光学精密工程2017，Vol.25Issue(5)：1135-1141,7.DOI:10.3788/OPE.20172505.1135
焊接缺陷磁光成像动态检测与识别
Dynamic detection and recognition of welded defects based on magneto-optical imaging
摘要
Abstract
To realize automatic inspection of welded defects, a dynamic magneto-optical imaging non-destructive detection of weld surface and subsurface defects under alternating magnetic field excitation was researched.The welded defect magneto-optical imaging mechanism based on Faraday magneto optical effect was analyzed and employed to derive the relationship between excitation variation and dynamic magneto-optical imaging by combining with alternating magnetic field principle.The subsurface weld magneto-optical imaging feature test of low-carbon steel was investigated, verifying that the proposed method could be used to detect incomplete penetration defects of weld surface.Finally, dynamic magneto-optical image of high-strength steel weld feature was analyzed and weld defect classification model was constructed through Principal Component Analysis and Support Vector Machine (PCA-SVM) mode recognition method.The result shows that the proposed method can recognize weld features (penetration, crack, sag and perfectness) in high-strength steel weldment with the entire recognition rate of defect classification model reaches to 92.6%, subsequently the automatic inspection of weld surface and subsurface defects can be realized.关键词
动态磁光成像/焊接缺陷/交变磁场/模式识别Key words
dynamic magneto-optical imaging/welded defect/alternating magnetic field/pattern recognition分类
矿业与冶金引用本文复制引用
高向东,蓝重洲,陈子琴,游德勇,李国华..焊接缺陷磁光成像动态检测与识别[J].光学精密工程,2017,25(5):1135-1141,7.基金项目
国家自然科学基金资助项目(No.51675104) （No.51675104）
广东省科技计划资助项目(No.2016A010102015) （No.2016A010102015）
广州市科技计划资助项目(No.201510010089) （No.201510010089）
