Publication | CPAIOR 2020

Multi-speed Gearbox Synthesis Using Global Search and Non-convex Optimization

This paper applies generative design to multi-speed gear box, a type of complex mechanical system. The research investigates various techniques to solve a bi-level, combinatorial optimization problem, including non-convex optimization, graph isomorphism algorithm, best-first search, and estimation of distribution algorithm. These techniques could be also applied in the future to solve many relevant bi-level and combinatorial problems that our customer faces in both AEC and MFC domains.

Download publication


Multi-speed Gearbox Synthesis Using Global Search and Non-convex Optimization

Chiara Piacentini, Hyunmin Cheong, Mehran Ebrahimi, Adrian Butscher


We consider the synthesis problem of a multi-speed gearbox, a mechanical system that receives an input speed and transmits it to an outlet through a series of connected gears, decreasing or increasing the speed according to predetermined transmission ratios. Here we formulate this as a bi-level optimization problem, where the inner problem involves non-convex optimization over continuous parameters of the components, and the outer task explores different configurations of the system. The outer problem is decomposed into sub-tasks and optimized by a variety of global search methods, namely simulated annealing, best-first search and estimation of distribution algorithm. Our experiments show that a three-stage decomposition coupled with a best-first search performs well on small-size problems, and it outmatches other techniques on larger problems when coupled with an estimation of distribution algorithm.

Get in touch

Something pique your interest? Get in touch if you’d like to learn more about Autodesk Research, our projects, people, and potential collaboration opportunities.

Contact us