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Parametric Reconstruction of Glass Fiber-reinforced Polymer Composites from X-ray Projection Data-A Simulation Study.
Elberfeld, Tim; De Beenhouwer, Jan; den Dekker, Arnold J; Heinzl, Christoph; Sijbers, Jan.
Afiliação
  • Elberfeld T; 1imec-VisionLab, Department of Physics, University of Antwerp, Universiteitsplein 1, 2610 Antwerpen, Belgium.
  • De Beenhouwer J; 1imec-VisionLab, Department of Physics, University of Antwerp, Universiteitsplein 1, 2610 Antwerpen, Belgium.
  • den Dekker AJ; 1imec-VisionLab, Department of Physics, University of Antwerp, Universiteitsplein 1, 2610 Antwerpen, Belgium.
  • Heinzl C; 2Delft Center for Systems and Control, Delft University of Technology, Mekelweg 2, 2628 CD Delft, The Netherlands.
  • Sijbers J; 3Research Group X-Ray Computed Tomography, University of Applied Sciences Upper Austria, Stelzhamerstrasse 23, 4600 Wels, Austria.
J Nondestr Eval ; 37(3): 62, 2018.
Article em En | MEDLINE | ID: mdl-30636823
ABSTRACT
We present a new approach to estimate geometry parameters of glass fibers in glass fiber-reinforced polymers from simulated X-ray micro-computed tomography scans. Traditionally, these parameters are estimated using a multi-step procedure including image reconstruction, pre-processing, segmentation and analysis of features of interest. Each step in this chain introduces errors that propagate through the pipeline and impair the accuracy of the estimated parameters. In the approach presented in this paper, we reconstruct volumes from a low number of projection angles using an iterative reconstruction technique and then estimate position, direction and length of the contained fibers incorporating a priori knowledge about their shape, modeled as a geometric representation, which is then optimized. Using simulation experiments, we show that our method can estimate those representations even in presence of noisy data and only very few projection angles available.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: J Nondestr Eval Ano de publicação: 2018 Tipo de documento: Article País de afiliação: Bélgica

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: J Nondestr Eval Ano de publicação: 2018 Tipo de documento: Article País de afiliação: Bélgica