Optimal Design of Three-Dimensional Voxel Printed Multimaterial Lattice Metamaterials via Machine Learning and Evolutionary Algorithm

摘要

Mechanical metamaterials present tremendous potential through synergistic material distribution and structural design, but current approaches mainly focus on structure while neglecting the material distribution. The authors propose an optimized method for designing lattice metamaterials, considering both control aspects. This framework can be used to match multiple targets, such as reproducing the stress-strain and Poisson's ratio curves of biological tissues simultaneously, and to enable customized lateral profiles. This work expands the design space of lattice metamaterials and is promising for flexible electronics and soft robotics.