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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Calreticulin discriminates the proximal region at the N-glycosylation site of Glc1Man9GlcNAc2 ligand

Calreticulin (CRT) is well known as a lectin-like chaperone that recognizes Glc1Man9GlcNAc2 (G1M9)-glycoproteins in the endoplasmic reticulum (ER). However, whether CRT can directly interact with the aglycone moiety (protein portion) of the glycoprotein remains controversial. To improve our understanding of CRT interactions, structure-defined G1M9-derivatives with different aglycones (–OH, –Gly–NH{sub 2}, and –Gly–Glu–{sup t}Bu) were used as CRT ligands, and their interactions with recombinant CRT were analyzed using thermal shift analysis. The results showed that CRT binds strongly to a G1M9-ligand in the order –Gly–Glu–{sup t}Bu > –Gly–NH{sub 2} > –OH, which is the same as that of the reglucosylation of Man9GlcNAc2 (M9)-derivatives by the folding sensor enzyme UGGT (UDP-glucose: glycoprotein glucosyltransferase). Our results indicate that, similar to UGGT, CRT discriminates the proximal region at the N-glycosylation site, suggesting a similar mechanism mediating the recognition of aglycone moieties in the ER glycoprotein quality control system. - Highlights: • Glc1Man9GlcNAc2 (G1M9) ligands with different aglycones were chemically prepared. • Calreticulin (CRT) discriminates the aglycone of Glc1Man9GlcNAc2 (G1M9) ligand. • CRT binds with G1M9 ligands in a similar manner to folding sensor enzyme.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Machine Learning for Automated Weld Quality Monitoring and Control

Resistance Spot Welding (RSW) is a critical process in the automotive industry, valued for its cost-effectiveness, short cycle time, and robustness. However, achieving consistent high-quality joints remains challenging due to the complex interplay of various factors, like materials, processes, and manufacturing uncertainties, etc. Under the collaborative project between Oak Ridge National Laboratory (ORNL) and General Motors (GM), we have developed a robust and expansible machine learning (ML) framework aimed at enhancing quality control in RSW. By harnessing the power of machine learning, we have developed the ability to ensure every aspect of the welding process, from the initial process design stage to the final weld joint quality. The framework operates by analyzing a variety of data streams, including in-line process signals, process parameters, materials, and postprocessed weld joint data. Through this analysis, the models have been trained to detect deviations from optimal quality standards, leveraging their ability to identify signature data patterns and anomalies within in-line signals and construct complex correlations between these signals and weld quality parameters. Meanwhile, the machine learning framework is designed to adapt to a variety of materials, including high strength steels and aluminum alloys, etc. Its flexible architecture facilitates the incorporation of diverse data sources and features, enabling precise modeling and prediction across a broad range of material properties and weld quality variables. The expansible ML frameworks represent a promising transformation in weld quality monitoring and control, empowering industry to achieve high levels of efficiency, consistency, and reliability in manufacturing.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Machine Learning for Automated Weld Quality Monitoring and Control

Resistance Spot Welding (RSW) is a critical process in the automotive industry, valued for its cost-effectiveness, short cycle time, and robustness. However, achieving consistent high-quality joints remains challenging due to the complex interplay of various factors, like materials, processes, and manufacturing uncertainties, etc. Under the collaborative project between Oak Ridge National Laboratory (ORNL) and General Motors (GM), we have developed a robust and expansible machine learning (ML) framework aimed at enhancing quality control in RSW. By harnessing the power of machine learning, we have developed the ability to ensure every aspect of the welding process, from the initial process design stage to the final weld joint quality. The framework operates by analyzing a variety of data streams, including in-line process signals, process parameters, materials, and postprocessed weld joint data. Through this analysis, the models have been trained to detect deviations from optimal quality standards, leveraging their ability to identify signature data patterns and anomalies within in-line signals and construct complex correlations between these signals and weld quality parameters. Meanwhile, the machine learning framework is designed to adapt to a variety of materials, including high strength steels and aluminum alloys, etc. Its flexible architecture facilitates the incorporation of diverse data sources and features, enabling precise modeling and prediction across a broad range of material properties and weld quality variables. The expansible ML frameworks represent a promising transformation in weld quality monitoring and control, empowering industry to achieve high levels of efficiency, consistency, and reliability in manufacturing.

42 ENGINEERING↗
DOE OSTI2016

SU-F-T-237: The Imaging and Radiation Oncology Core (IROC) Cooperatives Activities Supporting the NCI’s National Clinical Trial Network

