Dominique Jeulin

Morphological segmentation of FIB‐SEM data of highly porous media

Nanoporous materials play an important role in modern batteries as well as fuel cells. The materials microstructure needs to be analyzed as it determines the electrochemical properties. However, the microstructure is too fine to be resolved by microcomputed tomography. The method of choice to analyze the microstructure is focused ion beam nanotomography (FIB‐SEM). However, the reconstruction of the porous 3D microstructure from FIB‐SEM image data in general has been an unsolved problem so far. In this paper, we present a new method using morphological operations. First, features are extracted from the data. Subsequently, these features are combined to an initial segmentation, that is then refined by a constrained watershed transformation. We evaluate our method with synthetic data, generated by a simulation of the FIB‐SEM imaging process. We compare the ground truth in the simulated data to the segmentation result. The new method is found to produce a much smaller error than existing techniques.

Stochastic modeling of a glass fiber reinforced polymer

Stochastic modeling of a material microstructure is in general composed of multiple steps. First, geometric properties of the sample are measured by image analysis. Second, an appropriate stochastic model is chosen and model parameters are estimated from the geometric properties. Third, additional characteristics are computed on the data set and on realizations of stochastic models to evaluate the quality of the fitting. In this article, we show how to measure geometric properties of a fiber system, estimate parameters for two different fiber models, and evaluate the realizations with orientation covariance and tortuosity of the fibers. The considered stochastic models are the newly developed bended fiber model, composed of a force-biased packing of ball chains, and the classical cherry-pit cylinder model. We show the advantages and limitations of both methods.

Boolean Random Functions

The notion of Boolean random functions is considered which is a generalization of Boolean random closed sets. Their construction is based on the combination of a sequence of primary random functions by the operation ∨ (supremum) or ∧ (infimum), and their main properties (among which the supremum or infimum infinite divisibility) are given in the case of scalar random functions built on Poisson point processes. Examples of applications to the modeling of rough surfaces are given.

3d Modeling of Dense Packings of Bended Fibers

For the simulation of fiber systems, there exist several stochastic models: systems of straight non overlapping fibers, systems of overlapping bending fibers, or fiber systems created by sedimentation. However, there is a lack of models providing dense, non overlapping fiber systems with a given random orientation distribution and a controllable level of bending. We present in this paper the recently developed stochastic model that generalizes the force-biased packing approach to fibers represented as chains of balls. The starting configuration is a boolean system of fibers modeled by random walks, where two parameters in the multivariate von Mises-Fisher orientation distribution control the bending. The points of the random walk are associated with a radius and the current orientation. The resulting chains of balls are interpreted as fibers. The final fiber configuration is obtained as an equilibrium between repulsion forces avoiding crossing fibers and recover forces ensuring the fiber structure. This approach can provide high volume fractions up to 72 %. Furthermore, we study the efficiency of replacing the boolean system by a more intelligent placing strategy, before starting the packing process. Experiments show that a placing strategy is highly efficient for intermediate volume fraction.

Direct estimation of austenitic grain dimensions in heat affected zones of a martensitic steel from EBSD images

In the context of automated analyses of electron‐backscattered‐diffraction images, we present in this paper a novel method to automatically extract morphological properties of prior austenitic grains in martensitic steels based on raw crystallographic orientation maps. This quantification includes the estimation of the mean chord length in specific directions, with in addition the reconstruction of the mean shape of austenitic grains inducing anisotropic shape properties. The approach is based on the morphological measure of covariance on a decision curve of grain fidelity per disorientation angle. These efforts have been motivated by the need of realistic microstructures to perform micromechanical studies of grain boundary localized damage phenomenons in steels, one example being the type IV fracture phenomenon occurring in welded joints of grade P91/P92 steel. This failure is attributed to a change of the microstructure due to thermal gradients arising during the welding process. To precisely capture the relationships between microstructural changes and mechanical fields localization in a polycrystalline aggregate, we first need to achieve a reasonable stochastic model of its microstructure, which relies on a detailed knowledge of the microstructural morphology. As martensitic steels possess multiscale microstructures composed of prior austenitic grains, packets and laths, a relevant modelling strategy has to be proposed to account for the observed hierarchies. With this objective, this paper focuses on the larger scale entities present in the microstructure, namely, the austenitic grains.