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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

The influence of flowing afterglow exposure on the degradation of various polymeric materials

Changes in polymer properties which result after exposure to an atomic oxygen-rich environment of a flowing afterglow are presented. Dynamic mechanical and Fourier Transform Infrared (FTIR) spectra as well as scanning electron microscopy micrographs were employed to monitor variations in mechanical properties and chemical and physical structure. Polymers analyzed include polystyrene, PEKK, and a polyester.

Letton, Alan↗
DOE OSTI2021

A study of real-world micrograph data quality and machine learning model robustness

Abstract Machine-learning (ML) techniques hold the potential of enabling efficient quantitative micrograph analysis, but the robustness of ML models with respect to real-world micrograph quality variations has not been carefully evaluated. We collected thousands of scanning electron microscopy (SEM) micrographs for molecular solid materials, in which image pixel intensities vary due to both the microstructure content and microscope instrument conditions. We then built ML models to predict the ultimate compressive strength (UCS) of consolidated molecular solids, by encoding micrographs with different image feature descriptors and training a random forest regressor, and by training an end-to-end deep-learning (DL) model. Results show that instrument-induced pixel intensity signals can affect ML model predictions in a consistently negative way. As a remedy, we explored intensity normalization techniques. It is seen that intensity normalization helps to improve micrograph data quality and ML model robustness, but microscope-induced intensity variations can be difficult to eliminate.

36 MATERIALS SCIENCE↗
NASA NTRS1993

Organometallic chemical vapor deposition and characterization of ZnGe(1-x)Si(x)P2-Ge alloys on GaP substrates

The epitaxial growth of ZnGe(1-x)Si(x)P2-Ge alloys on GaP substrates by open tube organometallic chemical vapor deposition (OMCVD) is reported. The chemical composition of the alloys characterized by energy dispersive X-ray spectroscopy shows that alloys with x up to 0.13 can be deposited on (001) GaP. Epitaxial growth with mirror smooth surface morphology was achieved for x less than or equal to 0.05. Transmission electron microscopy (TEM) micrographs of these alloys show specular epitaxy and the absence of microstructural defects indicating a defect density of less than 10(exp 7) cm(sup -2). Selected area electron diffraction pattern of the alloy shows that the epitaxial layer crystallizes in the chalcopyrite structure with relatively weak superlattice reflections indicating certain degree of randomness in the cation sublattice. Hall measurements show that the alloys are p-type, like the unalloyed films; the carrier concentration, however, dropped about 10 times from 2 x 10(exp 18) to 2 x 10(exp 17) cm(sup -3). Absorption measurements indicate that the band tailing in the absorption spectra of the alloy was shifted about 0.04 eV towards shorter wavelength as compared to the unalloyed material. Diodes fabricated from the n(+)-GaP/p-ZnSiP2-ZnGeP2-Ge heterostructure at x = 0.05 have a reverse break-down voltage of -10.8 V and a reverse saturation current density of approximately 6 x 10(exp -8) A/sq cm.

Xing, G. C.↗
DOE OSTI2024

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. In contrast to microstructural classification, supervised CNN models for segmentation tasks require pixel-wise annotation labels. However, 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. Microstructural characterization especially needs to be expedited for faster material discovery by changing alloy compositions. Here, in this study, we attempt to overcome such limitations by utilizing multimodal microscopy to generate labels directly instead of manual labeling. We combine scanning electron microscopy images of 347H stainless steel as training data and electron backscatter diffraction micrographs as pixel-wise labels for grain boundary detection as a semantic segmentation task. The viability of our method is evaluated by considering a set of deep CNN architectures. 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 naïve 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.

