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At least 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Machine Learning Enabled Sensor Fusion for In-Situ Defect Detection in Laser Powder Bed Fusion

Laser Powder Bed Fusion (L-PBF) Additive Manufacturing (AM) is among the metal 3D printing technologies most broadly adopted by the manufacturing industry. The current industry qualification paradigm for critical-application L-PBF parts relies heavily on expensive non-destructive inspection techniques such as X-Ray Computed Tomography (XCT), which significantly limits the use-cases of L-PBF. In situ monitoring of the process promises a less expensive alternative to ex situ testing, but existing sensor technologies and data analysis techniques struggle to detect sub-surface flaws (e.g., porosity and cracking) on production-scale L-PBF printers. RTX Technologies Research Center (RTRC) has licensed ORNL’s Peregrine software package – a printer- and camera-agnostic data analytics tool designed specifically for detecting process anomalies using in situ data collected during powder bed printing. The goal of this project was to feed temporally rich, multi-modal sensor data, including visible light, integrated near infrared (NIR), and spatially mapped co-axial melt pool thermal emission data into Peregrine to enable detection of subsurface flaws. XCT data was used as ground truth training data to allow Peregrine’s deep learning algorithms to recognize anomalies in these complex data streams in both test artifacts and industrially relevant geometries. Completion of this program has seen the successful implementation of multi-modal, multi-layer sensor data footprints for training of machine learning models in Peregrine. Flaws detected in XCT data have been successfully detected directly from this in situ data footprint, and initial analyses of the in situ probability-of-detection has been conducted, showing performance levels commensurate with traditional non-destructive evaluation (NDE) methods. The in situ monitoring methodology was then applied to an industrially relevant component that was using post-build NDE, highlighting the utility of the proposed method for hard-to-inspect AM components. As a direct result of this program, two journal manuscripts [1], [2] have been published in Additive Manufacturing, with additional manuscripts planned following program completion.

36 MATERIALS SCIENCE↗
NASA NTRS2002

Evolutionary Design of a Robotic Material Defect Detection System

During the post-flight inspection of SSME engines, several inaccessible regions must be disassembled to inspect for defects such as cracks, scratches, gouges, etc. An improvement to the inspection process would be the design and development of very small robots capable of penetrating these inaccessible regions and detecting the defects. The goal of this research was to utilize an evolutionary design approach for the robotic detection of these types of defects. A simulation and visualization tool was developed prior to receiving the hardware as a development test bed. A small, commercial off-the-shelf (COTS) robot was selected from several candidates as the proof of concept robot. The basic approach to detect the defects was to utilize Cadmium Sulfide (CdS) sensors to detect changes in contrast of an illuminated surface. A neural network, optimally designed utilizing a genetic algorithm, was employed to detect the presence of the defects (cracks). By utilization of the COTS robot and US sensors, the research successfully demonstrated that an evolutionarily designed neural network can detect the presence of surface defects.

Ballard, Gary↗
NASA NTRS1979

Advanced quantitative magnetic nondestructive evaluation methods - Theory and experiment

The paper reviews the scale of fatigue crack phenomena in relation to the size detection capabilities of nondestructive evaluation methods. An assessment of several features of fatigue in relation to the inspection of ball and roller bearings suggested the use of magnetic methods; magnetic domain phenomena including the interaction of domains and inclusions, and the influence of stress and magnetic field on domains are discussed. Experimental results indicate that simplified calculations can be used to predict many features of these results; the data predicted by analytic models which use finite element computer analysis predictions do not agree with respect to certain features. Experimental analyses obtained on rod-type fatigue specimens which show experimental magnetic measurements in relation to the crack opening displacement and volume and crack depth should provide methods for improved crack characterization in relation to fracture mechanics and life prediction.

