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Results for “nondestructive quality evaluation”

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 sources NASA NTRS DOE OSTI

NASA NTRS2014

Process for Nondestructive Evaluation of the Quality of a Crimped Wire Connector

A process and apparatus for collecting data for nondestructive evaluation of the quality of a crimped wire connector are provided. The process involves providing a crimping tool having an anvil and opposing jaw for crimping a terminal onto a stranded wire, moving the jaw relative to the anvil to close the distance between the jaw and the anvil and thereby compress the terminal against the wire, while transmitting ultrasonic waves that are propagated through the terminal-wire combination and received at a receiving ultrasonic transducer as the jaw is moved relative to the anvil, and detecting and recording the position of the jaw relative to the anvil as a function of time and detecting and recording the amplitude of the ultrasonic wave that is received at the receiving ultrasonic transducer as a function of time as the jaw is moved relative to the anvil.

Yost, William T.↗

NASA NTRS1998

Low-Cost Quality Control and Nondestructive Evaluation Technologies for General Aviation Structures

NASA's Advanced General Aviation Transport Experiments (AGATE) Program has as a goal to reduce the overall cost of producing private aviation aircraft while maintaining the safety of these aircraft. In order to successfully meet this goal, it is necessary to develop nondestructive inspection techniques which will facilitate the production of the materials used in these aircraft and assure the quality necessary to maintain airworthiness. This paper will discuss a particular class of general aviation materials and several nondestructive inspection techniques that have proven effective for making these inspections. Additionally, this paper will discuss the investigation and application of other commercially available quality control techniques applicable to these structures.

Cramer, K. Elliott↗

DOE OSTI2025

Combining Deep Learning and scatterControl for High-Throughput X-ray CT Based Non-Destructive Characterization of Large-Scale Casted Metallic Components

X-ray computed tomography (XCT) is essential for nondestructive evaluation and quality control of large-scale metal components. XCT imaging, however, faces significant challenges from metal artifacts, particularly those caused by Compton scattering, which degrade image quality and obscure critical details. Hardware-based solutions (e.g. scatterControl) offer advancements by intercepting scattered photons and reducing artifacts, but they can be time-consuming and require additional processing. Here, we propose modifying and leveraging a novel deep learning (DL) framework, Simurgh, to enhance and accelerate scatter correction in XCT. By combining scatterControl with DL-based artifact removal, we demonstrate significant reduction in scan time while producing high-quality reconstructions. Through extensive evaluation on industrial XCT data, we show that our methods reduce scan time by up to more than 10 x while preserving flaw detectability. Quantitative analysis across multiple segmentation techniques confirms that Simurgh-based reconstructions consistently outperform traditional Feldkamp-Davis-Kress, model-based iterative reconstruction, and commercial DL models in both pixel-level and task-specific evaluations, enabling scalable, high-throughput XCT workflows for characterization of large scale components in applications such as casting and metal additive manufacturing.

Complex metal parts↗

DOE OSTI2023

Di-CNN: Domain-Knowledge-Informed Convolutional Neural Network for Manufacturing Quality Prediction

In manufacturing, convolutional neural networks (CNNs) are widely used on image sensor data for data-driven process monitoring and quality prediction. However, as purely data-driven models, CNNs do not integrate physical measures or practical considerations into the model structure or training procedure. Consequently, CNNs’ prediction accuracy can be limited, and model outputs may be hard to interpret practically. This study aims to leverage manufacturing domain knowledge to improve the accuracy and interpretability of CNNs in quality prediction. A novel CNN model, named Di-CNN, was developed that learns from both design-stage information (such as working condition and operational mode) and real-time sensor data, and adaptively weighs these data sources during model training. It exploits domain knowledge to guide model training, thus improving prediction accuracy and model interpretability. A case study on resistance spot welding, a popular lightweight metal-joining process for automotive manufacturing, compared the performance of (1) a Di-CNN with adaptive weights (the proposed model), (2) a Di-CNN without adaptive weights, and (3) a conventional CNN. The quality prediction results were measured with the mean squared error (MSE) over sixfold cross-validation. Model (1) achieved a mean MSE of 6.8866 and a median MSE of 6.1916, Model (2) achieved 13.6171 and 13.1343, and Model (3) achieved 27.2935 and 25.6117, demonstrating the superior performance of the proposed model.

