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Appl. Sci. 2017, 7(3), 299; doi:10.3390/app7030299

Quality Monitoring for Laser Welding Based on High-Speed Photography and Support Vector Machine

1
School of Computer Science, South China Normal University, Guangzhou 510631, China
2
School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510090, China
3
School of Physics and Telecommunication Engineering, South China Normal University, Guangzhou 510631, China
*
Author to whom correspondence should be addressed.
Academic Editors: Jiwang Yan and Cem Selcuk
Received: 2 November 2016 / Revised: 14 February 2017 / Accepted: 10 March 2017 / Published: 20 March 2017
View Full-Text   |   Download PDF [3102 KB, uploaded 20 March 2017]   |  

Abstract

In order to improve the prediction ability of welding quality during high-power disk laser welding, a new approach was proposed and applied in the classification of the dynamic features of metal vapor plume. Six features were extracted through the color image processing method. Three features, including the area of plume, number of spatters, and horizontal coordinate of plume centroid, were selected based on the classification accuracy rates and Pearson product-moment correlation coefficients. A support vector machine model was adopted to classify the welding quality status into two categories, good or poor. The results demonstrated that the support vector machine model established according to the selected features had satisfactory prediction and generalization ability. The classification accuracy rate was higher than 90%, and the model could be applied in the prediction of welding quality during high-power disk laser welding. View Full-Text
Keywords: laser welding; metal vapor plume; Pearson product-moment correlation coefficient; support vector machines laser welding; metal vapor plume; Pearson product-moment correlation coefficient; 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.; Li, W. Quality Monitoring for Laser Welding Based on High-Speed Photography and Support Vector Machine. Appl. Sci. 2017, 7, 299.

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