (RSM). Although the FFD is known and easier compared with other experimental designs such as RSM and Taguchi approach and can provide the response information for all parameter’s levels and all interactions, the FFD is not feasible for large numbers of factors and levels because it will produce a huge number of runs.

Ultrasonic Welding of Polymer–Metal Hybrid Joints

29

Taguchi approach is one of the experimental designs that used a few numbers of experimental runs to estimate the mean values of the response. In spite of the fact that the Taguchi method is powerful design of DOE, it cannot identify parameter interactions because it is not testing the combinations of input parameters [21]. Therefore, the using of Taguchi design should be avoided when the relations between all the parameters are being tested. Response surface methodology (RSM) is a collection of statistical and mathematical techniques to build an experimental model. The objective of using the RSM is to optimize the output variable (response) that is affected by many input variables (parameters) [22]. In addition, the smooth functions of RSM can be reduced the associated numerical noise and thus improved the convergence of the optimization process [23]. Additionally, it deals efficiently with the effect of parameter interaction on the response and it considers a suitable method when the response expects as a curve [22]. Therefore, the usage of RSM is being increasingly adopted in the industry. According to the above, the response surface methodology (RSM) was used in the current study to create an efficient analytical model to determine the optimization of the lap shear strength (the response) that is a function of ultrasonic welding parameters (independent variables) and investigate the effect of welding parameters and its interaction on the joint strength. The analysis of RSM depends on the regressions and analysis of variance (ANOVA). Analysis of variance (ANOVA) is a statistical technique used to interpret and explain the experimental results. In addition, ANOVA technique is applied to find out the impact independent factors have on the dependent factor in a regression analysis [24]. Based on second-order equation in RSM, the model of the lap shear strength of this research has the form: LSS  b0 + b1 x1 + b2 x2 + b3 x3 + b4 x4 + b12 x12 + b13 x13 + b14 x14 + b23 x23 + b24 x24 + b34 x34 + b11 x12 + b22 x22 + b33 x32 + b44 x42

(1)

The variables x 1 , x 2 , x 3, and x 4 represent the vibration amplitude, welding time, welding force, and Al6082-T6 thickness factors, respectively. In addition, x 12 , x 13 , x 14 , x 23 , x 24 , and x 34 are the two-factor interactions. One of the most designs that used for fitting second-order model and creating experimental matrix to collect the data is central composite design (CCD). Thus, this research was adopted central composite design (CCD).

3 Results and Discussion The results of ANOVA showed that all main parameters and most of their interaction have been significant effects on the LSS results. According to the ANOVA, the chosen model of the LSS shows a high percentage of R-squared and that is equal to

30

A. Al-Obaidi and C. Majewski

98.42%. This high percentage refers to high quality of the chosen model and precise in predicting the value of the response. In addition, the standard deviation of the model is small (0.025) indicating that the average distance between the observed values of experimental runs is very close to the fitted line of the model. Thus, the model can be trusted and reliable.

3.1 Welding Parameters Main Parameters Vibration Amplitude The effect of vibration amplitude as a main welding parameter firstly and its significant interactions with other welding parameters as a second part was discussed and interpreted in this section. The amplitude has been found to have a linear relationship with mean LSS, as shown in Fig. 3. This relation was a result due to the significant effect of vibration amplitude on the dissipated energy that becomes heat and this dissipated energy varies with square of vibration amplitude, as shown in Eq. (2) [25]. Q

f ε2 E  2

(2)

where ε  2/3A h . The variables Q, f , 2, E  , A, and h are internal heat generation (dissipated energy) per unit volume, welding frequency (rad/s), strain amplitude, the loss modulus, vibration amplitude, and energy director height, respectively.

Main Effects Plot for LSS

Fig. 3 Relationship between vibration amplitude and mean of LSS

Fitted Means

Mean of LSS (MPa)

1.35 1.30 1.25 1.20 1.15 1.10 1.05 17

18

19

20

Vibration Amplitude (μm)

21

Ultrasonic Welding of Polymer–Metal Hybrid Joints

31 Main Effects Plot for LSS

Fig. 4 Relationship between welding force and mean of LSS

Fitted Means

Mean of LSS (MPa)

1.20 1.15 1.10 1.05 1.00 850

900

950

Welding Force (N)

1000

1050

Thus, if the vibration amplitude is increased, the dissipated energy increases dramatically. This dissipated energy promotes the ABS melting (ED portion) and thus interlocks it with Al6082-T6 surface and this was led to increase the joint strength. This trend behaviour and the significance of vibration amplitude in the ultrasonic welding process were mentioned in the literature [3, 26]. Welding Force The relationship between the welding force and the mean LSS is nonlinear (curvy). As can be shown in Fig. 4, the LSS increased a little by about 5% when the welding force increases from 850 to 930 N by sonotrode tip. The mean of LSS was decreased by 20% when the welding force was still increasing from 930 N to reach 1050 N. The reason of the nonlinear relationship between welding force and mean LSS that shown in Fig. 4 was because the welding force is important to transmit the energy from the USW machine to faying surface (surface to be joint) through the upper part (Al6082-T6). The increasing of force has increased the friction, and thus, it assists to increase the heat energy at welding surface. The excessive increase of force was reduced the efficient amplitude movement and thus decreased the LSS. Welding Time As with the welding force, the welding time was found to have a nonlinear effect on the mean of LSS, as can be illustrated in Fig. 5. By increasing the time of welding, the joint strength increased slightly or did not show a strong change on LSS to reach at the maximum value at 1.2 s. However, the mean of LSS decreased by about 15% at 2 s. The heat dissipation increases when welding time is increased [27] that implies the joint strength increase when the time of welding increased, but the strength was not improved when the time still increasing but rather decreased the strength of the joint. This behaviour happened because the heat dissipation still increased; i.e., the temperature at the interface area increased to reach higher than the glass transition temperature of ABS (it is about 107 °C). The viscosity of ABS has been dropped,

32

A. Al-Obaidi and C. Majewski Main Effects Plot for LSS

Fig. 5 Relationship between welding time and mean of LSS

Fitted Means

1.24

Mean of LSS (MPa)

1.22 1.20 1.18 1.16 1.14 1.12 1.10 1.08 1.06

1.0

1.2

1.4

1.6

1.8

2.0

Welding Time (sec)

Fig. 6 Comprising between two different welding time when another parameters constant

and the flow of molten materials starts to occur. Once material flow begins to occur, the alignment of welded samples was moved and the molten polymer squeezes out of the welding area, as shown in Fig. 6. Therefore, it can be clearly seen that the mean of LSS was decreased when the extended period of welding time after the flow was occurring. Whereas the short time produced a small welded area, the welding strength that produced was not strong enough. Al6082-T6 Thickness The results show a linear relationship between Al6082-T6 thickness and mean LSS, as shown in Fig. 7. This behaviour happens because the required energy for the welding decreased when the thickness of the upper sample (Al6082-T6) was increased [2]. The ultrasonic energy, which was coming from the sonotrode, was passed through the top sample (Al6082-T6) to propagate at the interface area between ABS and Al6082T6 as a heat. Then, the generated heat was conducted and dispersed in the Al6082-T6

Ultrasonic Welding of Polymer–Metal Hybrid Joints

33 Main Effects Plot for LSS

Fig. 7 Relationship between Al6082-T6 thickness and mean of LSS

Fitted Means

Mean of LSS (MPa)

1.25 1.20 1.15 1.10 1.05 1.0

1.2

1.4

1.6

1.8

2.0

Al6082 Thickness (mm) Main Effects Plot for LSS

Fig. 8 Relationship between ED shape and mean of LSS

Fitted Means

Mean of LSS (MPa)

1.250 1.225 1.200 1.175 1.150 TRI

SEMI-C

RECT

ED Shape

sample volume rapidly due to the high conductivity of Al6082-T6. Therefore, the amount of molten ABS that interlocked inside the pores of thick Al6082-T6 is less than at thin Al6082-T6 and thus less joint strength. To produce a strongly welding for thicker material, the ultrasonic welding energy should be increased. Energy Director Shape The results that obtained from the relationship between the energy director shape and the joint strength showed the triangular shape of energy director has a higher welding strength compared with semicircular and rectangular shapes, as shown in Fig. 8, while the rectangular shape had the lowest welding strength. The greatest concentration of ultrasonic welding energy occurs at the smallest contact surface and smallest volume. The volumes of triangular, semicircular, and rectangular ED are 90, 141.38, and 180 mm3 , respectively. Therefore, triangle shape of the energy director has been melted quicker than semicircular and rectangular shapes because it reaches to the above of its glass transition temperature (107 °C) quickly and starts to the flowing due to drop down the viscosity of ABS. After melting a portion of energy director, the molten polymer pressed by static welding force and

34

A. Al-Obaidi and C. Majewski Interaction Plot for LSS

Fig. 9 Effect of two-way parameter interactions (vibration amplitude and welding time) on LSS

Fitted Means

Vibration Am * Welding Time

Mean of LSS (MPa)

1.4 1.3 1.2 1.1

Welding Time 1 1.5 2

1.0 0.9 17

18

19

20

21

Vibration Amlitude (μm)

spread on the joint area. Hence, the high level of welding force was pushed out the molten polymer outside the joint zone and thus reduced the joint strength. Parameter Interactions The data from ANOVA showed a statically significant effect of interaction between vibration amplitude and welding time, as shown in Fig. 4. The highest LSS was achieved at highest vibration amplitude and lowest welding time. When increasing both of vibration amplitude and welding time, the LSS was increased due to increase the period of exposure the samples to amount of energy at the interface and thus melting a large portion of energy director. When the increasing of the welding time was exceeded, the LSS will decrease due to the long period of imposing the samples to the cycles of vibration and force and thus exceed the energy heat of welding on ABS sample, as shown in Fig. 9. The increasing of welding force from 850 to 950 N was resulted increasing in LSS in conjunction with increasing of vibration amplitude but continued of welding force increasing to reach 1050 N that was caused decreases in the joint strength by 20%. The reasons for that trend are the increasing of amplitude promotes the strength of the joint due to get the energy that led to melting the energy director, while the applied welding force allows to the molten energy director (ABS polymer) to flow, thus increasing the contact area between Al6082-T6 and ABS. When the applied welding force was too high, the large amount of molten energy director had been pushed out the welding region what causes a thin layer of contact material. In addition, it noticed the contact layer that produced from melting the energy director was oriented to the flow direction, and the direction of flow is transverse to the tensile force direction, and thus, the lap shear strength was decreased, as illustrated in Fig. 10. Decreasing the thickness of Al6082-T6 with increasing the welding amplitude was produced a highest LSS because of the large amount of energy that travelled through the thickness of the top sample (Al6082-T6). To achieve a suitable welding energy for thicker sample, it needs to increase the dissipated energy that implies

Ultrasonic Welding of Polymer–Metal Hybrid Joints

35

Fig. 10 Direction of molten polymer

Flow direction

4

ED Collapse (mm)

Fig. 11 ED collapse at 18.9 µm, 950 N, 1.5 s, and 1.5 mm thickness of Al6082

3

Tri

2

Semi-C

1

Rect

0

increase the vibration amplitude. Therefore, the highest value of the mean of LSS has been found at the highest level of vibration amplitude and thinner Al6082-T6. As mentioned before, the increasing of vibration amplitude was produced a large amount of dissipated energy that becomes heat and melts the energy director. The shape of energy director was promoted the heat when the triangular shape was utilized, as expressed later. In addition, the collapse of triangular shape was the highest compared with rectangular and semicircular shapes, as shown in Fig. 11. Thus, the amount of melting polymer was increased and that was led to increasing area contact and increasing the joint strength.

3.2 Predictive Model The significant main welding parameters and its significant interaction were identified in this section. To find the fit model of the lap shear strength in Eq. (1), the model coefficients should be founded by response surface regression in Minitab 17 software. The fit model equation is for each categorical parameter (ED shape), and thus, there are three-fit model equations for square root of LSS. The results of square root of LSS should be squared to get the real results of the LSS. Equation (3) is for triangular energy director shape:

36

A. Al-Obaidi and C. Majewski



(LSS)  −8.711 − 0.1074 · VA + 0.8436 · WT + 0.020505 · WF + 0.8251 · Al6082 thickness + 0.00313 ∗ (VA)2 − 0.1822 · (WT)2 − 0.000011(WF)2 − 0.2018 · ( Al6082 thickness)2 + 0.00542 · VA · WT + 0.000036 · VA · WF + 0.00748 · VA · Al6082 thickness − 0.000496 · WT · WF − 0.04668 · WT · Al6082 thickness − 0.000494 × WF ∗ Al6082 thickness

(3)

where VA vibration amplitude; WT welding time; WF welding force. Equation (4) is for semicircular energy director shape:  (LSS)  −8.801 − 0.1045 · VA + 0.8405 · WT + 0.02048 · WF + 0.8201 · Al6082 thickness + 0.00313 · (VA)2 − 0.1822 · (WT)2 − 0.000011(WF)2 − 0.2018 · ( Al6082 thickness)2 + 0.00542 · VA · WT + 0.000036 · VA · WF + 0.00748 · VA · Al6082 thickness − 0.000496 · WT · WF − 0.04668 · WT · Al6082 thickness − 0.000494 · WF · Al6082 thickness

(4)

Equation (5) is for rectangular energy director shape:  (LSS)  −8.833 − 0.1006 · VA + 0.8303 · WT + 0.020422 · WF + 0.8065 · Al6082 thickness + 0.00313 · (VA)2 − 0.1822 · (WT)2 − 0.000011(WF)2 − 0.2018 · ( Al6082 thickness)2 + 0.00542 · VA · WT + 0.000036 · VA · WF + 0.00748 · VA · Al6082 thickness − 0.000496 · WT · WF − 0.04668 · WT · Al6082 thickness − 0.000494 · WF · Al6082 thickness

(5)

According to the predictive models (Eqs. 3–5), the optimum values of welding parameters that produced the highest LSS were predicted, as shown in Table 4.

Ultrasonic Welding of Polymer–Metal Hybrid Joints Table 4 Optimum levels of welding parameters Vibration Welding time Welding force Al6082-T6 amplitude (s) (N) thickness (µm) (mm) 21

1.2

931

1

37

ED shape

Predicted LSS (MPa)

Tri

2.312

4 Conclusions This research has provided a comprehensive understanding of the ultrasonic welding for lightweight dissimilar materials. Although the importance of this type of joining in industries and engineering applications, there are very limited studies which done in this field. The results of the present study can be concluded in the following vital points: • The presence of pre-treatment for Al6082-T6 and ED for ABS is very essential to obtain dissimilar welding (Al6082-T6/ABS joint). • Neither holding time nor the thickness of lower sample (ABS) has a significant effect on LSS. • The welding parameters, oscillation amplitude, welding time, welding force, upper sample thickness (Al6082-T6), and ED shape, have a significant impact on the joint strength. • The joint strength was increased when welding time, welding force, oscillation amplitude increased, and Al6082-T6 decreased. • The excessive increasing of welding time and welding force had led to decrease LSS. • Al6082-T6 is better to be the upper sample than lower sample for getting strong joints. • The optimum values of parameters that were resulted in the highest LSS (2.312 MPa) are as follows: – – – – – –

Oscillation amplitude 21 µm; Welding time 1.2 s; Welding force 931 N; Al6082-T6 thickness 1 mm; Energy director is triangular shape; Input welding energy 468 J.

Overall the current research has been completed to deliver a deeper analysis and substantial progress in getting a comprehensive understanding of ultrasonic welding for lightweight dissimilar materials between metal and polymer.

38

A. Al-Obaidi and C. Majewski

References 1. Zain-Ul-Abdein M (2009) Experimental investigation and numerical simulation of laser beam welding induced residual stresses and distortions in aa 6056-t4 sheets for aeronautic application. Bibliography 41(2):48–60 2. AWS (2013) Welding handbook, 53 3. Raza SF (2015) Ultrasonic welding of thermoplastics. Sheffield 4. Benatar A, Eswaran RV, Nayar SK (1989) Ultrasonic welding of thermoplastics in the nearfield. Polym Eng Sci 29(23):1689–1698 5. Michael J (2009) Handbook of plastics joining 6. Troughton MJ (2008) Handbook of plastics joining—a practical guide. William Andrew Publishing 7. Ensminger D, Bond LJ (2011) Ultrasonics: fundamentals, technology and applications. CRC Press 8. Graff K (2005) Ultrasonic metal welding. In: New developments in advanced welding, pp 241–269 9. Neppiras EA (1965) Ultrasonic welding of metals. Ultrasonics 3(3):128–135 10. Ginzburg SK, Nosov YG (1967) Characteristics of diffusion processes in commercial iron during ultrasonic welding. Met Sci Heat Treat 9(4):306–308 11. Lewis WJ, Antonevich JN, Monroe RE et al (1960) Fundamental studies on the mechanism of ultrasonic welding 12. Sooriyamoorthy E, John Henry SP, Kalakkath P (2010) Experimental studies on optimization of process parameters and finite element analysis of temperature and stress distribution on joining of Al–Al and Al–Al2O3 using ultrasonic welding. Int J Adv Manuf Technol 55(5–8):631–640 13. AlSarraf, SZ (2013) A study of ultrasonic metal welding. University of Glasgow 14. Wagner G, Balle F, Eifler D (2013) Ultrasonic welding of aluminum alloys to fiber reinforced polymers. Adv Eng Mater 15(9):792–803 15. Wright NW (2012) Implementation of ultrasonic welding in the automotive industry. University of Manchester 16. Bakavos D, Prangnell PB (2010) Mechanisms of joint and microstructure formation in high power ultrasonic spot welding 6111 aluminium automotive sheet. Mater Sci Eng, A 527(23):6320–6334 17. Patel VK, Bhole SD, Chen DL (2011) Influence of ultrasonic spot welding on microstructure in a magnesium alloy. Scr Mater 65(10):911–914 18. Lee S (2013) Process and quality characterization for ultrasonic welding of lithium-ion batteries. Cell Physiol Biochem 32(32):645–654 19. Mason RL, Gunst RF, Hess JL (2003) Statistical design and analysis of experiments: with applications to engineering and science 20. Mathews PG (2005) Design of experiments with MINITAB. ASQ Quality Press 21. Roy RK (2001) Design of experiments using the Taguchi approach: 16 steps to product and process improvement. Wiley 22. Bradley N (2007) The response surface methodology. Indiana University South Bend, p73 23. Asghar A, Abdul Raman AA, Daud WMAW (2014) A comparison of central composite design and Taguchi method for optimizing Fenton process. Sci World J 2014:869120 24. Elangovan S, Prakasan K, Jaiganesh V (2010) Optimization of ultrasonic welding parameters for copper to copper joints using design of experiments. Int J Adv Manuf Technol 51(1–4):163–171 25. Eswaran R (1988) Near field ultrasonic welding of thermoplastics. The Ohio State University 26. Liu SJ, Lin WF, Chang BC et al (1999) Optimizing the joint strength of ultrasonically welded thermoplastics. Adv Polym Technol 18(2):125–135 27. Nonhof CJ, Luiten GA (1996) Estimates for process conditions during the ultrasonic welding of thermoplastics. Polym Eng Sci 36(9):1177–1183

Based on Multi-sensor of Roughness Set Model of Aluminum Alloy Pulsed GTAW Seam Forming Control Research Jiyong Zhong, Yanling Xu, Huabin Chen, Na Lv and Shanben Chen

Abstract For the automatic aluminum GTAW, weld backside width is an important feature to be regulated in real-time weld forming control. For most welding conditions, the weld backside condition is difficult to be monitored in real time. Therefore, this paper combines visual sensing, arc sensing, and sound sensing to extract weld feature information in real time. Based on the rough set model, the multi-information fusion is proposed, and a prediction model of the weld backside width is proposed to realize the backside width control. And a fuzzy controller with genetic improvement is designed. The multi-information fusion prediction model based on roughness set is used to control the weld backside width in real time, and the control of the robot aluminum alloy GTAW weld forming is realized. Keywords Weld forming control · Visual sensing · Arc sensing · Sound sensing Robot welding

1 Introduction The key point of welding automation and intelligent is the real-time acquisition and intelligent control of the dynamic characteristics of the molten pool during the welding process, which is also the difficulty and hotspot of welding research direction [1–3]. At present, most of the welding equipment or teaching reproduction-type robot used in the actual welding process, due to constant parameter configuration and robot movement trajectory immobilization, in the face of the wrong side, gap changes, abnormal heat dissipation conditions, and thermal deformation of the workpiece and other factors, interference, which cannot be real time for these changes and interference to make corrections, resulting in decreased weld quality, have been unable to meet the high standards of welding requirements. Thus, in recent years, the J. Zhong (B) · Y. Xu · H. Chen · N. Lv · S. Chen School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 S. Chen et al. (eds.), Transactions on Intelligent Welding Manufacturing, Transactions on Intelligent Welding Manufacturing, https://doi.org/10.1007/978-981-13-3651-5_3

39

40

J. Zhong et al.

researchers based on the development of intelligent control theory to obtain a new path of welding process modeling and control have made great progress here [4–7]. But further development and improvement are still the goal of welding intelligent control researchers. Document [8–10] used passive visual sensing technology to do a lot of study on the real-time pool image acquisition and information in welding process. Without active light source sensor or auxiliary light source imaging, the light of the pool area comes from the arc light, the black body radiation of pool or the workpiece, and the pool surface reflection arc. Due to the interference of the arc, filtering is to need, and then by the CCD will obtain pool image. Therefore, the passive visual sensor research direction is through the dimmer filter system to remove the welding area forming arc interference, while ensuring the quality of the pool image acquisition to obtain effective pool characteristics information. Xu [11] and others have obtained the real-time image information of welding seam by using passive vision sensing and carried out in-depth and effective research on the welding seam tracking control. Chen and Kong [12, 13] have in-depth studied the relationship between arc length and arc voltage. As a basis, they have studied the weld seam height tracking with the errors in the 0.5 mm. That promotes the development of control technology. On this basis, Xu et al. [14] used arc sensing to reduce the system error of arc control to 0.2 mm, which made a great progress in the research of arc sensing. For sound research on aluminum alloy GTAW, Lv [15] used sound signal to feedback arc length and realized the real-time control of welding process. That lays a theoretical foundation for the welding quality with multi-information fusion. In this paper, the vision sensor, arc sensor, and sound sensor are used for multiinformation fusion. The forming control of robot aluminum alloy GTAW welding seam is studied.

2 System Figure 1 is a schematic diagram of a robot welding control system. The internal components are sound sensor, vision sensor, voltage sensor, wire feed mechanism, signal acquisition circuit, welding machine, industrial computer, etc. The main hardware of the welding robot system: Yaskawa robot, OTC 500P welding, OTCAW-33/32 welding, HC-71 type wire feeding machine, robot axes positioner, the main console, by the central computer and data collection card. In addition, the system also comprises a water tank, gas bottle, gas flow controller, and related welding fixture protection. In the control system of robot pulsed TIG welding, IPC is the core. The main achievement of the following functions: the information exchange system built with computer, monitoring sensors, welding machine and robot running state; and then the design of communication circuit system, realization of control computer and robot and welding real-time information exchange, a start–stop signal to the robot, at the same time the operation of welding power source arc on/off, current, and the wire feeding speed; the after is visual information acquisition in the process of welding

Based on Multi-sensor of Roughness Set Model of Aluminum …

41

Fig. 1 Robot pulsed GTAW system schematic

seam, and the information of arc sound signal processing, real-time data collection and control of the robot and the rectification welding power supply parameters change. The test system in this paper is shown in Fig. 2. This system can realize real-time acquisition of the welding pool image, arc voltage signals and sound information, by modifying the welding parameters and the voltage of the robot to transmit motion correction, line seam tracking and control the quality of welding operation.

3 Multi-information Acquisition and Processing Robotic pulsed GTAW welding seam forming quality control system is based on multi-information acquisition and fusion; therefore, this paper designed the pulsed GTAW process of multi-sensor information acquisition and processing method. Information acquisition includes passive vision system for molten pool image information acquisition, arc sensing system for voltage information acquisition, and acoustic sensor system for welding sound information acquisition. Through this system, the welding seam and weld pool image, voltage signal, welding sound signal and other information can be collected in real time during the pulsed GTAW process. In order to realize automatic welding process with self-adjustment, and to realize automatic adjustment of robot motion parameters and welding parameters, the welding process needs to capture and process multi-information in real time and rapidly fuse characteristic parameters. Table 1 is the welding parameter in the process of multi-information acquisition and processing.

