Predicting the perforation capability of Kinetic Energy Projectiles using artificial neural networks

The U.S. Army requires the evaluation of new weapon and vehicle systems through the use of experimental testing and Vulnerability/Lethality (V/L) modeling & simulation (M&S). The current M&S methods being utilized often require significant amounts of time and subject matter expertise. This typically means that quick results cannot be provided when needed to address new threats encountered in theater. Recently there has been an increased focus on rapid results for M&S efforts that can also provide accurate results. Accurately modeling the penetration and residual properties of a ballistic threat as it progresses through a target is an extremely important part of determining the effectiveness of the threat against that target. This paper presents preliminary results from the training of an artificial neural network for the prediction of perforation of a monolithic metallic target plate.