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
fDate :
6/24/1905 12:00:00 AM
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;
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7278-6
DOI :
10.1109/IJCNN.2002.1007504
