In the paper a method using active thermography and a neural algorithm for material defect characterization is presented. Experimental investigations are conducted with the stepped heating method, so-called time-resolved infrared radiometry, for the test specimen made of a material with low thermal diffusivity. The results of the experimental investigations were used in simulations of artificial neural networks. Simulations are performed for three datasets representing three stages of the heating process occurring in the investigated sample. In this work, the simulation research aimed to determine the accuracy of defect depth estimation with the use of the mentioned algorithm is descibed

This work deals with the inverse problem associated to 3D crack identification inside a conductive material using eddy current measurements. In order to accelerate the time-consuming direct optimization, the reconstruction is provided by the minimization of a last-square functional of the data-model misfit using space mapping (SM) methodology. This technique enables to shift the optimization burden from a time consuming and accurate model to the less precise but faster coarse surrogate model. In this work, the finite element method (FEM) is used as a fine model while the model based on the volume integral method (VIM) serves as a coarse model. The application of the proposed method to the shape reconstruction allows to shorten the evaluation time that is required to provide the proper parameter estimation of surface defects.