Barton, J. R.↗
DOE OSTI2019

A Flaw Stability Analysis for the Savannah River Site 3H High-Level-Waste Evaporator - 19548

The Savannah River Site (SRS) 3H high-level-waste Evaporator (242-25H) entered service in January 2000 and was discovered to be leaking in February 2016. It was robotically repaired and resumed operation in July 2018. During initial restart in December 2017, salt deposits were discovered on the evaporator's lower shell near welds, indicating two more small leak sites. The leak sites have an estimated hydraulic diameter of about 0.1 mm or less - about the diameter of a human hair. The estimated leak rates are about a factor of 10,000 smaller than the robotically repaired leak. An API 579-1/ASME FFS-1 fitness-for-service evaluation determined that if the leak sites are cracks, then they would need to be about 60 mm long (∼ 2 inches) to actively and catastrophically grow. Furthermore, a crack of this length would have been detected earlier since it would leak thousands of liters per day. The evaporator was designed with engineered features that minimize risks posed by these small leaks. The path-forward involves operating the evaporator in a deliberate manner with enhanced surveillance. The 3H Evaporator is key to SRS safely and timely reducing its 130 million liter liquid high-level-waste inventory, closing remaining waste tanks, and reducing associated risks to workers, the public, and the environment. (author)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2007

Triboluminescent Materials for Smart Optical Damage Sensors for Space Applications

Triboluminescence is light that is produced by pressure, friction or mechanical shock. New composite materials are constantly being reengi neered in an effort to make lightweight spacecrafts for various NASA missions. For these materials there is interest in monitoring the con dition of the composite in real time to detect any delamination or cr acking due to damage, fatigue or external forces. Methods of periodic inspection of composite structures for mechanical damage such as ult rasonic testing are rather mature. However, there is a need to develop a new technique of damage detection for composites, which could dete ct cracking or delamination from any desired location within a materi al structure in real time. This could provide a valuable tool in the confident use of composite materials for various space applications. Recently, triboluminnescent materials have been proposed as smart sen sors of structural damage. To sense the damage, these materials can b e epoxy bonded or coated in a polymer matrix or embedded in a composi te host structure. When the damage or fracture takes place in the hos t structure, it will lead to the fracture of triboluminescent crystal s resulting in a light emission. This will warn, in real time, that a structural damage has occurred. The triboluminescent emission of the candidate phosphor has to be sufficiently bright, so that the light signal reaching from the point of fracture to the detector through a fiber optic cable is sufficiently strong to be detected. There are a large number of triboluminescent materials, but few satisfy the above criterion. Authors have synthesized a Eu based organic material know n as Europium tetrakis (dibenzoylmethide) triethylammonium .(EuD(sub 4)TEA), one of the bright triboluminescent materials, which is a pote ntial candidate for application as a damage sensor and could be made into a wireless sensor with the addition of microchip, antenna and el ectronics. Preliminary results on the synthesis and characterization of this material shall be presented.

Aggarwal, Mohan D.↗
NASA NTRS1993

An Application of a New Electromagnetic Sensor to Real-Time Monitoring of Fatigue Crack Growth in Thin Metal Plates

A major part of fracture mechanics is concerned with studying the initiation and propagation of fatigue cracks. This typically requires constant monitoring of crack growth during fatigue cycles which necessitates automation of the whole process. If the rate of crack growth can be determined the experimenter can vary externally controlled parameters such as load level, load cycle frequency and so on. Hence, knowledge of the precise location of the crack tip at any given time is very valuable. One technique currently available for measuring fatigue crack length is the DC potential drop method. The method, however, may be inaccurate if the direction of crack growth deviates considerably from what was assumed initially or the curvature of the crack becomes significant. Another approach is to digitize an optical image of the test specimen surface and then apply a pattern recognition technique to locate the crack tip, but this method is still under development. The present work is an initial study on applying eddy current-type probes to monitoring fatigue crack growth. The performance of two types of electromagnetic probes, a conventional eddy current probe and a newly developed self-nulling probe, was evaluated for the detection characteristics at and near the tips of fatigue cracks. The scan results show that the latter probe provides a very well defined local maximum in its output in the crack tip region suggesting the definite possibility of precisely locating the tip, while the former provides a somewhat ambiguous distribution of the sensor output in the same region. The paper is organized as follows: We start by reviewing the design and performance characteristics of the self-nulling probe and then describe the scan results which demonstrate the basic properties of the self-nulling probe. Next, we provide a brief description of the software developed for tracing a simulated crack and give a brief discussion of the main results of the test. The final section summarizes the major accomplishments of the present work and the elements of the future R&D needs.