47 OTHER INSTRUMENTATION↗

NASA NTRS1972

Proceedings of the Symposium on Welding, Bonding, and Fastening

Various technological processes to achieve lightweight reliable joining systems for structural elements of aircraft and spacecraft are considered. Joining methods, combinations of them, and nondestructive evaluation and quality assurance are emphasized.

Stein, B. A.↗

NASA NTRS1991

Flexible Interior-Impression-Molding Tray

Device used inside combustion chamber of complicated shape for nondestructive evaluation of qualities of welds, including such features as offset, warping, misalignment of parts, and dropthrough. Includes flexible polypropylene tray trimmed to fit desired interior surface contour. Two neodymium boron magnets and inflatable bladder attached to tray. Tray and putty inserted in cavity to make mold of interior surface.

Anders, Jeffrey E.↗

NASA NTRS1991

Probabilistic/Fracture-Mechanics Model For Service Life

Computer program makes probabilistic estimates of lifetime of engine and components thereof. Developed to fill need for more accurate life-assessment technique that avoids errors in estimated lives and provides for statistical assessment of levels of risk created by engineering decisions in designing system. Implements mathematical model combining techniques of statistics, fatigue, fracture mechanics, nondestructive analysis, life-cycle cost analysis, and management of engine parts. Used to investigate effects of such engine-component life-controlling parameters as return-to-service intervals, stresses, capabilities for nondestructive evaluation, and qualities of materials.

Watkins, T., Jr.↗

NASA NTRS1999

Nondestructive Evaluation Correlated with Finite Element Analysis

Advanced materials are being developed for use in high-temperature gas turbine applications. For these new materials to be fully utilized, their deformation properties, their nondestructive evaluation (NDE) quality and material durability, and their creep and fatigue fracture characteristics need to be determined by suitable experiments. The experimental findings must be analyzed, characterized, modeled and translated into constitutive equations for stress analysis and life prediction. Only when these ingredients - together with the appropriate computational tools - are available, can durability analysis be performed in the design stage, long before the component is built. One of the many structural components being evaluated by the NDE group at the NASA Lewis Research Center is the flywheel system. It is being considered as an energy storage device for advanced space vehicles. Such devices offer advantages over electrochemical batteries in situations demanding high power delivery and high energy storage per unit weight. In addition, flywheels have potentially higher efficiency and longer lifetimes with proper motor-generator and rotor design. Flywheels made of fiber-reinforced polymer composite material show great promise for energy applications because of the high energy and power densities that they can achieve along with a burst failure mode that is relatively benign in comparison to those of flywheels made of metallic materials Therefore, to help improve durability and reduce structural uncertainties, we are developing a comprehensive analytical approach to predict the reliability and life of these components under these harsh loading conditions. The combination of NDE and two- and three-dimensional finite element analyses (e.g., stress analyses and fracture mechanics) is expected to set a standardized procedure to accurately assess the applicability of using various composite materials to design a suitable rotor/flywheel assembly.

Abdul-Azid, Ali↗

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↗

NASA NTRS1980

Completion of evaluation of manufacturing processes for B/Al composites containing 0.2mm diameter boron fibers

Four fabricators produced a total of 54 B/1100 Al, B/6061 Al, and B/2024 Al panels for evaluation. The 8 ply unidirectional, 45 to 50 volume percent, panels were made using 0.20 mm diameter boron fibers which were obtained from a single supplier. Hot press consolidation was carried out in vacuum except for one set of dry woven tape panels which were hot pressed in air. A single testing contractor conducted nondestructive inspection, metallography, fractography and mechanical property tests. The mechanical property tests included 21 and 260 C tensile tests and 21 C shear tests. Panel quality, as measured by nondestructive evaluation, was generally good as were the 21 C tensile properties. The panels hot pressed in air delaminated in the shear tests. Shear strength values were lower in these panels. But tensile strengths were not affected by the delaminations because of the relation between the tensile loading direction and the delaminations. Composite tensile strength was found to be proportional to the volume percent boron and the aluminum matrix rather than to the tape used or fabrication technique. Suitability of these composites for 260 C service was confirmed by tensile tests.

Moore, T. J.↗

DOE OSTI2020

Quality Control of Additively Manufactured Metallic Structures with Machine Learning of Thermography Images

Additive manufacturing (AM) of high-strength metals is currently based on the laser powder bed fusion (LPBF) process, which can introduce internal material flaws, such as pores and anisotropy. Quality control (QC) requires nondestructive evaluation of actual AM structures. Flash thermography is a potentially promising QC technique because it is scalable to arbitrary structure size. However, the detection sensitivity of this method is limited by noise. We investigate separation of signal from noise in thermography images using several machine learning (ML) methods, including new spatial–temporal blind source separation and spatial–temporal sparse dictionary learning methods. Performance of the ML methods is benchmarked using thermography data obtained from imaging stainless steel 316L and Inconel 718 specimens produced by the LPBF method with imprinted calibrated porosity defects. The ML methods are ranked by F-score and execution runtime. Finally, the ML methods with higher accuracy require a longer runtime. However, this runtime is sufficiently short to perform QC within a realistic time frame.

