Impact-Resistant Metamaterials Design: Synergistic Innovation of Machine Learning and Additive Manufacturing

Impact-resistant metamaterials can efficiently regulate impact energy through artificial microstructure design, and have great potential in aerospace, vehicle engineering, and human protection fields. Traditional metamaterial design methods based on empirical trial and error and homogeneous structures are difficult to meet the multi-objective requirements of lightweight, high energy absorption, and controllable failure. Based on energy absorption mechanisms including plastic deformation, viscoelastic dissipation, friction dissipation, and fracture dissipation, three categories of design strategies are summarized: traditional homogeneous structures such as honeycomb, lattice, and foam; bio-inspired gradient structures including seashell and beetle forewing configurations; and reconfigurable topological structures. Combined with additive manufacturing processes such as selective laser melting and stereolithography, the influence of process-induced defects on dynamic mechanical performance is analyzed. The research progress in achieving end-to-end "performance-to-structure" inverse design and compensating for process errors by a generative adversarial network-reinforcement learning machine learning framework is elaborated. A vision for constructing an intelligent “design‒manufacture‒validation” full-chain verification platform is proposed. Future research should focus on advancing multi-objective optimization, cross-scale simulation, and intelligent verification platform development to collaboratively promote the integration of the “design‒manufacture‒validation” full chain.