Digital twin forward monitoring and reverse control method for intelligent manufacturing Systems

被引:0
|
作者
Han, Dongyang [1 ]
Xia, Tangbin [1 ]
Fan, Yijing [1 ]
Wang, Hao [1 ]
Xi, Lifeng [1 ]
机构
[1] School of Mechanical Engineering, State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai,200240, China
关键词
Smart manufacturing;
D O I
10.13196/j.cims.2023.0518
中图分类号
学科分类号
摘要
In addressing the monitoring and control issues within intelligent manufacturing Systems» a bidirectional digital twin management approach tailored for industry was introduced. The forward aspect of this approach involved creating twin entities through data mapping to off er monitoring Services, while the reverse aspect employed simula-tion-hased control optimization to enhance the behavior of physical entities, which achieved a fully closedToop control over the manufacturing process. By integrating real-world physical data and virtual twin data within a cyber-physical System, a multi-layer architecture was established. A multi-scale and multi-level twin modeling method was devised, and coupling model-based definitions and finite State machine techniques were integrated to construct twin scenarios of physical attributes and behavioral actions using the Unreal Engine. By amalgamating artificial intelligence with behavior Simulation models, the contextual data was incorporated into functional Services, so that the System could effectively harness fused data, analyze and evaluate equipment health Status and generate simulated behavioral controls for the manufacturing process. Finally, a platform was developed for a component manufacturing System to validate the maturity of the proposed model and the reliability of the twin technology. © 2024 CIMS. All rights reserved.
引用
收藏
页码:3419 / 3430
相关论文
共 50 条
  • [1] Enhanced State Monitoring and Fault Diagnosis Method for Intelligent Manufacturing Systems via RXET in Digital Twin Technology
    Li, Min
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2024, 15 (11) : 1264 - 1275
  • [2] Intelligent Manufacturing with Digital Twin
    Moeller, Dietmar P. F.
    Vakilzadian, Hamid
    Hou, Weyan
    2021 IEEE INTERNATIONAL CONFERENCE ON ELECTRO INFORMATION TECHNOLOGY (EIT), 2021, : 413 - 418
  • [3] Digital twin assisted intelligent machining process monitoring and control
    Bakhshandeh, Parsa
    Mohammadi, Yaser
    Altintas, Yusuf
    Bleicher, Friedrich
    CIRP JOURNAL OF MANUFACTURING SCIENCE AND TECHNOLOGY, 2024, 49 : 180 - 190
  • [4] Development and Application of Digital Twin Control in Flexible Manufacturing Systems
    Ullah, Asif
    Younas, Muhammad
    JOURNAL OF MANUFACTURING AND MATERIALS PROCESSING, 2024, 8 (05):
  • [5] Cloud-Based Digital Twin for Robot Integration in Intelligent Manufacturing Systems
    Anton, Florin
    Borangiu, Theodor
    Raileanu, Silviu
    Anton, Silvia
    ADVANCES IN SERVICE AND INDUSTRIAL ROBOTICS, RAAD 2020, 2020, 84 : 565 - 573
  • [6] Digital twin process and simulation operation control technology for intelligent manufacturing unit
    He, Yichao
    Zhang, Niansong
    Wang, Aimin
    4TH INTERNATIONAL CONFERENCE ON RELIABILITY ENGINEERING (ICRE 2019), 2020, 836
  • [7] Digital twin monitoring and simulation integrated platform for reconfigurable manufacturing systems
    Leng, Bohan
    Gao, Shuo
    Xia, Tangbin
    Pan, Ershun
    Seidelmann, Joachim
    Wang, Hao
    Xi, Lifeng
    ADVANCED ENGINEERING INFORMATICS, 2023, 58
  • [8] Enhancing Manufacturing Excellence with Digital-Twin-Enabled Operational Monitoring and Intelligent Scheduling
    Yang, Jingzhe
    Zheng, Yili
    Wu, Jian
    Wang, Yuejia
    He, Jinyang
    Tang, Lingxiao
    APPLIED SCIENCES-BASEL, 2024, 14 (15):
  • [9] Digital Intelligent Forward Design Method and Its Application in Manufacturing Equipment and Process
    Tan J.
    Gao M.
    Xu J.
    Wang L.
    Jia C.
    Zhang S.
    Wang K.
    Jixie Gongcheng Xuebao/Journal of Mechanical Engineering, 2023, 59 (19): : 111 - 125
  • [10] Reinforcement learning and digital twin-based real-time scheduling method in intelligent manufacturing systems
    Zhang, Lixiang
    Yan, Yan
    Hu, Yaoguang
    Ren, Weibo
    IFAC PAPERSONLINE, 2022, 55 (10): : 359 - 364