- 3.7Impact Factor
- 5.8CiteScore
- 17 daysTime to First Decision
Journal of Composites Science, Volume 8, Issue 10
October 2024 - 56 articles
Cover Story: This paper presents a fully convolutional neural network-based model that is capable of predicting stress fields over both the polymer and fiber components, substantially reducing the time required to evaluate the mechanical response of fiber-reinforced composites. The network predicts the stress field in 2D slices of tomography images of a fiber-reinforced polymer specimen. The trained model accurately captures stress distributions, especially around fibers, from microstructure images. Predictions take seconds on a laptop, compared to 92.5 hours for full finite element simulations on a high-performance cluster, offering a promising approach for fast structural analysis and damage site identification in fiber-reinforced composites. View this paper
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J. Compos. Sci. - ISSN 2504-477X