Purpose: The Imaging and Radiation Oncology Core (IROC) Cooperative has been active for the past two years supporting the National Clinical Trial Network and the details of that support are reported. Methods: There are six QA centers (Houston, Ohio, Philadelphia-RT, Philadelphia-DI, Rhode Island, St. Louis) providing an integrated RT and DI quality control program in support of the NCI’s clinical trials. The QA Center’s efforts are focused on assuring high quality data for clinical trials designed to improve the clinical outcomes for cancer patients worldwide. This program is administered through five core services: site qualification, trial design support, credentialing, data management, and case review. Results: IROC currently provides core support for 172 NCTN trials with RT, DI and RT/DI components. Many of these trials were legacy trial from the previous cooperative group program. IROC monitors nearly 1800 RT photon and 20 proton institutions. Over 28,000 beams outputs were monitored with 8% of the sites requiring repeat audits due to beam out of criteria. As part of credentialing, 950 QA phantoms have been irradiated, 515 imaging modalities evaluated and almost 4000 credentialing letters have been issued. In just year 2, 5290 RT and 4934 DI patient datasets were received (many using TRIAD) by IROC QA Centers to be prepared for review. During the past 2 years, a total of 6300 RT cases and 19,000 DI image sets were reviewed by IROC technical staff. To date, IROC has published 36 manuscripts. Conclusion: The QA services provided by IROC are numerous and are continually being evaluated for effectiveness, harmonized across all NCTN Groups and administered in an efficient and timely manner to enhance accurate and per protocol trial data submission. These efforts increase each NCTN Group’s ability to derive meaningful outcomes from their clinical trials. This work was supported by DHHS NIH grant 5U24CA180803.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2010

ICESat Lidar and Global Digital Elevation Models: Application to DESDynI

Geodetic control is extremely important in the production and quality control of topographic data sets, enabling elevation results to be referenced to an absolute vertical datum. Global topographic data with improved geodetic accuracy achieved using global Ground Control Point (GCP) databases enable more accurate characterization of land topography and its change related to solid Earth processes, natural hazards and climate change. The multiple-beam lidar instrument that will be part of the NASA Deformation, Ecosystem Structure and Dynamics of Ice (DESDynI) mission will provide a comprehensive, global data set that can be used for geodetic control purposes. Here we illustrate that potential using data acquired by NASA's Ice, Cloud and land Elevation Satellite (ICEsat) that has acquired single-beam, globally distributed laser altimeter profiles (+/-86deg) since February of 2003 [1, 2]. The profiles provide a consistently referenced elevation data set with unprecedented accuracy and quantified measurement errors that can be used to generate GCPs with sub-decimeter vertical accuracy and better than 10 m horizontal accuracy. Like the planned capability for DESDynI, ICESat records a waveform that is the elevation distribution of energy reflected within the laser footprint from vegetation, where present, and the ground where illuminated through gaps in any vegetation cover [3]. The waveform enables assessment of Digital Elevation Models (DEMs) with respect to the highest, centroid, and lowest elevations observed by ICESat and in some cases with respect to the ground identified beneath vegetation cover. Using the ICESat altimetry data we are developing a comprehensive database of consistent, global, geodetic ground control that will enhance the quality of a variety of regional to global DEMs. Here we illustrate the accuracy assessment of the Shuttle Radar Topography Mission (SRTM) DEM produced for Australia, documenting spatially varying elevation biases of several meters in magnitude.

Carabajal, Claudia C.↗
NASA NTRS1967

Coatings quality assurance, section VII

Quality control assurance for coatings used on satellites, and getter-ion sublimation pumps with controls and power supplies

RADIO ASTRONOMY EXPLORER /RAE/ SATELLITE↗
DOE OSTI2016

Defense Waste Processing Facility (DWPF) Durability-Composition Models and the Applicability of the Associated Reduction of Constraints (ROC) Criteria for High TiO 2 Containing Glasses

Radioactive high-level waste (HLW) at the Savannah River Site (SRS) has successfully been vitrified into borosilicate glass in the DWPF since 1996. Vitrification requires stringent product/process (P/P) constraints since the glass cannot be reworked once it has been poured into ten foot tall by two foot diameter canisters. A unique “feed forward” statistical process control (SPC) was developed for this control rather than relying on statistical quality control (SQC). In SPC, the feed composition to the DWPF melter is controlled prior to vitrification. In SQC, the glass product would be sampled after it is vitrified. Individual glass property-composition models form the basis for the “feed forward” SPC. The models transform constraints on the melt and glass properties into constraints on the feed composition going to the melter in order to determine, at the 95% confidence level, that the feed will be processable and that the durability of the resulting waste form will be acceptable to a geologic repository. The DWPF SPC system is known as the Product Composition Control System (PCCS). One of the process models within PCCS is known as the Thermodynamic Hydration Energy Reaction MOdel (THERMO™). The DWPF will soon be receiving increased concentrations of TiO 2 -, Na 2 O-, and Cs 2 O-enriched wastes from the Salt Waste Processing Facility (SWPF). The SWPF has been built to pretreat the high-curie fraction of the salt waste to be removed from the HLW tanks in the F- and H-Area Tank Farms at the SRS. In order to validate the existing TiO 2 term in THERMO™ beyond 2.0 wt% in the DWPF, new durability data were developed over the target range of 2.00 to 6.00 wt% TiO 2 and evaluated against the 1995 durability model. The durability was measured by the 7-day Product Consistency Test. This study documents the adequacy of the existing THERMO™ terms. It is recommended that the modified THERMO™ durability models and the modified property acceptable region limits for the durability constraints be incorporated in the next revision of the technical bases for PCCS and then implemented into PCCS. It is also recommended that an reduction of constraints of 4 wt% Al 2 O 3 be implemented with no restrictions on the amount of alkali in the glass for TiO 2 values ≥2 wt%. The ultimate limit on the amount of TiO 2 that can be accommodated from SWPF will be determined by the three PCCS models, the waste composition of a given sludge batch, the waste loading of the sludge batch, and the frit used for vitrification.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Quality Assurance Project Plan for Closure of the Central Facilities Area Sewage Treatment Plant Lagoon 3 and Land Application Area

