期刊
COMPUTERS IN INDUSTRY
卷 94, 期 -, 页码 75-81出版社
ELSEVIER
DOI: 10.1016/j.compind.2017.10.003
关键词
Laser welding; Support vector machine; Real-time monitoring; Feature selection; Welding quality
资金
- National Natural Science Foundation of China [51505158, 11674106]
- Natural Science Foundation of Guangdong Province [2014A030310153]
In this paper, an efficient quality monitoring system for monitoring high-power disk laser welding in real time was developed. Fifteen features of laser-induced metal vapor plume and spatters were extracted and support vector machine was adopted to establish a classifier to evaluate the welding quality. Feature selection method was employed to choose suitable features. The experiment results demonstrated that this method had satisfactory performance and could be applied to real-time monitoring application. (C) 2017 Elsevier B.V. All rights reserved.
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