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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A Semi-supervised Hybrid Machine Learning Framework for the Qualification of Resistance Spot Welds

• Industries requiring high structural integrity, including automotive, aerospace, and construction, place considerable significance on weld quality classification. • The inspection normally involves human expertise through predefined quality metrics that are subjective, error-prone, and time-intensive • The challenge to classification model development is the scarcity of labeled data and imbalanced distributions in the data that are labeled. • This work develops a new hybrid methodology that achieves clustering using KMeans++ together with supervised classification to overcome these challenges. • The ensemble-based classifiers were identified as optimal, with accuracy enhancements of up to 8% using the pseudo-labeled dataset. • The work provides practical insight into feature engineering and machine learning integration in industrial quality assurance applications.

Rogers, Jeremy K. [Savannah River National Laborat↗
DOE OSTI2021

Validation of a lightning protection system for a fusion-welded thermoplastic composite wind turbine blade tip

Fusion welding for thermoplastic composite wind turbine blades is being investigated as a replacement for adhesives in bond lines to reduce manufacturing time and cost and to promote ease of recyclability. Resistance welding uses an electrically conductive material, such as carbon fiber, embedded in the bond line to heat the joining surfaces and fuse them together under pressure. One main challenge with fusion joining for large wind blades is the ability to use this conductive heating element in the bond line while still protecting the blade from the catastrophic effects of a lightning strike. This paper outlines the design and manufacturing of a lightning protection system for a 5-m-long thermally welded blade tip. The outcomes of high voltage and high current lightning testing applied to the 5 m blade tip show that the proposed design was able to protect the fusion welded bond lines from significant lightning damage.

17 WIND ENERGY↗
DOE OSTI2021

Neutron diffraction illustrates residual stress behavior of welded alloys used as radioactive confinement boundary

Corrosion-resistant welded alloys are frequently used as a leak-tight boundary in critical applications that require confinement of hazardous and/or radioactive substances, including an increasing population of spent nuclear fuel (SNF) canisters. The behavior of residual stresses generated as a result of irregular elastic–plastic deformation during processes such as welding is one of today's key issues to a full understanding of the aging mechanisms that may compromise the confinement boundary. Whether such processes and any subsequent weld repairs, not subjected to post-weld heat treatment, would negatively affect the initial material by introducing through-thickness tensile stresses remains an open question. Here we report the first residual stress measurements using neutron diffraction on the welded joints of a SNF canister. We found significant tensile residual stresses in the as welded sample, indicating that initiation and through-thickness growth of cracks may be possible. Following repair, we observed a stress redistribution and introduction of beneficial compressive stresses. We anticipate our results will improve understanding of confinement susceptibility to aging and guide improvements in repair techniques.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Mechanisms of paint bake response in resistance spot-welded first and third generation AHSS

Implementation of 3rd generation advanced high strength steels (3GAHSS) in automotive structures requires careful consideration of joining process implications. Due to the presence of increased carbon equivalent in 3GAHSS such as 980 HF relative to commonly-applied 1st generation AHSS such as 980 DP, the resultant properties of the weld nugget and heat affected zones in resistance spot welded must be engineered to provide consistent and acceptable performance, specifically in cross-tension loading mode. Although the as-welded cross-tension strength (CTS) of 980 HF is less than 980 DP, there is a significant improvement of the CTS after paint baking in 980 HF welds. 980 DP steel welds do not exhibit such marked improvement upon baking. The mechanism of the strong and beneficial effect of paint baking on 980 HF in comparison to 980 DP is discussed in light of its effect on residual stress distributions and microstructures. Although residual stress (strain) reduction is observed via synchrotron x-ray diffraction for all tested steels, this effect is demonstrated as secondary to the performance restoration of the weld joint in cross tension. High-resolution electron channeling contrast imaging (ECCI) micrographs reveal that paint baking affects a more beneficial degree of martensite tempering in 980 HF as compared to 980 DP. The martensite tempering is also evidenced by both a reduction in nano-hardness of critical microstructural features and re-emergence of ductile fracture behavior following the paint baking process. The tempering of martensite is concluded to be the dominant mechanism in improving cross tension strength of 3GAHSS welds following paint baking.

