A generative adversarial active learning method for mechanical layout generation

Layout generation is frequently encountered in the field of mechanical design. The direct application of generative adversarial network, which was originally used to generate pixel-level images, usually cannot guarantee the interrelation between components such as the non-overlap requirement. In addition, the number and the size of components cannot be precisely controlled. These all constitute the characteristics of mechanical layout. To address the above problems, we propose a hierarchical layout generation generative adversarial network (LGGAN) for mechanical layout generation. The layout generator consists of three modules. The first is hierarchical layout generation, where the shape and distribution of components are generated separately using two neural networks. Such a hierarchical structure greatly improves the generation capacity. To reduce the accumulated noise when multiple components are added, a denoiser is included as the second module. The third module is a refinement step used to fine-tune the layouts, which adjusts the size of each component to the prescribed value. All of the three modules are neural network-based, and can be trained through backpropagation. Additionally, an active learning strategy for training the LGGAN is proposed, which allows LGGAN to converge with a small amount of training data in situations where getting a significant amount of training data is not possible. Quantitative and qualitative experiments demonstrate the effectiveness of LGGAN.