DocumentCode
1678545
Title
Application of support vector machines to quality monitoring in robotized arc welding
Author
Feng, YE ; Lun, Song Yong ; Di, Li ; Zong, Lai Yi
Author_Institution
Dept. of Mechatronic Eng., South China Univ. of Technol., Guangzhou, China
Volume
3
fYear
2002
fDate
6/24/1905 12:00:00 AM
Firstpage
2321
Lastpage
2326
Abstract
A quality monitoring method by means of support vector machines (SVM) for robotized gas metal arc welding (GMAW) is introduced. Through the feature extraction of the welding process, a SVM classifier is constructed to establish the relationship between the feature of process parameters and the quality of weld penetration. The results show that the method can be feasible for identifying defects online in welding production
Keywords
arc welding; feature extraction; industrial robots; learning automata; pattern classification; quadratic programming; quality control; GMAW; SVM classifier; feature extraction; quality monitoring; robotized gas metal arc welding; support vector machines; weld penetration quality; Automotive engineering; Condition monitoring; Feature extraction; Manufacturing processes; Production; Robots; Support vector machine classification; Support vector machines; Testing; Welding;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location
Honolulu, HI
ISSN
1098-7576
Print_ISBN
0-7803-7278-6
Type
conf
DOI
10.1109/IJCNN.2002.1007504
Filename
1007504
Link To Document