Inverse Design of Energy-Absorbing Metamaterials by Topology Optimization

Compared with the forward design method through the control of geometric parameters and material types, the inverse design method based on the target stress-strain curve is helpful for the discovery of new structures. This study proposes an optimization strategy for mechanical metamaterials based on a genetic algorithm and establishes a topology optimization method for energy-absorbing structures with the desired stress-strain curves. A series of structural mutation algorithms and design-domain-independent mesh generation method are developed to improve the efficiency of finite element analysis and optimization iteration. The algorithm realizes the design of ideal energy-absorbing structures, which are verified by additive manufacturing and experimental characterization. The error between the stress-strain curve of the designed structure and the target curve is less than 5%, and the densification strain reaches 0.6. Furthermore, special attention is paid to passive pedestrian protection and occupant protection, and a reasonable solution is given through the design of a multiplatform energy-absorbing structure. The proposed topology optimization framework provides a new solution path for the elastic-plastic large deformation problem that is unable to be resolved by using classical gradient algorithms or genetic algorithms, and simplifies the design process of energy-absorbing mechanical metamaterials.

Automated summary

Compared with forward design methods, the inverse design method based on the target stress-strain curve is useful for discovering new structures. This study proposes an optimization strategy for mechanical metamaterials using a genetic algorithm and establishes a topology optimization method for energy-absorbing structures with desired stress-strain curves. A series of structural mutation algorithms and design-domain-independent mesh generation methods are developed to improve the efficiency of finite element analysis and optimization iteration. The algorithm successfully designs ideal energy-absorbing structures, which are verified through additive manufacturing and experimental characterization. The designed structures have an error of less than 5% compared to the target stress-strain curve and achieve a densification strain of 0.6.