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.


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    Titel :

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


    Weitere Titelangaben:

    Optimierung der Radaufhängung eines Hochgeschwindigkeitszugs mit Hilfe eines neuronalen Netzwerks


    Beteiligte:
    Kim, Y.G. (Autor:in) / Park, C.K. (Autor:in) / Hwang, H.S. (Autor:in) / Park, T.W. (Autor:in)


    Erscheinungsdatum :

    2003


    Format / Umfang :

    9 Seiten, 13 Bilder, 3 Tabellen, 15 Quellen




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Print


    Sprache :

    Englisch




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