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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Computed Tomography by Combining Transmission and Scatter Radiography Methods

In 2015, approximately 13 billion tons of freight was transported in the US, roughly 40 tons per person. 40% of all freight on a ton-mile basis will be transported by railroads. Class I railroads have approximately 140,000 miles (224,000 km) of railroad track across the country. Approximately 3,200 crossties per mile secure the rails at the proper gauge, transmit loads to the ballast, and prevent lateral and longitudinal shifting. Wood crossties can fail in many ways including rotting, breaking, cracking, and splitting. Developing new technologies to inspect these crossties are necessary to help maintain and ensure safe railroad operations. The Georgetown Railroad Equipment Company partnered with the University of Florida to develop the Aurora Xi X-ray backscatter inspection system. This system generates 2D backscatter images at 25 mph to detect weakness in wood crossties long before the internal degradation is visible on the tie surface. The railroad industry has a need for a high-speed high resolution 3D inspection method to inspect approximately 20 million newly fabricated railroad crossties per year. Currently new ties are not radiographically inspected to reject internal flaws and verify that rot prevention treatments have adequately penetrated into the crosstie. Current spiral scan computed tomography (CT) systems are not designed for the 100 cm/s to 400 cm/s speed of a wood crosstie production line. Building on the technology developed for the 2D Aurora Xi system, a new 3D fan-beam inspection system and CT method is being developed. A previous scatter CT method by Arsenault and Hussein required moving the Xray source to many positions, averaging the X-ray spectra, and ignoring the Compton scatter change of energy. The proposed design and method combines transmission and scatter radiography in all directions without moving or rotating the X-ray sources or detectors. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Dual X-ray computed tomography-aided classification of melt pool boundaries and flaws in crept additively manufactured parts

In metal additive manufacturing (AM), understanding the process-structure-performance relationships requires a combination of multi-scale characterization techniques that allows for the measurement of the melt pool shape and boundary and classifying various defects and flaws in the AM parts. Such approaches can be destructive, only 2D in nature, or have a small field of view and can be complex to co-register and analyze. Here, in this work, we present a non-destructive 3D inspection technique that employs dual-energy X-ray computed tomography (XCT) along with a model-based iterative reconstruction (MBIR) and a new segmentation algorithm. The proposed approach and algorithm are not only capable of classifying and quantifying flaws such as pores, cracks, and inclusions, but they also allow for the extraction of microstructural features such as melt pool boundaries (MPB) and melt pool regions (MPR), that can help understand process-structure-performance relationships for alloys under study. As an exemplar application, we employed the method for characterization of an additively manufactured aluminum alloy crept under tensile stress at 300 °C for 1064 h. Our results demonstrate high quality segmentation and classification of various flaws and MPB and MPR, for the first time, using 3D X-ray CT inspection. The delineated MPB and MPR in the crept samples reveal the preferential growth paths of cracks that formed during creep deformation. The technique was used for successfully quantifying the characteristics (number of defects, their density, volume fraction, etc.) of the manufacturing-induced pores and creep-induced cracks, which is necessary to better understand the creep failure mechanisms of the material.

36 MATERIALS SCIENCE↗
NASA NTRS2010

X-Ray Computed Tomography Inspection of the Stardust Heat Shield

The "Stardust" heat shield, composed of a PICA (Phenolic Impregnated Carbon Ablator) Thermal Protection System (TPS), bonded to a composite aeroshell, contains important features which chronicle its time in space as well as re-entry. To guide the further study of the Stardust heat shield, NASA reviewed a number of techniques for inspection of the article. The goals of the inspection were: 1) to establish the material characteristics of the shield and shield components, 2) record the dimensions of shield components and assembly as compared with the pre-flight condition, 3) provide flight infonnation for validation and verification of the FIAT ablation code and PICA material property model and 4) through the evaluation of the shield material provide input to future missions which employ similar materials. Industrial X-Ray Computed Tomography (CT) is a 3D inspection technology which can provide infonnation on material integrity, material properties (density) and dimensional measurements of the heat shield components. Computed tomographic volumetric inspections can generate a dimensionally correct, quantitatively accurate volume of the shield assembly. Because of the capabilities offered by X-ray CT, NASA chose to use this method to evaluate the Stardust heat shield. Personnel at NASA Johnson Space Center (JSC) and Lawrence Livermore National Labs (LLNL) recently performed a full scan of the Stardust heat shield using a newly installed X-ray CT system at JSC. This paper briefly discusses the technology used and then presents the following results: 1. CT scans derived dimensions and their comparisons with as-built dimensions anchored with data obtained from samples cut from the heat shield; 2. Measured density variation, char layer thickness, recession and bond line (the adhesive layer between the PICA and the aeroshell) integrity; 3. FIAT predicted recession, density and char layer profiles as well as bondline temperatures Finally suggestions are made as to future uses of this technology as a tool for non-destructively inspecting and verifying both pre and post flight heat shields.