36 MATERIALS SCIENCE↗
NASA NTRS2013

Fuzzy Reasoning to More Accurately Determine Void Areas on Optical Micrographs of Composite Structures

Accomplishing the best-performing composite matrix (resin) requires that not only the processing method but also the cure cycle generate low-void-content structures. If voids are present, the performance of the composite matrix will be significantly reduced. This is usually noticed by significant reductions in matrix-dominated properties, such as compression and shear strength. Voids in composite materials are areas that are absent of the composite components: matrix and fibers. The characteristics of the voids and their accurate estimation are critical to determine for high performance composite structures. One widely used method of performing void analysis on a composite structure sample is acquiring optical micrographs or Scanning Electron Microscope (SEM) images of lateral sides of the sample and retrieving the void areas within the micrographs/images using an image analysis technique. Segmentation for the retrieval and subsequent computation of void areas within the micrographs/images is challenging as the gray-scaled values of the void areas are close to the gray-scaled values of the matrix leading to the need of manually performing the segmentation based on the histogram of the micrographs/images to retrieve the void areas. The use of an algorithm developed by NASA and based on Fuzzy Reasoning (FR) proved to overcome the difficulty of suitably differentiate void and matrix image areas with similar gray-scaled values leading not only to a more accurate estimation of void areas on composite matrix micrographs but also to a faster void analysis process as the algorithm is fully autonomous.

Dominquez, Jesus A.↗
DOE OSTI2022

Variant selection in laser powder bed fusion of non-spherical Ti-6Al-4V powder

The presence of α/α' on prior β/β grain boundaries directly impacts the final mechanical properties of the titanium alloys. The β/β grain boundary variant selection of titanium alloys has been assumed to be unlikely owing to the high cooling rates in laser powder bed fusion (L-PBF). However, we hypothesize that powder characteristics such as morphology (non-spherical) and particle size (50–120 µm) could affect the initial variant selection in L-PBF processed Ti-6Al-4V alloy by locally altering the cooling rates. Despite the high cooling rate found in L-PBF, the results showed the presence of β/β grain boundary α' lath growth inside two adjacent prior β grains. Electron backscatter diffraction micrographs confirmed the presence of β/β grain boundary variant selection, and synchrotron X-ray high-speed imaging observation revealed the role of the “shadowing effect” on the locally decreased cooling rate because of keyhole depth reduction and the consequent β/β grain boundary α' lath growth. The self-accommodation mechanism was the main variant selection driving force, and the most abundant α/α boundary variant was type 4 (63.26°//[ $\bar{10}$ 5 5 $\bar{3}$ ]). The dominance of Category II α lath clusters associated with the type 4 α/α boundary variant was validated using the phenomenological theory of martensite transformations and analytical calculations, from which the stress needed for the β→α' transformation was calculated.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Characterization of Virgin, Re-used, and Oxygen-reduced Copper Powders processed by the Plasma Spheroidization Process

We report that fabrication of parts with high mechanical properties heavily depend on the quality of powder deployed in the fabrication process. Copper powder in three different powder types were spheroidized using radio-frequency inductively coupled plasma (ICP) spheroidization process (TekSphero-15 system). The characterized powders include virgin powder as purchased from the powder manufacturer, powder used in electron beam powder bed fusion (EB-PBF) process, and reconditioned powder, which was used powder that underwent an oxygen-reduction treatment. The goal of spheroidizing these powder types was to evaluate the change in powder morphology, the possibility of enhancing the powder properties back to their as-received conditions, and assess oxygen reduction of the powder lots given their initial oxygen contents. Also, to investigate the impact of re-spheroidization on powder properties, the second round of spheroidization was performed on the already used-spheroidized powder. The impact of powder type on powder sphericity and particle size distribution was evaluated using the image analysis of scanning electron microscope (SEM) micrographs and laser diffraction, respectively. The spheroidized powder showed higher sphericity and more uniform particle size distribution overall. Depending on the powder collection bin, second round of spheroidization affected the powder sphericity differently. The possibility of deploying the plasma spheroidization process as an alternative oxygen-reduction technique was also investigated through tracking the powders’ oxygen content using inert gas fusion method before and after the spheroidization. The plasma spheroidized powder showed less oxygen content than the hydrogen-treated powder. The second round of spheroidization caused no change in oxygen content. The correlation between oxygen-reduction and created cracks was discussed and compared between plasma spheroidization and hydrogen-treatment. The plasma spheroidization process created a powder with higher sphericity, uniform particle size, and less oxygen content.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Austenitic parent grain reconstruction in martensitic steel using deep learning

