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Design and Optimization of a Control Framework for Robot Assisted Additive Manufacturing Based on the Stewart Platform

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Abstract

Additive manufacturing, also known as 3D printing, is an emerging technology. The existing additive manufacturing technologies deploy a 3-axis printing mechanism where the material accumulation grows only in the z-direction. This results in limited printing freedom. Apart from this, support structures are needed to print overhang structures. Removal of these supports ultimately reduces print quality. This paper proposes a novel robot-assisted additive manufacturing along with a control system framework, which possesses multi-directional printing without support structures. Taking the advantage of its high stiffness and high payload-to-weight ratio, a 6-degree of freedom Stewart platform manipulator is designed to substitute the printer build plate. The kinematics and dynamics of the manipulator is formulated. Then, an extended proportion-derivation sliding mode controller is designed for trajectory tracking. The modified grey wolf optimization algorithm is applied to compute the optimal controller parameters. The integral absolute error (IAE) is used as a cost function and its minimum value is reached in the iteration interval [75,100]. The analytical model simulation in MATLAB is run for 10 seconds, and the results show that the desired length trajectories of the six legs of the manipulator are achieved after 3.5 seconds. The performance of the analytical model is verified on the automated dynamic analysis of mechanical systems (ADAMS).

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Funding

This work was supported in part by the National Key Research and Development Program of China (No. 2021YFE0116300); National Natural Science Foundation of China under Grants 61773382, U1909204, 61773381, 61872365 and 61701471; Scientific Instrument Developing Project of the Chinese Academy of Sciences (Grant No. YZQT014); CAS Key Technology Talent Program (Zhen Shen); Guangdong Basic and Applied Basic Research Foundation under Grant 2021B1515140034; Foshan Science and Technology Innovation Team Project (2018IT100142); Youth Foundation of the State Key Laboratory for Management and Control of Complex Systems (Y6S9011F1G); Project IGSD-2020-014 of the Collaborative Innovation Center of Intelligent Green Manufacturing Technology and Equipment, Shandong.

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Authors and Affiliations

  1. State Key Laboratory for Management and Control of Complex Systems, Beijing Engineering Research Center of Intelligent Systems and Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Tariku Sinshaw Tamir, Gang Xiong, Xisong Dong, Qihang Fang, Sheng Liu, Ehtisham Lodhi, Zhen Shen & Fei-Yue Wang

  2. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China

    Tariku Sinshaw Tamir, Qihang Fang & Ehtisham Lodhi

  3. Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing, Cloud Computing Center, Chinese Academy of Sciences, Dongguan, 523808, China

    Gang Xiong

  4. Intelligent Manufacturing Center, Qingdao Academy of Intelligent Industries, Qingdao, 266109, China

    Zhen Shen

Authors
  1. Tariku Sinshaw Tamir
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  7. Zhen Shen
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Corresponding author

Correspondence to Zhen Shen.

Additional information

Tariku Sinshaw Tamir and Gang Xiong are the co-first authors.

Tariku Sinshaw Tamir received his B.Sc. degree in electrical engineering from Haramaya University, Haramaya, Ethiopia, in 2011, and his M.Sc. degree in control engineering from Addis Ababa University, Addis Ababa, Ethiopia, in 2015. He is currently working towards a Ph.D. degree at the State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, as well as the School of Artificial Intellegence, University of Chinese Academy of Sciences, Beijing, China. His research interests include modeling, control and optimization of complex systems, and intelligent manufacturing.

