  We investigate the formation of stress hotspots in polycrystalline materials
under uniaxial tensile deformation by integrating full field crystal plasticity
based deformation models and machine learning techniques to gain data driven
insights about microstructural properties. Synthetic 3D microstructures are
created representing single phase equiaxed microstructures for generic copper
alloys. Uniaxial tensile deformation is simulated using a 3-D full-field,
image-based Fast Fourier Transform (FFT) technique with rate-sensitive crystal
plasticity, to get local micro- mechanical fields (stress and strain rates).
Stress hotspots are defined as the grains having stress values above the 90th
percentile of the stress distribution. Hotspot neighborhoods are then
characterized using metrics that reflect local crystallography, geometry, and
connectivity. This data is used to create input feature vectors to train a
random forest learning algorithm, which predicts the grains that will become
stress hotspots. We are able to achieve an area under the receiving operating
characteristic curve (ROC-AUC) of 0.74 for face centered cubic materials
modeled on generic copper alloys. The results show the power and the
limitations of the machine learning approach applied to the polycrystalline
grain networks.
