吉林大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (6): 1854-1860.doi: 10.13229/j.cnki.jdxbgxb201706025

• Orginal Article • Previous Articles     Next Articles

Hidden defect metal magnetic memory identification for welded joints based on fuzzy membership and maximum likelihood estimation

XING Hai-yan1, GE Hua1, LI Si-qi2, YANG Wen-guang3, SUN Xiao-jun1   

  1. 1.School of Mechanical Science and Engineering, Northeast Petroleum University, Daqing 163318,China;
    2.School of Astronautics, Harbin Institute of Technology, Harbin 150001,China;
    3.Lanpec Technologies Limited, Lanzhou 730070,China
  • Received:2016-09-07 Online:2017-11-20 Published:2017-11-20

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Abstract: To overcome the difficulty of Metal Magnetic Memory (MMM) technique in identification and location of the hidden defect in welded joints, a method of Maximum Likelihood Estimation (MLE) and fuzzy membership degree is put forward. The feature law of critical hidden damage is studied by fatigue experiment of Q235 steel plate with incompletely penetrated weld joint. Four feature parameters are extracted, that are peak to peak value ΔHp(y), mean gradient Kave, maximal gradient Kmax, and maximal limit coefficient mmax. The normal distribution function is employed to calculate the probabilities of the four feature parameters. Then, MLE values are obtained to establish the MLE fuzzy membership model. This model can be used to identify the suspected defect position. Results show that the MLE fuzzy membership model can effectively locate the weld defect and provide a new tool of identification and location of weld hidden damage with MMM method.

Key words: materials synthesis and processing technology, metal magnetic memory(MMM) testing, weld hidden damage, membership functions, maximum likelihood estimation

CLC Number: 

  • TG441.7
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