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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Hyperspectral and Nanosecond Temporal Resolution Widefield Infrared Photothermal Heterodyne Imaging

Label-free, bond-selective imaging offers new opportunities for fundamental and applied studies in chemistry, biology, and materials science. Preventing its broader application to investigating spectrally congested specimens are issues related to low sensitivity as well as low spatial and temporal resolution. Here, we demonstrate a widefield, mid-infrared (MIR) photothermal imaging technique, called widefield Infrared Photothermal Heterodyne imaging (wIR-PHI), that massively parallelizes acquisition of MIR absorption data through use of a high-speed complementary metal-oxide-semiconductor camera. wIR-PHI possesses notable features that include: spatial resolution significantly below the MIR diffraction limit, hyperspectral imaging capabilities, high sensitivity, and ~100 ns temporal resolution. Here, the first two features are highlighted by hyperspectral imaging of proximally close poly(methyl methacrylate) (PMMA) and polystyrene (PS) nanoparticles where clear, bond-specific imaging of nanoparticles, separated by less than the MIR diffraction limit, is demonstrated. Sensitivity is highlighted by imaging individual PMMA and PS nanoparticles with radii between r = 97-556 nm. This leads to a current, peak absorption cross-section limit-of-detection of σ abs = 1.9× 10 –16 cm 2 . wIR-PHI's 100 ns temporal resolution is simultaneously demonstrated by observing the decay of photothermal contrast on individual nanoparticles on a ~200-6000 ns timescale. In whole, wIR-PHI’s dramatic increase in acquisition speed opens opportunities for future MIR kinetic imaging and spectroscopic studies of important chemical, biological, and material processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Boosting the signal-to-noise of low-field MRI with deep learning image reconstruction

Recent years have seen a resurgence of interest in inexpensive low magnetic field (< 0.3 T) MRI systems mainly due to advances in magnet, coil and gradient set designs. Most of these advances have focused on improving hardware and signal acquisition strategies, and far less on the use of advanced image reconstruction methods to improve attainable image quality at low field. We describe here the use of our end-to-end deep neural network approach (AUTOMAP) to improve the image quality of highly noise-corrupted low-field MRI data. We compare the performance of this approach to two additional state-of-the-art denoising pipelines. We find that AUTOMAP improves image reconstruction of data acquired on two very different low-field MRI systems: human brain data acquired at 6.5 mT, and plant root data acquired at 47 mT, demonstrating SNR gains above Fourier reconstruction by factors of 1.5- to 4.5-fold, and 3-fold, respectively. In these applications, AUTOMAP outperformed two different contemporary image-based denoising algorithms, and suppressed noise-like spike artifacts in the reconstructed images. The impact of domain-specific training corpora on the reconstruction performance is discussed. The AUTOMAP approach to image reconstruction will enable significant image quality improvements at low-field, especially in highly noise-corrupted environments.

42 ENGINEERING↗
DOE OSTI2022

A knock-on deuteron imager for measurements of fuel and hotspot asymmetry in direct-drive inertial confinement fusion implosions (invited)

A knock-on deuteron imager (KoDI) has been implemented to measure the fuel and hotspot asymmetry of cryogenic inertial confinement fusion implosions on OMEGA. Energetic neutrons produced by D–T fusion elastically scatter (“knock on”) deuterons from the fuel layer with a probability that depends on ρR. Deuterons above 10 MeV are produced by near-forward scattering, and imaging them is equivalent to time-integrated neutron imaging of the hotspot. Deuterons below 6 MeV are produced by a combination of side scattering and ranging in the fuel, and encode information about the spatial distribution of the dense fuel. Here, the KoDI instrument consists of a multi-penumbral aperture positioned 10–20 cm from the implosion using a ten-inch manipulator and a detector pack at 350 cm from the implosion to record penumbral images with magnification of up to 35×. Range filters and the intrinsic properties of CR-39 are used to distinguish different charged-particle images by energy along the same line of sight. Image plates fielded behind the CR-39 record a 10 keV x-ray image using the same aperture. A maximum-likelihood reconstruction algorithm has been implemented to infer the source from the projected penumbral images. The effects of scattering and aperture charging on the instrument point-spread function are assessed. Synthetic data are used to validate the reconstruction algorithm and assess an appropriate termination criterion. Significant aperture charging has been observed in the initial experimental dataset, and increases with aperture distance from the implosion, consistent with a simple model of charging by laser-driven EMP.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Ultrafast time-resolved 2D imaging of laser-driven fast electron transport in solid density matter using an x-ray free electron laser

