Results for “defectivity” — Page 100

NASA NTRS2014

Empirical and Face Validity of Software Maintenance Defect Models Used at the Jet Propulsion Laboratory

At the Mission Design and Navigation Software Group at the Jet Propulsion Laboratory we make use of finite exponential based defect models to aid in maintenance planning and management for our widely used critical systems. However a number of pragmatic issues arise when applying defect models for a post-release system in continuous use. These include: how to utilize information from problem reports rather than testing to drive defect discovery and removal effort, practical model calibration, and alignment of model assumptions with our environment.

software reliability↗

NASA NTRS2017

A Thermal Runaway Failure Model for Low-Voltage BME Ceramic Capacitors with Defects

Reliability of base metal electrode (BME) multilayer ceramic capacitors (MLCCs) that until recently were used mostly in commercial applications, have been improved substantially by using new materials and processes. Currently, the inception of intrinsic wear-out failures in high quality capacitors became much greater than the mission duration in most high-reliability applications. However, in capacitors with defects degradation processes might accelerate substantially and cause infant mortality failures. In this work, a physical model that relates the presence of defects to reduction of breakdown voltages and decreasing times to failure has been suggested. The effect of the defect size has been analyzed using a thermal runaway model of failures. Adequacy of highly accelerated life testing (HALT) to predict reliability at normal operating conditions and limitations of voltage acceleration are considered. The applicability of the model to BME capacitors with cracks is discussed and validated experimentally.

reliability↗

NASA NTRS2018

A Risk Analysis Tool for Estimating the Risk of Electrical Failures Due to Human Induced Defects

Aerospace electrical systems are required to withstand and adequately operate in extremely harsh environments that include, for example, high radiation exposure, temperature extremes, intense vibrational stress and drastic temperature cycling. The nature of aerospace electronics also demands high reliability since, with very few exceptions, there is no chance for hardware servicing or repairs. Common risk mitigation techniques for this type of situation are to perform a Reliability Analysis of the system throughout the development cycle, and to use electrical components that are regarded as “high reliability” because of additional controls and requirements applied in their design, manufacturing and testing. Unfortunately, studies have shown that even though these techniques are used, many systems fail to meet mission requirements well before the predicted lifetimes. This paper presents the analysis of failures of electrical parts, experienced during various stages of system development, at NASA Goddard Space Flight Center, Greenbelt MD, between the years 2001 and 2013. These components were subjected to qualification, screening and testing in which the goal was to ensure that the components would survive the stresses of the mission. The analysis categorizes failures by part type and failure mechanisms. One of the results of the analysis was the realization that a surprising proportion of failures experienced during system integration and testing were caused by human error (i.e. human induced defect). Further analysis included the determination of root failure mechanisms and any influencing factors contributing to these failures. The major causes of these defects were attributed to electrostatic damage (ESD), electrical overstress (EOS), mechanical overstress (MOS), and thermal overstress (TOS). Finally, the study proposes a risk analysis tool which incorporates these major causes for the failures, termed error-producing conditions (EPCs), and a proportionality factor representing the number of each type of failure that has occurred at the facility under study. These factors are quantified and used to communicate the risk of human induced defects for the assembly, integration and testing of space hardware based on the system’s electrical parts list. The new risk identification can trigger risk-mitigating actions more effectively, based on the presence of component categories or other hazardous conditions that have a history of failure due to human error.

Majewicz, Peter J.↗

NASA NTRS2020

Ultrasonic Lamb Wave Inspection of Composite Defects

Composites have been extensively used in aerospace engineering due to their advantages of light weight, high strength, and engineering design flexibility. Manufacturing defects such as wrinkle and porosity can affect the performance of the composites and may lead to failure in the end, while damage such as delamination, fiber fraction, and matrix cracking can directly cause failure of the composites. In this paper, a Lamb wave based nonintrusive nondestructive evaluation system, which employs piezoelectric transducer for actuation and scanning laser Doppler vibrometer for wavefield sensing, is presented for typical composite defect and damage inspection and evaluation. Two composite panels with different geometry (flat or curved) and with various embedded defects (wrinkle and delamination) are inspected using the nonintrusive Lamb wave system. Both the wrinkles and delamination are detected from the wavefield and approximately quantified through wavefield imaging methods.