Namkung, M.↗
DOE OSTI2025

Deep Learning Super-Resolution X-Ray Computed Tomography Algorithms for Additive Manufacturing

Industrial X-ray computed tomography (XCT) is a nondestructive method for inspection and characterization of additively manufactured (AM) materials and parts. In practice, the resolution of XCT can be limited by factors such as detector binning, restricted field of view for large-scale objects, system blur, motion during scanning, and acquisition settings. These limitations can reduce the detectability of critical flaws such as pores, cracks, and lack of fusion. Super-resolution (SR) techniques offer a promising solution for improving the effective resolution and image quality of XCT reconstructions without the need for expensive hardware upgrades or laborious, time-consuming scans. In particular, deep learning-based SR methods have garnered attention in recent years as powerful tools for reconstructing high-resolution volumes from low-resolution inputs. In this work, a novel deep learning-based SR method is proposed for XCT scans of AM parts, and compared against several existing state-of-the-art (SOTA) methods. The proposed method, Simurgh-SR, is built on the pre-existing Simurgh framework and consists of a 2.5D U-Net trained to map low-quality inputs containing noise and artifacts to high-quality reconstructions characterized by higher flaw contrast, better noise texture, and reduced artifacts. The experimental results demonstrate superior performance of Simurgh-SR in performing 4× SR on real industrial XCT scans of thick 316L components, enhancing the structural similarity score and peak signal-to-noise ratio (>7dB) compared to the LR counterpart while improving the F1-score for flaw detection by more than 2.3× when compared to alternative SOTA SR methods. This improvement enables more accurate and significantly faster characterization of metal AM components. Additionally, Simurgh-SR was trained for both 2X and 4X SR and performs effectively at both levels, enabling the use of a single model for various SR factors.

Rahman, Obaid [ORNL] (ORCID:0000000277810840)↗
NASA NTRS1972

Study of acoustic emission during mechanical tests of large flight weight tank structure

A polyphenylane oxide insulated, flight weight, subscale, aluminum tank was monitored for acoustic emissions during a proof test and during 100 cycles of environmental test simulating space flights. The use of a combination of frequency filtering and appropriate spatial filtering to reduce background noise was found to be sufficient to detect acoustic emission signals of relatively small intensity expected from subcritical crack growth in the structure. Several emission source locations were identified, including the one where a flaw was detected by post-test X-ray inspections. For most source locations, however, post-test inspections did not detect flaws; this was partially attributed to the higher sensitivity of the acoustic emission technique than any other currently available NDT method for detecting flaws.

Nakamura, Y.↗
NASA NTRS1973

Study of acoustic emission during mechanical tests of large flight weight tank structure

A PPO-insulated, flight-weight, subscale, aluminum tank was monitored for acoustic emissions during a proof test and during 100 cycles of environmental test simulating space flights. The use of a combination of frequency filtering and appropriate spatial filtering to reduce background noise was found to be sufficient to detect acoustic emission signals of relatively small intensity expected from subcritical crack growth in the structure. Several emission source locations were identified, including the one where a flaw was detected by post-test x-ray inspections. For most source locations, however, post-test inspections did not detect flaws; this was partially attributed to the higher sensitivity of the acoustic emission technique than any other currently available NDT method for detecting flaws. For these non-verifiable emission sources, a problem still remains in correctly interpreting observed emission signals.

