Machine learning-driven low-velocity impact response prediction and multi-objective optimization of origami metamaterial sandwich

Inspired by the hybrid topology design that integrates Miura origami and star-shaped honeycomb, this study proposes a novel origami metamaterial sandwich and employs machine learning to predict low-velocity impact response and perform multi-objective optimization. Through drop-weight impact experiments and finite element simulations, the dynamic mechanical response and deformation failure modes of the sandwich under low-velocity impact are systematically investigated. The results demonstrate that the origami-inspired topologies effectively transform the instantaneous complete fracture of traditional honeycombs into progressive crushing failure, thereby significantly enhancing impact resistance. Subsequently, a residual connection-enhanced deep learning model is developed, enabling rapid and precise end-to-end prediction of the complete low-velocity impact response, with computational efficiency substantially surpassing that of finite element simulations. Based on the developed deep learning model, parametric analysis of the key angles revealed their effects on the impact response and effective density, particularly the angle-induced load redistribution between panel tension-compression deformation and crease bending deformation, enabling functional switching between load-bearing and cushioning modes and providing a mechanistic basis for the active tunability of impact response and failure modes. Furthermore, by integrating reinforcement learning and Pareto front analysis, the trained deep learning model served as a surrogate model to achieve lightweight multi-objective optimization tailored for load-bearing and impact-mitigation protection requirements. At similar effective densities, the metamaterial enables broad-range tuning of peak force, offering significant advantages for developing customized protective structures for diverse scenarios. This research not only establishes a solid foundation for creating customizable high-performance impact protection structures but also advances the field toward a new paradigm of intelligent, on-demand design.