基于非负矩阵分解的熔池图像识别方法  

Image recognition of molten pool based on non-negative matrix factorization

作　　者：裴莹蕾 王克鸿[2] PEI Yinglei;WANG Kehong(Guiyang Big Data Industry Group Co.,Ltd.,Guiyang 550081,China;School of Materials Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China)

机构地区：[1]贵阳大数据产业集团有限公司,贵州贵阳550081 [2]南京理工大学材料科学与工程学院,江苏南京210094

出　　处：《计算机集成制造系统》2023年第3期930-937,共8页Computer Integrated Manufacturing Systems

基　　金：广东省重点领域研发计划资助项目(2018B090906004)。

摘　　要：为探求视觉传感智能识别焊接缺陷技术,利用电荷耦合器件(CCD)相机采集了熔化极气体保护焊(GMAW)熔池图像,分析了不同工艺条件下熔池动态变化过程及所对应的焊接缺陷,提出利用非负矩阵分解法对熔池图像进行解析,得到熔池图像的特征矩阵。通过最小二乘法计算未知焊接过程测试图像在特征矩阵的投影值,给出了焊接缺陷的自动识别方法。研究结果表明,焊接质量和熔池稳定程度有关联,熔池紊乱表现为熔池轮廓波动、浮渣离散等特点,熔池稳定程度下降伴随着焊接质量的下降,以及焊缝出现缺陷。利用非负矩阵分解法得到的熔池图像特征矩阵,能够对原始图像进行整体性描述(如熔池轮廓)和局部性描述(如浮渣区域、电弧区域等),具有物理可解释性,可用于识别焊接缺陷。To explore the technology of visual sensing intelligent identification of welding defects,the continuous images of welding pool in Gas Metal Arc Welding(GMAW)were acquired by Charge Coupled Device(CCD)to research the behavior of the welding pool as well as the corresponding welding defects.The nonnegative matrix factorization algorithm was used for feature extraction of the welding pool images.The recognition value of tested image in feature matrix was calculated by least square method.The automatic identification method of welding defects was given.The results showed that the welding quality was related to the stability of the weld pool.Specifically,the unstable weld pool was characterized by profile fluctuation,welding slag dispersion and so on.The decrease of molten pool stability was accompanied by the decrease of welding quality,which leaded to appear the weld defects.The feature matrix of weld pool image based on nonnegative matrix factorization algorithm could describe the original images in general(such as weld pool profile)and in part-base(such as weld slag profile),which had physical interpretability and could be used to identify welding defects.

关 键 词：非负矩阵分解 图像识别 焊接熔池 焊接缺陷 熔化极气体保护焊 

分 类 号：TG409[金属学及工艺—焊接]