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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.

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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Isolation of Single Donors in ZnO

The shallow donor in zinc oxide (ZnO) is a promising semiconductor spin qubit with optical access. Single indium donors are isolated in a commercial ZnO substrate using plasma focused ion beam (PFIB) milling. Quantum emitters are identified optically by spatial and frequency filtering. The indium donor assignment is based on the optical bound exciton transition energy and magnetic dependence. The emission stability of these single donors in terms of both intensity and frequency, alongside their transition linewidths less than twice the lifetime limit, highlight the promise of single In donors as optically accessible spin qubits. Here, the optical stability of single donors after FIB fabrication is promising for optical device integration required for scalable quantum technologies based on single donors in direct band gap semiconductors.

42 ENGINEERING↗
DOE OSTI2015

NREL Develops High-Speed Scanner to Monitor Fuel Cell Material Defects

This highlight describes results of recent work in which polymer electrolyte membrane fuel cell electrodes with intentionally introduced known defects were imaged and analyzed using a fuel cell scanner recently developed at NREL. The highlight is being developed for the September 2015 Alliance S&T Board meeting.

30 DIRECT ENERGY CONVERSION↗
NASA NTRSDate not supplied

Convolutional Neural Networks for Image Classification in Metal Selective Laser Meting Additive Manufacturing

Selective laser melting (SLM) is a metal additive manufacturing process that has several advantages such as the large range of metal materials that can be accommodated, 3D printing of complex shape components, the ability to adjust material properties, and cost reduction as expensive production equipment may not be required. Therefore, process monitoring is crucial in different stages of the component building. In this work, convolutional neural networks (CNNs) are investigated as a suitable technique for post-inspection of builds. The monitoring of manufactured parts was conducted by collecting computed tomography (CT) images and identifying defects. Five CNN models were implemented and tested for the classification of the CT images. The models were based on NASNetMobile and DenseNet121, and a custom built CNN model. The results of this work show that CNNs can be feasible and reliable for rapid monitoring and classification of defects in CT images from build fabrication using SLM.

Rodolfo Ledesma↗
DOE OSTI2025

Novel Solar Panel Defect Detection Hardware and Defect Analysis Software (CRADA Final Report)

The CRADA work involved 1) the development of a machine learning software architecture to automatically detect defects within Electroluminescence images of solar panels, and 2) demonstrations of the usefulness of UV Fluorescence (UVF) defect detection for a wide variety of defect types across many different solar panel technologies with different field or environmental chamber histories.

14 SOLAR ENERGY↗
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 OSTI2024

Unsupervised learning-enabled pulsed infrared thermographic microscopy of subsurface defects in stainless steel

Metallic structures produced with laser powder bed fusion (LPBF) additive manufacturing method (AM) frequently contain microscopic porosity defects, with typical approximate size distribution from one to 100 microns. Presence of such defects could lead to premature failure of the structure. In principle, structural integrity assessment of LPBF metals can be accomplished with nondestructive evaluation (NDE). Pulsed infrared thermography (PIT) is a non-contact, one-sided NDE method that allows for imaging of internal defects in arbitrary size and shape metallic structures using heat transfer. PIT imaging is performed using compact instrumentation consisting of a flash lamp for deposition of a heat pulse, and a fast frame infrared (IR) camera for measuring surface temperature transients. However, limitations of imaging resolution with PIT include blurring due to heat diffusion, sensitivity limit of the IR camera. We demonstrate enhancement of PIT imaging capability with unsupervised learning (UL), which enables PIT microscopy of subsurface defects in high strength corrosion resistant stainless steel 316 alloy. PIT images were processed with UL spatial–temporal separation-based clustering segmentation (STSCS) algorithm, refined by morphology image processing methods to enhance visibility of defects. The STSCS algorithm starts with wavelet decomposition to spatially de-noise thermograms, followed by UL principal component analysis (PCA), fine-tuning optimization, and neural learning-based independent component analysis (ICA) algorithms to temporally compress de-noised thermograms. The compressed thermograms were further processed with UL-based graph thresholding K-means clustering algorithm for defects segmentation. The STSCS algorithm also includes online learning feature for efficient re-training of the model with new data. For this study, metallic specimens with calibrated microscopic flat bottom hole defects, with diameters in the range from 203 to 76 µm, were produced using electro discharge machining (EDM) drilling. While the raw thermograms do not show any material defects, using STSCS algorithm to process PIT images reveals defects as small as 101 µm in diameter. To the best of our knowledge, this is the smallest reported size of a sub-surface defect in a metal imaged with PIT, which demonstrates the PIT capability of detecting defects in the size range relevant to quality control requirements of LPBF-printed high-strength metals.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Morpho-Hawk

