Together with researchers from PSI Paul Scherrer Institut, ETH Zürich and DTU Wind and Energy Systems our team at Chalmers University Technology have developed an accurate image-based finite element model of a carbon fibre reinforced composite based on tensor-tomography data with a voxel-size of 100 μm, approximately 15 times the fibre diameter. Compared to standard approaches the technique could increase the scanned volume by up to six orders of magnitude.

You find the paper recently published in npj Computational Materials via this link: X-ray scattering tensor tomography based finite elementmodelling of heterogeneous materials

The work was part of Dr Robert Auenhammer's thesis work at Chalmers University of Technology. He was shared first-author with Dr Jisoo Kim at KRISS, South Korea.

Abstract:

Among micro-scale imaging technologies of materials, X-ray micro-computed tomography has evolved as most popular choice, even though it is restricted to limited field-of-views and long acquisition times. With recent progress in small-angle X-ray scattering these downsides of conventional absorption-based computed tomography have been overcome, allowing complete analysis of the micro-architecture for samples in the dimension of centimetres in a matter of minutes. These advances have been triggered through improved X-ray optical elements and acquisition methods. However, it has not yet been shown how to effectively transfer this novel type of image data into a numerical model capable of accurately predicting the actual material properties. Here, a method is presented to numerically predict mechanical properties of a carbon fibre-reinforced polymer based on imaging data with a voxel-size of 100 μm corresponding to approximately fifteen times the fibre diameter. This extremely low resolution requires a completely new way of constructing the material's constitutive law based on the fibre orientation, the X-ray scattering anisotropy, and the X-ray scattering intensity. The proposed method combining the advances in X-ray imaging and the novel material model opens for an accurate tensile modulus prediction for volumes of interest between three to six orders of magnitude larger than those conventional carbon fibre orientation image-based models can cover.