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ABSTRACT
Reconfigurable machine tool (RMT) is the core facility of the reconfigurable manufacturing system (RMS), which can provide customised flexibility for RMS through reconfiguration. The reconfiguration of RMT is complicated due to unpredictable changes in demand and the flexibility of RMT, where new RMT should be designed to satisfy the new demand. The concept of digital twin of RMT is introduced to solve complex reconfiguration problems by executing reconfiguration experiments on high-fidelity virtual RMT. Considering the design processes of RMT during reconfiguration, three building blocks for digital twin of RMT should be studied thoroughly, including structure design, configuration generation, and configuration evaluation. First, the structure design of RMT for multi-part families is studied, including the design principles, module division, and design method. Second, the configuration generation process of RMT based on the results of the structure design is analysed, where quantitative description of configuration is proposed to facilitate the generation process. Third, configuration evaluation is presented to confirm the performance of each configuration based on kinematics analysis. Finally, a case study is provided to demonstrate the effectiveness of the proposed three building blocks for digital twin of RMT during reconfiguration to obtain suitable design scheme of RMT.
Acknowledgments
The authors are grateful to the anonymous reviewers and the editor for their comments and feedback, which helped us to improve this paper. All the authors have approved this manuscript for submission to the journal, and there are no conflicts of interest regarding the publication of this manuscript. The authors acknowledge the supporting fund, the China National Postdoctoral Program for Innovative Talents (BX20200053), the National Natural Science Foundation of China (51975056) and the National Ministries Project of China (201820329132).
Disclosure statement
No potential conflict of interest was reported by the author(s).
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Notes on contributors
Sihan Huang
Sihan Huang received B.S. degree in Mechanical Engineering from Beijing Ins-titute of Technology, China, in 2014 and Ph.D. degree in Mechanical Engineering from Beijing Institute of Technology, China, in 2020. He was a visiting Ph.D. student at University of Michigan-Ann Arbor, USA from 2017 to 2019. He is currently a postdoctoral fellow at Beijing Institute of Technology, China. His research interests include reconfigurable manufacturing systems, intelligent manufacturing and digital twin.
Guoxin Wang
Guoxin Wang received B.S. degree from Lanzhou Jiaotong University in 2001, a M.S. degree from Lanzhou Jiaotong University in 2004 and Ph.D. degree from Beijing Institute of Technology in 2007. He was a visiting scholar at University of Oklahoma, USA from 2014 to 2015. He is currently an associate professor at Beijing Institute of Technology, China. His current research interests include reconfigurable manufacturing systems, intelligent design, systems engineering and knowledge engineering.
Yan Yan
Yan Yan received B.S. degree in mechanical engineering from Beijing Institute of Technology, China, in 1989 and Ph.D. degree in mechanical engineering from Beijing Institute of Technology, China, in 2001. She is currently a professor at Beijing Institute of Technology, China. Her current research interests include reconfigurable manufacturing systems, intelligent design and knowledge engineering.