This data article presents a data set comprised of 54 synthetic 3D equiaxed polycrystalline microstructures, the microstructural descriptors for each grain and the stress fields resulting from two sets of crystal plasticity simulations mimicking uniaxial tensile deformation to a total strain of 2%. This is related to the research article entitled “Applied Machine Learning to predict stress hotspots II: Hexagonal Close Packed Materials” (Mangal and Holm, 2018). The microstructures were created using an open source Dream.3D software tool and the crystal plasticity simulations were carried out using elasto-viscoplastic fast Fourier transform (EVPFFT) method. Eight different kinds of HCP textures are represented with stochastically different microstructures with varying texture intensity for each texture kind. For each texture kind, between six and nine stochastically different microstructures with varying texture intensity (measured by multiples of random density (MRD)) are created. This dataset is freely available in two Mendeley Data archives “Synthetic HCP 3D polycrystalline microstructures with grain-wise microstructural descriptors and stress fields under uniaxial tensile deformation: Part One” and “Synthetic HCP 3D polycrystalline microstructures with grain-wise microstructural descriptors and stress fields under uniaxial tensile deformation: Part Two” located at http://dx.doi.org/10.17632/kt8hfg4t2p.1 and http://dx.doi.org/10.17632/nsfn6tw295.1 respectively for any academic, educational, or research purposes.
Mangal, A., & Holm, E. A. (2018). A dataset of synthetic hexagonal close packed 3D polycrystalline microstructures, grain-wise microstructural descriptors and grain averaged stress fields under uniaxial tensile deformation for two sets of constitutive parameters. Data in Brief, 21, 1833–1841. https://doi.org/10.1016/j.dib.2018.10.172