Two-stage neural algorithm for defect detection and characterization uses an active thermography

In the paper a two-stage neural algorithm for defect detection and characterization is presented. In order to estimate the defect depth two neural networks trained on data obtained using an active thermography were employed. The first stage of the algorithm is developed to detect the defect by a classification neural network. Then the defects depth is estimated using a regressive neural network. In this work the results of experimental investigations and simulations are shown. Further, the sensitivity analysis of the presented algorithm was conducted and the impacts of emissivity error and the ambient temperature error on the depth estimation errors were studied. The results were obtained using a test sample made of material with a low thermal diffusivity.