Characterization of multilayer structures in fiber reinforced polymer employing synchrotron and laboratory X-ray CT

Abstract

Specimens of carbon or glass fiber reinforced polymer can be imaged using both conventional laboratory X-ray micro-computed tomography equipment and synchrotron light sources. The image quality when using intense (partially) coherent synchrotron light is still superior, especially when applying phase-retrieval algorithms. In the resulting volume images, the fiber direction distribution and other mechanically relevant parameters such as volume fractions or layer thickness can be determined. In this contribution, we will demonstrate how fiber direction results can be used to detect regions with locally different fiber orientations in carbon or glass fiber reinforced polymer which arise in the molding process of such samples. To this end, we evaluate the three-dimensional fiber orientation tensor locally across the thickness of different specimens. For each resulting individual layer, we can automatically detect the layer thickness and the preferred fiber direction. These methods have been successfully applied to various commercial specimens. We will demonstrate results on volume images of samples from both synchrotron and laboratory micro-computed tomography and discuss the specific advantages and disadvantages in this application.

Detecting anomalies in fibre systems using 3-dimensional image data

“…The images are provided by the Institute for Composite Materials (IVW) in Kaiserslautern, see Figure 11. For a detailed description of the material we refer to [50]. We apply the change point analysis from Section 4 to real data with 970 × 1469 × 1217 voxels and the estimated radius of 3 voxels.…”

Structure Detection With Second Order Riesz Transforms

“…The local fiber orientation analysis based on the eigenvalue analysis of the Gauss Hessian matrix has been successfully applied for various materials, e.g., by Heieck et al (2017); Sliseris et al (2016); Wirjadi et al (2014). However, application of this method requires the fiber thickness as an input parameter.…”

Endpoint Detection of Partially Overlapping Straight Fibers using High Positive Gaussian Curvature in 3D images

“…Their macroscopic mechanical properties are significantly affected by the geometric features of the reinforcing fibers. The fiber volume content and local fiber orientation distribution are already accessible non-destructively (Wirjadi et al, 2014). However, other relevant characteristics such as fiber length distribution can be measured only very roughly based on burning the sample and estimating the fiber length distribution from the 2D image of the remaining fibers on a slide .…”