In this work we develop a deep convolutional architecture to estimate the prior austenite structure from observed martensite electron backscatter diffraction micrographs. A novel data augmentation strategy randomizes the global reference coordinate system which makes it possible to train our model from only four micrographs. The model is much faster than algorithmic approaches and generalizes well when applied to micrographs of a different material. Empirical evidence suggests the efficacy of the model depends on the scale of the microstructure and receptive field of the vision model. Furthermore, this work demonstrates that modern computer vision approaches are well suited for capturing complex spatial-orientation patterns present in orientation imaging micrographs.

36 MATERIALS SCIENCE↗
NASA NTRS1983

Lunar sample analysis

The evolution of the lunar regolith under solar wind and micrometeorite bombardment is discussed as well as the size distribution of ultrafine iron in lunar soil. The most important characteristics of complex graphite, sulfide, arsenide, palladium, and platinum mineralization in a pegmatoid pyroxenite of the Stillwater Complex in Montana are examined. Oblique reflected light micrographs and backscattered electron SEM images of the graphite associations are included.

Housley, R. M.↗
NASA NTRS1995

Germination, growth rates, and electron microscope analysis of tomato seeds flown on the LDEF

The tomato seeds were flown in orbit aboard the Long Duration Exposure Facility (LDEF) for nearly six years. During this time, the tomato seeds received an abundant exposure to cosmic radiation and solar wind. Upon the return of the LDEF to earth, the seeds were distributed throughout the United States and 30 foreign countries for analysis. The purpose of the experiment was to determine the long term effect of cosmic rays on living tissue. Our university analysis included germination and growth rates as well as Scanning Electron Microscopy and X-ray analysis of the control as well as Space-exposed tomato seeds. In analyzing the seeds under the Electron Microscope, usual observations were performed on the nutritional and epidermis layer of the seed. These layers appeared to be more porous in the Space-exposed seeds than on the Earth-based control seeds. This unusual characteristic may explain the increases in the space seeds growth pattern. (Several test results show that the Space-exposed seeds germinate sooner than the Earth-Based seeds. Also, the Space-exposed seeds grew at a faster rate). The porous nutritional region may allow the seeds to receive necessary nutrients and liquids more readily, thus enabling the plant to grow at a faster rate. Roots, leaves and stems were cut into small sections and mounted. After sputter coating the specimens with Argon/Gold Palladium Plasma, they were ready to be viewed under the Electron Microscope. Many micrographs were taken. The X-ray analysis displayed possible identifications of calcium, potassium, chlorine, copper, aluminum, silicon, phosphate, carbon, and sometimes sulfur and iron. The highest concentrations were shown in potassium and calcium. The Space-exposed specimens displayed a high concentration of copper and calcium in the two specimens. There was a significantly high concentration of copper in the Earth-based specimens, whereas there was no copper in the Space-exposed specimens.

Hammond, Ernest C., Jr.↗
NASA NTRS1997

Mechanical Properties of Cu-Cr-Nb Alloys

The chemical compositions of the alloys are listed. The alloying levels were near the values for stochiometric Cr2Nb. A slight excess of Cr was chosen for increased hydrogen embrittlement resistance. The microstructures of all Cu-Cr-Nb alloys were very similar. Two typical transmission electron microscope (TEM) micrographs are presented. The images show the presence of large mount of Cr2Nb precipitates in a nearly pure Cu matrix. The interactions between dislocations and precipitates are currently under investigations, but as the images demonstrates, the extremely fine (less then 15 nm) Cr2Nb are the primary strengtheners for the alloy.

