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Preprints, Working Papers, ... Year : 2023

Deep Learning Approaches for Dynamic Mechanical Analysis of Viscoelastic Fiber Composites

Abstract

The increased adoption of reinforced polymer (RP) composite materials, driven by eco-design standards, calls for a fine balance between lightness, stiffness, and effective vibration control. These materials are integral to enhancing comfort, safety, and energy efficiency. Dynamic Mechanical Analysis (DMA) characterizes viscoelastic behavior, yet there's a growing interest in using Machine Learning (ML) to expedite the design and understanding of microstructures. In this paper we aim to map microstructures to their mechanical properties using deep neural networks, speeding up the process and allowing for the generation of microstructures from desired properties.
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Dates and versions

hal-04250557 , version 1 (19-10-2023)

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Victor Hoffmann, Ilias Nahmed, Parisa Rastin, Guénaël Cabanes, Julien Boisse. Deep Learning Approaches for Dynamic Mechanical Analysis of Viscoelastic Fiber Composites. 2023. ⟨hal-04250557⟩
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