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Abstract

Additive manufacturing (AM) is a rapidly growing field as it allows the creation of complex parts which are unobtainable using traditional manufacturing techniques. However, the resultant microstructures generated from the AM processes vary from highly columnar to equiaxed grains. These variations in microstructure can have significant impact on the properties of a component. To investigate the mechanical response of the material to its microstructural geometrical entities, it is crucial to generate synthetic microstuctures. In this regard, modified Random Sequential Addition (RSA) along with Voronoi tessellation and tessellation merging algorithms have been implemented in the in-house tool Advanced Microstructure Generator (AMG) to generate three-dimensional synthetic microstructures. The existing capabilities of AMG to generate microstructures composed of equiaxed grains have been extended to include elongated grains. Data such as grain size distribution, grain orientations etc., obtained from Electron Backscatter Diffraction (EBSD) for the polycrystalline microstructure is approximated statistically and provided as input for AMG. The resultant microstructure is then qualitatively and quantitatively compared to the experimental one. Qualitatively speaking, the currently available techniques for polycrystalline microstructure generation produce voxelated structures. In comparison, the proposed method produces smooth surface features between the grains. However, optimization is being carried out with respect to its quantitative validation.