36 MATERIALS SCIENCE↗
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 OSTI2024

Modelling of cracking during beam oscillation laser welding of a creep-resistant nickel alloy

During high power laser welding of Inconel 740H, horizontal solidification cracks form at locations between approximately 70–80% of the weld depth. With the integration of circular beam oscillation, the laser energy density distribution and underlying thermo-mechanical conditions were altered. By coupling three-dimensional heat transfer, fluid flow, and stress modelling tools, the role that circular beam oscillation plays in the formation of the strain rates and stresses driving this cracking phenomenon was identified. While the addition of a circular oscillation pattern appeared to lower the stress levels along the solidification front, it did not eliminate the appearance of horizontal cracking. Only when the beam amplitudes reached 1.6 mm did solidification cracking disappear, but the weld depths were also significantly reduced.

Gao, Mingze↗
DOE OSTI2022

Mechanical joint performances of friction self-piercing riveted carbon fiber reinforced polymer and AZ31B Mg alloy

Carbon fiber reinforced polymer (CFRP) and AZ31B Mg alloy were joined by the friction self-piercing riveting (F-SPR) with different steel rivet shank sizes. With the increase of rivet shank size, lap shear fracture load and mechanical interlock distance increased. Ultrafine grains were formed at the joint in AZ31B as a result of dynamic recrystallization, which contributed to the higher hardness. Fatigue life of the CFRP-AZ31B joint was studied at various peak loads of 0.5, 1, 2, and 3 kN and compared with the resistance spot welded AZ31B-AZ31B from the open literature. The fatigue performance was better at higher peak load (>2 kN) and comparable to that of resistance spot welding of AZ31B to AZ31B at lower peak loads (<1 kN). From fractography, the crack initiation for lower peak load (<1 kN) case was observed at the fretting positions on the top and bottom surfaces of AZ31B sheet. When peak load was increased, fretting between the rivet and the top of AZ31B became more dominant to initiate a crack during fatigue testing.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Mitigating Corrosion in Mg Sheet in Conjunction with a Sheet-Joining Method that Satisfies Structural Requirements within Sub-assemblies

This work was undertaken as a LightMAT project funded by the DOE-Vehicles Technology Office. The goal of this work was to develop corrosion protection strategies that simultaneously mitigate corrosion and achieve Class-A surface finish for Mg components in automotive applications. While automotive metals such as steel and aluminum are protected against corrosion through a variety of coating schemes/packages, the efficacy of these existing coating schemes for Mg and Mg- joints is not clear and needs to be determined. Therefore, five commercially available coating schemes and two joining techniques (riveting and Arplas resistance spot welding) were evaluated. The corresponding individual Mg sheet coupons or Mg/Mg joint test coupons were provided by Magna that were then corrosion tested at PNNL using ASTM B117 procedure. The microstructures and mechanical properties of the coupons were analyzed to determine the effectiveness of the joining and corrosion mitigation strategies. Of the coating schemes evaluated, Henkel Bonderite MgC 2.0 pre-treatment + E-coat showed the best corrosion protection and surface finish for individual Mg coupons and Arplas resistance spot welded coupons. However, the strength of Mg/Mg welded joint was reduced after corrosion testing due to some corrosion at the weld nugget. Mg/Mg rivet joints in conjunction with Chemetall oxisilan pre-treatment + polyurethane coating showed good corrosion resistance and some discoloration on the surface finish. Coating schemes comprising pre-treatment with Alodine 5200 + E-coat or Bonderite 1455 + polyurethane coating, in conjunction with Al rivet joints, showed significant corrosion and extensive discoloration of the surface. We anticipate that the results from this work will provide useful guidance to the automotive industry in selecting the appropriate combinations of corrosion protection coatings and joining techniques to fabricate light-weight Mg-based automotive components.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Corrosion Protection and Dissimilar Material Joining for Next Generation Lightweight Vehicles

