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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

BM3DORNL

BM3DORNL is a high-performance, open-source library for removing streak and ring artifacts from computed-tomography (CT) data, developed for neutron imaging at Oak Ridge National Laboratory's Spallation Neutron Source (VENUS beamline) and applicable to X-ray CT as well. Ring artifacts — concentric rings in reconstructed slices caused by detector pixel-to-pixel response non-uniformities — appear as vertical streaks in the sinogram and degrade both image quality and quantitative analysis. BM3DORNL operates in the sinogram domain using an adaptation of the BM3D (block-matching and 3D collaborative filtering) algorithm (Dabov et al., 2007). It provides a dedicated streak-removal mode, a true multi-scale BM3D variant (after Mäkinen et al., 2021) that suppresses wide streaks single-scale methods miss, and an alternative Fourier–SVD method (~2.6× faster) combining FFT-based energy detection with rank-1 SVD. The computationally intensive core is implemented in Rust with parallel (Rayon) block matching, integral-image pre-screening, and optimized transforms, and is exposed through a simple Python API (with an optional GUI) so it integrates directly into existing tomography reconstruction pipelines. It processes both 2D sinograms and 3D sinogram stacks, is pip-installable for Linux and macOS, and is documented at https://bm3dornl.readthedocs.io.

Zhang, Chen [Oak Ridge National Laboratory (ORNL),↗
DOE OSTI2025

An Image-Plane Approach to Gravitational Lens Modeling of Interferometric Data

Strong gravitational lensing acts as a cosmic telescope, enabling the study of the high-redshift universe. Astronomical interferometers, such as the Atacama Large Millimeter/submillimeter Array (ALMA), have provided high-resolution images of strongly lensed sources at millimeter and submillimeter wavelengths. To model the mass and light distributions of lensing and source galaxies from strongly lensed images, strong lens modeling for interferometric observations is conventionally performed in the visibility space, which is computationally expensive. In this paper, we implement an image-plane lens modeling methodology for interferometric dirty images by accounting for noise correlations. We show that the image-plane likelihood function produces accurate model values when tested on simulated ALMA observations with an ensemble of noise realizations. We also apply our technique to ALMA observations of two sources selected from the South Pole Telescope survey, comparing our results with previous visibility-based models. Our model results are consistent with previous models for both parametric and pixelated source-plane reconstructions. We implement this methodology for interferometric lens modeling in the open-source software package lenstronomy.

Zhang, Nan [Illinois U., Urbana (main)] (ORCID:000↗
DOE OSTI2025

Elastic Stochastic Full Waveform Inversion (eSFWI)

This collaboration between Lawrence Livermore National Security, LLC (LLNS) as manager and operator of Lawrence Livermore National Laboratory (LLNL) and Chevron USA Inc., acting through its Chevron Technical Center division, aimed at developing next-generation computational methods for the Elastic Stochastic Full Waveform Inversion (eSFWI). Seismic imaging is heavily used in the oil and gas industry for identifying and operating subsurface reservoirs. Improved seismic imaging methods can improve productivity, lower costs, and improve operational and environmental safety. This CRADA demonstrated that new high-performance computing (HPC) architectures being rolled out over the next five years can enable unprecedented seismic imaging resolution when using eSFWI techniques to process active seismic data. An open-source computational mini-application was developed, capable of demonstrating near-peak performance for eSFWI algorithms on CPU and GPU enabled HPC platforms. Performance was demonstrated on LLNL HPC systems such as Lassen, as well as on Chevron systems. This project benefited Chevron USA Inc. by demonstrating the potential computational efficiency of their full waveform inversion capabilities used to characterize oil/gas reservoirs, which in turn benefits the public through potential increases in capabilities to perform analysis of leasing sites.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2025

