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

TL;DR

This paper presents a workflow combining 3D imaging, stochastic modeling, and numerical simulation to analyze and optimize the elastic properties of foam micro-structures, demonstrated on aluminum alloy foam samples.

Contribution

It introduces a method to generate virtual foam micro-structures from micro-CT data using stochastic geometry models and validates elastic property predictions against experimental data.

Findings

Model realizations match experimental elastic moduli

Stochastic models effectively replicate foam micro-structure

Simulation insights enable micro-structure optimization

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.