Designing MacPherson Suspension Architectures using Bayesian Optimization

TL;DR

This paper introduces a Bayesian optimization framework to automate the design of MacPherson suspension architectures, reducing time and cost by efficiently optimizing complex, high-dimensional, non-linear design parameters without requiring gradient information.

Contribution

The paper presents a novel Bayesian optimization approach with a two-tier convergence criterion for automating complex suspension design, demonstrated on an industry-relevant vehicle chassis problem.

Findings

Efficiently finds optimal suspension designs with fewer simulations.

Scalable and generalizable to high-dimensional design problems.

Convergence criteria are straightforward to implement in existing software.

Abstract

Engineering design is traditionally performed by hand: an expert makes design proposals based on past experience, and these proposals are then tested for compliance with certain target specifications. Testing for compliance is performed first by computer simulation using what is called a discipline model. Such a model can be implemented by a finite element analysis, multibody systems approach, etc. Designs passing this simulation are then considered for physical prototyping. The overall process may take months, and is a significant cost in practice. We have developed a Bayesian optimization system for partially automating this process by directly optimizing compliance with the target specification with respect to the design parameters. The proposed method is a general framework for computing a generalized inverse of a high-dimensional non-linear function that does not require e.g. gradient information, which is often unavailable from discipline models. We furthermore develop a two-tier convergence criterion based on (i) convergence to a solution optimally satisfying all specified design criteria, or (ii) convergence to a minimum-norm solution. We demonstrate the proposed approach on a vehicle chassis design problem motivated by an industry setting using a state-of-the-art commercial discipline model. We show that the proposed approach is general, scalable, and efficient, and that the novel convergence criteria can be implemented straightforwardly based on existing concepts and subroutines in popular Bayesian optimization software packages.