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Appl. Sci. 2017, 7(9), 884; doi:10.3390/app7090884

Real-time Monitoring for Disk Laser Welding Based on Feature Selection and SVM

1
School of Computer, South China Normal University, Guangzhou 510631, China
2
School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510090, China
3
College of Information Science and Engineering, Central South University, Changsha 410083, China
*
Author to whom correspondence should be addressed.
Received: 26 July 2017 / Revised: 25 August 2017 / Accepted: 25 August 2017 / Published: 28 August 2017
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Abstract

In order to automatically evaluate the welding quality during high-power disk laser welding, a real-time monitoring system was developed. The images of laser-induced metal vapor during welding were captured and fifteen features were extracted. A feature selection method based on a sequential forward floating selection algorithm was employed to identify the optimal feature subset, and a support vector machine (SVM) classifier was built to recognize the welding quality. The experiment results demonstrated that this method had satisfactory performance, and could be applied in laser welding monitoring applications. View Full-Text
Keywords: laser welding; feature selection; sequential forward floating; support vector machines laser welding; feature selection; sequential forward floating; support vector machines
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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. (CC BY 4.0).

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Wang, T.; Chen, J.; Gao, X.; Qin, Y. Real-time Monitoring for Disk Laser Welding Based on Feature Selection and SVM. Appl. Sci. 2017, 7, 884.

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