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Milling force coefficients-based tool wear monitoring for variable parameter milling

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Abstract

Tool wear is an important factor that affects the quality and machining accuracy of aeronautical structural parts in the milling process. It is essential to monitor the tool wear in titanium alloy machining. The traditional tool wear features such as root mean square (RMS), kurtosis, and wavelet packet energy spectrum are related to not only the tool wear status but also to the milling parameters, thus monitoring the tool wear status only under fixed milling parameters. This paper proposes a new method of online monitoring of tool wear using milling force coefficients. The instantaneous cutting force model is used to extract the milling force coefficients which are independent of milling parameters. The principal component analysis (PCA) algorithm is used to fuse the milling force coefficients. Furthermore, support vector machine (SVM) model is used to monitor tool wear states. Experiments with different machining parameters were conducted to verify the effectiveness of this method used for tool wear monitoring. The results show that compared to traditional features, the milling force coefficients are not dependent on the milling parameters, and using milling force coefficients can effectively monitor the transition point of cutters from normal wear to severe wear (tool failure).

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Funding

This work was financially supported by the National Key R&D Program of China (No. 2018YFB1701901) for Jun Zhang, the Key-Area R&D Program of Guangdong Province (No. 2020B090927002) for Huijie Zhang, the National Key R&D Program of China (No. 2018YFB1701901), the Major Science and Technology Project of Shaanxi Province (No. 2019zdzx01-01–02), and the China Postdoctoral Science Foundation (No. BX20180253, 219,945) for Xing Zhang.

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Authors and Affiliations

  1. State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Yanta District, Room A315 West Building 5, 99# Yanxiang Road, Xi’an 710054, Shaanxi, China

    Tianhang Pan, Jun Zhang, Xing Zhang, Wanhua Zhao, Huijie Zhang & Bingheng Lu

Authors
  1. Tianhang Pan
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  2. Jun Zhang
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  3. Xing Zhang
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  4. Wanhua Zhao
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  5. Huijie Zhang
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  6. Bingheng Lu
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Contributions

Tianhang Pan: methodology, data curation, experiment, validation, formal analysis, writing—original draft, review and editing; Jun Zhang: supervision, writing—review and editing; Xing Zhang: methodology, formal analysis, writing—review and editing; Wanhua Zhao: methodology, supervision, data curation, formal analysis; Huijie Zhang: experiment; Bingheng Lu: project administration.

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Correspondence to Wanhua Zhao.

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Pan, T., Zhang, J., Zhang, X. et al. Milling force coefficients-based tool wear monitoring for variable parameter milling. Int J Adv Manuf Technol 120, 4565–4580 (2022). https://doi.org/10.1007/s00170-022-08823-y

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