Analysis of high-power disk laser welding stability based on classification of plume and spatter characteristics

Classification of plume and spatter images was studied to evaluate the welding stability. A high-speed camera was used to capture the instantaneous images of plume and spatters during high power disk laser welding. Characteristic parameters such as the area and number of spatters, the average grayscale of a spatter image, the entropy of a spatter grayscale image, the coordinate ratio of the plume centroid and the welding point, the polar coordinates of the plume centroid were defined and extracted. Karhunen-Loeve transform method was used to change the seven characteristics into three primary characteristics to reduce the dimensions. Also, K-nearest neighbor method was used to classify the plume and spatter images into two categories such as good and poor welding quality. The results show that plume and spatter have a close relationship with the welding stability, and two categories could be recognized effectively using K-nearest neighbor method based on Karhunen-Loeve transform.

Elucidation of Metallic Plume and Spatter Characteristics Based on SVM During High-Power Disk Laser Welding

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.

基于数学模型的大功率碟形激光焊支持向量回归熔宽预测

在焊接质量控制过程中,一个重要的考察因素就是熔宽。焊接是一个多因素强耦合问题,为了研究预测熔宽变化情况,采用支持向量机回归熔宽预测。构造出支持向量回归模型,并利用实验数据验证其准确性,借助于BP神经网络作为对比,以检验支持向量回归熔宽预测的效果。结果表明,BP神经网络和支持向量都能够很好地用于大功率碟形激光焊接过程,其中SVR更加合适,并且支持向量机预测效果在步数相同的情况下明显优于BP神经网路的预测结果。SVR在N=3时取得最优值,可是计算量与计算时间随N值减小时反而会增加,在综合了多方面的因素后,最终确定N=10为最优解。