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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

Connecting Research and Practice: An Experience Report on Research Infusion with SAVE

NASA systems need to be highly dependable to avoid catastrophic mission failures. This calls for rigorous engineering processes including meticulous validation and verification. However, NASA systems are often highly distributed and overwhelmingly complex, making the software portion of these systems challenging to understand, maintain, change, reuse, and test. NASA's systems are long-lived and the software maintenance process typically constitutes 60-80% of the total cost of the entire lifecycle. Thus, in addition to the technical challenges of ensuring high life-time quality of NASA's systems, the post-development phase also presents a significant financial burden. Some of NASA's software-related challenges could potentially be addressed by some of the many powerful technologies that are being developed in software research laboratories. Many of these research technologies seek to facilitate maintenance and evolution by for example architecting, designing and modeling for quality, flexibility, and reuse. Other technologies attempt to detect and remove defects and other quality issues by various forms of automated defect detection, architecture analysis, and various forms of sophisticated simulation and testing. However promising, most such research technologies nevertheless do not make the transition from the research lab to the software lab. One reason the transition from research to practice seldom occurs is that research infusion and technology transfer is difficult. For example, factors related to the technology are sometimes overshadowed by other types of factors such as reluctance to change and therefore prohibits the technology from sticking. Successful infusion might also take very long time. One famous study showed that the discrepancy between the conception of the idea and its practical use was 18 years plus or minus three. Nevertheless, infusing new technology is possible. We have found that it takes special circumstances for such research infusion to succeed: 1) there must be evidence that the technology works in the practitioner's particular domain, 2) there must be a potential for great improvements and enhanced competitive edge for the practitioner, 3) the practitioner has to have strong individual curiosity and continuous interest in trying out new technologies, 4) the practitioner has to have support on multiple levels (i.e. from the researchers, from management, from sponsors etc), and 5) to remain infused, the new technology has to be integrated into the practitioner's processes so that it becomes a natural part of the daily work. NASA IV&V's Research Infusion initiative sponsored by NASA's Office of Safety & Mission Assurance (OSMA) through the Software Assurance Research Program (SARP), strives to overcome some of the problems related to research infusion.

Lindvall, Mikael↗
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↗
DOE OSTI2022

Automatic Detection of Defects in High-Reliability Components

Disastrous consequences can result from defects in manufactured parts—particularly the high consequence parts developed at Sandia. Identifying flaws in as-built parts can be done with nondestructive means, such as X-ray Computed Tomography (CT). However, due to artifacts and complex imagery, the task of analyzing the CT images falls to humans. Human analysis is inherently unreproducible, unscalable, and can easily miss subtle flaws. We hypothesized that deep learning methods could improve defect identification, increase the number of parts that can effectively be analyzed, and do it in a reproducible manner. We pursued two methods: 1) generating a defect-free version of a scan and looking for differences (PandaNet), and 2) using pre-trained models to develop a statistical model of normality (Feature-based Anomaly Detection System: FADS). Both PandaNet and FADS provide good results, are scalable, and can identify anomalies in imagery. In particular, FADS enables zero-shot (training-free) identification of defects for minimal computational cost and expert time. It significantly outperforms prior approaches in computational cost while achieving comparable results. FADS’ core concept has also shown utility beyond anomaly detection by providing feature extraction for downstream tasks.

47 OTHER INSTRUMENTATION↗
NASA NTRS2009

Measurement Variability of Vertical Scanning Interferometry Tool Used for Orbiter Window Defect Assessment

The ability to sufficiently measure orbiter window defects to allow for window recertification has been an ongoing challenge for the orbiter vehicle program. The recent Columbia accident has forced even tighter constraints on the criteria that must be met in order to recertify windows for flight. As a result, new techniques are being investigated to improve the reliability, accuracy and resolution of the defect detection process. The methodology devised in this work, which is based on the utilization of a vertical scanning interferometric (VSI) tool, shows great promise for meeting the ever increasing requirements for defect detection. This methodology has the potential of a 10-100 fold greater resolution of the true defect depth than can be obtained from the currently employed micrometer based methodology. An added benefit is that it also produces a digital elevation map of the defect, thereby providing information about the defect morphology which can be utilized to ascertain the type of debris that induced the damage. However, in order to successfully implement such a tool, a greater understanding of the resolution capability and measurement repeatability must be obtained. This work focused on assessing the variability of the VSI-based measurement methodology and revealed that the VSI measurement tool was more repeatable and more precise than the current micrometer based approach, even in situations where operator variation could affect the measurement. The analysis also showed that the VSI technique was relatively insensitive to the hardware and software settings employed, making the technique extremely robust and desirable

