Identification of Radiolytically-Active Thermal Transition Phases in Boehmite
Abstract not provided
Huestis, Patricia L.↗
SEARCH · Engineering Papers
Results for “Defect detection”
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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Design of robot grippers for binder jet products handling
Dimension accuracy, damage minimization, and defect detection are essential in manufacturing processes, especially additive manufacturing. These types of challenges may arise either during the manufacture of a product or its use. The repeatability of the process is vital in additive manufacturing systems. However, human users may lose concentration and, thus, would be a great alternative as an assistant. Depending on the nature of work, a robot’s fingers might vary, for example, mechanical, electrical, vacuum, two-fingers, and three-fingers. In addition, the end effector plays a vital role in picking up an object in the advanced manufacturing process. However, inbuilt robotic fingers may not be appropriate in different production environments. In this research presented here considering metal binder jet additive manufacturing, the two-finger end- effectors are proposed design, analysis, and experiment to pick up an object after completing the production process from a specific location. The final designs were further printed by using a 3D metal printer and installed in the existing robotic systems. These new designs are used successfully to hold the object from the specific location by reducing the contact force that was not possible with the previously installed end effector's finger. In addition, a numerical study was conducted in order to compare the flowability of the geometric shape of finger's free areas.
42 ENGINEERING↗
Visualizing heterogeneous dipole fields by terahertz light coupling in individual nano-junctions
The challenge underlying superconducting quantum computing is to remove materials bottleneck for highly coherent quantum devices. The nonuniformity and complex structural components in the underlying quantum circuits often lead to local electric field concentration, charge scattering, dissipation and ultimately decoherence. Here we visualize interface dipole heterogeneous distribution of individual Al/AlO$_{x}$/Al junctions employed in transmon qubits by broadband terahertz scanning near-field microscopy that enables the non-destructive and contactless identification of defective boundaries in nano-junctions at an extremely precise nanoscale level. Our THz nano-imaging tool reveals an asymmetry across the junction in electromagnetic wave-junction coupling response that manifests as hot (high intensity) vs cold (low intensity) spots in the spatial electrical field structures and correlates with defected boundaries from the multi-angle deposition processes in Josephson junction fabrication inside qubit devices. The demonstrated local electromagnetic scattering method offers high sensitivity, allowing for reliable device defect detection in the pursuit of improved quantum circuit fabrication for ultimately optimizing coherence times.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Process-dependent anisotropic thermal conductivity of laser powder bed fusion AlSi10Mg: impact of microstructure and aluminum-silicon interfaces
Purpose AlSi10Mg alloy is commonly used in laser powder bed fusion due to its printability, relatively high thermal conductivity, low density and good mechanical properties. However, the thermal conductivity of as-built materials as a function of processing (energy density, laser power, laser scanning speed, support structure) and build orientation, are not well explored in the literature. This study aims to elucidate the relationship between processing, microstructure, and thermal conductivity. Design/methodology/approach The thermal conductivity of laser powder bed fusion (L-PBF) AlSi10Mg samples are investigated by the flash diffusivity and frequency domain thermoreflectance (FDTR) techniques. Thermal conductivities are linked to the microstructure of L-PBF AlSi10Mg, which changes with processing conditions. The through-plane exceeded the in-plane thermal conductivity for all energy densities. A co-located thermal conductivity map by frequency domain thermoreflectance (FDTR) and crystallographic grain orientation map by electron backscattered diffraction (EBSD) was used to investigate the effect of microstructure on thermal conductivity. Findings The highest through-plane thermal conductivity (136 ± 2 W/m-K) was achieved at 59 J/mm 3 and exceeded the values reported previously. The in-plane thermal conductivity peaked at 117 ± 2 W/m-K at 50 J/mm 3 . The trend of thermal conductivity reducing with energy density at similar porosity was primarily due to the reduced grain size producing more Al-Si interfaces that pose thermal resistance. At these interfaces, thermal energy must convert from electrons in the aluminum to phonons in the silicon. The co-located thermal conductivity and crystallographic grain orientation maps confirmed that larger colonies of columnar grains have higher thermal conductivity compared to smaller columnar grains. Practical implications The thermal properties of AlSi10Mg are crucial to heat transfer applications including additively manufactured heatsinks, cold plates, vapor chambers, heat pipes, enclosures and heat exchangers. Additionally, thermal-based nondestructive testing methods require these properties for applications such as defect detection and simulation of L-PBF processes. Industrial standards for L-PBF processes and components can use the data for thermal applications. Originality/value To the best of the authors’ knowledge, this paper is the first to make coupled thermal conductivity maps that were matched to microstructure for L-PBF AlSi10Mg aluminum alloy. This was achieved by a unique in-house thermal conductivity mapping setup and relating the data to local SEM EBSD maps. This provides the first conclusive proof that larger grain sizes can achieve higher thermal conductivity for this processing method and material system. This study also shows that control of the solidification can result in higher thermal conductivity. It was also the first to find that the build substrate (with or without support) has a large effect on thermal conductivity.
