Publications
Detailed Information
Generation and Validation of Finite Element Models of Computed Tomography for Unidirectional Composites Using Supervised Learning-based Segmentation Techniques
Cited 0 time in
Web of Science
Cited 0 time in Scopus
- Authors
- 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
- Files in This Item:
- There are no files associated with this item.
Item View & Download Count
Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.