42

J. Zhong et al.

Fig. 2 Robot pulsed GTAW welding seam tracking and forming quality control system Table 1 Aluminum alloy pulsed GTAW experimental process parameters

Parameter

Value

Peak current Base current Pulse frequency

220 A 50 A 2 Hz

Wire feed speed

10 mm/s

Welding speed

3 mm/s

Duty cycle

50%

Gas flow Welding materials

15 L/min LF6

Tungsten diameter

3.2 mm

Plate thickness

3 mm

3.1 Image Acquisition and Processing In the robot GTAW automation, the central computer needs to obtain accurate information of weld pool and weld, to effectively process characteristic information in real time, and to control the welding parameters and the trajectory of the robot with the controller. Therefore, the first premise is to extract the weld image of the weld pool in the process of welding and to process the special information of the image in real time. In this paper, aluminum alloy GTAW plate butt welding and flange welding

Based on Multi-sensor of Roughness Set Model of Aluminum …

43

are taken as the object of study. Experiments are carried out with a uniform welding process, and the image acquisition and processing are analyzed and studied. In this paper, after analyzing the influence of the light reduction filter system and the image acquisition time on the acquisition of the welding image, we have determined a stable combination of the reduced light filter and fixed acquisition time. Through the verification test, it is concluded that the image acquisition parameters are as follows: filter center wavelength 660 nm, attenuation transmittance 15%, bandwidth 10 nm, combined to the reducer and filter, the image collection time is 50 ms after the falling edge. Figure 3 is the aluminum alloy GTAW continuous welding image with light filter system above parameters, it can be seen that the weld pool image is very clear, and it is fast and effective in the subsequent information extraction and image the characteristic of molten pool and weld processing, for real-time seam tracking and the forming quality greatly help control. Figure 4 is the image acquisition for flange. After the image information acquisition of the molten pool, we need to extract the weld pool edge, using the Canny operator to detect the molten pool contour. And the edge set is rough and not smooth, curve fitting is needed. The image processing is shown in Fig. 5. Column to scan the edge of the pool, the upper and lower edge points in the same column of longitudinal coordinates of the difference between the calculation, the maximum obtained is the pool width W .

Fig. 3 Continuous pool images of plate butt welding process

Fig. 4 Flange welding continuous images

44

J. Zhong et al.

Fig. 5 Welding pool image process

Fig. 6 Image process of curve seam and pool

The image processing method mentioned above can be useful to the straightline welding seam, and it is also suitable for the curve welding of common flange welding. The image processing of curve welding seam is also studied in this paper. After acquiring the clear curve seam image, it must be processed to extract the characteristic value of the image. The image processing of curve welds includes small window extraction, degraded image restoration, median filtering, edge detection, edge scanning, line fitting, and so on. Figure 6 is the image processing flow and result of the curve weld.

Based on Multi-sensor of Roughness Set Model of Aluminum …

45

The image processing above allows the surface width of the weld pool to be obtained. In order to obtain the surface height of molten pool, the shape from shading method is adopted to treat the weld pool. According to the imaging illumination equation of the molten pool surface, the three-dimensional reconstruction of the weld pool image is carried out based on the improved algorithm, and then the surface height of the molten pool is extracted. The three-dimensional reconstruction of the weld pool surface is as follows. (a) The parameter initialization parameters (η, b, k, ρs , ρd , ns , nc , nz ). (b) Preprocessing weld pool images including noise reduction, image enhancement, linear denoising, and segmentation of target molten pool. (c) Standardized input image. That is to normalize the image data. (d) SFS calculation. (e) Coordinate conversion. The coordinates of the objects computed from the above steps of SFS are camera coordinates. In order to extract the surface height of the weld pool, the coordinates need to be moved to the workpiece coordinates. The CCD coordinate system and the workpiece coordinate system are set to O-XYZ and o-xyz, respectively. The workpiece coordinate system takes the center of the weld pool (the surface point of the workpiece below the tungsten electrode) as the origin, and the welding direction follows the X-axis, and the direction perpendicular to the workpiece surface is the Z-axis. Then, the relationship between the two coordinate systems is: (X , Y , Z, 1)  (x, y, z, 1)TM

(1)

Among them, T and M are shift transform matrix and rotation transform matrix, respectively. ⎞ ⎛ 1 0 0 0 ⎜0 1 0 0⎟ ⎟ ⎜ (2) T ⎜ ⎟ 0 1 0⎠ ⎝0 −XT −YT −ZT 1 ⎡ ⎤ cos ϕ cos ψ(1 − cos θ ) − cos ϕ cos ψ(1 + cos θ ) sin ψ sin θ 0 ⎢ sin ϕ(sin ψ + cos ψ cos θ ) − sin ϕ sin ψ + cos ϕ cos ψ cos θ − cos ψ sin θ 0 ⎥ ⎢ ⎥ M ⎢ ⎥ ⎣ sin ϕ sin θ cos ϕ sin θ cos θ 0⎦ 0

0

0

1 (3)

Among them, θ , ψ, and ϕ are the Euler angle between two coordinate axis angles (angle of nutation, precession angle, and rotation angle).

46

J. Zhong et al.

(f) Output results. Figure 7 shows the original image of the weld pool, and the surface reconstruction results obtained by the improved algorithm are followed along the X-axis and the Y -axis through the surface height of the molten pool center.

Fig. 7 SFS calculation results of the weld pool surface a the input image; b 3D reconstructed surface; c the heights along x-axis going through the centre of the reconstructed surface; d the heights along y-axis going through the centre of the reconstructed surface

Based on Multi-sensor of Roughness Set Model of Aluminum …

47

3.2 Arc Voltage Collection and Processing In welding process, arc length is very important to weld formation. If the consistency of arc length is maintained in real time, then the weld formation can be effectively controlled. The information of arc length is difficult to be monitored directly, and the voltage monitoring is used to reflect the information of arc length. This paper adopts arc voltage sensing system which collects the arc voltage of TIG welding robot information, research and analysis of the signal characteristics of arc pressure, the global threshold method to eliminate noise by wavelet decomposition structure of the collected signal based on arc voltage. The experiment is designed to research on the relationship between arc length and arc voltage; arc height information extraction

(a) 20

Voltage/V

10

0

-10

-20

-30 0

5

10

15

20

25

30

35

40

45

300

350

400

450

50

Time/s

(b) 20 15

Voltage/V

10 5 0 -5 -10 -15 -20 0

50

100

150

200

250

500

Time/ms

Fig. 8 Aluminum GTAW arc voltage a raw data of 3 mm arc length; b voltage waveform in one cycle

48

J. Zhong et al.

Fig. 9 Test of 3–6 mm arc length gradient a voltage signal after denoising; b the relationship between voltage and arc length

pressure signal establishes a mathematical model of arc voltage characterization of arc length, and the accuracy of the arc voltage signal is verified by experiment. Set the tungsten pole to the welding piece with 3 mm height at constant, then carry on pulse GTAW test, and collect arc voltage signal online, as shown in Fig. 8a. From the figure, the collected signal is very stable, and it can be seen from the surface of the peak arc voltage remained at a certain value, corresponding to the arc stability. In order to further obtain the correspondence between arc and the arc voltage, the original data denoising, feature extraction and other operations are required. Figure 8b is a voltage waveform in a pulse period.

Based on Multi-sensor of Roughness Set Model of Aluminum …

49

(a)

Arc Sound

2 1 0 -1 -2 5

6

7

8

9

10

11

12

Sample Number

x 10 5

(b) Pulse peak

1

Arc Sound

0.5 0 -0.5 Pulse base

-1 7.8

7.9

8

8.1

8.2

Sample Number

8.3

8.4

x 105

Fig. 10 Original welding sound and amplified waveform a original arc acoustic signal; b amplified waveform

In order to obtain the direct relationship between arc voltage and arc length, the welding experiment with signal acquisition is carried out by using 3–6 mm arc length gradient growth method. After the noise reduction, the peak voltage positive half cycle signal is shown in Fig. 9a. After linear fitting, the result of Fig. 9b is obtained, and the relation is V = 3.06h + 23.016. In order to verify the accuracy of voltage arc length formula, the step test of 4–2 mm is carried out, and the accuracy of the real-time processing is about 0.27.

3.3 Arc Sound Signal Collection and Processing The arc sound raw signal is shown in Fig. 10a. As shown by the diagram, the sound signals maintain periodic characteristics consistent with the pulse waveform of the

50

J. Zhong et al.

(a)

(b)

0.03

Energy

Partial Pen. Full Pen. Over Pen.

0.02

Mean

80

0.01

Partil Pen. Full Pen. Over Pen.

60 40 20

0

0

-0.01 0

50

100

150

0

200

50

time/s

(c) 0.2

200

0.15

Partial Pen. Full Pen. Over Pen.

0.04

RMXS

Standard deviation

Partial Pen. Full Pen. Over Pen.

0.1

0.03 0.02 0.01 0

0.05 0

50

100

150

0

200

50

100

150

200

Time/s

Time/s

(e)

(f) Partil Pen. Full Pen. Over Pen.

0.4 0.3 0.2 0.1

15

Log Energy

0.5

covriation

150

(d)

0.25

0

100

Time/s

Partial Pen. Full Pen. Over Pen.

10 5 0 -5

0

50

100

150

200

0

time/s

50

100

150

200

Time/s

Fig. 11 The time domain feature extraction of arc acoustic signals a mean sound; b arc energy; c standard variation; d covariation; e RMS; f log energy (Color figure online)

welding current. Figure 10b is an amplified waveform of an acoustic signal. It can be seen that the sound signal is the same as the voltage signal, and the peak value is exchanged with the base value cycle. Figure 11 is the time domain feature extraction of arc acoustic signals. It is shown by the figure that the time domain characteristics of sound are very high discrimination in penetration identification, and the difference between direct penetration and non-penetration is also good.

4 Prediction Model of Back Weld Width Due to the actual welding process itself is a complex process, it is difficult to use a precise model or the traditional system identification methods (transfer function) to describe the actual process; rough set theory is a new method of data processing

Based on Multi-sensor of Roughness Set Model of Aluminum …

51

modeling that can deal with nonlinear process. Therefore, this section mainly uses the rough set theory to model the welding process of aluminum alloy GTAW and verifies the accuracy of the model. The results verify the accuracy and effectiveness of the proposed method and further lay a solid foundation for the design of the intelligent controller. In order to obtain the required experimental data, the pulsed GTAW current and the wire feeding speed test were carried out. Current range is 130–190 A, wire feed 1–20 mm/s, welding speed 3 mm/s, and plate thickness 3 mm. The 3 mm aluminum alloy plate was welded by random test to obtain the information of the weld pool and the width of the back weld pool. The prediction model of back width of weld pool was established by rough set model, and its regular model was obtained. By using the multi-information acquisition system, the welding image, voltage signal, sound characteristic signal, and so on are collected in real time. The current, voltage, wire feed speed, welding width and reinforcement, positive mean and sound signal energy, log energy, standard deviation, variance and eigenvalues can indirectly reflect back weld width changes, so they are used as input variables, back weld width as the output variables, establish the model of multi knowledge fusion rough set, a decision table is shown in Table 2. In order to verify the accuracy of the rough set prediction model of back width, the prediction accuracy of the rough set model with back width is validated. The forecast results are shown in Fig. 12. As shown, the backside width of rough set model results is quite good and can fully meet the aluminum alloy pulse GTAW in the process of prediction for the back-welding width and provide a solid technical foundation for the real-time control of weld forming quality.

5 Weld Forming Control Experiments In order to realize the predictive control of weld width at the back of weld seam for fuzzy controller of aluminum alloy pulse GTAW based on genetic algorithm, the welding experiments were carried out to find the best welding point and the range of welding standard adjustment. Figure 13 is a fuzzy controller based on this algorithm. For the welding process of pulse Aluminum Alloy GTAW, the welding peak current of I (A) is selected as the control variables, and the back weld width is used as the controlled amount, at the optimal point. The welding current in the 200–260 A stochastic variation of other parameters under the condition of constant specification for welding and mapping back weld width set is obtained and current in order to optimize the effectiveness of training controller. In order to verify the accuracy of the designed controller, the welding seam forming control experiment of three kinds of aluminum alloy plate welding with different shapes and sizes is carried out. The back width is 7 mm, the initial peak current is 240 A, and the wire feeding speed and welding speed are constant at 10 and 3 mm/s, respectively. The image, voltage, and sound signals are sampled with a pulse 2 Hz for one cycle. The model is used to predict the width of the back surface by using the

lg E

std

sd

18 5 11 … 17 15 6

1.15 1.1 1.05 … 0.35 0.3 0.29

0.04931 0.048722 0.044953 … 0.054704 0.054562 0.054218

1.488581 1.218236 0.953634 … 0.998395 0.920629 0.958006

4.08754 4.05769 3.93688 … 4.00568 3.98701 4.00193

0.09575 0.094332 0.088802 … 0.091911 0.091057 0.091739

0.005018 0.004857 0.004387 … 0.003872 0.00376 0.003899

44 52 40 … 76 72 48

251 226 263 … 216 245 230

se

149 137 142 … 151 151 150

ms

wb

h

I

w

v

Policy

Condition attribution

Table 2 Decision table of the backside width prediction

52 J. Zhong et al.

Based on Multi-sensor of Roughness Set Model of Aluminum …

53

Fig. 12 Seam backside width prediction based on RS a prediction result; b error analysis Fuzzy controller Optimized model based on GA

e Wb0=7

d/dt

Wb

I

¡÷ e Basic control rule model

Robot

Revised control rule model

Welding Process

RS Prediction Model

Pool image information, Audio signal, electrical signal

Sensor system

Fig. 13 Fussy control based on genetic algorithm

multi-information fusion model based on rough set. According to the prediction of back weld width values based on modified genetic algorithm, fussy controller gives the current error signal to control the welding process, by changing the pattern of the heat input, in order to control the quality of weld forming.

54

J. Zhong et al.

Fig. 14 Trapezoidal plate control test a weld front; b backside of the weld

(a)

(b) 240

8

235

Current/A

Wb/mm

7.5 7 6.5

230 225 220 215

6 0

10

20

30

40

50

Time/s

60

70

80

90

210 0

10

20

30

40

50

60

70

80

90

Time/s

Fig. 15 Result of trapezoidal plate control test a weld backside width; b curve of the current

Figure 14 is a weld image of a trapezoidal test plate controlled by a gradual cooling condition. Figure 15 is the welding process control curve of the trapezoidal welding plate. As can be seen from Fig. 14, the weld seam is flat and uniform, the weld pool width of the front and back welds is basically smooth and consistent, and the influence of the change of working conditions and heat accumulation is successfully eliminated, thus ensuring the forming quality of the weld. Figure 15 shows the control curve of the welding process, welding can be found in addition to start unstable arc when the back weld width prediction is not ideal, the normal welding condition of back weld width has remained at around 7 mm, the maximum error is less than 0.3 mm, and the forming quality of the weld has been well controlled.

Based on Multi-sensor of Roughness Set Model of Aluminum …

55

Fig. 16 Dumbbell plate control test a weld front; b backside of the weld

(b)

(a) 240

8

235

Current/A

Wb/mm

7.5 7 6.5

230 225 220 215

6 0

10

20

30

40

50

Time/s

60

70

80

90

210

0

10

20

30

40

50

60

70

80

90

Time/s

Fig. 17 Result of dumbbell plate control test a weld backside width; b curve of current

Figure 16 is the control experiment result of dumbbell-shaped test panel. As can be seen from the diagram, the front and back of the test plate are well formed and the width of the weld is smooth and consistent. Figure 17 is the process control curve of the dumbbell test plate. As can be seen from the diagram, the control system has successfully controlled the back width of the weld at the setting of 7 mm. The fluctuation range is less than 0.4 mm. The combination of Fig. 16 and Fig. 17 shows that the quality of weld formation is well controlled. Figure 18 is the control experiment result of the I-shaped test plate. The weld is well formed and the weld width of the positive and negative welds remains smooth and consistent. The experimental results show that the controller can effectively

56

J. Zhong et al.

Fig. 18 Shaped plate control test a weld front; b backside of the weld

(a)

(b)

8

240 235

Current/A

Wb/mm

7.5 7 6.5

230 225 220 215

6

210 0

10

20

30

40

50

Time/s

60

70

80

90

0

10

20

30

40

50

60

70

80

90

Time/s

Fig. 19 Result of the shaped plate control test a weld backside width; b curve of current

control the back of the weld seam for the real-time control of the mutant test panel. Figure 19 is the welding process control of the I-shaped test plate. As shown in the figures, the backside weld width remains at around 7 mm, and does not receive mutations affecting the test plate conditions. And the control system maintains a stable condition. The weld width error is less than 0.4 mm. That shows the real-time control of weld quality is quite good.

Based on Multi-sensor of Roughness Set Model of Aluminum …

57

6 Conclusions In this paper, online multi-information acquisition of welding pool for robot aluminum alloy GTAW is carried out. Pool image, arc voltage information, and arc sound signal are obtained. Based on the rough set model, the information of weld pool surface, arc length information, and sound signal is fused, and the real-time prediction of weld width at the back of weld seam is realized. A fuzzy controller based on genetic algorithm is designed to realize the welding seam forming control of robot aluminum alloy GTAW. The experimental results show that the control system can control weld formation well and effectively ensure the quality of weld formation. Acknowledgements This work is partly supported by the National Natural Science Foundation of China (51405298 and 61401275).

References 1. Lii HCW, Kottilingam S, Zee RH et al (2001) Infrared sensing techniques for penetration depth control of the submerged arc welding process. J Mater Process Technol 113(1–3):228–233 2. Zhao DB, Chen SB, Wu L et al (2001) Intelligent control for the shape of the weld pool in pulsed GTAW with filler metal. Weld J 80(11):253–260 3. Kannatey-Asibu E (1997) Milestone developments in welding and joining processes. ASME J Manuf Sci Eng 119(4B):801–810 4. Nagesh DS, Datta GL (2002) Prediction of weld bead geometry and penetration in shielded metal-arc welding using artificial neural networks. J Mater Process Technol 123(2):303–312 5. Di L, Katsunori I (2001) Neural network based self-organized fuzzy logic control in arc welding process. Eng Appl Artif Intell 14(2):115–124 6. Chen SB (2000) Intelligent methodology for sensing, modeling and control of pulsed GTAW: part 1—band-on-plate welding. Weld J 79(6):151–163 7. Chen SB (2000) Intelligent methodology for sensing, modeling and control of pulsed GTAW: part 2—Butt joint welding. Weld J 79(6):164–174 8. Zhang GJ, Yan ZH, Wu L (2006) Visual sensing of weld pool in variable polarity TIG welding of aluminum alloy. Trans Nonferrous Met Soc China 16(3):522–526 9. Li LP, Chen SB, Lin T (2005) The modeling of welding pool surface reflectance of aluminum alloy pulse GTAW. Mater Sci Eng A 394(1–2):320–326 10. Balfour C, Smith JS, AI-Shamma AI (2006) A novel edge feature correlation algorithm for real-time computer vision-based molten weld pool measurements. Weld J 85(1):1–8 11. Xu YL, Yu HW, Zhong JY et al (2012) Real-time image capturing and processing of seam and pool during robotic welding process. Ind Robot 39(5):513–523 12. Wei SC, Kong M, Lin T et al (2011) Three-dimensional space type welding seam tracking method with the composite sensor’s technology. Ind Robot 38(5):500–508 13. Chen B (2009) Study on the processing method of multi-sensor information fusion in pulsed GTAW. Dissertation, Shaihai Jiao Tong University 14. Xu YL, Zhong JY, Ding MY et al (2013) The acquisition and processing of real-time information for height racking of robotic GTAW process by arc sensor. Int J Adv Manuf Technol 65(5):1031–1043 15. Lv N, Zhong JY, Chen HB et al (2014) Real-time control of welding penetration during robotic GTAW dynamical process by audio sensing of arc length. Int J Adv Manuf Technol 74(1–4):235–249

Research on Adaptive Robust Control Algorithm for Delta Parallel Robots Chendi Lu, Xingang Miao, Su Wang and Chenxi Zhang

Abstract The adaptive control and robust control are designed to solve the uncertainty of control system. The model reference adaptive controller is able to adapt to unknown friction characteristics and parameter uncertainties and to reduce the following error. However, the following error between the actual output of the model reference adaptive controller and the expected output is still large at some point. Taking advantage of adaptive control and robust control, an adaptive robust control system is proposed in this paper which using a delta parallel robot as an example. Through simulate the control system under the Simulink platform, this paper analyzes and compares the joint position conditions of model reference adaptive control and adaptive robust control. Comparing two delta parallel robot control systems, the adaptive robust control system proposed in this paper is more ideal. Keywords Delta parallel robot · Adaptive control · Robust control · Simulink Simulation

1 Introduction In the field of robot control, many scholars conduct research on the follow-up control of the robot system uncertainty and propose a control scheme of adaptive control and robust control. Adaptive control [1] means that in order to achieve the corresponding expected control target, the control system can recognize the change of the controlled object’s parameters in real time and make real-time adjustments to the control law. The adaptive control system has high requirements on the real-time performance of the parameters. Therefore, the adaptive control system cannot ensure the stability of the system with uncertain parameters. Robust control can ensure the stability of the C. Lu · X. Miao (B) · S. Wang · C. Zhang Beijing Key Laboratory of Robot Bionics and Function Research, Beijing Engineering Research Center of Monitoring for Construction Safety, Beijing University of Civil Engineering and Architecture, Beijing 100044, China e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 S. Chen et al. (eds.), Transactions on Intelligent Welding Manufacturing, Transactions on Intelligent Welding Manufacturing, https://doi.org/10.1007/978-981-13-3651-5_4

59

60

C. Lu et al.

system and meet a certain degree of control objectives when the range of uncertainties can be fixed. Compared with adaptive control, the robust control conditions are more fixed and the control process is easier. Robust control can replace the adaptive control to identify the change of uncertain parameters and take the corresponding control rate to correct [2, 3]. In order to deal with the disturbances in the system, this paper proposes a robust follow-up control based on the delta parallel robot [4] and designs a robust controller through the strategy of reverse solving. The following error modulation parameters and damping terms are added to the system in order to speed up the convergence of the output following error. Then, the Lyapunov stability is used to analyze the global stability of the closed-loop control system. It is proved that stability of the state observer and the closed-loop control system meets the requirement that the following error approaches zero. Finally, the effectiveness of the method is proved by Simulink simulation.

2 Delta Parallel Robot Configuration Introduction A typical three-degree-of-freedom delta parallel robot consists of three parts: the motion branch, the motion platform and the static platform [5–7]. The motor connects the branch chain to the static platform. The rotation of the motor drives the branch chain to move the moving platform. By controlling the movement of the movable platform, the branch chain can transmit the movement to the motor. The length of the active arm is the distance from the center of the servo motor to the parallelogram linkage. The follower’s arm is a longer section of the parallelogram structure. The moving platform is the three-plane position of the center point between the slave arms. The application of follower parallelogram structure makes the moving platform always parallel to the static platform. This paper takes the delta parallel robot developed by our laboratory as its research object. Its mechanical structure is shown in Fig. 1.

3 Control System Design The parallelogram structure of the Delta robot mechanism makes the delta parallel robot motion platform always parallel to the static platform or the horizontal plane. The gravity term in the equation of motion can be neglected. Then the dynamic performance is as in Eq. (1): J (q)q¨ + F(q, q) ˙ + σ (q, q) ˙ t

(1)

Among them, q is the position coordinate vector of each joint, t is drive input torque vector for each joint, and σ (q, q) ˙ is modeling error.

Research on Adaptive Robust Control Algorithm …

61

Fig. 1 Delta parallel robot

Lemma Examine the following non-autonomous systems: x˙  g[x(t), t]

(2)

If there is a positive definite function V (x, t), satisfy μ1 x2 ≤ V (x, t) ≤ μ1 x2 , ∀x, ∀t ≥ 0 V˙ (x, t) ≤ −μ3 x2 + ω, ∀x, ∀t ≥ 0

(3) (4)

where μ1 > 0, μ2 > 0, μ3 > 0, ω > 0 are given constants, then for any initial state x(0), the following formula holds:  x(t) ≤

1  2 μ2 ω  x(0)2 e−μt + 1 − e−μt μ1 μμ1

(5)

Among them, μ

μ3 > 0. μ2

Based on the above lemma, a robust robot following controller can be designed and analyzed for its stability. The controller can ensure that for a bounded modeling

62

C. Lu et al.

error, the final value of following error is always bounded. When the model error is equal to zero, the tracking error is also zero. The inverse dynamic compensation as: t  u + J (q)q¨ + F(q, q) ˙ q˙d

(6)

Substituting Eq. (6) into Eq. (1) yields: J (q)¨e + F(q, q) ˙ e˙ + σ (q, q) ˙ u

(7)

Among them, e(t) is following error, u is auxiliary control input signal e(t)  q(t) − qd (t). Define auxiliary signals: γ  e˙ + α e, ˙ α>0

(8)

When w(q, q, ˙ e, e) ˙  J (q)α e˙ + F(q, q)αe, ˙ then Eq. (8) can be expressed as: J (q)γ˙  −F(q, q)γ ˙ + w − σ (q, q) ˙ + u.

