Automated Grain Boundary (GB) Segmentation and Microstructural Analysis in 347H Stainless Steel Using Deep Learning and Multimodal Microscopy

Austenitic 347H stainless steel offers superior mechanical properties and corrosion resistance required for extreme operating conditions such as high temperature. The change in microstructure due to composition and process variations is expected to impact material properties. Identifying microstructural features such as grain boundaries thus becomes an important task in the process-microstructure-properties loop. Applying convolutional neural network (CNN) based deep-learning models is a powerful technique to detect features from material micrographs in an automated manner. Manual labeling of the images for the segmentation task poses a major bottleneck for generating training data and labels in a reliable and reproducible way within a reasonable timeframe. In this study, we attempt to overcome such limitations by utilizing multi-modal microscopy to generate labels directly instead of manual labeling. We combine scanning electron microscopy (SEM) images of 347H stainless steel as training data and electron backscatter diffraction (EBSD) micrographs as pixel-wise labels for grain boundary detection as a semantic segmentation task. We demonstrate that despite producing instrumentation drift during data collection between two modes of microscopy, this method performs comparably to similar segmentation tasks that used manual labeling. Additionally, we find that na\"ive pixel-wise segmentation results in small gaps and missing boundaries in the predicted grain boundary map. By incorporating topological information during model training, the connectivity of the grain boundary network and segmentation performance is improved. Finally, our approach is validated by accurate computation on downstream tasks of predicting the underlying grain morphology distributions which are the ultimate quantities of interest for microstructural characterization

Reaction Dynamics of Rocket Propellant with Magnesium Oxide Nanoparticles

The combustion behavior of rocket propellant grade 2 (RP-2) was investigated as a function of magnesium oxide (MgO) nanoparticles (i.e., 20 nm diameter) added at varied concentrations. The MgO nanoparticles were surface-treated with a long-chain carboxylic acid to aid their dispersion in RP-2. The fuel droplet regression rate, surface tension, and heat of combustion of RP-2 with MgO nanoparticle additives were measured to characterize combustion behavior. Heat of combustion and surface tension measurements varied negligibly among all samples indicating that calorific output and surface tension are not controlling parameters influencing fuel combustion behavior. However, fuel droplet regression rates were considerably increased by adding 0.5 wt % MgO from 0.225 to 66.16 mm/s, which is an improvement by 2 orders of magnitude. Further analysis showed that MgO particles enhance diffusive heat transfer, which promotes nucleation and disruptive burning throughout the three stages of regression, heating/evaporation (stage 1), combustion of RP-2 (stage 2), and combustion of carboxylic acid dispersant (stage 3), and, thus, lead to improved fuel droplet combustion