Multi-scale stochastic morphological models for 3D complex microstructures

The analysis of 3D images of complex materials, once imaging and reconstruction steps have been thoroughly done, can provide essential information. This analysis can be largely enhanced by using a modeling of the observed media, based on a reduced set of interpretable parameters. Besides, a common feature to many materials as diverse as concrete, rocks, bones, nanomaterials or heterogeneous catalysts is a multi-scale morphology with the meaning that specific morphological features exist at various length scales. Access to these different length scales' information is essential in order to understand and modelize these materials. This is a central point in the optimization of the usage properties of these materials such as mechanical strength or mass transport, which need a preliminary characterization of their morphology with the help of an adequate model. We propose here a modelization based on the so-called multi-scale Boolean models, models which have been successfully related to some usage properties, of primary importance for the design of new microstructures. These models are based on a reduced set of parameters related to interpretable material manufacturing settings. We illustrate the use of these models for the following tasks: representation of real multi-scale material like alumina catalyst supports, estimation of critical percolation threshold and assessment of tortuosity and accessibility. In addition, their efficient computing and visualization are addressed using ”plug im!”, a signal and image processing modular open access software.