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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Corrosion susceptibility and chromium loss in Austenitic steels and Nickel-based alloys in molten FLiNaK at 700 °C

A comparative study was conducted to evaluate the corrosion susceptibility of 316L, 316H, Ni 200, Inconel 625, and Hastelloy N in molten FLiNaK at 700 °C for 100 h. Top-view and cross-sectional scanning electron microscopy (SEM) imaging, combined with energy-dispersive X-ray spectroscopy (EDS) mapping, was performed to investigate microstructural and compositional changes. SEM images were further processed by introducing a contrast threshold to map cavity distribution. Using EDS mapping, intergranular and intragranular Cr loss were separately characterized. The ranking of mass loss after corrosion, from highest to lowest, is as follows: 316H > 316L > Inconel 625 > Hastelloy N > Ni 200. Cr loss was found to be correlated with Mo concentration, in agreement with many previous studies, with higher Mo content resulting in reduced Cr loss. On the other hand, there is no evidence that the high carbon content in 316H enhances corrosion resistance. The relatively low solubility of carbon in austenite at the testing temperature limits the amount of dissolved carbon. Therefore, the carbon-retarded vacancy diffusion, and consequently the reduced Cr diffusion, were not observed.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Ion irradiation effects on Cr-coated zircaloy-4 surface wettability and pool boiling critical heat flux.

The concept of coating the currently used nuclear fuel cladding (zirconium-based alloy, typically Zircaloy-4 or Zirc-4) with an oxidation preventive layer is a progressing Accident tolerant Fuel (ATF) candidate alloys. The coated Zirc-4-based alloys could be a solution to suppress undesirable fast reaction kinetics with high-temperature steam. Zirc-4 has been the most preferred cladding material in pressurized water reactors (PWRs). Chromium (Cr) based alloys as a coating material provides excellent corrosion protection and good strength and wear resistance. In this paper, we present the surface wettability measurements and pool boiling Critical Heat Flux (CHF) for Cr-coated Zirc-4 claddings pre- and post-exposure to an ion irradiation environment. The wettability measurements, including static contact angle (contact angle, θ) and average surface roughness (surface roughness, Ra), are introduced for samples of different coating thicknesses (5–30 μm thick). The coatings fabricated by the cold spray of Cr-Al particles to 10 mm × 10 mm × 1.95 mm Zirc-4 substrates. Post fabrication, a Pilgering (cold rolling) process, was applied to finalize the coating thickness and resulted in a significant reduction in surface roughness of initially fabricated rough surfaces. The process produced three distinguished samples 5-μm unpolished (as machined), 5-μm, and 30-μm polished (cold rolled). The measurements are presented for the three surfaces and bare Zirc-4 as a baseline surface. The contact angle analyses were implemented in theoretical models from the literature to predict pool boiling CHF. Pool boiling experiments were conducted to measure the pool boiling CHF values and compare them to the predicted values. Scanning Electron Microscope (SEM) images and Energy Dispersive X-ray Spectroscopy (EDS) analysis was performed to characterize the surfaces for better understanding and interpreting the results. The SEM images showed localized surface damage due to ion irradiation. No recognized change in the measured surface roughness due to ion irradiation. The contact angles of irradiated Cr-coated surfaces are consistently higher (10°) than pre-irradiated surfaces. Decreasing the Cr-coating layer thickness resulted in lower contact angle pre- and post- ion irradiation. The predicted pool boiling CHF using the Kandlikar model is in good agreement with the experimentally measured CHF values within ±12% for all samples.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Factors controlling pore network development of thermally mature Early Palaeozoic mudstones from the Baltic Basin (N Poland)

