This article presents the design process for generating a shell-like structure from an activated bent auxetic surface through an inductive process based on applying deep learning algorithms to predict a numeric value of geometrical features. The process developed under the Material Intelligence Workflow applied to the development of (1) a computational simulation of the mechanical and physical behaviour of an activated auxetic surface, (2) the generation of a geometrical dataset composed of six geometric features with 3,000 values each, (3) the construction and training of a regression Deep Neuronal Network (DNN) model, (4) the prediction of the geometric feature of the auxetic surface's pattern distance, and (5) the reconstruction of a new shell based on the predicted value. This process consistently reduces the computational power and simulation time to produce digital prototypes by integrating AI-based algorithms into material computation design processes.


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    Titel :

    Regression-Based Inductive Reconstruction of Shell Auxetic Structures


    Weitere Titelangaben:

    Computational Design and Robotic Fabrication


    Beteiligte:
    Yuan, Philip F. (Herausgeber:in) / Chai, Hua (Herausgeber:in) / Yan, Chao (Herausgeber:in) / Li, Keke (Herausgeber:in) / Sun, Tongyue (Herausgeber:in) / Vivanco, Tomás (Autor:in) / Ojeda, Juan Eduardo (Autor:in) / Yuan, Philip (Autor:in)

    Kongress:

    The International Conference on Computational Design and Robotic Fabrication ; 2022 June 27, 2022 - July 05, 2022



    Erscheinungsdatum :

    2023-04-04


    Format / Umfang :

    11 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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