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Prediction of tool wear in sculpture surface by a new fusion method of temporal convolutional network and self-attention

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Abstract

The accuracy and quality of the workpiece significantly depends on the degree of tool wear. The monitoring and prediction of tool wear are very important. Regretfully, most of references study on the tool wear based on the model of plane milling. It is well known that the cutting force and tool wear are great difference on the sculpture surfaces. So the accuracy of existing methods need be further improved. Considering that tool wear is related to time series signals and the sculpture surface has the different curvature, a new method based on the fusion of a temporal convolutional network and self-attention mechanism (TCNA) is proposed to predict the tool wear both in sculpture surface and plane surface. A temporal convolutional network ensures the causality between the output and input data and the self-attention mechanism strengthens the connection between the current output and input data from past moments. Compared with the traditional deep learning algorithms such as convolutional neural network (CNN) and long short-term memory (LSTM), TCNA has an excellent result on tool wear prediction either in plane milling or sculpture surface milling; the mean squared error (MSE) of TCNA model is reduced by more than 26.59% and 37.95%. Therefore, TCNA could be more accurately used for the tool wear prediction in any arbitrary surface cutting.

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Funding

This work was supported by National Natural Science Foundation of China (52175456), CAEP Research Project (S22H0086), Sichuan Science and Technology Project (2021YFG0052), Central University Fundermental Reasearch Project (ZYGZ2019J032).

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Authors and Affiliations

  1. School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Wenjie Jia, Wei Wang, Ziwei Li & Hai Li

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  1. Wenjie Jia
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  2. Wei Wang
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  3. Ziwei Li
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  4. Hai Li
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Contributions

Wenjie Jia contributed to methodology, software, validation, writing—original draft, and project administration; Wei Wang contributed to conceptualization, resources, supervision, writing—review & editing; Ziwei Li contributed to software, validation, and project administration; Hai Li contributed to validation and formal analysis.

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Correspondence to Wei Wang.

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Jia, W., Wang, W., Li, Z. et al. Prediction of tool wear in sculpture surface by a new fusion method of temporal convolutional network and self-attention. Int J Adv Manuf Technol 121, 2565–2583 (2022). https://doi.org/10.1007/s00170-022-09396-6

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  • DOI: https://doi.org/10.1007/s00170-022-09396-6

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