📝 SummarXiv

Abstract

Mechanical metamaterials enable precise control over structural properties, but their design method remains challenging due to their complex structure. Although additive manufacturing has expanded geometric freedom, navigating this vast and complex design space still requires computationally intensive simulations or expert-driven processes. Recently, artificial intelligence (AI)-driven design approaches have emerged to address these limitations, but many studies restrict their scope to parametric representations, limiting their generative capacity to predefined shapes. Here, we present a point cloud-based generative framework that enables the inverse design of 3D metamaterial without parametric constraints. Trained on a number of structurally valid unit cells, the present machine learning model learns geometric patterns, mitigates common connectivity issues inherent in point cloud generation. The proposed model constructs a latent space organized by mechanical properties and naturally clustered by unit cell types. By sampling this latent space, our method supports both property-guided inverse design and generation of topologically gradient transition between distinct unit cell types. This approach facilitates inverse design of 3D metamaterials with high geometric complexity.