(9)

Theorem Let there be a positive definite function δ(e, e) ˙ such that for any σ (q, q) ˙ the following holds: σ (q, q) ˙ ˙ ≤ δ(e, e)

(10)

The feedback control law is designed to: u  −K γ − w − v

(11)

γ δ 2 (e, e) ˙ γ δ(e, e) ˙ +ε

(12)

Among them, v

⎤ k1 0 0 ⎥ ⎢ where ε > 0 is a given constant, K  ⎣ 0 k2 0 ⎦, k1 , k2 , k3 are given constants 0 0 k3 greater than zero. ⎡

Research on Adaptive Robust Control Algorithm …

63

Then for any e(0), e(t) always keep the final value bounded, that is, there are constants A, B, C such that σ (q, q) ˙ satisfies Eq. (11) arbitrarily, so that formula (13) holds: μt

e(t) ≤ Ae(0)e−αt + Be− 3 + C

(13)

Prove First, it is proved that the final value of γ (t) is bounded. Defining a positive definite function: V (t, γ ) 

1 T γ J (q)γ 2

(14)

According to the boundedness of the coefficient matrix J (q), there are positive numbers μ1 > 0, μ2 > 0, satisfying: μ1 γ 2 ≤ V (t, γ ) ≤ μ2 γ 2 , ∀γ

(15)

Oblique symmetry according to robot dynamics:

γ T J˙(q) − 2F(q, q) ˙ γ  0, ∀γ

(16)

Then calculated by Eq. (9): V˙  γ T (u + w − σ ). By the above theorem, finishing is available: V˙ ≤ −γ T kγ +

γ δ ≤ −γ T kγ + ε ≤ −μ3 γ 2 + ε γ δ + ε

(17)

where μ3 is a given positive number, and μ3 ≤ K. According to Eqs. (13) and (15) and the preceding section of Lemma, replace γ (t) with K . The auxiliary signal γ (t) should satisfy: γ (t)2 ≤

 μ2 ε  γ (0)2 e−μt + 1 − e−μt μ1 μ1 μ

(18)

According to Eq. (18), there are: γ (t) ≤

√

X + Y e−μt ≤

√

X+

√

μ

Y e− 2 t

(19)

Among them: X

μ2 ε ε γ (0)2 − , Y  . μ1 μ μ1 μ1 μ

From definition (8) available: t

e(t)  e(0)e−αt + ∫ e−α(t−s) γ (s)ds. 0

(20)

64

C. Lu et al.

It is noted that the components of the following error vector e(t) are e1 (t), e2 (t), and e3 (t), respectively; the components of the auxiliary vector γ (s) are γ1 (s), γ2 (s), and γ3 (s). Then the absolute values of both sides of Eq. (21) are equivalent to: t

|ei (t)| ≤ |ei (t)|e−αt + ∫ e−α(t−s) |γi (s)|ds, (i  1, 2, 3)

(21)

0

Add the above three equations and use the Euclidean norm relation: t

e(t) ≤ 3e(0)e−αt + 3 ∫ e−α(t−s) γ (s)ds

(22)

0

Substituting Eq. (19) into Eq. (22), collation is available: μ

e(t) ≤ Ce−αt + De− 2 t + E, ∀t

(23)

Among them: √ √ 3 X 6 Y − , C  3e(0) − α 2α − μ

√ 6 Y D , 2α − μ

√ 2 X E . α

In summary, it can be seen that when t is sufficiently large, the norm of the following error is less than or equal to the value of E. When a robust control law such as Eq. (12) can obtain a better control effect, ε should be reduced as much as possible so that a better control effect can be obtained. When ε  0, E  0, the following error uniformly approaches zero in the control law (11).

4 Simulation Examples In this article, a three-degree-of-freedom delta parallel robot is selected as a controlled object. The coordinates x and y are used to represent the operating plane of the robot. The expected orbit coordinates and the robot nominal parameters are listed as follows (Table 1) The parameters can be measured by the delta parallel robot which designed and manufactured by our laboratory. Desired trajectory in the Cartesian coordinate plane: π 1 xd  − cos t, 4 2

yd 

1 (1 − cos π t). 5

The initial position of the controlled object is the coordinate value while the controlled object uses the angle value. Therefore, the initial coordinate value must

Research on Adaptive Robust Control Algorithm … Table 1 Robot nominal parameters Joint Quality (kg) Length (m) 1 2 3

0.765 0.765 0.765

0.25 0.25 0.25

65

Centroid to axis distance Moment of inertia (m) (kg m2 ) 0.15 0.15 0.15

0.05 0.05 0.05

be converted to the initial angle value. The following formulas are used to solve inverse kinematics problems:   2 x + yd2 − l22 − l32 π q3d  − 2 arccos d 2 2l2 l3  q2d  − arccos

Order ρ0 

⎧ ⎨

yd , xd arctan xydd ,

arctan

xd2 + yd2 − l12 − l22 2l1l2

xd ≥ 0



 , τ0  arccos

xd2 +yd2 +l12 −l22

√

 , q1d can be

2l1 xd2 +yd2 ⎩π + xd < 0  ρ0 − τ0 , q2d > 0 . expressed as q1d  ρ0 + τ0 , q2d ≤ 0 Therefore, the desired trajectory of each joint q1 , q2 , and q3 of the robot arm is .. qd (t), q˙d (t), and qd (t), respectively. The initial position of the robot end is set by the S function module to [x0 y0 ]T  [0.1 0.1]T . The initial position of the ideal track is [xd0 yd0 ]T  [−0.25 0]T . Model T ing error is σ (q, q) ˙  sin 6γ1 sin 6γ2 sin 6γ3 . The robust control law t adopts formula (6), and the control input u adopts formula (11). The gain of the control law is k1  k2  k3  50, taking α  60, ε  0.01. The bounding function of the model error is δ(e, e) ˙  3 tanh(2.6γ ). The Simulink simulation control system is shown in Fig. 2. The simulation results are recorded as the tracking curves for each joint, as shown in Fig. 3. Compare the control effects of both under the separate adaptive control [7–9] and the adaptive robust control, it can be seen that the fitting degree of the two response curves in adaptive robust control is higher. It shows that the actual output is closer to the expected output, and the system overshoot is smaller at the corresponding moment.

66

C. Lu et al.

Fig. 2 Adaptive robust control system simulation diagram

a) Model reference adaptive control joints under 1 position dynamic response curves

b) Adaptive robust control joints under 1 position dynamic response curves Fig. 3 Model reference adaptive control and adaptive robust control for position control of joints in delta parallel robots

Research on Adaptive Robust Control Algorithm …

c) Model reference adaptive control joints under 2 position dynamic response curves

d) Adaptive robust control joints under 2 position dynamic response curves

e) Model reference adaptive control joints under 3 position dynamic response curves

f) Adaptive robust control joints under 3 position dynamic response curves Fig. 3 (continued)

67

68

C. Lu et al.

5 Conclusions Due to the non-linearity, uncertainty, and unobservable characteristics of some unknowns, the control system of the delta parallel robot is crucial to the control performance of the robot. Combine the adaptive control and robust control, this paper processes a modified control system which is the adaptive robust control design. The simulation results show that the adaptive robust control can not only maintain the control objectives of the original model reference adaptive control system, but also achieve better control results. Acknowledgements This research was supported by the Beijing Key Laboratory of Robot Bionics and Function Research (BZ0337).

References 1. Liu X, Chen Y (2015) Adaptive control of a class of robot systems with multiple uncertainties. J Univ Electron Sci Technol China 44(01):61–66 2. Zhang W, Liu J, Hu G (2015) Stability analysis of robust multiple model adaptive control systems. Acta Autom Sinica 41(1):113–121 3. Fakhari V, Ohadi A, Talebi HA (2015) A robust adaptive control scheme for an active mount using a dynamic engine model. J Vib Control 21(11):2223–2245 4. Zhang Y (2016) Research progress of delta parallel robot. Mach Tools Hydraulics 40(21):16–20 5. Wei C, Zhou Y (2007) Application of Simulink in Lyapunov stability analysis teaching of control system. J Baise Univ 3(01):96–99 6. Wei Y, Li W, Zhang L et al (2018) Analysis of working space of delta robot based on positive kinematics solution. Mach Manuf Autom 47(01):173–175 7. Liu X (2018) Research on delta parallel robot control system. Dissertation, Beijing Jianzhu University 8. Wu J (2017) Speed adaptive control algorithm for permanent magnet synchronous motor. Dissertation, Jiangsu University 9. Li S, Yao Y, Prince Z (1999) A study on adaptive friction compensation method. J Motor Control 03:129–133

Weld Bead Penetration State Recognition in GTAW Process Based on a Human Auditory Perception Model Yanfeng Gao, Qisheng Wang, Yanfeng Gong and Linran Huang

Abstract The state of weld bead penetration is a crucial factor that affects the service performance of the welding products. Since arc sound signals contain abundant information of welding process, they were usually adopted to monitor the penetration states of weld bead online. However, the arc sound signals are susceptible to the environment noise, so they are seldom applied in industrial practice. In this study, a human auditory perception model was proposed to identify the penetration states in GTAW process. In this model, an auricle and middle ear transformation function were adopted firstly to remove partial noise in the arc sound signals. Then through simulating the functions of human ear basement membrane, a gamma-tone frequency resolution algorithm was used to decompose the arc sound signals into 64 channels. At last, based on the short-time energies of arc sound in these channels, the feature vectors were built to identify the penetration states. The experimental results show that the proposed method has high accuracy in recognition rates and strong antinoise interference capabilities. The human auditory perception model proposed in this study has potential practical applications in industrial environment. Keywords Weld penetration · Auditory mode · State recognition · Arc sound

1 Introduction To obtain a high service performance of the welding products, it is crucial to monitor and control penetration in the process of welding. However, online weld penetration detection is considered a challenging job because it is difficult to directly measure the weld pool penetration during welding process. The signals of arc sound, light, electric current, and voltage are closely related to the welding quality, so they are adopted extensively in the monitoring of welding process. The welding arc sound Y. Gao (B) · Q. Wang · Y. Gong · L. Huang School of Aeronautic Manufacturing Engineering, Nanchang Hangkong University, Nanchang 330063, China e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 S. Chen et al. (eds.), Transactions on Intelligent Welding Manufacturing, Transactions on Intelligent Welding Manufacturing, https://doi.org/10.1007/978-981-13-3651-5_5

69

70

Y. Gao et al.

signals contain abundant information about welding process. Compared to other online sensing technologies, such as high-speed photography and spectral analysis method, the acquisition of arc sound signals is relatively simple. Therefore, many researchers adopted arc sound signals to monitor the quality of welding online. Kaskinen and Mueller [1] studied the generation mechanism of arc sound and the acoustic arc length control technology. Drouet and Nadeau [2] developed an arc length monitoring technique with an acoustic voltmeter. Ma et al. [3] proposed a concept of welding arc sound tone channel and built its equivalent electrical model and adopted this model to detect the flow of shielding gas and the distance of the contact tip o workpiece. Fan et al. [4] concluded that CO2 welding arc sound energy changes proportionally with the amount of spatter loss and used it to monitor the stability of the welding process. Liu et al. [5] analyzed the characteristics of arc sound signals and used them to sense the states of welding penetration. Pal et al. [6] analyzed the arc sound signals, welding currents, and voltages in time and frequency domains to identify the metal transfer modes. Lv et al. [7] propose an auditory attention method to optimize the extraction of the arc sound signal and built a back propagation artificial neural network to identify the penetration state. Song et al. [8] studied the relationship between the welding sound and penetration states in variable polarity plasma arc welding and to identify the penetration states of no keyhole, keyhole, and cutting modes. In general, these previous works made a great contribution to the online quality monitoring of welding processes. However, there are few practical applications about arc sound monitoring in the industry that have been reported up to now. It is because the arc sounds are very susceptible to the industrial background noise. Therefore, the penetration sensing in a noised industrial environment becomes one of the most critical issues in the area of welding process monitoring. It is well known that an experienced welder can evaluate the welding states through listening to the arc sound even in a noisy environment. These ordinary experiences show that human auditory system has extremely strong anti-noise interference ability. To simulate human auditory system, researchers proposed some different auditory filter models based on the response mechanism of cochlea to outside sounds. Two typical auditory filter models are the resonance filter model [9] and the gamma-tone filter model [10]. In the past few years, these auditory system models have been applied in many areas such as speech recognition, underwater acoustic recognition [11–13]. However, there is no report about the applications of auditory system models in the welding arc sounds signal disposing. In this study, a human auditory perception model was built to identify the penetration states in a GTAW process. Firstly, this model employs an auricle and middle ear transformation function to remove some of noise in the arc sound signals. Secondly, it adopts a gamma-tone frequency resolution algorithm to decompose the arc sound signals into different frequency channels. Then, it computes the short-time energy of arc sounds in these channels. At last, it builds a support vector machine to identify the penetration states of the weld bead. This paper provides a novel method to identify the penetration states in GTAW process. The results of the study are helpful to monitor and control penetration of welding process in an industrial environment.

Weld Bead Penetration State Recognition in GTAW Process …

71

2 Experiment System In experiments, a series of non-wire-feed GTAW welding processes were implemented. A JASIC-TIG300S DC gas tungsten welding power source was used in these welding processes. Q235 mild steel with thickness 4 mm was used as the base metal to be molted and welded. Pure argon (99%) at a flow rate 10 L min−1 was utilized to protect the arc and the molten pool. A CRY331 microphone and a CRY506 signal amplifier were fixed on the arc torch at a distance of 200 mm from the tungsten electrode and point to the weld pool. A USB3202 acquisition card and a computer were used to collect the welding sound signals with a frequency of 40 kHz. The experimental equipment was shown in Fig. 1. To acquire the different penetration states and identify them, four groups of experiments were implemented in this study, and the parameters were shown in Table 1. Therefore, the welding penetration states would change with the welding speed. To get the penetration states in the different welding processes, the cross sections of weld beads were observed with a microscope. Figure 2 shows the penetration states in the different welding processes.

ide

wa

y

Fig. 1 Experimental equipment

u YG

Mircophone

X Gu

idew

ay

Base metal

Weld gun

Table 1 Welding parameters Welding parameters Experiment 1

Experiment 2

Experiment 3

Experiment 4

Tungsten electrode diameter (mm)

1.2

1.2

1.2

1.2

Arc air flow (L/min)

10

10

10

10

Arc height (mm)

10

10

10

10

Welding current (A)

110

110

110

110

Welding speed (mm/min)

47

35

24

12

72

Y. Gao et al.

(a) 47mm/min

(b) 35mm/min

(c) 24mm/min

(d) 12mm/min

Fig. 2 Weld bead formations in different welding speed

Figure 2 shows that with the weld speed decreasing from 47 to 12 mm/min, the depth of weld pool penetration increases correspondingly. In general, there are three typical types of penetration states in GTAW process, which are non-penetration, full penetration, and excessive penetration. In the non-penetration state, the arc is too weak to penetrate the base metal and there is no visible change on the back side, such as Fig. 2a, b. In the full penetration state, there is noticeable weld bead on the back side of base metal. Here, Fig. 2c can be considered as a kind of full penetration state and Fig. 2d is a kind of excessive penetration state.

3 Human Welder Auditory Perception Model 3.1 Principle of Human Welder Auditory Perception In an ordinary welding process, through watching the weld pool surface and hearing the arc sounds, a skilled welder usually assesses the process and adjusts the welding parameters to produce desirable welds. In the hearing process of a human welder, the arc sounds are firstly received by auricle and then transformed by middle ear and perceived by cochlea. After frequency resolving in cochlea, the auditory characteristics such as loudness and timbre of the arc sounds are extracted, and then through the transformation of auditory nervous system, the arc sounds are perceived by brain. To simulate the process, a human welder auditory perception model is proposed in this study, and the diagram of it is shown in Fig. 3. The arc sounds firstly were preprocessed by traditional filter method. Then an auricle and middle ear transformation function are built to remove some of noise in the arc sound signals. And then a gamma-tone frequency resolution algorithm is adopted to decompose the arc sound signals into different frequency channels. A

Weld Bead Penetration State Recognition in GTAW Process …

73

Auditory perception process Arc sounds

Pre-processing

Transformation in auricle and middle ear

Frequency resolving in cochlea

Feature vector extraction

Short energy analysis

Identification of penetration

Fig. 3 Illustration of a human welder auditory perception model

short-time energy analysis is used to compute the energy of the arc sound signals in different frequency channels. Based on the distribution of short energy in the different frequency channels, a feature vector is built and a support vector machine is adopted to identify the penetration states.

3.2 The Original Arc Sounds Figure 4 shows the original arc sound signals of four experiments, in which the welding speed decreases from 47 to 12 mm/min while the other welding parameters keep fixed. For each of the experiment, the timescale of the arc sound signals was 25 s. It is can be observed from Fig. 4 that in one experiment process, the most of the signals are uniform in the timescale of 25 s, but there are some low-frequency fluctuations such as from 15 to 20 s shown in Fig. 4a, from 10 to 15 s shown in Fig. 4b, c, from 5 to 10 s shown in Fig. 4d. It is known from Fig. 2 that in experiment 1 and 2, the weld beads are belonging to the non-penetration types, while in experiment 3 and 4 the weld beads are belonging to full penetration and excessive penetration types, respectively. The characteristics of arc sound in time domain show that from the state of non-penetration to excessive penetration, there are no obvious changes. Moreover, the amplitudes of the arc sounds are significantly affected by the distance and the position angle of microphone to the weld pool. Therefore, it is relatively difficult to identify the state of penetration only in the time domain of the arc sound signals. The producing of weld sounds is usually considered as a nonstationary random process, but during a short-time range of 80–100 ms, the process can be considered as a stable one. Short-time energy analysis is a general method of analyzing unstable signals in a time domain and commonly applied to dispose the acoustic signals. To get the short-time energy, the arc sound signals should be separated into some segments firstly. Define each segment of short-time signals as one frame, then the short-time energy of the nth frame E n can be calculated by Eq. (1).

74

Y. Gao et al.

(a) Welding speed 47mm/min

(b) Welding speed 35mm/min

(c) Welding speed 24mm/min

(d) Welding speed 12mm/min

Fig. 4 Original arc sound signals

En 

n 

[x(m)ω(n − m)]2

mn−(N −1)

(1)

Weld Bead Penetration State Recognition in GTAW Process …

75

where x(m) is the arc sound signal, ω is a window function, N is the length of the window function. In this study, a Hamming window was adopted, whose mathematical description is shown in Eq. (2).   n , 0≤n ≤ N −1 (2) ω(n)  0.54 − 0.46 cos 2π N −1 Based on the researches of physiological acoustics, if the interval of two sounds occurred was less than 100 ms, human ear would not distinguish them [14]. To get a short-time stable welding process, the time range in this study was set as 100 ms, so the length of window was chosen as 4000. The short-time energy of the arc sound signals shown in Fig. 4 was calculated based on Eq. (1), and the results were shown in Fig. 5. It is observed that the short-time energies of these experiments sharply change in the time when the original arc sound signals fluctuate with low frequency. Therefore, the welding processes in the four groups of experiments are unstable processes. It is very difficult to distinguish the penetration states from original arc sounds.

3.3 The Arc Sound After Auricle and Middle Ear Transformation Human ear consists of auricle, middle ear, and cochlea. The arc sound firstly is received and amplified by auricle and then transformed to middle ear. In middle ear, the low frequency less than 1 kHz and the high frequency more than 10 kHz of the sounds are depressed, and the middle frequency of the sounds are amplified and transformed to cochlea. American national standards institute (ANSI) proposed a transform function of auricle and middle ear. In this function, the sensitive frequency range of sound is set as 3–4 kHz. Based on the function, the arc sound signals were disposed and the results were shown in Fig. 6. It is observed from Fig. 6, compared to the signals in Fig. 4, that the arc sound signals transformed by auricle and middle ear are smoother, and the low-frequency fluctuations do not exist anymore. Compared to Fig. 6a, b, the amplitudes of the arc sound signals in Fig. 6c, d are more uniform. The short-time energies of the signals in Fig. 6 were calculated, and the results were shown in Fig. 7. Figure 7 shows that the short-time energy of experiment 1 has two major fluctuations during the range of time 0–5 s and the range of time 22 to 25 s. In experiment 2, the short-time energy decreases gradually in the whole time range and fluctuates with middle amplitudes. In experiment 3 and experiment 4, the short-time energies change very little in the whole process, so the arc signals are more stable in these two groups of experiments. In general, compared to Fig. 5, the short-time energy in Fig. 7 is more stable.

76

Y. Gao et al.

(a) Welding speed 47mm/min

(b) Welding speed 35mm/min

(c) Welding speed 24mm/min

(d) Welding speed 12mm/min

Fig. 5 Short-time energy of original arc sounds

3.4 Gamma-Tone Frequency Resolution Researches of physiological acoustics show that cochlea is an important organ in human auditory system. Basement membrane is the basic organ to receive sounds in cochlea. Basement membrane has a frequency selection function. Like shaking a silk ribbon, the maximum response amplitudes take place at the bottom of basement membrane for the high-frequency sound input, while the maximum response

Weld Bead Penetration State Recognition in GTAW Process …

77

(a) Welding speed 47mm/min

(b) Welding speed 35mm/min

(c) Welding speed 24mm/min

(d) Welding speed 12mm/min

Fig. 6 Arc sound signals transformed by auricle and middle ear

amplitudes take place at the top of basement membrane for the low-frequency sound input. Therefore, basement membrane decomposes the complex sound into different amplitudes and positions based on the frequency of sound. Greenwood [15] found that the characteristic frequency in specific position of basement membrane could be calculated by Eq. (3).

78

Y. Gao et al.

(a) Welding speed 47mm/min

(b) Welding speed 35mm/min

(c) Welding speed 24mm/min

(d) Welding speed 12mm/min

Fig. 7 Short-time energy of arc sound signals after transformed by auricle and middle ear

  f  A 10a(L−x) − k

(3)

where L is the length of basement membrane; f is the characteristic frequency; x is the position of the characteristic frequency; a and k are constant. To simulate the functions of basement membrane, some auditory filtering models have been proposed in recent years. In these proposed modes, gamma-tone model has relatively better properties to simulate the frequency selection function of basement membrane and was adopted by many researchers. The impulse response function adopted in gamma-tone model is expressed in Eq. (4).

Weld Bead Penetration State Recognition in GTAW Process …

gm (t)  At n−1 e−2π Bm t cos(2π f m t + ϕm ), t ≥ 0

79

(4)

where 1 ≤ m ≤ M; ϕm is the phase of signal; A is the gain of filter; M is the number of filters; n is the order of filter; f m is the center frequency of the filter, i.e., the characteristic frequency of basement membrane; Bm is the transform frequency of f m in the equivalent rectangle breadth (ERB), Bm  1.019ERB( f m )

(5)

The ERB of each filter could be acquired by Eq. (6). ERB( f m )  24.7(0.00437 f m + 1)

(6)

Therefore, it is only needed to select the number of filters M, and the center frequency of filters f m to acquire the response characteristics of the filters. Normally, f m could be calculated by Eqs. (7) and (8). ν

9.26(ln( f H + 228.7) − ln( f L + 228.7)) M mν f m  ( f H + 228.7)e− 9.26 − 228.7

(7) (8)

where f H is the maximum cutoff frequency of the filter; f L is minimum cutoff frequency of the filter; ν is the overlap factor of the filters, i.e., overlap percentage between adjacent two filters. In this study M, f L , and f H were set as 64, 1 Hz, and 20 kHz, respectively. So the arc sound signals were decomposed into 64 channels. The distributions of arc sound signals in the 64 channels could be acquired through gamma-tone frequency resolution method, and the penetration states could be distinguished based on the distributions. Because it is difficult for all of the arc sound signals in the 64 channels, some of the channels are selected randomly. The arc sound signals in the selected channels are shown in Fig. 8. Figure 8 shows that the arc sound signals in these channels have different characteristics. For experiment 1 and experiment 2, the arc sound signals have relatively high amplitudes in the low-frequency channels, such as the blue and green curves in Fig. 8a, b. For experiment 3 and experiment 4, the arc sound signals have relatively high amplitudes in the high-frequency channels, such as the pink curves in Fig. 8c, d. Therefore, the arc sound signals in the different penetration states have their particular distributions in the 64 channels. Based on their arc sound signal distribution features, the states of penetration could be identified.

80

Y. Gao et al.

(a) Welding speed 47mm/min (experiment 1)

(c) Welding speed 24mm/min (experiment 3)

(b) Welding speed 35mm/min (experiment 2)

(d) Welding speed 12mm/min (experiment 4)

Fig. 8 Arc sound signals decomposed by gamma-tone method

4 Feature Extraction and Penetration States Identification 4.1 Feature Vector Construction To extract the arc sound features in different penetration states, the short-time energies of arc sound signals in each of channels and their average values were calculated in this study. Figure 9 shows the average short-time energies of arc sounds in 64 channels of the four experiments. It was observed from Fig. 9 that the distribution of average short-time energy of each experiment has its own particular feature. For example, there are two peaks (at the 47th and the 57th channels) in the short-time energy curve of experiment 1, but the first peak is lower than the second one. However, the first peak is higher than the second one in the curve of experiment 2. Even experiment 1 shows the same trend as experiment 3, the ratio of the heights of the two peaks is different. Therefore, based on the shapes of the short-time energy curve, the penetration of the weld bead could be identified.

Weld Bead Penetration State Recognition in GTAW Process …

81

Fig. 9 Distribution of average short-time energy of the four experiments

To eliminate the influence of the distance of a microphone to the arc, which affects the absolute value of the short-time energy, a unit feature vector was constructed in this study. It was observed from Fig. 9 that the main differences between the four experiments are in the three channels of 47th, 48th, and 57th. The center frequencies of the three channels are 4672, 5009, and 9303 Hz, respectively. Therefore, the average short-time energies in these three channels were selected to construct the unit feature vector of each experiment. To verify the effectiveness of the proposed method, each of the four experiments in Table 1 was repeated three times. The arc sound signals in each of welding process were divided into many segments with the length of time 100 ms. 250 egments of the arc sound signals were selected randomly, and the unit feature vectors of them were constructed and plotted in the Cartesian coordinate system. The results were shown in Fig. 10. Figure 10 shows that the four experiments were to be obviously distinguished by the unit feature vectors of them. Therefore, it is easy to identify the penetration states with the proposed method.

