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

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Published Jun 21, 2018
DOI https://doi.org/10.3221/IGF-ESIS.45.12

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Salaheddine Harzallah
University Built Environmental Research Lab., Civil Engineering Faculty, University of Sciences and Technology HouariBoumediene, B.P 32 El Alia Bab Ezzouar, Algiers 16111 Algeria.
R. Rebhi
University Built Environmental Research Lab., Civil Engineering Faculty, University of Sciences and Technology HouariBoumediene, B.P 32 El Alia Bab Ezzouar, Algiers 16111 Algeria.
M. Chabaat
University Built Environmental Research Lab., Civil Engineering Faculty, University of Sciences and Technology HouariBoumediene, B.P 32 El Alia Bab Ezzouar, Algiers 16111 Algeria.
A. Rabehi
Laboratoire de Micro-électronique Appliquée, Université Djillali Liabès de Sidi Bel Abbés, BP 89, 22000 Sidi Bel Abbés, Algeria

Abstract

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 Artificial Neural Network (ANN) that simulates mapping between Eddy current signals and crack profiles. 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  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

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    Issue
    Vol. 12 No. 45 (2018): July 2018
    Section
    Miscellanea

    How to Cite

    Harzallah, S., Rebhi, R., Chabaat, M., & Rabehi, A. (2018). Eddy current modelling using multi-layer perceptron neural networks for detecting surface cracks. Frattura Ed Integrità Strutturale, 12(45), 147–155. https://doi.org/10.3221/IGF-ESIS.45.12

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