Padula, Santo, II↗
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↗
DOE OSTI2026

Establishing an acoustic-property relationship in laser powder bed fusion with machine learning

Quality control of Laser Powder Bed Fusion (PBF-LB) additively manufactured parts is an important hurdle inhibiting the technology’s use structural applications. Acoustic monitoring of the laser powder bed fusion process can detect defects in-situ that are known to degrade mechanical properties. However, processing-structure-property (PSP) relationships are required to extrapolate from detected defects to part performance. Here, this study explores how acoustics may be a suitable signature linking processing conditions to properties, thus effectively substituting for structure in the PSP relationship. Establishing such a relationship would enable a part’s mechanical performance to be directly predicted from its acoustic signature, reducing the need for destructive testing or microstructural analysis to ensure a part will meet performance requirements. One hundred CoCrFeMnNi high entropy alloy tensile bars were printed across 13 process conditions in a series of 6 prints. The acoustic signatures of these tensile bars were used to train machine learning models to predict each part’s mechanical properties. By using both process information and acoustic information to predict mechanical properties, yield strength was predicted 18% more accurately and ductility to failure was predicted 10% more accurately than is achieved when using duplicate parts to predict part performance. Finally, individual acoustic frequencies were investigated to determine why acoustic signatures improve mechanical property predictions and the potential physical origins of these signatures. This work demonstrates how blending acoustics, process information, and machine learning can provide in-situ diagnostics of mechanical properties and improve the reliability of the PBF-LB process.

Acoustic emission↗
DOE OSTI2025

Multi-physics melt pool modeling and process optimization for laser direct energy deposition of Nb-based refractory C103: Defect formation, geometric precision, and process mapping

Recent developments in additive manufacturing (AM) technology have reignited interest in the fabrication of the Nb-based refractory C103 alloy offering solutions to the challenges posed by traditional manufacturing methods. However, the limited numerical and experimental studies on laser direct energy deposition (DED) of C103 have hindered the understanding of the relationships between process parameters and build quality. This has made it challenging to consistently produce parts with the desired quality and microstructure suitable for critical applications. In this study, we focus on optimizing the laser DED process for C103 by employing a hybrid approach that combines experimental techniques and computational fluid dynamics (CFD). This approach facilitates the development of process maps for defect detection and geometric precision. To achieve this, multi-layer C103 samples were fabricated using laser DED under various process parameters, enabling the creation of a process map for defect detection. Additionally, a multi-physics, multiphase simulation framework was developed within a high-performance computing (HPC) environment to establish process maps for geometric precision. Using these process maps, printability windows were identified for achieving both the desired geometric accuracy and defect-free prints. It was observed that prints with a power-to-velocity (P/V) ratio close to unity resulted in defect-free outcomes. This study provides a foundation for reducing design lead time and rejected parts, ultimately optimizing the laser DED process for C103.

Defect formation and geometric precision↗
NASA NTRS2002

Finding Feasible Abstract Counter-Examples

A strength of model checking is its ability to automate the detection of subtle system errors and produce traces that exhibit those errors. Given the high computational cost of model checking most researchers advocate the use of aggressive property-preserving abstractions. Unfortunately, the more aggressively a system is abstracted the more infeasible behavior it will have. Thus, while abstraction enables efficient model checking it also threatens the usefulness of model checking as a defect detection tool, since it may be difficult to determine whether a counter-example is feasible and hence worth developer time to analyze. We have explored several strategies for addressing this problem by extending an explicit-state model checker, Java PathFinder (JPF), to search for and analyze counter-examples in the presence of abstractions. We demonstrate that these techniques effectively preserve the defect detection ability of model checking in the presence of aggressive abstraction by applying them to check properties of several abstracted multi-threaded Java programs. These new capabilities are not specific to JPF and can be easily adapted to other model checking frameworks; we describe how this was done for the Bandera toolset.