Morpho-Hawk applies projectional mathematical morphology in a fundamentally new way. Morpho-Hawk extracts object information from digital images by filtering with simple geometrical figures such as rectangles or simple curves. Two core algorithms are used to accomplish this: 1) Object Detection and Feature Extraction from Images: Using projectional morphology, Morpho-Hawk finds features of interest within an image, such as contours, shapes, colors, infrared spectra, and more. Because Morpho-Hawk identifies features based on shape or form, the method can uniquely handle different image or object conditions and directions. MorphoHawk can evaluate all possible images of the analyzed scene using the same transformations that are applied just to one image of that scene. Hence, recognizing a defect within an image or part of an image provides information to assist in recognizing other objects in the image. In addition, known background image information can be morphologically subtracted out in order to focus on the appearance or disappearance of the features of interest. 2) Object Analysis: Upon detection of an object of interest, Morpho-Hawk can analyze the object based learned information from prior images or end-user defined criteria for certain object of special interest by selecting feature (e.g., color, size, shape, apparent volume) and/or symbol (e.g., letter, number). The novel algorithm can analyze targeted objects, even as conditions of such as illumination, shadows, and spectral regions change strongly in following images as compared to the original image. This enables robust recognition, interpolation and prediction. As the analysis shows the presence of these features in the optical signal, the algorithm can make a determination based on user defined probability. The object information can be stored for later analysis using simple morphological data compression methods and/or retention of the original optical images.

Glazoff, MichaelV.↗
DOE OSTI2020

Refractive Imaging of Air Shock Above Microscale Defects in Pentaerythritol Tetranitrate (PETN) Films

Physical vapor deposition (PVD) of high explosives can produce energetic samples with unique microstructure and morphology compared to traditional powder processing techniques, but challenges may exist in fabricating explosive films without defects. Deposition conditions and substrate material may promote microcracking and other defects in the explosive films. Here, we investigate effects of engineered microscale defects (gaps) on detonation propagation and failure for pentaerythritol tetranitrate (PETN) films using ultra-high-speed refractive imaging and hydrocode modelling. Observations of the air shock above the gap reveal significant instabilities during gap crossing and re-ignition.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Cathodoluminescence for the 21st century: Learning more from light

Cathodoluminescence (CL) is the emission of light from a material in response to excitation by incident electrons. The technique has had significant impact in the characterization of semiconductors, minerals, ceramics, and many nanostructured materials. Since 2010, there have been a number of innovative developments that have revolutionized and expanded the information that can be gained from CL and broadened the areas of application. While the primary historical application of CL was for spatial mapping of luminescence variations (e.g., imaging dark line defects in semiconductor lasers or providing high resolution imaging of compositional variations in geological materials), new ways to collect and analyze the emitted light have expanded the science impact of CL, particularly at the intersection of materials science and nanotechnology. Current developments include (1) angular and polarized CL, (2) advances in time resolved CL, (3) far-field and near-field transport imaging that enable drift and diffusion information to be obtained through real space imaging, (4) increasing use of statistical analyses for the study of grain boundaries and interfaces, (5) 3D CL including tomography and combined work utilizing dual beam systems with CL, and (6) combined STEM/CL measurements that are reaching new levels of resolution and advancing single photon spectroscopy. This focused review will first summarize the fundamentals and then briefly describe the state-of-the-art in conventional CL imaging and spectroscopy. We also review these recent novel experimental approaches that enable added insight and information, providing a range of examples from nanophotonics, photovoltaics, plasmonics, and studies of individual defects and grain boundaries.

14 SOLAR ENERGY↗
DOE OSTI2018

Imaging charge carriers in potential-induced degradation defects of c-Si solar cells by scanning capacitance microscopy

We report on nm-resolution imaging of charge-carrier distribution around local potential-induced degradation (PID) shunting defects using scanning capacitance microscopy. We imaged on cross sections of heavily field-degraded module areas, cored out and selected by mm-scale photoluminescence imaging. We found localized areas with abnormal carrier behavior induced by the PID defects: the apparent n-type carrier extends vertically into the absorber to ~1-2 um from the cell surface, and laterally in similar lengths; in defect-free areas, the n-type carrier extends ~0.5 um, which is consistent with the junction depth. For comparison, we also investigated areas of the same module exhibiting the least PID stress, and we found no such heavily damaged junction area. Instead, we found slightly abnormal carrier behavior, where the carrier-type inversion in the absorber did not occur, but the p-type carrier concentration changed slightly in a much smaller lateral length of ~300 nm. These nano-electrical findings may indicate a possible mechanism that the existing extended defects, which may not be significantly harmful to cell performance, can be changed by PID to heavily damaged junction areas.