High-power, short-pulse laser-driven fast electrons can rapidly heat and ionize a high-density target before it hydrodynamically expands. The transport of such electrons within a solid target has been studied using two-dimensional (2D) imaging of electron-induced Kα radiation. However, it is currently limited to no or picosecond scale temporal resolutions. Here, we demonstrate femtosecond time-resolved 2D imaging of fast electron transport in a solid copper foil using the SACLA x-ray free electron laser (XFEL). An unfocused collimated x-ray beam produced transmission images with sub-micron and ~10 fs resolutions. The XFEL beam, tuned to its photon energy slightly above the Cu K-edge, enabled 2D imaging of transmission changes induced by electron isochoric heating. Time-resolved measurements obtained by varying the time delay between the x-ray probe and the optical laser show that the signature of the electron-heated region expands at ~25% of the speed of light in a picosecond duration. Time-integrated Cu Kα images support the electron energy and propagation distance observed with the transmission imaging. The x-ray near-edge transmission imaging with a tunable XFEL beam could be broadly applicable for imaging isochorically heated targets by laser-driven relativistic electrons, energetic protons, or an intense x-ray beam.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Fast Local Spatial Verification for Feature-Agnostic Large-Scale Image Retrieval

Images from social media can reflect diverse viewpoints, heated arguments, and expressions of creativity, adding new complexity to retrieval tasks. Researchers working on Content-Based Image Retrieval (CBIR) have traditionally tuned their algorithms to match filtered results with user search intent. However, we are now bombarded with composite images of unknown origin, authenticity, and even meaning. With such uncertainty, users may not have an initial idea of what the search query results should look like. For instance, hidden people, spliced objects, and subtly altered scenes can be difficult for a user to detect initially in a meme image, but may contribute significantly to its composition. It is pertinent to design systems that retrieve images with these nuanced relationships in addition to providing more traditional results, such as duplicates and near-duplicates — and to do so with enough efficiency at large scale. In this work, we propose a new approach for spatial verification that aims at modeling object-level regions using image keypoints retrieved from an image index, which is then used to accurately weight small contributing objects within the results, without the need for costly object detection steps. We call this method the Objects in Scene to Objects in Scene (OS2OS) score, and it is optimized for fast matrix operations, which can run quickly on either CPUs or GPUs. It performs comparably to state-of-the-art methods on classic CBIR problems (Oxford 5K, Paris 6K, and Google-Landmarks), and outperforms them in emerging retrieval tasks such as image composite matching in the NIST MFC2018 dataset and meme-style imagery from Reddit.

42 ENGINEERING↗
DOE OSTI2025

Single-Objective Airy Light-Sheet Imaging

Despite its massive potential, standard light-sheet imaging (LSI) faces key challenges, such as the incompatibility with common sample mounting techniques and low-resolution imaging. Single-objective LSI attempts to address these issues but often suffers from limited fields-of-view and throughput rates, or requires multiple optics that increase costs, alignment complexity, and losses. To overcome these challenges of standard single-objective LSI, we introduce single-objective Airy light-sheet imaging (SoALSI). SoALSI leverages the extraordinary self-acceleration properties of the Airy beam, achieving 5× higher imaging rates and enhanced imaging efficiency than standard single-objective LSI. Here, we demonstrate SoALSI’s versatility through rigorous contrast and resolution characterizations and by high-resolution imaging of diverse biological specimens, including malaria parasite-infected red blood cells and plant root tissue. SoALSI seamlessly integrates with any standard inverted microscope frame, enabling broader accessibility for the bioimaging community to explore biological processes in a wide range of specimens with enhanced resolution and imaging contrast.

