Reliable software systems design: defect prevention, detection, and containment
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software design↗
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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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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↗
A Screening Method Using Pulsed Power Combined with Infrared Imaging to Detect Pattern Defects in Bulk Metal Foil or Thin Film Resistors
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Jay Brusse↗
Structural Integrity Evaluation of the Lear Fan 2100 Aircraft
An in-situ nondestructive inspection was conducted to detect manufacturing and assembly induced defects in the upper two wing surfaces (skin s) and upper fuselage skin of the Lear Fan 2100 aircraft E009. The effects of the defects, detected during the inspection, on the integrity of the structure was analytically evaluated. A systematic evaluation was also conducted to determine the damage tolerance capability of the upper wing skin against impact threats and assembly induced damage. The upper wing skin was divided into small regions for damage tolerance evaluations. Structural reliability, margin of safety, allowable strains, and allowable damage size were computed. The results indicated that the impact damage threat imposed on composite military aircraft structures is too severe for the Lear Fan 2100 upper wing skin. However, the structural integrity is not significantly degraded by the assembly induced damage for properly assembled structures, such as the E009 aircraft.
Composites↗
Thermal Inspection of a Composite Fuselage Section Using a Fixed Eigenvector Principal Component Analysis Method
A composite fuselage aircraft forward section was inspected with flash thermography. The fuselage section is 24 feet long and approximately 8 feet in diameter. The structure is primarily configured with a composite sandwich structure of carbon fiber face sheets with a Nomex(Trademark) honeycomb core. The outer surface area was inspected. The thermal data consisted of 477 data sets totaling in size of over 227 Gigabytes. Principal component analysis (PCA) was used to process the data sets for substructure and defect detection. A fixed eigenvector approach using a global covariance matrix was used and compared to a varying eigenvector approach. The fixed eigenvector approach was demonstrated to be a practical analysis method for the detection and interpretation of various defects such as paint thickness variation, possible water intrusion damage, and delamination damage. In addition, inspection considerations are discussed including coordinate system layout, manipulation of the fuselage section, and the manual scanning technique used for full coverage.
Zalameda, Joseph N.↗
Thermographic Inspections Save Skins and Prevent Blackouts
Scanning thermography involves heating a component s surface and subsequently measuring the surface temperature, using an infrared camera to identify structural defects such as corrosion and disbonding. It is a completely noninvasive and noncontacting process. Scans can detect defects in conventional metals and plastics, as well as in bonded aluminum composites, plastic- and resinbased composites, and laminated structures. The apparatus used for scanning is highly portable and can cover the surface of a test material up to six times faster than conventional thermography. NASA scientists affirm that the technology is an invaluable asset to the airlines, detecting potential defects that can cause structural failure.In 1996, ThermTech Services, Inc., of Stuart, Florida, approached NASA in an effort to evaluate the technology for application in the power and process industries, where corrosion is of serious concern. ThermTech Services proceeded to develop the application for inspecting boiler waterwall tubing at fossil-fueled electric-generating stations. In 1999, ThermTech purchased the rights to NASA s patented technology and developed the specialized equipment required to apply the inspecting method to power plant components. The ThermTech robotic system using NASA technology has proved to be extremely successful and cost effective in performing detailed inspections of large structures such as boiler waterwalls and aboveground chemical storage tanks. It is capable of inspecting a waterwall, tank-wall, or other large surfaces at a rate of approximately 10 square feet per minute or faster.
Source record↗
Detection of internal defects in a liquid natural gas tank by use of infrared thermography
The use of an infrared scanning technique to detect defects in the secondary barrier of a liquid natural gas tank is described. The method works by detecting leak-caused temperature differences as low as 0.2 K, but can provide only an approximate idea of the extent of the defect. The nondestructive method was tested in a study of a LNG tank already at its location in a ship; the secondary barrier was located inside the tank wall. Defective areas indicated by the infrared radiometric measurements were confirmed by other probe techniques and by physical examination.
