Diffusion models for topology optimization in 3D printing applications

Abstract

Structural topology optimization, a critical aspect of engineering design in aerospace, mechanical, and civil engineering, seeks to discover the ideal physical structure for optimizing mechanical performance, particularly, in 3D structures. In recent times, Generative Adversarial Networks (GANs) and autoencoders have gained popularity as an alternative to traditional iterative topology optimization methods. However, these models often pose challenges in terms of training, exhibit limited generalizability, and prioritize mimicking optimal structures while neglecting factors such as manufacturability and mechanical compliance. In response to these limitations, we introduce an architecture based on conditional diffusion models aimed at achieving topology optimization that is not only performance-aware but also manufacturability-aware, specifically for 3D structures. Our approach incorporates a surrogate model-based guidance strategy that actively promotes structures with low compliance and excellent manufacturability. The results of our method surpass those of a state-of-the-art conditional GAN and conditional variational autoencoder (VAE), reducing the average error in physical performance by a factor of eight and generating eleven times fewer infeasible samples. By introducing diffusion models to 3D topology optimization, we demonstrate the superior capabilities of conditional diffusion models in engineering design synthesis applications as well. Furthermore, our work offers a broader framework for tackling engineering optimization problems using diffusion models and external performance, all while considering constraint-aware guidance.