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Tool wear monitoring based on the combination of machine vision and acoustic emission

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Abstract

Tool condition monitoring (TCM) has developed several mature methods to improve processing efficiency. However, existing methods either require the removal of the tool from the machining system for individual monitoring or extensive data processing, manual labeling, and empirical judgment on whether to replace the tool. A TCM method combining machine vision and acoustic emission (AE) is proposed in this paper. Based on the structural similarity index (SSIM) algorithm, the relationship between tool speed and camera frame number is established. Through machine learning (ML) and neural network (NN) methods, the mapping between the wear mount extracted by the machine vision method and the AE feature vector is constructed, and the tool monitoring model is established. Verified by the data set obtained by the milling test, the TCM model established by the proposed can achieve a recognition accuracy of 96.11%, and the root mean square error (RMSE) predicted by the model is 0.0106. This method has proved to be practical and versatile in TCM.

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Data availability

Some or all data generated or used during the study are available from the corresponding author by request.

Code availability

Some or all code used during the study are available from the corresponding author by request.

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Acknowledgements

This research has been financially supported by the National Natural Science Foundation of China (No. 51975504, 51475404), the Natural Science Foundation of Hunan Province (No. 2021JJ30676), Provincial Natural Science Foundation of Hunan for Distinguished Young Scholars (2022JJ10045), and Postgraduate Scientific Research Innovation Project of Hunan Province (No. CX20210519, XDCX2022Y103).

Funding

This work was supported by the National Natural Science Foundation of China (No. 51975504, 51475404), the Natural Science Foundation of Hunan Province (No. 2021JJ30676), Provincial Natural Science Foundation of Hunan for Distinguished Young Scholars (2022JJ10045), and Postgraduate Scientific Research Innovation Project of Hunan Province (No. CX20210519, XDCX2022Y103).

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Authors and Affiliations

  1. School of Mechanical Engineering, Xiangtan University, Xiangtan, 411105, China

    Meiliang Chen, Mengdan Li, Linfeng Zhao & Jiachen Liu

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  1. Meiliang Chen
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  2. Mengdan Li
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  3. Linfeng Zhao
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  4. Jiachen Liu
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Contributions

Meiliang Chen performed the experiment and contributed to analysis and manuscript; Mengdan Li contributed to the conception of the study and was a major contributor in writing the manuscript; Linfeng Zhao contributed significantly to analysis and manuscript preparation; Jiachen Liu performed the experiment and wrote the manuscript.

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Correspondence to Mengdan Li.

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Chen, M., Li, M., Zhao, L. et al. Tool wear monitoring based on the combination of machine vision and acoustic emission. Int J Adv Manuf Technol 125, 3881–3897 (2023). https://doi.org/10.1007/s00170-023-11017-9

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