Kantsios, A. G.↗
Nondestructive Evaluation of Carbon Fiber Reinforced Polymers
The American Society of Mechanical Engineers Boiler and Pressure Vessel Code requires repair and replacement of safety-related piping materials to meet the original Construction Code; however, no construction criteria currently exist for carbon fiber reinforced polymer (CFRP) materials in the nuclear industry. Although nondestructive examination (NDE) techniques for cast and wrought ferritic and austenitic steels are well established, licensees are increasingly deploying novel materials such as CFRP for which assessment of the use of NDE is required, as the inspectability of such materials and the influence of manufacturing processes on inspectability remain insufficiently understood. To address these gaps, Pacific Northwest National Laboratory (PNNL) evaluated the fabrication of several CFRP repair mockups and the effectiveness of NDE methods to support regulatory review of CFRP repairs in nuclear power plant applications. Representative flat-plate mockups containing realistic fabrication defects were manufactured and inspected using manual and automated tap testing, dynamic response spectroscopy (DRS), and ultrasonic testing (UT). The study identified significant fabrication variability, particularly in controlling defect size and epoxy thickness, which strongly influenced defect detectability by NDE methods. Tap testing was effective for shallow defects in thin epoxy configurations but unreliable for thicker repairs and deeper flaws. DRS demonstrated higher sensitivity to dry spots but produced unconfirmed indications for thicker plates, requiring further validation. Conventional UT, particularly at 1.0 MHz, showed the strongest overall capability for detecting a range of defects, although performance degraded with increasing repair thickness and complexity. The results highlight the need to better define critical defect sizes, develop reliable mockup fabrication methods, and validate NDE methods needed to support CFRP repairs.
36 MATERIALS SCIENCE↗
Effect of Thermal Diffusivity on the Detectability of TNDE
The effect of thermal diffusively on the defect detectability in Carbon/Epoxy composite panels by transient thermography is presented in this paper. A series of Finite Element Models were constructed and analyzed to simulate the transient heat transfer phenomenon during Thermographic Non-destructive Evaluation (TNDE) of composite panels with square defects. Six common carbon fibers were considered. The models were built for composites with various combinations of fibers and volumetric ratios. Finite Element Analysis of these models showed the trends of the detectable range and the maximum thermal contrast versus the thermal diffusivity of various composites. Additionally, the trends of defect size to depth ratio and the thermal contrast has been investigated.
Zhao, Junduo↗
Accurate and rapid acoustic damage characterization in complex structures using sparse sensor networks and deep learning models
Damage diagnosis in critical components is essential for ensuring the safety and reliability of operations across industries, spanning manufacturing, aerospace, and energy. Traditional acoustic nondestructive testing methods primarily focus on detecting defects through the direct scattering of single-mode incident waves from the damage, which limit their applicability to simple structures and small inspection areas. Our earlier research demonstrated that machine learning algorithms combined with sparse sensor networks can identify critical defect signatures even from multiply scattered, multi-mode acoustic signals, indicating the potential for improved defect inspection in complex, real-world structures. In this work, we demonstrate the successful implementation of this approach in a fixed sensor configuration to rapidly and accurately detect simulated defects in a geometrically complex, real-world structure, a brake rotor hub. Three different types of defects were physically simulated on the surface of the hub, and the collected data were used to train an autoencoder-based deep learning model. Two models were tested, one using single measurements and the other using multiple measurements taking advantage of the spatial distribution of the sensor network. After training, the multi-measurement model achieved 100 % accuracy in identifying, classifying, and locating unseen, unique damages. This work illustrates the potential of the proposed method for a wide range of industrial applications.
36 MATERIALS SCIENCE↗
In-situ sensor monitoring of multi-class gas porosity formation in laser powder bed fusion using convolutional neural network
In-situ monitoring of defect formation remains a significant challenge in the laser powder bed fusion (LPBF) process. Recent advances have enabled real-time defect detection with machine learning and in-situ sensing technologies; however, most studies focus on binary classification of keyhole pores, limiting nuanced multi-class pore differentiation and formation mechanisms. This work introduces a multi-class pore detection framework (no pore, small pores < 15 µm, and large pores > 15 µm) by leveraging photodiode sensor data alongside high-fidelity synchrotron X-ray imaging. The 15 µm threshold is selected to distinguish between two fundamentally different defect mechanisms, following the physical size-mechanism boundary established by prior high-resolution synchrotron X-ray characterization of Al6061 LPBF. Distinguishing these classes is critical because large keyhole pores are structurally detrimental, whereas small gas pores are often benign, requiring different process control strategies. Thermal emission monitoring data collected simultaneously with high-speed X-ray imaging at the Stanford Synchrotron Radiation Lightsource (SSRL), are correlated with subsurface melt pool dynamics to establish ground truth. Continuous Wavelet Transform (CWT) with optimized parameters converts the photodiode time-series signals into time–frequency images, facilitating feature extraction. Convolutional Neural Networks (CNN) are then applied for real-time multi-class pore classification in an average inference time of 1 ms per signal window. It achieves 79% accuracy and an Area Under the Receiver Operating Characteristic curve (AUC ROC) score of 0.89 with five-fold cross-validation. The results demonstrate that coupling CWT-based feature engineering with CNN architecture enables reliable multi-class pore detection in Al6061 builds using affordable in-situ sensors. This approach advances scalable and affordable quality assurance in additive manufacturing by moving beyond binary defect detection toward more nuanced classification of porosity mechanisms with in-situ sensors and machine learning.