airy beam↗
DOE OSTI2020

Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2

Motion-activated wildlife cameras (or “camera traps”) are frequently used to remotely and noninvasively observe animals. The vast number of images collected from camera trap projects has prompted some biologists to employ machine learning algorithms to automatically recognize species in these images, or at least filter-out images that do not contain animals. These approaches are often limited by model transferability, as a model trained to recognize species from one location might not work as well for the same species in different locations. Furthermore, these methods often require advanced computational skills, making them inaccessible to many biologists. We used 3 million camera trap images from 18 studies in 10 states across the United States of America to train two deep neural networks, one that recognizes 58 species, the “species model,” and one that determines if an image is empty or if it contains an animal, the “empty-animal model.” Our species model and empty-animal model had accuracies of 96.8% and 97.3%, respectively. Furthermore, the models performed well on some out-of-sample datasets, as the species model had 91% accuracy on species from Canada (accuracy range 36%–91% across all out-of-sample datasets) and the empty-animal model achieved an accuracy of 91%–94% on out-of-sample datasets from different continents. Our software addresses some of the limitations of using machine learning to classify images from camera traps. By including many species from several locations, our species model is potentially applicable to many camera trap studies in North America. We also found that our empty-animal model can facilitate removal of images without animals globally. We provide the trained models in an R package (MLWIC2: Machine Learning for Wildlife Image Classification in R), which contains Shiny Applications that allow scientists with minimal programming experience to use trained models and train new models in six neural network architectures with varying depths.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Automated bubble analysis of high-speed subcooled flow boiling images using U-net transfer learning and global optical flow

Capturing and analyzing the bubble dynamics is crucial to improving the understanding of boiling heat transfer mechanisms and predicting boiling heat transfer coefficient and boiling crisis. High speed video (HSV) imaging has been used for decades towards this end. Still, there is no universal approach to quantitatively analyze bubble dynamics from HSV images. In this study, we propose a data-driven post-processing approach to segment, track, and identify wall-attached vapor bubbles from HSV images of the boiling process in subcooled flow conditions. Firstly, we employ a transfer learning framework with a U-Net-based convolution neural network (CNN) architecture to detect and segment bubbles in HSV images of diverse contrast and surface texture using very little data (e.g., 10 images) for training. Then, we evaluate the trained CNN model with 100 ground-truth images, and the validation results show that the model accuracy and precision in detecting the optical footprint of bubbles are higher than 90%. Finally, we suggest a criterion to identify a condensing bubble based on the divergence of the bubble displacement, which is calculated from sequential segmented bubble images using a global optical flow code. Using this combination of machine learning and optical flow, we can identify nucleation sites and track the growth of bubbles nucleating at each site to quantify nucleation site density, nucleation frequency, and other fundamental boiling parameters. The proposed system is validated using results obtained on a special heater, which enables both infrared (IR) thermometry and HSV imaging on a metallic surface. We compare the fundamental boiling parameters obtained by the two different diagnostics. The results show good agreement. In conclusion, the difference between the measurements of nucleation site density, averaged nucleation frequency, and averaged growth time performed with the two techniques is always within ± 20% and mostly ± 10% of the values measured with IR thermometry.

42 ENGINEERING↗
DOE OSTI2020

An image-driven machine learning approach to kinetic modeling of a discontinuous precipitation reaction

Micrograph quantification is an essential component of several materials science studies. Machine learning methods, in particular convolutional neural networks, have previously demonstrated performance in image recognition tasks across several disciplines (e.g. materials science, medical imaging, facial recognition). Here, we apply these well-established methods to develop an approach to microstructure quantification for kinetic modeling of a discontinuous precipitation reaction in a case study on the uranium-molybdenum system. Prediction of material processing history based on image data (classification), calculation of area fraction of phases present in the micrographs (segmentation), and kinetic modeling from segmentation results were performed. Results indicate that convolutional neural networks represent microstructure image data well, and segmentation using the k-means clustering algorithm yields results that agree well with manually annotated images. Classification accuracies of original and segmented images are both 94% for a 5-class classification problem. Kinetic modeling results agree well with previously reported data using manual thresholding. The image quantification and kinetic modeling approach developed and presented here aims to reduce researcher bias introduced into the characterization process, and allows for leveraging information in limited image data sets.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Automated defect identification in electroluminescence images of solar modules

