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Segmentation of openings in non-crimp fabric using deep learning


Citation

Md Ali, Syaril Azrad and Norhellme, Nornajme and Ahmad Rashidi, Syazwan and Mahmud Zuhudi, Nurul Zuhairah and Abd Aziz, Noor Zuhaira (2024) Segmentation of openings in non-crimp fabric using deep learning. Journal of Aeronautics, Astronautics and Aviation, 57 (3S). pp. 1-10. ISSN 1990-7710

Abstract

Non-Crimp Fabrics (NCF) are widely used in industries like aerospace due to their lightweight and high-strength properties. Accurate characterization of openings in NCF is essential for maintaining material quality and performance. Traditional machine vision methods, such as histogram-based and edge detection approaches, often struggle with complex NCF patterns and rely heavily on manual feature engineering. This study applies deep learning models U-Net, LinkNet, and DeepLabv3+ for the binary segmentation of openings in carbon fiber NCF. These models use a ResNet18 encoder, pre-trained on ImageNet, to automatically extract robust features and improve generalization. To address data limitations, we augmented the collected NCF dataset to increase variability. The results show that U-Net, LinkNet, and DeepLabv3+ achieved Intersection over Union (IoU) scores of 0.7419, 0.7228, and 0.7172, respectively, demonstrating their effectiveness in segmenting openings in NCF. This study presents a method that enhances segmentation accuracy and improves ability to generalize across varying data conditions, providing a more adaptable approach to NCF characterization with potential applications for other advanced materials.


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Additional Metadata

Item Type: Article
Divisions: Faculty of Engineering
Publisher: Aeronautical and Astronautical Society of the Republic of China
Keywords: Deep learning; Non-crimp fabrics; Fiber-reinforced composite; Convolutional neural networks; Composite quality control; Transfer learning
Depositing User: Ms. Che Wa Zakaria
Date Deposited: 08 Jul 2025 02:24
Last Modified: 08 Jul 2025 02:24
URI: http://psasir.upm.edu.my/id/eprint/117868
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