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Application of X-ray computed tomography for the virtual permeability prediction of fiber reinforcements for liquid composite molding processes: A review

期刊

COMPOSITES SCIENCE AND TECHNOLOGY
卷 184, 期 -, 页码 -

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.compscitech.2019.107828

关键词

Liquid composite molding; Permeability; Compaction; Micro CT

资金

  1. Khalifa University of Science and Technology

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X-ray computed tomography (XCT) combined with computer simulations have proved to be an extremely powerful and versatile tool for material characterization in recent years. The use of XCT for measuring reinforcement permeability in the liquid composite molding (LCM) processes is a topic of great interest. This is mainly because current LCM characterization approaches involve costly, time-consuming, and tedious experimental procedures. Existing numerical permeability computation procedures use geometric models of the reinforcements that do not always capture either the realistic fiber architectures or the deformations associated with the compaction process. CT-scans can extract information that can simultaneously yield the compaction response and permeability values utilizing a single sample, potentially saving substantial labor and material costs. Herein, we present a detailed review outlining how the XCT system can be used as a process characterization tool to gather useful and high quality 3D images which then can be used to generate computational models to determine both the compaction response and the virtual permeability of complex fiber reinforcements for LCM processes. This article also reviews current types of equipment, X-ray power requirements, voxel sizes, resolutions, and unit cell size effects on permeability computation. The aspects relating to microstructural characteristics, such as tow geometry changes, inter and intra tow gap variations during compaction, which have direct influence on reinforcement permeability, are also discussed. This paper also highlights key limitations associated with the permeability predictions faced at various stages and identifies where improvements can be made.

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