This quality assurance project plan describes the technical requirements and quality assurance activities of the environmental data collection/analyses operations to close Central Facilities Area Sewage treatment Plant Lagoon 3 and the land application area. It describes the organization and persons involved, the data quality objectives, the analytical procedures, and the specific quality control measures to be employed. All quality assurance project plan activities are implemented to determine whether the results of the sampling and monitoring performed are of the right type, quantity, and quality to satisfy the requirements for closing Lagoon 3 and the land application area.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1974

Management approach recommendations. Earth Observatory Satellite system definition study (EOS)

Management analyses and tradeoffs were performed to determine the most cost effective management approach for the Earth Observatory Satellite (EOS) Phase C/D. The basic objectives of the management approach are identified. Some of the subjects considered are as follows: (1) contract startup phase, (2) project management control system, (3) configuration management, (4) quality control and reliability engineering requirements, and (5) the parts procurement program.

Source record↗
NASA NTRS1980

Production readiness verification testing

A Production Readiness Verification Testing (PRVT) program has been established to determine if structures fabricated from advanced composites can be committed on a production basis to commercial airline service. The program utilizes subcomponents which reflect the variabilities in structure that can realistically be expected from current production and quality control technology to estimate the production qualities, variation in static strength, and durability of advanced composite structures. The results of the static tests and a durability assessment after one year of continuous load/environment testing of twenty two duplicates of each of two structural components (a segment of the front spar and cover of a vertical stabilizer box structure) are discussed.

James, A. M.↗
NASA NTRS1991

Development of a portable multispectral thermal infrared camera

The purpose of this research and development effort was to design and build a prototype instrument designated the 'Thermal Infrared Multispectral Camera' (TIRC). The Phase 2 effort was a continuation of the Phase 1 feasibility study and preliminary design for such an instrument. The completed instrument designated AA465 has application in the field of geologic remote sensing and exploration. The AA465 Thermal Infrared Camera (TIRC) System is a field-portable multispectral thermal infrared camera operating over the 8.0 - 13.0 micron wavelength range. Its primary function is to acquire two-dimensional thermal infrared images of user-selected scenes. Thermal infrared energy emitted by the scene is collected, dispersed into ten 0.5 micron wide channels, and then measured and recorded by the AA465 System. This multispectral information is presented in real time on a color display to be used by the operator to identify spectral and spatial variations in the scenes emissivity and/or irradiance. This fundamental instrument capability has a wide variety of commercial and research applications. While ideally suited for two-man operation in the field, the AA465 System can be transported and operated effectively by a single user. Functionally, the instrument operates as if it were a single exposure camera. System measurement sensitivity requirements dictate relatively long (several minutes) instrument exposure times. As such, the instrument is not suited for recording time-variant information. The AA465 was fabricated, assembled, tested, and documented during this Phase 2 work period. The detailed design and fabrication of the instrument was performed during the period of June 1989 to July 1990. The software development effort and instrument integration/test extended from July 1990 to February 1991. Software development included an operator interface/menu structure, instrument internal control functions, DSP image processing code, and a display algorithm coding program. The instrument was delivered to NASA in March 1991. Potential commercial and research uses for this instrument are in its primary application as a field geologists exploration tool. Other applications have been suggested but not investigated in depth. These are measurements of process control in commercial materials processing and quality control functions which require information on surface heterogeneity.

Osterwisch, Frederick G.↗
NASA NTRS2007

Measuring Image Navigation and Registration Performance at the 3-Sigma Level Using Platinum Quality Landmarks

Geostationary Operational Environmental Satellite (GOES) Image Navigation and Registration (INR) performance is specified at the 3- level, meaning that 99.7% of a collection of individual measurements must comply with specification thresholds. Landmarks are measured by the Replacement Product Monitor (RPM), part of the operational GOES ground system, to assess INR performance and to close the INR loop. The RPM automatically discriminates between valid and invalid measurements enabling it to run without human supervision. In general, this screening is reliable, but a small population of invalid measurements will be falsely identified as valid. Even a small population of invalid measurements can create problems when assessing performance at the 3-sigma level. This paper describes an additional layer of quality control whereby landmarks of the highest quality ("platinum") are identified by their self-consistency. The platinum screening criteria are not simple statistical outlier tests against sigma values in populations of INR errors. In-orbit INR performance metrics for GOES-12 and GOES-13 are presented using the platinum landmark methodology.

Carr, James L.↗
DOE OSTI2021

aoso3.b1

aoso3.b1 with quality control

54 ENVIRONMENTAL SCIENCES↗