Advanced Search
GAO Xiangdong, XIE Yilong, CHEN Ziqin, YOU Deyong. Fractal feature detection of high-strength steel weld defects by magnetooptical imaging[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2017, 38(7): 1-4. DOI: 10.12073/j.hjxb.20150803001
Citation: GAO Xiangdong, XIE Yilong, CHEN Ziqin, YOU Deyong. Fractal feature detection of high-strength steel weld defects by magnetooptical imaging[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2017, 38(7): 1-4. DOI: 10.12073/j.hjxb.20150803001

Fractal feature detection of high-strength steel weld defects by magnetooptical imaging

More Information
  • Received Date: August 02, 2015
  • A new nondestructive testing method based on the principle of magnetic-optical imaging was investigated in this paper. Fractal dimension detection was applied to extract features and estimate the optimal scale on the magnetic-optical images of high strength steel that has micro weld defects on the surface. The extracted features of weld defects were trained and tested by using classification algorithm Adabost to build weld defect characteristics and realize automatic identification of magnetic optical images for high strength steel surface defects. The experimental results show that the characteristics of high strength steel weld defects can be detected by using magnetic-optical imaging. Furthermore, the position, shape and category of weld defects can be identified by fractal dimension analysis based on the magnetic-optical images.
  • 赵洪运,刘洪伟.22MnB5超高强钢焊接组织与性能[J].焊接学报,2014,35(2): 67-69,78.Zhao Hongyun,Liu Hongwei.Microstructure and properties of TIG welded 22MnB5 ultra high strength steel[J].Transactions of the China Welding lnstitution,2014,35(2): 67-69,78.[2] 吴一全,叶志龙,万 红,等.基于Contourlet和KPCA的焊接缺陷图像特征提取[J].焊接学报,2014,35(7): 18-21,104.Wu Yiquan,Ye Zhilong,Wan Hong,etal.Image feature extraction of welding defects based on Contourlet and KPCA[J].Transactions of the China Welding lnstitution,2014,35 (7): 18-21,104.[3] Kazantseva I G,Lemahieub I,Salova G I,etal.Statistical detection of defects in radiographic images in nondestructive testing[J].Signal Processing,2002,82: 791-801.[4] Gao X D,Chen Y Q.Detection of micro gap weld using magneto-optical imaging during laser welding[J].International Journal of Advanced Manufacturing Technology,2014,73(1): 23-33.[5] 高向东,甄任贺.微间隙焊缝磁光成像检测方法[J].焊接学报,2014,35(4): 11-14.Gao Xiangdong,Zhen Renhe.A method to detect micro weld joint based on magneto-optical imaging[J].Transactions of the China Welding Institution,2014,35(4): 11-14.[6] 那 彦,邓昆明.基于分形特征和平均梯度的多聚焦图像融合[J].电子科技,2015,28(6): 68-71.Na Yan,Deng Kunming.Fusion of multi-focus images based on a combination of the weighting fractal features and average gradient[J].Electronic Science and Technology,2015,28(6): 68-71.[7] Lin P L,Huang P W,Lee C H,etal.Automatic classification for solitary pulmonary nodule in CT image by fractal analysis based on fractional Brownian motion model[J].Pattern Recognition,2013,46(12): 3279-3287.
  • Related Articles

    [1]MA Nüjie, GAO Xiangdong, DAI Xinxin, ZHANG Nanfeng. Magnetic field characteristic simulation and magneto-optical imaging detection of weld cracks[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2019, 40(9): 77-81. DOI: 10.12073/j.hjxb.2019400239
    [2]CHEN Yuquan, GAO Xiangdong. Neural network compensation for micro-gap weld detection by magneto-optical imaging[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2016, 37(10): 33-36.
    [3]MO Ling, GAO Xiangdong, XIAO Zhenlin, CHEN Xiaohui. Weld detection of laser welding using magneto-optical color imaging[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2016, 37(1): 37-40.
    [4]GAO Xiangdong, LIU Yi, ZHANG Chi. Recognition of magneto-optical image of micro-gap weld using fractal dimension method[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2014, 35(12): 11-14.
    [5]GAO Xiangdong, ZHEN Renhe. A method to detect micro weld gap based on magneto-optical imaging[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2014, 35(4): 11-14.
    [6]YAN Xiaocheng, LI Zhiyong, GUO Yong, LI Yan. Correlation dimension analysis of current in GMAW welding[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2012, (5): 65-68.
    [7]GAO Fei, WANG Kehong, LIANG Yongshun, ZHAN Lanlan, ZHANG Yan. A multi-scale fractal image segmentation method for arc welding pool[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2011, (11): 33-36.
    [8]LIU Pengfei, SHAN Ping, LUO Zhen. Detection method of spot welding based on fractal and support vector machine[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2007, (12): 38-42.
    [9]WU Hua-zhi, GUO Hai-ding, GAO De-ping, XU kai-wang. Establishment of fatigue Fractal damage evolution equation on TC11 Ti alloy welded joint[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2005, (12): 93-95,107.
    [10]WU Hua-zhi, Guo Hai-ding, Gao De-ping. Fractal damage evolution model of low-cycled fatigue in welded joint[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2003, (1): 88-90.
  • Cited by

    Periodical cited type(10)

    1. 刘许亮. 基于改进粒子滤波的焊缝磁光成像增强. 电子器件. 2023(01): 96-102 .
    2. 税法典,陈世强. 基于机器视觉的数据线焊接缺陷检测. 无损检测. 2023(08): 67-72 .
    3. 刘倩雯,叶广文,马女杰,高向东. 焊接微缺陷磁光成像检测有限元分析. 精密成形工程. 2022(03): 94-101 .
    4. 代欣欣,高向东,郑俏俏,季玉坤. 焊缝缺陷磁光成像模糊聚类识别方法. 焊接学报. 2021(01): 54-57+101 . 本站查看
    5. 王付军,刘兰英. 基于微焦点X射线的SMT焊点缺陷检测仿真. 计算机仿真. 2020(09): 428-431 .
    6. 甄任贺,熊建斌,周卫. 基于磁荷理论的微间隙焊缝磁光成像规律研究. 电焊机. 2019(07): 84-88 .
    7. 陈廷艳,梁宝英,罗瑜清. 基于神经网络的焊缝宽度预测方法研究. 机电信息. 2019(30): 88-89+91 .
    8. 王春草,高向东,李彦峰,张南峰. 磁光成像无损检测方法的研究现状与展望. 制造技术与机床. 2019(11): 31-37 .
    9. 王春草,高向东,李彦峰,张南峰. 磁光成像无损检测方法的研究现状与展望. 制造技术与机床. 2019(11): 31-37 .
    10. 张佳莹,丛森,刚铁,林尚扬. 基于频率–相位编码信号激励的焊缝超声检测分析. 焊接学报. 2018(07): 7-11+41+129 . 本站查看

    Other cited types(7)

Catalog

    Article views (508) PDF downloads (133) Cited by(17)

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return