Infrared thermography (IRT) proved to be a valuable technique in non-destructive testing due to its contactless nature, real-time application, and wide area coverage. However, the major hurdle to widespread application of IRT is quantification; more specifically quantifying the depth of defects from the acquired thermograms in low conductive host materials. In this paper, an artificial neural network (NN) is employed to detect defects depth in a composite sample, coupled with a Pulsed Thermography PT setup. A Carbon Fiber Reinforced Polymer (CFRP) coupon with 16 embedded defects was designed via 3D printing; to precisely control the defects morphology and depth. Firstly; the current study presents a proof of concept using a Multiphysics FEM simulation model of the inspection process, to generate the training sets of data, so that the developed NN is assessed in a deterministic (noise free) environment. Then, the proposed NN was further tested experimentally to validate its accuracy and performance. The accuracy of the developed NN for the synthetic data was more than 97% and for the experimental data was around 90%.
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