4.7 Article

Fiber identification of braided composites using micro-computed tomography

期刊

COMPOSITES COMMUNICATIONS
卷 27, 期 -, 页码 -

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ELSEVIER SCI LTD
DOI: 10.1016/j.coco.2021.100813

关键词

Braided composites; Micro-computed tomography; Fiber identification

资金

  1. Natural Sciences and Engineering Research Council (NSERC) Canada [RGPIN-2018-05899]

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Braided composites contain interwoven fibers embedded in a matrix material, requiring advanced measurement methods for accurate characterization. Micro-computed tomography (mu CT) was used for high-resolution imaging of braided composites, allowing identification and measurement of individual fibers within yarns. Through Avizo and XFiber analysis, properties such as fiber orientation angles and diameter were obtained, providing a roadmap for accurate modeling of braided composite unit cell geometries.
Braided composites contain interwoven fibers that are embedded in a matrix material. Advanced measurement methods are required to accurately measure and characterize braided composites due to their interwoven composition. Micro-computed tomography (mu CT) is an X-ray based measurement method that allows for the internal structure of objects to be examined. High-resolution mu CT of braided composites allows for their internal geometry to be accurately measured. Braid samples were measured with a voxel size of 1.0 mu m(3), which resulted in a field of view of 4.904 x 4.904 x 3.064 mm(3). With this field of view, individual fibers within the braid yarns could be identified and measured. The scientific visualization software package Avizo and the XFiber extension was used to identify and measure braid yarn fibers from the collected mu CT measurements. Fiber properties such as orientation angles (phi and theta), curved fiber length, tortuosity, and fiber diameter were obtained. Additionally, finite element mesh geometries of the braid yarns within a braided structure were created. The presented methodology provides a roadmap for the accurate modeling of braided composite unit cell geometries using high-resolution mu CT data.

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