  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.
