Digital twin-enhanced laser cutting production system design and operation

被引:0
|
作者
Fu, Zhuorui [1 ]
Zhao, Ning [1 ]
Luo, Lei [1 ]
机构
[1] Univ Sci & Technol Beijing, Sch Mech Engn, Beijing, Peoples R China
基金
国家重点研发计划; 中国国家自然科学基金;
关键词
Digital twin; production system design; design and operation optimization; laser cutting; OPTIMIZATION; FRAMEWORK;
D O I
10.1080/0951192X.2024.2328041
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The design and operation of laser cutting production systems (LCPS) are critical for reducing cost and improving efficiency. In this study, a digital twin (DT) approach for the LCPS is proposed to enhance their design and operation. The DT is generated by mapping the parameters and characteristics of the physical entities in the LCPS as well as the operational logic into the virtual space. Using simulation technology, virtual LCPS corresponding to various designs are enumerated and evaluated rapidly in a virtual space. Consequently, the best design is identified and used as the adopted design. The DT generated in the design phase can be further employed in the operational phase of the LCPS. By inputting the actual production order and collecting the real-time data of physical LCPS, the production performance is predicted and optimized by the DT. Genetic algorithm is applied to optimize the schedule. Finally, the practicality, effectiveness, and the implementation of the proposed approach are verified using two numerical experiments and a practical case study.
引用
收藏
页码:228 / 254
页数:27
相关论文
共 50 条
  • [1] A digital twin-enhanced system for engineering product family design and optimization
    Lim, Kendrik Yan Hong
    Zheng, Pai
    Chen, Chun-Hsien
    Huang, Lihui
    JOURNAL OF MANUFACTURING SYSTEMS, 2020, 57 : 82 - 93
  • [2] Digital twin-enhanced opportunistic maintenance of smart microgrids based on the risk importance measure
    Dui, Hongyan
    Zhang, Songru
    Dong, Xinghui
    Wu, Shaomin
    RELIABILITY ENGINEERING & SYSTEM SAFETY, 2025, 253
  • [3] Digital Twin-Enhanced Deep Reinforcement Learning for Resource Management in Networks Slicing
    Zhang, Zhengming
    Huang, Yongming
    Zhang, Cheng
    Zheng, Qingbi
    Yang, Luxi
    You, Xiaohu
    IEEE TRANSACTIONS ON COMMUNICATIONS, 2024, 72 (10) : 6209 - 6224
  • [4] Digital twin-enhanced robotic system for remote diesel engine assembly defect inspection
    Wang, Kai
    Wang, Xiang
    Tan, Chao
    Dong, Shijie
    Zhao, Fang
    Lian, Shiguo
    INDUSTRIAL ROBOT-THE INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH AND APPLICATION, 2025, 52 (02): : 266 - 276
  • [5] Design of Digital Twin Cutting Experiment System for Shearer
    Miao, Bing
    Li, Yunwang
    Guo, Yinan
    SENSORS, 2024, 24 (10)
  • [6] Digital Twin-enhanced Approach for Supply Chain Disruption Management in Manufacturing Shop Floors
    Lim, Kendrik Yan Hong
    Seif, Alejandro
    Agarwal, Nimisha
    Nam Tuan Le
    2021 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT (IEEE IEEM21), 2021, : 848 - 852
  • [7] Design and Implementation of a Digital Twin System for Log Rotary Cutting Optimization
    Zhao, Yadi
    Yan, Lei
    Wu, Jian
    Song, Ximing
    FUTURE INTERNET, 2024, 16 (01)
  • [8] Digital Twin-Enhanced Adaptive Traffic Signal Framework under Limited Synchronization Conditions
    Zhu, Hong
    Sun, Fengmei
    Tang, Keshuang
    Wu, Hao
    Feng, Jialong
    Tang, Zhixian
    SUSTAINABILITY, 2024, 16 (13)
  • [9] Securing the vetaverse: Web 3.0 for decentralized Digital Twin-enhanced vehicle-road safety
    Siddiqi, Sadia Jabeen
    Saleh, Sana
    Jan, Mian Ahmad
    Tariq, Muhammad
    FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2025, 164
  • [10] Digital Twin-Enhanced Control for Fuel Cell and Lithium-Ion Battery Hybrid Vehicles
    Kang, Xu
    Wang, Yujie
    Jiang, Cong
    Chen, Zonghai
    BATTERIES-BASEL, 2024, 10 (07):