4.2 Penetration States Identification To identify the penetration states of weld bead based on the constructed unit feature vector, a support vector machine model was built in this study. In the experiments, each of the four experiments in Table 1 was repeated three times and 450 segments of the arc sound signals with the length of time 100 ms in each of the experiments were selected randomly. There were 50 segments of the arc sound signals which were chosen randomly as the training data. To examine the anti-noise capability of the proposed method, a series of white noise with different signal–noise ratios were

82

Y. Gao et al.

Fig. 10 Unit feature vectors of the four experiments

Table 2 Identification results Signal–noise Recognition rates ratio (SNR)

∞ 17 dB 14 dB 12 dB 10 dB 8 dB

Experiment 1 (%)

Experiment 2 (%)

Experiment 3 (%)

Experiment 4 (%)

Whole experiments (%)

98 98.44 98 98.22 99.11 99.11

100 100 100 100 100 100

98.67 99.56 100 98.67 95.11 73.56

100 100 100 100 100 100

99.03 99.42 99.42 99.35 98.32 92.06

added into the original arc sound signals. The identification results were shown in Table 2. In general, the original signal would be distorted when the SNR is less than 70 dB. In Table 2, the least of the SNR was set as 8 dB, so there is very strong interference of noise. The data in Table 2 show that there is a high correct recognition rate for the different penetration states even in the strong interference of white noise. The results show that the proposed method has a high anti-noise capability for the identification of weld bead penetration states.

Weld Bead Penetration State Recognition in GTAW Process …

83

5 Conclusions (1) Arc sound signals contain abundance information about the welding process and could be adopted to online monitor the penetration states of the weld bead. The short-time energy analysis shows that the original arc sound signal is an unstable process, which is difficult to be used directly to monitor the welding process. (2) A human auditory perception model was proposed in this study. The auricle and middle ear transformation functions and a gamma-tone frequency resolution algorithm are used in this model to extract the feature vectors from the arc sound signals. Based on the feature vectors, the weld penetration states were identified. The experiment results show that the proposed human auditory perception model has high correct recognition rates for the weld penetration states. (3) To examine the anti-noise capability of the proposed method, a series of white noise with different signal–noise ratios were added into the original arc sound signals. The identification results of these noise-interfered arc sound signals show that the whole correct recognition rate is higher than 92% even in the SNR of 8 dB condition. The human auditory perception model proposed in this study has potential practical applications in industrial environment. Acknowledgements The authors gratefully acknowledge the support from the National Natural Science Foundation of China (51465043).

References 1. Kaskinen P, Mueller G (1986) Acoustic arc length control. In: Proceedings of an international conference on trends in welding research, pp 763–765 2. Drouet MG, Nadeau F (1982) Acoustic measurement of the arc voltage applicable to arc welding and arc furnaces. J Phys E Sci Instrum 15(3):268 3. Ma Y, Chen J, Liang W (2005) Parametric modeling of the arc sound in GMAW for on-line quality monitoring. J Mech Eng 41(11):109–114 4. Fan D, Shi Y, Ushio M (2001) Investigation of CO2 welding arc sound: correlation of welding arc sound signal with welding spatter. Trans JWRI 30:29–33 5. Liu L, Lan H, Zheng H et al (2010) Relationship between arc sound signal and penetration status in MIG welding. J Mech Eng 46(14):79–84 6. Pal K, Bhattacharya S, Pal SK (2010) Investigation on arc sound and metal transfer modes for on-line monitoring in pulsed gas metal arc welding. J Mater Process Tech 210(10):1397–1410 7. Lv N, Xu Y, Li S et al (2017) Automated control of welding penetration based on audio sensing technology. J Mater Process Technol 250:81–98 8. Song S, Chen H, Lin T et al (2016) Penetration state recognition based on the doublesound-sources characteristic of VPPAW and hidden Markov Model. J Mater Process Technol 234:33–44 9. Lyon RF, Mead C (1998) An analog electronic cochlea. Acoust Speech Signal Process 36(7):1119–1134 10. Patterson RD, Moore BCJ (1986) Auditory filters and excitation patterns as representations of frequency resolution. In: Proceedings of frequency selectivity in hearing London, Academic Press, pp 123–177

84

Y. Gao et al.

11. Ma Y, Chen K, Wang N et al (2009) Application of auditory spectrum-based features into acoustic target recognition. Acta Acust 34(2):142–150 12. Tucker S, Brown G (2003) Modelling the auditory perception of size, shape and material: applications to the classification of transient sonar sounds. In: Proceedings of 114th convention audio engineering society, Amsterdam, Netherlands 13. Tucker S, Brown G (2002) Investigating the perception of the size, shape and material of damped and free vibrating plates. Department of Computer Science, University of Sheffield, Sheffield, U.K., Tech. Rep CS-02-10 14. Glasberg BR, Moore BCJ (2002) A model of loudness applicable to time-varying sounds. J Audio Eng Soc 50(5):331–342 15. Greenwood DD (1998) A cochlear frequency-position function for several species—29 years later. J Acoust Soc Am 87(6):2592–2605

Experimental Method of Mechanical Melting Point in A6N01-T5 Aluminum Alloy Lichun Meng, Xiaohong Sun, Yongming Cheng, Gongxiang Zhao and Jijin Xu

Abstract A novel experimental method was proposed to measure the mechanical melting point of A6N01-T5 aluminum alloy. Before test, characteristic parameters of thermal cycle were set according to the welding thermal cycle curves with different peak temperatures. The dimensions of sample were normalized. Based on thermal–mechanical simulation test, the dynamic stress data were acquisitioned and saved. The relationship between the residual stress and peak temperature was established. Based on the measured and predicted evolution of residual stress, the mechanical melting point of A6N01-T5 aluminum alloy was confirmed and validated. Keywords Mechanical melting point · Thermal–mechanical simulation test Welding thermal cycle · A6N01-T5 aluminum alloy

1 Introduction The lightweight design is an important developing trend in the high-speed train. Aluminum alloy with its low density, high strength, excellent extrusion resistance, good weldability becomes one of the most ideal structural materials for lightweight vehicles. A6N01-T5 aluminum alloy is widely applied in the manufacture of aluminum alloy car body on high-speed train, such as the roof, the side wall. Welding is usually employed to fabricate or assemble these structures. Metal inert gas welding (MIG) is the most commonly used welding method in aluminum alloy welding. However, due to local rapid heating and cooling during welding process, the welding residual stress is inevitable to be generated around the weld bead [1]. Welding-induced residual stress is one of the most critical factors, which influences the final weld quality L. Meng · X. Sun · Y. Cheng CRRC Qingdao Sifang Co., Ltd., Qingdao 266111, China G. Zhao · J. Xu (B) Key Lab of Shanghai Laser Manufacturing and Materials Modification, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 S. Chen et al. (eds.), Transactions on Intelligent Welding Manufacturing, Transactions on Intelligent Welding Manufacturing, https://doi.org/10.1007/978-981-13-3651-5_6

85

86

L. Meng et al.

and the service life of the welded structure [2, 3]. Therefore, the knowledge of the residual stress state is a major issue and is required a deep understanding to master the in-service mechanical behavior. Welding numerical simulation by the finite element method (FEM) becomes a particularly interesting tool to predict the residual stresses resulting from the welding process. However, in order to obtain the accurate simulated results, the numerical simulation faces many challenges [4]. As we know, welding residual stress is induced by the residual compressive plastic strain. The residual compressive plastic strain increases with the increase of welding temperature. However, it is obviously incorrect to treat inelastic strain at high temperature as plastic strain. In order to predict accurately the welding residual stress, a mechanical melting point (MMP) was proposed to handle the inelastic strain at high temperature in the welding numerical simulation. Above this temperature range, creep, recovery, and recrystallization processes are active, so inelastic strain tends to appear as creep strain without further work hardening, and prior work hardening is progressively dissipated by recovery and recrystallization [5]. In present, MMP was usually assumed, while lack of experimental approach. Deng et al. [6] considered that it was reasonable to set the MMP of 316L stainless steel to be 1000 °C. Muránsky et al. selected a temperature of 1050 °C as MMP in numerical weld analyses of 316L stainless steel [7]. Moreover, they also employed a two-stage MMP to simulate a three-pass bead-in-slot weld [8]. A lower temperature was 800 °C, and an upper temperature was 1300 °C. In our previous work, MMP of 316L stainless steel was set to be 800 °C [1, 9–11]. According to the above researches, the deviation of the assumed MMP is large. For the MMP of aluminum alloy, the related research is little. In this work, a novel experimental method was established to measure the MMP of A6N01-T5 aluminum alloy. Characteristic parameters of thermal cycle and specimen dimension were normalized based on thermal–mechanical simulation test. According to the evolution of residual stress, the MMP of A6N01-T5 aluminum alloy was confirmed.

2 Experimental Procedures 2.1 Materials and Methods In this study, A6N01-T5 aluminum alloy was selected. It was usually used in the roof, the side wall on the high-speed train. T5 is a state of the heat treatment in which the alloy cools in the process of high-temperature molding, and artificial aging is conducted subsequently. Its chemical compositions are listed in Table 1. A Satoh test method was developed to imitate the welding thermal processes of different fields in welded joint using Gleeble 3500. During test process, the temperature, force, and time were recorded and saved automatically. Figure 1 shows the flowchart of test.

Experimental Method of Mechanical Melting Point in A6N01-T5 … Table 1 Chemical compositions of A6N01-T5 (wt%) Mg Si Zn Fe Cu Mn 0.64

0.60

0.03

0.13

0.009

0.11

87

Cr

Ti

Al

0.002

0.034

Others

Fig. 1 Flowchart of test

2.2 Thermal Cycle Parameters In Satoh test, in order to obtain the accurate thermal simulation results, the characteristic parameters of welding thermal cycle need to be extracted based on FEM. Figure 2 shows welding temperature distribution contour and the welding thermal cycle curves at different peak temperatures. According to the welding thermal cycle curves, a two-step heating procedure was employed to simulate the welding thermal process, as shown in Fig. 3. The characteristic parameters of thermal cycle include the peak temperature at the first step (T max1 ), the peak temperature at the second step (T max2 ), the heating rate at the first step (vh1 ), the heating rate at the second step (vh2 ), the heating time at the first step (t h1 ), and the heating time at the second step (t h2 ). The cooling mode is air cooling. The values of characteristic parameters are listed in Table 2.

88

L. Meng et al.

(a) Temperature contour

(b) Temperature curves Fig. 2 Welding temperature distribution contour and the welding thermal cycle curve at different peak temperatures (Color figure online)

2.3 Dimension of Sample During Satoh test, the sample must meet the two requirements: (1) The sample cannot be deformed during the thermal cycle process; (2) the measured heating rate is in accordance with the preset heating rate. Based on the standard test of sample size, the shape and size of the sample were determined, as shown in Fig. 4.

Experimental Method of Mechanical Melting Point in A6N01-T5 …

89

Fig. 3 Characteristic parameters of thermal cycle

Table 2 Values of characteristic parameters Peak The first step temperature (°C) vh1 (°C/s) t h1 (s) T max1 (°C) 550 510 490 470 450 400 350 300 250

250 230 220 210 200 175 150 125 100

2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0

500 460 440 420 400 350 300 250 200

The second step vh2 (°C/s)

t h2 (s)

T max2 (°C)

25 25 25 25 25 25 25 25 25

2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0

550 510 490 470 450 400 350 300 250

2.4 MMP Measurement Different test peak temperatures were selected as follows: 250, 300, 350, 400, 450, 470, 490, 510, and 550 °C. The measuring steps were as follows: (1) Before testing, the width and thickness of samples were measured with Vernier caliper and recorded; (2) a set of thermocouple was welded on the center of the sample to measure the test temperature. Compared with the preset heating speed and temperature, the input power of Gleeble 3500 was adjusted to control the heating speed and the peak temperature; (3) the sample was installed and fixed with the special tools in Gleeble 3500 test machine; (4) according to the characteristic parameters of thermal cycle, the test program was written; (5) the test was carried out, and the temperature, force, and time were collected and saved automatically; (6) data processing was carried out to obtain the MMP. In order to ensure the reliability of the test results, three or five samples were tested at each peak temperature.

90

L. Meng et al.

Fig. 4 Shape and size of sample

Fig. 5 Stress evolution process during the thermal simulation (Color figure online)

3 Results and Discussion 3.1 Stress Evolution Figure 5 shows the stress evolution process during the thermal simulation. From Fig. 5, the following results can be obtained:

Experimental Method of Mechanical Melting Point in A6N01-T5 …

91

(1) During heating process, the compressive stress is generated due to thermal expansion. The compressive stress increases linearly with the increase of temperature at the low-temperature stage (20 ~ 250 °C). But when the temperature reaches about 250 °C, the compressive stress reaches the maximum and is about −150 MPa. (2) When the temperature continues to increase, the compressive stress begins to decrease. The main reason is that the higher the temperature, the lower the yield strength of the material. Another reason is the transformation of thermal strain caused by temperature to plastic strain. When the temperature is over 400 °C, the downward trend of compressive stress slows down and the compressive stress is close to zero with the increase in temperature. (3) During the cooling period, the compressive stress is changed to the tensile stress. The tensile stress increases with the increase of temperature. The final residual stress is shown as tensile stress

3.2 MMP According to the test results, the final residual stresses at different peak temperatures were obtained. The final residual stresses at the same peak temperature were averaged, and then the relationship between the final residual stress and the peak heating temperature was plotted, as shown in Fig. 6. The red line is a curve obtained by nonlinear fitting processing. It can be found that the relationship between the final residual stress and the peak temperature presents a basically linear increase at the low-temperature stage. As the temperature continues to increase, the final residual stress increases slowly. When the temperature reaches 370 °C, the ultimate residual stress reaches the maximum, and then the final residual stress decreases gradually with the increase of temperature. The stress remains unchanged when the temperature reaches 520 °C. From Fig. 6, the final residual stress is not monotonous increase with the increase of the peak temperature. The reason is that inelastic strain at high temperature tends to appear as creep strain without further work hardening, and prior work hardening is progressively dissipated by recovery and recrystallization. The temperature is the start temperature of MMP when the final residual stress reaches the maximum value and is the end temperature of MMP when the final residual stress decreases and then remains unchanged. Therefore, for A6N01-T6 alloy, the range of MMP is 370 ~ 520 °C.

92

L. Meng et al.

Fig. 6 Relationship between the final residual stress and the peak heating temperature

3.3 Numerical Simulation Validation In order to validate the accuracy of MMP, the finite element method was employed to simulate the thermal cycle process using the software code SYSWELD. The predicted stress evolution was compared with the measured data. The whole three-dimensional model was meshed as shown in Fig. 7. In order to improve calculating speed and convergence, a nonuniform mesh was adopted. The mesh was finer at the center of specimen due to high-temperature gradient and coarser far away from the center. The model contains 2496 eight-node hexahedral elements and 3555 nodes.

Fig. 7 Finite element model

Experimental Method of Mechanical Melting Point in A6N01-T5 …

93

(a) Temperature contours

(b) Temperature comparison Fig. 8 Thermal calculating results (Color figure online)

The initial temperature was set to 20 °C. Heat losses due to convection and radiation were considered. Temperature-dependent thermal properties and mechanical properties and the softening model of A6N01-T6 were given in Ref. [12]. According to Fig. 6, the MMP was set to 520 °C. In thermal simulation, the peak temperature of 550 °C was selected. The thermal cycle curve was employed on the uniform temper-

94

L. Meng et al.

Fig. 9 Residual stress distribution contour

Fig. 10 Stress evolution comparison between simulated and measured (Color figure online)

ature zone as a heat source model. In mechanical calculation, a full rigid constraint was imposed at both the ends of specimen. Figure 8 shows the thermal calculating results. From Fig. 8, the maximum temperature is distributed at the center of the sample. The temperature decreases gradually away from the center, which conforms to the temperature variation law of the thermal–mechanical simulation test process. The predicted temperature curve is in good agreement with the measured.

Experimental Method of Mechanical Melting Point in A6N01-T5 …

95

Figure 9 shows the residual stress distribution contour. The parallel section is mainly characterized by tensile stress. The stress distribution is very uniform. The stress value is about 100 MPa, and the maximum tensile stress is 127 MPa, which is distributed in the transition zone of the specimen. The stress evolution comparison between simulated and measured is plotted in Fig. 10. It can be seen that the predicted stress evolution trend is consistent with the measured data, which validates the accuracy of MMP.

4 Conclusions The measuring method of mechanical melting point was established based on thermal–mechanical simulation test. The final residual stress is not monotonous increase with the increase of the peak temperature. For A6N01-T6 alloy, the start temperature of mechanical melting point is 370 °C and the end temperature is 520 °C. The predicted stress evolution trend has good agreement with the measured, which validates the accuracy of MMP.

References 1. Xu JJ, Gilles P, Duan YG (2012) Simulation and validation of welding residual stresses based on non-linear mixed hardening model. Strain 48(5):406–414 2. Kong FR, Ma JJ, Kovacevic R (2011) Numerical and experimental study of thermally induced residual stress in the hybrid laser-GMA welding process. J Mater Process Tech 211(6):1102–1111 3. Jiang WC, Yucai Zhang, Woo WC (2012) Using heat sink technology to decrease residual stress in 316L stainless steel welding joint: finite element simulation. Int J Press Ves Pip 92:56–62 4. De A, DebRoy T (2011) A perspective on residual stresses in welding. Sci Technol Weld Joining 16(3):204–208 5. Smith MC, Smith AC (2009) NeT bead-on-plate round robin: comparison of residual stress predictions and measurements. Int J Press Ves Pip 86(1):79–95 6. Deng D, Zhang CH, Pu XW et al (2017) Influence of material model on prediction accuracy of welding residual stress in an austenitic stainless-steel multi-pass butt-welded joint. J Mater Eng Perform 26:1494–1505 7. Muránsky O, Hamelin CJ, Smith MC et al (2012) The effect of plasticity theory on predicted residual stress fields in numerical weld analyses. Comp Mater Sci 54(1):125–134 8. Muránsky O, Smith MC, Bendeich PJ et al (2012) Comprehensive numerical analysis of a three-pass bead-in-slot weld and its critical validation using neutron and synchrotron diffraction residual stress measurements. Int J Solids Struct 49(9):1045–1062 9. Xu JJ, Gilles P, Duan YG et al (2012) Temperature and residual stress simulations of the NeT single-bead-on-plate specimen using SYSWELD. Int J Press Ves Pip 99–100(1):51–60 10. Xu JJ, Chen JY, Duan Y et al (2017) Comparison of residual stress induced by TIG and LBW in girth weld of AISI 304 stainless steel pipes. J Mater Process Tech 248(1):178–184 11. Xu JJ (2014) Effect of material hardening model on welding residual stresses of 316L stainless steel. Trans China Weld Inst 35(3):97–100 12. Fan YY, Xu JJ, Meng LC (2017) Welding softening character and numerical simulation of A6N01S-T5 aluminum alloy. Trans China Weld Inst 38(7):77–82

Mathematical Modeling and Workspace Analysis for Photographic Robot Xuedong Li, Xingang Miao and Su Wang

Abstract The four-link manipulator can realize the characteristics of various motion trajectories to achieve multi-angle, precise positioning and high-quality shooting operations. A new method is proposed to determine the membership degree of the photographic robot workspace. In this paper, we use the DH digital modeling method to establish the positive kinematics model of the photographic robot and solve the kinematics positive solution of photographic robot. We also utilize the numerical analysis method to analyze the working space of the angle between the robot manipulator and guide rail, which will obtain the workspace, boundary point coordinates and geometry to measure and divide membership of a complex workspace into several simple working subspaces, and then get the decision conditions of the comprehensive workspace. Finally, the simulations are performed using MATLAB for verification. The working space of the photographic robot is determined, and the limit range of the robot operation is obtained. Keywords Photographic robot · DH modeling · Work space analysis Determination of membership degree

1 Introduction The photographic robot manipulator uses a four-bar linkage mechanism, which is widely used in mechanical design and has a variety of configurations. The four-bar linkage mechanism can realize a variety of motion trajectory curves and motion laws, can directly complete an actuator required by a certain trajectory, and can transmit a large power, and can realize a manipulation mechanism for long-distance transmission. Therefore, the four-bar linkage manipulator is applied to the photographic X. Li · X. Miao (B) · S. Wang Beijing Engineering Research Center of Monitoring for Construction Safety, Beijing Key Laboratory of Robot Bionics and Function Research, Beijing University of Civil Engineering and Architecture, Beijing 100044, China e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 S. Chen et al. (eds.), Transactions on Intelligent Welding Manufacturing, Transactions on Intelligent Welding Manufacturing, https://doi.org/10.1007/978-981-13-3651-5_7

97

98

X. Li et al.

robot, so that the camera mounted on the front end of the manipulator has the characteristics of large range of motion, small space limitation, accurate positioning, and many angles during shooting. Using the characteristics of the four-link crank-rocker mechanism, the working space of the photographic robot is analyzed, and the forward kinematics model of the photographic robot is established by DH modeling method. The literatures [1–5] used the statistical method to analyze the working space of the robot. Together, these reports analyze boundaries of the XY two-dimensional plane at the terminus of the manipulator at different positions. Firstly, using established kinematics modeling, the end-position range of the photographic robot is obtained. Boundary details and geometric equation are also obtained. Secondly, the literatures [6–8] analyzed the method of determining the membership degree of the target point workspace and uses MATLAB software to simulate and display it graphically. Finally, the simulation results in [9–11] proved the validity of modeling and workspace analysis—thus measuring the actual working space of the photographic robot.

2 Photographic Robot Structure The overall structure diagram of the photographic robot and the three-dimensional diagram of the four-link manipulator are shown in Figs. 1 and 2. The overall structure of the photographic robot consists of horizontal rails, vertical rails, and robots. The horizontal guide rail has a length of 12,000 mm, and the vertical guide rail has a length of 6000 mm. The horizontal and vertical guide rails allow the robot to have two degrees of freedom in horizontal and vertical directions. Form a vertical plane and define the horizontal direction as the Y -axis. The vertical direction is Z-axis. The robot L has a length of 800 mm and is combined with another rotating shaft so that it can rotate about a vertical axis, so that the manipulator has a degree of freedom perpendicular to the vertical plane, and the specified direction is the X-axis. The three rotating shafts at the front end of the manipulator have three degrees of freedom for the end to rotate around the XYZ-axis, that is, the three-axis position adjustment structure, and the manipulator L maintains the end posture unchanged during the swinging process.

3 Positive Kinematics Positive kinematics determines the pose of the end effector in Cartesian space given the joint angle vector of the robot and the geometry of the member. The photographic robot is modeled by DH method. The robot has a total of six axes, which are a horizontal linear motion axis d1 , a vertical motion axis d2 , a mechanical arm L that oscillates about a vertical axis, and three attitude adjustment axes θ4 , θ5 , θ6 . The attached coordinate system of the connecting rod is shown in Fig. 3.

Mathematical Modeling and Workspace Analysis for Photographic …

Fig. 1 Photographic robot overall structure

Fig. 2 Four-link manipulator three-dimensional figure

99

100

X. Li et al.

Fig. 3 Link coordinate system

The effect of the motion on the Y -axis is reduced to d1 , which is recorded as d1 , and the motion of the four-bar linkage mechanism has the following constraints:  d1  L cos ϕ2 (1) d3  L sin ϕ2 The coordinate system X n − Z n is transformed into the adjacent coordinate system X n+1 − Z n+1 as follows: (1) Rotate θn+1 around the Z n axis such that X n and X n+1 are parallel to each other. (2) Translate the dn+1 distance along the Z n axis such that X n and X n+1 are collinear. (3) Translate the distance an+1 along the already rotated axis X n such that the origins of X n and X n+1 coincide, and rotate the Z n axis around X n by an+1 such that Z n and Z n+1 Axis alignment. After the above three steps, a coordinate system is transformed to the next coordinate system, because the transformations that are passed are performed relative to the coordinate system itself, so all transformation matrices are right-multiplied, and: n

Tn+1  An+1  Rot(z, θn+1 ) × Trans(0, 0, dn+1 ) × Trans(an+1 , 0, 0) × Rot(x, αn+1 ) ⎤ ⎡ Cθn+1 −Sθn+1 Cαn+1 Sθn+1 Sαn+1 an+1 Cθn+1 ⎥ ⎢ Sθ ⎢ n+1 Cθn+1 Cαn+1 −Cθn+1 Sαn+1 an+1 Sθn+1 ⎥ (2) ⎢ ⎥ ⎣ 0 Sαn+1 Cαn+1 dn+1 ⎦ 0

0

0

1

The link parameters of the photographing robot are shown in Table 1 as follows.

