Physics-informed neural network model for predicting projectile mass erosion integrating cutting and thermal melting mechanisms

High-velocity penetration of projectiles into concrete induces intense thermomechanical interactions that lead to substantial projectile mass erosion, thereby compromising structural integrity and ballistic stability. Accurate mass erosion prediction is therefore critical for warhead lethality assessment. To address this challenge, we develop a physics-informed neural network (PINN) model for predicting projectile mass erosion at velocities ranging from 345 m/s to 1852 m/s. The model integrates physics-based constraints from cutting and thermal melting mechanisms through a composite loss function. Data augmentation and dimensionality reduction techniques are applied to improve model generalization across diverse impact conditions. Training and validation are conducted on an augmented dataset comprising 283 samples, derived from 10 published studies. The model’s generalization capability is rigorously evaluated on an independent set of 13 new penetration experiments with ogive-nosed projectiles made of three distinct materials. Compared to a purely data-driven neural network (NN) model, the PINN model reduces the average relative error on the validation set from 16.3% to 14.5% and improves the proportion of predictions within a 20% relative error margin from 67.3% to 71.2%. On an independent test set, the PINN model demonstrates superior accuracy over both the data-driven neural network model and conventional theoretical models, achieving an average relative error of 11.7%, with 38.5% and 84.6% of the predictions falling within 10% and 20% relative error margins, respectively. By enabling accurate mass erosion prediction under high-velocity conditions, the proposed methodology supports weight-optimized penetrator design and directly contributes to terminal effectiveness improvement.