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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.

This paper propose a new concept of an eddy current (EC) multi-element sensor for the characterization of carbon fiber-reinforced polymers (CFRP) to evaluate the orientations of plies in CFRP and the order of their stacking. The main advantage of the new sensors is the flexible parametrization by electronical switching that reduces the effort for mechanical manipulation. The sensor response was calculated and proved by 3D finite element (FE) modeling. This sensor is dedicated to nondestructive testing (NDT) and can be an alternative for conventional mechanical rotating and rectangular sensors.

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