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Research on digital twin monitoring system for large complex surface machining

Author

Listed:
  • Tian-Feng Qi

    (Beijing Jiaotong University)

  • Hai-Rong Fang

    (Beijing Jiaotong University)

  • Yu-Fei Chen

    (Beijing Jiaotong University)

  • Li-Tao He

    (Beijing Jiaotong University)

Abstract

With the rapid development of aerospace, the large complex curved workpiece is widely used. However, the lack of digital monitoring and detection in the current manufacturing process leads to the low efficiency of the parts produced and processed, and quality consistency cannot be guaranteed. Aiming at the problems of low degree of virtual visualization and insufficient monitoring ability of large complex surface machining, a framework of large complex surface machining monitoring system based on digital twin technology was proposed. The digital research of intelligent processing monitoring system is carried out from six dimensions. By studying the key technologies of virtual twin model construction, multi-source data acquisition and transmission, and virtual-real mapping relationship construction, a digital twin monitoring system for large complex surface machining is developed. Finally, the feasibility and effectiveness of the twin system are verified by a real scene, and it provides a reference for monitoring the machining process of large complex curved workpieces.

Suggested Citation

  • Tian-Feng Qi & Hai-Rong Fang & Yu-Fei Chen & Li-Tao He, 2024. "Research on digital twin monitoring system for large complex surface machining," Journal of Intelligent Manufacturing, Springer, vol. 35(3), pages 977-990, March.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:3:d:10.1007_s10845-022-02072-2
    DOI: 10.1007/s10845-022-02072-2
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    References listed on IDEAS

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    1. Gaurav Garg & Vladimir Kuts & Gholamreza Anbarjafari, 2021. "Digital Twin for FANUC Robots: Industrial Robot Programming and Simulation Using Virtual Reality," Sustainability, MDPI, vol. 13(18), pages 1-22, September.
    2. Fei Tao & Fangyuan Sui & Ang Liu & Qinglin Qi & Meng Zhang & Boyang Song & Zirong Guo & Stephen C.-Y. Lu & A. Y. C. Nee, 2019. "Digital twin-driven product design framework," International Journal of Production Research, Taylor & Francis Journals, vol. 57(12), pages 3935-3953, June.
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