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. The data from the computational simulations are fed to artificial neural networks to train them to predict and characterize defect sizes and shapes. The predictions from the neural networks are compared to the actual dimensions from the computational models. These predictions show a high level of accuracy especially when quantifying thermal image information and using it to train the neural network. This accuracy is around 10% and 19% for predicting defect depth in plates and pipes, respectively. This hybrid approach has the advantage of not relying on experimental data (experiments were used only for validation) and predicting damage shape and size. This suggests that the methodology used in this study combining lock-in thermography experiments, computational simulations, and ANNs is a viable method for a potential nondestructive testing (NDT) method for detecting embedded defects within composite pipes in real applications. What makes this approach attractive is that it can be used with live thermal images that can be fed directly into the ANN model.