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Random Auxetic Porous Materials from Parametric Growth Processes

Jonàs Martínez 1
1 MFX - Matter from Graphics
LORIA - ALGO - Department of Algorithms, Computation, Image and Geometry, Inria Nancy - Grand Est
Abstract : We introduce a computational approach to optimize random porous materials via parametric growth processes. We focus on the problem of minimizing the Poisson's ratio of a two-phase porous random material, which results in an auxetic material. Initially, we perform a parametric optimization of the growth process. Afterward, the optimized parametric growth process implicitly generates an auxetic random material. Namely, the growth process intrinsically entails the formation of an auxetic material. Our approach enables the computation of large-scale auxetic random materials in commodity computers. We also provide numerical results indicating that the computed auxetic materials have close to isotropic linear elastic behavior and physical tests revealing auxetic behavior.
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https://hal.inria.fr/hal-02911788
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Submitted on : Monday, July 5, 2021 - 6:49:12 PM
Last modification on : Friday, April 1, 2022 - 3:57:15 AM

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Jonàs Martínez. Random Auxetic Porous Materials from Parametric Growth Processes. Computer-Aided Design, Elsevier, 2021, 139, ⟨10.1016/j.cad.2021.103069⟩. ⟨hal-02911788v3⟩

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