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Research on Tool Wear Detection Method Using Deep Residual Network

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Book cover Intelligent Robotics and Applications (ICIRA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13016))

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Abstract

During part machining, as the tool usage time and the number of passes increase, the cutting edge of the tool gradually wears out. As a tool for parts processing, the degree of wear of cutting tools has an important influence on the quality of parts processing. In order to understand the tool wear in time and ensure the quality of parts processing, this paper proposes a tool wear monitoring method based on deep learning. The application of deep residual network in tool wear degree monitoring is investigated, the overall scheme of tool wear degree monitoring is designed, and the deep learning scheme is implemented in the PyTorch framework. The vibration and force signals from multiple parallel experiments are first collected by sensors. The signals are analyzed in time and frequency using the short-time Fourier trans-form, after which the transform results are used as the input of the ResNet34 deep residual network for supervised training. Finally, the model effect is tested using test data. The research results show that the accuracy rate of using the deep residual network to monitor the degree of tool wear can reach 98%, which has high monitoring accuracy.

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Authors and Affiliations

  1. Key Laboratory of High-Efficiency and Clean Mechanical Manufacture, National Demonstration Center for Experimental Mechanical Engineering Education, School of Mechanical Engineering, Shandong University, Jinan, 250061, China

    Qian Zhou, Kai Guo, Jie Sun & Vinothkumar Sivalingam

  2. Research Center for Aeronautical Component Manufacturing Technology and Equipment, Shandong University, Jinan, 250061, China

    Qian Zhou, Kai Guo, Jie Sun & Vinothkumar Sivalingam

Authors
  1. Qian Zhou
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  2. Kai Guo
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  3. Jie Sun
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  4. Vinothkumar Sivalingam
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Corresponding author

Correspondence to Kai Guo .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Xin-Jun Liu

  2. Tsinghua University, Beijing, China

    Zhenguo Nie

  3. Beihang University, Beijing, China

    Jingjun Yu

  4. Tsinghua University, Beijing, China

    Fugui Xie

  5. Shandong University, Shandong, China

    Rui Song

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Zhou, Q., Guo, K., Sun, J., Sivalingam, V. (2021). Research on Tool Wear Detection Method Using Deep Residual Network. In: Liu, XJ., Nie, Z., Yu, J., Xie, F., Song, R. (eds) Intelligent Robotics and Applications. ICIRA 2021. Lecture Notes in Computer Science(), vol 13016. Springer, Cham. https://doi.org/10.1007/978-3-030-89092-6_51

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  • DOI: https://doi.org/10.1007/978-3-030-89092-6_51

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-89091-9

  • Online ISBN: 978-3-030-89092-6

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