Fatima Barrarat

This study investigates the capability of high-frequency eddy-current (HFEC) testing for the characterization of carbon fiber-reinforced polymer (CFRP) laminates layup by analyzing the angular response of a transmitter-receiver (T-R) eddy-current probe. Three-dimensional finite-element simulations were performed to analyze the distribution of induced currents and the angular resistance response in laminates with different fiber orientations and stacking sequences. The simulations demonstrate that the angular positions of the response peaks identify the fiber orientations, whereas their relative amplitudes provide quantitative information on the ply stacking sequence. The numerical predictions were validated experimentally using an HFEC imaging system. Both numerical and experimental results show that the measured HFEC response is governed by the combined effects of probe orientation, fiber orientation, and scan direction, demonstrating the potential of the proposed approach for non-destructive characterization of CFRP laminate layup.

The purpose of this paper is to investigate the optimal parameters for the design and use of eddy current (EC) sensors for non-destructive testing (NDT), aimed at crack characterization by data inversion. A methodology has been established to evaluate the performance of these sensors in the detection of opening cracks and to compare their performances in various measurement conditions. Detection and inversion algorithms were developed and implemented in order to use the most efficient and optimal structure, to quantify the detection quality of defects and to estimate their geometric characteristics. This study has produced very encouraging results in terms of detection performance and data inversion.

Fast estimation is a critical feature of the proposed modeling approach as a “forward” fast solver that has a key role in solving the “inverse” problem involved in crack sizing, especially for real‐time applications. To this end, two different inversion methods have been proposed to estimate the crack depth. The first one uses an interpolation model of the 3D direct model simulation data. The second approach is based on artificial neural networks (ANN). In this article, the rotating uniform eddy current (RUEC) probe is used to detect the normal magnetic component Bz which is defined as the characteristic signal for reconstructing the crack length and depth concurrently. Using the first approach, The Bz characteristic 3D surface is presented, and this can be modeled by a fitted polynomial interpolation equation. Thus, the crack depth can be inverted using this equation referring to the experimental or simulated Bz signal and the crack length. The ANN is outlined to determine the crack depth based on the simulated Bz signature. The move from the first inversion method to the second was flexible and useful, in which the interpolation model is used as a defect signal generator for fast building of a large and efficient database for ANN training. Both of the proposed methods proved the objectivity and accuracy of the inversion results and offered more robust engineering support for automated NDT, reducing production time and increasing productivity.

The exploitation of nondestructive eddy current testing (NDT‐EC) has become a capital necessity. Therefore, the development of a fast tool for the eddy‐current signal inversion is necessary. This paper proposes an inversion of signals coming from the NDT‐EC sensor response using machine learning methods, to reconstruct the length and depth of the defect and obtain its geometric characterization by solving the inverse problem. In this context, a database comprising the impedance of the sensor‐cracked part system (constituting the crack signature) was constructed from a 3D finite element simulation and validated by an experimental companion. The machine learning algorithms were trained using this database. The results show that the defect can be quantified using these developed approaches. The numerical approach can replace the expensive experimental investigation or the optimization algorithm that has a prohibitive computing time. The results show that the approaches developed estimated the desired parameters of the crack with good precision. These different machine learning methods for solving the inverse problem were implemented using the MATLAB software.