Analysis of magneto-optical imaging characteristics of weld defects under magnetic field excitation
-
摘要: 为了研究磁场激励下焊接缺陷磁光成像特征，以激光焊低碳钢板为试验对象，采用恒定磁场和50Hz交变磁场对焊接缺陷进行励磁，并由磁光成像传感器实时获取焊接缺陷区域磁场分布，进行了理论分析和实验验证。取得了恒定磁场和交变磁场励磁下厚度分别为1mm，2mm和3mm低碳钢（Q235）焊接缺陷的磁光图像，并与COMSOL模拟结果进行对比；通过加权平均图像融合技术将交变磁场中获得的焊接缺陷磁光图像进行融合。结果表明，与恒定磁场励磁相比，采用交变励磁获得的焊接缺陷信息更加准确、快速和完整，并且有效避免了焊接缺陷信息的遗漏。此研究为提高焊接缺陷检测效率提供了依据。Abstract: In order to study the characteristics of magnetic-optical images of weld defects excited by magnetic field, low carbon steel of laser welding was used as test object and the welding defects were excited by constant magnetic field and 50Hz alternating magnetic field. Real-time magnetic field distribution of welding defect area was obtained by magnetic-optical imaging sensor. Through theoretical analysis and experimental verification, the magneto-optic images of welding defects of low carbon steel (Q235) of thickness of 1mm, 2mm and 3mm with constant magnetic field and alternating magnetic field were obtained, and then, were compared with COMSOL simulation results. The weighted average image fusion technique was used to fuse the magnetic-optical images of welding defects in the alternating magnetic field. The results show that, compared with the excitation of constant magnetic field, the welding defect information obtained by the alternating excitation is more accurate, fast and complete, and avoids the omission of welding defect information effectively. This study provides the basis for improving the detection efficiency of welding defects.
-
-
![]()
![]()
Figure 4. Gray-scale image of magneto-optic images and 100 columns of gray value in constant magnetic field
![]()
Figure 5. y component of magnetic field strength of weld surfacein constant magnetic field
![]()
Figure 7. Three consecutive frames of magneto-optical grayscale graph under alternating excitation
![]()
Figure 9. Gray graphs and gray value curves after the fusion of magnetic and optical images
Table 1 Test conditions of magneto-optical imaging
weldment material weldment size(length×width×thickness)/mm excitation method excitation frequency f/Hz imaging pixels/pixel low-carbon steel 100×50×1(2 or 3) constant magnetic field no 400×400 alternating magnetic field 50Hz 400×400 
下载: 导出CSV
Table 2 Pixel extremum of the magneto-optical image of the welding defects
plate thickness h/mm defect type x1/pixel x2/pixel difference x/pixel actual width/mm 1 0.1mm wide crack 168 209 41 0.402 2 0.05mm wide crack 186 210 24 0.235 3 splash 94 112 18 0.176
下载: 导出CSV
-
[1] ZHANG Y L, ZHANG H Ch, ZHAO J X, et al. Review of non-destructive testing for remanufacturing of high end equipment[J]. Journal of Mechanical Engineering, 2013, 53(7):80-90(in Chinese). http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jxgcxb201307012
[2] KOSCHNY M, LINDNER M. Magneto-optical sensors accurately analyze magnetic field distribution of magnetic materials[J]. Advanced Materials & Processes, 2012, 170(2):13-16. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=2fba62e4b6f5f26f7f607702d4d03bf8
[3] 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. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=0bf5b6f796bd63bf47b5f3d35e9e6138
[4] GAO X D, WU J J. Approach of detecting and tracking micro weld joint based on magneto optical imaging[J]. Journal of Mechanical Engineering, 2015, 51(4):71-77(in Chinese). DOI: 10.3901/JME.2015.04.071
[5] HU X Ch, LUO F L, HE B Z, et al. Pulsed alternating current field measurement technique for defect identification and quantification[J]. Journal of Mechanical Engineering, 2011, 47(4):17-22(in Chinese). DOI: 10.3901/JME.2011.04.017
[6] GAO X D, LAN Ch Zh, YOU D Y, et al. Weldment nondestructive testing using magneto-optical imaging induced by alternating magnetic field[J]. Journal of Nondestructive Evaluation, 2017, 36(3):55. DOI: 10.1007/s10921-017-0434-4
[7] DENG Y, LIU X, UDPA L. Magneto-optic imaging for aircraft skins inspection:a probability of detection study of simulated and experimental image data[J]. IEEE Transactions on Reliability, 2011, 61(4):1-8. http://ieeexplore.ieee.org/document/6328299/
[8] RICHERT H, SCHMIDT H, LINDNER S, et al. Dynamic magneto-optical imaging of domains in grain oriented electrical steel[J]. Steel Research, 2016, 87(2):232-240. DOI: 10.1002/srin.v87.2
[9] GAO X D, LI G H, XIAO Zh L, et al. Detection and classification of welded defects by magneto-optical imaging based on multi-scale wavelet[J]. Optics and Precision Engineering, 2016, 24(4):930-936(in Chinese). DOI: 10.3788/OPE.
