Machine Learning Neural-Network Predictions for Grain-Boundary Strain Accumulation in a Polycrystalline Metal

Abstract

Microstructural features such as grain boundaries play a significant role in the macroscopic plastic response of polycrystalline metals. However, a quantitative link between plastic strain accumulation at grain boundaries and material response in plasticity dominated phenomena is still lacking. Here we seek to develop predictive relations between a material’s granular microstructure and the accumulation of plastic strains at the microstructural level during plastic deformation. A single-input neural network approach was applied to predict the residual plastic strain fields at regions surrounding grain boundaries of an austenitic stainless steel. The neural network was trained on data obtained by applying a very-high resolution digital image correlation (DIC) experimental technique that allows the measurement of grain-scale strains aligned to the underlying microstructure obtained from electron backscatter diffraction (EBSD) scans. The neural-network-predicted and the DIC-measured strain fields showed good correlation for most of the tested cases. Best individual agreement was found when each microstructure was used to predict fields in its own case. However, best overall average predictions were seen when multiple samples were used for the network training. The results showed that the local geometrical angle between a grain boundary and the loading axes is in many cases a good predictor for the accumulation of strains at the given boundary. The expected limitations of this single parameter approach (grain boundary angle alone cannot be a good predictor for varying strains along a straight grain boundary, for example) were seen as the reason for the situations where predictions were not as good.