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

Xin Tong, Qiang Liu (), Yinuo Zhou and Pengpeng Sun
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Xin Tong: Beihang University
Qiang Liu: Beihang University
Yinuo Zhou: Beihang University
Pengpeng Sun: Beihang University

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 1, No 31, 568 pages

Abstract: 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.

Keywords: Digital twin; Adaptive control; Virtual machining; Cutting force estimation (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10845-023-02193-2

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