Search NASA⌕ Search

SEARCH · Search NASA

Results for “defect imaging”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

High frequency QRS ECG predicts ischemic defects during myocardial perfusion imaging

Background: Changes in high frequency QRS components of the electrocardiogram (HF QRS ECG) (150-250 Hz) are more sensitive than changes in conventional ST segments for detecting myocardial ischemia. We investigated the accuracy of 12-lead HF QRS ECG in detecting ischemia during adenosine tetrofosmin myocardial perfusion imaging (MPI). Methods and Results: 12-lead HF QRS ECG recordings were obtained from 45 patients before and during adenosine technetium-99 tetrofosmin MPI tests. Before the adenosine infusions, recordings of HF QRS were analyzed according to a morphological score that incorporated the number, type and location of reduced amplitude zones (RAZs) present in the 12 leads. During the adenosine infusions, recordings of HF QRS were analyzed according to the maximum percentage changes (in both the positive and negative directions) that occurred in root mean square (RMS) voltage amplitudes within the 12 leads. The best set of prospective HF QRS criteria had a sensitivity of 94% and a specificity of 83% for correctly identifying the MPI result. The sensitivity of simultaneous ST segment changes (18%) was significantly lower than that of any individual HF QRS criterion (P<0.001). Conclusions: Analysis of 12-lead HF QRS ECG is highly sensitive and specific for detecting ischemic perfusion defects during adenosine MPI stress tests and significantly more sensitive than analysis of conventional ST segments.

Rahman, Atiar↗
DOE OSTI2022

STEM imaging of irradiation induced defects in Eurofer97 steel variants irradiated in the EUROfusion collaboration

Ten exploratory variants of Eurofer97 reduced activation ferritic martensitic (RAFM) steel were irradiated in HFIR to ~2.94 – 32.4 dpa, 300±30 °C as a part of the EUROfusion collaboration. The irradiations were performed in rabbit capsules. ES21-22 capsule were used for irradiating SS-J3 tensile samples while M4CVN bend bar samples were irradiated in ES31-35 capsules. The mechanical properties and multi-length scale characterization of the microstructures of these ten steels, designated as H, I, P, J, K, L, M, N, O, P and reference E, in the nonirradiated form was reported in "Mechanical properties and microstructure characterization of unirradiated Eurofer-97 steel variants for the EUROfusion project, ORNL/SPR-2018/882," while their irradiated properties and some microstructure analysis was reported in "Post-irradiation examination of Eurofer97 steel variants irradiated to 2.5 dpa, ~300 °C in HFIR for the EUROfusion program, ORNL/SPR-2020/1440." Here, additional microstructures using TEM/STEM and STEM-EDX of the irradiated steels is presented. Characterization was performed at the Low Activation Materials Development and Analysis (LAMDA) laboratory.

36 MATERIALS SCIENCE↗
NASA NTRS2018

A Screening Method Using Pulsed-Power Combined with Infrared Imaging to Detect Pattern Defects in Bulk Metal Foil or Thin Film Resistors

Bulk metal foil and thin film resistors occasionally contain localized defects within the etched resistor pattern. Common defects of this type include in-line constrictions referred to as notches, unremoved resistor material bridges between adjacent pattern lines, and embedded non-conductive particles in the resistor material. Such defects are prone to fracture due to thermomechanical fatigue during powered operation, especially power cycling, resulting in positive resistance change and open circuit failure modes. Common screening methods of optical microscopy, short time overload power tests (e.g., 6.25x rated power for 5 seconds) and burn-in (e.g., 1.5x rated power for 100 hours) are useful, but they are not always effective at removing devices with such defects. An improved method has been developed to screen for localized resistor pattern defects. The method involves the application of brief, high power electrical pulses at a low duty cycle while inspecting the resistor with a high resolution, high speed infrared camera. The following test conditions were found to be suitable for this purpose: 6.25x rated power, 1 to 5 pulses, 50 ms pulse width and 10% duty cycle. During the power pulsing, localized constrictions in the resistor pattern are identifiable as hot spots via the infrared camera. Reject criteria can be established based on observations of hot spots. To assess its effectiveness, a total of two hundred eighty (280) surface mount foil resistors (40 each from 7 different lots) were screened using this infrared technique. The screening identified twenty-nine (29) resistors with significant hot spots in the resistor pattern. All 280 resistors were then subjected to an industry standard 10,000 hour life test at 1x rated power at 70°C with power cycled for 90 minutes on and 30 minutes off. During life testing, three resistors exhibited positive resistance change failure mode. All three failures were due to thermomechanical fatigue failure of localized bridge defects at the locations identified via infrared screening. The results of this evaluation illustrate the benefits of the pulsed-power infrared detection screening technique to identify reliability suspect foil or thin film resistors that may escape common screening methods.

