x射线计算机断层扫描碳纤维增强聚合物样品中编织和编织纤维束的视觉分类

Johannes Weissenböck , Arindam Bhattacharya , Bernhard Plank , Christoph Heinzl , Johann Kastner
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引用次数: 6

摘要

近年来,碳纤维增强聚合物(CFRP)等先进复合材料在许多应用领域(如汽车、航空和休闲工业)得到了应用。这些材料的特点是刚度和强度高,同时重量轻。特别是机织碳纤维增强材料由于其织物结构而具有优异的力学性能。为了分析和开发织物,了解单个纤维束的过程是很重要的。工业3D x射线计算机断层扫描(XCT)作为一种无损检测方法,可以分辨这些单独的纤维束。本文采用Bhattacharya等[6]的方法对两种新型CFRP试件进行纤维束提取,展示了我们的发现。一个标本包含三轴编织层在RTM6树脂和另一个标本编织双对角线层。此外,我们还展示了分离单个纤维束和计算单个纤维束特征所需的步骤,这对于后验视觉分析和探索是必不可少的。我们进一步展示了织物中单个纤维束的分类,以支持领域专家感知XCT扫描样本的编织结构。
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Visual classification of braided and woven fiber bundles in X-ray computed tomography scanned carbon fiber reinforced polymer specimens

In recent years, advanced composite materials such as carbon fiber reinforced polymers (CFRP) are used in many fields of application (e.g., automotive, aeronautic and leisure industry). These materials are characterized by their high stiffness and strength, while having low weight. Especially, woven carbon fiber reinforced materials have outstanding mechanical properties due to their fabric structure. To analyze and develop the fabrics, it is important to understand the course of the individual fiber bundles. Industrial 3D X-ray computed tomography (XCT) as a nondestructive testing method allows resolving these individual fiber bundles. In this paper, we show our findings when applying the method of Bhattacharya et al. [6] for extracting fiber bundles on two new types of CFRP specimens. One specimen contains triaxial braided plies in an RTM6 resin and another specimen woven bi-diagonal layers. Furthermore, we show the required steps to separate the individual bundles and the calculation of the individual fiber bundles characteristics which are essential for the posterior visual analysis and exploration. We further demonstrate the classification of the individual fiber bundles within the fabrics to support the domain experts in perceiving the weaving structure of XCT scanned specimens.

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Contents Editorial Board Editorial Board Contents Comparison of surface-based and image-based quality metrics for the analysis of dimensional computed tomography data
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