Bayesian topology optimization for efficient design of origami folding structures

Abstract

Bayesian optimization (BO) is a popular method for solving optimization problems involving expensive objective functions. Although BO has been applied across various fields, its use in structural optimization area is in its early stages. Origami folding structures provide a complex design space where the use of an efficient optimizer is critical. In this work for the first time we demonstrate the ability of BO to solve origami-inspired design problems. We use a Gaussian process (GP) as the surrogate model that is trained to mimic the response of the expensive finite element (FE) objective function. The ability of this BO-FE framework to find optimal designs is verified by applying it to well-known origami design problems. We compare the performance of the proposed approach to traditional gradient-based optimization techniques and genetic algorithm methods in terms of ability to discover designs and computational efficiency. BO has many user-defined components/parameters and intuitions for these for structural optimization are currently limited. In this work, we study the role of hyperparameter tuning and the sensitivity of Bayesian optimization to the quality and size of the initial training set. Taking a holistic view of the computational expense, we propose various heuristic approaches to reduce the overall cost of optimization. Our results show that Bayesian optimization is an efficient alternative to traditional methods. It allows for the discovery of optimal designs using fewer finite element solutions, which makes it an attractive choice for the non-convex design space of origami fold mechanics.