FPGA-accelerated SpeckleNN with SNL for real-time X-ray single-particle imaging

We present the implementation of a specialized version of our previously published unified embedding model, SpeckleNN, for real-time speckle pattern classification in X-ray Single-Particle Imaging (SPI), using the SLAC Neural Network Library (SNL) on an FPGA platform. This hardware realization transitions SpeckleNN from a prototypic model into a practical edge solution, optimized for running inference near the detector in high-throughput X-ray free-electron laser (XFEL) facilities, such as those found at the Linac Coherent Light Source (LCLS). To address the resource constraints inherent in FPGAs, we developed a more specialized version of SpeckleNN. The original model, which was designed for broader classification across multiple biological samples, comprised ~5.6 million parameters. The new implementation, while reducing the parameter count to 64.6K (a 98.8% reduction), focuses on maintaining the model's essential functionality for real-time operation, achieving an accuracy of 90%. Furthermore, we compressed the latent space from 128 to 50 dimensions. This implementation was demonstrated on the KCU1500 FPGA board, utilizing 71% of available DSPs, 75% of LUTs, and 48% of FFs, with an average power consumption of 9.4W according to the Vivado post-implementation report. The FPGA performed inference on a single image with a latency of 45.015 microseconds at a 200 MHz clock rate. In comparison, running the same inference on an NVIDIA A100 GPU resulted in an average power consumption of ~73W and an image processing latency of around 400 microseconds. Our FPGA-accelerated version of SpeckleNN demonstrated significant improvements, achieving an 8.9 × speedup and a 7.8 × reduction in power consumption compared to the GPU implementation. Key advancements include model specialization and dynamic weight loading through SNL, which eliminates the need for time-consuming FPGA design re-synthesis, allowing fast and continuous deployment of models (re)trained online. These innovations enable real-time adaptive classification and efficient vetoing of speckle patterns, making SpeckleNN more suited for deployment in XFEL facilities. This implementation has the potential to significantly accelerate SPI experiments and enhance adaptability to evolving experimental conditions.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

AAPM Truth‐based CT (TrueCT) reconstruction grand challenge

Background: This Special Report summarizes the 2022, AAPM grand challenge on Truth-based CT image reconstruction. Purpose: To provide an objective framework for evaluating CT reconstruction methods using virtual imaging resources consisting of a library of simulated CT projection images of a population of human models with various diseases. Methods: Two hundred unique anthropomorphic, computational models were created with varied diseases consisting of 67 emphysema, 67 lung lesions, and 66 liver lesions. The organs were modeled based on clinical CT images of real patients. The emphysematous regions were modeled using segmentations from patient CT cases in the COPDGene Phase I dataset. For the lung and liver lesion cases, 1–6 malignant lesions were created and inserted into the human models, with lesion diameters ranging from 5.6 to 21.9 mm for lung lesions and 3.9 to 14.9 mm for liver lesions. The contrast defined between the liver lesions and liver parenchyma was 82 ± 12 HU, ranging from 50 to 110 HU. Similarly, the contrast between the lung lesions and the lung parenchyma was defined as 781 ± 11 HU, ranging from 725 to 805 HU. For the emphysematous regions, the defined HU values were −950 ± 17 HU ranging from −918 to −979 HU. The developed human models were imaged with a validated CT simulator. The resulting CT sinograms were shared with the participants. The participants reconstructed CT images from the sinograms and sent back their reconstructed images. Further, the reconstructed images were then scored by comparing the results against the corresponding ground truth values. The scores included both task-generic (root mean square error [RMSE] and structural similarity matrix [SSIM]), and task-specific (detectability index [d’] and lesion volume accuracy) metrics. For the cases with multiple lesions, the measured metric was averaged across all the lesions. To combine the metrics with each other, each metric was normalized to a range of 0 to 1 per disease type, with “0” and “1” being the worst and best measured values across all cases of the disease type for all received reconstructions. Results: The True-CT challenge attracted 52 participants, out of which 5 successfully completed the challenge and submitted the requested 200 reconstructions. Across all participants and disease types, SSIM absolute values ranged from 0.22 to 0.90, RMSE from 77.6 to 490.5 HU, d’ from 0.1 to 64.6, and volume accuracy ranged from 1.2 to 753.1 mm3. The overall scores demonstrated that participant “A” had the best performance in all categories, except for the metrics of d’ for lung lesions and RMSE for liver lesions. Participant “A” had an average normalized score of 0.41 ± 0.22, 0.48 ± 0.32, and 0.42 ± 0.33 for the emphysema, lung lesion, and liver lesion cases, respectively. Conclusions: The True-CT challenge successfully enabled objective assessment of CT reconstructions with the unique advantage of access to a diverse population of diseased human models with known ground truth. This study highlights the significant potential of virtual imaging trials in objective assessment of medical imaging technologies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

