Geometrically-driven generation of mechanical designs through deep convolutional GANs

Abstract

Despite the freedom Additive Manufacturing (AM) offers when manufacturing organic shapes, it still requires some geometrical criteria to avoid a part's collapse during printing. The most synergetic design approach to AM is Topology Optimization (TO), which finds an optimal free-form given mechanical constraints. However, it is hard for TO to integrate these layout geometry-related constraints and it seldom proposes printable shapes. Therefore, this work leverages the Deep Learning (DL) capability to handle spatial correlations within the mechanical design process by integrating the layout and mechanical constraints at the conceptual level. It proposes a DL-layout-driven solution (DL-TO) trained via a triple-discriminator Generative Adversarial Network (GAN) framework. The DL-TO's performance is demonstrated by generating mechanically valid 2D designs conforming with layout constraints in a fraction of a second. DL-TO's creativity is illustrated by its capability to generate designs with unseen input constraints (passive/active elements) and to propose several shapes for the same input mechanical constraints, a task that is hard for a traditional TO to achieve.