A quality monitoring method by means of support vector machines (SVM) forrobotized gas metal arc welding (GMAW) is introduced. Through the feature extraction of the weldingprocess signal, a SVM classifier is constructed to establish the relationship between the feature ofprocess parameters and the quality of weld penetration. Under the samples obtained from auto partswelding production line, the learning machine with a radial basis function kernel shows goodperformance. And this method can be feasible to identity defect online in welding production.
