Obtaining an accurate three-dimensional (3D) structure of a porous microstructure is important for assessing the material properties based on finite element analysis. While directly obtaining 3D images of the microstructure is impractical under many circumstances, two sets of methods have been developed in the literature to generate (reconstruct) 3D microstructure from its 2D images: one characterizes the microstructure based on certain statistical descriptors, typically two-point correlation function and cluster correlation function, and then performs an optimization process to build a 3D structure that matches those statistical descriptors; the other method models the microstructure using stochastic models like a Gaussian random field (GRF) and generates a 3D structure directly from the function. The former obtains a relatively accurate 3D microstructure, but the optimization process can be very computationally intensive, especially for problems with a large image size; the latter generates a 3D microstructure quickly but sacrifices the accuracy. A hybrid optimization approach of modeling the 3D porous microstructure of random isotropic two-phase materials is proposed in this paper, which combines the two sets of methods and hence maintains the accuracy of the correlation-based method with improved efficiency. The proposed technique is verified for 3D reconstructions based on silica polymer composite images with different volume fractions. A comparison of the reconstructed microstructures and the optimization histories for both the original correlation-based method and our hybrid approach demonstrates the improved efficiency of the approach.

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