Abstract
The ballistic clay is used to assess protection performance of bulletproof vests. Applying a clay block behind targets conservatively provides similar behavior to the human body as a non-negligible boundary condition. Although understanding and interpreting RP deformation behavior is important, there is no distinctive model covering various impact conditions. We established a numerical model consolidated by a deep neural network which can determine physical input parameters and an empirical formula to understand RP behavior under various impact conditions referring to 59 cases, including our own experiments. The numerical model achieved high levels of accuracy of over 92.6% only when the diameter of the indenter was ø44.5 mm and ø63.5 mm. On the other hand, the empirical model achieved high levels of averaged accuracy of 87.4% for all the cases. The proposed empirical and numerical models can be utilized for assessing protection performance and interpreting impact event of bulletproof vests.
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