Dissertation Defense by John R. Auten Sr.
Title: Predicting the Terminal Ballistics of Kinetic Energy Projectiles using Artificial Neural Networks
Date & Time: November 17, 2016; 12:30-2:30pm
Location: YR 459, Towson University
Abstract: The U.S. Army requires the evaluation of new weapon and vehicle systems through the use of experimental testing and Modeling & Simulation (M&S). Traditional M&S has worked well over the years, but can be a lengthy process and often cannot provide quick results for studies involving new threats encountered in theater. So, there is increased focus on rapid M&S efforts that can provide accurate and fast 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 dissertation presents research on the application of Artificial Neural Networks (ANNs) to the prediction of the terminal ballistics of Kinetic Energy Projectiles (KEPs). This research shows that ANNs can be used to model the terminal ballistics of KEPs and that they are capable of being used for single element and multiple element targets. It is also shown that the runtimes of an ANN are drastically faster than the current state-of-the-art model. Another improvement in performance is realized by removing the need for input preparation by a Subject Matter Expert (SME) prior to using the methodology for an analysis.