Understanding the formation of pore space, especially in low porosity shales (as source rocks and as unconventional resources), is critical to the oil and gas industry, since pores control the space available for hydrocarbon and participate in hydrocarbon transport. We examined 87 Ordovician and Silurian mudstone samples collected from four wells located in the Pomeranian part of the Baltic Basin (northern Poland), one of the primary Polish targets for hydrocarbon exploration. These samples represent the Pelplin, the Paslek, the Jantar, the Prabuty, and the Sasino Formations, which still requires more detailed porosity studies. Our study aimed to identify factors controlling porosity development, by applying bulk techniques (organic petrology and TOC analyses, quantitative mineralogy, and porosimetry) as well as nano- to microscale techniques (thin section petrography, electron microscopy). The studied samples are mainly argillaceous mudstones. The results of porosimetry measurements, combined with image analysis, indicate that the pores of all studied rocks are dominated by micropores (pores < 2 nm in diameter), mesopores (2-50 nm in diameter) and small macropores. The SEM images showed three main pore types: a) voids related to clay mineral aggregates, b) pores inside organic matter particles, and c) pores between other mineral grains. In the Jantar and Sasino mudstones, the organic matter content and its thermal maturity control porosity. The occurrence of solid bitumen in the rocks from these formations reduces samples’ mesoporosity because of the pore-clogging effect. In contrast, in the Paslek and Prabuty Formations, there is low organic matter content and specific surface area and the volume of mesopores increase with clay minerals content. In the Pelplin mudstones, there are no prevailing factors controlling porosity. As a result, we suggest that a combination of SEM image analysis and dual liquid porosity (DLP) measurements is a powerful method to assess porosity available for petroleum flow in mudstones.

03 NATURAL GAS↗
DOE OSTI2025

A quantitative imaging framework for lithium morphology: Linking deposition uniformity to cycle stability in lithium metal batteries

Characterizing the morphology of lithium (Li) is crucial for developing long-lasting lithium metal batteries. It is well established that more uniform Li deposition correlates with better cell performance. Li morphology is often characterized through qualitative analysis of scanning electron microscopy (SEM) images; however, there are no widely accepted metrics to quantitatively describe deposition uniformity. Here, we propose a framework to quantify uniformity through SEM image analysis via the index of dispersion (ID) metric, which is defined and presented in the context of Li metal batteries. We also explore experimental impacts of sampling protocols onIDmeasurements. Our results demonstrate that theIDmetric is highly sensitive to variations in deposition uniformity, including the coexistence and uniformity of multiple morphologies, uniformity within a single morphology, and particle size distribution uniformity. Furthermore, it is demonstrated that uniformity, as measured by theID, can be related to the average potential of Li||Li symmetric cells over cycling. Higher capacity cycling leads to more pronounced changes in bothIDand average cell potential. Local minima/maxima are found consistently in bothIDand average cell potential immediately before cells short-circuit, which we suggest may indicate a collapse of the microstructure prior to failure. We put forward this framework as a more robust approach to quantify Li deposition uniformity, advancing the development of Li metal batteries that are safer and longer lasting.

Science & Technology - Other Topics↗
DOE OSTI2025

Wafer-scale waveguide sidewall roughness scattering loss characterization by image processing

Photonic integrated circuits (PICs) are vital for developing affordable, high-performance optoelectronic devices that can be manufactured at an industrial scale, driving innovation and efficiency in various applications. Optical loss of modes in thin film waveguides and devices is a critical measure of their performance. Thin film growth, lithography, masking, and etching processes are imperfect processes that introduce significant sidewall and top-surface roughness and cause dominating optical losses in waveguides and photonic structures. This roughness, as perturbations couple light from guided to far-field radiation modes, leads to scattering losses that can be estimated from theoretical models. Typically, with UV-based lithography, sidewall roughness is significantly larger than wafer-top surface roughness. Atomic force microscopy (AFM) imaging measurement gives a 3D and high-resolution roughness profile, but the measurement is inconvenient, costly, and unscalable for large-scale PICs and at wafer-scale. Here, we evaluate the sidewall roughness profile based on 2D high-resolution scanning electron microscope (SEM) imaging. We characterized the loss on two homemade nitride and oxide films on 3-inch silicon wafers with 12 waveguide devices on each and correlated the scattering loss estimated from a 2D image-based sidewall profile and theoretical Payne model. The lowest loss of guided fundamental transverse electric (TE 0 ) mode is found at 0.075 dB/cm at 633 nm across 24 devices, a record at visible wavelength. Our work shows 100% success (edge continuity span exceeding 95% of image width/height) in edge detection in image processing of all images to estimate autocorrelation function and optical mode loss. These demonstrations offer valuable insights into waveguide sidewall roughness and a comparison of experimental and 2D SEM image processing based loss estimations with applications in loss characterization at wafer-scale PICs.