Mccauley, B. O.↗
DOE OSTI2024

Magnetostrictive EMAT for Nuclear Spent Fuel Canisters

One approach for mitigation of stainless steel (SS) spent fuel canisters (SFCs) is to coat the welds or even the entire SFC with a cold-sprayed nickel coating. The coating can serve as a magnetostrictive interface between an electromagnetic acoustic transducer (EMAT) and the SS plate material. The EMAT can send ultrasound into the canister and “listen” for reflections caused by chloride-initiated stress corrosion cracking (CISCC) or other damage mechanisms that may lead to leaks if undetected and unmitigated. Here, this project outlines an approach for monitoring the integrity of SFCs using magnetostrictive EMAT sensors, which emit and detect discontinuity-initiated reflections indicative of pits or cracks likely arising from CISCC. Flat 13 mm SS plates with welds similar to actual canister welds were prepared, with flat-bottom holes 3 mm and 6 mm deep and 6 mm in diameter placed within the weld heat-affected zone (HAZ). The most effective sensor configurations were able to detect critical discontinuities more than 2 m away. Although a single sensor could be robotically deployed, the large volume covered by each sensor could support a small number of permanently mounted sensors, equipped with an external connector for periodic interrogation. The HAZ of a hypothetical SFC cylindrical surface could be effectively monitored with just 16 sensors.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

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↗
DOE OSTI2025

Exploring Capability of Multimodal Foundation Model for Image-based Fault Detection of Photovoltaic Modules

Multimodal Foundation Model (MFM), like ChatGPT and Gemini, have emerged as powerful tools for their exceptional natural language processing capabilities and their emerging potential in image analysis. This paper investigates the application of MFMs for photovoltaic (PV) fault detection through image analysis, focusing on ChatGPT 4.0 and Gemini 1.5 Pro. Three types of PV images and the corresponding common PV faults are detected: bird droppings using visible images, cell cracks via electroluminescence (EL) images, and hotspots using infrared (IR) images. Among the two models, Gemini 1.5 Pro demonstrated superior performance, achieving near-perfect results with an average F1 score of 0.97, consistently outperforming ChatGPT 4.0 in accuracy and reliability. Unlike traditional machine learning (ML) models, MFMs can operate in a zero shot manner that does not require additional training by the user, and the input images are not limited by size, angle, scope, or PV technology. The strong adaptability and user-friendliness make MFM a promising tool for analyzing PV images and advancing health monitoring for PV modules.

Li, Baojie↗
DOE OSTI2026

Spatiotemporal Learning in Power Modules: Wavelet-Enhanced Forecasting of Thermomechanical Degradation

Detecting internal defects in power electronics packages is critical for their performance and reliability, especially under extreme operating conditions, as these defects can lead to catastrophic failure if not properly addressed. Confocal scanning acoustic microscopy (C-SAM) plays a key role in the nondestructive evaluation of bond layer degradation within a power electronics package by detecting defects such as delamination, voids, and cracks. However, accurately quantifying and predicting these defects from C-SAM images remains a significant challenge due to the low noise-to-signal ratio, which typically arises from both imaging process and bond patterns itself. In this paper, we explore machine learning strategies for processing C-SAM images and providing predictive models of defect growth. We use C-SAM images of sintered copper and sintered silver samples, which are obtained under accelerated thermal experiments, as the representative dataset for our study. We investigate the effect of Fourier transforms and wavelet transforms on these datasets to remove high-frequency noise and address noise across multiple scales with histogram equalization to enhance the contrast and improve the visibility of defects. As a result, defect boundaries can be clearly distinguished, enabling more accurate tracking of their growth over time. We then employ different time-series forecasting algorithms on the denoised images to formulate an image-based lifetime prediction model. Statistical models and deep-learning techniques are trained on images obtained in the early stages of thermal shock, and defect growth in the later stages is predicted. Our work serves as a preliminary attempt to improve the accuracy of lifetime prediction models of power electronics packages, which is critical under extreme operating environments.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Report on Hydrogen Content Measurements of Yttrium Hydrides

This report describes the hydrogen content measurements of yttrium samples as irradiated at Advanced Test Reactor as part of the Microreactor Program. Irradiated samples were prepared at the facilities of Analytical Research Laboratory (ARL) of Materials and Fuels Complex (MFC). Hydrogen content measurements were performed on reduced size specimens using an inert gas fusion analyzer. For a single sample, replicate samples were prepared and tested to improve statistics of results. Uncertainty analyses were conducted using two approaches to determine the hydrogen content variations in the samples. Hydrogen content of irradiated specimens were both lower and higher than the expected values, indicating stoichiometry variations. Hydrogen was detected in all samples, even for the cracked capsule’s specimens. Results also suggested a potential hydrogen redistribution is present between samples inside the capsules.