Solar photovoltaic (PV) modules are susceptible to manufacturing defects, mishandling problems or extreme weather events that can limit energy production or cause early device failure. Trained professionals use electroluminescence (EL) images to identify defects in modules, however, field surveys or inline image acquisition can generate millions of EL images, which are infeasible to analyze by rote inspection. Here, we develop a rapid automatic computer vision pipeline (~0.5 seconds/module) to analyze EL images and identify defects including cracks, intra-cell defects, oxygen-induced defects, and solder disconnections. Defect identification is achieved with a machine learning model (Random Forest, ResNet models and YOLO) trained on 762 manually-labeled EL images of PV modules. We compare model performance on an imbalanced real-world validation set containing 134 EL images and determine that ResNet18 and YOLO are the optimal models; we next evaluated these models on a dedicated testing set (129 module images) with resulting macro F1 scores of 0.83 (ResNet18) and 0.78 (YOLO). Using a field EL survey of a PV power plant damaged in a vegetation fire, we analyze 18,954 EL images (2.4 million cells) and inspect the spatial distribution of defects on the solar modules. The results find increased frequency of ‘crack’, ‘solder’ and ‘intra-cell’ defects on the edges of the solar module closest to the ground after fire. We also find an abnormal increase of striation rings on cells which were assumed to be caused mainly in fabrication process. Our methods are published as open-source software. It can also be used to identify other kinds of defects or process different types of solar cells with minor modification on models by transfer learning.

14 SOLAR ENERGY↗
DOE OSTI2021

A time-resolved, in-chamber x-ray pinhole imager for Z

We have commissioned a new time-resolved, x-ray imaging diagnostic for the Z facility. The primary intended application is for diagnosing the stagnation behavior of Magnetized Liner Inertial Fusion (MagLIF) and similar targets. We have a variety of imaging systems at Z, both time-integrated and time-resolved, that provide valuable x-ray imaging information, but no system at Z up to this time provides a combined high-resolution imaging with multi-frame time resolution; this new diagnostic, called TRICXI for Time Resolved In-Chamber X-ray Imager, is meant to provide time-resolved spatial imaging with high resolution. The multi-frame camera consists of a microchannel plate camera. A key component to achieving the design goals is to place the instrument inside the Z vacuum chamber within 2 m of the load, which necessitates a considerable amount of x-ray shielding as well as a specially designed, independent vacuum system. Here, a demonstration of the imaging capability for a series of MagLIF shots is presented. Predictions are given for resolution and relative image irradiance to guide experimenters in choosing the desired configuration for their experiments.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Geometrical-Based Generative Adversarial Network to Enhance Digital Rock Image Quality

X-ray microcomputed tomography (micro-CT) is a common tool for the study of porous media structures and properties. High-quality micro-CT data are required to accurately capture pore structures. Acquiring high-quality micro-CT data, however, is not always possible, owing to application limitations and experimental constraints. Therefore, we propose a geometrical-based generative adversarial network (GAN) to rapidly restore noisy micro-CT images to their clean counterparts. The training data and related ground-truth (GT) data are scanned for 7 min and 9.5 h, respectively. To evaluate the performance of the geometrical-based GAN, a 6003 voxel image that has never been used for training is reconstructed and compared with the corresponding GT image. Histogram matching and linear normalization are implemented to adjust the histogram of the reconstructed image to that of the GT image. A watershed-based segmentation method is then applied to delineate pore and solid phases. Lastly, we measure the Minkowski functionals and petrophysical properties, including absolute permeability, pore size distribution, drainage capillary pressure-saturation curve, and imbibition relative permeability, to estimate the physical accuracy of the denoised image. The results show that the proposed geometrical-based GAN can accurately restore noisy micro-CT data. By reducing the scanning time from 9.5 h to 7 min, the expenditure of collecting micro-CT can be decreased significantly. This is particularly important for applications where time-lapse images of a dynamic process are required, high-throughput imaging is necessary for real-time data analysis, or where the quantification of large sample volumes is required.

58 GEOSCIENCES↗
DOE OSTI2026

High-speed quantitative X-ray multi-contrast imaging with deep learning based modulated pattern analysis

The advent of X-ray multi-contrast imaging methods, providing absorption, phase, and dark-field images, holds tremendous promise for complementary and non-destructive visualization of inner structures within materials and bio-samples. However, the low efficiency in measuring and analyzing X-ray modulated patterns has hindered their application in high-resolution in situ imaging. In this work, the Enhanced Scanning Pattern-based Imaging Neural Network (ESPINNet) is introduced as a powerful tool for achieving high-speed, high-resolution quantitative imaging. ESPINNet is faster than correlation-based speckle tracking methods such as XSVT and UMPA, and provides a balanced performance in terms of resolution and speed for data collection by using fewer scanning images. In comparison with our previously developed neural network, ESPINNet introduces the capability to generate dark-field images, further enhancing its versatility. By leveraging scanning patterns, ESPINNet significantly improves resolution and measurement precision. Furthermore, its adaptability to various modulation patterns, including those produced by sandpaper, coded masks, or gratings, ensures broad applicability. These features enable real-time 2D and 3D multi-contrast imaging, positioning ESPINNet as a transformative solution for applications in materials science and biomedical research, particularly for high-speed and in situ measurements.