ExtremeMETA: High-speed Lightweight Image Segmentation Model by Remodeling Multi-channel Metamaterial Imagers

Deep neural networks (DNNs) have heavily relied on traditional computational units, such as CPUs and GPUs. However, this conventional approach brings significant computational burden, latency issues, and high power consumption, limiting their effectiveness. This has sparked the need for lightweight networks such as ExtremeC3Net. Meanwhile, there have been notable advancements in optical computational units, particularly with metamaterials, offering the exciting prospect of energy-efficient neural networks operating at the speed of light. Yet, the digital design of metamaterial neural networks (MNNs) faces precision, noise, and bandwidth challenges, limiting their application to intuitive tasks and low-resolution images. In this study, we proposed a large kernel lightweight segmentation model, ExtremeMETA. Based on ExtremeC3Net, our proposed model, ExtremeMETA maximized the ability of the first convolution layer by exploring a larger convolution kernel and multiple processing paths. With the large kernel convolution model, we extended the optic neural network application boundary to the segmentation task. To further lighten the computation burden of the digital processing part, a set of model compression methods was applied to improve model efficiency in the inference stage. The experimental results on three publicly available datasets demonstrated that the optimized efficient design improved segmentation performance from 92.45 to 95.97 on mIoU while reducing computational FLOPs from 461.07 MMacs to 166.03 MMacs. The large kernel lightweight model ExtremeMETA showcased the hybrid design’s ability on complex tasks.

large convolution kernel↗
DOE OSTI2025

Boosting Noise2Inverse via enhanced model selection for denoising computed tomography data

Synchrotron-based x-ray tomographic imaging enables the examination of the internal structure of materials at high spatial and temporal resolution. Experimental constraints can impose dose and time limits on the measurements, introducing a higher level of noise and artifacts in the reconstructed images. Deep learning has emerged as a powerful tool to remove noise from reconstructed images. Recently, the Noise2Inverse method was designed specifically for denoising reconstructed images without requiring paired noisy and clean images. This method creates multiple statistically independent reconstructions used to pair the data in which training involves transforming one reconstruction into the other, and vice versa. Originally designed to be used after a fixed number of epochs, we see in practice that this approach may not produce the optimal model and may unnecessarily waste computational resources. Therefore, we propose an alternative method of identifying the best model during training that aligns with the Noise2Inverse method. During validation, we compare the model output of the multiple reconstructions among each other. We hypothesize that the best model is the one that produces images with the highest similarity, implying a convergence in the predicted material properties and absorption values. To compare model outputs, we consider the absolute error, square error, structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and cosine similarity. We evaluate our method on two simulated tomography datasets and two, real-world, low-contrast, high-energy x-ray tomography datasets. We show our approach is more effective at determining the best model, up to an increase of 12.50% and 12.53% in SSIM and PSNR, respectively, while only requiring a fifth of the training time compared to the original approach.

