Advanced Search
DAI Xinxin, ZHENG Qiaoqiao, JI Yukun, GAO Xiangdong. Numerical simulation and experimental analysis of magnetic field distribution of magneto-optical imaging in weld defects[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2019, 40(12): 102-108. DOI: 10.12073/j.hjxb.2019400321
Citation: DAI Xinxin, ZHENG Qiaoqiao, JI Yukun, GAO Xiangdong. Numerical simulation and experimental analysis of magnetic field distribution of magneto-optical imaging in weld defects[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2019, 40(12): 102-108. DOI: 10.12073/j.hjxb.2019400321
shu

Numerical simulation and experimental analysis of magnetic field distribution of magneto-optical imaging in weld defects

  • Guangdong Provincial Welding Engineering Technology Research Center, Guangdong University of Technology, Guangzhou 510006, China

More Information
  • Received Date: July 07, 2019
    Graphical Abstract
    • Abstract
  • Taking the weld defects of laser butt welding as the object, the analysis method of magnetic leakage field of weld defects was studied based on numerical simulation. The three dimension finite models of the weld defects detection of laser butt welding was established by Magnet. The relationship between different geometrical defects and their leakage magnetic field signals was compared and analyzed by using the theory of leakage magnetic field, and verified by experiments. The numerical simulation results show that the deeper the crack depth is, the larger the magnetic induction intensity is. The magnetic induction intensity decreases with increase of the angle and width of unfused and concave pits. Magnetic leakage signal can be used as the basis of weld defect detection. Besides, the magneto-optical image was segmented by RGB segmentation method and the features were extracted. Fuzzy c-mean clustering (FCM) was used to identify different weld defects. The results show that the recognition rate is good.
    • FullText(HTML)
    • References (13)
  • 李晓延, 武传松, 李午申. 中国焊接制造领域学科发展研究[J]. 机械工程学报, 2012, 48(6):19-31
    Li Xiaoyan, Wu Chuansong, Li Wushen. Research on the development chinese welding manufacturing[J]. Journal of Mechanical Engineering, 2012, 48(6):19-31
    冯吉才, 王厚勤, 张秉刚, 等. 空间焊接技术研究现状及展望[J]. 焊接学报, 2015, 36(6):107-112
    Feng Jicai, Wang Houqin, Zhang Bingang, et al. Research status and prospects of space welding technology[J]. Transactions of the China Welding Institution, 2015, 36(6):107-112
    迟大钊, 刚 铁, 赵立彬. 线聚焦超声波法焊接缺陷识别[J]. 焊接学报, 2015, 36(5):29-32
    Chi Dazhao, Gang Tie, Zhao Libin. Line focusing ultrasonic welding defect identification[J]. Transactions of the China Welding Institution, 2015, 36(5):29-32
    Zhang L, Zhang Y, Dai B, et al. Welding defect detection based on local image enhancement[J]. IET Image Processing, 2019, 13(13):2647-2658.
    Kumar S, Menaka M, Venkatraman B. Simulation and experimental analysis of austenitic stainless steel weld joints using ultrasonic phased array[J]. Measurement Science and Technology, 2019, 31(2):024005-1-11.
    Miao R, Gao Y, Ge L, et al. Online defect recognition of narrow overlap weld based on two-stage recognition model combining continuous wavelet transform and convolutional neural network[J]. Computers in Industry, 2019, 112:103115-1-11.
    Gao X, Mo L, You D, et al. Tight butt joint weld detection based on optical flow and particle filtering of magneto-optical imaging[J]. Mechanical Systems and Signal Processing, 2017, 96:16-30.
    Li Y, Gao X, Zheng Q, et al. Weld cracks nondestructive testing based on magneto-optical imaging under alternating magnetic field excitation[J]. Sensors and Actuators A:Physical, 2019, 285:289-299.
    Yan S, Chao Z, Rui L, et al. Theory and application of magnetic flux leakage pipeline detection[J]. Sensors, 2015, 15(12):31036-31055.
    Li Y, Tian G, Ward S. Numerical simulation on magnetic flux leakage evaluation at high speed[J]. NDT & E International, 2006, 39(5):367-373.
    Silva R R D, Siqueira M H S, Souza M P V D, et al. Estimated accuracy of classification of defects detected in welded joints by radiographic tests[J]. Ndt & E International, 2005, 38(5):335-343.
    Liao T W. Classification of welding flaw types with fuzzy expert systems[J]. Expert Systems with Applications, 2003, 25(1):101-111.
    高向东, 梁剑斌, 刘桂谦, 等. 大功率光纤激光焊熔透状态模糊聚类识别方法[J]. 焊接学报, 2017, 38(5):22-25
    Gao Xiangdong, Liang Jianbin, Liu Guiqian, et al. Fuzzy clustering identification method for high power fiber laser welding penetration state[J]. Transactions of the China Welding Institution, 2017, 38(5):22-25
    • Related Articles

  • Related Articles

    [1]HONG Lei1, YANG Xiaolan2, ZHONG Dongping3. Feature extraction and analysis of weld seam stripe line on slope analysis method[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2017, 38(8): 91-94. DOI: 10.12073/j.hjxb.20151012002
    [2]CHEN Haiyong, DU Xiaolin, DONG Yan. Tiny visual feature extraction of random changing weld[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2016, 37(5): 97-101.
    [3]WU Yiquan, YE Zhilong, WAN hong, GANG Tie. Feature extraction for welding defect image based on contourlet transform and kernel principal component analysis[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2014, 35(7): 17-21,104.
    [4]CHEN Haiyong, SUN Hexu, XU De. An image feature extraction method for a certain kind of narrowgap weld seam[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2012, (1): 61-64.
    [5]LIU Suyi, LI Bing, ZHANG Hua, JIA Jianping. Feature extraction and image processing for underwater weld with laser vision[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2011, (9): 45-48.
    [6]LIU Lijun, WANG Qi, LAN Hu, ZHENG Hongyan, LI Feng. Feature extraction of arc sound signal in MIG welding based on linear prediction analysis[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2010, (7): 35-38.
    [7]LIU Lijun, LAN Hu, WEN Jianli, YU Zhongwei. Feature extraction of penetration arc sound in MIG welding via wavelet packet frequency-band energy[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2010, (1): 45-49.
    [8]LI Yuan, WANG Qinglin, XU De, TAN Min. Image processing and features extraction of molten pool for pipe welding[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2008, (8): 57-60.
    [9]LI Yuan, XU De, SHEN Yang, TAN Min. A image processing and features extraction method for structured light image of welding seam[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2006, (9): 25-30.
    [10]ZHANG Peng-xian, ZHANG Hong-jie, MA Yue-zhou, CHEN Jian-hong. On-line quality estimation of resistance spot welding based on extraction of signals feature[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2005, (9): 52-57.

  • Cited by

    Periodical cited type(1)

    1. 卢慧. 基于量子多元宇宙算法的磁光成像增强. 激光与红外. 2022(12): 1787-1795 .

    Other cited types(4)

Catalog

    Get Citation
    PDF
    XML
    Article views (342) PDF downloads (58) Cited by(5)
    Turn off MathJax
    Article Contents
    Abstract
    References
    Related Articles

    Website Copyright © Editorial Office of Transactions of the China Welding Institution, Welding Journals Publishing HouseTel：0451-86323218Address：No. 2077, Chuangxin Road, Songbei District, Harbin

    Supported by: Beijing Renhe Information Technology Co. Ltd    Baidu Analytics

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return