Data mining techniques can be used to help chassis developers design and create suspension models and make tyre related decisions. Techniques for extracting information from large data sets are helpful tools for ensuring that chassis engineers can make the best possible decisions in the early development stages. The ability to repeatedly reslice and quickly zoom in and out of data sets in order to consider different aspects is an added advantage of having an organized data repository. Furthermore, data-based rapid model development techniques can automatically generate highly tuned models specific to current development needs. Data mining can also support the parameterisation process for simplified physical tyre models and even provide a solid foundation for initial predictive modelling without having to resort to a detailed, fully physical FEM model that can only be developed in conjunction with the tyre manufacturer. This paper largely serves as an introduction to the concepts. As more data and more models are generated, further research and development is also being conducted to fully explore the potential for applications of data mining to tyre data within the framework of chassis development.


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    Titel :

    Application of data mining techniques to tire data within the framework of chassis development


    Beteiligte:
    Harris, Mark (Autor:in) / Eckstein, Lutz (Autor:in)


    Erscheinungsdatum :

    2014


    Format / Umfang :

    16 Seiten, Bilder, 1 Quelle


    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




    Application of data mining techniques to tire data within the framework of chassis development

    Harris,M. / Eckstein,L. / Univ.Aachen,DE | Kraftfahrwesen | 2014



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