X-ray at-wavelength metrology↗
DOE OSTI2024

Zone plate-based extreme ultraviolet mask microscope with through-pellicle imaging capability

Mirror-based and zone plate-based imaging systems are being used in actinic extreme ultraviolet (EUV) reticle review tools. With regard to zone plates, a short working distance is advantageous in terms of the required spectral bandwidth, manufacturability, and potential throughput and imaging performance. Zone plates therefore typically have a short working distance. The industry has adopted the use of an EUV pellicle to protect the photomask. Imaging photomask through-pellicle requires a working distance larger than 2.5 mm. A zone-plate-based EUV mask microscope with a 3-mm working distance has been commissioned at beamline 11.3.2 of the Advanced Light Source. Through-pellicle imaging at an exposure time of 2 s is demonstrated. The instrument achieves an image contrast of 95% on large features on a photomask with a tantalum-based absorber. Imaging down to 45-nm half pitch (mask scale) is demonstrated. A NILS of 2.55 is achieved on 60-nm half-pitch (mask scale) lines and spaces. These results demonstrate that zone-plate-based imaging systems can meet the requirements of an actinic EUV mask review tool in terms of imaging performance and throughput in an instrument compatible with EUV pellicles.

Extreme ultraviolet↗
DOE OSTI2020

Chlorophyll fluorescence imaging captures photochemical efficiency of grain sorghum (Sorghum bicolor) in a field setting

Abstract Background Photosynthesis is one of the most important biological reactions and forms the basis of crop productivity and yield on which a growing global population relies. However, to develop improved plant cultivars that are capable of increased productivity, methods that can accurately and quickly quantify photosynthetic efficiency in large numbers of genotypes under field conditions are needed. Chlorophyll fluorescence imaging is a rapid, non-destructive measurement that can provide insight into the efficiency of the light-dependent reactions of photosynthesis. Results To test and validate a field-deployed fluorescence imaging system on the TERRA-REF field scanalyzer, leaves of potted sorghum plants were treated with a photosystem II inhibitor, DCMU, to reduce photochemical efficiency (F V /F M ). The ability of the fluorescence imaging system to detect changes in fluorescence was determined by comparing the image-derived values with a handheld fluorometer. This study demonstrated that the imaging system was able to accurately measure photochemical efficiency (F V /F M ) and was highly correlated ( r = 0.92) with the handheld fluorometer values. Additionally, the fluorescence imaging system was able to track the decrease in photochemical efficiency due to treatment of DCMU over a 7 day period. Conclusions The system’s ability to capture the temporal dynamics of the plants’ response to this induced stress, which has comparable dynamics to abiotic and biotic stressors found in field environments, indicates the system is operating correctly. With the validation of the fluorescence imaging system, physiological and genetic studies can be undertaken that leverage the fluorescence imaging capabilities and throughput of the field scanalyzer.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

End–to–End Metasurface Design for Temperature Imaging via Broadband Planck‐Radiation Regression

A theoretical framework is presented for temperature imaging from long-wavelength infrared (LWIR) thermal radiation (e.g., 8–12 µm) through the end-to-end design of a metasurface-optics frontend and a computational-reconstruction backend. A new nonlinear reconstruction algorithm, “Planck regression”, is introduced to reconstruct the temperature map from a gray scale sensor image, even in the presence of severe chromatic aberration, by exploiting black body and optical physics particular to thermal imaging. This algorithm is combined with an end-to-end approach that optimizes manufacturable, single-layer metasurfaces to yield the most accurate reconstruction. The designs demonstrate high-quality, noise-robust reconstructions of arbitrary temperature maps (including completely random images) in simulations of an ultra-compact thermal-imaging device. Here, it is also shown that Planck regression is much more generalizable to arbitrary images than a straightforward neural-network reconstruction, which requires a large training set of domain-specific images.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Computational models of direct and indirect X‐ray breast imaging detectors for in silico trials

