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Hybrid physics data-driven model-based fusion framework for machining tool wear prediction

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Abstract

Accurate tool wear prediction is of great significance to improve production efficiency, ensure product quality and reduce machining cost. This paper proposes a hybrid physics data-driven model-based fusion framework for tool wear prediction to improve low prediction accuracy of physical model and poor interpretation of data-driven model. In this framework, physical information and local features of sensor measurement signals are used as inputs to build a hybrid physics data-driven (HPDD) model. And data mining and physics principles are effectively integrated by using unlabeled samples for data expansion. Piecewise prediction is introduced to reduce difficulty in parameter estimation. Then, in order to manage prediction uncertainty of physical information and HPDD method, two prediction results are gradually combined based on Bayesian fusion mechanism to eliminate prediction error. Finally, the effectiveness of the proposed method is verified by experiment. Compared with existing methods, this method significantly improves prediction. The mean values of root mean square error (RMSE) and mean relative error (MARE) for tool wear prediction results are respectively 2.28 and 1.85.

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Funding

This work was supported in part by the National Natural Science Foundation of China under Grant No. 52075202, and in part by the Key Research and Development Program of Hubei Province, China, under Grant No. 2021AAB001.

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Authors and Affiliations

  1. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 400073, China

    Tianhong Gao, Haiping Zhu, Zhiqiang Lu & Shaowen Zhang

  2. School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan, 400073, China

    Jun Wu

  3. National Center of Technology Innovation for Intelligent Design and Numerical Control, Wuhan, 430074, China

    Haiping Zhu & Jun Wu

Authors
  1. Tianhong Gao
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  2. Haiping Zhu
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  3. Jun Wu
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  4. Zhiqiang Lu
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  5. Shaowen Zhang
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Contributions

The research progress is supported by contributions from all authors. Conceptualization, methodology, software, verification, formal analysis, and manuscript writing were implemented by Tianhong Gao. Conceptualization, resources, and review and editing were carried out by Haiping Zhu and Jun Wu. Investigation and data collation were realized by Zhiqiang Lu and Shaowen Zhang. The final draft read and approved by all authors.

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Correspondence to Haiping Zhu.

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Gao, T., Zhu, H., Wu, J. et al. Hybrid physics data-driven model-based fusion framework for machining tool wear prediction. Int J Adv Manuf Technol 132, 1481–1496 (2024). https://doi.org/10.1007/s00170-024-13365-6

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  • DOI: https://doi.org/10.1007/s00170-024-13365-6

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