When the penetrator collides with the target, the penetrator has different penetrating characteristics and residual velocity after penetration, according to the geometry of the penetrator. In this study, we optimized the geometry of the penetrator using the artificial neural network and the genetic algorithm to derive the best penetration performance. The Latin hypercube sampling method was used to collect the sample data, Simulation for predicting the behavior of the penetrator was conducted with the finite cavity pressure method to generate the training data for the artificial neural network. Also, the optimal hyper parameter was derived by using the Latin hypercube sampling method and the artificial neural network was used as the fitness function of the genetic algorithm to optimize the geometry of the penetrator. The optimized geometry presented the deepest penetration depth.

We investigated the protection capability of a plate against high speed projectiles demonstrating collision and penetration behaviors by finite element analysis. The element erosion method was used for penetration analysis, which showed that the speed of the projectile was slightly reduced by the collision with the protection plate. Protection capability was measured by the projectile’s attitude angle change because the damage of our tanks by projectiles was also dependent on the projectile-tank collision angle. When the length of the protection plate was sufficiently long, the projectile was severely deformed and incapacitated. In the case of a small plate, the projectile was deformed only in the collision region. Thus, projection capability was investigated by the change of attitude angle. The effect of collision angle, velocity, and length of the plate on the rotational and vertical velocities of the projectile was investigated.