  Integrated computational materials engineering (ICME) has significantly
enhanced the systemic analysis of the relationship between microstructure and
material properties, paving the way for the development of high-performance
materials. However, analyzing microstructure-sensitive material behavior
remains challenging due to the scarcity of three-dimensional (3D)
microstructure datasets. Moreover, this challenge is amplified if the
microstructure is anisotropic, as this results in anisotropic material
properties as well. In this paper, we present a framework for reconstruction of
anisotropic microstructures solely based on two-dimensional (2D) micrographs
using conditional diffusion-based generative models (DGMs). The proposed
framework involves spatial connection of multiple 2D conditional DGMs, each
trained to generate 2D microstructure samples for three different orthogonal
planes. The connected multiple reverse diffusion processes then enable
effective modeling of a Markov chain for transforming noise into a 3D
microstructure sample. Furthermore, a modified harmonized sampling is employed
to enhance the sample quality while preserving the spatial connection between
the slices of anisotropic microstructure samples in 3D space. To validate the
proposed framework, the 2D-to-3D reconstructed anisotropic microstructure
samples are evaluated in terms of both the spatial correlation function and the
physical material behavior. The results demonstrate that the framework is
capable of reproducing not only the statistical distribution of material phases
but also the material properties in 3D space. This highlights the potential
application of the proposed 2D-to-3D reconstruction framework in establishing
microstructure-property linkages, which could aid high-throughput material
design for future studies
