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Physics-informed interpretable machine learning method for DOC monitoring in peripheral milling

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Abstract

Online monitoring of depths of cut (DOC) is an essential way to avoid machining defects, such as over-cutting and machining chatter. Data-driven machine learning method has been widely used to build the monitoring algorithms. However, deficient training data and un-interpretable algorithm make it difficult for application. Therefore, a physics-informed interpretable machine learning algorithm was proposed. Firstly, a physical simulation model incorporated with DOC was established with the rotation speed and feed speed as its inputs and time-domain signals of milling force as its outputs. The output force signals were quantitatively presented by time domain and frequency domain features. Secondly, six dimensionless features, namely the kurtosis, skewness, waveform factor, peak factor, pulse factor, and margin factor of the resultant milling force, were explored through sensitivity analysis method. They were sensitive to DOC but insensitive to milling force coefficient, speed, and feed speed. Then, a quantitative relationship model between features and DOC was established using the least squares linear regression algorithm, which has an intrinsic interpretability. Finally, the model was trained by a labeled 100-group experimental data. The results show that the accuracy of the proposed model for DOC monitoring is higher than 90%.

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Data availability

All data generated or analyzed during this study were available by emailing to author (sunl@just.edu.cn).

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Funding

This work was supported by the National Natural Science Foundation of China (62203193), the National Natural Science Foundation of China (51605207), and the General Project of Natural Science Research for Institutions of Higher Education of Jiangsu Province of China (21KJB510016)

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Authors and Affiliations

  1. School of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, Jiangsu, China

    Guochao Li, Hao Zheng, Ru Jiang, Shixian Xu & Li Sun

  2. Department of Automation, Rocket Force University of Engineering, Xi’an, 710025, Shanxi, China

    Li Sun

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  1. Guochao Li
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  2. Hao Zheng
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  3. Ru Jiang
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  4. Shixian Xu
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Contributions

Guochao Li proposed the main analysis ideas, established the analysis framework, developed the physics simulation model, and helped to read and approve the final manuscript. Ru Jiang and Hao Zheng explored six dimensionless characteristics and established a quantitative relationship model between features and DOC. Shixian Xu and Li Sun provided analytical and linguistic guidance.

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Correspondence to Li Sun.

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Li, G., Zheng, H., Jiang, R. et al. Physics-informed interpretable machine learning method for DOC monitoring in peripheral milling. Int J Adv Manuf Technol 132, 179–191 (2024). https://doi.org/10.1007/s00170-024-13364-7

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