Artificial Neural Network Modelling of Glass Laminate Sample Shape Influence on the ESPI Modes

Abstract

The present work is devoted to the applications of artificial neural networks (ANN) for material design prediction. We have investigated the dependence of the generated mode frequency as a function of a sample thickness and a sample shape of glass laminate samples by electronic speckle interferometry (ESPI). The obtained experimental results for differently shaped (thickness, canting and rounding) glass laminate samples are compared with those of ANN. The coincidence of both experimental and simulated results is very good.