Wrinkles↗

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)↗

NASA NTRS2020

Fatigue Failures at Defective Grains in a Single Crystal Nickel-Base Superalloy

Single crystal nickel-base superalloys used as turbine engine blades can have much higher creep and fatigue resistance than many polycrystalline superalloys due to the absence of weak grain boundaries as well as the ability to orient the blades in the primary loading direction on to the low elastic modulus [001] orientation, which significantly lowers the stresses imposed on the blades. However, grain defects can occur in these crystals, impairing these advantages. In the present study, fatigue tests of a single crystal superalloy uncovered unexpected defective grains in the test specimens, which initiated premature fatigue failures. The characteristics of these defective grains and the associated failures were evaluated and related to the observed fatigue lives.

superalloy↗

NASA NTRSDate not supplied

Towards Integrated Computational Materials Engineering for Quantifying Performance Impacts of Microstructure and Defect Interactions in Powder Bed Fusion Parts

Powder bed fusion (PBF) additive manufacturing (AM) enables the creation of parts with complexity and functionality levels that were previously impossible with traditional manufacturing methods. By modifying the laser power, hatch spacing, or the numerous other processing parameters, the PBF process supports the production of a wide set of materials and geometries. However, that same process parameter design flexibility causes the process-design space of PBF to be massive and expensive to explore experimentally. Another challenge is quality variation across a build. As a part is being built, geometric variance between locations, such as at a thin-wall section vs. the bulk material, may cause the specified processing parameters to no longer be acceptable for producing defect-free printing. Furthermore, if the processing parameters deviate during the print process, it is difficult to assess if the part will still perform satisfactorily. Integrated Computational Materials Engineering (ICME) provides a way to understand and address these various challenges. This talk will present advancements in process-structure simulations of PBF at NASA Langley Research Center. The ability to simulate grain-scale PBF microstructures using the Physically Based Monte Carlo method will be demonstrated and compared to experimental measurements. Techniques for simulating three-dimensional lack-of-fusion and keyhole porosity defects based on the specific processing conditions and approaches for integrating the two porosity prediction techniques alongside the computational microstructure evolution models will be shown. Finally, the integration of simulated PBF microstructures, embedded process defects, and crystal plasticity finite element models to elucidate the interaction of porosity and microstructure on micromechanical fields will be demonstrated. These integrated techniques demonstrate an example of using ICME to relate processing to performance for PBF AM materials. With continued maturity, it is hoped that such ICME approaches will lead to next-generation computational-materials supported qualification and certification of AM parts.

Additive manufacturing↗

NASA NTRSDate not supplied

Discerning Cell Surface Defects with 3D Optical Profilometry

This investigation seeks to establish better criteria that can be used for qualitative visual screening of cylindrical battery cells through exploration of various defects across multiple cylindrical Lithium-Ion battery cells. This investigation focuses on INR18650s and INR21700s cells from Molicel, LG, and Samsung. The principal instrument used is a high-definition 3D optical profilometer with measurement accuracy less than 0.5 microns. Through this effort, a catalog was built discerning minor surface imperfections from significant flaws. The catalog generated covers a comparison of axial and radial scratches, less than 5 microns, greater than 5 microns and greater than 15 microns, as well as the difference between surface discoloration, early-stage pitting, and late stages of corrosion. An exploration is made into cell button defects and crimp shoulder defects. When ultra-high-power optical magnification and three-dimension topological tools are not immediately available, this catalog will help determine if an individual cell is usable, worth further investigation, or failing. The purpose of this catalog is to be a field guide used for quick reference of a potential issue, with the goal of increasing screening throughput while reducing the need for costly examination. Trade names and trademarks are used in this abstract for identification only. Their usage does not constitute an official endorsement, either expressed or implied, by the National Aeronautics and Space Administration.

battery↗

NASA NTRSDate not supplied

Discerning Cell Surface Defects with 3D Optical Profilometry

This investigation seeks to establish better criteria that can be used for qualitative visual screening of cylindrical battery cells through exploration of various defects across multiple cylindrical Lithium-Ion battery cells. This investigation focuses on INR18650s and INR21700s cells from Molicel, LG, and Samsung. The principal instrument used is a high-definition 3D optical profilometer with measurement accuracy less than 0.5 microns. Through this effort, a catalog was built discerning minor surface imperfections from significant flaws. The catalog generated covers a comparison of axial and radial scratches, less than 5 microns, greater than 5 microns and greater than 15 microns, as well as the difference between surface discoloration, early-stage pitting, and late stages of corrosion. An exploration is made into cell button defects and crimp shoulder defects. When ultra-high-power optical magnification and three-dimension topological tools are not immediately available, this catalog will help determine if an individual cell is usable, worth further investigation, or failing. The purpose of this catalog is to be a field guide used for quick reference of a potential issue, with the goal of increasing screening throughput while reducing the need for costly examination. Trade names and trademarks are used in this abstract for identification only. Their usage does not constitute an official endorsement, either expressed or implied, by the National Aeronautics and Space Administration.

