Using data driven methods to predict stress hotspots in materials.
This work focuses on integrating crystal plasticity based deformation models and machine learning techniques to gain data driven insights about the microstructural properties of polycrystalline metals. Inhomogeneous stress distribution in materials leads to the development of stress hotspots in polycrystalline metals under uniaxial tensile deformation. Synthetic 3D microstructures have been created to represent single phase equiaxed microstructures for alpha-Titanium and Copper alloys. A viscoplastic self consistent (VPSC) model is used to optimize the Voce hardening parameters to match the experimental stress-strain curve. Uniaxial tensile deformation is simulated using an image-based Fast Fourier Transform (FFT) technique, which provides full field solutions for 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. After identifying stress hotspots by thresholding stress values, we characterize their neighborhoods 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 0.82 for hexagonal close packed and 0.74 for face centered cubic materials. The results show the power and the limitations of the machine learning approach applied to the polycrystalline grain networks.
Publisher URL: http://arxiv.org/abs/1711.00118
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