During deep penetration laser welding,there exist plume(weak plasma) and spatters,which are the results of weld material ejection due to strong laser heating.The characteristics of plume and spatters are related to welding stability and quality.Characteristics of metallic plume and spatters were investigated during high-power disk laser bead-on-plate welding of Type 304 austenitic stainless steel plates at a continuous wave laser power of 10 kW.An ultraviolet and visible sensitive high-speed camera was used to capture the metallic plume and spatter images.Plume area,laser beam path through the plume,swing angle,distance between laser beam focus and plume image centroid,abscissa of plume centroid and spatter numbers are defined as eigenvalues,and the weld bead width was used as a characteristic parameter that reflected welding stability.Welding status was distinguished by SVM(support vector machine) after data normalization and characteristic analysis.Also,PCA(principal components analysis) feature extraction was used to reduce the dimensions of feature space,and PSO(particle swarm optimization) was used to optimize the parameters of SVM.Finally a classification model based on SVM was established to estimate the weld bead width and welding stability.Experimental results show that the established algorithm based on SVM could effectively distinguish the variation of weld bead width,thus providing an experimental example of monitoring high-power disk laser welding quality. During deep penetration laser welding, there exist the plume (weak plasma) and spatters, which are the results of weld paper ejection due to strong laser heating. These characteristics of plume and spatters are related to welding stability and quality. Characteristics of metallic plume and spatters were investigated during high-power disk laser bead-on-plate welding of Type 304 austenitic stainless steel plates at a continuous wave laser power of 10 kW. Xen ultraviolet and visible sensitive high-speed camera was used to capture the metallic plume and spatter images .Plume area, laser beam path through the plume, swing angle, distance between laser beam focus and plume image centroid, abscissa of plume centroid and spatter numbers are defined as eigenvalues, and the weld bead width was used as a characteristic parameter that reflected welding stability.Welding status was distinguished by SVM (support vector machine) after data normalization and characteristic analysis.Also, PCA (principal components analy sis) feature extraction was used to reduce the dimensions of feature space, and PSO (particle swarm optimization) was used to optimize the parameters of SVM. Finally a classification model based on SVM was established to estimate the weld bead width and welding stability. Experimental results show that the established algorithm based on SVM could effectively distinguish the variation of weld bead width, thus providing an experimental example of monitoring high-power disk laser welding quality.