Optimization for truss design using Bayesian optimization
This work provides a baseline for AI-based optimization in expensive engineering domains, specifically finite element analysis, but is incremental as it applies an existing method to a new application area.
The authors tackled the problem of optimizing truss geometry for maximum load-bearing capacity without yielding to stress, using Bayesian optimization to efficiently handle computationally expensive finite element analysis evaluations, achieving a sample-efficient approach for design optimization.
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.