Laser powder bed fusion, Multi-class pores, In-sit↗
New detection method for rolling element and bearing defects
Instrument for detecting defects in rolling elements of bearings is described. Detection depends on rate at which rolling elements impact defect and establishes envelope amplitude of ball resonant frequency. Block diagram of instrument is provided and results obtained in conducting tests are reported.
Burchill, R. F.↗
System and process for detecting and monitoring surface defects
A system and process for detecting and monitoring defects in large surfaces such as the field joints of the container segments of a space shuttle booster motor. Beams of semi-collimated light from three non-parallel fiber optic light panels are directed at a region of the surface at non-normal angles of expected incidence. A video camera gathers some portion of the light that is reflected at an angle other than the angle of expected reflectance, and generates signals which are analyzed to discern defects in the surface. The analysis may be performed by visual inspection of an image on a video monitor, or by inspection of filtered or otherwise processed images. In one alternative embodiment, successive predetermined regions of the surface are aligned with the light source before illumination, thereby permitting efficient detection of defects in a large surface. Such alignment is performed by using a line scan gauge to sense the light which passes through an aperture in the surface. In another embodiment a digital map of the surface is created, thereby permitting the maintenance of records detailing changes in the location or size of defects as the container segment is refurbished and re-used. The defect detection apparatus may also be advantageously mounted on a fixture which engages the edge of a container segment.
Mueller, Mark K.↗
High-Throughput Uniformity and Defect Monitoring in Low-Temperature Electrolysis Porous Transport Layers Using X-Ray Radiography
Effective quality control (QC) for manufacturing proton exchange membrane water electrolysis (PEMWE) components is critical to enabling widespread adoption of the technology for hydrogen generation. This study investigates X-ray radiography as a novel, high-throughput, potentially in-line QC technique for detecting defects and assessing material property distributions in titanium-based porous transport layers (PTLs) which constitute a crucial component of low temperature PEMWE stacks. We obtain radiographs of a set of fifteen PTLs and model their absorbance of the broadband radiation as a second-order polynomial to account for the non-monoenergetic radiation source used in this study. The resulting model serves as a basis for predicting the areal density and porosity distributions of the PTLs. We find radiography successful in detecting multiple instances of defects, including holes/depressions, cracks, and excess material on the surface or in the pores of the material, demonstrating its potential as a robust in-line QC tool for PTL manufacturing.
08 HYDROGEN↗
Effect of threshold parameters on infrared segmentation methods for porosity detection in electron beam powder bed fusion
In-situ process monitoring has seen significant interest in additive manufacturing to address qualification and certification goals. This is especially prevalent in metal powder bed fusion processes such as electron beam powder bed fusion (PBF-EB), with layer-wise infrared imaging being commonly used to detect defects. Here, this work compares two different segmentation methods (static thresholding and statistical thresholding) used for detecting porosity from in-situ infrared imaging data for PBF-EB. Samples were manufactured at a variety of focus offset values to induce porosity. Then, the segmented infrared images were compared to ex-situ X-ray computed tomography scans, which served as a ground-truth reference for objective evaluation. Through this analysis framework, the influential parameters, static threshold and N-value (number of standard deviations above the mean pixel value), respectively, for both image segmentation methods were analyzed and compared for their effects on porosity detection. With optimal parameter settings, the two methods had similar porosity detection performance, but the statistical method performed better under a larger variety of parameter settings.
