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国家自然科学基金(10251009001000001)

作品数:2 被引量:10H指数:2
发文基金:国家自然科学基金国家教育部博士点基金更多>>
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Study of Dynamic Features of Surface Plasma in High-Power Disk Laser Welding被引量:8
2012年
High-speed photography was used to obtain the dynamic changes in the surface plasma during a high-power disk laser welding process.A color space clustering algorithm to extract the edge information of the surface plasma region was developed in order to improve the accuracy of image processing.With a comparative analysis of the plasma features,i.e.,area and height,and the characteristics of the welded seam,the relationship between the surface plasma and the stability of the laser welding process was characterized,which provides a basic understanding for the real-time monitoring of laser welding.
王腾高向东Katayama SEIJI金小莉
关键词:表面等离子体激光焊接动态特征高速摄影
Elucidation of Metallic Plume and Spatter Characteristics Based on SVM During High-Power Disk Laser Welding被引量:2
2015年
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.
高向东刘桂谦
关键词:SVM
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