Deep Learning Approaches for Dynamic Mechanical Analysis of Viscoelastic Fiber Composites

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...

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Bibliographic Details
Published in:arXiv.org 2023-10
Main Authors: Hoffmann, Victor, Nahmed, Ilias, Rastin, Parisa, Cabanes, Guénaël, Boisse, Julien
Format: Article
Language:English
Subjects:
Artificial neural networks
Deep learning
Design standards
Dynamic mechanical analysis
Fiber composites
Machine learning
Mechanical properties
Microstructure
Vibration control
Viscoelasticity
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Summary: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.
ISSN:2331-8422