No abstract

This study offers experimental observation of the effect of low strain conditions (e \ 10%) on abnormal grain growth (AGG) in Nickel-200. At such conditions, stored mechanical energy is low within the microstructure enabling one to observe the impact of increasing mechanical deformation on the early onset of AGG compared to a control, or nondeformed, equivalent sample. The onset of AGG was observed to occur at specific pairings of compressive strain and annealing temperature and an empirical relation describing the influence of thermal exposure and strain content was developed. The evolution of low-R coincident site lattice (CSL) boundaries and overall grain size distributions are quantified using electron backscatter diffraction preceding, at onset and during ensuing AGG, whereby possible mechanisms for AGG in the low strain regime are offered and discussed.

Grain boundary engineering (GBE) is a thermomechanical processing technique used to control the distribution, arrangement, and identity of grain boundary networks, thereby improving their mechanical properties. In both GBE and non-GBE metals, the phenomena of abnormal grain growth (AGG) and its contributing factors is still a subject of much interest and research. In a previous study, GBE was performed on minimally strained (ε < 10%), commercially pure Nickel-200 via cyclic annealing, wherein unique onset temperature and induced strain pairings were identified for the emergence of AGG. In this study, crystallographic segmentation of grain orientations from said experiments are leveraged in tandem with image processing to quantify growth rates for abnormal grains within the minimally strained regime. Advances in growth rates are shown to vary directly with initial strain content but inversely with initiating AGG onset temperature. A numeric estimator for advancement rates associated with AGG is also derived and presented.

bhjared@sandia.gov, f) jmrodel@sandia.gov, g) bsalzbr@sandia.govAbstract. The intrinsic relation between structure and performance is a foundational tenant of most all materials science investigations. While the specific form of this relation is dictated by material system, processing route and performance metric of interest, it is widely agreed that appropriate characterization of a material allows for greater accuracy in understanding and/or predicting material response. However, in the context of additive manufacturing, prior models and expectations of material performance must be revisited as performance often diverges from traditional values, even among well explored material systems. This work utilizes micro-computed tomography to quantify porosity and lack of fusion defects in an additively manufactured stainless steel and relates these metrics to performance across a statistically significant population using high-throughput mechanical testing. The degree to which performance in additively manufactured stainless steel can and cannot be correlated to detectable porosity will be presented and suggestions for performing similar experiments will be provided.

Mechanical serial sectioning is a highly repetitive technique employed in metallography for the rendering of 3D reconstructions of microstructure. While alternate techniques such as ultrasonic detection, micro-computed tomography, and focused ion beam milling have progressed much in recent years, few alternatives provide equivalent opportunities for comparatively high resolutions over significantly sized cross-sectional areas and volumes. To that end, the introduction of automated serial sectioning systems has greatly heightened repeatability and increased data collection rates while diminishing opportunity for mishandling and other userintroduced errors. Unfortunately, even among current, stateof-the-art automated serial sectioning systems, challenges in data collection have not been fully eradicated. Therefore, this paper highlights two specific advances to assist in this area; a non-contact laser triangulation method for assessment of material removal rates and a newly developed graphical user interface providing real-time monitoring of experimental progress. Both are shown to be helpful in the rapid identification of anomalies and interruptions, while also providing comparable and less error-prone measures of removal rate over the course of these long-term, challenging, and innately destructive characterization experiments.