ESTIMATING FIBRE DIRECTION DISTRIBUTIONS OF REINFORCED COMPOSITES FROM TOMOGRAPHIC IMAGES

Authors

  • Oliver Wirjadi Fraunhofer ITWM
  • Katja Schladitz Fraunhofer ITWM
  • Prakash Easwaran Fraunhofer ITWM
  • Joachim Ohser Hochschule Darmstadt

DOI:

https://doi.org/10.5566/ias.1489

Keywords:

composites, fibre direction distribution, image analysis, orientation tensor, tomography

Abstract

Fibre reinforced composites constitute a relevant class of materials used chiefly in lightweight constructions for example in fuselages or car bodies. The spatial arrangement of the fibres and in particular their direction distribution have huge impact on macroscopic properties and, thus, its determination is an important topic of material characterisation. The fibre direction distribution is defined on the unit sphere, and it is therefore preferable to work with fully three-dimensional images of the microstructure as obtained, e.g., by computed micro-tomography. A number of recent image analysis algorithms exploit local grey value variations to estimate a preferred direction in each fibre point. Averaging these local results leads estimates of the volume-weighted fibre direction distribution. We show how the thus derived fibre direction distribution is related to quantities commonly used in engineering applications. Furthermore, we discuss four algorithms for local orientation analysis, namely those based on the response of anisotropic Gaussian filters, moments and axes of inertia derived from directed distance transforms, the structure tensor, or the Hessian matrix. Finally, the feasibility of these algorithms is demonstrated for application examples and some advantages and disadvantages of the underlying methods are pointed out.

Author Biographies

  • Katja Schladitz, Fraunhofer ITWM
    image processing department, research group 3D image analysis and modelling of microstructures, senior researcher
  • Prakash Easwaran, Fraunhofer ITWM
    image processing department, research group 3D image analysis and modelling of microstructures, PhD student
  • Joachim Ohser, Hochschule Darmstadt
    Fachbereich Mathematik und Naturwissenschaften, professor

References

Advani S, Tucker C (1987). The use of tensors to describe and predict fibre orientation in short fiber composites. J Rheology 31:751–84.

Altendorf H, Decenci`ere E, Jeulin D, Peixoto P, Deniset-Besseau A, Angelini E, Mosser G, Schanne-Klein MC (2012). Imaging and 3d morphological analysis of collagen fibrils. Journal of Microscopy 247:161–75.

Altendorf H, Jeulin D (2009). 3d directional mathematical morphology for analysis of fiber orientations. Image Analysis Stereology 28:143–53.

Altendorf H, Jeulin D (2011). Random-walk-based stochastic modeling of three-dimensional fiber systems. Phys Rev E 83:041804.

Bunge H (1993). On-line determination of texture-dependent materials properties. J Nondestructive Evaluation 12:3–11.

Clarke A, Archenhold G, Davidson N (1995). A novel technique for determining the 3D spatial distribution of glass fibres in polymer composites. Composites Science and Technology 55:75–91.

Eberhardt C, Clarke A (2001). Fibre-orientation measurements in short-glass-fibre composites. Part I: automated, high-angular-resolution measurement by confocal microscopy. Composites Science and Technology

:1389–400.

Eberly D, Gardner R, Morse B, Pizer S, Scharlach C (1994). Ridges for image analysis. J Mathematical Imaging and Vision 4:353–73.

Fisher N, Lewis T, Embleton B (1987). Statistical Analysis of Spherical Data. Cambridge, UK: Cambridge University Press.

Fliege J, Maier U (1999). The distribution of points on the sphere and corresponding cubature formulae. IMA J Numerical Analysis 19:317–34.

Frangi A, Niessen W, Vincken K, Viergever M (1998). Multiscale vessel enhancement filtering. In: Proc. Medical Image Computing and Computer-Assisted Intervention.

Hu MK (1962). Visual pattern recognition by moment invariants. IRE Transactions on Information Theory 8:179–87.

Kiderlen M, Pfrang A (2005). Algorithms to estimate the rose of directions of a spatial fibre system. J Microscopy 219:50–60.

