Application of magneto-optical imaging magnetic flux leakage characteristics in contour reconstruction of welding defects

Xiang-dong GAO; Xiao-hu ZHOU; Yan-feng LI; Xin-xin DAI; Nan-feng ZHANG

doi:10.3788/OPE.20192708.1863

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Application of magneto-optical imaging magnetic flux leakage characteristics in contour reconstruction of welding defects

Information Sciences | 更新时间：2020-08-13

- Application of magneto-optical imaging magnetic flux leakage characteristics in contour reconstruction of welding defects
- Optics and Precision Engineering Vol. 27, Issue 8, Pages: 1863-1869(2019)
- 作者机构：
  
  广东工业大学广东省焊接工程技术研究中心，广东广州 510006
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20192708.1863
  CLC： TG115
- Received：07 November 2018，
  
  Accepted：27 January 2019，
  
  Published：15 August 2019
- 稿件说明：
移动端阅览
Xiang-dong GAO, Xiao-hu ZHOU, Yan-feng LI, et al. Application of magneto-optical imaging magnetic flux leakage characteristics in contour reconstruction of welding defects[J]. Optics and precision engineering, 2019, 27(8): 1863-1869.
DOI：

Xiang-dong GAO, Xiao-hu ZHOU, Yan-feng LI, et al. Application of magneto-optical imaging magnetic flux leakage characteristics in contour reconstruction of welding defects[J]. Optics and precision engineering, 2019, 27(8): 1863-1869. DOI： 10.3788/OPE.20192708.1863.

摘要

为了实现焊接缺陷的检测与评估，提出将交变磁场激励下磁光成像的漏磁特征应用于焊接缺陷的轮廓重构当中，建立漏磁重构模型，研究焊接缺陷的二维轮廓特征。首先根据交变磁场下的漏磁场的形成机理，讨论漏磁场分量

，

两种漏磁信号与缺陷轮廓存在的关系。再利用数值模拟方法获取数据，训练其广义回归神经网络(Generalized Regression Neural Network，GRNN)来确定该模型并说明漏磁场信号可以实现缺陷轮廓重构。最后，将磁光成像漏磁特征的数据应用于模型训练，确定重构的可行性。试验结果表明，应用磁光成像漏磁特征的图像数据与仿真获得的轮廓重构规律一致，能够实现焊接缺陷二维轮廓重构。在一定范围内，缺陷深度越大(不小于0.45 mm)，重构效果越好。

Abstract

In this study

to detect and evaluate welding defects effectively

magnetic flux leakage characteristics of magneto-optic imaging under alternating magnetic field excitation are proposed for application in the contour reconstruction of welding defects. A magnetic flux leakage reconstruction model was also established to study the two-dimensional contour characteristics of welding defects. First

based on the formation mechanism of a leakage magnetic field under an alternating magnetic field

the relationship between the two leakage magnetic field component signals (

and

) and the defect contour was discussed. Second

a generalized regression neural network was trained using numerical simulation data

to determine the model and to show that the leakage magnetic field signal can achieve defect contour reconstruction. Finally

the data derived from magneto optic imaging magnetic leakage characteristics were applied to the training of the model

to determine the feasibility of reconstruction. Experimental results show that the image data of the magnetic flux leakage characteristics are consistent with the contour reconstruction rule obtained through simulation

and a two-dimensional contour reconstruction of welding defects can be realized. Within a specific range

the greater (no less than 0.45 mm) the depth of the defect

the better the reconstruction effect.

关键词

Keywords

references

高向东, 蓝重洲, 陈子琴, 等.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.

GAO X D, LAN CH ZH, CHEN Z Q, et al .. Dynamic detection and recognition of welded defects based on magneto-optical imaging[J]. Opt. Precision Eng., 2017.25(5):1135-1141.(in Chinese)

彭丽莎, 黄松岭, 赵伟, 等.漏磁检测中的缺陷重构方法[J].电测与仪表, 2015, 52(13):1-6.