The Arconic Technology Center working with Honda R&D Americas, LLC and the Ohio State University evaluated the corrosion performance of several multi-material conditions and demonstrated the production worthiness of the Resistance Spot Riveting (RSR™) process. RSR is a new technology being developed by Howmet Fastening Systems (formerly Arconic, Inc.) that employs a fastener that is installed using conventional resistance spot welding equipment to produce multi-material joints. The goal of the 3-year project was to demonstrate the use of RSR to join aluminum to steel and aluminum to carbon fiber composites on a prototype scale. Deployment of this technology would help the automotive industry achieve an additional 10 to 20% weight reduction over high strength steels. These weight-savings to the body in white generally translates to 2.5-5.0% of overall vehicle curb weight. The resulting total weight-savings could provide a 1.5% to 3.0% total improvement in fuel efficiency for vehicles that incorporate RSR for multi-material joining. The RSR technology addresses several production barriers to achieving DOE’s fuel efficiency targets including eliminating the need of additional capital for new joining technologies and the flexibility to process conventional steel and multi-material structures with the same equipment. Additionally, the trend towards ultra-high strength steels limits the availability of conventional joining technologies that can effectively process these multi-material combinations. In order to accomplish these goals, the following program milestones were completed by the team: 1) Developed RSR process parameters, producing multi-material joints for mechanical testing and corrosion assessments. 2) Conducted corrosion evaluation of RSR and baseline joints assembled between automotive type aluminum alloys, steels, and carbon fiber using several corrosion mitigation strategies. 3) Developed a production ready feed system and integrate into a robotic resistance spot welding station to simulate automotive production conditions. 4) Produced demonstration assemblies for testing and evaluation.

36 MATERIALS SCIENCE↗
DOE OSTI2022

High Strength Steel-Aluminum Components by Vaporizing Foil Actuator Welding

This project aimed to address the challenge of effectively welding dissimilar materials—high-strength steel and high-strength aluminum for creating lightweight, multi-material automotive components. For automotive companies, reducing weight of a vehicle is critical task regulated by the government to solve the issue of greenhouse gas emissions. Production of lightweight cars and trucks can be achieved by substitution of current all-steel structures with multi-material lightweight structures that include high strength-to-weight-ration materials such as high-strength steels, aluminum alloys, magnesium alloys, titanium alloys, coupled with lightweight designs. This requires dissimilar metal welding, which is challenging for state of the art joining processes such as resistance spot welding. The cycle of melting-cooling-freezing during traditional welding that can easily ruin the designed outstanding properties of the advanced base metals, such as aluminum alloys, making the welded area much weaker than the base metals. To weld two different metals with great difference in melting points, such as aluminum and steel, it’s even more difficult or impossible because of the formation of brittle intermetallic compounds at the welded interface. In this project, a novel welding method, developed at OSU, was selected for validation and development. This novel technology enables welding by impact without melting and proves to be robust to join various dissimilar lightweight metals. Termed as vaporizing foil actuator welding or VFAW, the technology uses a thin aluminum foil that is rapidly vaporized by a high current pulse to produce an explosive-like pressure pulse to drive one metallic piece into another at the high speed required for impact welding. This project entailed development of the early-stage welding technology in terms of (a) the consumables, the welding apparatus and the power sources, (b) coupon scale screening of many material combinations including corrosion studies, (c) computational modeling and design of the welded interface as well as of the multi-material prototype component, and (d) mechanical testing for strength and durability at coupon scale and to a certain extent the prototype scale. The all-steel engine cradle of 2016 Chevrolet Cruze was chosen as the baseline prototype component. The target set for the project was to demonstrate a 20% weight reduction at a cost premium of less than $\$ $5/lb saved without compromising on baseline mechanical properties. At project completion, a 12% lighter prototype component was demonstrated with an estimated cost premium of $\$ $9.8/lb saved. Besides prototype level demonstration of the technology, this project also enabled elevation of the technology’s readiness level to where a hydraulically actuated welding head was developed and made ready for deployment at a research and development facility for Tier 1 automotive supplier.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Di-CNN: Domain-Knowledge-Informed Convolutional Neural Network for Manufacturing Quality Prediction

