Abstract

This work presents the influence of ballistic testing variables on residual velocity of projectile and absorbed energy of aluminum 1100-H12 using design of experiments (DOE) and artificial neural network (ANN) approach. Simulations have been carried out with three process variables: Projectile nose shape, impact velocity and target thickness. Taguchi’s technique has been employed for experimental investigation. Trials were planned using an L 18 (6¹x3³) orthogonal array with 18 combinations of testing variables was selected to assess the influence of various factors. Optimum testing variable combination was achieved by using analysis of signal to noise (S/N) ratio. Simulations were performed using Ansys Autodyn 3 D code. Simulated and experimental results were compared with each other and found well. The predictions of the ANN model, simulation results were in good agreement with the experimental data taken from literature.

摘要

这项工作使用实验设计（DOE）和人工神经网络（ANN）方法，提出了弹道测试变量对铝1100-H12弹丸残余速度和吸收能量的影响。使用三个过程变量进行了仿真：弹头形状，冲击速度和目标厚度。田口的技术已用于实验研究。试验计划使用L 18（6 ¹ x3 ³选择具有18个测试变量组合的正交数组以评估各种因素的影响。通过使用信噪比（S / N）的分析获得了最佳的测试变量组合。使用Ansys Autodyn 3 D代码进行仿真。将模拟结果和实验结果进行了比较，并发现了很好的结果。人工神经网络模型的预测，仿真结果与文献记载的实验数据吻合良好。