screening↗
NASA NTRS2018

A Screening Method Using Pulsed-Power Combined with Infrared Imaging to Detect Pattern Defects in Bulk Metal Foil or Thin Film Resistors

Bulk metal foil and thin film resistors occasionally contain localized defects within the etched resistor pattern. Common defects of this type include in-line constrictions referred to as notches, unremoved resistor material bridges between adjacent pattern lines, and embedded non-conductive particles in the resistor material. Such defects are prone to fracture due to thermomechanical fatigue during powered operation, especially power cycling, resulting in positive resistance change and open circuit failure modes. Common screening methods of optical microscopy, short time overload power tests (e.g., 6.25x rated power for 5 seconds) and burn-in (e.g., 1.5x rated power for 100 hours) are useful, but they are not always effective at removing devices with such defects. An improved method has been developed to screen for localized resistor pattern defects. The method involves the application of brief, high power electrical pulses at a low duty cycle while inspecting the resistor with a high resolution, high speed infrared camera. The following test conditions were found to be suitable for this purpose: 6.25x rated power, 1 to 5 pulses, 50 ms pulse width and 10% duty cycle. During the power pulsing, localized constrictions in the resistor pattern are identifiable as hot spots via the infrared camera. Reject criteria can be established based on observations of hot spots. To assess its effectiveness, a total of two hundred eighty (280) surface mount foil resistors (40 each from 7 different lots) were screened using this infrared technique. The screening identified twenty-nine (29) resistors with significant hot spots in the resistor pattern. All 280 resistors were then subjected to an industry standard 10,000 hour life test at 1x rated power at 70°C with power cycled for 90 minutes on and 30 minutes off. During life testing, three resistors exhibited positive resistance change failure mode. All three failures were due to thermomechanical fatigue failure of localized bridge defects at the locations identified via infrared screening. The results of this evaluation illustrate the benefits of the pulsed-power infrared detection screening technique to identify reliability suspect foil or thin film resistors that may escape common screening methods.

screening↗
NASA NTRS2007

High-frequency Electrocardiogram Analysis in the Ability to Predict Reversible Perfusion Defects during Adenosine Myocardial Perfusion Imaging

Background: A previous study has shown that analysis of high-frequency QRS components (HF-QRS) is highly sensitive and reasonably specific for detecting reversible perfusion defects on myocardial perfusion imaging (MPI) scans during adenosine. The purpose of the present study was to try to reproduce those findings. Methods: 12-lead high-resolution electrocardiogram recordings were obtained from 100 patients before (baseline) and during adenosine Tc-99m-tetrofosmin MPI tests. HF-QRS were analyzed regarding morphology and changes in root mean square (RMS) voltages from before the adenosine infusion to peak infusion. Results: The best area under the curve (AUC) was found in supine patients (AUC=0.736) in a combination of morphology and RMS changes. None of the measurements, however, were statistically better than tossing a coin (AUC=0.5). Conclusion: Analysis of HF-QRS was not significantly better than tossing a coin for determining reversible perfusion defects on MPI scans.