Ellis, David L.↗
NASA NTRS1998

Contactless Growth of ZnSe Single Crystals by Physical Vapor Transport

ZnSe crystals were grown by self-seeded physical vapor transport (PVT) technique in the horizontal configuration. The source materials were heat treated by H2 reduction to remove the oxide followed by baking under dynamic vacuum to adjust the source composition toward that of congruent sublimation. Contactless growth of ZnSe single crystals have been performed consistently using three different source materials. The crystals grew away from the wall during the later stage of the growth with large (110) facets tend to align parallel to the gravity direction. The Scanning Electron Micrography (SEM) micrographs and the Atomic Force Microscope (AFM) images showed that large (110) terraces and steps dominate the as-grown facets. The measured residual gas pressures in the processed ampoules agree well among various source materials and the major components were CO and H2. No preferred growth direction was found. The one-dimensional diffusion model on the mass flux of a multi-species PVT system was employed to analyze the conditions for contactless growth. The calculated thermal profile for supersaturation is very close to the thermal profile measured inside the empty furnace bore in the region of contactless growth. The effects of convective flows in the vapor phase inside the ampoule on the growth processes are discussed.

Su, Ching-Hua↗
NASA NTRS2010

Examination of Scanning Electron Microscope and Computed Tomography Images of PICA

Micrographs of PICA (Phenolic Impregnated Carbon Ablator) taken using a Scanning Electron Microscope (SEM) and 3D images taken with a Computed Tomography (CT) system are examined. PICA is a carbon fiber based composite (Fiberform ) with a phenolic polymer matrix. The micrographs are taken at different surface depths and at different magnifications in a sample after arc jet testing and show different levels of oxidative removal of the charred matrix (Figs 1 though 13). CT scans, courtesy of Xradia, Inc. of Concord CA, were captured for samples of virgin PICA, charred PICA and raw Fiberform (Fig. 14). We use these images to calculate the thermal conductivity (TC) of these materials using correlation function (CF) methods. CF methods give a mathematical description of how one material is embedded in another and is thus ideally suited for modeling composites like PICA. We will evaluate how the TC of the materials changes as a function of surface depth. This work is in collaboration with ETH-Zurich, which has expertise in high temperature materials and TC modeling (including CF methods).

Lawson, John W.↗
NASA NTRS1972

Profiling with the electron microscope.

Discussion of a profiling technique using a scanning electron microscope for obtaining depth information on a single micrograph of a small specimen. A stationary electron beam is used to form a series of contamination spots in a line across the specimen. Micrographs obtained by this technique are useful as a means of projection and display where stereo viewers are not practical.

Vedder, J. F.↗
DOE OSTI2022

Deep Neural Network Algorithm for CMC Microstructure Characterization and Variability Quantification

Microstructure characterization and variability quantification are crucial for understanding ceramic matrix composites (CMCs) mechanical behavior and deformation mechanisms across length scales. Traditionally, analyses of the micrographs obtained from microscopy are labor-intensive. However, with the vast improvement in computer vision (CV) and deep learning (DL), an automated algorithm can be designed to extract essential microstructure variability from micrographs which can then be used to construct a statistically representative volume element (SRVE). The DL-based algorithm spans the taxonomy of microstructure analyses, including semantic segmentation of microstructure constituents, secondary phases, matrix/fiber interface, and defects, and quantifying the microstructure variability in terms of probability distributions. In this work, C/SiNC and SiC/SiNC CMCs microstructures are semantically segmented through a deep convolutional neural network, followed by variability quantification through the implementation of a fully connected regression layer, hence forming a deep regression network. The deep regression network operates in a feedforward regime, in which the neuron output signal traverses through the network in a unidirectional manner. The weight tensor associated with each layer is updated through a backpropagation stochastic gradient descent approach. The input gray-scale image obtained through in-house scanning electron microscope and confocal microscope micrographs is augmented through affine transformations to increase the training set size, which is then processed through four strided convolutional layers. This compresses the image resolution by half at each layer while increasing the image depth by applying different filters (image encoding). The class activation maps (CAMs) corresponding to the applied filters highlight the key architectural features and assist with the semantic segmentation of the microstructure.

Hamza, Mohamed H.↗