Magneto-optical Imaging Detection and Strong Classification of Weld Defects in Rotating Magnetic Field

被引：0
|
作者：
Gao X. [1 ]
Zheng Q. [1 ]
Wang C. [1 ]
机构：
[1] Guangdong Provincial Welding Engineering Technology Research Center, Guangdong University of Technology, Guangzhou
来源：
Jixie Gongcheng Xuebao/Journal of Mechanical Engineering | 2019年 / 55卷 / 17期
关键词：
BP-AdaBoost strong classifier; Finite element simulation; Magneto-optical detection; PCA; Rotating magnetic field; Weld defects;
D O I：
10.3901/JME.2019.17.061
中图分类号：
学科分类号：
摘要：
Accurate detection and classification of weld defects plays an important role in ensuring the quality of welding products. A magneto-optical imaging nondestructive detection method of weld defects under rotating magnetic field excitation is studied. The mechanism of magneto-optical imaging of weld defects based on Faraday magneto-optical effect is analyzed, the magnetic field distribution of weld defects is analyzed by using finite element simulation, and a best lift-off degree is found to collect magneto-optical images. The mechanism of rotating magnetic field is analyzed, a pair of cross yokes is used to generate a rotating magnetic to excite the weldment. Also, a magneto-optical sensor is used to obtain the images of weld defects. Using PCA to extract and reduce the dimension of gray-scale features of column pixels in magneto-optical images, the AdaBoost algorithm combined with BP neural network is used to establish a BP-AdaBoost weld defects classification model. Experimental results show that the proposed method can effectively improve the recognition accuracy of No. 45 steel weld joint defects (curve crack, linear crack, sag, less penetration), the overall recognition rate of BP-AdaBoost weld defects classification model reaches 98.88%, and the detection and classification of weld defects can be realized effectively. © 2019 Journal of Mechanical Engineering.
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页码：61 / 67
页数：6
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