Eddy current modelling using multi-layer perceptron neural networks for detecting surface cracks

A new method for computing fracture mechanics parameters using computational Eddy Current Modelling by Multi-layer Perceptron Neural Networks for detecting surface cracks. The method is based upon an inverse problem using an Arti?cial Neural Network (ANN) that simulates mapping between Eddy current...

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Bibliographic Details
Published inFrattura ed integritá strutturale Vol. 12; no. 45; pp. 147 - 155
Main Authors S. Harzallah, R. Rebhi, M. Chabaat, A. Rabehi
Format Journal Article
LanguageEnglish
Published Gruppo Italiano Frattura 01.07.2018
Subjects
3D-FEM-EC
Artificial Neural Network
Cracks
Inverse Problems
Multi-layer Perceptron
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Summary:A new method for computing fracture mechanics parameters using computational Eddy Current Modelling by Multi-layer Perceptron Neural Networks for detecting surface cracks. The method is based upon an inverse problem using an Arti?cial Neural Network (ANN) that simulates mapping between Eddy current signals and crack pro?les. Simultaneous use of ANN by MLP can be very helpful for the localization and the shape classification of defects. On the other side, it can be described as the task of reconstructing the cracks and damage in the plate profile of an頠 inspected specimen in order to estimate its material properties. This is accomplished by inverting eddy current probe impedance measurements that are recorded as a function of probe position, excitation frequency or both. In eddy current nondestructive evaluation, this is widely recognized as a complex theoretical problem whose solution is likely to have a significant impact on the detection of cracks in materials
ISSN:1971-8993
1971-8993
DOI:10.3221/IGF-ESIS.45.12