Version 1
: Received: 27 August 2024 / Approved: 27 August 2024 / Online: 28 August 2024 (02:55:49 CEST)
How to cite:
Duan, J.; Cao, G.; Ma, G.; Yu, Z.; Shao, C. Research on On-Line Monitoring of Grinding Wheel Wear Based on Multi-sensor. Preprints2024, 2024081958. https://doi.org/10.20944/preprints202408.1958.v1
Duan, J.; Cao, G.; Ma, G.; Yu, Z.; Shao, C. Research on On-Line Monitoring of Grinding Wheel Wear Based on Multi-sensor. Preprints 2024, 2024081958. https://doi.org/10.20944/preprints202408.1958.v1
Duan, J.; Cao, G.; Ma, G.; Yu, Z.; Shao, C. Research on On-Line Monitoring of Grinding Wheel Wear Based on Multi-sensor. Preprints2024, 2024081958. https://doi.org/10.20944/preprints202408.1958.v1
APA Style
Duan, J., Cao, G., Ma, G., Yu, Z., & Shao, C. (2024). Research on On-Line Monitoring of Grinding Wheel Wear Based on Multi-sensor. Preprints. https://doi.org/10.20944/preprints202408.1958.v1
Chicago/Turabian Style
Duan, J., Zhenglin Yu and Changshun Shao. 2024 "Research on On-Line Monitoring of Grinding Wheel Wear Based on Multi-sensor" Preprints. https://doi.org/10.20944/preprints202408.1958.v1
Abstract
The state of the grinding wheel in the grinding process directly affects the surface quality of the workpiece. The monitoring of the wear state of the grinding wheel can effectively identify the wear information of the grinding wheel and timely and effectively repair the grinding wheel. In this paper, a multi-sensor fusion precision grinding wheel wear intelligent monitoring system is established for typical difficult-to-machine aerospace material titanium alloy, and a multi-eigenvalue fusion algorithm based on improved support vector machine (SVM) is proposed. The extracted sound emission signal wavelet packet decomposition coefficient mean square value, force signal grinding force ratio, and vibration signal effective value are used as the input of the improved support vector machine, and the identification strategy is adjusted by the entropy weight evaluation method. The results show that the monitoring system established in this paper can effectively identify the wear state of the grinding wheel, and the monitoring accuracy can reach more than 92%
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