08 HYDROGEN↗
NASA NTRS1985

Influence of load interactions on crack growth as related to state of stress and crack closure

Fatigue crack propagation (FCP) after an application of a low-high loading sequence was investigated as a function of specimen thickness and crack closure. No load interaction effects were detected for specimens in a predominant plane strain state. However, for the plane stress specimens, initially high FCP rates after transition to a higher stress intensity range were observed. The difference in observed behavior was explained by examining the effect of the resulting closure stress intensity values on the effective stress intensity range.

Telesman, J.↗
NASA NTRS1993

RSRM nozzle actuator bracket/lug fracture mechanics qualification test

This is the final report for the actuator bracket/lug fracture mechanics qualification test. The test plan (CTP-0071) outlined a two-phase test program designed to answer questions about the fracture criticality of the redesigned solid rocket motor (RSRM) nozzle actuator bracket. An analysis conducted using the NASA/FLAGRO fracture mechanics computer program indicated that the actuator bracket might be a fracture critical component. In the NASA/FLAGRO analysis, a simple lug model was used to represent the actuator bracket. It was calculated that the bracket would fracture if subjected to an actuator stall load in the presence of a 0.10 in. corner crack at the actuator attachment hole. The 0.10 in. crack size corresponds to the nondestructive inspection detectability limit for the actuator bracket. The inspection method used is the dye penetrant method. The actuator stall load (103,424 lb) is the maximum load which the actuator bracket is required to withstand during motor operation. This testing was designed to establish the accuracy of the analytical model and to directly determine whether the actuator bracket is capable of meeting fracture mechanics safe-life requirements.

Kelley, Peggy↗
NASA NTRS2005

Probabilistic Fracture Mechanics Analysis of the Orbiter's LH2 Feedline Flowliner

Work performed by Southwest Research Institute (SwRI) as part of an Independent Technical Assessment (ITA) for the NASA Engineering and Safety Center (NESC) is summarized. The ITA goal was to establish a flight rationale in light of a history of fatigue cracking due to flow induced vibrations in the feedline flowliners that supply liquid hydrogen to the space shuttle main engines. Prior deterministic analyses using worst-case assumptions predicted failure in a single flight. The current work formulated statistical models for dynamic loading and cryogenic fatigue crack growth properties, instead of using worst-case assumptions. Weight function solutions for bivariant stressing were developed to determine accurate crack "driving-forces". Monte Carlo simulations showed that low flowliner probabilities of failure (POF = 0.001 to 0.0001) are achievable, provided pre-flight inspections for cracks are performed with adequate probability of detection (POD)-specifically, 20/75 mils with 50%/99% POD. Measurements to confirm assumed POD curves are recommended. Since the computed POFs are very sensitive to the cyclic loads/stresses and the analysis of strain gage data revealed inconsistencies with the previous assumption of a single dominant vibrant mode, further work to reconcile this difference is recommended. It is possible that the unaccounted vibrational modes in the flight spectra could increase the computed POFs.

Bonacuse, Peter J.↗
DOE OSTI2021

The Application of Convolutional Neural Networks (CNNs) to Recognize Defects in 3D-Printed Parts

Cracks and pores are two common defects in metallic additive manufacturing (AM) parts. In this paper, deep learning-based image analysis is performed for defect (cracks and pores) classification/detection based on SEM images of metallic AM parts. Three different levels of complexities, namely, defect classification, defect detection and defect image segmentation, are successfully achieved using a simple CNN model, the YOLOv4 model and the Detectron2 object detection library, respectively. The tuned CNN model can classify any single defect as either a crack or pore at almost 100% accuracy. The other two models can identify more than 90% of the cracks and pores in the testing images. In addition to the application of static image analysis, defect detection is also successfully applied on a video which mimics the AM process control images. The trained Detectron2 model can identify almost all the pores and cracks that exist in the original video. This study lays a foundation for future in situ process monitoring of the 3D printing process.

36 MATERIALS SCIENCE↗