# Functional Generative Design of Mechanisms with Recurrent Neural Networks and Novelty Search

## TL;DR

This paper introduces a novel method using recurrent neural networks and novelty search to automatically generate diverse, functional 3D-printed mechanisms, overcoming high-dimensional search challenges and physical unpredictability.

## Contribution

It presents an indirect encoding approach with RNNs evolved via novelty search for designing effective, diverse mechanisms, outperforming traditional genetic algorithms.

## Key findings

- Generated mechanisms are more geometrically diverse.
- Designed mechanisms show higher functional effectiveness.
- Physical testing confirms the reliability of evolved designs.

## Abstract

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.