Application of machine-learning approaches with physics-informed oversampling to determine ballistic limit curves

The proposed study investigates the application of physics-informed oversampling in machine-learning models to approximate ballistic curves and ballistic limit velocities that represent the resistance of targets made from S355 steel under impacts of 7.62 × 51 mm P80 (0.308 Win) small-calibre projectiles. Through a detailed terminal ballistic experimental investigation, we determined ballistic limit velocities and corresponding limit curves for four target thicknesses: 20 mm, 16 mm, 8 mm, and 5 mm. The experimental results for the four impact configurations were used to validate the finite element analysis, resulting in an additional set of 11 ballistic limit curves and ballistic limit velocities for a total of 15 configurations of S355 steel plates, with thicknesses ranging from 4 mm to 18 mm. Finite element simulations complemented the experimental data and further characterised the effects of varying plate thickness on protection level. Several machine-learning approaches, including deep neural networks (DNN), Random Forests (RF), Support Vector Machines (SVM), and XGBoost (XGB), were trained on a part of the data. Their efficiency and accuracy in predicting experimentally identified ballistic limit features were then evaluated for the remaining data subset. By leveraging available data and incorporating specific physical mechanisms into machine-learning algorithms, the study analyses the applicability and limitations of these approaches.