Identification of weld defects using magneto-optical imaging

被引：8

作者：

Gao, Xiangdong ^{[1
]}

Du, Liangliang ^{[1
]}

Xie, Yilong ^{[1
]}

Chen, Ziqin ^{[1
]}

Zhang, Yanxi ^{[1
]}

You, Deyong ^{[1
]}

Gao, Perry P. ^{[2
]}

机构：

[1] Guangdong Univ Technol, Guangdong Prov Welding Engn Technol Res Ctr, Guangzhou 510006, Guangdong, Peoples R China

[2] US China Youth Educ Solut Fdn, New York, NY 10019 USA

来源：

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY | 2019年 / 105卷 / 1-4期

基金：

中国国家自然科学基金;

关键词：

Magneto-optical imaging; Defect detection; Weld cracks; Imaging enhancement; RADIATION;

D O I：

10.1007/s00170-019-04401-x

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The weld cracks of the high-strength steel are identified by magneto-optical imaging. The background and basic principle of micro-crack inspection after welding by magneto-optical imaging (MOI) are discussed. The key point is to adopt continuous fuzzy enhancement on the basis of fuzzy set theory, to improve the degree of separation of welding crack and weld and solve the problem of uneven magnetic surface of high-strength steel. The experiment of restoring the magneto-optical image is carried out by using the algorithm of unevenness of crack magneto-optical imaging of high strength steel. After restoration, the PSNR data of magneto-optical image is large, indicating that image quality is greatly improved. According to the characteristics of magneto-optical imaging method, an array crack identification model of laser welding is established by using principal component analysis (PCA) method and support vector machine (SVM). The test results validate that our proposed method can efficiently extract the features of welding cracks and improve the precision of detecting welding cracks.

引用

页码：1713 / 1722

页数：10

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