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 signal,a SVM classifier is constructed to establish the relationship between the feature of process parameters and the quality of weld penetration. Under the samples obtained from auto parts welding production line, the learning machine with a radial basis function kernel shows good performance. And this method can be feasible to identify defect online in welding production.
