Computationally fast morphological descriptor-based microstructure reconstruction algorithms for particulate composites

In this paper, we propose a series of novel algorithms for microstructure reconstruction based on constituent morphologies. The proposed algorithm can generate statistically equivalent representative volume elements (RVE) from shape repository consisting of the actual particles from micro-computed tomography (CT) images. The reconstruction algorithm is also general in that it can adopt any morphological descriptors. CDFs errors of the morphological descriptors between the targeted and reconstructed RVEs are minimized within the proposed algorithmic framework. High reliability of the microstructure reconstructions stems from unique and efficient design of the algorithmic framework. One of the novelties of the proposed algorithm is that it does not rely on time-consuming two-point correlation function (TPCF) calculations while it can replicate distributions keeping realistic morphologies of particles. To verify the proposed reconstruction algorithm, the TPCF and homogenized effective properties were compared between targeted and reconstructed RVEs. Moreover, mechanical responses of the targeted and reconstructed RVEs are evaluated and compared by 3D finite element analyses.