Identification of materials with magnetic characteristics by neural networks
Author
Nazlibilek, S.
Ege, Y.
Kalender O.
Sensoy, M.G.
Karacor, D.
Sazli, M.H.
Date
2012Source Title
Measurement: Journal of the International Measurement Confederation
Print ISSN
0263-2241
Volume
45
Issue
4
Pages
734 - 744
Language
English
Type
ArticleItem Usage Stats
141
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121
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Abstract
In industry, there is a need for remote sensing and autonomous method for the identification of the ferromagnetic materials used. The system is desired to have the characteristics of improved accuracy and low power consumption. It must also autonomous and fast enough for the decision. In this work, the details of inaccurate and low power remote sensing mechanism and autonomous identification system are given. The remote sensing mechanism utilizes KMZ51 anisotropic magneto-resistive sensor with high sensitivity and low power consumption. The images and most appropriate mathematical curves and formulas for the magnetic anomalies created by the magnetic materials are obtained by 2-D motion of the sensor over the material. The contribution of the paper is the use of the images obtained by the measurement of the perpendicular component of the Earth magnetic field that is a new method for the purpose of identification of an unknown magnetic material. The identification system is based on two kinds of neural network structures. The MultiLayer Perceptron (MLP) and the Radial Basis Function (RBF) network types are used for training of the neural networks. In this work, 23 different materials such as SAE/AISI 1030, 1035, 1040, 1060, 4140 and 8260 are identified. Besides the ferromagnetic materials, three objects are also successfully identified. Two of them are anti-personal and anti-tank mines and one is an empty can box. It is shown that the identification system can also be used as a buried mine identification system. The neural networks are trained with images which are originally obtained by the remote sensing system and the system is operated by images with added Gaussian white noises. © 2012 Elsevier Ltd. All rights reserved.
Keywords
Anisotropic magnetoresistive sensor (AMR)Magnetic anomaly
Magnetic materials
Neural networks
Remote sensing
Anisotropic magnetoresistive sensors
Anti-tank mines
Buried mines
Earth magnetic fields
Gaussian white noise
High sensitivity
Low Power
Low-power consumption
Magnetic anomalies
Magnetic characteristic
Mathematical curves
Multi layer perceptron
Neural network structures
Remote sensing system
Sensing mechanism
Anisotropy
Explosives
Ferromagnetic materials
Geomagnetism
Magnetic materials
Radial basis function networks
Remote sensing
Neural networks
Permalink
http://hdl.handle.net/11693/21490Published Version (Please cite this version)
http://dx.doi.org/10.1016/j.measurement.2011.12.017Collections
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