Khurana, Mohit (ORCID:0000000207898672)↗
DOE OSTI2020

MOCVD Growth and Characterization of ZnSnN 2

Since the first few reports of its synthesis in 2011 and 20121-3, interest in ZnSnN 2 for diverse applications, including photovoltaics1,3,4, visible light photocatalysis5, and optoelectronics, has been growing. Novel nitride-based LED device designs promise high gains in efficiency at emission wavelengths into the green, amber and even red.6-8 Their realization requires the precise insertion of an ultrathin layer of ZnGeN 2 or ZnSnN 2 into the active region of an InGaAs-GaAs LED structure. While thin films of ZnSnN 2 have been synthesized by RF sputtering1,9 and by MBE 2,10,11, and thin films of ZnGeN 2 , ZnSiN 2 and their alloys by MOCVD, the synthesis of ZnSnN 2 thin films by MOCVD has not yet been reported. It is a challenging goal, given the narrow temperature window between efficient activation of ammonia and the thermal decomposition of ZnSnN 2 at near-atmospheric pressures. We report here the results of studies of ZnSnN 2 films grown on r- and c-plane sapphire substrates, GaN templates on sapphire, and In 0.15 Ga 0.85 N templates on GaN-on-sapphire, at temperatures from 500 °C to 560 °C, using tetramethyltin (TMSn), diethylzinc (DEZn), germane and ammonia. Chamber pressures were varied between 200 and 500 torr. In addition, the ratios of the cation precursors and the total cation-to-anion precursor ratios were varied. Single crystal growth, as evidenced by 2θ-ω x-ray diffraction scans, was achieved on all of these substrates. Wurtzitic (0002) ZnSnN 2 diffraction were observed at 2θ ~ 32.90 for the samples grown on c-sapphire, GaN (0001) and InGaN (0001) templates, and wurtzitic (11-20) peak at 2θ ~ 54.80 for the films grown on r-sapphire. Higher pressures and higher temperatures promoted better crystallinity and more continuous films, as revealed by scanning electron microscopy (SEM) images and 2θ-ω x-ray diffraction peak widths and intensities. Crystal quality was observed to be highly dependent on growth temperature. For example, in one series a 10 °C change in growth temperature resulting in a factor of 15 increase in diffraction peak intensity. SEM images show that films grown on the GaN templates had better uniformity in nucleation than those grown on sapphire. The presence of satellite peaks around the Bragg peak in the XRD 2θ-ω scan for a film grown on a GaN template, and the absence of satellite peaks for the films grown simultaneously on c- and r-sapphire, also indicate superior quality for the film grown on GaN. HAADF-STEM images of films grown on these templates showed smooth interfaces at the film-substrate boundary and show growth rates as high as 167 nm/h, considerably higher than reported previously for MBE growth.9,10 Electron diffraction patterns show single crystallinity. Rocking curves show FWHM as low as 0.100. RMS surfaces roughnesses, measured by atomic force microscopy, are of the order of 3 nm for 1µm x 1µm areas. Current work is focusing on optimizing growth on InGaN templates. These templates are promising for their relatively lower lattice mismatch with ZnSnN 2 . Their use is a next step toward producing LED device structures.

