Numerical microstructure model of NiTi wire reconstructed from 3D-XRD data

journal article in Web of Science

en

In this paper, the grain microstructure and strain partitioning in a polycrystalline NiTi wire subjected to tensile loading was reconstructed from an experimental 3D-XRD dataset. The reconstruction of a volume containing more than 8000 stressed grains involved optimization with respect to both the geometrical features and material elastic properties. The geometrical features of the microstructure were reconstructed using Laguerre tessellations based on the experimental 3D-XRD dataset. Two different algorithms fitting Laguerre tessellations were applied in order to assess the sensitivity of the reconstruction to the choice of the algorithm. The material properties in terms of elastic anisotropy were refined from an initial published value to minimize the mismatch between experiment and simulation using an optimization algorithm based on linear elasticity simulations. As a result of this, we constructed a numerical microstructure model that statistically matches the experimentally probed material in terms of positions and sizes of grains as well as partitioning of elastic strain and stress in the microstructure (average elastic properties and standard deviations of piecewise constant components of elastic strain and stress tensors in grains).

In this paper, the grain microstructure and strain partitioning in a polycrystalline NiTi wire subjected to tensile loading was reconstructed from an experimental 3D-XRD dataset. The reconstruction of a volume containing more than 8000 stressed grains involved optimization with respect to both the geometrical features and material elastic properties. The geometrical features of the microstructure were reconstructed using Laguerre tessellations based on the experimental 3D-XRD dataset. Two different algorithms fitting Laguerre tessellations were applied in order to assess the sensitivity of the reconstruction to the choice of the algorithm. The material properties in terms of elastic anisotropy were refined from an initial published value to minimize the mismatch between experiment and simulation using an optimization algorithm based on linear elasticity simulations. As a result of this, we constructed a numerical microstructure model that statistically matches the experimentally probed material in terms of positions and sizes of grains as well as partitioning of elastic strain and stress in the microstructure (average elastic properties and standard deviations of piecewise constant components of elastic strain and stress tensors in grains).

elastic anisotropy; elasticity; microstructure; microstructure reconstruction; 3D-XRD; Laguerre tessellation; finite element method

15.05.2020

IOP PUBLISHING LTD

BRISTOL

0965-0393

28

5

055007–055007

25