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.
Regression-Based Inductive Reconstruction of Shell Auxetic Structures
Computational Design and Robotic Fabrication
The International Conference on Computational Design and Robotic Fabrication ; 2022 June 27, 2022 - July 05, 2022
2023-04-04
11 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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