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At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
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↗
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↗
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↗
DOE OSTI2020

The LUX-ZEPLIN (LZ) radioactivity and cleanliness control programs

LUX-ZEPLIN (LZ) is a second-generation direct dark matter experiment with spin-independent WIMP-nucleon scattering sensitivity above 1.4×10 –48 cm 2 for a WIMP mass of 40GeV/c 2 and a 1000 days exposure. LZ achieves this sensitivity through a combination of a large 5.6 t fiducial volume, active inner and outer veto systems, and radio-pure construction using materials with inherently low radioactivity content. The LZ collaboration performed an extensive radioassay campaign over a period of six years to inform material selection for construction and provide an input to the experimental background model against which any possible signal excess may be evaluated. The campaign and its results are described in this paper. We present assays of dust and radon daughters depositing on the surface of components as well as cleanliness controls necessary to maintain background expectations through detector construction and assembly. Finally, examples from the campaign to highlight fixed contaminant radioassays for the LZ photomultiplier tubes, quality control and quality assurance procedures through fabrication, radon emanation measurements of major sub-systems, and bespoke detector systems to assay scintillator are presented.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Erratum to: The LUX-ZEPLIN (LZ) radioactivity and cleanliness control programs

LUX-ZEPLIN (LZ) is a second-generation direct dark matter experiment with spin-independent WIMP-nucleon scattering sensitivity above 1.4 × 10 -48 cm 2 for a WIMP mass of 40 GeV/c 2 and a 1000 d exposure. LZ achieves this sensitivity through a combination of a large 5.6 t fiducial volume, active inner and outer veto systems, and radio-pure construction using materials with inherently low radioactivity content. The LZ collaboration performed an extensive radioassay campaign over a period of six years to inform material selection for construction and provide an input to the experimental background model against which any possible signal excess may be evaluated. The campaign and its results are described in this paper. We present assays of dust and radon daughters depositing on the surface of components as well as cleanliness controls necessary to maintain background expectations through detector construction and assembly. Finally, examples from the campaign to highlight fixed contaminant radioassays for the LZ photomultiplier tubes, quality control and quality assurance procedures through fabrication, radon emanation measurements of major sub-systems, and be spoke detector systems to assay scintillator are presented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