Damage characterization of embedded defects in composites using a hybrid thermography, computational, and artificial neural networks approach.

This work presents a hybrid thermography, computational, and Artificial Neural Networks (ANN) approach to characterize beneath the surface defects in composites. Computational simulations are created to model thermography experiments carried out on composite plates with controlled damage in the form of drilled holes. The computational models are then extended to create hypothetical composite component geometries of plates and pipes with embedded defects of varying sizes and shapes.