CT↗
DOE OSTI2024

Optical Control of Adaptive Nanoscale Domain Networks

Adaptive networks can sense and adjust to dynamic environments to optimize their performance. Understanding their nanoscale responses to external stimuli is essential for applications in nanodevices and neuromorphic computing. However, it is challenging to image such responses on the nanoscale with crystallographic sensitivity. Here, the evolution of nanodomain networks in (PbTiO 3 ) n /(SrTiO 3 ) n superlattices (SLs) is directly visualized in real space as the system adapts to ultrafast repetitive optical excitations that emulate controlled neural inputs. The adaptive response allows the system to explore a wealth of metastable states that are previously inaccessible. Their reconfiguration and competition are quantitatively measured by scanning x-ray nanodiffraction as a function of the number of applied pulses, in which crystallographic characteristics are quantitatively assessed by assorted diffraction patterns using unsupervised machine-learning methods. The corresponding domain boundaries and their connectivity are drastically altered by light, holding promise for light-programable nanocircuits in analogy to neuroplasticity. Phase-field simulations elucidate that the reconfiguration of the domain networks is a result of the interplay between photocarriers and transient lattice temperature. The demonstrated optical control scheme and the uncovered nanoscopic insights open opportunities for the remote control of adaptive nanoscale domain networks.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Towards the Stable Chelation of Radioantimony(V) for Targeted Auger Theranostics

Abstract Antimony‐119 ( 119 Sb) is one of the most attractive Auger‐electron emitters identified to date, but it remains practically unexplored for targeted radiotherapy because no chelators have been identified to stably bind this metalloid in vivo. In a departure from current studies focused on chelator development for Sb(III), we explore the chelation chemistry of Sb(V) using the tris‐catecholate ligand TREN‐CAM. Through a combination of radiolabeling, spectroscopic, solid‐state, and computational studies, the radiochemistry and structural chemistry of TREN‐CAM with 1XX/nat Sb(V) were established. The resulting [ 1XX Sb]Sb–TREN‐CAM complex remained intact for several days in human serum, signifying high stability under biological conditions. Finally, the first in vivo single photon emission computed tomography and positron emission tomography imaging studies were carried out using 117 Sb, the diagnostic analogue of 119 Sb. These studies revealed marked differences in the uptake and distribution of activity in mice administered unchelated [ 117 Sb]Sb(OH) 6 – versus [ 117 Sb]Sb–TREN‐CAM, suggesting that 117 Sb is largely retained by TREN‐CAM over the time course of the study. Collectively, these findings demonstrate the most physiologically stable complex of no‐carrier‐added 1XX Sb yet reported, offering new promise for the clinical implementation of radioantimony in nuclear medicine. Our results also establish the feasibility of 117 Sb as an elementally matched partner to 119 Sb for theranostic applications.

Olson, Aeli P. [Department of Medical Physics Univ↗
DOE OSTI2025

Towards the Stable Chelation of Radioantimony(V) for Targeted Auger Theranostics

Antimony-119 ( 119 Sb) is one of the most attractive Auger-electron emitters identified to date, but it remains practically unexplored for targeted radiotherapy because no chelators have been identified to stably bind this metalloid in vivo. In a departure from current studies focused on chelator development for Sb(III), we explore the chelation chemistry of Sb(V) using the tris-catecholate ligand TREN-CAM. Through a combination of radiolabeling, spectroscopic, solid-state, and computational studies, the radiochemistry and structural chemistry of TREN-CAM with 1XX/nat Sb(V) were established. The resulting [ 1XX Sb]Sb–TREN-CAM complex remained intact for several days in human serum, signifying high stability under biological conditions. Finally, the first in vivo single photon emission computed tomography and positron emission tomography imaging studies were carried out using 117 Sb, the diagnostic analogue of 119 Sb. These studies revealed marked differences in the uptake and distribution of activity in mice administered unchelated [ 117 Sb]Sb(OH) 6 – versus [ 117 Sb]Sb–TREN-CAM, suggesting that 117 Sb is largely retained by TREN-CAM over the time course of the study. Collectively, these findings demonstrate the most physiologically stable complex of no-carrier-added 1XX Sb yet reported, offering new promise for the clinical implementation of radioantimony in nuclear medicine. In conclusion, our results also establish the feasibility of 117 Sb as an elementally matched partner to 119 Sb for theranostic applications.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Increased AGE Cross-Linking Reduces the Mechanical Properties of Osteons

