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Volume 13
Issue 2
10.3390/machines13020115
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Tool Condition Monitoring Model Based on DAE–SVR

by
Xiaoning Sun
1,
Zhifeng Yang
2,
Maojin Xia
3,
Min Xia
4,
Changfu Liu
1,*,
Yang Zhou
1 and
Yuquan Guo
1
1
School of Mechanical Engineering, LiaoNing Petrochemical University, Fushun 113001, China
2
School of Mechanical Engineering, Shenyang Polytechnic College, Shenyang 110045, China
3
China Petroleum Engineering & Construction Corp. Beijing Branch, Beijing 100101, China
4
School of Engineering, Lancaster University, Lancaster LA1 4YW, UK
*
Author to whom correspondence should be addressed.
Machines 2025, 13(2), 115; https://doi.org/10.3390/machines13020115
Submission received: 31 December 2024 / Revised: 24 January 2025 / Accepted: 30 January 2025 / Published: 1 February 2025
(This article belongs to the Section Machines Testing and Maintenance)
Versions Notes

Abstract

Cutting tools are executive components in metal processing, and tool wear directly affects the quality of the workpiece and processing efficiency; monitoring the change in its state is crucial to avoid accidents and ensure the safety of workers. The traditional monitoring model cannot compress a large amount of cutting data effectively, failing to obtain reliable feature data, and there are some defects in generalization ability and monitoring accuracy. For this purpose, this article takes milling cutters as the research object, and it integrates signals from force sensors, vibration sensors, and acoustic emission sensors, combining the advantages of the denoising autoencoder (DAE) model in data compression and the high monitoring accuracy of the support vector regression (SVR) model, to establish a tool wear monitoring model based on DAE–SVR. The results show that compared with traditional DAE and SVR models in multiple datasets, the maximum improvement in monitoring performance (MAE) is 43.58%.
Keywords: multi-sensor signal fusion; monitoring of tool wear status; DAE–SVR; monitoring accuracy multi-sensor signal fusion; monitoring of tool wear status; DAE–SVR; monitoring accuracy

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MDPI and ACS Style

Sun, X.; Yang, Z.; Xia, M.; Xia, M.; Liu, C.; Zhou, Y.; Guo, Y. Tool Condition Monitoring Model Based on DAE–SVR. Machines 2025, 13, 115. https://doi.org/10.3390/machines13020115

AMA Style

Sun X, Yang Z, Xia M, Xia M, Liu C, Zhou Y, Guo Y. Tool Condition Monitoring Model Based on DAE–SVR. Machines. 2025; 13(2):115. https://doi.org/10.3390/machines13020115

Chicago/Turabian Style

Sun, Xiaoning, Zhifeng Yang, Maojin Xia, Min Xia, Changfu Liu, Yang Zhou, and Yuquan Guo. 2025. "Tool Condition Monitoring Model Based on DAE–SVR" Machines 13, no. 2: 115. https://doi.org/10.3390/machines13020115

APA Style

Sun, X., Yang, Z., Xia, M., Xia, M., Liu, C., Zhou, Y., & Guo, Y. (2025). Tool Condition Monitoring Model Based on DAE–SVR. Machines, 13(2), 115. https://doi.org/10.3390/machines13020115

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