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In-process surface quality monitoring of the slender workpiece machining with digital twin approach

Author

Listed:
  • Kaibo Lu

    (Taiyuan University of Technology)

  • Zhen Li

    (Taiyuan University of Technology)

  • Andrew Longstaff

    (University of Huddersfield)

Abstract

In-process monitoring of production quality plays a significant role in intelligent manufacturing. Both part deformation and vibration happen simultaneously in machining processes. They are two prominent issues that can affect the surface quality of machined parts, especially those with low rigidity. The purpose of this study is to explore a hybrid modeling solution to simultaneously monitor the diametrical errors and early chatter vibrations when turning a slender workpiece. A generic analytical model of the slender workpiece turning is formulated based on dynamics of machining. It is proved that the system complies with the principle of superposition. Accordingly, the explicit expressions free from cutting force modeling that is widely used in literature are derived for characterizing the finished surface quality with analytical and finite element methods, respectively. A data-driven model is also developed using the wavelet packet transform to the displacement signals. The independent decompositions of the displacement signals are then correlated with both the dimensional error model and the turning chatter model. Interconnecting the dynamics-based model and the data-driven model contributes to a digital-twin prototype, which allows for in-process detection of the geometrical distortion and the onset of chatter on the part surface. Finally, two different machining cases were performed to verify the proposed methodology. The results show that the developed model consisting of the formulated deflection correlation and chatter indicator is capable of simultaneously evaluating and detecting the dimensional error prediction and the early-onset chatter. By comparison with the analytical modeling, the high fidelity digital twin using the finite element modeling could exhibit higher prediction accuracy. The proposed monitoring strategy could provide a pragmatic approach to online quality control for intelligent machining of flexible workpieces on the shop floor.

Suggested Citation

  • Kaibo Lu & Zhen Li & Andrew Longstaff, 2025. "In-process surface quality monitoring of the slender workpiece machining with digital twin approach," Journal of Intelligent Manufacturing, Springer, vol. 36(3), pages 2039-2053, March.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:3:d:10.1007_s10845-024-02353-y
    DOI: 10.1007/s10845-024-02353-y
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    References listed on IDEAS

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    1. Baifan Zhou & Tim Pychynski & Markus Reischl & Evgeny Kharlamov & Ralf Mikut, 2022. "Machine learning with domain knowledge for predictive quality monitoring in resistance spot welding," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 1139-1163, April.
    2. Kendrik Yan Hong Lim & Pai Zheng & Chun-Hsien Chen, 2020. "A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1313-1337, August.
    3. Noel P. Greis & Monica L. Nogueira & Sambit Bhattacharya & Catherine Spooner & Tony Schmitz, 2023. "Stability modeling for chatter avoidance in self-aware machining: an application of physics-guided machine learning," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 387-413, January.
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