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DAI Xinxin, GAO Xiangdong, ZHENG Qiaoqiao, JI Yukun. A method of fuzzy clustering identification for weld defects by magneto-optical imaging[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2021, 42(1): 54-57. DOI: 10.12073/j.hjxb.20200525001
Citation: DAI Xinxin, GAO Xiangdong, ZHENG Qiaoqiao, JI Yukun. A method of fuzzy clustering identification for weld defects by magneto-optical imaging[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2021, 42(1): 54-57. DOI: 10.12073/j.hjxb.20200525001
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A method of fuzzy clustering identification for weld defects by magneto-optical imaging

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

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  • Received Date: May 24, 2020
  • Available Online: March 23, 2021
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    • Abstract
  • Weldment of high strength steel (HSS) in laser welding was used as the research object, and a magneto-optical imaging detection method based on Faraday magnetic rotation effect was studied. By applying alternating-current power and changing the size of induced magnetic field of welds, a magneto-optical sensor was used to capture the magneto-optical images. The gray-level occurrence matrix (GLCM) texture features of the specific area of magneto-optical images were extracted and analyzed. For accurately detecting weld defects and classifying the type of defects, a fuzzy clustering identification model was established. Different calculation results of weld defects by adjusting the fuzzy index, the input characteristic numbers and the sample of weld defects of fuzzy c-mean clustering (FCM) were compared and the identification effect of weld defects were analyzed. Experimental results show that the fuzzy C-means clustering is effective for identification of the weld defects, which also has a better identification effect on cracks, incomplete penetrations, sags and different forms of same kinds of weld defects.
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    • References (10)
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