In manufacturing, convolutional neural networks (CNNs) are widely used on image sensor data for data-driven process monitoring and quality prediction. However, as purely data-driven models, CNNs do not integrate physical measures or practical considerations into the model structure or training procedure. Consequently, CNNs’ prediction accuracy can be limited, and model outputs may be hard to interpret practically. This study aims to leverage manufacturing domain knowledge to improve the accuracy and interpretability of CNNs in quality prediction. A novel CNN model, named Di-CNN, was developed that learns from both design-stage information (such as working condition and operational mode) and real-time sensor data, and adaptively weighs these data sources during model training. It exploits domain knowledge to guide model training, thus improving prediction accuracy and model interpretability. A case study on resistance spot welding, a popular lightweight metal-joining process for automotive manufacturing, compared the performance of (1) a Di-CNN with adaptive weights (the proposed model), (2) a Di-CNN without adaptive weights, and (3) a conventional CNN. The quality prediction results were measured with the mean squared error (MSE) over sixfold cross-validation. Model (1) achieved a mean MSE of 6.8866 and a median MSE of 6.1916, Model (2) achieved 13.6171 and 13.1343, and Model (3) achieved 27.2935 and 25.6117, demonstrating the superior performance of the proposed model.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Prediction of Aluminum to Steel RSW Joint Failure (CRADA 405)

General Motors, LLC has developed a resistance spot welding process to join aluminum sheet to steel for body-in-white manufacturing. Efforts to develop the process for any one joint configuration (combination of alloy and thicknesses) is intensive. To aid in process understanding and eventual reduction in process development efforts, an initiative was created to develop an finite element model of the various destructive test configurations (lap shear, cross-tension, and coach peel) to help predict the failure mode and load displacement curves under tensile testing. This report describes the finite element modeling approach, the means by which local material properties were determined, as well as model validation and refinement efforts.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Crack Growth Rate and Fracture Toughness of Stainless Steel Welds in Light Water Reactor Environment

Austenitic stainless steel welds play a crucial role in maintaining the structural integrity of light water reactors. Within the reactor core, stainless steel welds endure a harsh service environment including elevated temperature, corrosive coolant, and intense neutron irradiation. Long-term exposure to such a severe service environment could lead to performance degradation. To ensure the safe and economic operation of light water reactors, this study performed crack growth rate and fracture toughness tests on irradiated and thermally aged weld samples harvested from decommissioned reactor components. In a low-corrosion-potential environment, stainless steel welds showed resistance to stress corrosion cracking, and the measured crack propagation rates were comparable to those of wrought stainless steels under similar test conditions. However, a marked decline in fracture toughness was observed in weld samples, particularly in irradiated samples. Fracture instability was observed at ~0.8 dpa, significantly lower than those observed in wrought stainless steels. Further research at higher doses is needed to evaluate the dose dependence of irradiation-induced embrittlement, and to understand the interplay between thermal aging and neutron irradiation in stainless steel welds.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Extending Ultrasonic Welding Techniques to New Material Pairs (FY 2023 Annual Progress Report)

Modern multimaterial vehicles require joining of various lightweight materials, such as aluminum (Al) and magnesium (Mg) alloys and carbon fiber reinforced polymers (CFRP), with advanced high-strength steels together to form a high-performance and lightweight body structure. A variety of joining methodologies (e.g., resistance spot welding, adhesive bonding, linear fusion welding, hemming, clinching, bolting, riveting) have been attempted by the automotive industry to join different materials. Often, these joining techniques are limited to only certain material combinations. For capital and operational cost, automobile original equipment manufacturers need to limit the number of joining technologies implemented on an assembly line.

36 MATERIALS SCIENCE↗