Abstract
Design optimization has been performed for the suspension system of high speed train. Neural network and design of experiment (DOE) have been employed to build a meta-model for the system with 29 design variables and 46 responses. A combination of fractional factorial design and D-optimality design was used as an approach to DOE in order to reduce the number of experiments to a more practical level. As a result, only 66 experiments were enough. The 46 responses were divided into four performance index groups such as ride comfort, derailment quotient, unloading ratio and stability index. Four meta-models for each index group were constructed by use of neural network. For the learned meta-models, multi-criteria optimization was achieved by differential evolution. The results show that the proposed methodology yields a highly improved design in the ride comfort, unloading ratio and stability index.
