Tsarouchas, D and Markaki, AE (2011) Extraction of fibre network architecture by X-ray tomography and prediction of elastic properties using an affine analytical model. Acta Materialia, 59. pp. 6989-7002. ISSN 1359-6454Full text not available from this repository.
This paper proposes a method for extracting reliable architectural characteristics from complex porous structures using micro-computed tomography (μCT) images. The work focuses on a highly porous material composed of a network of fibres bonded together. The segmentation process, allowing separation of the fibres from the remainder of the image, is the most critical step in constructing an accurate representation of the network architecture. Segmentation methods, based on local and global thresholding, were investigated and evaluated by a quantitative comparison of the architectural parameters they yielded, such as the fibre orientation and segment length (sections between joints) distributions and the number of inter-fibre crossings. To improve segmentation accuracy, a deconvolution algorithm was proposed to restore the original images. The efficacy of the proposed method was verified by comparing μCT network architectural characteristics with those obtained using high resolution CT scans (nanoCT). The results indicate that this approach resolves the architecture of these complex networks and produces results approaching the quality of nanoCT scans. The extracted architectural parameters were used in conjunction with an affine analytical model to predict the axial and transverse stiffnesses of the fibre network. Transverse stiffness predictions were compared with experimentally measured values obtained by vibration testing. © 2011 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.
|Uncontrolled Keywords:||Elastic properties Fibre networks Orientation Porosity X-ray tomography|
|Divisions:||Div C > Materials Engineering|
Div C > Biomechanics
|Depositing User:||Cron Job|
|Date Deposited:||07 Mar 2014 11:22|
|Last Modified:||26 Jan 2015 03:35|