Role of multi-response principal component analysis in reliability-based robust design optimization: an application to commercial vehicle design

Abstract

The Taguchi method is a widely used conventional approach for robust design that combines experimental design with quality loss functions. However, this method can be only used in a single-response problem. In this study, we propose the use of principal component analysis (PCA) to consider multi-response problems in the Taguchi method and to investigate the influence factor of a cab suspension system. We compute the normalized quality loss for each response and perform PCA to calculate the multi-response performance index. In this study, control factors with three level combinations and noise factors with random sampling from each normal distribution are considered. Additionally, we applied multi-objective reliability based robust design optimization (RBRDO) to accommodate design uncertainties and its data scattering based on rational probabilistic approaches. This is used to develop the reliability assessment and reliability based design optimization and corresponds to an integrated method that accounts for the design robustness in the objective function and reliability in the constraints.