PENG L, HUANG S L, ZHAO W, et al .. Defect reconstruction method in magnetic flux leakage detection[J]. Electrical Measurement & Instrumentation, 2015, 52(13):1-6. (in Chinese)

韩文花, 阙沛文.基于遗传优化算法的二维漏磁缺陷重构[J].中国石油大学学报(自然科学版), 2006, 30(1):138-141.

HAN W H, QUE P W. Reconstruction of two dimensional magnetic flux leakage defect based on genetic algorithm[J]. Journal of China University of Petroleum(Edition of Natural Science) , 2006, 30(1):138-141. (in Chinese)

RAVAN M, SADAGHI S H H, MOINI R. Using a wavelet network for reconstruction of fatigue crack depth profile from AC field measurement signals[J]. Ndt & E International , 2007, 40(7):537-544.

YUAN X C, WANG C L, ZUO X Z, et al .. A method of 2D defect profile reconstruction from magnetic flux leakage signals based on improved particle filter[J]. Insight-Non-Destructive Testing and Condition Monitoring , 2011, 53(3):152-155.

BETTA G, FERRIGNO L, LARACCA M, et al.. Fast 2D crack profile reconstruction by image processing for eddy-current testing[C]. Metrology for Aerospace. IEEE , 2015: 341-345.

蹇清平.基于漏磁检测的油管缺陷量化研究[D].成都: 西南石油大学, 2015. http://cdmd.cnki.com.cn/Article/CDMD-10615-1016005913.htm

JIAN Q P. Quantification of Tubing Defects Based on Magnetic Flux Leakage Detection [D]. Chengdu: Southwest Petroleum University, 2015. (in Chinese)

王鹤.漏磁检测信号分析与缺陷评价实验研究[D].武汉: 华中科技大学, 2014. http://cdmd.cnki.com.cn/Article/CDMD-10487-1015014007.htm

WANG H. Experimental Study on Signal Analysis and Defect Evaluation of Magnetic Leakage Detection [D]. Wuhan: Huazhong University of Science and Technology, 2014. (in Chinese)

廖肖晓, 周绍骑, 白金春.三轴交流漏磁检测矩形缺陷信号特征分析[J].重庆理工大学学报, 2016, 30(9):106-112.

LIAO X X, ZHOU SH Q, BAI J CH. Signal analysis of tri-axial ac-mfl inspection for therectangular defect[J]. Journal of Chongqing University of Technology , 2016, 30(9):106-112. (in Chinese)

张腊梅, 陈泽茜, 邹斌.基于3D卷积神经网络的PolSAR图像精细分类[J].红外与激光工程, 2018, 47(7):17-24.

ZHANG L M, CHEN J Q, ZOU B, Fine classification of polarimetric SAR images based on 3D convolutional neural network[J]. Infrared and Laser Engineering, 2018, 47(7):17-24. (in Chinese)

程玉华, 周肇飞, 尹伯彪.磁光/涡流实时成像检测系统的研究[J].光学精密工程, 2006, 14(5):797-801.

CHEN Y H, ZHOU ZH F, YIN B B.Study on magneto optical/eddy current image system for real-time testing[J]. Opt. Precision Eng., 2006, 14(5):797-801. (in Chinese)

GAO X D, CHEN Y Q, YOU D Y, et al .. Detection of micro gap weld joint by using magneto-optical imaging and Kalman filtering compensated with RBF neural network[J]. Mechanical Systems & Signal Processing , 2017, 84:570-583.

陈琦, 徐熙平, 姜肇国, 等.基于光场相机的深度面光场计算重构[J].光学精密工程, 2018, 26(3):708-714.

CHEN Q, XU X P, JIANG ZH G, et al ..Light field computional reconstruction from focal planes based on field camera[J]. Opt. Precision Eng., 2018, 26(3):708-714. (in Chinese)

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