X-ray scattering tensor tomography based finite element modelling of heterogeneous materials

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作者
Robert M. Auenhammer
Jisoo Kim
Carolyn Oddy
Lars P. Mikkelsen
Federica Marone
Marco Stampanoni
Leif E. Asp
机构
[1] Chalmers University of Technology,Material and Computational Mechanics, Department of Industrial and Materials Science
[2] Technical University of Denmark,Composites Manufacturing and Testing, Department of Wind and Energy Systems
[3] University and ETH Zürich,Institute for Biomedical Engineering
[4] Paul Scherrer Institut,Swiss Light Source
[5] GKN Aerospace Sweden,Department of Automation and Composite Technologies
[6] Korea Research Institute of Standards and Science,Advanced Instrumentation Institute
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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 small-angle X-ray scattering 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 presented 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.
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