Authors

Xiaojian Liu, Xinyu Lu, Huishu Jia, Wei Zeng, Wentian Li, Yangjian Ji, Guodong Yi, Kang Wang, Lemiao Qiu, Shuyou Zhang

Abstract

Developing high-fidelity digital twin models for electric spindles requires rigorously predicting the coupled thermal-vibration dynamics, where friction-induced heat actively drives parameter drift such as thermal stiffening and viscosity reduction. While structure-preserving frameworks like Port-Hamiltonian Neural Networks and Dissipative Hamiltonian Neural Networks excel in energy-based modeling, they predominantly treat dissipation via resistive ports or energy sinks, implicitly assuming an isothermal environment, failing to capture the reciprocal thermo-mechanical feedback, leading to substantial errors in vibration predictions. To bridge this gap, we propose a Physics-Informed Thermodynamical Hybrid Modeling Method based on the GENERIC formalism and the dissipative Hamiltonian dynamics. A Thermodynamical Hamiltonian Neural Network (THNN) is introduced to identify temperature-dependent constitutive parameters within a thermodynamically consistent physical model, rigorously tracking the conversion of dissipated mechanical work into entropy and temperature evolution and thereby closing the thermo-mechanical loop by design. Through a comprehensive proof-of-concept validated under diverse benchmark scenarios, including noise robustness and parameter sensitivity analyses, we demonstrate that THNN reduces the MAE by 44.1% compared to state-of-the-art baselines while maintaining strict adherence to the First and Second Laws of Thermodynamics. The compact architecture (4.9 K parameters) achieves superior accuracy through physics-informed structural constraints rather than model capacity, establishing a methodological approach for vibration prediction of electric spindles.

Keywords

dissipative hamiltonian system, electric spindles, hybrid modeling, non-linear dynamics, physics-informed neural network

Citation

  • Journal: Journal of Intelligent Manufacturing
  • Year: 2026
  • Volume:
  • Issue:
  • Pages:
  • Publisher: Springer Science and Business Media LLC
  • DOI: 10.1007/s10845-026-02961-w

BibTeX

@article{Liu_2026,
  title={{Physics-informed thermodynamical Hamiltonian hybrid modeling method for vibration prediction of electric spindles}},
  ISSN={1572-8145},
  DOI={10.1007/s10845-026-02961-w},
  journal={Journal of Intelligent Manufacturing},
  publisher={Springer Science and Business Media LLC},
  author={Liu, Xiaojian and Lu, Xinyu and Jia, Huishu and Zeng, Wei and Li, Wentian and Ji, Yangjian and Yi, Guodong and Wang, Kang and Qiu, Lemiao and Zhang, Shuyou},
  year={2026}
}

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