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Application of cutting power consumption in tool condition monitoring and wear prediction based on Gaussian process regression under variable cutting parameters

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Abstract

Tool wear is inevitable in actual manufacturing, especially in extreme processing conditions for machining difficult-to-cut materials. The monitoring of the tool state has an important influence on the surface quality and dimensional accuracy of the precision parts. In the previous studies, the original total power consumption is usually used to predict tool wear while ignoring the cutting power consumption accounts for a small proportion of the total power consumption of machine tools. Therefore, the accuracy is difficult to achieve the expected target. For better prediction results, a novel prediction method based on net cutting power consumption by Gaussian process regression (GPR) with ARD Matern 5/2 kernel is proposed in this study. Firstly, the physical model of net cutting power consumption is established. Then, tool wear under fixed working conditions is predicted by using the net cutting power consumption and GPR, and the advantage of the proposed method in this study is verified by comparing it with the existing methods. Finally, the proposed method is verified to obtain better prediction performance with variable cutting parameters than using total power consumption with the neural network. This study reveals that low-cost sensors like power meter can be used as an important supplement to monitoring tool conditions in the industry and also provides a research basis for predicting tool wear under different cutting conditions.

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The authors confirm that the data supporting the findings of this study are available within the article.

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Funding

The authors would like to acknowledge the financial support provided by the National Natural Science Foundation of China (grant number 51905442), the National Major Science and Technology Projects of China (grant number J2019-VII-0001–0141), and the Aeronautical Science Foundation of China (grant number 2020Z045053001).

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, 710072, Shaanxi, China

    Biyao Qiang, Kaining Shi & Junxue Ren

  2. Key Laboratory of High Performance Manufacturing for Aero Engine, Ministry of Industry and Information Technology, Northwestern Polytechnical University, Xi’an, 710072, Shaanxi, China

    Biyao Qiang, Kaining Shi, Pan Zhao & Junxue Ren

  3. Engineering Research Center of Advanced Manufacturing Technology for Aero Engine, Ministry of Education, Northwestern Polytechnical University, Xi’an, 710072, Shaanxi, China

    Biyao Qiang, Kaining Shi, Pan Zhao & Junxue Ren

  4. Virtual Manufacturing Group, Smart Manufacturing Division, Advanced Remanufacturing and Technology Centre, 3 Cleantech Loop, #01-01, CleanTech Two, Singapore, 637143, Singapore

    Ning Liu

  5. School of Intelligent Manufacturing and Control Technology, Xi’an Mingde Institute of Technology, Xi’an, 710124, Shaanxi, China

    Pan Zhao

Authors
  1. Biyao Qiang
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  2. Kaining Shi
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  4. Pan Zhao
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  5. Junxue Ren
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Contributions

All authors contributed to the study’s conception and design. Biyao Qiang conducted a Matlab software simulation and wrote the first draft of the manuscript. Kaining Shi proposed the conception of this work and performed the data analyses. Ning Liu contributed significantly to the analysis and manuscript preparation. Pan Zhao performed the machining experiment. Junxue Ren reviewed and edited the manuscript. All authors read and approved the final manuscript.

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Correspondence to Kaining Shi or Ning Liu.

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Qiang, B., Shi, K., Liu, N. et al. Application of cutting power consumption in tool condition monitoring and wear prediction based on Gaussian process regression under variable cutting parameters. Int J Adv Manuf Technol 124, 37–50 (2023). https://doi.org/10.1007/s00170-022-10459-x

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