36 MATERIALS SCIENCE↗

NASA NTRS1972

Summary of the nondestructive evaluation session

Advanced material systems and designs require improved nondestructive evaluation (NDE) techniques for quality control, failure analysis and properties monitoring. Some advanced NDE techniques such as infrared scanning systems, laser holographic applications and the use of the Mossbauer effect are outlined. Applications of these techniques are discussed for evaluating the new space shuttle external insulation materials, aircraft structures and engine components, and precursors to crack initiation and growth.

Stinebring, R. C.↗

NASA NTRSDate not supplied

Near Infrared In-situ Measurements for the Detection of Keyhole Porosity in Autogenous Ti-6AL-4V Welds

Certification of additive manufactured metal parts requires nondestructive evaluation (NDE) to ensure build quality. NDE can be performed during the build process or post build. For large parts with complex geometries, post build NDE can be challenging. In-situ NDE potentially provides a way to perform the inspection layer by layer. This work explores the use of a high speed near infrared (NIR) camera that is focused in-line with a laser to obtain high spatial and temporal resolution thermal imagery of the melt pool and associated cooling areas. The thermal data is obtained during a laser melting process using Ti-6AL-4V plate and of particular interest is the detection of keyhole porosity. Keyhole porosity can result from non-optimal build conditions, such as excessive laser power at a given laser scanning speed, that creates an entrapped bubble. The NIR measured melt pool and cooling areas are processed to detect keyhole porosity. The results are compared to X-ray CT results for validation.

additive manufacturing↗

NASA NTRSDate not supplied

Near Infrared In-situ Measurements for the Detection of Keyhole Porosity in Autogenous Ti-6AL-4V Welds

Certification of additive manufactured metal parts requires nondestructive evaluation (NDE) to ensure build quality. NDE can be performed during the build process or post build. For large parts with complex geometries, post build NDE can be challenging. In-situ NDE potentially provides a way to perform the inspection layer by layer. This work explores the use of a high speed near infrared (NIR) camera that is focused in-line with a laser to obtain high spatial and temporal resolution thermal imagery of the melt pool and associated cooling areas. The thermal data is obtained during a laser melting process using a Ti-6Al-4V plate and of particular interest is the detection of keyhole porosity. Keyhole porosity can result from non-optimal build conditions, such as excessive laser power at a given laser scanning speed, that creates an entrapped bubble. The NIR measured melt pool and cooling areas are processed to detect keyhole porosity. The results are compared to X-ray CT results for validation. Small keyhole pores buried deep were not detectable with this technique, however, some larger subsurface elongated pores along with some open surface pores did show some promise for detectability.

additive manufacturing↗

NASA NTRS1989

Quality Evaluation By Acousto-Ultrasonic Testing Of Composites

Promising nondestructive-testing method based on ultrasonic simulation of stress waves. Report reviews acousto-ultrasonic technology for nondestructive testing. Discusses principles, suggests advanced signal-analysis schemes for development, and presents potential applications. Acousto-ultrasonics applied principally to assess defects in laminated and filament-wound fiber-reinforced composite materials. Technique used to determine variations in such properties as tensile, shear, and flexural strengths and reductions in strength and toughness caused by defects. Also used to evaluate states of cure, porosities, orientation of fibers, volume fractions of fibers, bonding between fibers and matrices, and qualities of interlaminar bonds.

Vary, Alex↗

DOE OSTI2025

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↗

DOE OSTI2025

Evaluation and Development of Phase Array Ultrasonic Testing (PAUT) System for Additively Manufactured Parts

36 MATERIALS SCIENCE↗

NASA NTRS1982

Metal honeycomb to porous wireform substrate diffusion bond evaluation

Two nondestructive techniques were used to evaluate diffusion bond quality between a metal foil honeycomb and porous wireform substrate. The two techniques, cryographics and acousto-ultrasonics, are complementary in revealing variations of bond integrity and quality in shroud segments from an experimental aircraft turbine engine.

Vary, A.↗