AI-aided design and multi-scale optimization of mechanical metastructures with controllable anisotropy

Anisotropic mechanical metamaterials with controllable properties are crucial for additive manufacturing design. However, manually regulating microstructural anisotropy remains challenging. This study introduces a method for artificial intelligence-aided design (AIAD) and mechanical metastructures optimization (MMO) to achieve extensive multi-scale structural enhancements. The approach involves compiling a comprehensive database of lattice materials with anisotropic characteristics. This is achieved by manipulating the central node position and rod diameter of a cubic-BCC microstructure. Homogenization theory then determines the elastic tensor of each microstructure. A 3D convolutional neural network (3D-CNN) maps the relationship between geometric properties and mechanical performance. An inverse design model based on a backpropagation neural network (NN) and parametric design acquires microstructures with desired elastic tensor attributes. Finally, a novel optimization approach for large-scale multiscale structures applies this method to control structural anisotropy. The resulting material distribution resembles a truss, significantly improving structural performance.