Gang Xiong received his B.Eng. and M.Eng. degrees from Xi’an University of Science and Technology, Xi’an, China, in 1991 and 1994, respectively, and his Ph.D. degree from Shanghai Jiao Tong University, Shanghai, China, in 1996. From 1996 to 1998, he was a Postdoctor and Associate Scientist with Zhejiang University, Hangzhou, China. From 1998 to 2001, he was a Senior Research Fellow with Tampere University of Technology, Tampere, Finland. From 2001 to 2007, he was a Specialist and Project Manager with Nokia Corporation, Finland. In 2007, he was a Senior Consultant and Team Leader with Accenture and Chevron, USA. In 2008, he was the Deputy Director of the Informatization Office, Chinese Academy of Science (CAS), Beijing, China. In 2009, he started his present position as a Research Scientist and now he is a full professor with the State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, CAS. In 2011, he became Deputy Director of Cloud Computing Center, CAS. His research interests include parallel control and management, modeling and optimization of complex systems, cloud computing and big data, intelligent manufacturing, and intelligent transportation systems.

Xisong Dong received his B.Sc. degree in applied mathematics in 2001 and his Ph.D. degree in control theory and control engineering in 2007 from the University of Science and Technology Beijing, China. He is currently an Associate Professor at the State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences. He has participated more than 30 academic or technical projects funded by Chinese 973, 863, NSFC, etc. He has authored more than 120 papers. His interests include the modeling and analysis of complex systems, intelligent control, and intelligent transportation systems.

Qihang Fang received his B.E. degree in automation from Wuhan University, Wuhan, China, in 2020. He is currently pursuing a Ph.D. degree in control theory and control engineering from the University of Chinese Academy of Sciences, Beijing, China. His current research interests include 3D printing and machine learning.

Sheng Liu received his Ph.D. degree in mechatronic engineering from Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China, in 2007. He is currently an associate professor with the State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy for Sciences, 100190, Beijing, China. His research area includes combinatorial optimization, cutting and packing.

Ehtisham Lodhi received his M.Sc. degree in electrical (power electronics and power drives) engineering from Beihang University, Beijing, China in 2017. Currently, he is pursuing his Ph.D. degree in Control Theory and Control Engineering at the Institute of Automation, Chinese Academy of Sciences, Beijing, China. His research interest includes power electronics, renewable technologies, and faults detection in PV system by using artificial intelligence techniques.

Zhen Shen received his B.E. degree in automation and his Ph.D. degree in control science and engineering from Tsinghua University, Beijing, China, in 2004 and 2009, respectively. He is currently an associate professor with the State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, and also with the Intelligent Manufacturing Center, Qing-dao Academy of Intelligent Industries, Qingdao, China. He has authored about 50 referred journal and conference papers, and holds 15 authorized patents of China and 5 US patents. His current research interests include intelligent manufacturing and complex systems. Dr. Shen was a recipient of the 2005 “Outstanding Achievement Award” from United Technology Research Center, the 2018 Second Class Award of China Industry-University-Research Cooperation Innovation Achievement, and the 2019 Second Class Prize of the 9th Wu Wenjun Artificial Intelligence Science and Technology Progress Award.

Fei-Yue Wang received his Ph.D. degree in computer and systems engineering from the Rensselaer Polytechnic Institute, Troy, NY, USA, in 1990. He joined the University of Arizona in 1990 and became a Professor and the Director of the Robotics and Automation Laboratory and the Program in Advanced Research for Complex Systems. In 1999, he founded the Intelligent Control and Systems Engineering Center at the Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, China, and in 2002, he was appointed as the Director of the Key Laboratory of Complex Systems and Intelligence Science, CAS. In 2011, he became the Director of the State Key Laboratory for Management and Control of Complex Systems. His research focuses on methods and applications for parallel intelligence, social computing, and knowledge automation. He is a fellow of INCOSE, IFAC, ASME, AAAS, and IEEE. Currently, he is the President of CAA’s Supervision Council, IEEE Council on RFID, and Vice President of IEEE Systems, Man, and Cybernetics Society.

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Tamir, T.S., Xiong, G., Dong, X. et al. Design and Optimization of a Control Framework for Robot Assisted Additive Manufacturing Based on the Stewart Platform. Int. J. Control Autom. Syst. 20, 968–982 (2022). https://doi.org/10.1007/s12555-021-0058-4

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