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A digital twin-driven cutting force adaptive control approach for milling process

Author

Listed:
  • Xin Tong

    (Beihang University
    Jiangxi Research Institute of Beihang University)

  • Qiang Liu

    (Beihang University
    Jiangxi Research Institute of Beihang University)

  • Yinuo Zhou

    (Beihang University
    Beijing Engineering Technological Research Center of High-Efficient & Green CNC Machining Process and Equipment)

  • Pengpeng Sun

    (Beihang University
    Beijing Engineering Technological Research Center of High-Efficient & Green CNC Machining Process and Equipment)

Abstract

With intelligent manufacturing development, applying adaptive control technology in the machining process is an effective way to increase productivity and quality. However, adaptive control alone cannot control cutting forces effectively when cutting conditions have excessive change. In this study, a digital twin of the milling process is introduced to cutting force adaptive control for system robustness and efficiency. The cutting force is indirectly measured based on the feed drive current using a Kalman filter, and unknown parameters in the estimation model are identified. A virtual machining system model is established based on online data communication and geometric operation. In addition, the machining state is predicted and introduced into the adaptive control algorithm based on the integrated digital twin for cutting force constraint control. Finally, rough milling of an S-shape specimen is carried out as the cutting experiment to verify the credibility and efficiency of the digital twin-driven cutting force adaptive control.

Suggested Citation

  • Xin Tong & Qiang Liu & Yinuo Zhou & Pengpeng Sun, 2025. "A digital twin-driven cutting force adaptive control approach for milling process," Journal of Intelligent Manufacturing, Springer, vol. 36(1), pages 551-568, January.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:1:d:10.1007_s10845-023-02193-2
    DOI: 10.1007/s10845-023-02193-2
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    References listed on IDEAS

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    1. Jinjiang Wang & Lunkuan Ye & Robert X. Gao & Chen Li & Laibin Zhang, 2019. "Digital Twin for rotating machinery fault diagnosis in smart manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 57(12), pages 3920-3934, June.
    2. Max Schwenzer & Sebastian Stemmler & Muzaffer Ay & Adrian Karl Rüppel & Thomas Bergs & Dirk Abel, 2022. "Model predictive force control in milling based on an ensemble Kalman filter," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 1907-1919, October.
    3. Xin Tong & Qiang Liu & Shiwei Pi & Yao Xiao, 2020. "Real-time machining data application and service based on IMT digital twin," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1113-1132, June.
    4. Zengya Zhao & Sibao Wang & Zehua Wang & Shilong Wang & Chi Ma & Bo Yang, 2022. "Surface roughness stabilization method based on digital twin-driven machining parameters self-adaption adjustment: a case study in five-axis machining," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 943-952, April.
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