Microstructure characterization and stochastic modeling of open-cell foam based on μCT-image analysis

Foam is a cellular material whose mechanical properties are strongly determined by its complex microstructure. To study the microstructure, at first a foam characterization based on $\mu$CT image processing is required. Here we present an image segmentation procedure and determine the foam's characteristics using the lattice cell-based concept of intrinsic volumes. Information like porosity, pore size distribution, and ligament shape are derived. These data are then employed as input for the generation of stochastic foam volume elements with the corresponding morphology. The introduced microstructural characterization and foam generation procedures are validated by an inverse analysis, that is, by a $\mu$CT image analysis of the stochastic foam volume element. Additionally, an example investigation of industrial polyurethane foam proves the concepts.