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Tool condition monitoring in the milling process based on multisource pattern recognition model

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Abstract

In the milling process of metallic parts, appropriate tool conditions are essential to reducing processing faults and ensuring manufacturing quality. However, the existing condition monitoring methods are usually limited by recognizing intermediate abnormal states during milling processing, which is inefficient and impractical for real practical applications. Therefore, this paper proposes a tool condition monitoring (TCM) method in the milling process based on multisource pattern recognition and state transfer paths. First, the improved K-means clustering method is used to generate multiple patterns of tool wear. Second, a multisource pattern recognition model framework is developed, and multiple observation windows and the pattern transfer path are considered in the multisource pattern recognition model. Finally, PHM2010 datasets are used to verify the feasibility of the proposed method, and the results demonstrate the applicability of the proposed method in practice for tool condition monitoring.

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Data Availability

The datasets during the current study are available in the “PHM Data Challenge 2010” database (https://www.Phmsociety.org/competition/phm/10S).

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Acknowledgements

The authors acknowledge the financial support of the National Natural Science Foundation of China (No. 51705015) and the National Defense Fundamental Research Foundation of China (No. JCKY2018203C005).

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Authors and Affiliations

  1. School of Reliability and Systems Engineering, Beihang University, Beijing, China

    Wei Dai, Kui Liang & Tingting Huang

  2. Beijing Hangxing Machinery Manufacturing Co., Ltd., Beijing, China

    Zhiyuan Lu

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  1. Wei Dai
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  2. Kui Liang
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Contributions

Wei Dai contributed to the conception and methodology and wrote the original draft of the study. Kui Liang performed supervising and writing a review of the manuscript. Tingting Huang contributed significantly to the analysis, review, and editing of the manuscript. Zhiyuan Lu helped perform the analysis with constructive discussions [16].

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Correspondence to Tingting Huang.

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Dai, W., Liang, K., Huang, T. et al. Tool condition monitoring in the milling process based on multisource pattern recognition model. Int J Adv Manuf Technol 119, 2099–2114 (2022). https://doi.org/10.1007/s00170-021-08012-3

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