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arXiv · 2501.16194

3D image based stochastic micro-structure modelling of foams for simulating elasticity

Anne Jung·Claudia Redenbach·Katja Schladitz·Sarah Staub
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Abstract

Image acquisition techniques such as micro-computed tomography are nowadays widely available. Quantitative analysis of the resulting 3D image data enables geometric characterization of the micro-structure of materials. Stochastic geometry models can be fit to the observed micro-structures. By alteration of the model parameters, virtual micro-structures with modified geometry can be generated. Numerical simulation of elastic properties in realizations of these models yields deeper insight on the influence of particular micro-structural features. Ultimately, this allows for an optimization of the micro-structure geometry for particular applications. Here, we present this workflow at the example of open cell foams. Applicability is demonstrated using an aluminum alloy foam sample. The structure observed in a micro-computed tomography image is modeled by the edge system of a random Laguerre tessellation generated by a system of closely packed spheres. Elastic moduli are computed in the binarized micro-CT image of the foam as well as in realizations of the model. They agree well with the results of a compression test on the real material.

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Anne Jung, Claudia Redenbach, Katja Schladitz, Sarah Staub. 2025-01-27. 3D image based stochastic micro-structure modelling of foams for simulating elasticity. https://doi.org/10.1007/978-3-031-04496-0_11

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