Design Optimization of Alloy Wheels Based on a Dynamic Cornering Fatigue Test Using Finite Element Analysis and Multi-Additional Sampling of Efficient Global Optimization

Abstract

An alloy wheel is generally a symmetrically shaped part integral to a vehicle because its weight and strength can improve driving performance. Therefore, alloy wheel design is essential, and a novel design method should be considered. Currently, the Multi-Additional Sampling Efficient Global Optimization (MAs-EGO) has been proposed and widely implemented in various fields of engineering design. This study employed a surrogate model to maximize Expected Hypervolume Improvement (EHVI) for multi-objectives by increasing multi-sampling per iteration to update a surrogate model and evaluate an optimal point for alloy wheel design. Latin Hypercube Sampling (LHS) was used to generate an initial design of an alloy wheel, including the thickness and width of the spoke wheel. The maximum principal stress according to the dynamic cornering fatigue simulation was then evaluated for risk of failure using Finite Element (FE) analysis. The objectives were to minimize both the principal stress and weight of the symmetric alloy wheel. The Kriging method was used to construct a surrogate model, including a Genetic Algorithm (GA), which was performed to maximize hypervolume improvement to explore the next additional sampling point, and that point was also an optimal point for the process when computation had converged. Finally, FE results were validated through a designed apparatus to confirm the numerical solution. The results exhibit thatMulti-Additional Sampling Efficient Global Optimization can achieve an optimal alloy shape. The maximum principal stress distribution occurs in the spoke area and exhibits a symmetrical pattern around the axis following the cyclic bending load. The optimal design point of the alloy wheel can reduce 20.181% and 3.176% of principal stress and weight, respectively, compared to the initial design. The experimental results are consistent trend in the same direction as FEA results.