Jayatunga, Benthara Hewage Dinushi↗
DOE OSTI2023

Improving robustness for model discerning synthesis process of uranium oxide with unsupervised domain adaptation

The quantitative characterization of surface structures captured in scanning electron microscopy (SEM) images has proven to be effective for discerning provenance of an unknown nuclear material. Recently, many works have taken advantage of the powerful performance of convolutional neural networks (CNNs) to provide faster and more consistent characterization of surface structures. However, one inherent limitation of CNNs is their degradation in performance when encountering discrepancy between training and test datasets, which limits their use widely. The common discrepancy in an SEM image dataset occurs at low-level image information due to user-bias in selecting acquisition parameters and microscopes from different manufacturers. Therefore, in this study, we present a domain adaptation framework to improve robustness of CNNs against the discrepancy in low-level image information. Furthermore, our proposed approach makes use of only unlabeled test samples to adapt a pretrained model, which is more suitable for nuclear forensics application for which obtaining both training and test datasets simultaneously is a challenge due to data sensitivity. Through extensive experiments, we demonstrate that our proposed approach effectively improves the performance of a model by at least 18% when encountering domain discrepancy, and can be deployed in many CNN architectures.

scanning electron microscopy↗
DOE OSTI2022

A Study on Surface Texture and Wettability of Femtosecond Laser Treated Aluminum Alloys

This research aims to study the effects of femtosecond laser treatment on surface texture and wettability properties. The first portion of this research studies 2024-T3 aluminum alloy concerning surface texture (e.g., roughness), wettability, and surface free energy. SEM images help aid the researchers in detecting minuscule changes in surface texture. Profilometry extracts topographical data from the surface. A contact angle goniometer measures contact angle, which follows the sessile drop method. Owens, Wendt, Rabel, and Kaelble's method calculates the surface free energy in conjunction with the contact angle. Femtosecond laser modification produces relatively uniform surface morphology at the macroscale. When looking at the microscale, surface morphology differs depending on the interplay between material melting and self-assembly, where the surface becomes rougher after laser treatment. Contact angle results show that all but the control sample and sample 7 exhibit hydrophobic behavior, i.e., contact angle >90°, with Sample 1 having the most significant angle of 115°. The effects of laser parameters on surface energy is apparent. More work is needed to establish correlations among these three quantities: contact angle, surface energy, and laser parameters. The second portion of this research studies aluminum alloy 6061 as the subject material with the primary goal of creating a superhydrophobic surface. A central composite design creates the laser surface texturing design of experiments. SEM images help aid the researchers in detecting minuscule changes in surface texture. A 3D optical profiler extracts topographical data from the surface. The sessile drop method incorporates a contact angle goniometer to measure the contact angle. The response surface method builds a second-order polynomial model for the contact angle and obtains optimized parameters to maximize the contact angle. This research shows that laser surface texturing can generate a wide range of surface profiles and roughness values with geometric features ranging from hundreds of μm to submicron. All three laser parameters (pulse energy, pulse duration, repetition rate) affect surface roughness and contact angle to some degree. To quantify the relationship between the contact angle, pulse energy, and pulse repetition rate, a response surface model for Al 6061 is identified and used to find the optimal conditions of E=214 μJ, tp=10 ps, and fp=2427 Hz with a predicted maximum contact angle of 161°. A confirmation experiment produces a contact angle of 168°, in good agreement with the predicted value.

Angell, Dakota↗
DOE OSTI2026

Predicting U 3 O 8 powder processing conditions: An AI/ML approach analyzing deep learning embeddings of SEM micrographs

High-resolution SEM images of uranium-oxide powders encode micro- and nanoscale clues to their synthesis route and calcination temperature. We trained a ResNet-50 model on 11 commercial-scale U₃O₈ classes, ammonium diuranate (ADU) or uranyl peroxide (H₂O₂) precursors calcined at temperatures ranging from 400 to 750 °C and added a 256-D projection head before the classifier to analyze the learned representation. The best of eight seeds reached 92.4 % accuracy on reserved testing data, but our focus is the structure of the embedding space rather than the accuracy and labels. We quantify class relatedness in the original 256-D space using centroid similarity and distributional distances, and we use Uniform Manifold Approximation Projection (UMAP) for visualization. ‘Unknown’ images from different preparation methods, SEM operators, and from the literature localized near the expected classes under a nearest-centroid analysis without retraining, as well as clustered in similar UMAP space. In conclusion, this embedding-centered workflow complements black-box classification by providing quantitative, similarity-based comparisons of U₃O₈ morphologies and reduces storage space by up to 98 % for image data used in millisecond vector search comparisons.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Effect of Coaxial Electrode Geometry on the Electric Field Enhancement Factor for a High Voltage Vacuum Gap

