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Generation and Validation of Finite Element Models of Computed Tomography for Unidirectional Composites Using Supervised Learning-based Segmentation Techniques

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Authors

Kim, Taeyi; Jin, Seong-Won; Kim, Yeong-Bae; Lim, Jae Hyuk; Kim, Yunho

Issue Date
2023-12
Publisher
한국복합재료학회
Citation
Composites Research, Vol.36 No.6, pp.395-401
Abstract
In this study, finite element modeling of unidirectional composite materials of the computed tomography (CT) was conducted using a supervised learning-based segmentation technique. Firstly, Micro-CT scan was performed to obtain the raw volume of unidirectional composite materials, providing microstructure information. From the CT volume images, actual microstructure of the cross-section of unidirectional composite materials was extracted by the labeling process. Then, a U-net deep learning model was trained with a small number of raw images as inputs and their labeled images as outputs to generate a segmentation model. Subsequently, most of remaining images were input to the trained U-net deep learning model to segment all raw volume for identifying complex microstructure, which was used for the generation of finite element model. Finally, the fiber volume fraction of the finite element model was compared with that of experimentally measured volume to validate the appropriateness of the proposed method.
ISSN
2288-2103
URI
https://hdl.handle.net/10371/202303
DOI
https://doi.org/10.7234/composres.2023.36.6.395
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  • College of Engineering
  • Department of Aerospace Engineering
Research Area Smart composites, Space environments

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