Tragardh, Elin↗
DOE OSTI2019

Detection and quantitation of right ventricular reversible perfusion defects by stress SPECT myocardial perfusion imaging: A proof-of-principle study

Background: In patients with right dominant coronary circulation, the right ventricular (RV) myocardium and the inferior region of the left ventricular (LV) myocardium share a common source of blood flow. We hypothesized that stress/rest SPECT myocardial perfusion imaging (MPI) could detect reversible perfusion defects in the RV in some patients with LV inferior wall perfusion abnormalities. Material and Methods: We identified 2 groups of patients with LV inferior wall perfusion defects (with or without defects in other regions of LV myocardium) from our database. Patients in group 1 (n = 17) had reversible perfusion defects in the RV free wall by visual analysis, while patients in group 2 (n = 17) did not. The images were processed with filtered back projection and, separately, with iterative reconstruction. The images were then re-processed using an automated quantitative software that is specifically designed to include the RV in the region of interest. Results: There were 76% men in group 1 and 94% in group 2 (P <0.05). The mean age was 65±20 in group 1 vs. 63±18 years in group 2 (P < 0.05). The stress type was exercise in 30% in group 1 and 35% in group 2, with the remaining patients studied with pharmacological stress testing (P = NS). The presence of RV reversible perfusion defects using filtered back projection was more evident in 13 patients (75%), while it was better seen with iterative reconstruction in 4 patients (25%). By automated analysis, the RV reversible perfusion defect size was 19 ± 14% of RV myocardium. Conclusion: This proof-of-principle study demonstrates that reversible RV perfusion defects suggestive of ischemia can be detected by SPECT myocardial perfusion imaging in some patients with LV inferior ischemia by visual analysis and can be quantitated by automated programs. Further studies on the diagnostic and prognostic relevance of assessing RV ischemia on SPECT MPI are needed.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
NASA NTRS2010

Portable Handheld Optical Window Inspection Device

The Portable Handheld Optical Window Inspection Device (PHOWID) is a measurement system for imaging small defects (scratches, pits, micrometeor impacts, and the like) in the field. Designed primarily for window inspection, PHOWID attaches to a smooth surface with suction cups, and raster scans a small area with an optical pen in order to provide a three-dimensional image of the defect. PHOWID consists of a graphical user interface, motor control subsystem, scanning head, and interface electronics, as well as an integrated camera and user display that allows a user to locate minute defects before scanning. Noise levels are on the order of 60 in. (1.5 m). PHOWID allows field measurement of defects that are usually done in the lab. It is small, light, and attaches directly to the test article in any orientation up to vertical. An operator can scan a defect and get useful engineering data in a matter of minutes. There is no need to make a mold impression for later lab analysis.

Ihlefeld, Curtis↗
DOE OSTI2025

Bayesian SegNet for Semantic Segmentation with Improved Interpretation of Microstructural Evolution During Irradiation of Materials

Understanding the relationship between the evolution of microstructures of irradiated LiAlO2pellets and tritium diffusion, retention and release could improve predictions of tritium performance. Given expert-labeled segmented images of irradiated and unirradiated pellets, we trained Deep Convolutional Neural Networks to segment images into defect, grain, and boundary classes. Qualitative microstructural information was calculated from these segmented images to facilitate the comparison of unirradiated and irradiated pellets. We tested modifications to improve the sensitivity of the model, including incorporating meta-data into the model and utilizing uncertainty quantification. The predicted segmentation was similar to the expert-labeled segmentation for most methods of microstructural qualification, including pixel proportion, defect area, and defect density. Overall, the high performance metrics for the best models for both irradiated and unirradiated images shows that utilizing neural network models is a viable alternative to expert-labeled images.

