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Feature extraction of the wear state of a deep hole drill tool based on the wavelet fractal dimension of the current signal

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Abstract

Given that wavelet transform and fractal theory reveal the self-similarity characteristics of objects from macro to micro levels, this study proposes a wavelet fractal dimension (WFD) to extract the fractal dimension feature of the wear state of a deep hole drill bit by using binary wavelet function as the scale. Weierstrass–Mandelbrot fractal functions with different theoretical fractal dimensions are introduced to evaluate the accuracy of WFD. Four methods for defining fractal dimensions are applied to estimate the fractal dimension of the current signal from the spindle motor in deep hole machining processing. Then, the variation law of the estimated value of the fractal dimensions with drill wear is investigated. Results show that the estimated value of WFD presents the smallest error compared with the theoretical value. Moreover, compared with other methods, the WFD of the current signal provides the strongest correlation with drill bit wear, which offers accurate characteristics for the monitoring of tool wear state.

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Acknowledgments

This work was supported by the Natural Science Foundation Research Project of Shaanxi Province (No.2021JQ-488) and Doctor’s Research Foundation of Xi’an University of Technology (Grant Number 102-451120014).

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Authors and Affiliations

  1. School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an, Shaanxi, 710048, P.R. China

    Chao Peng, Jianming Zheng, Ting Chen, Zhangshuai Jing, Weichao Shi & Shijie Shan

  2. Ankang University, Ankang, Shaanxi, 725000, P.R. China

    Chao Peng

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  1. Chao Peng
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  2. Jianming Zheng
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  3. Ting Chen
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  4. Zhangshuai Jing
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  5. Weichao Shi
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Corresponding author

Correspondence to Jianming Zheng.

Additional information

Chao Peng is currently pursuing a doctorate in Xi’an University of Technology, Xi’an, China. His research interests include deep hole drilling and monitoring of machining process.

Jianming Zheng is a Professor and Doctoral Supervisor of Xi’an University of Technology, Xi’an, China. His research interests mainly include vibration drilling, deep hole cutting, and fault diagnosis.

Ting Chen is currently pursuing a doctorate in Xi’an University of Technology, Xi’an, China. Her research interests include vibration drilling and monitoring of tapping process.

Zhangshuai Jing is currently pursuing a doctorate in Xi’an University of Technology, Xi’an, China. His research interests include incremental forming using single point ultrasonic vibration.

Weichao Shi is an Associate Professor and Master’s Supervisor in Xi’an University of Technology, Xi’an, China. His research interests include microvision image processing technology and intelligent control technology.

Shijie Shan is currently pursuing a doctorate in Xi’an University of Technology, Xi’an, China. His research interests include fault diagnosis and image processing technology.

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Peng, C., Zheng, J., Chen, T. et al. Feature extraction of the wear state of a deep hole drill tool based on the wavelet fractal dimension of the current signal. J Mech Sci Technol 38, 2211–2221 (2024). https://doi.org/10.1007/s12206-024-0404-6

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  • DOI: https://doi.org/10.1007/s12206-024-0404-6

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