在这项研究中,我们深入研究了射弹的迷人动力学及其与材料的相互作用,专注于剩余速度-射弹击中目标后保持的速度。这个参数很关键,特别是在考虑各种环境中的保护屏障的设计时。传统的测量剩余速度的方法很麻烦,资源密集型,偶尔不一致。为了应对这些挑战,我们通过MATLABR2021a介绍了一种使用人工神经网络(ANN)模型的创新方法。这个计算机化的工具,在先前研究的丰富数据集上训练,可以通过考虑多个因素来预测剩余速度,包括弹丸的初始速度,它的材料和形状,和目标的厚度。本文精心详述了发展,培训,和人工神经网络模型的验证,与Recht-Ipson模型等传统方法相比,突出了其卓越的准确性。与Recht-Ipson模型相比,开发的ANN模型表现出卓越的性能。在培训期间,它表现出0.0259的平均绝对百分比误差(MAPE)和1.5993的均方根误差(RMSE)。对于验证,MAPE为0.0295,RMSE为2.2056。相比之下,Recht-Ipson模型显示更高的错误,MAPE和RMSE值分别为0.2349和14.1791。此外,我们讨论了神经网络模型不仅预测剩余速度而且预测吸收能量的潜力,展示其多功能性。我们的发现的实际意义是巨大的。从在城市环境中设计更安全的基础设施到增强军事应用中的装甲系统,神经网络模型的预测可以成为创新的基石。
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.