Engineering↗
Remote Monitoring and Diagnostics of Pitch-Bearing Defects in an MW-Scale Wind Turbine Using Pitch Symmetrical-Component Analysis
Recently, multiple wind turbine failure databases have reviewed that the pitch system is one of the subassemblies with the highest failure rates and largest contributors to the overall downtime. Therefore, there has been an increasing interest to provide remote health monitoring for wind turbine pitch system. While most of the research articles are discussing pitch actuation system (hydraulic or electric actuator) faults only, there is very limited research on pitch-bearing-defect detection. This article provides a remote and hardware-free solution to monitor multiaxis pitch-bearing health condition called pitch symmetrical-component analysis. It leverages readily available low-resolution (100 Hz) electrical measurements, mechanical measurements, and control signals from the existing pitch control platform, and innovatively applies symmetrical-component analysis in multiphase ac system to multiaxis pitch control system and introduces multiaxis pitch-bearing degradation trending curves. This hardware-free solution can be directly applied to the existing wind turbines and successfully give the wind farm operator an early warning before multiaxis pitch bearing fails. It has been proved to be accurate, low cost, and has minimum impacts on turbine normal operation, and has been validated by field data from several North America MW-scale wind farms. This approach turns out to be the first hardware-free (no additional hardware needed) method to remotely monitor and diagnose multiaxis wind turbine pitch-bearing condition.
17 WIND ENERGY↗
Layer-wise Imaging Dataset from Powder Bed Additive Manufacturing Processes for Machine Learning Applications (Peregrine v2021-03)
This dataset contains layer-wise powder bed images from three different powder bed printing technologies â laser powder bed fusion, electron beam powder bed fusion, and binder jetting. This dataset was collected and annotated using the internally-developed Peregrine software tool and is designed primarily to facilitate research into anomaly defect detection using image segmentation or similar techniques. A total of 20 layers are provided for each printing technology, with each layer of data consisting of one or more calibrated images and an annotation file containing pixel-wise ground truth labels. The ground truths were labeled by domain experts, typically printer technicians. Data in this release were collected at Oak Ridge National Laboratory between 2016 and 2020 and were compiled in March 2021.
36 MATERIALS SCIENCE↗
Layer-wise Imaging Dataset from Powder Bed Additive Manufacturing Processes for Machine Learning Applications (Peregrine v2022-10)
This release consists of six datasets which together include multi-modal layer-wise powder bed images from two different powder bed printing technologies. These datasets are designed primarily to facilitate the development and testing of new computer vision and machine learning based anomaly and defect detection algorithms. The authors provide both training data with corresponding ground truth pixel masks and evaluation data with corresponding baseline prediction pixel masks made by a trained neural network. The laser powder bed fusion (L-PBF) datasets are sourced from EOS M290 and AddUp FormUp 350 printers and the binder jet (BJ) dataset is sourced from an ExOne M-Flex printer. The materials represented in these datasets include 17-4 PH Stainless Steel, DMREF, Inconel 718, Maraging Steel, and H13 Steel. The sensor imaging modalities represented include visible-light (VL), temporally-integrated (i.e., long duration exposure) near-infrared (TI-NIR), and wide-band infrared (IR).
