Non-destructive characterization of fiber orientation in reinforced SMC as input for simulation based design. (15th January 2017) - Schladitz, Katja; Büter, Andreas; Godehardt, Michael; Wirjadi, Oliver; Fleckenstein, Johanna; Gerster, Tobias; Hassler, Ulf; Jaschek, Katrin; Maisl, Michael; Maisl, Ute; Mohr, Stefan; Netzelmann, Udo; et al.
Abstract: The macroscopic properties of materials are strongly influenced by their microstructure. This holds in particular for fiber reinforced composites where fiber distribution and orientation are crucial for the reinforcement to serve its purpose. This essential microstructural information can be obtained from high-resolution images using appropriate methods for quantitative image analysis. Sheet molding compounds feature a very dense layered system of reinforcing fibers and a particularly strong X-ray absorption. Therefore, in this case, state-of-the-art fiber orientation analysis based on 3D images obtained by X-ray microtomography faces problems. In this paper, we determine the local fiber orientation in each pixel by the orientation of the anisotropic Gaussian filter yielding the strongest filter response. Hence, the local fiber orientation can be computed without identifying individual fibers. From the thus determined area weighted orientation distribution, the degree of anisotropy and the main fiber orientation are derived. This extremely robust analysis method is applied to 2D slice images from scanning acoustic microscopy and high-resolution 3D microtomography. We show that the Gaussian filter based fiber orientation analysis method yields comparable results for both imaging techniques. Moreover, comparison with fatigue tests performed on the same specimens proves the image analytically determined fiber orientation and the failure behaviour to be strongly correlated. In particular, a critical degree of anisotropy could be identified. For degrees of anisotropy higher than this limit, the samples behave mechanically like a uniaxial material. The paper thus provides experimentally validated evidence for calibrating micro-mechanical models for subsequent simulation of macroscopic material properties using the combination of high-resolution imaging techniques and quantitative image analysis.