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Tool wear prediction under missing data through prioritization of sensor combinations

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Abstract

It is significant to ensure stable machining quality that accurately monitors the tool wear. Sensor signals are mainly used to predict tool wear. Therefore, complete sensor signals are crucial for tool wear prediction. To obtain comprehensive information on the process for improving prediction accuracy and ensuring the effectiveness of the prediction model when sensor data are missing, this paper proposes a tool wear prediction scheme through prioritization of sensor combinations under missing data. In this prediction scheme, the optimal feature subset of the sensor combination is obtained using kernel principal component analysis (KPCA) optimized by maximum information coefficient (MIC); that is, the MIC is employed to select the gamma value of the KPCA. Meanwhile, random forests are utilized to determine the prioritization of sensor combinations based on the obtained optimal feature subset. With the occurrence of signal loss, prediction errors exist. Thus, the sensor combination that replaces the sensor with missing data is selected according to the determined priority order. The effectiveness of the proposed scheme is verified through sensor combinations based on the cutting force, vibration, and acoustic emission during the milling process of TC18.

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Data availability

The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.

Code availability

Not applicable.

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Funding

This research is supported by the National Natural Science Foundation of China (NSFC) (Grant No. 51665005 and Grant No. 52165062), the Guangxi Natural Science Foundation Program (Grant No. 2020JJD160004 and 2019JJB160048), and the project of improving the basic scientific research ability of young and middle-aged teachers of colleges and universities in Guangxi (Grant No. 2020KY10014).

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Authors and Affiliations

  1. Guangxi Key Laboratory of Manufacturing Systems and Advanced Manufacturing Technology, Guangxi University, Nanning, 530004, China

    Zhenjun Wu, Yujia Li, Yonghui Chen, Jian Feng, Junyan Ma & ** Liao

  2. Department of Mechanical and Marine Engineering, Beibu Gulf University, 535011, Qinzhou, China

    Juan Lu

Authors
  1. Zhenjun Wu
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  2. Juan Lu
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  3. Yujia Li
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  4. Yonghui Chen
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  6. Junyan Ma
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  7. ** Liao
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Contributions

Zhenjun Wu contributed to the process and summary of the study and wrote the manuscript. Zhenjun Wu, Juan Lu, Yujia Li, Yonghui Chen, and Jian Feng designed and carried out the experiment. Junyan Ma and ** Liao guided the concept, direction, and experiments of research. Zhenjun Wu tested the results and analyzed the data.

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Correspondence to ** Liao.

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Wu, Z., Lu, J., Li, Y. et al. Tool wear prediction under missing data through prioritization of sensor combinations. Int J Adv Manuf Technol 120, 2715–2729 (2022). https://doi.org/10.1007/s00170-022-08916-8

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