Descriptor-based methodology for statistical characterization and 3D reconstruction of microstructural materials

3D reconstructions of heterogeneous microstructures are important for assessing material properties using advanced simulation techniques such as finite element analysis (FEA). Nevertheless, for many materials systems like polymer nanocomposites, only 2D microstructural images are available even with the state-of-the-art imaging techniques. This paper proposes a new descriptor-based methodology for reconstructing 3D particle-based heterogeneous microstructures based on 2D images. The proposed methodology characterizes a 2D microstructural morphology using a small set of microstructure descriptors covering features including material composition, dispersion status, and phase geometry, and then reconstructs statistically equivalent microstructures in a 3D space based on the 3D descriptors derived from 2D characterization and a few reasonable assumptions. Our approach is the most useful when the direct 3D microstructure analysis, such as 3D tomography, is not available due to either high cost or difficulties in sample preparations. Other practical features of descriptor-based characterization include low dimensionality, which enables optimal parametric design of microstructures, as well as physically meaningful mapping of processing related material parameters. In reconstruction, the proposed algorithm is capable to generate large size 3D structures at a low computational cost. Furthermore, since the algorithm is stochastic, it can be used to construct both Representative Volume Element (RVE) and Statistical Volume Element (SVE) for FEA studies. We demonstrate the proposed methodology by characterizing and reconstructing polymer nanocomposites.