Deep Generative Design: Integration of Topology Optimization and Generative Models
This work addresses the need for automated design exploration in engineering, offering an incremental improvement by combining existing techniques for enhanced generative design.
The study tackled the problem of generating diverse and performance-optimized designs by integrating topology optimization with deep generative models, resulting in a framework that produced designs with better aesthetics, diversity, and robustness compared to previous methods.
Deep learning has recently been applied to various research areas of design optimization. This study presents the need and effectiveness of adopting deep learning for generative design (or design exploration) research area. This work proposes an artificial intelligent (AI)-based design automation framework that is capable of generating numerous design options which are not only aesthetic but also optimized for engineering performance. The proposed framework integrates topology optimization and deep generative models (e.g., generative adversarial networks (GANs)) in an iterative manner to explore new design options, thus generating a large number of designs starting from limited previous design data. In addition, anomaly detection can evaluate the novelty of generated designs, thus helping designers choose among design options. The 2D wheel design problem is applied as a case study for validation of the proposed framework. The framework manifests better aesthetics, diversity, and robustness of generated designs than previous generative design methods.