  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.
