Digital twin-enabled machining process modeling

被引:0
|
作者
Liu, Jinfeng [1 ]
Wen, Xiaojian [1 ]
Zhou, Honggen [1 ]
Sheng, Sushan [1 ]
Zhao, Peng [1 ]
Liu, Xiaojun [2 ]
Kang, Chao [1 ]
Chen, Yu [1 ]
机构
[1] School of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang,212003, China
[2] School of Mechanical Engineering, Southeast University, Nanjing,211198, China
关键词
Process design;
D O I
暂无
中图分类号
学科分类号
摘要
Considering the new generation of information technology, the digitalization and intellectualization of the machining process have become the major core in intelligent manufacturing. The complex and diverse requirements, as well as the processing sites force the machining sequence to move towards cyber-physical integration. This paper presents a multidimensional modeling approach for machining processes, by introducing Digital Twin (DT) technology. The method is oriented towards the design and execution phases of the machining process and is used to support intelligent machining. The working mechanism of modeling, simulation, prediction and control of machining process is described based on the interpretation of the modeling and application methods of machining process design, inspection process, fault diagnosis and quality prediction, as based on digital twin technology. Finally, key components of diesel engines are targeted as test objects, demonstrating increased material removal rate by 5.1%, reduced deformation by 22.98% and 30.13%, respectively, verifying the effectiveness of the applied framework and the proposed method. © 2022
引用
收藏
相关论文
共 50 条
  • [41] Digital twin-enabled anomaly detection for built asset monitoring in operation and maintenance
    Lu, Qiuchen
    Xie, Xiang
    Parlikad, Ajith Kumar
    Schooling, Jennifer Mary
    AUTOMATION IN CONSTRUCTION, 2020, 118
  • [42] Digital twin-enabled adaptive scheduling strategy based on deep reinforcement learning
    GAN XueMei
    ZUO Ying
    ZHANG AnSi
    LI ShaoBo
    TAO Fei
    Science China(Technological Sciences), 2023, (07) : 1937 - 1951
  • [43] Digital Twin-Enabled Optical Network Automation: Power Re-Optimization
    Sun, Chenyu
    Yang, Xin
    Charlet, Gabriel
    Stavrou, Photios A.
    Pointurier, Yvan
    2024 OPTICAL FIBER COMMUNICATIONS CONFERENCE AND EXHIBITION, OFC, 2024,
  • [44] Digital Twin-enabled Collaborative Data Management for Metal Additive Manufacturing Systems
    Liu, Chao
    Le Roux, Leopold
    Korner, Carolin
    Tabaste, Olivier
    Lacan, Franck
    Bigot, Samuel
    JOURNAL OF MANUFACTURING SYSTEMS, 2022, 62 : 857 - 874
  • [45] QoE Fairness Resource Allocation in Digital Twin-Enabled Wireless Virtual Reality Systems
    Feng, Jie
    Liu, Lei
    Hou, Xiangwang
    Pei, Qingqi
    Wu, Celimuge
    IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, 2023, 41 (10) : 3355 - 3368
  • [46] Digital twin-enabled multi-robot system for collaborative assembly of unorganized parts
    Oo, Kyaw Htet
    Koomsap, Pisut
    Ayutthaya, Duangthida Hussadintorn Na
    JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION, 2025, 44
  • [47] Quality-Aware Massive Content Delivery in Digital Twin-Enabled Edge Networks
    Gao, Yun
    Liao, Junqi
    Wei, Xin
    Zhou, Liang
    CHINA COMMUNICATIONS, 2023, 20 (02) : 1 - 13
  • [48] Text-to-Metaverse: Towards a Digital Twin-Enabled Multimodal Conditional Generative Metaverse
    Elhagry, Ahmed
    PROCEEDINGS OF THE 31ST ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA, MM 2023, 2023, : 9336 - 9339
  • [49] Mobility-Aware Utility Maximization in Digital Twin-Enabled Serverless Edge Computing
    Li, Jing
    Guo, Song
    Liang, Weifa
    Wang, Jianping
    Chen, Quan
    Xu, Wenchao
    Wei, Kang
    Jia, Xiaohua
    IEEE TRANSACTIONS ON COMPUTERS, 2024, 73 (07) : 1837 - 1851
  • [50] Digital Twin-enabled AI Enhancement in Smart Critical Infrastructures for 5G
    Gai, Keke
    Xiao, Qiang
    Qiu, Meikang
    Zhang, Guolei
    Chen, Jianyu
    Wei, Yihang
    Zhang, Yue
    ACM TRANSACTIONS ON SENSOR NETWORKS, 2022, 18 (03)