Pasareanu, Corina S.↗
DOE OSTI2020

Detection of Defects in Additively Manufactured Metallic Materials with Machine Learning of Pulsed Thermography Images

Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of nuclear reactor parts. AM of metallic structures for nuclear energy applications is currently based on laser powder bed fusion (LPBF) process, which can introduce internal material flaws, such as pores and anisotropy. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures due to exposure to high temperature, radiation and corrosive environment in a nuclear reactor. Quality control (QC) requires nondestructive evaluation (NDE) of actual AM structures. Pulsed thermography is a potentially promising QC technique because it is scalable to arbitrary structure size. However, detection sensitivity of this method is limited by noises. We investigate separation of signal from noise in thermography images using several machine learning (ML) methods, including new spatio-temporal blind source separation (STBSS) and spatio-temporal sparse dictionary learning (STSDL) methods. Performance of the ML methods is benchmarked using thermography data obtained from imaging stainless steel 316L and Inconel 718 specimens produced LPBF method with imprinted calibrated porosity defects. The ML methods are ranked by F-score and execution runtime. The ML methods with higher accuracy require longer run time. However, this runtime is sufficiently short to perform QC within a realistic time frame.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Multi-Task Learning of Scanning Electron Microscopy and Synthetic Thermal Tomography Images for Detection of Defects in Additively Manufactured Metals

One of the key challenges in laser powder bed fusion (LPBF) additive manufacturing of metals is the appearance of microscopic pores in 3D-printed metallic structures. Quality control in LPBF can be accomplished with non-destructive imaging of the actual 3D-printed structures. Thermal tomography (TT) is a promising non-contact, non-destructive imaging method, which allows for the visualization of subsurface defects in arbitrary-sized metallic structures. However, because imaging is based on heat diffusion, TT images suffer from blurring, which increases with depth. We have been investigating the enhancement of TT imaging capability using machine learning. In this work, we introduce a novel multi-task learning (MTL) approach, which simultaneously performs the classification of synthetic TT images, and segmentation of experimental scanning electron microscopy (SEM) images. Synthetic TT images are obtained from computer simulations of metallic structures with subsurface elliptical-shaped defects, while experimental SEM images are obtained from imaging of LPBF-printed stainless-steel coupons. MTL network is implemented as a shared U-net encoder between the classification and the segmentation tasks. Results of this study show that the MTL network performs better in both the classification of synthetic TT images and the segmentation of SEM images tasks, as compared to the conventional approach when the individual tasks are performed independently of each other.

36 MATERIALS SCIENCE↗
NASA NTRS1980

Detecting insulation defects in metal/plastic films

Simple apparatus checks insulation between plastic and metal surfaces. Film can be inspected more accurately; apparatus can spot minute electrical contaminants between plastic and metal films. Steel roller connected to high-range ohmmeter is guided over entire plastic area of test sample. Roller weighs 2 lb. (0.9 kg), which effectively translates into 250-psi (1.76X10 to 6th power -N/sq m) contact pressure at plastic surface sufficient to locate microscopic defects.

Buggle, R. N.↗
NASA NTRS2002

Scanning Electron Microscope Mapping System Developed for Detecting Surface Defects in Fatigue Specimens

An automated two-degree-of-freedom specimen positioning stage has been developed at the NASA Glenn Research Center to map and monitor defects in fatigue specimens. This system expedites the examination of the entire gauge section of fatigue specimens so that defects can be found using scanning electron microscopy (SEM). Translation and rotation stages are driven by microprocessor-based controllers that are, in turn, interfaced to a computer running custom-designed software. This system is currently being used to find and record the location of ceramic inclusions in powder metallurgy materials. The mapped inclusions are periodically examined during interrupted fatigue experiments. The number of cycles to initiate cracks from these inclusions and the rate of growth of initiated cracks can then be quantified. This information is necessary to quantify the effect of this type of defect on the durability of powder metallurgy materials. This system was developed with support of the Ultra Safe program.

Bonacuse, Peter J.↗
NASA NTRS2018

Automated Fiber Placement Defects: Automated Inspection and Characterization

Automated Fiber Placement (AFP) is an additive composite manufacturing technique, and a pressing challenge facing this technology is defect detection and repair. Manual defect inspection is time consuming, which led to the motivation to develop a rapid automatic method of inspection. This paper suggests a new automated inspection system based on convolutional neural networks and image segmentation tasks. This creates a pixel by pixel classification of the defects of the whole part scan. This process will allow for greater defect information extraction and faster processing times over previous systems, motivating rapid part inspection and analysis. Fine shape, height, and boundary detail can be generated through our system as opposed to a more coarse resolution demonstrated in other techniques. These scans are analyzed for defects, and then each defect is stored for export, or correlated to machine parameters or part design. The network is further improved through novel optimization techniques. New training instances can also be created with every new part scan by including the machine operator as a post inspection check on the accuracy of the system. Having a continuously adapting inspection system will increase accuracy for automated inspections, cutting down on false readings.

Sacco, Christopher↗