[10] CHEN Y H, ZHOU Z F, YIN B B. Study on magneto optical/eddy current imaging system for real time testing[J]. Optics and Precision Engineering, 2006, 14(5):797-801(in Chinese). http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=gxjmgc200605014
[11] LE M, LEE J, SHOJI T. A simulation of magneto-optical eddy current imaging[J]. NDT & E International, 2011, 44(8):783-788. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=2a19e415098c9778c4d922a370941b1a
[12] GAO X D, HUANG G X, XIAO Zh L, et al. Micro weld detection based on magneto-optical imaging and wavelet multi-scale fusion[J]. Transactions of the China Welding Institution, 2016, 37(4):1-4(in Chinese). http://en.cnki.com.cn/Article_en/CJFDTOTAL-HJXB201604001.htm
[13] HAGHIGHAT M B A, AGHAGOLZADEH A, SEYEDARABI H. Multi-focus image fusion for visual sensor networks in DCT domain[J]. Computers & Electrical Engineering, 2011, 37(5):789-797. http://dl.acm.org/citation.cfm?id=2064186
[14] PRAKASH O, KUMAR A, KHARE A. Pixel-level image fusion scheme based on steerable pyramid wavelet transform using absolute maximum selection fusion rule[C]//International Conference on Issues and Challenges in Intelligent Computing Techniques. New York, USA: IEEE, 2014: 765-770.
[15] ZHENG W, SUN X Q, LI Zh. Image fusion based on Shearlet transform and region characteristics[J]. Laser Technology, 2015, 39(1):50-56(in Chinese). http://www.en.cnki.com.cn/Article_en/CJFDTotal-JGJS201501010.htm
[16] LI H H, GUO L, LIU H. Research on image fusion based on the second generation curvelet transform[J]. Acta Photonica Sinica, 2006, 26(5):657-662(in Chinese). http://d.old.wanfangdata.com.cn/Periodical/ygxb201703009
[17] GAO Y, WANG A M, WANG F H, et al. Application of improved wavelet transform algorithm in image fusion[J]. Laser Technology, 2013, 37(5):690-695(in Chinese). http://en.cnki.com.cn/Article_en/CJFDTOTAL-JGJS201305029.htm
[18] ZHU P, LIU Z Y, HUANG Zh H. Infrared polarization and intensity image fusion based on dual-tree complex wavelet transform and sparse representation[J/OL].(2017-05-08)[2017-11-07].http://kns.cnki.net/kcms/detail/61.1235.O4.20170508.1426.116.html (in Chinese).
-
期刊类型引用(4)
1. 崔文超, 郭瑞民, 王德发, 董贺伟. 分布反馈激光器温度与电流控制研究. 激光技术. 2019(04): 1-5 . 
本站查看
2. 陈垚至. 混沌激光通信网络波分复用传输系统设计. 激光杂志. 2018(10): 96-101 . 百度学术
3. 吴艳玲, 唐穗欣. 激光传感器的机器人运动控制研究. 激光杂志. 2017(01): 127-130 . 百度学术
4. 苏文芝, 申玉霞. 光纤信道混沌激光通信故障的提取与识别. 激光杂志. 2017(01): 144-147 . 百度学术
其他类型引用(0)
计量
- 文章访问数: 5
- HTML全文浏览量: 0
- PDF下载量: 6
- 被引次数: 4