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dc.contributor.authorHuang, Jintang
dc.contributor.authorHuang, Sihan
dc.contributor.authorMoghaddam, Shokraneh K.
dc.contributor.authorLu, Yuqian
dc.contributor.authorWang, Guoxin
dc.contributor.authorYan, Yan
dc.contributor.authorShi, Xuejiang
dc.date.accessioned2024-09-05T16:15:00Z
dc.date.available2024-09-05T16:15:00Z
dc.date.issued2024-07-30
dc.identifier.citationHuang , J , Huang , S , Moghaddam , S K , Lu , Y , Wang , G , Yan , Y & Shi , X 2024 , ' Deep Reinforcement Learning-Based Dynamic Reconfiguration Planning for Digital Twin-Driven Smart Manufacturing Systems With Reconfigurable Machine Tools ' , IEEE Transactions on Industrial Informatics , vol. PP , no. 99 , 10614748 , pp. 1-12 . https://doi.org/10.1109/TII.2024.3431095
dc.identifier.issn1551-3203
dc.identifier.otherIeee: 10.1109/TII.2024.3431095
dc.identifier.otherORCID: /0000-0001-8864-0229/work/166985649
dc.identifier.urihttp://hdl.handle.net/2299/28132
dc.description© 2024 The Author(s). This is an open access article under the Creative Commons Attribution-Non Commercial-No Derivatives CC BY-NC-ND licence, https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.description.abstractSmart 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.en
dc.format.extent12
dc.format.extent3660990
dc.language.isoeng
dc.relation.ispartofIEEE Transactions on Industrial Informatics
dc.subjectSmart manufacturing
dc.subjectPlanning
dc.subjectProduction
dc.subjectManufacturing systems
dc.subjectOptimization
dc.subjectDigital twins
dc.subjectDeep reinforcement learning
dc.subjectreconfigurable machine tools (RMTs)
dc.subjectreconfiguration planning
dc.subjectsmart manufacturing systems
dc.subjectdigital twin
dc.subjectIndustry 4.0
dc.subjectInformation Systems
dc.subjectElectrical and Electronic Engineering
dc.subjectControl and Systems Engineering
dc.subjectComputer Science Applications
dc.titleDeep Reinforcement Learning-Based Dynamic Reconfiguration Planning for Digital Twin-Driven Smart Manufacturing Systems With Reconfigurable Machine Toolsen
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
dc.contributor.institutionDepartment of Engineering and Technology
dc.description.statusPeer reviewed
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85202299377&partnerID=8YFLogxK
dc.identifier.urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10614748
rioxxterms.versionofrecord10.1109/TII.2024.3431095
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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