Application of Metal Magnetic Memory Testing Technology to the Detection of Stress Corrosion Defect

The damage of equipment manufactured with ferromagnetic materials in service can be effectively detected by Metal Magnetic Memory Testing (MMMT) technology, which has received extensive attention in various industry fields. The effect of stress or strain on Magnetic Flux Leakage (MFL) signals of fer...

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Main Authors: Bingxun Zhao, Kai Yao, Libo Wu, Xinglong Li, Yue-Sheng Wang
Format: Article
Language:English
Published: MDPI AG 2020-10-01
Series:Applied Sciences
Subjects:
Online Access:https://www.mdpi.com/2076-3417/10/20/7083
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author Bingxun Zhao
Kai Yao
Libo Wu
Xinglong Li
Yue-Sheng Wang
author_facet Bingxun Zhao
Kai Yao
Libo Wu
Xinglong Li
Yue-Sheng Wang
author_sort Bingxun Zhao
collection DOAJ
description The damage of equipment manufactured with ferromagnetic materials in service can be effectively detected by Metal Magnetic Memory Testing (MMMT) technology, which has received extensive attention in various industry fields. The effect of stress or strain on Magnetic Flux Leakage (MFL) signals of ferromagnetic materials has been researched by many scholars for assessing stress concentration and fatigue damage. However, there is still a lack of research on the detection of stress corrosion damage of ferromagnetic materials by MMMT technology. In this paper, the electrochemical corrosion system was designed for corrosion experiments, and three different experiments were performed to study the effect of corrosion on MFL signals. The distribution of MFL signals on the surface of the specimen was investigated. The results indicated that both the normal component <i>H</i><sub>n</sub> and tangential component <i>H</i><sub>t</sub> of MFL signals presented different signal characteristics when the specimen was subjected to different working conditions. Finally, two characterization parameters, <i>S</i><sub>n</sub> and <i>S</i><sub>t</sub>, were defined to evaluate the corrosion degree of the specimen, and <i>S</i><sub>t</sub> is better. The direct dependence of corrosion depth on the parameter was developed and the average error rates between the predicted and measured values are 8.94% under the same working condition. Therefore, the expression can be used to evaluate the corrosion degree of the specimen quantitatively. The results are significant for detecting and assessing the corrosion defect of ferromagnetic materials.
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spelling doaj.art-f1e30d912da6449a83c091542a3a6b902023-11-20T16:46:09ZengMDPI AGApplied Sciences2076-34172020-10-011020708310.3390/app10207083Application of Metal Magnetic Memory Testing Technology to the Detection of Stress Corrosion DefectBingxun Zhao0Kai Yao1Libo Wu2Xinglong Li3Yue-Sheng Wang4Department of Mechanics, School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, ChinaDepartment of Mechanics, School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, ChinaDepartment of Mechanics, School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, ChinaDepartment of Mechanics, School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, ChinaDepartment of Mechanics, School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, ChinaThe damage of equipment manufactured with ferromagnetic materials in service can be effectively detected by Metal Magnetic Memory Testing (MMMT) technology, which has received extensive attention in various industry fields. The effect of stress or strain on Magnetic Flux Leakage (MFL) signals of ferromagnetic materials has been researched by many scholars for assessing stress concentration and fatigue damage. However, there is still a lack of research on the detection of stress corrosion damage of ferromagnetic materials by MMMT technology. In this paper, the electrochemical corrosion system was designed for corrosion experiments, and three different experiments were performed to study the effect of corrosion on MFL signals. The distribution of MFL signals on the surface of the specimen was investigated. The results indicated that both the normal component <i>H</i><sub>n</sub> and tangential component <i>H</i><sub>t</sub> of MFL signals presented different signal characteristics when the specimen was subjected to different working conditions. Finally, two characterization parameters, <i>S</i><sub>n</sub> and <i>S</i><sub>t</sub>, were defined to evaluate the corrosion degree of the specimen, and <i>S</i><sub>t</sub> is better. The direct dependence of corrosion depth on the parameter was developed and the average error rates between the predicted and measured values are 8.94% under the same working condition. Therefore, the expression can be used to evaluate the corrosion degree of the specimen quantitatively. The results are significant for detecting and assessing the corrosion defect of ferromagnetic materials.https://www.mdpi.com/2076-3417/10/20/7083metal magnetic memory testing technologystress corrosionferromagnetic materialscharacterization parameters
spellingShingle Bingxun Zhao
Kai Yao
Libo Wu
Xinglong Li
Yue-Sheng Wang
Application of Metal Magnetic Memory Testing Technology to the Detection of Stress Corrosion Defect
Applied Sciences
metal magnetic memory testing technology
stress corrosion
ferromagnetic materials
characterization parameters
title Application of Metal Magnetic Memory Testing Technology to the Detection of Stress Corrosion Defect
title_full Application of Metal Magnetic Memory Testing Technology to the Detection of Stress Corrosion Defect
title_fullStr Application of Metal Magnetic Memory Testing Technology to the Detection of Stress Corrosion Defect
title_full_unstemmed Application of Metal Magnetic Memory Testing Technology to the Detection of Stress Corrosion Defect
title_short Application of Metal Magnetic Memory Testing Technology to the Detection of Stress Corrosion Defect
title_sort application of metal magnetic memory testing technology to the detection of stress corrosion defect
topic metal magnetic memory testing technology
stress corrosion
ferromagnetic materials
characterization parameters
url https://www.mdpi.com/2076-3417/10/20/7083
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