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Tool wear monitoring based on an improved convolutional neural network

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Abstract

Tool condition is the key factor affecting the quality and efficiency of precision cutting of parts. As tool wear is inevitable during machining, tool wear status during machining must be regularly monitored. This study proposes a combined convolutional neural network and support vector machine (SVM) approach for tool wear status monitoring. First, 1D cutting force data are wavelet-transformed and converted into 2D spectrogram. Second, the leaky-ReLU activation function is adopted to enhance network robustness. Third, an SVM classifier is used to replace the traditional Softmax function to improve the model generalization capability. Finally, the cutting force signal of the tool used for the machining of the aero-engine integral blisk is verified. The accuracy of the constructed network model can reach 98.28 %. Moreover, the proposed model has a simple structure, requires a small number of parameters, and has good robustness and reliability.

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Abbreviations

FT :

Fourier transform

WT :

Wavelet transform

CNN :

Convolutional neural network

ReLU :

Rectified linear unit

SVM :

Support vector machine

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (51965051) and the Natural Science Foundation of Inner Mongolia (2019LH05029).

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Authors and Affiliations

  1. School of Mechanical Engineering, Inner Mongolia University of Technology, Hohhot, China

    Jia-Wei Zhao, Shi-Jie Guo, Lin Ma, Hao-Qiang Kong & Nan Zhang

Authors
  1. Jia-Wei Zhao
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  2. Shi-Jie Guo
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  3. Lin Ma
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  4. Hao-Qiang Kong
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  5. Nan Zhang
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Corresponding author

Correspondence to Nan Zhang.

Additional information

Nan Zhang is an Associate Professor of the School of Mechanical Engineering at Inner Mongolia University of Technology. She received a Ph.D. degree in Mechatronic Engineering from Northwestern Polytechnical University in 2021. Her research fields include machining mechanism, condition monitoring, residual life prediction and system reliability.

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Zhao, JW., Guo, SJ., Ma, L. et al. Tool wear monitoring based on an improved convolutional neural network. J Mech Sci Technol 37, 1949–1958 (2023). https://doi.org/10.1007/s12206-023-0332-x

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  • DOI: https://doi.org/10.1007/s12206-023-0332-x

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