Krause M, Hausherr J, Burgeth B, Herrmann C, Krenkel W (2010).

Determination of the fibre orientation in composites using the structure tensor and local x-ray transform. J Material Science 45:888–96.

Lampert C, Wirjadi O (2006). An optimal non-orthogonal separation of the anisotropic Gaussian convolution filter. IEEE Trans Image Processing 15:3501–13.

Lee J, Chung M, Alexander A (2006). Evaluation of anisotropic filters for diffusion tensor imaging. In: Proc. 3rd IEEE Int. Symp. Biomedical Imaging.

Lee Y, Lee S, Youn J, Chung K, Kang T (2002). Characterization of fiber orientation in short fiber reinforced composites with an image processing technique. Materials Research Innovations 6:65–72.

Mecke J, Nagel W (1980). Stationäre räumliche Faserprozesse und ihre Schnittzahlrosen. Elektron Informationsverarb Kyb 16:475–83.

Nagel W (1983). Dünne Schnitte von stationären räumlichen Faserprozessen. Math Operationsforsch Stat Ser Stat 14:569–76.

Ohser J, Schladitz K (2009). 3D Images of Materials Structures: Processing and Analysis. Weinheim: Wiley VCH.

Ohser J, Steinbach B, Lang C (1998). Efficient texture analysis of binary images. Journal of Microscopy 192:20–8.

Prokop RJ, Reeves AP (1992). A survey of moment-based techniques for unoccluded object representation and recognition. CVGIP Graph Models Image Process 54:438–60.

Riplinger M, Spiess M (2012). Asymptotic properties of the approximate inverse estimator for directional distributions. Advances in Applied Probability 44:954–76.

Robb K, Wirjadi O, Schladitz K (2007). Fiber orientation estimation from 3D image data: Practical algorithms, visualization, and interpretation. In: Proc. Int. Conf. Hybrid Intelligent Systems.

Rosenfeld A, Pfaltz JL (1966). Sequential operations in digital picture processing. J ACM 13:471–94.

Sandau K, Ohser J (2007). The chord length transform and the segmentation of crossing fibres. J Microscopy 226:43–53.

Tan J, Elliot J, Clyne T (2006). Analysis of tomography images of bonded fibre networks to measure distributions. Advanced Engineering Materials 8:495–500.

Tankyevych O, Talbot H, Dokladal P, Passat N (2009). Direction-adaptive grey-level morphology. Application to 3d vascular brain imaging. In: Proc. 16th IEEE International Conference on Image Processing (ICIP).

Weickert J (1999). Coherence-enhancing diffusion filtering. International Journal of Computer Vision 31:111–27.

Wirjadi O (2009). Models and Algorithms for Image-Based Analysis of Microstructures. Ph.D. thesis, Technische Universität Kaiserslautern.

Wirjadi O, Schladitz K, Rack A, Breuel T (2009). Applications of anisotropic image filters for computing 2d and 3d-fiber orientations. In: Proc. 10th Europ. Congr. Stereology and Image Analysis.

Yang H, Lindquist B (2000). Three-dimensional image analysis of fibrous materials. In: Proc. SPIE Applications of Digital Image Processing XXIII, vol. 4115.

Young I, van Vliet L (1995). Recursive implementation of the Gaussian filter. Signal Processing 44:139–51.

Zhu YT, Blumenthal WR, Lowe TC (1997). Determination of non-symmetric 3-d fiber-orientation distribution and average fiber length in short-fiber composites. Journal of Composite Materials 31:1287–301.

Downloads

Published

2016-12-08

Issue

Section

Original Research Paper

How to Cite

Wirjadi, O., Schladitz, K., Easwaran, P., & Ohser, J. (2016). ESTIMATING FIBRE DIRECTION DISTRIBUTIONS OF REINFORCED COMPOSITES FROM TOMOGRAPHIC IMAGES. Image Analysis and Stereology, 35(3), 167-179. https://doi.org/10.5566/ias.1489

Most read articles by the same author(s)

<< < 1 2 3 > >>