An efficient simulation-based search method for reliability-based robust design optimization of mechanical components

Reliability-based robust design optimization (RBRDO) aims to minimize the variation in the system, and ensure the levels of failure probability of the system. Despite significant improvements on RBRDO, several challenges have been emerging. First, the existing implementations of RBRDO are complex to apply them to design problems. Second, an efficient method of optimum search is needed to enhance the RBRDO process. To address these issues, in this work, a simulation-based search method for RBRDO is proposed by utilizing Monte-Carlo Simulation and Artificial Neural Network. Specifically, to accurately select an optimum searching direction and step lengths, a search vector based on correlation coefficients between design variables and responses is put forward. This proposed method is applied to the design of a car handle to show its effectiveness and efficacy. Results demonstrates that this method enables to efficiently and effectively find reliable and robust designs under uncertainty compared to the deterministic case.