TY - JOUR
T1 - Parametric Reconstruction of Glass Fiber-reinforced Polymer Composites from X-ray Projection Data—A Simulation Study
AU - Elberfeld, Tim
AU - De Beenhouwer, Jan
AU - den Dekker, Arnold J.
AU - Heinzl, Christoph
AU - Sijbers, Jan
PY - 2018/9/1
Y1 - 2018/9/1
N2 - 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.
AB - 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.
KW - GFRP
KW - Glass fiber reinforced polymer
KW - Materials science
KW - Modeling of micro-structures
KW - Parametric model
KW - Tomography
KW - μ CT
UR - http://www.scopus.com/inward/record.url?scp=85050729929&partnerID=8YFLogxK
U2 - 10.1007/s10921-018-0514-0
DO - 10.1007/s10921-018-0514-0
M3 - Article
SN - 0195-9298
VL - 37
JO - Journal of Nondestructive Evaluation
JF - Journal of Nondestructive Evaluation
IS - 3
M1 - 62
ER -