We present an experimental analysis of the change in the electric field enhancement factor with varying gap size and penetration depth (P.D) of cathode into anode for a coaxial vacuum gap, diagnosed using Fowler–Nordheim analysis and optical imaging via scanning electron microscope (SEM) and time integrated Digital single lens reflex camera (DSLR). Data were collected on the Coaxial Gap Breakdown Machine (240 A, 25 kV, 150 ns, 0.1 Hz). Experiments using five different gap sizes at nine different P.Ds are compared over runs comprising 50 shots for each case. The results show a strong link between enhancement factor and gap size, with P.D and surface topology. For large gap sizes, 150, 330, and 700 μm, the average enhancement factor value increases with increasing P.D. For smaller gap sizes, 50 and 100 μm, the average enhancement factor decreases with P.D. SEM imaging before and after plasma formation for each gap size allows for quantifying surface finish, microprotrusion growth, average blast diameter, and an estimation of the surface area breakdowns occupy. Time integrated DSLR imaging analysis of the gap at each shot allows for a determination of the distribution of breakdowns about the circumference of the gap for each case tested. Here, the Fowler–Nordheim analysis allows for a quantitative analysis of the surface roughness of all gap sizes tested. Results show that for large gap sizes, the gap geometry and increasing area of breakdown is the main cause for increasing average enhancement factor. For small gap sizes, the dominant driving factor for small average enhancement factors—that subsequently decrease with P.D—is significant changes in surface topology due to an increased number of breakdowns.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

A fine pore-preserved deep neural network for porosity analytics of a high burnup U-10Zr metallic fuel

Abstract U-10 wt.% Zr (U-10Zr) metallic fuel is the leading candidate for next-generation sodium-cooled fast reactors. Porosity is one of the most important factors that impacts the performance of U-10Zr metallic fuel. The pores generated by the fission gas accumulation can lead to changes in thermal conductivity, fuel swelling, Fuel-Cladding Chemical Interaction (FCCI) and Fuel-Cladding Mechanical Interaction (FCMI). Therefore, it is crucial to accurately segment and analyze porosity to understand the U-10Zr fuel system to design future fast reactors. To address the above issues, we introduce a workflow to process and analyze multi-source Scanning Electron Microscope (SEM) image data. Moreover, an encoder-decoder-based, deep fully convolutional network is proposed to segment pores accurately by integrating the residual unit and the densely-connected units. Two SEM 250 × field of view image datasets with different formats are utilized to evaluate the new proposed model’s performance. Sufficient comparison results demonstrate that our method quantitatively outperforms two popular deep fully convolutional networks. Furthermore, we conducted experiments on the third SEM 2500 × field of view image dataset, and the transfer learning results show the potential capability to transfer the knowledge from low-magnification images to high-magnification images. Finally, we use a pre-trained network to predict the pores of SEM images in the whole cross-sectional image and obtain quantitative porosity analysis. Our findings will guide the SEM microscopy data collection efficiently, provide a mechanistic understanding of the U-10Zr fuel system and bridge the gap between advanced characterization to fuel system design.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Argon broad ion beam sectioning and high resolution scanning electron microscopy imaging of hydrated alite

Highlights: • The native fibrous outer and dense inner C-S-H structure are preserved by using low energy argon broad ion beam sectioning. • Possible artefacts of the preparation and imaging of hydrated alite are shown and discussed. • An approach for a semi automated pore analysis of the obtained high resolution images is demonstrated. Scanning electron microscopy (SEM) imaging is able to visualize micro- to nano-structures of cement and concrete. A prerequisite is that the sample preparation preserves the native structure of the specimen. In this study, argon broad ion beam (BIB) sectioning is compared to state-of-the-art sample preparation (resin embedding, polishing) for hydrated alite. Additionally, it is investigated if during BIB, sample cooling is beneficial to avoid deterioration of cement hydrates. The aim is to quantitatively measure pore size distributions in hardened alite pastes. Therefore, not only optimized sample preparation but also optimized imaging conditions are investigated. Finally, it is demonstrated that by image analysis pores down to a diameter of 5 nm in hydrated alite pastes can be quantitatively analysed.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Alignment of Au nanorods along de novo designed protein nanofibers studied with automated image analysis

