Automatic Determination of Fiber-Length Distribution in Composite Material Using 3D CT Data

Abstract

Determining fiber length distribution in fiber reinforced polymer components is a crucial step in quality assurance, since fiber length has a strong influence on overall strength, stiffness, and stability of the material. The approximate fiber length distribution is usually determined early in the development process, as conventional methods require a destruction of the sample component. In this paper, a novel, automatic, and nondestructive approach for the determination of fiber length distribution in fiber reinforced polymers is presented. For this purpose, high-resolution computed tomography is used as imaging method together with subsequent image analysis for evaluation. The image analysis consists of an iterative process where single fibers are detected automatically in each iteration step after having applied image enhancement algorithms. Subsequently, a model-based approach is used together with a priori information in order to guide a fiber tracing and segmentation process. Thereby, the length of the segmented fibers can be calculated and a length distribution can be deduced. The performance and the robustness of the segmentation method is demonstrated by applying it to artificially generated test data and selected real components.

Non-destructive automatic determination of aspect ratio and cross-sectional properties of fibres

“…The fibres may thus be partially hollow and partially solid, hindering the use of above methods. Algorithms introduced in [22][23][24] are based on analysis of the local orientation of fibres in order to separate them from each other, and thus seem to be better suited for fibres of irregular shape.…”

Evaluation of Computed Tomography Data from Fibre Reinforced Polymers to Determine Fibre Length Distribution

“…(Fu et al, 2002, Clarke et al, 1995 Recently, three dimensional methods based on X-ray tomography (CT) have also been developed. (Shen et al, 2004;Kastner et al, 2008b;Teßmann et al, 2010) Depending on the tomographic method used, the results can only be estimates or detailed analyses. Since data quality is determined by resolution as well as the appearance of artefacts, at the moment the best CT-data quality can be achieved by Synchrotron scanning (SCT).…”

Extraction of Curved Fibers From 3d Data

“…Alternative evaluation criteria could be e.g. the smallest gray-value, the variance of gray-values or a function introduced in Teßmann et al (2010) which takes advantage of the relation of cylindrical structures to the eigenvalues of its Hessian matrices and emphasize those voxel which exhibit the greatest likelihood of belonging to the center lines of fibers.…”

“…For example, the length and curvature of single fibers influence their mechanical strength, stiffness, and electrical resistance (Teßmann et al, 2010), where the arrangement and the form of fibers can be changed by the production process or as a result of degradation phenomena. To better understand the coherence of the microstructure of the considered material and the physical processes therein, the development of stochastic models describing the geometry of GDL is of great importance.…”

“…Therefore, in order to build an adequate single-fiber model, we first need to develop a segmentation algorithm which automatically extracts single fibers from tomographic 3D data. In the literature, some works on this topic has been done so far (Altendorf and Jeulin, 2009;Dokládal and Jeulin, 2009;Teßmann et al, 2010). In this paper, we propose another algorithm for singlefiber extraction which is developed in particular for densely packed systems of strongly curved fibers.…”