A data-driven approach for predicting the ballistic resistance of elastoplastic materials

Abstract

Data-driven methods and machine learning methods provide efficient and accurate approaches for solving impact problems. In this paper, a data-driven approach is proposed for numerical simulations of ballistic impact behavior for elastoplastic materials. An enhanced rate-dependent scheme is employed for improving the predictability of the data-driven constitutive model. A new method that introduces a stress triaxiality indicator to three separate constitutive models is proposed to consider the discrepancy between the mechanical responses of materials under tension, compression, and shear. Additionally, a modified Bai-Wierzbicki fracture criterion considering the strain rate effect and the stress-state effect is used to evaluate the fracture behavior of the materials during impact simulations. Subsequently, a compatible numerical implementation algorithm that considers loading, unloading, and reverse loading is established to enable the application of the data-driven approach in finite element simulations. Numerical validation of the proposed data-driven approach is conducted through several simple loading examples, such as cyclic loading, torsional loading, and tension–torsion combined loading. The data-driven approach is then employed to simulate the ballistic impact behavior of Ti-6Al-4V targets of different thicknesses that are struck by blunt projectiles. Impact properties—including the relationship between residual velocity and impact velocity, ballistic limit velocities, and fracture paths—are comprehensively studied. The results demonstrate the reasonable predictability and accuracy of the data-driven approach when applied to ballistic impact simulations.