Oostrom, Marjolein T.↗
DOE OSTI2024

Deep-learning based artificial intelligence tool for melt pools and defect segmentation

Accelerating fabrication of additively manufactured components with precise microstructures is important for quality and qualification of built parts, as well as for a fundamental understanding of process improvement. Accomplishing this requires fast and robust characterization of melt pool geometries and structural defects in images. This paper proposes a pragmatic approach based on implementation of deep learning models and self-consistent workflow that enable systematic segmentation of defects and melt pools in optical images. Deep learning is based on an image-to-image translation–conditional generative adversarial neural network architecture. An artificial intelligence (AI) tool based on this deep learning model enables fast and incrementally more accurate predictions of the prevalent geometric features, including melt pool boundaries and printing-induced structural defects. We present statistical analysis of geometric features that is enabled by the AI tool, showing strong spatial correlation of defects and the melt pool boundaries. The correlations of widths and heights of melt pools with dataset processing parameters show the highest sensitivity to thermal influences resulting from laser passes in adjacent and subsequent layer passes. The presented models and tools are demonstrated on the aluminum alloy and datasets produced with different sets of processing parameters. However, they have universal quality and could easily be adapted to different material compositions. The method can be easily generalized to microstructural characterizations other than optical microscopy.

additive manufacturing↗
DOE OSTI2018

Direct observations of crystal defects in polycrystalline diamond

Highlights: • Electron channeling contrast imaging of crystal defects in polycrystalline diamond • Introducing acid etching as a novel protocol for diamond surface preparation • Characterization of {111} twins, {111} stacking faults and {111} helical dislocations in diamond using ECCI and EBSD - Abstract: Crystal defects are abundant in synthetic diamond produced by chemical vapor deposition (CVD). We present the first images of crystal defects in a bulk polycrystalline CVD diamond sample using general electron channeling contrast imaging (ECCI) in a field emission scanning electron microscope (FE-SEM). For enhancement of channeling contrast of this material, we introduce a novel protocol for diamond surface preparation that involves acid etching. Using this protocol, we imaged three types of crystal defects including twins, stacking faults and dislocations. Each defect was identified based on its appearance in electron channeling contrast (ECC) micrographs. We analyzed grains containing twins and dislocations using electron backscatter diffraction (EBSD) crystal orientation mapping. We found a large population of grains that contained Σ3 type twins on {111} planes with a 60°<111> angle–axis pair of misorientation for twin boundaries. In addition, we identified {111} stacking faults and {111} helical dislocations. These observations are in agreement with reports of crystal defects in CVD diamond thin foils studied by a transmission electron microscope (TEM).

36 MATERIALS SCIENCE↗
DOE OSTI2016

Non-blinking single-photon emitters in silica

Samples for single-emitter spectroscopy are usually prepared by spin-coating a dilute solution of emitters on a microscope cover slip of silicate based glass (such as quartz). Here, we show that both borosilicate glass and quartz contain intrinsic defect colour centres that fluoresce when excited at 532 nm. In a microscope image the defect emission is indistinguishable from spin-coated emitters. The emission spectrum is characterised by multiple peaks with the main peak between 2.05 and 2.20 eV, most likely due to coupling to a silica vibration with an energy that varies between 160 and 180 meV. The defects are single-photon emitters, do not blink, and have photoluminescence lifetimes of a few nanoseconds. Furthermore, photoluminescence from such defects may previously have been misinterpreted as originating from single nanocrystal quantum dots.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

pnnl/defect_detection

The code takes expert-labeled segmented images of irradiated and unirradiated pellets and trains DeepConvolutional Neural Networks to segment these images into defects, backgrounds, and boundaries. The code calculated qualitative microstructural information from these segmented images to facilitate the comparison of unirradiated and irradiated pellets

Oostrom, Marjolein↗
DOE OSTI2025

Non-destructive identification of absorptive damage sites by photoluminescence brightening

We present a non-destructive photoluminescence (PL) imaging strategy for probing damage growth behavior in fused silica under laser irradiation. Transient wide-field PL image measurements were conducted at varying excitation intensities to investigate the dynamics of radiative defect generation and annihilation in laser-induced damage sites. Our results reveal that, while low excitation intensities predominantly induce PL quenching due to defect annihilation, high-intensity excitation (>0.3 GW/cm 2 at 532 nm) triggers pronounced PL brightening (PLB), indicative of radiative defect generation. Image analysis demonstrates that PLB events are strongly correlated with subsequent damage growth (10 J/cm 2 at 351 nm), whereas overall PL intensity is not. No optically detectable morphological changes were observed during PLB, confirming the non-destructive nature of the technique. We propose that PLB arises from defect-assisted absorption of multiple photons at pre-existing absorptive centers, which enhances local absorptivity and initiates further defect formation. These findings establish PLB monitoring as a sensitive diagnostic tool for identifying damage sites with elevated absorptivity, enabling targeted maintenance and improved laser damage management in optical materials.