Abstract The osteon is the primary structural component of bone, contributing significantly to its unique toughness and strength. Despite extensive research on osteonal structure, the properties of osteons have not been fully investigated, particularly within the context of bone fragility diseases like type 2 diabetes mellitus (T2DM). This study aims to isolate osteons from bovine bone, simulate the effects of increased advanced glycation end-products (AGEs) in T2DM through ribosylation, and evaluate the mechanical properties of isolated osteons. Osteons extracted from the posterior section of bovine femur mid-diaphysis were processed to achieve a sub-millimeter scale for microscale imaging. Subsequently, synchrotron radiation micro-computed tomography was employed to precisely localize and isolate the osteon internally. While comparable elastic properties were observed between control and ribosylated osteons, the presence of AGEs led to decreased strain to failure. Young’s modulus was quantified (9.9 ± 4.9 GPa and 8.7 ± 3 GPa, respectively), aligning closely with existing literature. This study presents a novel method for the extraction and isolation of osteons from bone and shows the detrimental effect of AGEs at the osteonal level.

Materials Science↗
DOE OSTI2025

Feeding state-dependent neuropeptidergic modulation of reciprocally interconnected inhibitory neurons biases sensorimotor decisions in Drosophila

Abstract An animal’s feeding state changes its behavioral priorities and thus influences even nonfeeding-related decisions. How the feeding state information is transmitted to nonfeeding-related circuits and what circuit mechanisms are involved in biasing nonfeeding-related decisions remain open questions. By combining calcium imaging, neuronal manipulations, behavioral analysis and computational modeling, we determined that the competition between different aversive responses to mechanical cues is biased by changes in the feeding state. We found that this effect is achieved by the differential modulation of two different types of reciprocally connected inhibitory neurons promoting opposing actions. This modulation results in a more frequent active type of response and, less frequently, a protective type of response if larvae are fed sugar than when they are fed a balanced diet. Information about the internal state is conveyed to inhibitory neurons through homologs of the vertebrate neuropeptide Y, which is known to be involved in regulating feeding behavior.

Science & Technology - Other Topics↗
DOE OSTI2024

Biomechanical drivers of the evolution of butterflies and moths with a coilable proboscis

Current biomechanical models suggest that butterflies and moths use their proboscis as a drinking straw pulling nectar as a continuous liquid column. Our analyses revealed an alternative mode for fluid uptake: drinking bubble trains that help defeat drag. We combined X-ray phase-contrast imaging, optical video microscopy, micro-computed tomography, phylogenetic models of evolution and fluid mechanics models of bubble-train formation to understand the biomechanics of butterfly and moth feeding. Our models suggest that the bubble-train mechanism appeared in the early evolution of butterflies and moths with a proboscis long enough to coil. We propose that, in addition to the ability to drink a continuous column of fluid from pools, the ability to exploit fluid films by capitalizing on bubble trains would have expanded the range of available food sources, facilitating diversification of Lepidoptera.

Palaoro, Alexandre V.↗
DOE OSTI2024

Z Line VISAR Analysis with the LineVISAR SMASH Class

The Z line VISAR system (ZLV) is a spatially-resolved velocimeter that measures surface velocities in high-energy density experiments on the Z Pulsed Power Facility to facilitate the investigation of fusion, power flow, and dynamic material physics. The data measured in these experiments are analyzed with the LineVISAR SMASH class, a MATLAB software suite that provides tools for data importation, streak image correction, spatiotemporal registration, wrapped phase computation, phase unwrapping, shock handling, and velocity calculation. This report overviews the LineVISAR class, discusses its use, provides example implementations, and supplies analysis specifics not typically recorded in journal publications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

In Search of Optimum Fresh-Cut Raw Material: Using Computer Vision Systems as a Sensory Screening Tool for Browning-Resistant Romaine Lettuce Accessions