Infrared imaging↗
Leveraging artificial intelligence and advanced food processing techniques for enhanced food safety, quality, and security: a comprehensive review
Artificial intelligence is emerging as a transformative force in addressing the multifaceted challenges of food safety, food quality, and food security. This review synthesizes advancements in AI-driven technologies, such as machine learning, deep learning, natural language processing, and computer vision, and their applications across the food supply chain, based on a comprehensive analysis of literature published from 1990 to 2024. AI enhances food safety through real-time contamination detection, predictive risk modeling, and compliance monitoring, reducing public health risks. It improves food quality by automating defect detection, optimizing shelf-life predictions, and ensuring consistency in taste, texture, and appearance. Furthermore, AI addresses food security by enabling resource-efficient agriculture, yield forecasting, and supply chain optimization to ensure the availability and accessibility of nutritious food resources. This review also highlights the integration of AI with advanced food processing techniques such as high-pressure processing, ultraviolet treatment, pulsed electric fields, cold plasma, and irradiation, which ensure microbial safety, extend shelf life, and enhance product quality. Additionally, the integration of AI with emerging technologies such as the Internet of Things, blockchain, and AI-powered sensors enables proactive risk management, predictive analytics, and automated quality control. By examining these innovations' potential to enhance transparency, efficiency, and decision-making within food systems, this review identifies current research gaps and proposes strategies to address barriers such as data limitations, model generalizability, and ethical concerns. These insights underscore the critical role of AI in advancing safer, higher-quality, and more secure food systems, guiding future research and fostering sustainable food systems that benefit public health and consumer trust.
AI↗
Near-Infrared Photon-Counting Camera for High-Sensitivity Observations
The dark current of a transferred-electron photocathode with an InGaAs absorber, responsive over the 0.9-to-1.7- micron range, must be reduced to an ultralow level suitable for low signal spectral astrophysical measurements by lowering the temperature of the sensor incorporating the cathode. However, photocathode quantum efficiency (QE) is known to reduce to zero at such low temperatures. Moreover, it has not been demonstrated that the target dark current can be reached at any temperature using existing photocathodes. Changes in the transferred-electron photocathode epistructure (with an In- GaAs absorber lattice-matched to InP and exhibiting responsivity over the 0.9- to-1.7- m range) and fabrication processes were developed and implemented that resulted in a demonstrated >13x reduction in dark current at -40 C while retaining >95% of the approximately equal to 25% saturated room-temperature QE. Further testing at lower temperature is needed to confirm a >25 C predicted reduction in cooling required to achieve an ultralow dark-current target suitable for faint spectral astronomical observations that are not otherwise possible. This reduction in dark current makes it possible to increase the integration time of the imaging sensor, thus enabling a much higher near-infrared (NIR) sensitivity than is possible with current technology. As a result, extremely faint phenomena and NIR signals emitted from distant celestial objects can be now observed and imaged (such as the dynamics of redshifting galaxies, and spectral measurements on extra-solar planets in search of water and bio-markers) that were not previously possible. In addition, the enhanced NIR sensitivity also directly benefits other NIR imaging applications, including drug and bomb detection, stand-off detection of improvised explosive devices (IED's), Raman spectroscopy and microscopy for life/physical science applications, and semiconductor product defect detection.
Jurkovic, Michael↗
Detection of Objects Hidden in Highly Scattering Media Using Time-Gated Imaging Methods
Non-intrusive and non-invasive optical imaging techniques has generated great interest among researchers for their potential applications to biological study, device characterization, surface defect detection, and jet fuel dynamics. Non-linear optical parametric amplification gate (NLOPG) has been used to detect back-scattered images of objects hidden in diluted Intralipid solutions. To directly detect objects hidden in highly scattering media, the diffusive component of light needs to be sorted out from early arrived ballistic and snake photons. In an optical imaging system, images are collected in transmission or back-scattered geometry. The early arrival photons in the transmission approach, always carry the direct information of the hidden object embedded in the turbid medium. In the back-scattered approach, the result is not so forth coming. In the presence of a scattering host, the first arrival photons in back-scattered approach will be directly photons from the host material. In the presentation, NLOPG was applied to acquire time resolved back-scattered images under the phase matching condition. A time-gated amplified signal was obtained through this NLOPG process. The system's gain was approximately 100 times. The time-gate was achieved through phase matching condition where only coherent photons retain their phase. As a result, the diffusive photons, which were the primary contributor to the background, were removed. With a large dynamic range and high resolution, time-gated early light imaging has the potential for improving rocket/aircraft design by determining jets shape and particle sizes. Refinements to these techniques may enable drop size measurements in the highly scattering, optically dense region of multi-element rocket injectors. These types of measurements should greatly enhance the design of stable, and higher performing rocket engines.
Galland, Pierre A.↗