36 MATERIALS SCIENCE↗
Layer-wise Imaging Dataset from Powder Bed Additive Manufacturing Processes for Machine Learning Applications (Peregrine v2022-10.1)
This release consists of six datasets which together include multi-modal layer-wise powder bed images from two different powder bed printing technologies. These datasets are designed primarily to facilitate the development and testing of new computer vision and machine learning based anomaly and defect detection algorithms. The authors provide both training data with corresponding ground truth pixel masks and evaluation data with corresponding baseline prediction pixel masks made by a trained neural network. The laser powder bed fusion (L-PBF) datasets are sourced from EOS M290 and AddUp FormUp 350 printers and the binder jet (BJ) dataset is sourced from an ExOne M-Flex printer. The materials represented in these datasets include 17-4 PH Stainless Steel, GammaPrint-700, Inconel 718, Maraging Steel, and H13 Steel. The sensor imaging modalities represented include visible-light (VL), temporally-integrated (i.e., long duration exposure) near-infrared (TI-NIR), and wide-band infrared (IR). To download the dataset: (1) Create a Globus account. (2) Create a Globus Endpoint on your computer. (3) Transfer the dataset from the OLCF DOI-DOWNLOADS Collection to your Collection. Common troubleshooting steps: (a) Confirm that the transfer is going from OLCF DOI-DOWNLOADS to your Collection. (b) Create an exception for Globus in your antivirus software so that it can create an Endpoint. (c) Manually create a Globus access directory (where the data will be downloaded) by going to the Preferences > Access tab.
36 MATERIALS SCIENCE↗
Synthesis and Single Crystals of Refractory Oxides of Lanthanides and Thorium
At the completion of this program, we can report that we developed a considerable degree of technical improvements in our ability to perform hydrothermal reactions at high temperatures and pressures. We can now routinely perform reactions at 700-750°C and 200 MPa. Currently we are in the process of exploiting this new technology synthesizing a range of exotic new materials investigating relatively poorly understood materials. Our initial efforts focused on the chemistry of rare earth oxides with tetravalent and pentavalent oxides. We recently published a study of the lanthanides with Nb 5+ and Ta 5+ ions, where we grew oxides such as RENdO 4 and RETaO 4 as high quality single crystals. These compounds were targeted as potential hosts for luminescent and scintillation materials, particularly given that they are among the densest oxide hosts and hence have good potential as absorbers for high energy radiation like X-rays and gamma rays. We also isolated a range of unusual new rare earth tantalates with very complex structures. indicating that the chemistry is very sensitive to conditions. We performed some fairly comprehensive examinations of the solid-state chemistry of rare earth ions with various tetravalent metal ions especially Si 4+ , Ge 4+ , Sn 4+ and Ti 4+ . Given the potential role of rare earth silicates in immobilizing radioactive waste elements in long-term storage, and the similarity of our hydrothermal fluids with known geological conditions, this chemistry continues to be relevant. We prepared an extensive series of new lanthanide germanates (e.g. RE 13 Ge 6 O 31 (OH), BaRE 10 (GeO 4 ) 4 O 8 ). and found that there is there is almost no overlap between the chemistry of the rare earth silicates. Stannic oxide (SnO 2 ) is much more refractory and requires higher temperatures and of mineralizer concentrations. One significant result is the growth of RE 2 Sn 2 O 7 pyrochlore single crystals. These are of interest because the rare earth stannate pyrochlores are known to display a wide range of magnetic frustration such as spin ice behavior. We grew high quality single crystals of rare earth germanate and stannate pyrochlores and this led to a collaboration with Professor Kate Ross at Colorado State. Preliminary measurements, indicate that the crystals contain no detectable defects or site disorder. Initial neutron diffraction on single crystals was performed at Oak Ridge, and more detailed experiments involving the Ross group are underway at both NIST and ORNL. This particular chemistry has turned out to be the most potentially significant work on this project and the collaborative effort with Prof. Ross is the topic of a DoE renewal project on quantum materials. Our initial foray into the hydrothermal chemistry of rare earth titanates has also been very promising and a range of cubic and polar ferroic phases of the light rare earths RE 2 Ti 2 O 7 (RE = La - Pr) in the P2 1 phase. We also discovered an interesting new phase Ce 2 Ti 4 O 11 that can have implications in heavy metal immobilization and storage. along with a series of new rare earth titanates (La 5 Ti 4 O 15 (OH) Sm 3 TiO 5 (OH) 3 and Lu 5 Ti 2 O 11 (OH) with exceptionally complex structures. One interesting sidelight has been high temperature hydrothermal chemistry terbium, including the growth of large crystals of TbO(OH). This is not a new compound but it is the first time it has been grown as large single crystals. The Tb atom density is almost as high as that in Tb 2 O 3 and has a very high Verdet constant (ca. 70), making it a very attractive candidate as a Faraday rotator. Unfortunately it is not in a cubic structure but he material is hard, stable, pure and inexpensive, so should still be an attractive Faraday oscillator. We recently received a patent on this material. We also synthesized K 2 Tb(Ge 2 O 7 ) containing stable octahedral Tb 4+ ions, which appears to be the first example of a well-characterized Tb 4+ complex. Given that Tb 4+ has been proposed as a benign surrogate for more treacherous tetravalent ions such as Cf 4+ and Bk 4+ , we think that Tb 4+ silicates can be a particularly useful study for actinide immobilization and related work. We also began reaction studies with rare earths and both ReO 2 and RuO 2 . These resulted in large single crystals of species like RE 5 Ru 2 O 12 , RE 4 Re 2 O 11 , REReO 4 and RE 2 ReO 5 . Several of these samples have already been sent to ORNL for magnetic and neutron diffraction studies.
