Although the functionality and performance are both important aspects in vehicle design, an automotive form is a crucial factor in determining a consumer's image perception and purchase decision. Hence, an effective tool for designing successful automotive form was suggested. Based on Kansei engineering principles, the relationship between the profile chracteristics and the consumer's image perception is established using a Back-Propagation Neural (BPN) network. A Computer Aided Design (CAD) tool, which uses the trained BPN to predict the consumer perception of an automobile profile expressed in the form of a numerical definition, is constructed using Visual Basic software. The performance of the CAD prototype tool is verified by comparing its predictions to the actual consumer perception evaluations. A good similarity is identified between the two sets of results. Therefore, the developed tool provides designers with powerful means of creating automotive designs from a consumers image perception perspective.


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    Titel :

    A neural network-based computer aided design tool for automotive form design


    Weitere Titelangaben:

    Neuronalnetzbasiertes, rechnergestütztes Design-Tool für den Entwurf von Automobilform


    Beteiligte:
    Chang, Yu-Ming (Autor:in) / Chen, Hung-Yuan (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2007


    Format / Umfang :

    14 Seiten, 8 Bilder, 4 Tabellen, 30 Quellen




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Print


    Sprache :

    Englisch




    A neural network-based computer aided design tool for automotive form design

    Chang,Y.M. / Chen,H.Y. / National Cheng Kung Univ.,TW | Kraftfahrwesen | 2007



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