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A hybrid remaining useful life prediction method for cutting tool considering the wear state

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Abstract

Accurate cutting tool remaining useful life (RUL) prediction is of significance to guarantee the cutting quality and minimize the production cost. Recently, physics-based and data-driven methods have been widely used in the tool RUL prediction. The physics-based approaches may not accurately describe the time-varying wear process due to a lack of knowledge for underlying physics and simplifications involved in physical models, while the data-driven methods may be easily affected by the quantity and quality of data. To overcome the drawbacks of these two approaches, a hybrid prognostics framework considering tool wear state is developed to achieve an accurate prediction. Firstly, the mapping relationship between the sensor signal and tool wear is established by support vector regression (SVR). Then, the tool wear statuses are recognized by support vector machine (SVM), and the results are put into a Bayesian framework as prior information. Thirdly, based on the constructed Bayesian framework, the tool wear model parameters are updated iteratively by the sliding time window and particle filter algorithm. Finally, the tool wear state space and RUL can be predicted accordingly using the updating tool wear model. The validity of the proposed method is demonstrated by a high-speed machine tool experiment. The results show that the presented approach can effectively reduce the uncertainty of tool wear state estimation and improve the accuracy of RUL prediction.

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Data availability

Data related to this work will be provided upon request.

Code availability

Part of the code can be provided upon request for noncommercial purpose.

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Funding

This work is supported by the National Natural Science Foundation of China (Grant No. 51475425).

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Authors and Affiliations

  1. College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang, 310014, People’s Republic of China

    Yifan Li, Yongyong Xiang, Baisong Pan & Luojie Shi

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  1. Yifan Li
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  2. Yongyong Xiang
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  3. Baisong Pan
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  4. Luojie Shi
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Contributions

Yifan Li: conceptualization, methodology, software, writing—original draft preparation. Yongyong Xiang: investigation, writing—original draft preparation. Baisong Pan: data curation, software, validation, writing—reviewing and editing. Luojie Shi: resources, supervision.

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Correspondence to Baisong Pan.

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Li, Y., Xiang, Y., Pan, B. et al. A hybrid remaining useful life prediction method for cutting tool considering the wear state. Int J Adv Manuf Technol 121, 3583–3596 (2022). https://doi.org/10.1007/s00170-022-09417-4

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