Materials science↗
DOE OSTI2015

Contactless Electroluminescence Imaging for Cell and Module Characterization

Module performance can be characterized by imaging using baseline and periodic images to track defects and degradation. Both thermal images, which can be acquired during sunny operating conditions, and photoluminescence images, which could be acquired at night, can be collected without electrical connection. Electroluminescence (EL) images, which are useful to detect many types of defects such as cracks, interconnect and solder faults, and resistances, have typically required electrical connection to drive current in the cells and modules. Here, a contactless EL imaging technique is proposed, which provides an EL image without the need for electrical connection to drive current through the module. Such EL imaging has the capability to be collected at night without disruption to daytime power generation.

14 SOLAR ENERGY↗
DOE OSTI2023

Application of a deep learning semantic segmentation model to helium bubbles and voids in nuclear materials

Imaging nanoscale radiation-induced defects using the transmission electron microscope (TEM) is a key factor in the successful implementation of materials for nuclear energy structural applications. Analyzing each defect in a TEM micrograph is currently a manual task. To identify the defects in a single image can take anywhere from 15 min to an hour and a project can require the analysis of anywhere from tens to ≥ 100 images. Here, we use artificial intelligence (AI) models to automate this task. For simplification, we evaluated images with only a single type of defect; helium bubbles. Additionally, we performed semantic segmentation of these helium bubble defects in electron microscopy images of irradiated FeCrAl alloys using a deep learning DefectSegNet model. This model, which was previously used to classify crystal defects, is inspired by the classic DenseNet and U-Net image segmentation models. It claims high spatial resolution, but has poor performance at object boundaries. Our paper improves the DefectSegNet model’s application by adding two new features. First, the DefectSegNet model is applied not only to perform calculation pixel-wise but also object (or feature) wise. Because object-wise metrics are directly relevant to our final goal of detecting bubbles, whereas pixel-wise classification is only an intermediate step, it is an important part of our study. Second, a distance map loss (DML) function has been added to increase its performance at object boundaries. It is crucial to accurately represent defects boundaries, especially bubbles, in order to track the bubble-induced swelling caused by irradiation. The boundary-focused DML function is also compared to other loss functions like Cross-entropy, Weighted Binary Cross Entropy (WBCE), Dice and Intersection over Union (IOU). Finally, by incorporating new features, we found a marked improvement on segmentation quality and better shape preservation at the boundaries and areas of the bubbles.

42 ENGINEERING↗
DOE OSTI2017

Simulation of Laser Ultrasonics for Detection of Surface-Connected Rail Defects

Laser ultrasonic produces frequencies in the MHz range, enabling high accuracy and a strong ability to detect rail surface defects. This paper mainly studied on the simulation of detecting surface-connected rail defects on 60 kg rails with laser ultrasonic, established the finite element model of laser-excited ultrasonic Rayleigh wave, carried out the simulation, and verified the effectiveness of the technology through experiments. To solve the problem that laser ultrasonic is insensitive to the width of defects in actual detection, and unable to make quantitative detection of defects, this paper established a new model on the basis of improving the original model that has been verified, exciting ultrasonic at the two sides at the same time of a rail with two staggered beams of laser separately to detect irregular scratch defects on rail surface, and two groups of signal data were received through two probes. Each group of data can present the half-profile information of defects, and further form two detection images of the defect. At last, the two detection images were combined into a complete image through image processing. The results of the experiment indicate that the technology studied offers a new method for the effective quantitative detection of surface-connected defects on rail.

42 ENGINEERING↗