  Consumer-grade 3D printers have made it easier to fabricate aesthetic objects
and static assemblies, opening the door to automated 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
too 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 is to produce 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 created and evolved using novelty search. The
elite solutions of each generation are 3D printed to evaluate their functional
performance on a physical test platform. The system is able to discover
sequential design rules that are difficult to discover with other methods.
Compared to direct encoding evolved with Genetic Algorithms (GAs), its designs
are geometrically more diverse and functionally more effective. It therefore
forms a promising foundation for the generative design of 3D-printed,
functional mechanisms.
