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Abstract
A compact and tractable representation of the grain structure of a material is an extremely valuable tool when carrying out an empirical analysis of the material’s microstructure. Tessellations have proven to be very good choices for such representations. Most widely used tessellation models have convex cells with planar boundaries. Recently, however, a new tessellation model — called the generalised balanced power diagram (GBPD) — has been developed that is very flexible and can incorporate features such as curved boundaries and non-convexity of cells. In order to use a GBPD to describe the grain structure observed in empirical image data, the parameters of the model must be chosen appropriately. This typically involves solving a difficult optimisation problem. In this paper, we describe a method for fitting GBPDs to tomographic image data. This method uses simulated annealing to solve a suitably chosen optimisation problem. We then apply this method to both artificial data and experimental 3D electron backscatter diffraction (3D EBSD) data obtained in order to study the properties of fine-grained materials with superplastic behaviour. The 3D EBSD data required new alignment and segmentation procedures, which we also briefly describe. Our numerical experiments demonstrate the effectiveness of the simulated annealing approach (compared to heuristic fitting methods) and show that GBPDs are able to describe the structures of polycrystalline materials very well.
Notes
No potential conflict of interest was reported by the authors.