Data-driven learning of impactor strength properties from shock experiments with additively manufactured materials

In this work we demonstrate a method for leveraging high-fidelity, multi-physics simulations of high-speed impacts in a particular manufactured material to encode prior information regarding the impactor material's strength properties. Our simulations involve a material composed of stacked cylindrical ligaments impacted by a high-velocity aluminum plate. We show that deep neural networks of relatively simple architecture can be trained on the simulations to make highly-accurate inferences of the strength properties of the impactor material. We detail our neural architectures and the considerations that went into their design. In addition, we discuss the simplicity of our network architecture which lends itself to interpretability of learned features in radiographic observations.