The weight and modal performance of the vehicle wheels are two essential factors that affect the driving comfort of a vehicle. The main objective of this study is to present an efficient approach to reduce the weight and enhance the modal performance of the wheel by reducing the design time and computational cost. The alloy wheel rim is often used for lightweight wheel design. In this study, an approach is presented for the lightweight design of alloy wheel rims. An intelligent approach based on neural networks (NNs) is introduced to predict the optimum design parameters of the wheel rim during the wheel design phase and to improve the wheel optimization process. The Latin hypercube and Hammersley designs of the experimental methods were used to obtain a training dataset with finite element analysis. The NN and multiple linear regression (MLR) models were trained to predict the weight, first-mode frequency, and displacement values. A multi-objective genetic algorithm was employed to optimize the design decisions based on the predicted values. It was used to compute the optimum results with both the NN and MLR models for a better prediction accuracy of the wheel rim design parameters. The proposed approach allows designers to optimize design decisions and evaluate design modifications during the early stages of the wheel development phase. The surrogate-based optimization method plays an important role in the wheel rim optimization process, particularly when the optimization model is established based on computationally expensive finite element simulations, testing, and prototypes. The results show the effectiveness of the NN-combined genetic optimization approach in predicting the responses and optimizing the design decisions for the alloy wheel rim design by reducing engineering time and computational cost.

车轮的重量和模态性能是影响车辆驾驶舒适性的两个重要因素。本研究的主要目的是提出一种有效的方法，通过减少设计时间和计算成本来减轻车轮的重量并提高车轮的模态性能。合金轮辋常用于轻量化车轮设计。在这项研究中，提出了一种合金轮辋的轻量化设计方法。引入了一种基于神经网络 (NNs) 的智能方法，用于在车轮设计阶段预测轮辋的最佳设计参数并改进车轮优化过程。实验方法的拉丁超立方体和 Hammersley 设计用于获得有限元分析的训练数据集。NN 和多元线性回归 (MLR) 模型经过训练以预测重量、第一模式频率和位移值。采用多目标遗传算法根据预测值优化设计决策。它用于计算 NN 和 MLR 模型的最佳结果，以更好地预测轮辋设计参数的准确性。所提出的方法允许设计人员在车轮开发阶段的早期阶段优化设计决策并评估设计修改。基于代理的优化方法在轮辋优化过程中发挥着重要作用，特别是在基于计算昂贵的有限元模拟、测试和原型建立优化模型时。