Mathematical Modeling and Workspace Analysis for Photographic … Table 1 Photographic robot D-H parameter

101

#

θ

d

a

α

U-0

0

0

d3

−90°

0-1

0

d1 − d1

0

90°

1-2

90°

d2

0

90°

2-3

0

d3

0

0

3-4

oo

d3

0

90°

4-5

θ5 + 90◦

0

0

90°

5-H

θ6

0

0

−90°

Bring the link parameters into the formula to get: ⎤ ⎤ ⎡ ⎡ 10 0 0 001 1 0 00 ⎥ ⎥ ⎢ ⎢ 0 0 −1 ⎢1 0 0 0 ⎥ ⎢0 0 1 0⎥ ⎢ ⎢ ⎢ ⎥, A1  ⎢  ⎥, A2  ⎢ ⎣ 0 −1 0 0 ⎦ ⎣ 0 1 0 d1 − d1 ⎦ ⎣0 1 0 0 0 01 000 00 0 1 ⎤ ⎡ ⎡ ⎤ cos(θ4 + π ) 0 sin(θ4 + π ) 0 100 0 ⎥ ⎢0 1 0 0 ⎥ ⎢ ⎢ ⎢ sin(θ4 + π ) 0 − cos(θ4 + π ) 0 ⎥ ⎥ ⎢ ⎥, ⎥, A4  ⎢ ⎣ 0 0 1 d3 ⎦ ⎣ 0 1 0 0⎦ 000 1 0 0 0 1 ⎤ ⎡ cos(θ5 + π/2) 0 sin(θ5 + π/2) 0 ⎥ ⎢ ⎢ sin(θ5 + π/2) 0 − cos(θ5 + π/2) 0 ⎥ ⎢ ⎥, ⎣ 0 1 0 0⎦ 0 0 0 1 ⎡ ⎤ cos θ6 0 − sin θ6 0 ⎢ ⎥ ⎢ sin θ6 0 cos θ6 0 ⎥ ⎢ ⎥ ⎣ 0 −1 0 0⎦ 0 0 0 1 ⎡

A0

A3

A5

A6

⎤ 0 0⎥ ⎥ ⎥, d2 ⎦ 1

(3)

Then the pose matrix of the end effector: TH  A0 × A1 × A2 × A3 × A4 × A5 × A6 ⎡ ⎤ c5 c6 −s5 −c5 s6 d3 ⎢ c s c − s s c c −s c − c s s d − d  ⎥ ⎢ 4 5 6 4 6 4 5 4 6 4 5 6 1 1⎥ ⎢ ⎥ ⎣ c4 s6 + s4 s5 c6 s4 c5 c4 c6 − s4 s5 s6 d2 ⎦ 0

0

0

1

(4)

102

X. Li et al.

4 Photographic Robot Workspace Analysis The robot workspace refers to the range of motion that can be achieved in the Cartesian space at the robot’s terminal point. The accessible position of the terminus on the XY -plane is the upper and lower bottom surfaces, and the range of motion in the Z-axis direction is sufficiently high to constitute a space enclosed by the cylinder surface. The analysis of this workspace focuses on the reachable position analysis of the photographic robot on the XY two-dimensional plane. In this paper, the angle formed by the manipulator L and the Y -axis is ϕ, the direction is defined to start in the negative direction of the Y -axis, and the counterclockwise rotation is positive. According to the structure of the robot L and the position where the positive and negative limit are installed, points A and and positive limit positions

B are the negative

of the Y -axis. And when ϕ ∈ 0, π2 and ϕ ∈ π2 , π have reachable ranges, the two ranges correspond to different kinematics. Figures 4 and 5 show two geometrical diagrams of the four-bar linkage at different angles in a two-dimensional plane.

Fig. 4 Four-bar linkage geometry sketch (a)

Fig. 5 Four-bar linkage geometry sketch (b)

Mathematical Modeling and Workspace Analysis for Photographic …

103

4.1 Analysis of the Range of the Robot in the XY 2D Plane When ϕ Is an Acute Angle

When the value of ϕ is a subset of 0, π2 , that mean ϕ is an acute angle, as shown in Fig. 6, where point A is the origin of the XY plane. As shown in the figure, when ϕ is an acute angle,  AB N is the minimum value of ϕ, starting from point A and analyzing point B. G and N are the two extreme positions that the end of the manipulator L can reach at two points A and B when ϕ takes the minimum value; F and D are when the ϕ is π2 , the end of the manipulator L is at two points A and B that two extreme positions can be reached. The shaded area GNDF is the range that can be reached by the end effector of the area when ϕ is an acute angle, denoted as a.

4.2 Analysis of the Range of the Robot in the XY 2D Plane When ϕ Is an Obtuse Angle

When the value of ϕ is a subset of ϕ ∈ π2 , π , that mean ϕ is an obtuse angle, as shown in Fig. 7. As shown in the figure, when ϕ is an obtuse angle, it is analyzed from the right extreme position B point of the Y -axis to point A. Point C is the limit position of the manipulator at the end of the B position, where ϕ   ABC is the maximum value; D is the position of the actuator at the end of the B position, where ϕ   AB D  π2 . When the entire four-bar linkage manipulator moves in the negative direction of the Y -axis, the area covered by the end is similar to the shape of the hatched shadow area when ϕ is an acute angle, but the range on the X-axis is narrow; when the manipulator moves to P. When the point moves and continues to point A, the included position area is completely covered by a, the intersection of the two areas is H, and the plane area enclosed by CDEH is b.

Fig. 6 ϕ is an acute angle region analysis

104

X. Li et al.

Fig. 7 ϕ is an obtuse angle when the area analysis

Fig. 8 XY -plane work area

4.3 Manipulator Integrated Boundary Analysis in XY-Plane Combining the above two defined domain working ranges, the operating range of the end effector of the photographic robot on the XY -plane is obtained, as shown in Fig. 8. As shown in the above figure, if the intersection of the plane areas a and b is c, then a, b, and c jointly form the XY -plane working area. The boundaries are thus the GN line segment, NH arc, HC line segment, CD arc, FD line segment, and GF arc. Analysis can be obtained for each segment boundary line equation: (a) The line segment GN is parallel to the Y -axis, and the equation is:  x  Cx GY ≤ y ≤ Ny

(5)

Mathematical Modeling and Workspace Analysis for Photographic …

105

(b) Arc NH, the equation is: ⎧ 2 2 2 ⎪ ⎪ ⎨ x + (y − B y )  L N X ≤ x ≤ Hx ⎪ ⎪ ⎩ N y ≤ y ≤ Hy

(c) The line segment HC is parallel to the Y -axis, and its equation is:  x  Hx Hx ≤ y ≤ C y

(6)

(7)

(d) Arc CD, the equation is: ⎧ 2 2 2 ⎪ ⎪ ⎨ x + (y − B y )  L C X ≤ x ≤ Dx ⎪ ⎪ ⎩ Dy ≤ y ≤ C y

(e) Line segment FD, whose equation is:  x  Fx 0 ≤ y ≤ Dy

(8)

(9)

(f) Arc GF, the equation is: ⎧ 2 2 2 ⎪ ⎨x + y  L G X ≤ x ≤ Fx ⎪ ⎩ G y ≤ y ≤ Fy

(10)

5 Determination Method and Simulation 5.1 Target Point Workspace Membership Degree Determination Method Based on the above boundary analysis, determining whether the target point belongs to the workspace is based mainly on whether the XY coordinates of the target belong to the XY two-dimensional workspace plane decision. However, since the working plane boundary is composed of multiple arcs and line segments, and the kinematic inverse solution corresponding to a and b regions is different, it is cumbersome to directly use the boundary equation inequality method to determine membership

106

X. Li et al.

of the target point workspace. In this paper, we will use the method of membership degree to transform the complex workspace into a simpler working subspace. We will analyze it one by one. And finally, obtain the judgment conditions of the integrated workspace. The matrix vector of the target point position is:

tarP  t PX , t Py , t Pz , 1

(11)

The work plane shown in Fig. 8 is divided into a GMF area, an MNDF area, and an HCD area. The subordinates of the projection of the point t in the XY -plane in three plane regions are judged one by one. (1) Determine whether the two-dimensional coordinate point (t Px , t Py ) is within the GMF area, that is: ⎧ 2 2 2 ⎪ ⎪ ⎨ t Px + t Py ≤ L G X ≤ t Px ≤ Fx ⎪ ⎪ ⎩ G y ≤ t Py ≤ Fy

(12)

(2) Determining whether the two-dimensional coordinate point (t Px , t Py ) is within the MNDF region, that is: ⎧ 2 2 2 ⎪ ⎪ ⎨ t Px + (t Py − B y ) ≥ L (13) M X ≤ t Px ≤ Fx ⎪ ⎪ ⎩ 0 ≤ t Py ≤ D y (3) Determine whether the two-dimensional coordinate point (t Px , t Py ) is within the HCD area, that is:  t Px2 + (t Py − B y )2 ≤ L 2 (14) H X ≤ t Px ≤ Dx

5.2 Workspace Membership Degree MATLAB Simulation The above describes the method of judging the designated degree of workspace that transforms the complex workspace into the working subspace. This section will use MATLAB to simulate and verify the validity of the method. Specific parameter setting: The starting point of the test position is the zero position of each axis of the robot. In other words, the Cartesian coordinates are XYZ  (564.0851, −1049.1484, 0), the unit is mm; ABC (0, 0, 0), the unit is degree.

Mathematical Modeling and Workspace Analysis for Photographic … Fig. 9 Workspace membership MATLAB simulation

107

14000 12000

Y/mm

10000 8000 6000 4000 2000 0 -2000 -2000

0

2000

4000

6000

8000

10000 12000

X/mm

In the rectangular range larger than the working space of the robot arm, 300 points are generated by a random algorithm, and the average is distributed in the rectangular area. The points in the working space are represented by blue “*”, and the points outside the working space are marked with red. “+” indicates that the simulation results are as shown in Fig. 9. The rule of determining all positions: as long as the subspace point coordinate requirement is met, both the decision procedure is exited and the position of the decision point is identified on the image. The range of rectangular regions that generate random points is: ⎧ ⎪ ⎨ 0 ≤ x ≤ 1500 (15) −2000 ≤ y ≤ 14000 ⎪ ⎩ z0 Among the 300 random points selected in this simulation, 109 belonged to the workspace and 191 did not belong to the workspace. From the simulation results in the figure, it is shown that the blue points are all within the boundary of the workspace analyzed in Sect. 4, indicating that the membership degree determination method using a complex workspace to transform into a simple working subspace is effective. At the same time, the trapezoidal area where the blue point is located is the actual working space of the photographic robot.

108

X. Li et al.

6 Conclusions This paper proposes to apply the four-bar linkage manipulator to the photographic robot and analyze the robot hand working space. The photographic robot model was established by DH modeling method, and the workspace analysis was carried out. The photographic robot workspace is obtained by transforming the complex workspace into a simple working subspace. The simulation results prove the effectiveness of the mathematical modeling method. The spatial analysis results show that the robot workspace has no holes and a large working range. Finally, the actual working space of the photographic robot was determined, and the experimental results were in line with expectations.

References 1. Xinke L, Xingang M, Wang S (2017) Research on working space of 2-redundancy freedom 8 axis lighting robot based on analytic method. Mech Des Res 33(06):31–34 2. Jingjie H, Wang S, Xingang M (2015) Mathematical modeling and workspace analysis of welding visual tracking robot. J Shanghai Jiaotong Univ 49(03):319–322 3. Di C, Cunyi X, Tie Z (2009) Analysis and simulation of spraying robot working space based on monte carlo method. Mach Des Manuf 03:161–162 4. Baofeng L (2011) Working space analysis and parameter identification of six-DOF space robot. Dissertation, Beijing University of Posts and Telecommunications 5. Yanhui H (2010) Analysis and simulation of the working space and error compensation of industrial robots. Dissertation, Henan Polytechnic University 6. Li M. Research on design and simulation of small palletizer robot mechanism based on MATLAB. Dissertation, Tianjin University of Science and Technology 7. Hui W, Jinhong G, Dianjun W et al (2015) Workspace analysis and simulation of 6-DOF modular robot. Mach Des Manuf 05:192–195 8. Wang J, Gong C (2008) Motion control of industrial robot SV3 based on ACL-Win. In: Proceedings of the IEEE international conference on automation and logistics 9. Minzhou L, Jian F, Jianghai Z (2015) Technology development and application of industrial robots. Mach Manuf Autom 01:1–4 10. Yong Z, Jianxin Z (2004) A new method for solving workspace of robots. Mach Tool Hydraul 04:66–67 11. Fujun T (2015) Cable tight coupling multi-robot modeling and workspace analysis. Dissertation, Lanzhou Jiaotong University

The Regulation of Laser-Arc Hybrid Welding Source on TC4 Ti Alloy to 304 Stainless Steel Joints with Interlayers Hongyang Wang, Gang Song, Zhonglin Hou and Liming Liu

Abstract Laser-arc hybrid welding source is used to join TC4 Ti alloy to 304 stainless steel with Cu interlayer and adhesive. The influences of the welding source and the interlayers on the microstructures and intermetallic in the welding joint are analyzed elaborately. The welding joint shows a layer structure with the effect of laser-arc hybrid welding source. The Ti–Fe intermetallic (IMC) is nearly eliminated by the Cu interlayer, which reduces harmful effect of IMC obviously. More Ti–Cu intermetallic is found in the transition zone, which influences the property of the laser-arc welding TC4 Ti alloy to 304 stainless steel joints. The surface state and heat transfer process in the welding joint are changed. The property of the dissimilar joint is improved by the laser-arc hybrid welding source and the addition of interlayers. Keywords Laser-arc hybrid welding · Adhesive · Heat transform · Ti Stainless steel · Intermetallics

1 Introduction Titanium alloys are expected to be much more widely used for implant materials in the medical and dental fields because of their superior biocompatibility, corrosion resistance and specific strength compared with other metallic implant materials [1, 2]. Still, with the increasing demand of lightweight, the application of Ti alloys increase rapidly year after year. Stainless steel’s resistance to corrosion and staining, low maintenance, and familiar luster make it an ideal material for many applications [3, 4]. H. Wang (B) · G. Song · Z. Hou · L. Liu School of Material Science and Engineering, Key Laboratory of Liaoning Advanced Welding and Joining Technology, Dalian University of Technology, Dalian 116024, Liaoning, China e-mail: [email protected] Z. Hou School of Material Science and Engineering, University of Science and Technology Liaoning, Anshan 114051, Liaoning, China © Springer Nature Singapore Pte Ltd. 2019 S. Chen et al. (eds.), Transactions on Intelligent Welding Manufacturing, Transactions on Intelligent Welding Manufacturing, https://doi.org/10.1007/978-981-13-3651-5_8

109

110

H. Wang et al.

There are many applications in which weldments are made from metals of different compositions, such as strength and lightweight. The problem of making welds between dissimilar metals relates to the transition zone between the metals and the intermetallic compounds formed in this transition zone. The welding process of the Ti and steel has been reported by many researchers. Laser welding method is used to join the Ti alloy and the stainless steel by Chen [5]. When the laser is approaching to the stainless steel, the tensile strength of the joint is better. Shanmugarajan B. found that the laser waveform made effect on the tensile strength of the laser welding Ti alloy and 304 stainless steel [6]. Ishida used the FSW welding method to joint eh 304SUS and the Ti alloy [7], the tensile shear load of the joint was 3.83 kN cm−1 , and the peel strength was 1.5 kN. In some cases, it was still necessary to use a third metal that was soluble with each metal in order to produce a successful joint. The effect of interlayer on the Ti and stainless-steel welding joint was widely discussed, such as Ni, V, Cu [8–14]. Wang and Tomashchuk both found that the property of the joint was improved by the addition of the Cu interlayer. The Fe–Ti intermetallic was inhibited by the addition of the element Cu. But in welding process, with the addition of the Cu interlayer, some Cu–Ti intermetallic was found in the transition zone, which still made harmful effect on the joint [9, 10]. How to eliminate the harmful influence of the interlayer on the welding process is a key problem to realize the favorable joining between titanium alloy and stainless steel. The laser-arc hybrid welding technology was often used in dissimilar metal welding process [15–17]. The synergy of laser and arc heat source will make obviously effect on the microstructures and improve the property of the joint. In the process of laser-arc hybrid welding, the effective change of the interlayer was introduced, and the structural characteristics of the interface were expected to increase the strength of the joint. With the regulation of the hybrid welding source, it could be obtained several different welding structures and form different types of welding joints. In this paper, the Cu and adhesive were added into the laser-arc hybrid welding TC4 Ti ally to 304 stainless steel (304SS) processes, which is used to eliminate the harmful effect of Ti–Fe intermetallic. The microstructure and elements distribution of the dissimilar joint are observed carefully. The influences of the welding source and the interlayers on the dissimilar welding joint are discussed completely.

2 Experiments Samples of extruded TC4 Ti alloy (1.0 mm) and 304 stainless steel (1.0 mm) were used during the laser-arc-adhesive hybrid welding process. The configuration and dimensions of the laser-arc-adhesive hybrid welding specimen used throughout the current work are shown in Fig. 1. The adhesive used in the experiment was an epoxy adhesive made by 3 M Co. The 0.08 mm thickness of Cu interlayer and 0.1 mm thickness of adhesive were used in the experiments. The experiment uses a 1000 W laser and Tungsten Inert Gas (TIG) source supply to form a laser-TIG hybrid welding system. The pulsed YAG solid-state laser is used in the experiment. The arc is a direct

The Regulation of Laser-Arc Hybrid Welding …

111

Fig. 1 Configuration of the laser-arc-adhesive hybrid welding specimen Table 1 Welding parameters of laser-arc hybrid welding processes Laser power (W) TIG (A) Defocus (mm) Dla (mm) 390–650

90

2.5

2

Speed (mm min−1 ) 1100

current TIG. The angle between the laser and the TIG arc was about 45° during the test. The TIG current is kept constant. The welding parameters are shown in Table 1. After welding, the adhesive is cured under 170 °C for 1 h. Tensile shear testing of samples is performed at room temperature using an Instron-type testing machine with 2 mm • min−1 cross-head speed. The resultant welds sliced using an electron discharge machine were grounded with SiC paper and micro-polished using 0.5 mm Al2 O3 powder. The transverse section samples of joints were prepared without being etched and analyzed by an EPMA to measure the distributions of elements in the fusion zone. The microstructures of laser-arc and laser-arc-adhesive joints fusion zone were observed by SEM. The elements and phases in the weld zone were analyzed using energy dispersive spectroscopy (EDS). According to the characteristics of the dissimilar metals, the samples of the Ti alloys and SS 304 for SEM were etched in a mixing acid.

3 Results and Discussions 3.1 The Effect of Welding Source on the Microstructure of the Joint In laser-arc hybrid welding process, the welding speed was about 1000 mm/min, and the current of the TIG welding process was 90 A, and the average power of

112

H. Wang et al.

(a) The microstructure of the transition zone

(b) Microstructure at Ti-Cu interface

(c) Microstructure at Cu-Fe interface

Fig. 2 Microstructure of the laser-arc hybrid welding of Ti to steel joints

the pulse laser was about 500 W. In this welding process, the TC4 Ti alloy and Cu interlayer were melted completely by the laser-arc hybrid heat source, and the 304SS was melted a little, as shown in Fig. 2a. The addition of the Cu interlayer avoids the direct metallurgical reaction between the titanium alloy and the stainless steel, which restrained the formation of Ti–Fe brittle intermetallic compound. The Cu interlayer did not mix with the Ti and stainless steel, which still shows the continuous layered structure. The phase structure approaching the Ti–Cu interface was Ti–Cu intermetallic, whose thickness was about 20 µm, as shown in Fig. 2b. From the EDS analyses of the Area A, it consisted of 78.82 at.% of Cu, 19.90 at.% of Ti and 1.28 at.% of Al, which should be composed the Ti–Cu intermetallics and Cu–Ti hypereutectic phase. The XRD results of the fracture are shown in Fig. 3, which can be used to prove the EDS analysis results. Through the above analysis, it can be found that the microstructure of the interlayer of the titanium alloy and the stainless-steel welded joint is mainly divided into three parts: Ti–Cu intermetallic compound, copper-based solid solution, and Fe–Cu eutectoid reaction mixture.

The Regulation of Laser-Arc Hybrid Welding …

113

Fig. 3 X-ray diffraction pattern of the fracture surfaced of the Ti–Cu joint

Fig. 4 Tensile shear loads of laser-arc hybrid welding TC4 Ti alloy and 304 stainless steel joints with different laser powers

Figure 4 shows the results of tensile shear test in different laser powers. The property of the joint increased firstly and then decreased. The microstructure of the weld joint was observed under different conditions. The overall structure is similar to that shown in Fig. 2, except the thickness of the Ti–Cu intermetallic at the Ti–Cu interface. In order to better explain the state of the interface, we observed the elements distribution at the interface under different laser beam powers. When the average laser power was about 390 W, the joint was failed at the Cu–Fe interface. The thickness of the diffusion layer between the elements Fe and Cu is about 5 µm, as shown in Fig. 5. When the average laser power was about 600 W, the joint was failed at the Ti–Cu interface. As the increasing of the laser powers, more Ti–Cu intermetallic was found at the Ti–Cu interface and the thickness of the intermetallic layer was about 40–50 µm, which made obvious harmful effect on the property of the joint. As the average laser power was about 485 W, the strength of

114

H. Wang et al.

Fig. 5 EPMA of TC4 Ti alloy and 304 stainless steel laser-arc hybrid welding joint with different laser powers

the Ti–Cu and Cu–Fe interfaces was nearly same; thus, the joint was failed at the transition zone. From the above results, it could be found that the cross section of the laser-arc hybrid welding joint showed obvious laminar transition structure. Therefore, the welding mode in this laser-arc hybrid welding joint was in conductive mode, which made the heat transform in the joint smoothly and stable. In the hybrid welding process, the TIG arc formed a stable fusion zone on the Ti alloy, and do not act on the Cu interlayer directly, which provided a state for the heat transfer control on the interface. The thickness of transform layers as 5, 13, and 20 µm was increased gradually with the increase of laser beam powers. Still, the diffusion and reaction on the Ti–Fe interface were controlled precisely by the regulation of the laser beam power in the hybrid welding process, which was made obviously effect on the property of the joints. This kind of precise control of interface structure could only be

The Regulation of Laser-Arc Hybrid Welding …

115

obtained in the laser-arc hybrid welding process. In dissimilar welding process, the regulatory characteristics of the laser beam were enlarged by the welding arc, which was obviously better than that of simple laser welding process.

3.2 The Effect of the Adhesive on the Welding Process and Microstructures Laser-arc-adhesive hybrid welding TC4 Ti alloy to 304SS joint was done in same conditions with the laser-arc hybrid welding process. The tensile shear loads of the laser-arc-adhesive hybrid welding joints without adhesive cured are shown in Fig. 6. It was found that the best result of the tensile shear load was obtained in laser-arcadhesive welding joint, which was 3.85 kN cm−1 , compared with 3.30 kN cm−1 in laser-arc hybrid welding joint. After the adhesive was cured, the shear load of the laser-arc-adhesive bonding joint was 5.22 kN cm−1 . The microstructures of the transition zone in laser-arc-adhesive welding joint are shown in Fig. 7a. The different areas of the transition zone are shown in Fig. 7b–d. In order to know the phase compositions in the transition zone, the EDS analysis of different areas was done in same conditions. From the EDS analyses of the Area b (Fig. 8b), it consisted of 78.82 at.% of Cu, 19.90 at.% of Ti, and 1.28 at.% of Al, which is mainly composed of the Cu–Ti hypoeutectic phase. The microstructure of the Cu–Ti hypoeutectic (Area b) phase was the dense acicular structure in laserarc-adhesive hybrid welding joint, which was obviously different from the reticular structure that in laser-arc hybrid welding joint. From the EDS analyses of the Area c (Fig. 7c), it consisted of 81.10 at.% of Cu, 16.34 at.% of Ti, and 2.56 at.% of Al, which was mainly composed of the Cu–Ti hypoeutectic and eutectic phase. From the EDS analyses of the Area d (Fig. 7d), the reticular structure consisted of 84.44

Fig. 6 Tensile shear loads of laser-arc-adhesive hybrid welding TC4 Ti alloy and 304 stainless steel joints with different laser powers

116

H. Wang et al.

(a) Microstructure of the transition zone

(c) Microstructure approach Ti-Cu interface

(b) Microstructure at Ti-Cu interface

(d) Microstructure at Cu-Fe interface

(e) Microstructure approach Cu-Fe interface Fig. 7 Microstructure of the laser-arc-adhesive bonding of Ti to steel joints

The Regulation of Laser-Arc Hybrid Welding …

117

Fig. 8 EPMA results approach the Ti alloy in laser-TIG-adhesive hybrid welding joint

Fig. 9 EPMA results approach the 304SS in laser-TIG-adhesive hybrid welding joint

at.% of Cu, 12.77 at.% of Ti, and 2.79 at.% of Al, which was mainly composed of the Cu–Ti hypereutectic phase. And the Area e (Fig. 7e) consisted of 93.76, 2.91 at.% of Ti, 2.26 at.% of Al, and 1.07 at.% of Fe, which was mainly composed of the Cu–Ti–Fe solid solution. In order to understand the distribution of the Cu, Ti, and Fe elements in the transition zone, the element distribution results of the Ti–Cu and Cu–Fe interfaces are shown in Figs. 8 and 9. The distribution of the Ti and Cu elements was uniform in the transition zone, which was different from that gradient distribution in laser-arc hybrid welding joint, as shown in Fig. 4. The thickness of the Ti–Cu intermetallic was less than 10 µm. And some Fe element was found at the Ti–Cu interface. Therefore, it could deduce that the addition of the adhesive makes influence on the element diffusion in welding process. From the above results, it could be found that the heat transform in the dissimilar welding process is influenced by the hybrid welding sources and the adhesive interlayer. The property of the joints was still changed with the laser beam powers, but it was higher than that of laser-arc hybrid welding joint, which was mainly influenced by the addition of the adhesive. The adhesive was a new kind of interlayer, which did not change the reaction between the elements. But the adhesive changed the surface tension of the alloy and the heat transform in the welding process. With the thermal effect of the laser-arc welding source, the adhesive would be decomposed into

118

H. Wang et al.

Fig. 10 Effect of adhesive evaporation on the interacting between laser beam and steel

several kinds of gases, which should have a nearly similar characteristic with that of metal vapor in hybrid welding process. The adhesive stayed at the surface of the steel where was still between the Cu interlayer and steel. Thus, in welding process, the adhesive mainly influences the thermal effect of laser beam power but not the TIG arc. When laser beam was acting on the adhesive decomposition vapor, it would absorb the laser beam energy continuously. The laser absorptivity of this adhesive decomposition vapor was obviously higher than that of steel, which was nearly 50%. Therefore, the temperature of the decomposition vapor would be increased quickly through the energy level transition, ionization, and molecule shocking. As the adhesive decomposition vapor temperature increases, there would be thermal conductivity and radiation effect between the adhesive decomposition vapor and the Al alloy, as shown in Fig. 10. In this welding process, the heat adsorbed by Ti and Cu alloy is nearly no change, as the adhesive is brushed on the surface of the steel. Still the adhesive could be decomposed into some gases, which make effect on the distribution of the elements. Therefore, the addition of the adhesive makes little effect on the reaction between the Ti and Cu, but changes the distribution of the IMC, which is helpful for the property of the joint. At the same time, laser energy being absorbed by the vapor could be transferred to the steel by the thermal conductivity and radiation. Through this kind of energy transforming way, laser beam energy could be indirectly absorbed by the steel through the adhesive decomposition vapor, which had a higher efficiency than that of free electrons in steel. Therefore, the temperature of the steel would be increased with the addition of the adhesive, and more Fe element reacted with the

The Regulation of Laser-Arc Hybrid Welding …

119

Cu element in relatively lower welding power, which was helpful for the limit of the IMC. In dissimilar welding process, the laser-arc hybrid welding source and the adhesive changed the heat transform in the welding process, which could be used to control the elements reactions precisely.

