Roma Plastilina No. 1 clay has been widely used as a conservative boundary condition in bulletproof vests, namely to play the role of a human body. Interestingly, the effect of this boundary condition on the ballistic performance of the vests is indiscernible. Moreover, back face deformation should be characterized by measuring the indentation in the deformed clay, which is important for determining the lethality of gunshots. Therefore, several studies have focused on modeling not only bulletproof vests but also the clay backing material. Despite various attempts to develop a suitable numerical model, determining the appropriate physical parameters that can capture the high-strain-rate behavior of clay is still challenging. In this study, we predicted indentation depth in clay using an artificial neural network (ANN) and determined the optimal material parameters required for a finite element method (FEM)-based model using an inverse tracking method. Our ANN-FEM hybrid model successfully optimized high-strain-rate material parameters without the need for any independent mechanical tests. The proposed novel model achieved a high prediction accuracy of over 98% referring impact cases.
翻译:值得注意的是,这一边界条件对防弹背心弹道性能的影响是无法分辨的。此外,背面变形的特征应该是测量变形粘土的缩进,这对于确定枪声的致命性十分重要。因此,一些研究不仅侧重于防弹背心的模型,而且侧重于粘土背料材料的模型。尽管曾试图开发一个适当的数字模型,确定能够捕捉高强度粘土行为的适当物理参数,但这种边界条件对防弹背心的弹性能的影响仍然具有挑战性。在这项研究中,我们预测了使用人工神经网络(ANN)在粘土中的缩进深度,并用反跟踪方法确定了基于一定元素方法(FEM)模型所需的最佳物质参数。我们的ANN-FEM混合模型成功地优化了高强度材料参数,而无需任何独立的机械测试。拟议的新模型的预测精确度超过了98%。