|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
||Digital Image Correlation Based Machine Learning Predictions for Grain-boundary Strain Accumulation in a Polycrystalline Metal
||Renato Vieira, John Lambros
|On-Site Speaker (Planned)
Non-uniform accumulation of plastic strains at the grain scale in polycrystalline metals is a precursor to damage and eventual crack nucleation during fatigue. Here we investigate the accumulation of plastic strains at the microscale using a high-resolution digital image correlation (HiDIC) technique in conjunction with electron backscatter diffraction (EBSD). With the large datasets resulting from the HiDIC experiments, we develop machine-learning algorithms capable of predicting the accumulation of strains at the grain scale from microstructural and load inputs. As a proof of concept, a neural-network has been trained to correlate grain boundary orientation with the resulting local strains surrounding boundary mantle regions. The obtained neural-network predictions show good correlation with the DIC-measured strain fields for most measured cases. These 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 around that boundary.
||Machine Learning, Iron and Steel, Characterization