McNamara, Karen M.↗
DOE OSTI2018

Kinematic fixtures to enable multi-material printing and rapid non-destructive inspection during two-photon lithography

Here, two-photon lithography (TPL) is a polymerization based technique that enables additive manufacturing of millimeter scale parts with submicron features. TPL equipment is often based on retrofitted optical microscopes that lack precise registration capabilities. Consequently, slow and error-prone visual alignment to fiducials is necessary if registration to pre-existing features is required. Herein, we have designed, built, and tested precise kinematic fixtures that are repeatable to within ±315 nm (3σ value) and passively register the build surface to TPL equipment with an accuracy of ±1.7 μm. This enables one to sequentially print with multiple materials by building the structures directly on top of the kinematic fixtures. In addition, the same fixtures passively register to an X-ray computed tomography (CT) system to enable non-destructive 3D inspection that is integrated with the fabrication process. These fixtures (i) provide a practical means to handle micro-scale parts during non-destructive imaging, (ii) reduce the set-up time for X-ray CT from more than an hour to less than a few minutes, and (iii) eliminate operator uncertainty from the multi-material printing and imaging process. As such, these fixtures enable new printing and imaging functionalities that are critical for high-quality additive manufacturing of multi-material polymer parts with microscale and submicron features.

42 ENGINEERING↗
NASA NTRS2011

Image Analysis via Soft Computing: Prototype Applications at NASA KSC and Product Commercialization

This slide presentation reviews the use of "soft computing" which differs from "hard computing" in that it is more tolerant of imprecision, partial truth, uncertainty, and approximation and its use in image analysis. Soft computing provides flexible information processing to handle real life ambiguous situations and achieve tractability, robustness low solution cost, and a closer resemblance to human decision making. Several systems are or have been developed: Fuzzy Reasoning Edge Detection (FRED), Fuzzy Reasoning Adaptive Thresholding (FRAT), Image enhancement techniques, and visual/pattern recognition. These systems are compared with examples that show the effectiveness of each. NASA applications that are reviewed are: Real-Time (RT) Anomaly Detection, Real-Time (RT) Moving Debris Detection and the Columbia Investigation. The RT anomaly detection reviewed the case of a damaged cable for the emergency egress system. The use of these techniques is further illustrated in the Columbia investigation with the location and detection of Foam debris. There are several applications in commercial usage: image enhancement, human screening and privacy protection, visual inspection, 3D heart visualization, tumor detections and x ray image enhancement.

Dominguez, Jesus A.↗
NASA NTRS1990

Space shuttle main engine computed tomography applications

For the past two years the potential applications of computed tomography to the fabrication and overhaul of the Space Shuttle Main Engine were evaluated. Application tests were performed at various government and manufacturer facilities with equipment produced by four different manufacturers. The hardware scanned varied in size and complexity from a small temperature sensor and turbine blades to an assembled heat exchanger and main injector oxidizer inlet manifold. The evaluation of capabilities included the ability to identify and locate internal flaws, measure the depth of surface cracks, measure wall thickness, compare manifold design contours to actual part contours, perform automatic dimensional inspections, generate 3D computer models of actual parts, and image the relationship of the details in a complex assembly. The capabilities evaluated, with the exception of measuring the depth of surface flaws, demonstrated the existing and potential ability to perform many beneficial Space Shuttle Main Engine applications.

