Phase segmentation of uncured prepreg X-Ray CT micrographs
Phase segmentation of uncured prepreg X-Ray CT micrographs
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DOI：
10.1016/j.compositesa.2021.106527
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发表时间：
2021-06-26
影响因子：
8.7
通讯作者：
Kratz, James
中科院分区：
文献类型：
--
作者：
Galvez-Hernandez, Pedro;Gaska, Karolina;Kratz, James
本文探讨的方法，以调查空隙和干纤维面积在预浸渍对齐碳纤维增强环氧树脂铺层。将深度学习分割方法与传统阈值技术进行比较，以识别通过对未固化层压板的样品进行微CT扫描获得的层间空隙（截留空气）和干燥区域（不饱和纤维床）相。这两种方法的性能进行了定量评估，在三个地区的利益具有不同程度的孔隙度，从低1%到高25%。深度学习在层间空隙和干燥区域的分割方面始终优于阈值。此外，深度学习提高了检测小空隙的能力，并且能够准确地分割空隙率小于2%的体积中的空隙，而阈值技术在这项任务中失败了。最后，将深度学习应用于显微CT扫描中干燥区域的分割，提供了比阈值更清晰的结果，而无需滤波。
This paper explores methods to investigate voids and dry fibre areas in pre-impregnated aligned carbon fibre reinforced epoxy lay-ups. A deep learning segmentation approach was compared to conventional thresholding techniques to characterise the interlaminar voids (entrapped air) and dry areas (unsaturated fibre bed) phases obtained by micro-CT scanning of samples from uncured laminates. The performance of both approaches was quantitatively assessed in three regions of interest having different levels of porosity, ranging from a low 1% to a high 25%. Deep learning consistently outperformed thresholding in the segmentation of both interlaminar voidage and dry areas. Furthermore, deep learning improved the ability to detect small voids and was able to accurately segment voids in volumes with less than 2% voidage, whereas thresholding techniques fail in this task. Finally, the application of deep learning to the segmentation of dry areas in micro-CT scans provided sharper results than thresholding, without needing filtering.