battery↗

NASA NTRSDate not supplied

Defect Detection in ISC Cylinder Using Spoof Plasmon Polaritons

NASA is exploring the use of microwave spoof plasmon polaritons to detect defects in integrally stiffened cylinders. This work investigates the detection of simulated defects in the form of aluminum wires as small as 10 mm long and 3.1 mm diameter that are placed in between stiffeners of an ISC. The wires simulate manufacturing inclusions defects, and the sum of the delta impedance is shown to increase with increasing volume of added metal.

spoof↗

DOE OSTI2015

PROGRESS IN CHARACTERIZATION OF PRECIPITATES AND DEFECT STRUCTURES IN Mg+ ION IMPLANTED CUBIC SILICON CARBIDE

This report describes the progress of our current experimental effort on Mg+ ion implanted 3C-SiC. Following our initial study [ ] that suggests possible formation of Mg2Si and MgC2 precipitates as well as tetrahedral voids in 24Mg+ ion implanted 3C-SiC, we have designed specific experiments to confirm the results and examine the inclusions and defects. Relatively low fluence (5.0×1015 24Mg+/cm2) implantation in 3C-SiC was performed to reduce defect concentrations and isolate individual defect features for characterization. In addition, 25Mg+ isotope was implanted in 3C-SiC to the same previously applied ion fluence (9.6×1016 ions/cm2) for atom probe tomography (APT) study of precipitates. Each set of the samples was annealed at 1573 K for 2, 6 and 12 h, respectively. The depth profiles of the implanted Mg were measured using secondary ion mass spectrometry (SIMS) before and after the annealing steps. The samples are currently being analyzed using transmission electron microscopy (TEM) and APT.

Environmental Molecular Sciences Laboratory↗

DOE OSTI2016

Determination of Experimental Fuel Rod Parameters using 3D Modelling of PCMI with MPS Defect

An in-reactor experiment is being designed in order to validate the pellet-cladding mechanical interaction (PCMI) behavior of the BISON fuel performance code. The experimental parameters for the test rod being placed in the Halden Research Reactor are being determined using BISON simulations. The 3D model includes a missing pellet surface (MPS) defect to generate large local cladding deformations, which should be measureable after typical burnup times. The BISON fuel performance code is being developed at Idaho National Laboratory (INL) and is built on the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework. BISON supports both 2D and 3D finite elements and solves the fully coupled equations for solid mechanics, heat conduction and species diffusion. A number of fuel performance effects are included using models for swelling, densification, creep, relocation and fission gas production & release. In addition, the mechanical and thermal contact between the fuel and cladding is explicitly modelled using a master-slave based contact algorithm. In order to accurately predict PCMI effects, the BISON code includes the relevant physics involved and provides a scalable and robust solution procedure. The depth of the proposed MPS defect is being varied in the BISON model to establish an optimum value for the experiment. The experiment will be interrupted approximately every 6 months to measure cladding radial deformation and provide data to validate BISON. The complete rodlet (~20 discrete pellets) is being simulated using a 180° half symmetry 3D model with MPS defects at two axial locations. In addition, annular pellets will be used at the top and bottom of the pellet stack to allow thermocouples within the rod to measure the fuel centerline temperature. Simulation results will be presented to illustrate the expected PCMI behavior and support the chosen experimental design parameters.

BISON↗

DOE OSTI2016

Analysis of Experimental Fuel Rod Parameters using 3D Modelling of PCMI with MPS Defect

An in-reactor experiment is being designed in order to validate the pellet-cladding mechanical interaction (PCMI) behavior of the BISON fuel performance code. The experimental parameters for the test rod being placed in the Halden Research Reactor are being determined using BISON simulations. The 3D model includes a missing pellet surface (MPS) defect to generate large local cladding deformations, which should be measureable after typical burnup times. The BISON fuel performance code is being developed at Idaho National Laboratory (INL) and is built on the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework. BISON supports both 2D and 3D finite elements and solves the fully coupled equations for solid mechanics, heat conduction and species diffusion. A number of fuel performance effects are included using models for swelling, densification, creep, relocation and fission gas production & release. In addition, the mechanical and thermal contact between the fuel and cladding is explicitly modelled using a master-slave based contact algorithm. In order to accurately predict PCMI effects, the BISON code includes the relevant physics involved and provides a scalable and robust solution procedure. The depth of the proposed MPS defect is being varied in the BISON model to establish an optimum value for the experiment. The experiment will be interrupted approximately every 6 months to measure cladding radial deformation and provide data to validate BISON. The complete rodlet (~20 discrete pellets) is being simulated using a 180° half symmetry 3D model with MPS defects at two axial locations. In addition, annular pellets will be used at the top and bottom of the pellet stack to allow thermocouples within the rod to measure the fuel centerline temperature. Simulation results will be presented to illustrate the expected PCMI behavior and support the chosen experimental design parameters.

