This work introduces a new design algorithm to optimize progressively folding thin-walled structures and in order to improve automotive crashworthiness. The proposed design algorithm is composed of three stages: conceptual thickness distribution, design parameterization, and multi-objective design optimization. The conceptual thickness distribution stage generates an innovative design using a novel one-iteration compliant mechanism approach that triggers progressive folding even on irregular structures under oblique impact. The design parameterization stage optimally segments the conceptual design into a reduced number of clusters using a machine learning K-means algorithm. Finally, the multi-objective design optimization stage finds non-dominated designs of maximum specific energy absorption and minimum peak crushing force. The proposed optimization problem is addressed by a multi-objective genetic algorithm on sequentially updated surrogate models, which are optimally selected from a set of 24 surrogates. The effectiveness of the design algorithm is demonstrated on an S-rail thin-walled structure. The best compromised Pareto design increases specific energy absorption and decreases peak crushing force in the order of 8% and 12%, respectively.


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    Titel :

    Thin-Walled Compliant Mechanism Component Design Assisted by Machine Learning and Multiple Surrogates


    Weitere Titelangaben:

    Sae Technical Papers


    Beteiligte:
    Tovar, Andres (Autor:in) / Detwiler, Duane (Autor:in) / Nutwell, Emily (Autor:in) / Liu, Kai (Autor:in)

    Kongress:

    SAE 2015 World Congress & Exhibition ; 2015



    Erscheinungsdatum :

    2015-04-14




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch





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