4 Conclusions In summary, TC4 Ti alloy and 304SS were joined successfully by laser-arc hybrid welding technology with interlayers. The property of the dissimilar welding joints was decided by the laser-arc hybrid welding source and the interlayers, which changed the heat transform in the welding process. In conductive welding mode, the interface reactions between the elements are decided by the laser beam power, and the thickness of the IMC could be controlled by the regulation of the welding source power. The addition of the adhesive changed the surface state of the steel, and the reaction on the interface was enhanced, which improved the structure on the interface. Therefore, the laser-arc hybrid welding source and the adhesive provided a new method for the precisely controlling of the dissimilar welding process. Acknowledgements The authors gratefully acknowledge the support of the National Natural Science Foundation of China (U1764251), the Science Fund for Creative Research Groups of NSFC (51621064).

References 1. Nakaia M, Niinomia M, Akahorib T et al (2012) Microstructural factors determining mechanical properties of laser-welded Ti-4.5 Al-2.5 Cr-1.2 Fe-0.1 C alloy for use in next-generation aircraft. Mater Sci Eng A 550:55–65 2. Gangwar K, Mamidal R, Sanders GD (2017) Friction stir welding of near α and α + β titanium alloys: metallurgical and mechanical characterization. Metals 7(12):565 3. Reitemeyer D, Schult V, Syassen F et al (2013) Laser welding of large scale stainless steel aircraft structures. Phys Procedia 41:106–111 4. Ba¸syi˘git AB, Kurt A (2017) Investigation of the weld properties of dissimilar S32205 duplex stainless steel with AISI 304 steel joints produced by arc stud welding. Metals 7(3):77 5. Chen S, Zhang M, Huang J et al (2014) Microstructures and mechanical property of laser butt welding of titanium alloy to stainless steel. Mater Des 53:504–511 6. Shanmugarajan B, Padmanabham G (2012) Fusion welding studies using laser on Ti–SS dissimilar combination. Opt Laser Eng 50:1612 7. Ishida K, Gao Y, Nagatsuka K et al (2015) Microstructures and mechanical properties of friction stir welded lap joints of commercially pure titanium and 304 stainless steel. J Alloy Compd 630:172–177 8. Wang T, Zhang B, Feng J et al (2012) Effect of a copper filler metal on the microstructure and mechanical properties of electron beam welded titanium–stainless steel joint. Mater Charact 73:104–113 9. Wang T, Zhang B, Chen G et al (2013) High strength electron beam welded titanium-stainless steel joint with V/Cu based composite filler metals. Vacuum 94:41–47

120

H. Wang et al.

10. Tomashchuk I, Sallamand P, Andrzejewski H et al (2011) The formation of intermetallics in dissimilar Ti6Al4V/copper/AISI 316 L electron beam and Nd: YAG laser joints. Intermetallics 19(10):1466–1473 11. Kundu S, Roy D, Chatterjee S et al (2012) Influence of interface microstructure on the mechanical properties of titanium/17-4 PH stainless steel solid state diffusion bonded joints. Mater Des 37:560–568 12. Sam S, Kundu S, Chatterjee S (2012) Diffusion bonding of titanium alloy to micro-duplex stainless steel using a nickel alloy interlayer: interface microstructure and strength properties. Mater Des 40:237–244 13. Kundu S, Sam S, Chatterjee S (2011) Evaluation of interface microstructure and mechanical properties of the diffusion bonded joints of Ti–6Al–4V alloy to micro-duplex stainless steel. Mater Sci Eng A 528(15):4910–4916 14. Lee MK, Lee JG, Choi YH et al (2010) Interlayer engineering for dissimilar bonding of titanium to stainless steel. Mater Lett 64(9):1105–1108 15. Wang HY, Liu LM (2014) Analysis of the influence of adhesives in laser weld bonded joints. Int J Adhes Adhes 52:77–81 16. Wang HY, Liu LM, Liu F (2013) The characterization investigation of laser-arc-adhesive hybrid welding of Mg to Al joint using Ni interlayer. Mater Des 50:463–466 17. Wang HY, Song G (2017) Influence of adhesive and Ni on the interface between Mg and Fe in the laser-TIG-adhesive hybrid welding joint. Int J Precis Eng Manuf 17(6):823–827

A Machine Vision-Based Multifunctional Image Processing Platform Baoming Li and Peiquan Xu

Abstract With the development of society, robots gradually replace the human beings. The research of machine vision sensing is particularly important. This paper combines the open-source and the free OpenCV2.4.9 computer vision library, Daheng Imavision, and its development kit and VS2013 to achieve the docking. The format conversion of image data captured by industrial Imavision is successful, which adapts to the direct processing of image by OpenCV library function. In order to ensure the accuracy of image captured and processed, the camera is calibrated and corrected based on the platform. Based on MFC interface, modules such as “sub-pixel corner detection and processing,” “mouse center point extraction,” and “measurement of plane and stereoscopic distance” are developed. Combined with the OpenCV library function, a series of algorithm is developed to implement the interface function. The experimental verification and error analysis are carried out by using the captured image. Keywords OpenCV · MFC interface · Camera calibration · Image processing

1 Introduction With the arrival of industry 4.0, the demand for the intelligent machine is getting higher and higher. The premise of machine intelligent realization is that the robot can get a lot of information from the sensing device, then select and judge the information, and finally feed back the information. In order to facilitate the development and application of machine vision researchers, many visual libraries for computer vision have been developed, such as MATLAB, which provides a large number of mathematical functions, but it runs slowly and cannot be directly applied to industrial production. OpenCV is a B. Li · P. Xu (B) College of Materials Engineering, Shanghai University of Engineering Science, Shanghai 201620, China e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 S. Chen et al. (eds.), Transactions on Intelligent Welding Manufacturing, Transactions on Intelligent Welding Manufacturing, https://doi.org/10.1007/978-981-13-3651-5_9

121

122

B. Li and P. Xu

cross-platform computer vision library. It can run on a variety of operating systems and contains a wealth of image processing functions [1]. In recent years, many researchers have carried out a lot of research on image processing based on OpenCV. MPD Albuquerque et al. [2] have used the OpenCV library and the LibSVM library to develop a high-speed image processing algorithm applied to multifaceted asymmetric radiation from the edge detection in C/C++ language. The result presents a correct detection rate of 93.3% and an average image processing rate of 650 frames per second. Wei et al. [3] proposed a method for the implementation of lane detection under Android system based on Open Source Computer Vision Library and used OpenCV to process image with several processing techniques, and the method was verified on Android Virtual Device at last. In the field of face recognition, Guo et al. [4] used the OpenCV image processing library and QT as the system interface framework to realize the recognition of human faces on the Tiny210 platform, Adaboost algorithm and PCA algorithm are used to detect and recognize the face, and the face recognition rate is very high after the test. Raihan et al. [5] made use of OpenCV to enable the system to capture, identify, and analyze the features of the image. Based on the image subtraction technique, the residual characteristics of the subtraction results are detected, and the PCB is quickly checked to find the defective parts. In order to achieve precise distance measurement, Sasaki et al. [6] proposed a simple and low-cost method for measuring workshop distance based on geometric similarity. The size of the object is known by visible light communication. The results show that the error rate of the method is less than 5% at almost all measuring points. Zhang et al. [7] introduced a visual method for distance measurement, using Fourier–Mellin transform to calculate the scaling parameters between images taken at different locations. Finally, the pinhole model is used to calculate the distance from the optical center of the camera to the object through these two parameters. In addition, many scholars have done a lot of image processing research based on OpenCV [8–11]. Compared with other interface platforms, MFC is not flexible and convenient enough to use, so few researchers have developed the industrial camera and built an image processing platform based on MFC in the VS compiler environment. Since the image acquisition module in the development kit of large constant camera is developed based on MFC, it is easier to develop the interface by using MFC on the basis of the development kit than others. Before image acquisition, camera calibration and correction have been finished from the software development environment building. The design of image processing interface and algorithm development, experimental verification, error analysis, summary and prospect, and other aspects of research have been carried out.

A Machine Vision-Based Multifunctional Image …

123

2 The Configuration of the Development Environment 2.1 The Configuration of Environmental Variables To implement the docking of OpenCV2.4.9, VS2013, and Daheng Imavision modeling MER-500-7UC-L, first of all, make sure that the three parts are installed successfully. Then, user variables and system variables under computer environment variables need to be configured, which can enable the command code to automatically search files in the specified directory. The configuration of environmental variables is shown in Fig. 1.

2.2 The Configuration of Project Attributes The three parts are configured under the VS platform to achieve the docking. Opening the “SDK” development kit of Daheng Imavision and selecting “GxSingleCamColor” project under “VC SDK” are to realize the selection of development environment. In order to configure the image processing function of the Daheng Imavision development kit and the library function of OpenCV in the same compiler environment, the project attributes of VS need to be configured. Corresponding to the configuration of “containing directory” and “add-in directory,” directory search is implemented in OpenCV and included files in Daheng Imavision source code. Similarly, the configuration of “Library Directory” and “additional library directory” can automatically

Fig. 1 Configuration of environment variables in the development environment configuration

124

B. Li and P. Xu

Fig. 2 Configuration of project properties in VS

search their respective “lib” files. Adding the “lib” file of Daheng Imavision and OpenCV to “additional dependency” catalog can link and use their function and classes in the compiler environment. With the above configuration, the docking of Daheng Imavision, OpenCV, and VS can be finished. The required configuration project is shown in Fig. 2.

3 Image Processing and Error Analysis 3.1 Camera Calibration In order to increase the light intensity, a lens is usually added to the front of the camera. As is known, it is difficult to produce an ideal lens and keep the lens parallel to the imager. Although the lens is used to solve the problem of light intensity, distortion is introduced. Therefore, the distortion caused by the introduction of the additional lens need to be corrected. Radial distortion and tangential distortion correspond to the problem of lens manufacture and parallelism between lens and imager. There is no radial distortion at the center of the lens (r  0), so the first three items of Taylor series expansion are used to describe quantitatively the radial distortion [12]. This formula introduces three parameters (k 1 , k 2 , k 3 ). In the formula, “(X, Y )” is the original coordinate of the distortion point on the image, and “(X j , Y j )” is the new coordinate after correction. The formula can be expressed as follows, ⎧   ⎨ X j  X 1 + k1 r 2 + k2 r 4 + k3 r 6 (1)   ⎩ Y j  Y 1 + k1 r 2 + k2 r 4 + k3 r 6

A Machine Vision-Based Multifunctional Image …

125

Fig. 3 Calibrated images captured by using the Daheng industrial camera

The tangential distortion is caused by the deviation of the angle, and the vector endpoints change along tangent direction. Therefore, the two-distortion coefficient is introduced (p1 , p2 ), and the formula can be expressed as follows ⎧   ⎨ X j  X + 2k1 Y + p2 r 2 + 2X 2 (2)   ⎩ Y j  Y + [ p1 r 2 + 2Y 2 + 2 p2 X ] To sum up, there are five distortion parameters that need to be calculated, so they are placed in a 5 × 1 matrix corresponding to [k 1 , k 2 , p1 , p2 , k 3 ]. This calibration is accomplished by using the “CameraCalibrator” class. First, the calibration image is captured, and the checkerboard template of 41 mm × 41 mm is calibrated, as shown in Fig. 3. The final calibration results can be obtained by using the library function of OpenCV to calibrate and calculate, and the calibration results are shown in Fig. 4. Thus, distortion correction of the camera is realized by “remap ()” function of OpenCV library function.

3.2 Format Conversion of Digital Images The Daheng Imavision itself does not support the OpenCV library function. Therefore, how to realize OpenCV library function directly processing image data from Daheng Imavision has been the difficulty of Daheng Imavision developers. The image captured by the Daheng Imavision is in the format of “RAW”, which is the original

126

B. Li and P. Xu

Fig. 4 Calibration result

data format that converts the light signal captured to the digital signal with the CCD image sensor, and this conversion has not entered any compression, so its arranged format is in the type of “Byte”. When Daheng Imavision processes image data, the image data is not arranged in a “Mat” format instead of creating a rectangular object by the “RECT” class. The “Mat” class is composed of a matrix head and a matrix that stores image data, which is essentially different from the “RECT” class and also can explain the reason of the direct processing of the image without directly using the OpenCV library function. The image data captured by Daheng Imavision can be processed by the OpenCV library function, so it must be solved from the root of the data arrangement. The object of “Mat” was defined in the callback function of the camera. According to the actual situation of the selected cameras, the corresponding resolution of the corresponding type is found to be 2592 × 1944, so as to array the data of the “Mat” object. The main code to convert the “Byte” type to the “Mat” type is shown as follows. Mat frame(Size(2592, 1944), CV_8UC3, pDlg->m_pBufferRGB)

3.3 Image Processing The image processing platform is mainly based on the MFC interface. Control buttons and key data required on the MFC interface are added to control the image processing. Three image processing modules have been developed, and the results of image processing achieved by each module are shown in Fig. 5.

A Machine Vision-Based Multifunctional Image …

127

Fig. 5 MFC interface module developed and its image processing result achieved

Module 1 is “sub-pixel corner detection of the image”; it can process the extracted corner points to obtain the information of the center, the radius, and so on, by using the “cornerSubPix ()” function to achieve the image’s sub-pixel corner detection. The “circle ()” function can be used to draws circles and the center of a circle, and the “putText ()” function was used to output the relevant information of the circle. The corner points selected are marked by the “putText ()” function, so the corner points can be selected by the “Edit Box”. Clicking on the “Button” button of each module can realize the load of the image, confirmation of point position and display control. Module 2 is complementary to a function of module 1. Although corner points extracted from module 1 are more precise, the corners selection is restricted. Module 2 selects the points by controlling the mouse, which used the “if (event  = CV_EVENT_LBUTTONDOWN)” function to extract the corners of interest. Module 3 is mainly used to measure the length of the object on the same plane and the measurement of the distance between the camera lens and the object. It is mainly based on the two-point distance formula to find the object with known size on the detected image as a ruler to measure. Supposing that the pixel coordinates of two points are A (x 1 , y1 ), B (x 2 , y2 ), and “AB” represents the distance of the image coordinates of the two points. The calculation formula is shown as follows,

128

B. Li and P. Xu

Fig. 6 Schematic diagram of stereoscopic distance measurement

AB 



(x1 − x2 ) · (x1 − x2 ) + (y1 − y2 ) · (y1 − y2 )

(3)

Assuming that the distance from the ruler is “d”, the actual length of the object measured is “L”, so, L

A1 B1 ·d AB

(4)

The detection of three-dimensional distance is done mainly by the principle of similar triangles, as shown in Fig. 6. According to the user manual of Daheng Imavision, it is found that the focal length of this type of industrial camera is “f  12 mm” and the pixel size is “a  2.2 µm”. Assuming that the actual length of the selected calibration object is “L 1 ”, the length of the object on the image is “L 2 ” and the distance from the object to the camera is “S”, so, L 2  AB · a L2 f  S L1

(5) (6)

It can be deduced from Eqs. (5) to (6) S

L1 · f AB · a

(7)

A Machine Vision-Based Multifunctional Image …

129

4 Error Analysis Because the image plane of the camera and the plane of the measured object are hard to achieve the ideal parallelism, the minor error of measurement results of module 1 mainly from the angle of image acquisition. Compared with module 1, module 2 has high flexibility in selecting points, but it produced error in the selection of point by using mouse, angle of acquisition, precision of calculation, and so on. Judging from the test results, the error is small and the result is excellent. For module 3, the center distance of the camera to the door is 5.8 m, the height of the door is 195 cm, the stereo distance of the image measurement data is 6.2 m, and the height of the door is 192.338 cm. After testing, the error of the stereoscopic distance is about 6.9%, and the measurement error of the door height is about 1.4%. From the results of measurement, the error of plane length detection is smaller, but the error of three-dimensional distance detection is larger. When the measured object is far away from the camera, the image is seriously distorted. Therefore, it is difficult to select the ruler and the edge of the image accurately to measure. So, for module 3, the error of stereo distance measurement is related to the camera lens of the object; if the distance between the object and the camera enables the camera to capture a clear image, the measurement results will be more ideal.

5 Conclusions This paper successfully built the docking environment of Daheng Imavision, VS, and OpenCV. The OpenCV library function is used to calibrate and correct the distortion of the Daheng industrial Imavision, and the digital format is converted to adapt to OpenCV. The direct processing of the captured image by the OpenCV library function is realized. Based on the built platform, three image processing modules based on MFC interface are designed and each module is implemented by using OpenCV library function, and the developed modules are tested and verified by the acquisition of image data captured by Daheng Imavision. The results show that the developed module has good measurement results on the plane image, but the measurement error of the stereo distance is larger. Therefore, to improve the accuracy of measurement and the intellectualization of the measurement process, further researches on stereoscopic vision-based need to be carried out on this platform. In addition, the interface platform design and algorithm implementation also need further improvement and optimization. Acknowledgements This work is financially supported by the National Natural Science Foundation of China (51475282) and the Graduate Innovation Project (17KY0515).

130

B. Li and P. Xu

References 1. Kaehler A, Bradski G (2016) Learning OpenCV 3: computer vision in C++ with the Opencv library. O’Reilly Media, Inc, America, pp 1681–1682 2. Albuquerque MPD, Albuquerque MPD, Chacon GT et al (2012) High-speed image processing algorithms for real-time detection of MARFEs on JET. IEEE Trans Plasma Sci 40(12):3485–3492 3. Wei XX, Meng L (2013) A method to implementation of lane detection under android system based on OpenCV. In: Intelligent technologies and engineering systems. Lecture notes in electrical engineering, vol 234. Springer, New York, pp 115–121 4. Guo ZH, Yuan JY, Wu FJ et al (2016) Research on face recognition technology based on Open CV. Electronics World, pp 105–106 5. Raihan F (2018) PCB defect detection USING OPENCV with image subtraction method. In: 2017 International conference on information management and technology (ICIMTech), IEEE, pp 204–209 6. Sasaki N, Iijima N, Uchiyama D (2015) Development of ranging method for inter-vehicle distance using visible light communication and image processing. In: 2015 15th international conference on control, automation and systems (ICCAS), IEEE, Korea, pp 666–670 7. Zhang H, Wang L, Jia R et al (2009) A distance measuring method using visual image processing. In: 2009 2nd international congress on image and signal processing, IEEE, China, pp 1–5 8. Pulli K, Baksheev A et al (2012) Real-time computer vision with OpenCV. Commun ACM 55(6):61–69 9. Deepthi RS, Sankaraiah S (2011) Implementation of mobile platform using Qt and OpenCV for image processing applications. In: 2011 IEEE conference on open systems, Malaysia, pp 284–289 10. Burden J, Cleland M et al (2010) Tracking a single cyclist during a team changeover on a velodrome track with Python and OpenCV. Procedia Eng 2(2):2931–2935 11. Gadhe NB, Lande BK, Meshram BB (2012) Intelligent system for detecting, modeling, classification of human behavior using image processing, machine vision and OpenCV. Int J Adv Res Comput Eng Technol 1(4):266–267 12. Chennamma HR, Rangarajan L (2010) Image splicing detection using inherent lens radial distortion. Int J Comput Sci Issues 7(6):149–158

The Formation and Control of Porosity During GMA Welding of Galvanized Steel Yingming Wu, Chao Hu and Xizhang Chen

Abstract GMA lap welding of 1.4 mm thick galvanized steel DP780 was conducted in this investigation. Effects of different welding modes, heating inputs, and assembly conditions on the porosity in weld bead were examined by X-ray nondestructive detection. The experimental results reveal that the number of pores in the weld bead formed with double pulse (DP) mode is the minimum, compared to those formed under direct current (DC), pulse (P), cold metal transfer (CMT) and cold metal transfer and pulse (CMT+P) welding modes. The porosities ratios of the weld bead obtained using high heating input are improved compared to those obtained employing low heating input in DC, P, DP and CMT welding modes, respectively. The escape of zinc vapors through the reserved gap of 1 mm between steel plates effectively lessens the weld porosity, while the number of pores in the weld evidently increases as a copper liner board placed below galvanized steel plates for lap welding. Keywords GMAW · Porosity · Galvanized steel · X-ray nondestructive detection

1 Introduction Automobile lightweight is one of the purposes for the development of automobile industry nowadays. At present, the main effective approaches include utilizing aluminum and magnesium alloy materials instead of traditional steel material [1, 2], optimizing the design of the structure, employing aluminum/steel composite structure and so on. However, due to the low strength of Al/steel welding joint and the weak weldability of aluminum and magnesium alloy [3], main load-bearing components, such as column and beam, still prefer to steel material for the guarantee of the safety of automobile [4]. Existing researches demonstrate that the thickness of automotive Y. Wu · X. Chen (B) School of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, China e-mail: [email protected] Y. Wu · C. Hu · X. Chen School of Material Science and Engineering, Jiangsu University, Zhenjiang 212013, China © Springer Nature Singapore Pte Ltd. 2019 S. Chen et al. (eds.), Transactions on Intelligent Welding Manufacturing, Transactions on Intelligent Welding Manufacturing, https://doi.org/10.1007/978-981-13-3651-5_10

131

132

Y. Wu et al.

body plate ranging from 1.0 to 1.2 mm reduces by 0.2–0.5 mm accompanying with the 15–20% decrease of body weight and the 8–10% decrease of fuel consumption as using high-strength steel plate instead of traditional steel plate in automotive body [5, 6], which leads to a large number of researchers to study high-strength steel. Dual phase steel has great potential application in automotive body attributable to the characteristics of good formability, high rate of energy absorption, low yield ratio, high efficiency of initial work-hardening and so on [7–9]. The microstructure characteristics of dual phase steel are that hard martensite distributes on the ferrite grain boundaries or within the ferrite grain balancing the strength and toughness of steel. The proportion of the martensite phase generally ranging from 5 to 30% determines the strength of dual phase steel [10]. Moreover, the corrosion of ordinary steel easily appears in damp environment affecting the appearance and service life of steel. Galvanizing technology is widely used in automotive industry as its advance in corrosion resistance of steel [11]. Galvanized layer not only provides the physical shielding effect of the steel plate, but also supplies matrix steel with the electrochemical protection. As the local zinc layer is damaged, the galvanized layer and matrix steel are exposed in the corrosion environment to form a primary battery in which zinc layer acts as the anode to be oxidized while iron acts as the cathode to be protected, and the dense substance produced by the corrosion of zinc also brings a reduction of the reaction rate. Previous studies indicate that the lifetime of galvanized steel is several times or even more than ten times higher than conventional steel plate, remarkably boosting corrosion resistance [12]. Gas metal arc welding (GMAW) compared with other welding methods possesses unique advantages including high production efficiency, easy to realize sautomation, low invest, easy to achieve thin plate welding and so on. However, arc pressure makes part of the zinc vapor restricted in the molten pool for arc welding resulting in the formation of blowholes in welding seam, which affects the performance of the welding joint [13]. In this investigation, GMAW was performed with the lap configuration of 1.4 mm thick DP780 galvanized steel to discuss the sensitivity of porosity in the welding seam formed under direct current (DC), pulse (P), double pulse (DP), cold metal transfer (CMT) and cold metal transfer and pulse (CMT+P) welding modes, respectively, and to analyze the factors affecting the formation of porosity. Experiments conducted in this study aimed at finding a way to achieve high speed welding of galvanized dual phase steel plate and a low porosity ratio in welding seam.

