Stochastic modeling of the polygonal microstructures of alloys using representative microscopic images

Abstract

This paper proposes an algorithm to generate a statistically equivalent microstructure of alloys for polygonal mesh-based finite element modeling. Morphological and crystallographic characteristics obtained from scanning electron microscopy images of 2D surface slices are employed to generate the mesh. Titanium alloy microstructures are considered as an example to demonstrate the proposed method. The mean and standard deviation of the grain area distribution obtained from the images are employed as the statistical parameters. The algorithm uses the image data and applies perturbation using a distortion factor and Voronoi tessellation to generate the statistically equivalent polygonal microstructure that captures the randomness of grain shape and size iteratively. The grain area distribution of the simulated microstructure closely matches with the actual distribution. Lower-order elements (quadrilateral) on the domain boundary get introduced that may create artifacts when analyzed with the finite element method. A method to eliminate these artifacts is presented with an analysis of stress concentrations occurring due to grain orientation mismatch. Effective mechanical properties, deformation, and fracture behavior as a function of various microstructural parameters like grain area and crystallographic orientations can be analyzed using the polygonal finite element mesh.