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A physics-informed machine learning model for surface roughness prediction in milling operations

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Abstract

Surface roughness has played a crucial role in determining the quality and performance in service of the machined workpiece. To enhance the performance of the final product, it is necessary to quantify the final surface roughness accurately. To this end, massive physical models and data-driven methods have been devoted to modeling surface roughness. However, a high-performance physical and data-driven surface roughness prediction model is often subject to the complex modeling process and data insufficient in the milling process. To this end, a physics-informed neural network for surface roughness prediction in milling operations is proposed in this paper. By using the proposed method, the physical knowledge can be incorporated into the deep learning prediction model, which can effectively reduce the complexity and data dependencies in the modeling phase. To verify the applicability and accuracy of the model, cutting tests were conducted using various workpieces, cutting tools, and process parameters. The results demonstrated that the proposed method can effectively reduce the data dependence while depicting high performance, which is more reliable to be applied in the manufacturing industries.

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Funding

This study was financially supported by the National General Program of National Natural Science Foundation (No. 52175453), and the Graduate Research and Innovation Foundation of Chongqing, China (Grant No. CYB22011).

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Authors and Affiliations

  1. State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing, 400030, China

    Pengcheng Wu, Yufeng Li, Yan He, Rui Zhong & Jinsen He

  2. Shanghai Aerospace Equipments Manufacturer Co., Ltd, Shanghai, 200245, China

    Haicong Dai

Authors
  1. Pengcheng Wu
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  2. Haicong Dai
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  3. Yufeng Li
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  4. Yan He
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  5. Rui Zhong
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  6. Jinsen He
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Contributions

Pengcheng Wu: Paper idea, conceptualization, supervision, methodology and formal analysis. Haicong Dai: methodology. Yufeng Li: writing-original draft preparation. Yan He: investigation, validation. Jinsen He and Rui Zhong: writing-reviewing and editing. All the authors contributed to the final manuscript.

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Correspondence to Yufeng Li.

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Wu, P., Dai, H., Li, Y. et al. A physics-informed machine learning model for surface roughness prediction in milling operations. Int J Adv Manuf Technol 123, 4065–4076 (2022). https://doi.org/10.1007/s00170-022-10470-2

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  • DOI: https://doi.org/10.1007/s00170-022-10470-2

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