Research on methods of defect classification based on metal magnetic memory |
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| Affiliation: | 1. School of Mechanical Engineering, Jiangnan University, Wuxi 214122, China;2. The Key Laboratory for Advanced Food Manufacturing Equipment Technology of Jiangsu Province, Wuxi 214122, China;3. College of Electrical and Information Engineering, Hunan University, Changsha 410082, China;1. Dept.of Mechanical Engineering, Annamacharya Institute of Science & Technology, Rajampet, India;2. N.N.R. Group of Institutions, Rangareddy, India;3. Narasimha reddy Engineering college, India;1. Department of Physics, University of Warwick, Coventry, CV4 7AL, UK;2. Department of Mechanical Engineering, Imperial College London, London, SW7 2AZ, UK;3. Department of Aerospace Engineering & Engineering Mechanics, University of Cincinnati, Cincinnati, OH 45221, USA;1. College of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, China;2. Department of Mechanical and Industrial Engineering, University of Toronto, 5 King’s College Road, Toronto, M5S 3G8, Canada;3. Department of Industrial and Information Engineering and Economics, University of L’Aquila, Piazzale E. Pontieri no. 1, 67100 Monteluco di Roio – L’Aquila (AQ), Italy;4. Tomsk Polytechnic University, Lenin Av., 30, Tomsk 634050, Russia;5. School of Computer Sciences, Federal University of Uberlândia, 2121 Av. Joao Naves de Avila, Uberlândia 38400-902, Brazil;6. Department of Electrical and Computer Engineering, Computer Vision and Systems Laboratory (CVSL), Laval University, Quebec city, G1V 0A6, Canada;1. School of Mechano-Electronic Engineering, Xidian University, Xi’an 710071, Shaanxi, PR China;2. School of Aerospace Science and Technology, Xidian University, Xi’an 710071, Shaanxi, PR China;3. Key Laboratory of Equipment Efficiency in Extreme Environment, Ministry of Education, PR China |
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| Abstract: | Metal magnetic memory (MMM) technique can evaluate early damages of ferromagnets and search possible defect locations, while just classifies the defect types roughly. To promote study in this area, the magnetic gradient tensor (MGT) of the self-magnetic leakage field (SMLF) on the fracture zone of crack and stress concentration was measured using a tri-axis magnetometer. From measured results, both the plane and the vertical characteristics of SMLF distributions were discussed. To remove the influence of the measuring direction on experimental results, a new parameter of the analytical signal of magnetic gradient tensor (AMGT) was introduced to determine the location and boundary of the defect. Then, the vertical features were acquired by measuring the plane distributions of AMGT under different lift offs. Through analyzing the vertical features, it was concluded that change rule of the maximum AMGT can be used to predict the defect type. At last, the explanation of the relationship between the vertical feature and the defect type was discussed, which can give some useful inspirations to researchers on magnetic leakage field testing. |
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| Keywords: | Metal magnetic memory Defect classification Analytical signal of magnetic gradient tensor Plane features Vertical features |
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