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Tool wear status monitoring under laser-ultrasonic compound cutting based on acoustic emission and deep learning

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Abstract

In order to solve the problem of untimely monitoring of tool wear status during laser-ultrasonic compound cutting (LUCC), which leads to the reduction of process accuracy and product quality damage, a method of tool wear status monitoring based on acoustic emission (AE) and deep learning is proposed in this paper. The AE signal is collected and analyzed in the frequency and time-frequency domains by using Fourier transform (FT) and wavelet packet decomposition (WPD), it is found that the AE signal energy is mainly concentrated in the 1st, 2nd, 3rd, and 4th frequency bands, and the energy of each frequency band in the signal increases gradually when the tool wear degree increases, however, the percentage of energy of each frequency band in the total energy shows different change trend. According to the amount of flank wear of the tool, the tool wear degree is divided into four tool wear states. The SVM model and BP neural network model were established based on MATLAB software to realize the tool wear state monitoring. The results show that the tool wear monitoring based on BP neural network is better than that of SVM for tool wear status monitoring. The model identification correct rate reaches 99 %, which is more suitable for the tool wear state monitoring designed in this paper.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 51075127), the Key Scientific Research Project of Colleges and Universities in Henan Province, China (No. 23A460019) and the Doctoral Fund of Henan Polytechnic University in Henan Province, China (No. B2018-23).

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Authors and Affiliations

  1. College of Mechanical and Power Engineering, Henan Polytechnic University, Jiaozuo, 454003, People’s Republic of China

    Changjuan Zhang, Junhao Wang, Yongjing Cao & Feng Jiao

Authors
  1. Changjuan Zhang
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  2. Junhao Wang
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  3. Yongjing Cao
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  4. Feng Jiao
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Corresponding author

Correspondence to Changjuan Zhang.

Additional information

Changjuan Zhang graduated with a doctoral degree in Mechanical Design, Manufacturing, and Automation from Henan Polytechnic University. Her research interests include precision manufacturing and precision ultra-precision machining in the field of mechanical design, manufacturing, and automation.

Junhao Wang is currently pursuing a master’s degree. He is currently studying for his master’s degree at Henan Polytechnic University. His research focus is on intelligent identification of tool wear in precision machining.

Yongjing Cao is currently pursuing a master’s degree. He is currently studying for his master’s degree at Henan Polytechnic University. His research focus is on laser-ultrasonic composite machining.

Feng Jiao is a high-level scientific and technological innovation talent in Henan Province and serves as the academic and technical leader of the Education Department. He currently holds the position of Vice Dean in the School of Mechanical and Power Engineering at Henan Polytechnic University, and is also responsible for the major of Mechanical Design, Manufacturing, and Automation. His research interests include efficient precision machining techniques for difficult-to-machine materials, ultrasonic machining technology and equipment, composite machining technology, and more.

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Zhang, C., Wang, J., Cao, Y. et al. Tool wear status monitoring under laser-ultrasonic compound cutting based on acoustic emission and deep learning. J Mech Sci Technol 38, 2411–2421 (2024). https://doi.org/10.1007/s12206-024-0419-z

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  • DOI: https://doi.org/10.1007/s12206-024-0419-z

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