Optimization for truss design using Bayesian optimization

TL;DR

This paper demonstrates the use of Bayesian optimization to efficiently design mechanical trusses by maximizing load capacity while minimizing stress, leveraging finite element analysis despite its high computational cost.

Contribution

It introduces a Bayesian optimization framework for truss design that reduces the number of expensive finite element analysis evaluations needed for optimal shape determination.

Findings

Bayesian optimization outperforms traditional methods in sample efficiency.

The approach effectively balances exploration and exploitation in the design space.

Results provide a baseline for AI-driven optimization in engineering applications.

Abstract

In this work, geometry optimization of mechanical truss using computer-aided finite element analysis is presented. The shape of the truss is a dominant factor in determining the capacity of load it can bear. At a given parameter space, our goal is to find the parameters of a hull that maximize the load-bearing capacity and also don't yield to the induced stress. We rely on finite element analysis, which is a computationally costly design analysis tool for design evaluation. For such expensive to-evaluate functions, we chose Bayesian optimization as our optimization framework which has empirically proven sample efficient than other simulation-based optimization methods. By utilizing Bayesian optimization algorithms, the truss design involves iteratively evaluating a set of candidate truss designs and updating a probabilistic model of the design space based on the results. The model is used to predict the performance of each candidate design, and the next candidate design is selected based on the prediction and an acquisition function that balances exploration and exploitation of the design space. Our result can be used as a baseline for future study on AI-based optimization in expensive engineering domains especially in finite element Analysis.