Statistical aspects of grain-level strain evolution and reorientation during the heating and elastic-plastic loading of a Ni-base superalloy at elevated temperature

Abstract

The advancement of microstructure-sensitive models for elevated temperature applications requires experimental data at the relevant length scales and usage environments. Towards this goal, we studied the evolution of grain-level strains in a Ni-based superalloy during heating and subsequent loading at elevated temperature. The specimen was heated using two halogen lamps in a furnace designed explicitly for the special load frame used for the synchrotron-based experiment. High energy X-ray diffraction was used to non-destructively characterize the initial 3D microstructure, confirm the temperature of the specimen, and monitor the evolution of grain-level strains, stresses, and orientations through the elastic-plastic transition. The distributions of grain-level normal strains were observed to broaden with elastic loading, whereas elastic-plastic loading led to development of local intergranular “hotspots”, or activation of plastic slip within select grains, that both broaden and skew the grain-level strain distributions. These grain-level strain distributions were fit to a generalized extreme value (GEV) distribution function. The GEV shape parameter is observed to change significantly at the onset of macroscopic plasticity. At the grain scale, more grain reorientation (plasticity) occurred for grains oriented for lower strength-to-stiffness ratios. During the onset of plastic deformation for LSHR at 660∘C load shedding from lower strength-to-stiffness-oriented grains to higher strength-to-stiffness-oriented grains was observed. Additionally, based on the grain-averaged reorientation axis, it was found that LSHR has a preference for activation of multiple slip systems at 660∘C. We conclude with a discussion on the critical need for statistical analysis of such mesoscale data obtained in situ at elevated temperature for the verification/validation of the next generation of microstructure-sensitive models.