Neural network based defect detection and depth estimation in TNDE

Abstract

For many years, applications of the TNDE (Thermographic NonDestructive Evaluation) technique has been limited due to the complex non-linearity nature of related inversion problems such as defect depth estimation. Artificial neural networks have recently obtained success in revealing and providing quantitative information concerning defects in TNDE. In this paper, a three dimensional thermal model for non-homogenous materials such as carbon fiber reinforced plastic (CFRP) is first given. The modeling results are compared with the analytical solution based on Duhamel's theorem. Two back propagation neural networks (NN) as defect detector and depth estimator are then presented. Finally, simulated and experimental results are presented and discussed.