EXTRACTION OF CURVED FIBERS FROM 3D DATA
DOI:
https://doi.org/10.5566/ias.v32.p57-63Keywords:
fiber extraction, synchrotron tomography, stochastic model, fiber-based material, non-wovenAbstract
A segmentation algorithm is proposed which automatically extracts single fibers from tomographic 3D data of fiber-based materials. As an example, the algorithm is applied to a non-woven material used in the gas diffusion layer of polymer electrolyte membrane fuel cells. This porous material consists of a densely packed system of strongly curved carbon fibers.Our algorithm works as follows. In a first step, we focus on the extraction of skeletons, i.e., center lines of fibers. Due to irregularities like noise or other data artefacts, it is only possible to extract fragments of center lines. Thus, in a second step, we consider a stochastic algorithm to adequately connect these parts of center lines to each other, with the general aim to reconstruct the complete fibers such that the curvature properties of real fibers are reflected correctly. The quality of the segmentation algorithm is validated by applying it to simulated test data.
References
Altendorf H, Jeulin, D (2009). Fiber separation
from local orientation and probability maps. In:
Wilkinson M, Roerdink J, eds.
Abstract book of the 9th Int. Symp on Math. Morph., 3336.
University of Groningen, The Netherlands.
Burger W, Burge, MJ (2007). Digital Image
Processing: An Algorithmic Introduction Using
Java. Berlin: Springer.
Dokldal P, Jeulin D (2009). 3-D Extraction of
Fibres from Microtomographic Images of Fibre-
Reinforced Composite Materials. Mathematical
Morphology and Its Application to Signal and
Image Processing, Lecture Notes in Computer
Science, Volume 5720.
Springer Berlin Heidelberg, 2009, p. 12.
Gaiselmann G, Thiedmann R, Manke I, Lehnert
W, Schmidt V (2012). Stochastic 3D modeling
of fiber-based materials. Computational Materials
Science 59, 75-86.
Gaiselmann G, Froning D, Tötzke C, Quick C,
Manke I, Lehnert W, Schmidt V (2012). Stochastic
D modeling of non-woven materials with wetproofing
agent. Preprint (submitted).
Hartnig C, Jörissen L, Kerres J, Lehnert W, Scholta
J (2008). Polymer electrolyte membrane fuel cells
(PEMFC). In: M. Gasik (ed.), Materials for Fuel
Cells. Cambridge: Woodhead Publishing, 101–184.
Hoshen J, Kopelman R (1976). Percolation and
cluster distribution. I. Cluster multiple labeling
technique and critical concentration algorithm.
Physical Review B 14, 3438–3445.
Mathias MF, Roth J, Fleming J, Lehnert, W (2003).
Diffusion Media materials and characterisation. In:
W. Vielstich, A. Lamm and H. Gasteiger (eds.),
Handbook of Fuel Cells. London: J.Wiley & Sons,
-537.
Soille P (2003). Morphological Image Analysis:
Principles and Applications. 2nd ed., Berlin:
Springer.
Teßmann M, Mohr S, Gayetsky S, Hassler U, Hanke
R, Greiner G (2010). Automatic Determination of
Fiber-Length Distribution in Composite Material
Using 3D CT Data. EURASIP Journal on
Advances in Signal Processing 2010, Article ID
Thiedmann R, Fleischer F, Lehnert W, Schmidt V
(2008). Stochastic 3D modelling of the GDL
structure in PEM fuel cells, based on thin section
detection. Journal of the Electrochemical Society
, B391–B399.
