Detection model of invisible weld defects by magneto-optical imaging at rotating magnetic field directions

被引:15
作者
Li, Yanfeng [1 ]
Gao, Xiangdong [1 ]
Zhang, Yanxi [1 ]
You, Deyong [1 ]
Zhang, Nanfeng [1 ]
Wang, Congyi [1 ]
Wang, Chuncao [1 ]
机构
[1] Guangdong Univ Technol, Guangdong Prov Welding Engn Technol Res Ctr, Higher Educ Mega Ctr, 100 West Waihuan Rd, Guangzhou 510006, Guangdong, Peoples R China
基金
中国国家自然科学基金;
关键词
Magneto-optical imaging; Invisible weld defects; Rotating magnetic field directions; Gray level co-occurrence matrix; OPTICAL SENSOR; INSPECTION;
D O I
10.1016/j.optlastec.2019.105772
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
Magneto-optical (MO) imaging non-destructive testing (NDT) system excited by rotating magnetic field is proposed for feature extraction and detection classification of invisible arbitrary-angle weld defects. The magnetic field direction of the rotating magnetic field changes periodically with time, and the magnitude and direction of the leakage magnetic field on the weldment also changes periodically. A finite element analysis (FEA) model of weldment is established to study the distribution of rotating alternating current field at different transient time. The correctness of the rotating alternating current field theory is verified by FEA. Based on the Faraday rotation effect, the relationship between the imaging characteristics of weld defect MO image and the leakage magnetic field intensity is analyzed. The gray value of MO image can match the corresponding leakage magnetic field intensity. MO imaging NDT experiments are performed on invisible weld defects, including surface crack, subsurface crack, and non-penetration. The gray-level co-occurrence matrix (GLCM) method is used to extract texture features of the MO image of weld defects, and the texture features of the images can reflect the leakage magnetic field characteristics of the defects. These texture features of MO images are used as input data for the defect classification model established using support vector machine (SVM). This model is evaluated by weld defect detection experiments and shows that it can effectively and accurately classify invisible arbitrary-angle weld defects.
引用
收藏
页数:11
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