Physically reliable 3D styled shape generation via structure-aware topology optimization in unified latent space

Abstract

We propose a novel approach to structure-aware topology optimization (SATO) to generate physically plausible multi-component structures with diverse stylistic variations. Traditional TO methods often operate within a discrete voxel-defined design space, overlooking the underlying structure-aware, which limits their ability to accommodate stylistic design preferences. Our approach leverages variational autoencoders (VAEs) to encode both geometries and corresponding structures into a unified latent space, capturing part arrangement features. The design target is carefully formulated as a topology optimization problem taking the VAE code as design variables under physical constraints, and solved numerically via analyzing the associated sensitivity with respect to the VAE variables. Our numerical examples demonstrate the ability to generate lightweight structures that balance geometric plausibility and structural performance with much enhanced stiffness that outperforms existing generative techniques. The method also enables the generation of diverse and reliable designs, maintaining structural integrity throughout, via a direct smooth interpolation between the optimized designs. The findings highlight the potential of our approach to bridge the gap between generative design and physics-based optimization by incorporating deep learning techniques.