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.
Keywords: Austenitic grains; EBSD images; covariance function; heat affected zones; martensite microstructure; size measure; steel P91 and P92.
© 2015 The Authors Journal of Microscopy © 2015 Royal Microscopical Society.