Fracture and Structural Integrity e-ISSN 1971-8993 Open access
Miscellanea Vol. 12, No. 45 (2018)

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

Author and affiliation details
  • Salaheddine Harzallah Corresponding author University Built Environmental Research Lab., Civil Engineering Faculty, University of Sciences and Technology HouariBoumediene, B.P 32 El Alia Bab Ezzouar, Algiers 16111 Algeria. sharzallah@usthb.dz
  • 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
DOI
10.3221/IGF-ESIS.45.12
Pages
147-155
Published
Published inVol. 12, No. 45 (2018)Go to the issue

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

Keywords

Article history

  1. ReceivedUntil the next stage: 22 days
  2. Revision requestedUntil the next stage: 33 days
  3. RevisedUntil the next stage: 48 days
  4. AcceptedUntil the next stage: 4 days
  5. Published

How to cite

APA

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

IEEE

S. Harzallah, R. Rebhi, M. Chabaat, and A. Rabehi, “Eddy current modelling using multi-layer perceptron neural networks for detecting surface cracks,” Fracture and Structural Integrity, vol. 12, no. 45, pp. 147–155, 2018, doi: 10.3221/IGF-ESIS.45.12.

MLA

Harzallah, Salaheddine, et al. “Eddy current modelling using multi-layer perceptron neural networks for detecting surface cracks.” Fracture and Structural Integrity, vol. 12, no. 45, 2018, pp. 147–155. https://doi.org/10.3221/IGF-ESIS.45.12.

Chicago

Harzallah, Salaheddine, R. Rebhi, M. Chabaat, and A. Rabehi. 2018. “Eddy current modelling using multi-layer perceptron neural networks for detecting surface cracks.” Fracture and Structural Integrity 12 (45): 147–155. https://doi.org/10.3221/IGF-ESIS.45.12.

Harvard

Harzallah, S., Rebhi, R., Chabaat, M. and Rabehi, A. (2018) ‘Eddy current modelling using multi-layer perceptron neural networks for detecting surface cracks’, Fracture and Structural Integrity, 12(45), pp. 147–155. doi: 10.3221/IGF-ESIS.45.12.

Vancouver

Harzallah S, Rebhi R, Chabaat M, Rabehi A. Eddy current modelling using multi-layer perceptron neural networks for detecting surface cracks. Fracture and Structural Integrity. 2018;12(45):147-155. doi:10.3221/IGF-ESIS.45.12

BibTeX

@article{harzallah2018, author = {Harzallah, Salaheddine and Rebhi, R. and Chabaat, M. and Rabehi, A.}, title = {Eddy current modelling using multi-layer perceptron neural networks for detecting surface cracks}, journal = {Fracture and Structural Integrity}, year = {2018}, volume = {12}, number = {45}, pages = {147--155}, doi = {10.3221/IGF-ESIS.45.12}, url = {https://doi.org/10.3221/IGF-ESIS.45.12}, issn = {1971-8993} }

RIS

TY - JOUR AU - Harzallah, Salaheddine AU - Rebhi, R. AU - Chabaat, M. AU - Rabehi, A. TI - Eddy current modelling using multi-layer perceptron neural networks for detecting surface cracks JO - Fracture and Structural Integrity T2 - Fracture and Structural Integrity PY - 2018 DA - 2018/06/21 VL - 12 IS - 45 SP - 147 EP - 155 DO - 10.3221/IGF-ESIS.45.12 UR - https://doi.org/10.3221/IGF-ESIS.45.12 SN - 1971-8993 ER -

Licence

Copyright © 2018 Salaheddine Harzallah, R. Rebhi, M. Chabaat, A. Rabehi.

Copyright 
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