Sporny, Richard F.↗
NASA NTRSDate not supplied

3D Localization of Defects in Facility Inspection

Wind tunnels are crucial facilities that support the aerospace industry. However, these facilities are large, complex, and pose unique maintenance and inspection requirements. Manual inspections to identify defects such as cracks, missing fasteners, leaks, and foreign objects are important but labor and schedule intensive. Our goal is to utilize small Unmanned Aircraft Systems (sUAS) and computer vision-based analysis to automate the inspection of the interior and exterior of NASA’s critical wind tunnel facilities. We detect missing fasteners as our defect class, and detect existing fasteners to provide potential future missing fastener sites for preventative maintenance. These detections are done on both 2D raw images and in 3D space to provide a visual reference and real world location to facilitate repairs. A dataset was created consisting of images taken along a grid-like pattern of an interior tunnel section in the AEDC National Full-Scale Aerodynamics Complex (NFAC) at NASA Ames Research Center. Our method uses object detection to create image level bounding boxes of the fasteners and missing fasteners, then uses photogrammetry to create a mapping from 2D image locations to 3D real world locations. The image level bounding boxes and the 2D to 3D mapping are then combined to determine the 3D location of the defects. We describe the data collection, photogrammetry, and computer vision techniques used for object detection as well as a quantitative analysis of the method.

Small Unmanned Aircraft Systems (sUAS)↗
DOE OSTI2022

Segmentation of tomography datasets using 3D convolutional neural networks

Dendritic microstructures are ubiquitous in nature and are the primary solidification morphologies in metallic materials. Techniques such as X-ray computed tomography (XCT) have provided new insights into dendritic phase transformation phenomena. However, manual identification of dendritic morphologies in microscopy data can be both labor intensive and potentially ambiguous. The analysis of 3D datasets is particularly challenging due to their large sizes (terabytes) and the presence of artifacts scattered within the imaged volumes. Here, in this study, we trained 3D convolutional neural networks (CNNs) to segment 3D datasets. Three CNN architectures were investigated, including a new version of FCDenseNet which we extended to 3D. We show that using hyperparameter optimization (HPO) and fine-tuning techniques, both 2D and 3D CNN architectures outperform the previous state of the art. The 3D U-Net architecture trained in this study produced the best segmentations according to quantitative metrics (intersection-over-union of 95.56% and a boundary displacement error of 0.58 pixels), while 3D FCDense produced the smoothest boundaries and best segmentations according to visual inspection. The trained 3D CNNs are able to segment entire 852 × 852 × 250 voxel 3D volumes in only ~60 s, thus hastening the progress towards a deeper understanding of phase transformation phenomena such as dendritic solidification.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

In-Space Inspection Needs: Opportunities for advanced NDE tools such as x-ray CT for additively manufactured parts, in-situ resource utilization, geological applications, and more

It is now 50 years since the first human presence on the surface of the Moon and as we strive to return with women and men in the next few years, we embrace new technical challenges, goals, and innovative solutions to address 21st century objectives. These new ambitions carry fresh challenges and risks, with the field of NDE playing an increasingly more relevant role towards meeting these essential goals. In recent years, more advanced NDE tools have triggered a rapid expansion of applications for the space industry. In particular, x-ray Computed Tomography (CT) has proven to be a trusted and powerful asset for spaceflight hardware inspection, as well as applied geotechnical analysis for natural materials (e.g., rocks, soils) for NASA and across industry. However, such methods have yet to be extended to “deep space” applications such as those that are now part of the US National Space Policy Directive (SPD-1) and the accelerated push to return humans to the Moon (i.e., Artemis). For this reason, advancing these powerful Earth-based laboratory methods via new technologies, integrated computational solutions, and creative engineering approaches is directly aligned with national space policies, as well as with multiple NASA Strategic Plan priorities. The use of x-ray CT at scales as fine as a few microns or smaller can identify spacecraft part failure modes relevant to quality assurance for flight hardware and AM parts such as those recently developed for ISS. This technology could also identify valuable metallic phases within geological materials (i.e., rocks or drill cores), enabling resource-relevant triage of samples for In-Situ Resource Utilization (ISRU) and high science value sample return to Earth laboratories. There is also significant application for 3D imaging tools for medical use such as inspecting protective gear as well as bone density degradation studies which are critical in establishing a sustained presence in space. Timing for development of these tools for space use is advantageous as we prepare for new opportunities in the next few years and recognize recent commercial technology advancements which make it feasible. Moreover, as NASA strives to take full advantage of developments in AM technologies, including In-Space Manufacturing (ISM), it is widely recognized that NDE tools such as CT will play an essential role in acceptance of these parts for widespread use. New in-space 3D inspection tools with complimentary technology such as AI-based automated feature recognition (accelerated by machine learning), rapid compositional analysis, and advanced sample manipulation, would be a game-changing step toward a new class of crew-based laboratory sensors once human outposts on the Moon are established.

In-Space Non-Destructive Evaluation↗