2 Experimental Procedures 2.1 Experimental Material and Equipment DP780 galvanized steel sheets with the size of 150 mm × 100 mm × 1.4 mm were employed in this investigation. The microstructure of DP780 galvanized steel is

The Formation and Control of Porosity …

133

Fig. 1 Microstructure of DP780 galvanized steel

shown in Fig. 1, in which the proportion of martensite is about 15%. The lowest yield strength and tensile strength of DP780 galvanized steel (as received) are 420 and 780 MPa, respectively. ER70S-6 welding wire with the diameter of 1.2 mm produced by American Lincoln Company and shielding gas produced by Air Liquide with 90% argon and 10% CO2 were employed. The chemical compositions of DP780 galvanized steel and ER70S-6 welding wire are exhibited in Table 1 and the mechanical properties of DP780 zinced sheet are shown in Table 2. Fronius TPS5000 CMT digital welding machine including direct current (DC), pulse (P), double pulse (DP), cold metal transfer (CMT) and cold metal transfer and pulse (CMT+P) welding modes was employed with the rated current of 500 A. Welding torch was controlled by ABB IRB1520 robot arm in welding process. The welding platform is displayed in Fig. 2. The specimens were detected by X-ray nondestructive testing instrument after welding.

2.2 Experimental Methods Lap welding of DP780 galvanized steel sheet was conducted by Fronius TPS5000 CMT digital welding machine along the length direction of the steel plate. The length of the welding seam obtained after welding is about 130 mm, and the configuration of the welded joint is shown in Fig. 3. The actual welded joint of DP780 galvanized steel sheets is displayed in Fig. 4. Shielding gas flux and genuine length of welding wires in welding were 25 L/min and 12 mm, respectively. Torch work angle and weld travel angle are 70° and 80°, respectively, as exhibited in Figs. 5 and 6. Experiment one: GMAW of DP780 galvanized steel sheets was performed with welding speed of 6 mm/s under DC, P, DP, CMT and CMT+P welding modes,

DP780 ER70S-6

0.09 0.08

C

0.14 0.92

Si 1.99 1.48

Mn 0.006 –

P 0.002 –

S

Table 1 Chemical compositions of DP780 steel and ER70S-6 welding wire (wt%) Al 0.04 0.02

Cr – 0.02

Mo – 0.01

Ni – 0.04

134 Y. Wu et al.

The Formation and Control of Porosity … Table 2 Mechanical properties of DP780 steel Yield strength Re /MPa Tensile strength Rm /MPa 500–650 Fig. 2 Welding platform

Fig. 3 Schematic diagram of welded joint

Fig. 4 Actual welded joint of DP780 galvanized steel sheets

≥780

135

Elongation percentage after fracture A/% ≥10

136

Y. Wu et al.

Fig. 5 Schematic diagram of torch work angle

Fig. 6 Schematic diagram of weld travel angle

Table 3 Welding parameters in different modes Sample Mode Wire feeding Welding Welding speed speed current I/A V f /(m min−1 ) V w /(mm s−1 ) 1# 2# 3# 4# 5#

DC P DP CMT CMT+P

2.2 2.5 2.6 3.6 2.6

6 6 6 6 6

95 80 83 132 80

Welding voltage U/V

Line energy E/(J mm−1 )

15.9 18.9 19.1 12.1 20.3

251.8 252.0 264.2 266.2 243.6

respectively. According to the range of wire feeding speed under 6 mm/s welding speed in each mode, high wire feeding speed was employed as far as possible on the basis of forming excellent welding seam. Welding parameters under different modes are shown in Table 3. Susceptibilities of porosity in welding seams under different welding modes were investigated. Experiment two: Two wire feeding speeds were employed for the welding of DP780 galvanized steel sheets under DC, P, DP and CMT welding modes due to the welding parameter range was narrow with 6 mm/s welding speed in CMT+P welding mode. The welding parameters are exhibited in Table 4. The susceptibilities of porosity in welding seams with different line energies under DC, P, DP, CMT welding modes were analyzed. Experiment three: Two groups of comparative experiment were executed with the same welding parameters under pulse welding mode. GMA lap welding for two DP780 galvanized steel plates, between which placed a 1 mm thick steel sheet was performed, compared to that with no gap, to evaluate the effect of the gap between steel plates on weld porosities of lap joints for DP780 galvanized steel sheets. In addition, GMA lap welding for DP780 galvanized steel sheets with a copper liner board underneath was performed, compared to that with no cooling device, to assess the effect of heat dissipation on welding porosities of lap joints.

The Formation and Control of Porosity …

137

Table 4 Welding parameters with 6 mm/s welding speed in different modes Sample Mode Wire feeding Welding Welding Welding speed speed current I/A voltage U/V V f /(m min−1 ) V w /(mm s−1 ) 6# 7# 8# 9# 10# 11# 12# 13#

DC DC P P DP DP CMT CMT

2.2 2.0 2.5 2.2 2.6 2.4 3.6 3

6 6 6 6 6 6 6 6

95 85 80 71 83 77 132 124

Table 5 Welding parameters in different assembly conditions Sample Mode Welding wire Welding Welding feeding speed current I/A speed V w /(mm s−1 ) V f /(m min−1 )

Line energy E/(J mm−1 )

15.9 15.7 18.9 18.4 19.1 18.8 12.1 11.8

251.8 222.4 252.0 217.7 264.2 241.3 266.2 243.9

Welding voltage U/V

Assembly condition

No gap and cooling device 1 mm gap

14#

P

3.2

10

102

20.1

15#

P

3.2

10

102

20.1

16#

P

3.2

10

102

20.1

With a copper liner underneath

Experimental parameters are shown in Table 5. The susceptibilities of porosity in weld bead under different assembly conditions were analyzed. The surface of steel plates was scrubbed with acetone removing dirt and grease before welding. The X-ray detection of specimens was conducted with X-ray nondestructive testing instrument after welding to appraise porosities in welding seams.

3 Experimental Results and Discussion 3.1 Experimental Results Stomatal distributions of welded specimens with welding speed of 6 mm/s under DC, P, DP, CMT and CMT+P welding modes are shown in Fig. 7. As shown in Fig. 7, the number of pores in the weld bead of CMT mode is the maximum with the similar line energy and these blowholes, one next to one in the internal of 0–2 mm, formed in a straight line along the direction of welding in the

138

Y. Wu et al.

whole weld bead. The number of pores is the minimum in the weld bead of DP mode, only two pores appearing in arc-ending side due to low temperature. Moreover, the pores in weld are spherical-shape under DC, P and CMT modes as heating inputs in these welding processes are relatively uniform. The sizes of these pores are relatively large. However, the pores in weld under CMT+P mode are mostly strip-type due to the high heating input of pulse mode resulting in that partial zinc vapors on the surface of steel plates run into the molten pool in the alternate process of CMT mode and the pulse mode, while the low heating input as turned into CMT welding mode leading to a few zinc vapors, which have no time to escape during cooling process, restricted in weld bead. Stomatal distributions of welded specimens with 6 mm/s welding speed for different line energies under DC, P, DP, CMT welding modes are shown in Fig. 8. As exhibited in Fig. 8, the number of pores in weld of CMT mode is the maximum for DP780 galvanized steel plates. These pores are produced in a straight line in the interval less than 2 mm along the weld bead. Furthermore, the numbers of pores in the weld of DC and P modes significantly decrease compared to that of CMT mode. There is almost no pore in the welding seam of DP mode except few ones in arcending side because of low temperature. In addition, it is clear that the weld beads obtained under high line energy possess a lower porosity of gas hole, compared to those obtained under low line energy, in each welding mode, respectively. Stomatal distributions of welded samples in different assembly conditions under P welding mode are shown in Fig. 9. As exhibited in Fig. 9, with the equal welding parameters in P mode, the porosity percentage of weld in sample 14#, in which several extremely small pores existed, employing no additional assembly is slightly higher than that in sample 15# holding a 1 mm gap between steel plates. What’s more, the porosity ratio of weld in sample 16#, under which is a copper liner board, is severely higher than those in sample 14# and 15#, and the gas holes appear, one by one in the interval of 0–1 mm, in a straight line along the center line of weld in sample 16#.

Fig. 7 Stomatal distributions of specimens through X-ray nondestructive detection in different modes: 1# under DC mode; 2# under P mode; 3# under DP mode; 4# under CMT mode; 5# under CMT+P mode

The Formation and Control of Porosity …

139

Fig. 8 Stomatal distributions of specimens through X-ray nondestructive detection with different line energies under different modes: 6#, 7# under DC mode; 8#, 9# under P mode; 10#, 11# under DP mode; 12#, 13# under CMT mode; 6#, 8#, 10#, 12# with high line energies; 7#, 9#, 11#, 13# with low line energies

Fig. 9 Stomatal distributions of samples through X-ray nondestructive detection with different assembly conditions under P mode: 14# no gap and cooling device; 15# 1 mm gap; 16# with a copper liner board underneath

3.2 Discussion In the welding process of CMT mode, the welding current is almost zero in the period of droplet transition, leading to the low heating input and the rapid cooling of molten pool metal. Part of zinc vapors retained in molten pool has no enough time to escape, giving rise to a large number of gas holes produced along the center of weld. Compared with CMT mode, heating inputs are higher in DC and P modes and high temperature of molten pool with the lower coefficient of viscosity is maintained in a relatively longer time, bringing about lower porosity ratio in weld attributed to the easy escape of partial zinc vapors. CMT+P mode in which the whole welding heating input is higher than that of CMT mode consisted of alternating CMT and P processes. Pulse peak current causes high heating input giving enough time for zinc bubbles in molten pool to escape. Hence, the porosity in the weld of CMT+P mode is significantly improved compared to that of CMT mode. Cooperative pulse in DP

140

Y. Wu et al.

mode has a stirring effect on molten metal, combining small bubbles into large ones leading to zinc bubbles more easily to float [14, 15]. Finally, there is almost no pore in weld bead except the arc-ending side. High temperature of molten pool is held longer with higher line energy of welding in each mode, lengthening the floating time of zinc bubbles, bringing about less and smaller pores in welding seam. In contrast, with lower line energy of welding, the rapid cooling of molten pool metal provides little time for zinc bubbles to escape giving rise to the degradation of porosity in the weld. What’s more, with the increase of welding current, the pressure of arc to molten pool also enlarges causing the increase of concave degree of molten pool surface, which is beneficial to the float of zinc bubbles owing to the short distance between molten pool surface and zinc bubbles. The reserved gap of 1 mm between steel plates in sample 15# is beneficial to the diffusion of zinc vapors, reducing the amount of zinc vapors entering into molten pool. High temperature duration of molten pool is shortened as a copper liner board is placed below the welding joint during cooling process, making it difficult for zinc bubbles to escape. In practical welding process, a certain gap between steel plates is in favor of the disappearance of blowholes in the welding seam, whereas the excessive gap also brings poor weld. And the number of pores in the weld bead increases as the acceleration of cooling. Thus, appropriately large heating input and proper welding mode are significant for the improvement of porosity in the welding seam.

4 Conclusions (1) With the welding speed of 6 mm/s, the number of pores in the weld bead formed with DP welding mode is the minimum, when choosing as high speed as possible in the range of reasonable welding parameters for each welding mode. The porosity ratio of the weld bead formed with CMT welding mode is the highest because of low heating input. (2) The line energy in each welding mode has a great influence on weld porosity. High line energy leading to long duration of high temperature of molten pool and high welding current causing great pressure to the surface of molten pool are both beneficial for the zinc bubbles to escape, reducing the number of pores in the welding seam. (3) Assembly condition has significant impacts on the porosity of weld. The diffusion of zinc vapors from the reserved gap of 1 mm between steel plates effectively reduces the weld porosity, while the number of pores in the welding seam evidently increases as a copper liner board placed below steel plates for lap welding.

The Formation and Control of Porosity …

141

References 1. Windmann M, Röttger A, Kügler H et al (2017) Microstructure and mechanical properties of the heat-affected zone in laser-welded/brazed steel 22MnB5-AA6016 aluminum/AZ31 magnesium alloy. J Mater Process Technol 247:11–18 2. Huang JK, He XY, Guo YN et al (2017) Joining of aluminum alloys to galvanized mild steel by the pulsed DE-GMAW with the alternation of droplet transfer. J Manuf Processes 25:16–25 3. He BL (2011) Researching status and developing trend of the effect of ultrasonic impact on fatigue properties of magnesium weld joints. J East China Jiaotong Univ 28(3):73–77 4. Wang NN, Qiu RF, Cui LH et al (2014) Research status of welding-brazing between aluminum alloy and steel. Light Alloy Fab Technol 42(1):13–17 5. Ye P, Shen JP, Wang GY et al (2006) Current status and development of light-weighting high strength steel used in automobiles. Mat Mech Eng 30(3):4–7 6. Wang L (1997) High tensile strength IF steel sheet for automotive applications. Baosteel Technol 1:58–61 7. Huang PF, Xiong W, Tang C et al (2014) Microstructure and tensile shear property of high strength DP780 steel MAG welding lap joints. Mat Mech Eng 38(7):20–24 8. Zhang XH, Mao WM, Zhu GH et al (2008) Research and development and production of cold rolled ultra-high strength dual-phase steel for automobile purpose. Wisco Technol 46(3):54–58 9. Tian ZQ, Tang D, Jian HT et al (2009) Research and production status of dual phase steels for automobiles. Mat Mech Eng 33(4):1–5 10. Jiang HT, Tang D, Mi ZL (2007) Latest progress in development and application of advanced high strength steels for automobiles. J Iron Steel Res 19(8):1–6 11. Song J, Gao H (2015) Effect of cladding materials on the process window and electrode life. Electric Weld Mach 45(4):187–189 12. Wei YS (2011) Types of galvanized steel sheet and its application in automobiles. Mat Appl 197:51–56 13. Mei LF, Chen GY, Yan DB et al (2015) Impact of inter-sheet gaps on laser overlap welding performance for galvanized steel. J Mater Process Technol 226:157–168 14. Liu WM, Lu FG, Tang XH et al (2012) Research on sensibility to gas holes at different welding modes by GMAW welding for DP590GA. Cast Forging Weld 41(5):145–147 15. Wu CS (2008) Welding process and molten pool behaviors. Machinery Industry, Beijing, p 184

Part III

Short Papers and Technical Notes

Optimization of SURF Algorithm for Image Matching of Parts Hongyan Duan, Xiaoyu Zhang and Wensi He

Abstract SURF algorithm, which is featured with high speed and good robustness, is a common algorithm for feature points detection. But this algorithm has such drawbacks as the separating capacity of its feature points descriptor is low and principal direction of feature points is not accurate, which can easily cause less image matching pairs and high mismatching ratio. Therefore, an improved SURF algorithm is proposed to increase matching pairs effectively and raise accuracy of matching. For the algorithm, two new-type feature sets are added, and 128-dimensional feature descriptor is established. The method of combining Euclidean distance with cosine similarity match is adopted to match feature points. Then RANSAC method is used to lower mismatching ratio, thus achieving robots’ parts-matching in the indoor environment. The experiment result shows that under the conditions of images rotating, zooming, blurring, lighting and view change, the improved algorithm has better robustness. Besides, its matching ratio and accuracy are higher than standard SURF algorithm. Keywords Image processing · Improved SURF algorithm · Image matching Cosine similarity · RANSAC algorithm

1 Introduction In recent years, machine vision has been widely used in the positioning and recognition of robots. Compared with laser and ultrasonic sensors, the visual sensor has the advantages of high accuracy and high speed. It can collect rich information, and it has been widely used in robot systems. Image matching is an important part of visual positioning. By matching two pictures of the same scene taken under different conditions, the position of the object in the two images is determined through comH. Duan (B) · X. Zhang · W. He College of Mechanical and Electrical Engineering, Lanzhou University of Technology, Lanzhou 730050, China e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 S. Chen et al. (eds.), Transactions on Intelligent Welding Manufacturing, Transactions on Intelligent Welding Manufacturing, https://doi.org/10.1007/978-981-13-3651-5_11

145

146

H. Duan et al.

puter analysis and processing. According to the matching algorithm, matching can be divided into two types: methods based on region matching and feature matching. Feature-based matching method has the advantages of fast calculation speed, good robustness, etc. It is a commonly used matching method [1]. SIFT (Scale-invariant feature transform) [2] is the most commonly used detection algorithm of feature point and has invariant characteristics in translation, scaling and rotation of a picture. However, the SIFT algorithm consumes much time and reduces the speed of operation due to the use of pictures of different scales in the downsampling process. The SURF algorithm proposed by Bay et al. [3, 4] and Mikolajczk inherits the invariance of SIFT and uses the integral image and template scaling to approximate the size variation of an image, making the operation speed of feature point about 3 times faster than SIFT [5]. However, the SURF algorithm is affected by the direction of the pixel gradient in the local area when finding the main direction of the feature point, resulting in inaccurate main direction. In the matching process, only the Euclidean distance is used as a criterion. When there is too much noise in the image or there is a large difference in the matching objects, a large error may occur. Lienhart [6] and others built a haar-like feature set to improve the discrimination and computational efficiency of algorithm descriptors. Jia [7] and others use the “return” shape square neighbor instead of the circular four-quadrant to construct a 32-dimension descriptor based on the distance relationship with the feature points and to improve the computational efficiency, but the algorithm finds few matching points on a surface-smooth part. Yan [8] and others use feature point pairs to construct Delaunay triangular meshes and restrict the geometric correlation of feature point pairs according to triangle edge information, thus effectively improving the matching accuracy. Xie [9] and others use the SURF algorithm to achieve underwater image registration, and the approval speed is increased by about five times. Fu [10] and others use Mahalanobis distance to calculate the feature points of the first match, eliminate false matching points, improve the matching accuracy and achieve a good positioning effect. In view of the above-mentioned literature, this paper proposes an improved SURF matching optimization algorithm, which adds two kinds of wavelet features to the SURF algorithm and enhances the distinguishability of the feature descriptors in the linear and diagonal directions. Then, the method of cosine similarity and Euclidean distance is used to perform the second matching to remove the pseudo-feature points. Finally, the RANSAC algorithm is used to further reduce the false matching rate. In the case of ensuring the speed of operation, the feature recognition and matching accuracy are improved.

Optimization of SURF Algorithm for Image Matching of Parts

147

2 SURF Algorithm and Haar-like Feature Set 2.1 SURF Algorithm SURF is a local feature description algorithm with high efficiency and high robustness. It not only keeps the rotation and scale scaling constant, but also maintains a certain degree of stability for changes in illumination, viewing angle, affine transformation and noise [11]. SURF uses Hessian matrix H(x, σ ) to detect feature points [12]. Each pixel can be solved as a Hessian matrix, where X represents the coordinates of the feature points, σ represents the scale, and L x x (x, σ ) is the convolution of input image and 2 Gaussian second-order differential ∂∂x 2 g(σ ). g(σ ) is a Gaussian function. Then the second-order standard Gaussian function is used in the filter, and the second-order partial derivative is calculated by the convolution of the specific nucleus to calculate the H matrix. The expression is as Eq. (1):   L x x (x, σ ) L x y (x, σ ) (1) H (x, σ )  L x y (x, σ ) L yy (x, σ ) Because the feature points in the image need to be scale-independent, Gaussian filtering is performed on the feature points to eliminate the correlation of the feature points, and Hessian calculation is performed. L(x, t) represents the image at different resolutions and the calculation formula is as follows Eqs. (2) and (3) ∂ 2 g(t) ∂x2 L(x, t)  G(t) · I (x, t) G(t) 

(2) (3)

Bay proposes replacing the Gaussian second-order differential operator (L x x , L x y , L yy ) with a box filter for the purpose of simplifying calculations, so the weight w  0.9 is introduced to eliminate errors. The Hessian matrix determinant is shown as (4): det(Happrox)  Dx x D yy − (0.9Dx y )2

(4)

The Hessian matrix determinant for each pixel is compared with the threshold to remove pixels with low thresholds. The non-maximal suppression was then applied to the qualified pixels and 26 points in the three-dimensional domain. Finally, subpixel level feature points are obtained by the linear difference method. In the reserved feature point area, the horizontal and vertical Haar wavelet feature sums of all the feature points in the fan-shaped area are counted in units of 60° sectors, and then the 60° sector rotates at certain intervals, and after one revolution, the feature and the

148

H. Duan et al.

maximum are obtained. The fan direction serves as the main direction of the feature point, thus ensuring the rotation invariance of the algorithm. After the main direction of the feature point is selected, a square frame is selected around it, and the side length is 20 s (s is the scale of the feature point). The direction is the main direction of the feature point, and then it is divided into 16 sub-areas, and the horizontal and vertical Haar wavelet features of   each sub-area are counted. A fourdimensional vector V  dx, |dx|, dy, |dy| is formed and normalized to form a 16 × 4 64-dimensional SURF description operator, which makes the algorithm robust to changes in brightness.

2.2 The Haar-like Feature Set The Haar-like feature set designed by Lienhart et al. enriches the simple feature description and enhances computational efficiency. It contains 15 common features, as shown in Fig. 1, including 4 edge features (Fig. 1a–d); 8 line features (Fig. 1e–l); 2 center-surrounding features (Fig. 1m, n) and 1 diagonal feature (Fig. 1o). These features are independent of the horizontal and vertical scales and can generate rich and complete feature sets. Each feature set can be considered as a filter. And the weight of the black part is 1 and the weight of the white part is −1, and it can be combined with the integral graph algorithm. For example, the line feature graph 1e has a height of 2 and the width is 6, which can be expressed as Eq. (5): const int dl_s[NL][5]  {{0, 0, 2, 2, −1}, {2, 0, 4, 2, 1}, {4, 0, 6, 2, −1}}

(5)

wherein NL represents the number of black and white rectangles and 5 represents five parameters in each set. The first and the second parameters represent the integration start coordinate, and the third and fourth parameters represent the integrated end point

Fig. 1 Haar-like feature set

Optimization of SURF Algorithm for Image Matching of Parts

149

coordinates. The fifth coordinates represent the weights. The experiment proves that additional features significantly enhance the performance of the learning system and improve the performance of the detection system.

3 The Optimization of the Matching Algorithm The SURF algorithm only uses two features (a) and (b) in Fig. 1 in constructing the descriptors and the feature distinguishing ability is weak, resulting in incomplete feature description. In the feature point matching process, due to the similarity of the local point field information of the image, the matching degree of two different feature point descriptors exceeds the matching degree of the feature descriptors of the same point. And because only the Euclidean distance matching feature is adopted and the feature descriptions are not considered, the correlation between sub-vectors leads to a high false match rate. Therefore, in the process of constructing descriptors, this paper adds two feature sets to improve the distinguishing ability of features, and then adds cosine similarity matching and RANSAC algorithm to eliminate the mismatched pair.

3.1 The Improved SURF Descriptors The level (Fig. 1a) and vertical (Fig. 1b) feature set descriptors used in SURF are weakly discriminating and now the (e) and (o) features of the Haar-like feature set are added to describe linear and diagonal features. The original linear feature (e) is 2 in height and 6 in width, black in the middle, and white in both sides. The sampling interval is 6 s × 2 s. In order to unify the size of the filter and improve the computational efficiency, its size is changed to a height of 4, a width of 4 and a sampling interval of 4 s × 4 s. Diagonal features are as follows. Its height is 4 and width is 4; it is black and white; its sampling interval is 4 s × 4 s as shown in Fig. 2. After the main direction is selected, a square frame with a side length of 20 s is selected around the feature point, and then the frame is divided into 16 sub-areas.

Fig. 2 Improved surf descriptors

150

H. Duan et al.

Fig. 3 Process diagram

The Haar wavelet features in four directions of level (dx), vertical (dy) linear (dl) and diagonal (dd) of 25 pixels in each per sub-area are counted. An 8-dimensional vector is formed, as shown in Eq. (6), and normalized to form a 16 × 8 128-dimensional description operator, as shown in Fig. 3.         |dx|, |dy|, |dl|, |dd| V  dx, dy, dl, dd, (6)

3.2 Vector Space Cosine Similarity Matching The criteria for judging the similarity of two vectors in space are generally as follows: distance measurement method and similarity function method. The distance measurement method judges the degree of difference between vectors based on the distance existing in the vector space. The similarity function uses the size of the function value to indicate the degree of difference between the two vectors. The Euclidean distance and cosine similarity are commonly used distance measures and similarity measures. This article combines two methods to remove redundant matching pairs by setting the threshold of the similarity function. Cosine similarity measure is to calculate the degree of similarity between feature points, which transforms the coordinates of feature points into vector space and obtains the cosine value between adjacent feature points. The value of the cosine value represents the degree of similarity between feature points. The larger the cosine value is, the more similar the feature points are. The specific method is to use the matching points of the two images determined by the Euclidean distance as the input points of the cosine similarity matching. Then the cosine value of the corresponding matching point is calculated and it is compared with the threshold K. For two vectors x and y, the cosine similarity expression is shown in Eq. (7):

Optimization of SURF Algorithm for Image Matching of Parts

S(x, y) 

n

i1 x i yi  n 2 2 1/2 i1 x i i1 yi

(x, y)  n x · y

151

(7)

3.3 The RANSAC Algorithm The RANSAC algorithm is a classical algorithm in image matching. It uses the idea of hypothesis, verification and correction to use all measured data and sets the threshold to divide the measured data into inliers and outliers. By continuously assuming, iterating and verifying, we finally obtain a globally optimal model. The estimation steps in this paper are as follows: 1. N pairs of feature point pairs after the second match are used as candidate point pairs, and three-point pairs are randomly selected to calculate the mapping matrix M. 2. Calculate the distance between the remaining N-3 group point pair and the candidate matching point after the M matrix transformation. 3. If the distance is less than the set threshold, the candidate points are classified as internal points, and the number of internal points is counted. If the distance is greater than the threshold, the points are classified as external points. 4. Select another 3 pairs of matching points. Repeat steps 1–3. After iterating for t times, select the mapping matrix with the largest number of interior points as the optimal model. The inner point is the correct matching pair.

