Predictions of the design decisions for vehicle alloy wheel rims using neural network

Abstract

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.