Eddy current nondestructive evaluation of metallic plates electrical conductivity using artificial neural networks based inverse problem

Authors

  • Abdelkader Bouhlal
  • Nasreddine Nait-Said
  • Fatima-Zohra Louai
  • Said Touati

DOI:

https://doi.org/10.54021/seesv5n1-057

Keywords:

Eddy Current Nondestructive Evaluation (ECNDE), Inverse Problem, Artificial Neural Networks (ANNs), Finite Element Method (FEM), COMSOL multiphysics

Abstract

The most used method and extensively studied in the literature for conductors’ characterization is Eddy Current Nondestructive Evaluation (ECNDE) due to its significant advantages, such as its ability to preserve the integrity of the structures or materials to be examined during manufacturing or a regular in-service nondestructive testing. Various approaches are developed for the Eddy Current measurement of the electrical conductivity. In the present work, the evaluation of the electrical conductivity is treated as an inverse problem. In pursuit of this aim, a combination is established between Eddy currents evaluation and artificial neural networks (ANN) to evaluate the electrical conductivity of homogeneous metallic plates from eddy-current probe impedance measurements. For this purpose, an experimental setup is developed, including a bobbin-coil probe, metallic plates (target), data acquisition and signal processing systems. Finally, experimental conductivity values of various metallic plates using ANN are compared with those obtained using four-point measurements of direct current potential drop (DCPD) made on the same plates and very good agreement is obtained.

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Published

2024-04-09

How to Cite

Bouhlal, A., Nait-Said, N., Louai, F.-Z., & Touati, S. (2024). Eddy current nondestructive evaluation of metallic plates electrical conductivity using artificial neural networks based inverse problem. STUDIES IN ENGINEERING AND EXACT SCIENCES, 5(1), 1093–1116. https://doi.org/10.54021/seesv5n1-057