4 Experiment and Result Analysis The experimental computer CPU frequency is 2.2 GHz; the memory is 4 GB; the software is written by the development environment of Visual Studio 2010, and the Open CV computer vision library is used. The camera and robot arm used are shown in Fig. 4. The binocular camera is used to take 5 sets of pictures of each part under the four changes of scale, brightness, blur, and angle of view, and then the left and right cameras are used to take pictures. The cosine similarity matching threshold K is selected as 0.9. To verify the performance of the improved algorithm, the experiment compares the matching logarithm, the subjective matching visual graph and the matching accuracy. The improved algorithm of this paper is represented by HSURF, and the images are captured by the left and right cameras under each change condition. The matching logarithmic line graph is shown in Fig. 5. The abscissa shows the five matching patterns of scale, brightness, blur and angle of view in turn. The ordinate represents the matching logarithm. One of the matches for each condition change is shown below. Figures 6, 7, 8 and 9 are subjective visual match diagrams of scale, brightness, blur and perspective

152

H. Duan et al.

Fig. 4 Experimental scene

change, respectively. Figure 6 is a photograph of the robot arm moving upward by 0.3 m. Figure 7 is a photograph of the aperture rotating at a dark angle of 30°. Figure 8 is a photograph of the focal length adjusted by 15°. Figure 9 is a photograph of the robot arm rotated 30° to the right. Figures 10, 11, 12 and 13 are line graphs of the matching accuracy when the image rotation and scale, brightness, blur, and viewing angle change. The abscissa indicates the matching of the right and left camera-captured pictures under each condition change, and the ordinate indicates the matching accuracy rate for verification improvement. The SURF algorithm and SIFT algorithm are added to test and improve algorithm performance and to make the experimental results more complete. From the above experimental data analysis, it can be found that after adding the descriptors of the linear and diagonal features, the improved algorithm significantly increases the number of pairs of feature matches, and has stronger feature description capabilities than the SURF and SIFT algorithms. It can find feature points on the surface of a component with not obvious features. Compared with SURF and SIFT algorithms, the proposed algorithm has better matching performance, and the images can show good robustness when scale, brightness, blur and perspective change.

Optimization of SURF Algorithm for Image Matching of Parts

Fig. 5 Matching pairs under different conditions

(a) SURF

(b) HSURF

Fig. 6 Scale change

(a) SURF Fig. 7 Brightness change

(b) HSURF

153

154

H. Duan et al.

(a) SURF

(b) HSURF

Fig. 8 Fuzzy change

(a) SURF Fig. 9 View change

Fig. 10 Scale change

(b) HSURF

Optimization of SURF Algorithm for Image Matching of Parts

Fig. 11 Brightness change

Fig. 12 Fuzzy change

155

156

H. Duan et al.

Fig. 13 View change

5 Conclusions Aiming at the shortcomings of SURF algorithm, such as low separation ability of feature points descriptor and inaccurate main direction of feature points. In this paper, the linear and diagonal features of the Haar-like feature set are added to the SURF descriptor, which improves the discriminating ability of the descriptors. It uses cosine similarity and Euclidean distance to combine the two matching methods, and finally uses the RANSAC algorithm to further eliminate mismatching. And the SURF algorithm is optimized to solve the problem of poor feature distinguishing ability and high mismatch rate. As can be seen from Figs. 10, 11, 12 and 13, the matching accuracy of HSURF algorithm is mostly higher than that of SURF algorithm and SIFT algorithm when scale, brightness, blur and angle of view change. Especially when the angle of view is changed, the matching accuracy of the HSURF algorithm is higher than that of the other two algorithms. The experimental results show that the improved algorithm has strong feature description ability and robustness under picture scale, blur, brightness change and viewing angle change.

Optimization of SURF Algorithm for Image Matching of Parts

157

References 1. Ding NN, Liu YY, Zhang Y et al (2012) Fast image registration based on SURF-DAISY algorithm and randomized kd trees. J Optoelectron Laser 23(7):1395–1402 2. Lowe DG (2004) Distinctive image features from scale-invariant key points. Int J Comput Vis 60(2):91–110 3. Wang M, Yang K, Hua XS et al (2010) Towards a relevant and diverse search of social images. IEEE Trans Multimed 12(8):829–842 4. Bay H, Ess A, Tuytelaars T et al (2008) Speeded-up robust features. Comput Vis Image Underst 110(3):404–417 5. Bauer J, Sünderhauf N, Protzel P (2007) Comparing several implementations of two recently published feature detectors. IFAC Proc Vol 40(15):143–148 6. Lienhart R, Kuranov A, Pisarevsky V (2003) Empirical analysis of detection cascades of boosted classifiers for rapid object detection. Dagm 2781:297–304 7. Jia L, Fu W, Wen W et al (2013) Image matching based on improved sift algorithm. Chin J Sci Instrum 34(5):1107–1112 8. Yan ZG, Jiang JG, Guo D (2014) Image matching based on surf feature and delaunay triangular meshes. Acta Autom Sin 40(6):1216–1222 9. Xie Y, Li X, Lu J et al (2010) Underwater images real-time registration method based on surf. J Comput-Aided Des Comput Graph 22(12):2215–2220 10. Fu W, Qin C, Liu J et al (2011) Matching and location of image object based on sift algorithm. Chin J Sci Instrum 32(1):163–169 11. Hu M, Chen J, Shi C (2015) Three-dimensional mapping based on SIFT and RANSAC for mobile robot. In: IEEE international conference on cyber technology in automation, control, and intelligent systems. IEEE, pp 139–144 12. Huang L, Chen C, Shen H et al (2015) Adaptive registration algorithm of color images based on surf. Measurement 66:118–124

Research on Implicit Genetic Inverse Solution Algorithm for Eight-DOF Mechanical Arm of Photography Robot Qi Dong, Xingang Miao, Su Wang and Xingai Peng

Abstract In this paper, an implicit genetic algorithm is proposed to solve the inverse kinematics problem of a robot with redundant degree of freedom. The robots use DH model calibration. Any two-dimensional double-redundant vector is chosen as the individual, and the kinematics of the photography robot is solved by genetic algorithm. In an infinite inverse solution, an effective inverse solution is achieved by optimizing the objective function. MATLAB is used to verify the optimal solution. Optimizing the posture of the robot can improve the robot’s position and posture accuracy. The photography robot is installed on the welding robot head, and the accuracy of the welding spot can be improved through image positioning. Keywords Photography robot · Inverse kinematics Redundant degrees of freedom · Implicit genetic algorithm

1 Introduction The inverse kinematics of a robot with redundant degrees of freedom (DOF) is expressed as a solution to a nonlinear equation. At present, the research of genetic algorithm to solve robot with redundant DOF mainly focuses on the optimization of robot joint space by using genetic algorithm [1]. The fitness function of the minimum position error and the minimum angle of each axis is taken as the objective optimization function of the method. With this method, the accuracy can achieve the desired effect. However, this method can only optimize the position of the robot, and the posture cannot be optimized, which leads to a deviation between the optimum value and the target value. In this paper, the implicit genetic algorithm is used to Q. Dong · X. Miao (B) · S. Wang Beijing Key Laboratory of Robot Bionics and Function Research, Beijing University of Civil, Engineering and Architecture, Beijing 100044, China e-mail: [email protected] X. Peng China Petroleum Pipeline Engineering Corporation, Langfang 065001, China © Springer Nature Singapore Pte Ltd. 2019 S. Chen et al. (eds.), Transactions on Intelligent Welding Manufacturing, Transactions on Intelligent Welding Manufacturing, https://doi.org/10.1007/978-981-13-3651-5_12

159

160

Q. Dong et al.

optimize the inverse of the objective function, not the traditional genetic algorithm. In this method, implicit gene added to the explicit expression of the fitness function in variable is discarded, the dimension of the solution space is reduced, the optimal solution is found, and the target pose and pose problem of the robot are effectively improved. Combining a photography robot with a welding robot helps to accurately capture welding points.

2 A Kinematic Model of Photography Robot The type of photography robot studied is eight DOF. Its connecting rods are rigid. Based on this, a kinematic model of the robot is established. The study of kinematics model mainly solves the problem of robot positioning, especially for describing the positional relationship between connecting rod [2, 3]. The three-dimensional model of the photography robot is shown in Fig. 1. There are eight motion axes in the photography robot; refer to the number ➀–➇ in Fig. 1. They are the bottom of the linear motion axis r 1 , the bottom ring rotation axis θ 2 , the top of the ring structure pitch rotation axis θ 3 , the top linear motion axis r 4 , the top line structure distal pitch rotation axis θ 5 , the end actuator attitude adjustment rotation axis θ 6 , pitch axis θ 7 , and roll axis θ ee . This paper uses the DH method to model the camera. In consideration of the need for calibration, the six-variable modeling method is used for the end actuator transformation matrix. In this paper, the coordinate system {i} is fixed on the connecting rod i, and the origin is on the axis of the joint shaft of the connecting rod. Thus, the establishment of the photography robot linkage coordinate system is shown in Fig. 2. When the photography robot is in the zero state, the movement amount of each motor shaft is zero. The position and posture of the photography robot are shown in Fig. 2.

Fig. 1 Three-dimensional structure of photography robot

Research on Implicit Genetic Inverse Solution Algorithm …

161

Fig. 2 Kinematic model of photography robot Table 1 Photography robot kinematic link parameters table Exchange i θ r

l

α

0 0

π /2 −π /2

W –0 0–1

0 1

π /2 0

0 r 1 + r 10

1–2

2

θ 2 − π /2

r2

0

−π /2

2–3

3

θ 3 − π /2

0

l3

−π /2

3–4

4

0

r 4 + r 40

0

π /2

4–5

5

θ 5 + π /2

0

0

−π /2

5–6

6

θ6

r6

0

π /2

6–7

7

θ 7 − π/2

0

0

−π /2

Based on the DH and six-variable model, the kinematic link parameters of the photography robot were obtained, as shown in Table 1. Among them, r 10  1000, r 2  300, l3  333, r 40  2500, r 6  963. In the coordinate system {i} state, rotate θ around the current Z axis, then move r along the Z axis, then move along the X axis, and finally rotate the X axis around the X axis, which is Rot(Z, θ ) · Trans(Z, r) · Trans(X , l) · Rot(X , α) The transformation matrix formula of DH modeling is

(1)

162

Q. Dong et al.

⎤ cos θ − cos α · sin θ sin θ · sin α l · cos θ ⎥ ⎢ ⎢ sin θ cos α · sin θ − cos θ · sin α l · sin θ ⎥ ⎢ ⎥ ⎦ ⎣ 0 sin α cos α r 0 0 0 1 ⎡

ADH

So, the position and posture of the end actuator coordinate system in the world coordinate system are T ee  A0 · A1 · A2 · A3 · A4 · A5 · A6 · A7 · Aee

(2)

Here, Ai represents a transformation matrix from the ith coordinate system to the ith coordinate system. And the Aee is expressed as: ⎡ ⎤ cos θee sin θee 0 0 ⎢ ⎥ ⎢ − sin θee cos θee 0 0 ⎥ (3) Aee  ⎢ ⎥ ⎣ 0 0 1 0⎦ 0 0 01

3 Photography Robot Double-Redundant Implicit Genetic Inverse Solution Algorithm 3.1 Photography Robot Motion Optimization Objective Function Using mathematical language to describe the kinematics model of the photography robot, the objective function of motion optimization is obtained. Set the current status of the photography robot as T  CRCS  r1C θ2C θ3C r4C θ5C θ6C θ7C θeeC

(4)

The photography’s target (inverse solution) state is: T  CRTS  r1T θ2T θ3T r4T θ5T θ6T θ7T θeeT

(5)

Eliminate the concept of positive and negative turns, using the absolute value of the movement of the photography robot’s each axis as a measure of the movement size. Get: T  CRMDA  |r1 | |θ2 | |θ3 | |r4 | |θ5 | |θ6 | |θ7 | |θee |

(6)

Research on Implicit Genetic Inverse Solution Algorithm …

163

Considering the load moment of inertia of each motor shaft, let the motion weight of each movement axis be, 

CRMW  w1 w2 w3 w4 w5 w6 w7 wee   50 200 100 50 30 1 1 1 (7) The principle of weights assignment is that the greater the moment of the load inertia on the motor shaft, the larger the corresponding weight, and the smaller the load inertia, the smaller the corresponding weight. The smaller the function value of the photography robot objective function, the better the optimization performance, and the larger the function value, the worse the optimization performance. On the basis of the above, the objective function of photography robot optimization is proposed. CRMJD  CRMW · CRMDA  w1 · |r1 | + w2 · |θ2 | + w3 · |θ3 | + w4 · |r4 | + w5 · |θ5 | + w6 · |θ6 | + w7 · |θ7 | + wee · |θee |

(8)

The weight assignment principle is that the bigger the load inertia moment of the motor shaft is, the larger the corresponding weight is, and the smaller the load inertia moment is, the smaller the corresponding weight is. Therefore, when calculating the objective optimization function, the axis with large inertia moment, the smaller the amount of movement after optimization, the smaller the inertia moment of axis, the greater the amount of movement after optimization. The smaller the function value of the objective function of the photography robot movement optimization, the better the optimization performance is, and the larger the function value is, the worse the optimization performance is. The ideal state is to select the CRMJD function value’s smallest inverse solution in many infinitely inverse solutions in a short period of time, to avoid jitter caused by excessive acceleration.

3.2 Fitness Function of Photography Robots Implicit Genetic Inverse Algorithm (1) Fitness Function of Photography Robots Implicit Genetic Inverse Algorithm The fitness function is a function that you want to be optimized for. It is called the objective function in the classical function algorithm [4, 5]. This article uses MATLAB genetic algorithm toolbox settings, that is, to find the minimum value of the objective function. Taking the weighted range of motion of photography robot as

164

Q. Dong et al.

Table 2 Dual-redundant implicit genetic inverse solution simulation parameters Parameter name Parameter value Main code Number of genetic variables Population size

NVARS  2

2 nPS  100

options  gaoptimset (options, ‘PopulationSize’, nPS)

Cutoff algebra

10

options  gaoptimset (options, ‘Generations’, 10)

Fitness function

HijGAFcn

[vR1, f1 val]  ga(@HijGAFcn, NVARS, [], [], [], [], LB, UB, [], options)

the objective function of motion optimization [6, 7], this function is included in the fitness function HijGAFcn. HijGAFcn is the fitness function of the inverse which is Fitness Function  HijGAFcn. (2) Photography Robot Inverse Solution Format Setting Determine a set of photography robot redundancy parameters to get four inverse solutions. In determining a set of redundant parameters, the inverse solution set of the photography robot inverse solution based on the CRTS setting is set as: ⎤ ⎡ r1TN1 θ2TN1 θ3TN1 r4TN1 θ5TN1 θ6TN1 θ7TN1 θeeTN1 V alidity1 nRF1 ⎥ ⎢ ⎢ r1TN2 θ2TN2 θ3TN2 r4TN2 θ5TN2 θ6TN2 θ7TN2 θeeTN2 V alidity2 nRF2 ⎥ ⎥ ⎢ NPLCRTS  ⎢ ⎥ ⎣ r1TN3 θ2TN3 θ3TN3 r4TN3 θ5TN3 θ6TN3 θ7TN3 θeeTN3 V alidity3 nRF3 ⎦ r1TN4 θ2TN4 θ3TN4 r4TN4 θ5TN4 θ6TN4 θ7TN4 θeeTN4 V alidity4 nRF4 In the above data, the first row and the second line for the upper arm type, the third line and the fourth line for the lower arm type [8]. Line 1 and Line 3 are an end actuator posture adjustment 3 combination of axis posture combinations, and rows 2 and 4 for the other end of the actuator posture adjustment 3 combination of axis posture combinations. Validity is an 8-bit binary number, from low to high, corresponding to axis 1–8, 0 for valid, 1 for invalid. The fitness function value of this inverse solution is stored in nRF [3]. After obtaining the inverse solution set, select the effective solution of the minimum value of the fitness function as the fitness function value of the redundant parameter combination. If there is no valid solution, then set that this value is greater than any effective inverse of the fitness function value; in this case set to 600,000.

Research on Implicit Genetic Inverse Solution Algorithm …

165

4 Simulation of the Double-Redundancy Physical Constraint Implicit Genetic Inverse Solution Algorithm Set the target point to paT. Set the current position of the photography robot joint space to CRCS genetic algorithm key parameters as shown in Table 2 [9, 10]. Set the current state of the photography to the initial zero state, which is

T CRCS  0 0 0 0 0 0 0 0

(9)

The target point is a homogeneous matrix:

Fig. 3 Adaptation of minimum and average fitness values with genetic algebra results after four experiments

166

Q. Dong et al.

Fig. 4 Target poses of the double-redundant model implicit heuristic inverse solution with the population size of 100

⎡

1 ⎢ ⎢0 paT  ⎢ ⎣0 0

0 1 0 0

⎤ 0 5000 ⎥ 0 1500 ⎥ ⎥ 1 1500 ⎦ 0 1

(10)

The mean and minimum values of fitness functions vary with the genetic algebra as shown in Fig. 3. Figure 3a, b is the adaptation of minimum and average fitness values with genetic algebra. The algorithm takes about 104.5 s in each test. According to the genetic algorithm optimization results, the fitness function in the 10th generation to obtain the minimum value 71,660. At this point, the corresponding fitness function value of the individual is:

  (11) r1 r4  1423 6.415 The complete and effective inverse solution of the photography robot is: CRTS 

[1423 0.5271 −0.01280 6.415 −2.60721 0 1.049213 −2.61446]

(12)

The state of the photography robot when the robot reaches the target point is shown in Fig. 4, where light gray line is for the initial position of the robot position and dark black line for the target position.

Research on Implicit Genetic Inverse Solution Algorithm …

167

5 Conclusions According to the characteristics of the DOF robot and its stability requirements during shooting, the principle is that the shaft inertia is small, the inertia of the shaft is large, and the motion with the least movement is weighted. We propose the weighted motion of the photography robot. The range of objective function of weighted moving object optimization for camera robot is proposed. According to the characteristics and requirements of optimization, the implicit genetic inverse algorithm of photography robot is proposed, and the optimal solution of the implicit genetic inverse solution of dual redundant physical constraints space is obtained. The conclusion is verified by MATLAB simulation. After several experiments, the average of the fitness function is close enough to the minimum of the fitness function, and the optimization result converges.

References 1. Denavit J, Hartenberg RS (1995) A kinematic notation for lower-pair mechanisms based on matrices. J Appl Mech 21(5):215–221 2. Xiangfeng Ma (1991) Robot mechanics. Machinery Industry Press, Beijing 3. Craig JJ (2010) Introduction to robotics—mechanics and control. mechanics and control. Addison-Wesley Publication Company, Boston, pp 388–423 4. The MathWorks, Inc. (2006) Genetic algorithm and direct search toolbox user’s guide 5. Wang Su, Jingjie He (2015) The present situation and analysis of the development of photography robot. Adv Motion Picture Technol 1(6):45–48 6. Yang X, Wang H, Zhang C et al (2010) A method for mapping the boundaries of collision-free reachable workspaces. Mech Mach Theory 45(7):1024–1033 7. Ali MA, Park HA, Lee CSG (2010) Closed-form inverse kinematic joint solution for humanoid robots. In IEEE/RSJ international conference on intelligent robots and systems, pp 704–709 8. Ayusawa K, Nakamura Y (2012) Fast inverse kinematics algorithm for large DOF system with decomposed gradient computation based on recursive formulation of equilibrium. In IEEE/RSJ international conference on intelligent robots and systems, pp 3447–3452 9. Ye Tian, Xiaopeng Chen, Dongyong Jia et al (2011) Design and kinematics analysis of lightweight high-rigidity arm for humanoid robot. Robot 33(3):332–339 10. Mingxiao Dong, Yiqi Zhou (2000) A new method for inverse kinematics of PUMA robots. Combined Mach Tool Autom Manuf Technol 10:19–21

Information for Authors

Aims and Scopes Transactions on Intelligent Welding Manufacturing (TIWM) is authorized by Springer for periodical publication of research papers and monograph on intelligentized welding manufacturing (IWM). The TIWM is a multidisciplinary and interdisciplinary publication series focusing on the development of intelligent modelling, controlling, monitoring, and evaluating and optimizing the welding manufacturing processes related to the following scopes: • • • • • • • • • • • •

Scientific theory of intelligentized welding manufacturing Planning and optimizing of welding techniques Virtual and digital welding/additive manufacturing Sensing technologies for welding process Intelligent control of welding processes and quality Knowledge modeling of welding process Intelligentized robotic welding technologies Intelligentized, digitalized welding equipment Telecontrol and network welding technologies Intelligentized welding technology applications Intelligentized welding workshop implementation Other related intelligent manufacturing topics

© Springer Nature Singapore Pte Ltd. 2019 S. Chen et al. (eds.), Transactions on Intelligent Welding Manufacturing, Transactions on Intelligent Welding Manufacturing, https://doi.org/10.1007/978-981-13-3651-5

169

170

Information for Authors

Submission Manuscripts must be submitted electronically in WORD version on online submission system: https://ocs.springer.com/ocs/en/home/TIWM2017. Further assistance can be obtained by emailing Editorial Office of TIWM, Dr. Yan Zhang: [email protected], or one of the Editors-in-Chief of TIWM.

Style of Manuscripts The TIWM includes two types of contributions in scopes aforementioned, the periodical proceedings of research papers and research monographs. Research papers include four types of contributions: Invited Feature Articles, Regular Research Papers, Short Papers, and Technical Notes. It is better to limit the full length of Invited Feature Articles in 20 pages; Regular Research Papers in 12 pages; and Short Papers and Technical Notes both in 6 pages. The cover page should contain: Paper title, Authors name, Affiliation, Address, Telephone number, Email address of the corresponding author, Abstract (100–200 words), Keywords (3–6 words), and the suggested technical area.

Format of Manuscripts The manuscripts must be well written in English and should be electronically prepared preferably from the template “splnproc1110.dotm” which can be downloaded from the website: http://rwlab.sjtu.edu.cn/tiwm/index.html. The manuscript including texts, figures, tables, references, and appendixes (if any) must be submitted as a single WORD file.

Originality and Copyright The manuscripts should be original, and must not have been submitted simultaneously to any other journals. Authors are responsible for obtaining permission to use drawings, photographs, tables, and other previously published materials. It is the policy of Springer and TIWM to own the copyright of all contributions it publishes and to permit and facilitate appropriate reuses of such published materials by others. To comply with the related copyright law, authors are required to sign a Copyright Transfer Form before publication. This form is supplied to the authors by the editor after papers have been accepted for publication and grants authors and their employers the full rights to reuse of their own works for noncommercial purposes such as classroom teaching.

Author Index

A Anwer Al-Obaidi, 21

P Peiquan Xu, 121

B Baoming Li, 121 Bintao Wu, 3

Q Qi Dong, 159 Qisheng Wang, 69

C Candice Majewski, 21 Chao Hu, 131 Chendi Lu, 59 Chenxi Zhang, 59

S Shanben Chen, 39 Stephen van Duin, 3 Su Wang, 59, 97, 159

G Gang Song, 109 Gongxiang Zhao, 85 H Hongyan Duan, 145 Hongyang Wang, 109 Huabin Chen, 39 Huijun Li, 3 J Jijin Xu, 85 Jiyong Zhong, 39 L Lichun Meng, 85 Liming Liu, 109 Linran Huang, 69 N Na Lv, 39

W Wensi He, 145 X Xiaohong Sun, 85 Xiaoyu Zhang, 145 Xingai Peng, 159 Xingang Miao, 59, 97, 159 Xizhang Chen, 131 Xuedong Li, 97 Y Yanfeng Gao, 69 Yanfeng Gong, 69 Yanling Xu, 39 Yingming Wu, 131 Yongming Cheng, 85 Z Zengxi Pan, 3 Zhonglin Hou, 109

© Springer Nature Singapore Pte Ltd. 2019 S. Chen et al. (eds.), Transactions on Intelligent Welding Manufacturing, Transactions on Intelligent Welding Manufacturing, https://doi.org/10.1007/978-981-13-3651-5

171