Deep Reinforcement Learning-Based Dynamic Reconfiguration Planning for Digital Twin-Driven Smart Manufacturing Systems With Reconfigurable Machine Tools

Smart manufacturing systems are a new paradigm in Industry 4.0 driven by the emerging information and communication technology and artificial intelligence that converge to digital twin, which are able to perceive, recognize, and handle the changes in demand and production. Reconfigurable machine too...

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Vydané v:	IEEE transactions on industrial informatics Ročník 20; číslo 11; s. 13135 - 13146
Hlavní autori:	Huang, Jintang, Huang, Sihan, Moghaddam, Shokraneh K., Lu, Yuqian, Wang, Guoxin, Yan, Yan, Shi, Xuejiang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.11.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Artificial intelligence Deep learning Deep reinforcement learning digital twin Digital twins Flexibility Industry 4.0 Machine tools Manufacturing Manufacturing systems Optimization Planning Production reconfigurable machine tools (RMTs) Reconfiguration reconfiguration planning Smart manufacturing smart manufacturing systems
ISSN:	1551-3203, 1941-0050
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Abstract	Smart manufacturing systems are a new paradigm in Industry 4.0 driven by the emerging information and communication technology and artificial intelligence that converge to digital twin, which are able to perceive, recognize, and handle the changes in demand and production. Reconfigurable machine tools (RMTs) can promote the flexibility of smart manufacturing systems. The fundamental problem lies in dynamically reconfiguring the RMTs in smart manufacturing systems efficiently and accurately by considering the flexibility of production precedence and operation sequences simultaneously. Therefore, in this article, a deep reinforcement learning-based reconfiguration planning method of digital twin-driven smart manufacturing systems with RMT is proposed to seek optimal reconfiguration policy online. The reconfiguration processes of smart manufacturing systems are modeled by considering reconfiguration cost, moving cost, and processing cost. Deep Q -network is adopted to explore the state space and action space to find the optimal reconfiguration scheme with the highest return. An industry case study is presented to demonstrate the effectiveness and efficiency of the proposed method, where the reconfiguration processes of a smart manufacturing system consisting of five RMTs for producing four parts are discussed.
AbstractList	Smart manufacturing systems are a new paradigm in Industry 4.0 driven by the emerging information and communication technology and artificial intelligence that converge to digital twin, which are able to perceive, recognize, and handle the changes in demand and production. Reconfigurable machine tools (RMTs) can promote the flexibility of smart manufacturing systems. The fundamental problem lies in dynamically reconfiguring the RMTs in smart manufacturing systems efficiently and accurately by considering the flexibility of production precedence and operation sequences simultaneously. Therefore, in this article, a deep reinforcement learning-based reconfiguration planning method of digital twin-driven smart manufacturing systems with RMT is proposed to seek optimal reconfiguration policy online. The reconfiguration processes of smart manufacturing systems are modeled by considering reconfiguration cost, moving cost, and processing cost. Deep Q -network is adopted to explore the state space and action space to find the optimal reconfiguration scheme with the highest return. An industry case study is presented to demonstrate the effectiveness and efficiency of the proposed method, where the reconfiguration processes of a smart manufacturing system consisting of five RMTs for producing four parts are discussed.
Author	Moghaddam, Shokraneh K. Wang, Guoxin Lu, Yuqian Yan, Yan Shi, Xuejiang Huang, Jintang Huang, Sihan
Author_xml	– sequence: 1 givenname: Jintang surname: Huang fullname: Huang, Jintang email: huangjt0119@163.com organization: School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China – sequence: 2 givenname: Sihan orcidid: 0000-0002-4086-2903 surname: Huang fullname: Huang, Sihan email: hsh@bit.edu.cn organization: School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China – sequence: 3 givenname: Shokraneh K. orcidid: 0000-0001-8864-0229 surname: Moghaddam fullname: Moghaddam, Shokraneh K. email: s.khashkhashimoghadam@herts.ac.uk organization: School of Physics, Engineering, and Computer Science, University of Hertfordshire, Hatfield, U.K – sequence: 4 givenname: Yuqian orcidid: 0000-0001-5954-0421 surname: Lu fullname: Lu, Yuqian email: yuqian.lu@auckland.ac.nz organization: Department of Mechanical and Mechatronics Engineering, The University of Auckland, Auckland, New Zealand – sequence: 5 givenname: Guoxin orcidid: 0000-0003-2363-8595 surname: Wang fullname: Wang, Guoxin email: wangguoxin@bit.edu.cn organization: School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China – sequence: 6 givenname: Yan orcidid: 0000-0001-8555-0156 surname: Yan fullname: Yan, Yan email: yanyan331@bit.edu.cn organization: School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China – sequence: 7 givenname: Xuejiang surname: Shi fullname: Shi, Xuejiang email: 81321763@qq.com organization: Hongyun Honghe Tobacco Group, Kunming, China
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SubjectTerms	Artificial intelligence Deep learning Deep reinforcement learning digital twin Digital twins Flexibility Industry 4.0 Machine tools Manufacturing Manufacturing systems Optimization Planning Production reconfigurable machine tools (RMTs) Reconfiguration reconfiguration planning Smart manufacturing smart manufacturing systems
Title	Deep Reinforcement Learning-Based Dynamic Reconfiguration Planning for Digital Twin-Driven Smart Manufacturing Systems With Reconfigurable Machine Tools
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