36 MATERIALS SCIENCE↗
International Meeting on Fuel Cell and Electrolyzer Quality Control: Summary Report
Quality control (QC) for both polymer electrolyte membrane fuel cell and electrolysis membrane electrode assembly (MEA) materials is a key challenge for scale-up and cost reduction. Developing methods for detecting defects, as well as measuring critical material properties and understanding the impact of as-manufactured variations in these materials on cell performance and lifetime, are critical barriers. To help address these needs, the National Research Council Canada (NRC), Fraunhofer Institute for Solar Energy Systems (ISE), and the National Renewable Energy Laboratory (NREL) have organized and facilitated a series of workshops on the topic, bringing together industry, academia, and research institutions from North America and Europe. Prior workshops in Canada and Germany have focused on the status of quality tool capabilities and identification of needed developments for fuel cells. These meetings have garnered an excellent response and follow-on attendance, with over 100 unique attendees.
30 DIRECT ENERGY CONVERSION↗
SIS-AOP Cueing/Segmenting Algorithm (FOA_SIS-AOP) Using the Sandia FOA 4.0 Framework
For machine vision, one of the most important operations is fast and effective object cueing or segmentation. Sandia National Labs has a long history of development and implementation of very fast and effective cueing/segmentation algorithms. This report covers the history, motivation and implementation of evolving frameworks (Sandia FOA Frameworks) upon which this long legacy of successful algorithms are built. The report describes the innovative microprocessor implementation, enabling extremely fast morphological processing, combined with a novel adaptive quantization front - end and a feature - based backend that resulted in Sandia developing fast and effective cueing in a wide variety of applications, from defect detection to SAR ATR. The report covers evolution from Sandia FOA 1.0 Framework (1995) to current Sandia FOA 4.0 Framework (2021). Requirements for the cueing algorithm for SIS - AOP (FOA_SIS - AOP) that drove the Sandia FOA 4.0 Framework development are discussed, along with information on how to use the Sandia FOA Frameworks.
97 MATHEMATICS AND COMPUTING↗
Sound Speeds of Solids from Ultrasonic Pulse Receiver Measurements
A common method of determining elastic material properties is utilizing an ultrasonic pulse receiver. This is a non-destructive test (NDT) causing no plastic deformation of the material. It requires a pulse generator, oscilloscope, and two transducers to measure the sound velocity of a material. Both longitudinal and shear sound velocities may be obtained by utilizing the appropriate transducer. Acoustic nondestructive testing methods are often used in manufacturing certification processes and for material defect detection in industry. These methods can also be utilized to directly measure the elastic moduli of a material without damaging the material. Overall, it is a low-cost experiment with minimal preparation, and is easy to use.
36 MATERIALS SCIENCE↗
Evaluation and Development of Phase Array Ultrasonic Testing (PAUT) System for Additively Manufactured Parts
This research focuses on the application of advanced ultrasonic testing techniques developed by The Phased Array Company (TPAC) for inspecting defects in additive manufacturing (AM) parts. Traditionally, X-ray computed tomography is the standard for inspecting AM components. Although, the long inspection and analysis time, along with relatively high cost make implementation difficult. Thus, an alternative nondestructive evaluation (NDE) approach is necessary to support quality assurance efforts within the field of AM. TPAC is recognized as a leader in ultrasonic testing innovation, deploying sophisticated algorithms such as Total Focusing Method (TFM) and Phased Wave Imaging (PWI) for ultrasonic data processing and interpretation. This work will explore how the TFM and PWI algorithms can assist defect detection within polymer AM parts. The AM field is seeking novel NDE methods to provide support within quality control and assurance efforts. Advanced ultrasonics inspection have the potential to fulfill this need.
