Modified Maximum Likelihood Estimation Metal Magnetic Memory Quantitative Identifying Model of Weld Defect Levels Based on Dempster–Shafer Theory

Author:

Xing Haiyan1,Xu Cheng12,Yi Ming1,Gao Shenrou12,Liu Weinan1

Affiliation:

1. School of Mechanical Science and Engineering, Northeast Petroleum University, Daqing 163318, China

2. School of Mechanical and Electronic Engineering, Qiqihar University, Qiqihar 161006, China

Abstract

Metal magnetic memory (MMM) is a nondestructive testing technology based on the magnetomechanical effect, which is widely used in the qualitative detection of stress concentration zones for welded joints. However, there is inevitable residual stress after welding, which brings the bottleneck of quantitative identification between the weld residual stress concentration and the early hidden damage. In order to overcome the bottleneck of quantitative identification of weld defect levels with MMM technology, a modified maximum likelihood estimation (MLE) MMM quantitative identifying model is first proposed. The experimental materials are Q235B welded plate specimens. Fatigue tension experiments were operated to find the MMM feature laws of critical hidden crack by comparing with synchronous X-ray detection results. Six MMM characteristic parameters, which are, ΔHp(x), Gxmax, Zxmax, ΔHp(y), Gymax and Zymax, are extracted corresponding to the normal state, the hidden crack state and the macroscopic crack, respectively. The MLE values of the six parameters are obtained by the kernel density functions with optimized bandwidth from the view of mathematical statistics. Furthermore, the modified MLE MMM quantitative identifying model is established based on D–S theory to overcome the partial overlap of MLE values among different defect levels, of which the uncertainty is as low as 0.3%. The verification result from scanning electron microscopy (SEM) is consistent with the prediction of the modified MLE MMM model, which provides a new method for quantitative identification of weld defect levels.

Funder

National Natural Science Foundation of China

the Natural Science Foundation of Heilongjiang Province of China

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

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