Predicting projectile residual velocities using an advanced artificial neural network model.

In this research, we delve into the fascinating dynamics of projectiles and their interactions with materials, with a keen focus on residual velocity - the speed a projectile retains after striking a target. This parameter is pivotal, especially when considering the design of protective barriers in various environments. Traditional methods of gauging residual velocity have been cumbersome, resource-intensive, and occasionally inconsistent. To address these challenges, we introduce an innovative approach using an Artificial Neural Network (ANN) model through MATLAB R2021a. This computerized tool, trained on a rich dataset from prior research, can predict residual velocities by considering multiple factors, including the initial speed of the projectile, its material and shape, and the thickness of the target. This paper meticulously details the development, training, and validation of the ANN model, highlighting its superior accuracy when compared to traditional methods like the Recht-Ipson model. The developed ANN model demonstrated remarkable performance compared to the Recht-Ipson model. During training, it exhibited a Mean Absolute Percentage Error (MAPE) of 0.0259 and a Root Mean Squared Error (RMSE) of 1.5993. For validation, MAPE was 0.0295, and RMSE was 2.2056. In contrast, the Recht-Ipson model displayed higher errors, with MAPE and RMSE values of 0.2349 and 14.1791, respectively. Furthermore, we discuss the potential of the ANN model in predicting not just residual velocities but also absorbed energy, showcasing its versatility. The practical implications of our findings are vast. From designing safer infrastructures in urban settings to enhancing armour systems in military applications, the ANN model's predictions can be a cornerstone for innovation.