99 GENERAL AND MISCELLANEOUS↗
Evaluation and Development of Phase Array Ultrasonic Testing (PAUT) System for Additively Manufactured Parts
This research focuses on the application of advanced ultrasonic testing techniques developed by The Phased Array Company (TPAC) for inspecting defects in additive manufacturing (AM) parts. Traditionally, X-ray computed tomography is the standard for inspecting AM components. Although, the long inspection and analysis time, along with relatively high cost make implementation difficult. Thus, an alternative nondestructive evaluation (NDE) approach is necessary to support quality assurance efforts within the field of AM. TPAC is recognized as a leader in ultrasonic testing innovation, deploying sophisticated algorithms such as Total Focusing Method (TFM) and Phased Wave Imaging (PWI) for ultrasonic data processing and interpretation. This work will explore how the TFM and PWI algorithms can assist defect detection within polymer AM parts. The AM field is seeking novel NDE methods to provide support within quality control and assurance efforts. Advanced ultrasonics inspection have the potential to fulfill this need.
36 MATERIALS SCIENCE↗
High-Throughput Characterization Tools/Algorithms To Outline Porosity Variability in AM Samples as a Function of Processing Conditions
This report documents the development and deployment of advanced algorithms and tools that enable high-throughput characterization for metal additive manufacturing (AM), with a particular focus on process parameter optimization and material/part qualification for nuclear applications. While the method ologies presented support diverse characterization techniques, the majority of the work is centered on AI-driven algorithms for X-ray computed tomography (XCT) to accelerate defect detection and materials analysis at scale.
36 MATERIALS SCIENCE↗
Evaluation of the Reconstruction Accuracy of the Ultrasound Model–Based Image Reconstruction (U-MBIR) Method for Concrete to Optimize Damage Detection
Reinforced concrete is a critical structural material used to construct nuclear power plants (NPPs). As such, its safety and performance must be thoroughly examined throughout the life cycle of the NPP infrastructure system. Ultrasonic measurements have been an industry standard for both surface and subsurface inspections. To support the development of these techniques, Oak Ridge National Laboratory (ORNL) has researched and developed advanced image reconstruction algorithms to capture internal damage. The results and discussion presented herein summarize the current state of the ultrasonic model–based iterative reconstruction (U-MBIR) algorithm developed at ORNL. In the work documented in this report, the U-MBIR methodology was applied to four sets of ultrasonic data collected from concrete specimens. The results demonstrate that the U-MBIR algorithm can successfully detect defects within the four concrete samples. The reconstruction images help identify the specimen thickness, regions of delamination, and location of rebar embedded within the concrete. The reconstruction images allow engineers and technicians to characterize the internal defects within concrete specimens and structural members. Ultimately, this knowledge can guide engineers in making informed decisions regarding the performance, safety, and reliability of structural materials (i.e., reinforced concrete) throughout the life cycle of NPPs.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Machine Learning for Additive Manufacturing of Functionally Graded Materials
Additive Manufacturing (AM) is a transformative manufacturing technology enabling direct fabrication of complex parts layer-by-layer from 3D modeling data. Among AM applications, the fabrication of Functionally Graded Materials (FGMs) has significant importance due to the potential to enhance component performance across several industries. FGMs are manufactured with a gradient composition transition between dissimilar materials, enabling the design of new materials with location-dependent mechanical and physical properties. This study presents a comprehensive review of published literature pertaining to the implementation of Machine Learning (ML) techniques in AM, with an emphasis on ML-based methods for optimizing FGMs fabrication processes. Through an extensive survey of the literature, this review article explores the role of ML in addressing the inherent challenges in FGMs fabrication and encompasses parameter optimization, defect detection, and real-time monitoring. The article also provides a discussion of future research directions and challenges in employing ML-based methods in the AM fabrication of FGMs.
36 - MATERIALS SCIENCE↗
Simurgh: A Framework for Cad-Driven Deep Learning Based X-Ray CT Reconstruction
High-resolution X-ray computed tomography (XCT) is an important technique for the inspection of additively manufactured (AM) parts. While XCT is typically used off-line to inspect a subset of manufactured parts, significantly accelerating measurement speed while retaining accuracy would enable use of XCT for in-line inspection to rapidly identify defects in each part as it is manufactured. Here, we propose a deep learning (DL) based approach that uses computer aided design (CAD) models of the AM parts and physics-based information to rapidly produce high-quality reconstructions from sparse XCT measurements without high quality ground truth data. Our approach uses a generative adversarial neural network (GAN) to produced realistic training data from the CAD-based simulations and a deep neural network that is trained using data from the first stage to produce accurate 3D reconstructions. Using experimental XCT data of metal parts, we demonstrate enhanced defect detection capabilities while dramatically reducing the scan time.
Ziabari, Amir↗