Application of support vector machines to quality monitoring in robotized arc welding

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.