BISON↗

DOE OSTI2018

Comparison of ex situ x-ray radiography and in situ monitoring to gain control over defects during laser powder bed fusion

As in any other laser welding technique, creation of pores and defects during laser powder bed fusion can lead to component failure and thus cannot be tolerated. Post-build inspection is required to ensure the printed parts are defects-free. These inspections can take time and are complicated particularly for large parts. A solution is to use in situ process monitoring to detect the creation of defects, which could in turn be corrected during the printing itself. However, the relationship between pore creation and in situ monitoring still needs to be understood. In this work, we use x-ray computed tomography to detect pores and correlate them to pyrometry and high speed thermal imaging signals collected during laser welding.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Comparison of ex situ x-ray radiography and in situ monitoring to gain control over defects during laser powder bed fusion

As in any other laser welding technique, creation of pores and defects during laser powder bed fusion can lead to component failure and thus cannot be tolerated. Post-build inspection is required to ensure the printed parts are defects-free. These inspections can take time and are complicated particularly for large parts. A solution is to use in situ process monitoring to detect the creation of defects, which could in turn be corrected during the printing itself. However, the relationship between pore creation and in situ monitoring still needs to be understood. In this work, we use x-ray computed tomography to detect pores and correlate them to pyrometry and high speed thermal imaging signals collected during laser welding.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2019

Nondestructive Evaluation of Concrete Specimens Representative of Nuclear Power Plants Containing Known Defects

Many known defects that develop in concrete over time threaten the safety and security of the infrastructure in nuclear power plants (NPPs). These defects also exist in other concrete infrastructures, but the harsher nuclear environment adds significant difficulty to their detection and correction. To monitor the structural health of a concrete facility, coring and other destructive testing methods are typically employed, but this is not the ideal solution for NPP concrete infrastructure. Developing a reliable nondestructive evaluation (NDE) method to evaluate the damage in these structures will greatly improve the long-term operation and safety of NPPs. The University of Tennessee, in collaboration with Oak Ridge National Laboratory, developed a large-scale experiment to study one known defect due to the reaction between hydroxyl ions of the alkalis in cement paste and the siliceous minerals present in some aggregates known as alkali-silica reactions (ASRs). Alkali-silica gel forms and swells due to its hygroscopic characteristic, causing cracking and material failure. This paper discusses the NDE measurements performed on the large concrete specimens and the reconstructed images from these measurements.

Ezell, N Dianne↗

DOE OSTI2021

Improvement of Front-Junction GaInP by Point-Defect Injection and Annealing: Preprint

Traditional front junction GaInP solar cells are radiation tolerant and can have good diffusion length, but have limited voltage and thus efficiency. Here, we investigate the impact of annealing on GaInP device performance. First, Zn-doped GaInP/AlGaInP double heterojunction structures are studied in order to investigate the impact of annealing in a simple structure. While standard anneals lower diffusion length and lifetime, annealing after injecting point-defects improves material quality, presumably by eliminating sources of non-radiative recombination. Atomic ordering is reduced by the anneal, which raises the device bandgap and requires consideration. These results can be used to improve front junction device performance while controlling the bandgap. Then, GaInP front-junction devices are created using anneals with and without point-defect injection. Voc and EQE trends follow the diffusion length and lifetime trends from the DH structures. Baseline devices have Woc of 0.484 V, raising to 0.504 V after a high temperature anneal. However, injecting point defects prior to the anneal results in a front junction device with Woc of 0.405 V and a diffusion length greater than 8 micrometers. Optimized front-junction GaInP devices with an ARC have ~20% efficiency without a rear reflector.

anneal↗

DOE OSTI2025

Fusion of Experiments and Simulations for Real-Time Identification of Pipeline Defects

In this study, we explored fusion of experiments and simulations for real time identification of pipeline defects across physical and non-physical domains. The challenges associated to data processing were addressed and a combined classification models was presented via CNN models. In addition, regression model based on XGBOOST is built to determine the defect location and defect dimension from data-driven features of guided wave signals captured by SMS fiber optic sensor.

deep learning↗