In this paper, we focus on exploring the directional assembly of anisotropic Au nanorods along de novo designed 1D protein nanofiber templates. Using machine learning and automated image processing, we analyze scanning electron microscopy (SEM) images to study how the attachment density and alignment fidelity are influenced by variables such as the aspect ratio of the Au nanorods, and the salt concentration of the solution. We find that the Au nanorods prefer to align parallel to the protein nanofibers. This preference decreases with increasing salt concentration, but is only weakly sensitive to the nanorod aspect ratio. While the overall specific Au nanorod attachment density to the protein fibers increases with increasing solution ionic strength, this increase is dominated primarily by non-specific binding to the substrate background, and we find that greater specific attachment (nanorods attached to the nanofiber template as compared to the substrates) occurs at the lower studied salt concentrations, with the maximum ratio of specific to non-specific binding occurring when the protein fiber solutions are prepared in 75 mM NaCl concentration.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Effects of deposition and annealing conditions on the crystallisation of NiTi thin films by e-beam evaporation

This Letter demonstrates the effects of deposition and annealing conditions on the crystallisation of nickel–titanium (NiTi) thin films deposited by the e-beam evaporation. NiTi thin films of 0.5 μm thick are fabricated on P-type (110) silicon substrates using the e-beam evaporation technique. We find that with the characterisations using the X-ray diffraction method, the results indicate that low e-beam deposition rate and argon employed annealing procedures can be beneficial to the formation of NiTi crystalline structure. Additionally, the as-deposited NiTi thin films without annealing procedures are amorphous, when deposited on the substrates at low temperatures. The differential scanning calorimetry studies show NiTi thin films formed by the e-beam evaporation are Ni-rich. Furthermore, SEM images illustrate that high-precision patterned NiTi thin film microstructures can be fabricated by combining with the e-beam evaporation and lift-off resist methods, without any wet or dry etching procedures. The proposed fabrication process for NiTi thin film microstructures shows great potential for application in shape memory alloy medical devices, as well as the micro-robotics.

36 MATERIALS SCIENCE↗
DOE OSTI2021

2D-to-3D image translation of complex nanoporous volumes using generative networks

Image-based characterization offers a powerful approach to studying geological porous media at the nanoscale and images are critical to understanding reactive transport mechanisms in reservoirs relevant to energy and sustainability technologies such as carbon sequestration, subsurface hydrogen storage, and natural gas recovery. Nanoimaging presents a trade off, however, between higher-contrast sample-destructive and lower-contrast sample-preserving imaging modalities. Furthermore, high-contrast imaging modalities often acquire only 2D images, while 3D volumes are needed to characterize fully a source rock sample. In this work, we present deep learning image translation models to predict high-contrast focused ion beam-scanning electron microscopy (FIB-SEM) image volumes from transmission X-ray microscopy (TXM) images when only 2D paired training data is available. We introduce a regularization method for improving 3D volume generation from 2D-to-2D deep learning image models and apply this approach to translate 3D TXM volumes to FIB-SEM fidelity. We then segment a predicted FIB-SEM volume into a flow simulation domain and calculate the sample apparent permeability using a lattice Boltzmann method (LBM) technique. Results show that our image translation approach produces simulation domains suitable for flow visualization and allows for accurate characterization of petrophysical properties from non-destructive imaging data.

