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金属蒸汽和飞溅图像特征分类的大功率盘形激光焊稳定性分析
引用本文:高向东,文茜,Seiji KATAYAMA.金属蒸汽和飞溅图像特征分类的大功率盘形激光焊稳定性分析[J].中国有色金属学会会刊,2013,23(12):3748-3757.
作者姓名:高向东  文茜  Seiji KATAYAMA
作者单位:[1]广东工业大学机电工程学院,广州,510006 [2]大阪大学接合科学研究所,大阪567-0047,日本
基金项目:Project (51175095) supported by the National Natural Science Foundation of China,Projects (10251009001000001,9151009001000020) supported by the Natural Science Foundation of Guangdong Province, China,Project (20104420110001) supported by the Specialized Research Fund for the Doctoral Program of Higher Education of China
摘    要:采用对金属蒸汽图像特征进行分类的方法来评估焊接过程的稳定性。使用高速摄像机实时获取大功率盘形激光焊接过程中金属蒸汽和飞溅图像,定义并提取飞溅面积和个数、飞溅灰度图像平均灰度和熵、金属蒸汽质心与焊接点的坐标比以及金属蒸汽质心的极坐标(矢径和极角)等7个金属蒸汽和飞溅特征。为实现降维,使用Karhunen-Loeve变换法将7维特征向量转换为3维特征向量,同时使用K近邻法将图像分成焊接质量较好与较差两类。实验结果表明,金属蒸汽及飞溅与焊接稳定性有密切的联系,使用K近邻法对Karhunen-Loeve变换后的图像进行分类可以获得较好的效果,实现焊接状况的评估。

关 键 词:大功率盘形激光焊  金属蒸气  飞溅  特征分类  稳定性
收稿时间:7 January 2013

Analysis of high-power disk laser welding stability based on classification of plume and spatter characteristics
Xiang-dong GAO,Qian WEN,Seiji KATAYAMA.Analysis of high-power disk laser welding stability based on classification of plume and spatter characteristics[J].Transactions of Nonferrous Metals Society of China,2013,23(12):3748-3757.
Authors:Xiang-dong GAO  Qian WEN  Seiji KATAYAMA
Affiliation:Xiang-dong GAO,Qian WEN,Seiji KATAYAMA(1. School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China; 2. Joining and Welding Research Institute, Osaka University, 11-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan)
Abstract: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.
Keywords:high-power disk laser welding  plume  spatter  feature classification  stability
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