The popularity of ready-to-eat (RTE) salads has prompted novel technology to prolong the shelf life of their ingredients. Fresh-cut romaine lettuce is widely used in RTE salads; however, its tendency to quickly discolor continues to be a challenge for the industry. Selecting the ideal lettuce accessions for use in RTE salads is essential to ensure maximum shelf life, and it is critical to have a practical way to assess and compare the quality of multiple lettuce accessions that are being considered for use in fresh-cut applications. Thus, in this work we aimed to determine whether a computer vision system (CVS) composed of image acquisition, processing, and analysis could be effective to detect visual quality differences among 16 accessions of fresh-cut romaine lettuce during postharvest storage. The CVS involved a post-capturing color correction, effective image segmentation, and calculation of a browning index, which was tested as a predictor of quality and shelf life of fresh-cut romaine lettuce. The results demonstrated that machine vision software can be implemented to replace or supplement the scoring of a trained panel and instrumental quality measurements. Overall visual quality, a key sensory parameter that determines food preferences and consumer behavior, was highly correlated with the browning index, with a Pearson correlation coefficient of −0.85. Other important sensory decision parameters were also strongly or moderately correlated with the browning index, with Pearson correlation coefficients of −0.84 for freshness, 0.79 for off odor, and 0.57 for browning. The ranking of the accessions according to quality acceptability from the sensory evaluation produced a similar pattern to those obtained with the CVS. This study revealed that multiple lettuce accessions can be effectively benchmarked for their performance as fresh-cut sources via a CVS-based method. Future opportunities and challenges in using machine vision image processing to predict consumer preferences for RTE salad greens is also discussed.

Agriculture↗
DOE OSTI2025

Advancing the Frontiers of Deep Learning for Low-Dose 3D Cone-Beam CT Reconstruction

X-ray computed tomography (CT) is an important noninvasive medical imaging modality for studying the structural details of internal organs. Image reconstruction in CT is an inverse problem of recovering an object's internal structure from the absorption profile of X-ray beams (sinogram) measured using a detector. The classical variational approach for CT reconstruction minimizes an energy functional using an appropriate iterative algorithm. Motivated by the success of deep learning (DL), researchers have begun to leverage training data and enhanced computing capabilities in recent years to produce high-fidelity reconstructed images. Nonetheless, much of the academic research in DL algorithms for CT has focused primarily on the two-dimensional setting (with simplified forward operators and noise model) for proofs-of-concept, and a comprehensive benchmarking of various classical and data-driven CT reconstruction approaches has not beenundertaken. The key objective of our CT reconstruction grand challenge was to promote methodological advancements for both classical and DL-based approaches for clinical CT with a reasonably accurately simulated 3D CT forward operator and noise model. We have utilized the publicly available LIDC-IDRI dataset and simulated sinograms and FDK images corresponding to two dose levels (clinical- and low-dose, constituting two tracks of the challenge) starting from the normal-dose images as the ground truth. In this paper, we summarize the motivation, context, and results of our challenge, and highlight the future research directions in DL for clinical CT.

X-ray tomography↗
DOE OSTI2025

Quantitative Comparisons of Image Quality for Flash X-Ray Detectors

Due to X-rays’ ability to penetrate materials, flash X-ray radiography can be used for high-speed measurements where direct optical access is not possible. Choice of detector has a pronounced impact on resulting image quality. Four different detector systems were evaluated with a 450kVp flash source to quantitatively compare image quality metrics. The scintillating digital detector had less image noise than the three different storage phosphor computed radiography detectors across all transmission levels, but lacked the spatial resolution of the computed radiography detectors. For the screens tested here, the HPX-DR digital system had the highest signal to noise ratio of 68.24 and contrast to noise ratio of 35.53, but had the lowest spatial resolution, resolving 2.5 line pairs per millimeter at 1.78% contrast. At a value of 37.59, the Flex GP imaging plate had a signal to noise value above its storage phosphor counterparts under a 450kVp flash source. For radiographic setups typically used for dynamic experiments, the Flex XL Blue and Flex HR detectors had signal to noise ratios of 18.44 and 26.56 respectively. The highest resolved spatial frequencies of the Flex GP, Flex XL Blue, and Flex HR with the flash source are 3.85, 5.00, and 3.85 line pairs per millimeter, respectively. In conclusion, the Flex GP detector had the best combination of signal to noise ratio, contrast to noise ratio, and spatial resolution under a flash source.

computed radiography↗
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↗