58 GEOSCIENCES↗
DOE OSTI2025

Multiscale Characterization of Additive Manufacturing Components with Computed Tomography, 3D X-ray Microscopy, and Deep Learning

Additive manufacturing (AM) facilitates the creation of complex-geometry parts, driving advancements in lightweight aerospace components, high-efficiency engine cooling channels, and customized medical implants. However, ensuring the quality and reliability of AM parts remains challenging due to internal defects, surface irregularities, porosity, and residual trapped powder, which are often inaccessible to traditional inspection methods. Recent developments in X-ray computed tomography (XCT) and 3D X-ray microscopy (XRM), particularly systems equipped with resolution-at-a-distance (RaaD™) capabilities, enable high-resolution, non-destructive evaluation of AM components across multiple scales, from sub-micrometer to macroscopic levels. This paper explores modern XCT and XRM techniques for multiscale characterization of AM parts, focusing on their ability to detect and analyze defects such as porosity, cracks, inclusions, and surface roughness, while offering insights into defect formation mechanisms, material properties, and process-induced variations. The integration of deep learning (DL) frameworks, including Simurgh, DeepRecon, and DeepScout, enhances XCT/XRM workflows by reducing scan times, improving resolution recovery, and enabling accurate defect detection even with limited projection data. These DL-based methods overcome limitations of traditional reconstruction techniques, enabling faster, more reliable characterization of dense materials like Inconel 718 and novel alloys such as AlCe. Applications include process parameter optimization, high-throughput quality control, and multistage AM process evaluation, with DL-enhanced workflows accelerating analysis times from weeks to days. Correlative imaging approaches further validate XCT and XRM data against scanning electron microscopy (SEM) images of physically sectioned samples, confirming the accuracy of DL-based reconstructions and enabling comprehensive defect analysis. While challenges remain in generalizing DL models to diverse materials and imaging conditions, improvements in resolution, noise reduction, and defect detection highlight the transformative potential of these methods. This multiscale and correlative approach enables precise identification and correlation of microstructural features with the overall performance of AM components. By integrating advanced XCT, XRM, and DL techniques, this paper demonstrates a significant leap forward in AM characterization, offering valuable insights into the relationships between processing parameters, microstructure, and part performance, and driving innovations that enhance the quality and reliability of AM products for demanding industrial applications.

Additive manufacturing↗
DOE OSTI2021

The Application of Convolutional Neural Networks (CNNs) to Recognize Defects in 3D-Printed Parts

Cracks and pores are two common defects in metallic additive manufacturing (AM) parts. In this paper, deep learning-based image analysis is performed for defect (cracks and pores) classification/detection based on SEM images of metallic AM parts. Three different levels of complexities, namely, defect classification, defect detection and defect image segmentation, are successfully achieved using a simple CNN model, the YOLOv4 model and the Detectron2 object detection library, respectively. The tuned CNN model can classify any single defect as either a crack or pore at almost 100% accuracy. The other two models can identify more than 90% of the cracks and pores in the testing images. In addition to the application of static image analysis, defect detection is also successfully applied on a video which mimics the AM process control images. The trained Detectron2 model can identify almost all the pores and cracks that exist in the original video. This study lays a foundation for future in situ process monitoring of the 3D printing process.

36 MATERIALS SCIENCE↗
DOE OSTI2026

INFLUENCE OF SUPPLEMENTARY ARGON GAS SHIELDING ON THE MICROSTRUCTURE AND MECHANICAL BEHAVIOR OF SS316L ADDITIVELY MANUFACTURED BY DIRECTED ENERGY DEPOSITION

The dissolution of atmospheric gases into parts additively manufactured by directed energy deposition (DED) has long been of concern, and stock shielding solutions have usually proven inadequate at fully removing atmospheric gases from weld beads. The objective of this work was to design a new supplementary argon gas shroud for a specific DED printer and evaluate its effect on microstructure and macroscopic properties of SS316L and the potential for printing more reactive metals such as Ti-6Al-4V. This was investigated by printing four SS316L sample blocks, two with and two without the shroud, each printed in two different orientations. These samples were subsequently evaluated with inert gas fusion (IGF), SEM/EDS imaging, and tensile tests. The shroud reduced oxygen content by 0.019-0.031%, and SEM imaging revealed fewer oxide inclusions. A more dramatic reduction of 0.13% oxygen and 0.171% nitrogen was observed by the inert gas fusion of Ti-6Al-4V samples. Overall, the results indicated the shroud was successful at reducing atmospheric gas uptake within the print region and may enable the printing of reactive metal powders in DED systems.

Helvie, Kyle↗