Using Neural Networks to Explore Structure-Property Relations in Bio-Inspired Impact-Resistant Structures

Inspired by biological structural designs observed in nature, this work explores the structure-property relationships in structures under dynamic transverse and longitudinal compression. The two primary structures analyzed are low porosity structures inspired by sheep horns and 2D extruded thin-walled structures inspired by design elements found in bamboo, beetles, and crabs. The low-porosity structures exhibit superior mechanical properties in nature, with porosity ranging from 1–5%, while the thin-walled structures provide insights into the effect of geometric feature interactions leading to high energy absorption during impact. The current work utilizes the gated recurrent unit (GRU) model to predict the mechanical response of the structures during the impact. The inputs used in GRU models were varied to present different techniques to predict stress-strain response for the structures at a given loading condition. The first method utilized the parametric representation of the geometric features, while the other used a combinatorial approach and autoencoders to prepare inputs for the GRU model. The ground-truth data was obtained using finite element simulations with the rate-dependent elastoplastic and Johnson-Cook material models. The trained models allow rapid evaluations of stress-strain response and allow the elimination of poor designs.