14 SOLAR ENERGY↗
DOE OSTI2020

Image charge interaction correction in charged-defect calculations

Charged-defect calculation using a periodic supercell is a significant class of problems in solid state physics. However, the finite supercell size induces an undesirable long-range image charge Coulomb interaction. Although a variety of methods have been proposed to eliminate such image Coulomb interaction, most of the previous schemes are based on a rough approximation of the defect charge screening. In this work, we present a rigorous derivation of the image charge interaction with a defect screening model where the use of a bulk macroscopic dielectric constant can be avoided. Here, we have verified this approach in comparison with a widely used approach for 12 different defects. Our correction scheme offers a much faster convergence concerning the supercell size for cases with considerable image charge interactions. In those cases, we also found that the nonlinear dielectric screening might play an important role. Our proposed defect screening model will also shed new light on understanding the defect screening properties and can be applied to other defect systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Synchrotron CT imaging of lattice structures with engineered defects

Understanding mechanical failure, crack propagation, and compressive behavior at the micrometer scale is essential for tailoring material properties for structural performance in cellular materials. Typically, modeling of traditional polymer foam materials is clouded by the lack of control in material morphology and its inherent stochastic structure. Additive manufacturing with sub-micrometer resolution provides a direct path for experimenters to specifically tailor structures needed by modelers to explicitly probe mechanical performance. Using laboratory-based 3D X-ray computed tomographic imaging (CT), the examination of deformation and damage provides a critical path to understand how these soft materials behave. Additionally, synchrotron CT yields realistic information at higher strain rates to directly validate the robustness of our finite element modeling. For this study, nanolithographic printing was employed to generate a series of engineered lattices with increasing levels of defects through the random removal of ligaments. These structures were mechanically tested and imaged with laboratory-based microCT. Additionally, synchrotron experiments were conducted in which the structures were imaged in 3D at 14 Hz during compression at a 0.4 s –1 strain rate. These 3D images show the changes in the structure as the ligaments bend, buckle and fracture in real time. This technique provides a robust framework for developing our methodologies and future exploration of engineered structures.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Forming an Edge Dislocation Image at Anomalous X-ray Transmission

The features of forming section and projection topography images of an edge dislocation normal to the crystal surface are considered for the case of X-ray anomalous transmission. Experimental images are analyzed using numerical simulation of the diffraction experiment. A new mechanism for image formation of defects arranged near the output crystal surface has been proposed. Differences between topography images of dislocations and rosettes of local misorientations of reflecting planes have been established.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Application of Weak-Beam Dark-Field STEM for Dislocation Loop Analysis

Nanoscale dislocation loops formed by irradiation can significantly contribute to both irradiation hardening and embrittlement of materials when subjected to extreme nuclear reactor environments. Here, this study explores the application of weak-beam dark-field (WBDF) scanning transmission electron microscopy (STEM) methods for quantitative irradiation-induced defect analysis in crystalline materials, with a specific focus on dislocation loop imaging and analysis. A high-purity Fe-5 wt% Cr model alloy was irradiated with 8 MeV Fe 2+ ions at 450°C to a fluence of 8.8 × 10 19 m -2 , inducing dislocation loops for analysis. While transmission electron microscopy (TEM) has traditionally been the primary tool for dislocation imaging, recent advancements in STEM technology have reignited interest in using STEM for defect imaging. This study introduces and compares three WBDF STEM methods, demonstrating their effectiveness in suppressing background contrasts, isolating defect information for dislocation loop type classification, providing finer dislocation line images for small loop analysis, and presenting inside–outside contrast for identifying loop nature. Experimental findings indicate that WBDF STEM methods surpass traditional TEM approaches, yielding clearer and more detailed images of dislocation loops. The study concludes by discussing the potential applications of WBDF STEM techniques in defect analysis, emphasizing their adaptability across various material systems beyond nuclear materials.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Imaging Unstained Synthetic Polymer Crystals and Defects on Atomic Length Scales Using Cryogenic Electron Microscopy

Properties of soft crystalline materials such as synthetic polymers are governed by locations of constituent atoms. Determining atomic-scale structures in these materials is difficult because they degrade rapidly when studied by electron microscopy, and techniques such as x-ray scattering average over volumes much larger than coherent blocks of the unit cells. We obtained cryo-electron microscopy images of self-assembled nanosheets of a peptoid polymer, made by solid-phase synthesis, in which we see a variety of crystalline motifs. A combination of crystallographic and single particle methods, developed for cryo-electron microscopy of biological macromolecules, was used to obtain high resolution images of the crystals. Individual crystals contain grains that are mirror images of each other with concomitant grain boundaries. We have used molecular dynamic simulations to build an atomic model of the crystal structure to facilitate the interpretation of electron micrographs. Direct visualization of crystalline grains and grain boundaries on atomic length scales represents a new level of information for the polymer field.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Defect identification in simulated Bragg coherent diffraction imaging by automated AI

X-ray Bragg coherent diffraction imaging is a powerful technique for operando and in situ materials characterization and provides a unique means of quantifying the influence of one-dimensional (1D) and two-dimensional (2D) material defects on material response. However, obtaining full images from raw x-ray diffraction data is nontrivial and computationally intensive, precluding real-time experimental feedback. Here, we present a machine learning approach to identify the presence of crystalline line defects (edge and screw) in samples from the raw, 2D, coherent diffraction data without the need for image reconstruction through iterative phase retrieval. Further, we compare different approaches to designing neural networks for this application and demonstrate the potential of automated ML (autoML) approaches.

36 MATERIALS SCIENCE↗