Design optimization for suspension system of high speed train using neural network

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.