Functional generative design of mechanisms with recurrent neural networks and novelty search

Consumer-grade 3D printers have made the fabrication of aesthetic objects and static assemblies easier, opening the door to automate the design of such objects. However, while static designs are easily produced with 3D printing, functional designs, with moving parts, are more difficult to generate: The search space is high-dimensional, the resolution of the 3D-printed parts is not adequate, and it is difficult to predict the physical behavior of imperfect, 3D-printed mechanisms. An example challenge for automating the design of functional, 3D-printed mechanisms is producing a diverse set of reliable and effective gear mechanisms that could be used after production without extensive post-processing. To meet this challenge, an indirect encoding based on a Recurrent Neural Network (RNN) is proposed and evolved using Novelty Search. The elite solutions of each generation are 3D printed to evaluate their functional performance in a physical test platform. The proposed RNN model successfully discovers sequential design rules that are difficult to discover with other methods. Compared to a direct encoding of gear mechanisms evolved with Genetic Algorithms (GAs), the designs produced by the RNN are geometrically more diverse and functionally more effective, thus forming a promising foundation for the generative design of 3D-printed, functional mechanisms.