A new tool based on artificial neural networks for the design of lightweight ceramic–metal armour against high-velocity impact of solids

Abstract

A new tool based on artificial neural networks (ANNs) has been developed for the design of lightweight ceramic–metal armours against high-velocity impact of solids. The tool developed predicts, in real-time, the response of the armour: impacting body arrest or target perforation are determined and, in the latter case, the residual mass and velocity of the impacting body are determined. A large set of impact cases has been generated, by FEM numerical simulation, in order to train and test the ANN. The impact cases consider different impacting body and target geometries, materials and impact velocities, all these parameters varying in a wide range that covers most common impact situations. The behaviour of the ceramic material under impact was simulated using a modified version of the model developed by Cortés et al. The ANN developed has a remarkable prediction ability and therefore it constitutes a complementary methodology to the conventional design techniques.