Abstract Background To facilitate in silico studies that investigate digital mammography (DM) and breast tomosynthesis (DBT), models replicating the variety in imaging performance of the DM and DBT systems, observed across manufacturers are needed. Purpose The main purpose of this work is to develop generic physics models for direct and indirect detector technology used in commercially available systems, with the goal of making them available open source to manufacturers to further tweak and develop the exact in silico replicas of their systems. Methods We recently reported on an in silico version of the SIEMENS Mammomat Inspiration DM/DBT system using an open‐source GPU‐accelerated Monte Carlo x‐ray imaging simulation code (MC‐GPU). We build on the previous version of the MC‐GPU codes to mimic the imaging performances of two other Food and Drug Administration (FDA)‐approved DM/DBT systems, such as Hologic Selenia Dimensions (HSD) and the General Electric Senographe Pristina (GSP) systems. In this work, we developed a hybrid technique to model the optical spread and signal crosstalk observed in the GSP and HSD systems. MC simulations are used to track each x‐ray photon till its first interaction within the x‐ray detector. On the other hand, the signal spread in the x‐ray detectors is modeled using previously developed analytical equations. This approach allows us to preserve the modeling accuracy offered by MC methods in the patient body, while speeding up secondary carrier transport (either electron–hole pairs or optical photons) using analytical equations in the detector. The analytical optical spread model for the indirect detector includes the depth‐dependent spread and collection of optical photons and relies on a pre‐computed set of point response functions that describe the optical spread as a function of depth. To understand the capabilities of the computational x‐ray detector models, we compared image quality metrics like modulation transfer function (MTF), normalized noise power spectrum (NNPS), and detective quantum efficiency (DQE), simulated with our models against measured data. Please note that the purpose of these comparisons with measured data would be to gauge if the model developed as part of this work could replicate commercially used direct and indirect technology in general and not to achieve perfect fits with measured data. Results We found that the simulated image quality metrics such as MTF, NNPS, and DQE were in reasonable agreement with experimental data. To demonstrate the imaging performance of the three DM/DBT systems, we integrated the detector models with the VICTRE pipeline and simulated DM images of a fatty breast model containing a spiculated mass and a calcium oxalate cluster. In general, we found that the images generated using the indirect model appeared more blurred with a different noise texture and contrast as compared to the systems with direct detectors. Conclusions We have presented computational models of three commercially available FDA‐approved DM/DBT systems, which implement both direct and indirect detector technology. The updated versions of the MC‐GPU codes that can be used to replicate three systems are available in open source format through GitHub.

Sengupta, Aunnasha↗
DOE OSTI2025

Multimodal Defect Imaging of Pure Tungsten Components Fabricated via Electron Beam Powder Bed Fusion

The utilization of additive manufacturing (AM) techniques for refractory materials in high-temperature environments has significantly expanded because of the ability to fabricate geometrically complex components. Electron beam powder bed fusion (EB-PBF), which provides lower residual stress, a cleaner vacuum environment, and better efficiency for high melting point, is one of the best-suited AM methods to produce advanced refractory components. However, the property variation attributed to the heterogeneous microstructure and process-induced defects has hindered the widespread adoption of EB-PBF-produced material like tungsten. While numerous in-situ monitoring and defect detection methods have been demonstrated for EB-PBF, a workflow that compares and evaluates process-induced abnormalities from different imaging perspectives is still limited. This study examines a feature-embedded tungsten component manufactured via the EB-PBF process to demonstrate the defect detection capabilities of a multimodal defect imaging workflow. The predefined and process-induced defects are evaluated by harnessing various imaging techniques, including in-situ electron imaging, layerwise near-infrared (NIR) imaging, post-build high-energy x-ray computed tomography (CT), and conventional destructive metallography. The results highlight the strengths and limitations of distinctive defect imaging techniques concerning specific defect types, sizes, and conditions. It was found that electron imaging can provide more abnormal detection capabilities while maintaining a higher measuring accuracy, against the conventional metallography in this case study, compared with NIR and CT imaging techniques.

36 MATERIALS SCIENCE↗