근사모델을 이용한 강건최적설계의 자동차부품 적용 사례

Abstract

It is common to apply CAE when developing structural automobile parts. In order to predict the structural performance of an automobile part, more accurate simulation is required, which increases the size of the numerical model. In addition, the optimization technique is applied to reduce weight and improve performance. Recently, optimization methods using metamodels such as response surface method, kriging, and neural network have been widely used. In this study, kriging is adopted to predict the structural performance of automobile part. In addition, it is often unreasonable to suggest performance as a deterministic value when predicting structural performance through CAE. In this study, we introduce a robust optimization scheme that considers uncertainties, and apply it to the design of automobile parts. In this study, the robust optimizations for a control arm and a ball joint were performed respectively. The uncertainties in the tolerances and material property were taken into consideration, and the control arm was optimized for the lightweight design. On the contrary, and the optimum design of the ball joint was suggested considering the pull-out strength. As a result, each optimal solution is presented as a distribution rather than a deterministic value.