Prediction of elastic wave propagation in composites using 3D CNN

Performing time-dependent finite element simulations for wave propagation in composites is a particularly complex task that consumes a lot of computational energy as it involves modeling the interactions between waves and various constituents that make up the composite material. In this study, we have developed a surrogate model of elastic wave propagation in composites based on three-dimensional conventional neural networks. The input to the model consists of a three-dimensional matrix representing the architecture of the composites and a vector representing the input waves, while the output is a vector representing the output elastic waves. After training the model using 60 000 randomly generated samples, it has shown high accuracy and efficiency in predicting the output elastic waves. This significantly reduces computational resources required to conduct simulation using commercial software, making it a more practical solution for real-world applications, such as composite optimization, nondestructive testing, and material characterization.