6 research outputs found
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
Friction-based riveting technique for AZ31 magnesium alloy
A new friction-based riveting technique, Rotating Hammer Riveting (RHR), is demonstrated to fully form AZ31 Mg rivet heads in a mere 0.23 s. Heat and pressure generated through severe plastic deformation during the process was sufficient to form the Mg rivet head without the need for a pre-heating operation. Due to preliminary twinning and followed by dynamic recrystallization, AZ31 Mg grains in the rivet head were refined during RHR, which enhance the formability of Mg rivets by triggering grain boundary sliding and reducing plastic anisotropy of Mg. In addition, RHR joints showed a metallurgical bond between the rivet head and top AZ31 Mg sheet, which eliminates a significant pathway for corrosion
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