Parameter Optimization of Off-Road Vehicle Frame Based on Sensitivity Analysis, Radial Basis Function Neural Network, and Elitist Non-dominated Sorting Genetic Algorithm

Abstract

The lightweight design of a vehicle can save manufacturing costs and reduce greenhouse gas emissions. For the off-road vehicle and truck, the chassis frame is the most important load-bearing assembly of the separate frame construction vehicle. The frame is one of the most assemblies with great potential to be lightweight optimized. However, most of the vehicle components are mounted on the frame, such as the engine, transmission, suspension, steering system, radiator, and vehicle body. Therefore, boundaries and constraints should be taken into consideration during the optimal process. The finite element (FE) model is widely used to simulate and assess the frame performance. The performance of the frame is determined by the design parameters. As one of the largest components of the vehicle, it has a lot of parameters. To improve the optimum efficiency, sensitivity analysis is used to narrow the range of the variables. In this paper, the frame of an off-road vehicle is analyzed and evaluated. Based on the concerning performance of the frame, and the sensitivity coefficient value, the thickness of some parts is chosen as the variables. The approximation model of the constraints, objectives, and variables is established by radial basis function neural network (RBF NN). The qualified stiffness coefficient is chosen as the constraint. The parameters of the new model are gained by the elitist non-dominated sorting genetic algorithm (NSGA-II). According to the comparison of the simulation result of the original and optimized model, the bend and torsional modal frequency are improved by 4.24% and 5.71%, respectively, while the mass of the vehicle frame is decreased by 6.13%. The optimal result can provide a better frame for the vehicle, and this method can be widely used in the design process of other vehicles.