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.

翻译：本文提出了一种结合计算机辅助有限元分析的机械桁架几何优化方法。桁架形状是决定其承载能力的关键因素。在给定参数空间内，我们的目标是寻找船体外壳参数，使其在满足不产生屈服应力的前提下最大化承载能力。我们采用有限元分析这一计算成本较高的设计评估工具。针对此类高成本评估函数，我们选择贝叶斯优化作为优化框架，该框架在样本效率方面已被实证优于其他基于仿真的优化方法。通过应用贝叶斯优化算法，桁架设计过程涉及对候选设计方案进行迭代评估，并基于评估结果更新设计空间的概率模型。该模型用于预测各候选方案的性能，并通过平衡探索与利用的采集函数选择下一个候选方案。本研究结果可作为高成本工程领域（尤其是有限元分析）中基于人工智能的优化方法研究的基准。