Deep Learning Approaches for Dynamic Mechanical Analysis of Viscoelastic Fiber Composites

2023/10/20 by Victor Hoffmann, Hoffmann, Victor, Ilias Nahmed +7
Engineering · #Artificial Intelligence (cs.AI) #Dynamics and Control of Mechanical Systems #FOS: Computer and information sciences #FOS: Physical sciences #Innovations in Concrete and Construction Materials #Machine Learning (cs.LG) #Materials Science (cond-mat.mtrl-sci) #Mechanical Engineering and Vibrations Research

paper · pdf · doi:10.48550/arxiv.2310.15188

openalex publication_date 2023/10/20 · openalex created_date 2023/10/21 · openalex updated_date 2026/07/28

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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