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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

CHARACTERIZATION OF Pro-Beam LOW VOLTAGE ELECTRON BEAM WELDING MACHINE

The purpose of this paper is to present and discuss data related to the performance of a newly acquired low voltage electron beam welding machine. The machine was made by Pro-Beam AG &Co. KGaA of Germany. This machine was recently installed at LANL in building SM -39; a companion machine was installed in the production facility. The PB machine is substantially different than the EBW machines typically used at LANL and therefore, it is important to understand its characteristics as well as possible. Our basic purpose in this paper is to present basic machine performance data and to compare those with similar results from the existing EBW machines. It is hoped that this data will provide a historical record of this machine’s characteristics as well as possibly being helpful for transferring welding processes from the old EBW machines to the PB machine or comparable machines that may be purchased in the future.

Electron Beam Welding, Electron Beam Measurement↗
DOE OSTI2018

ADVANCED WELDING TECHNOLOGY DEVELOPMENT FOR NUCLEAR REACTOR REPAIR

Welding is widely used for repair, maintenance, and upgrade of nuclear reactor components. As a critical technology for supporting the extension of nuclear power plant service lifetimes beyond 60 years, there has been an industry need in further developing welding technology for highly irradiated materials. During welding of irradiated materials, Helium, which is a transmutation byproduct from Boron and Nickel contained in the structural alloys, can coalesce into bubbles along grain boundaries in the material under driving forces of high temperature and thermal tensile stress. This leads to embrittlement and potential intergranular cracking in the Heat Affected Zone (HAZ) of the weld. In this paper, irradiated 304 stainless steel coupons containing 5, 10 and 20 ppm Boron prior to radiation were successfully welded by advanced laser beam welding (LBW) and friction stir welding (FSW). Facilities and welding machine design, welding processes development, cold material welded joints evaluation, process safety control and documentation, and irradiated material welding are discussed. Both laser and friction stir welded coupons of the irradiated material exhibited high welding quality and surface finish. No Helium induced welding defects were observed on the weld surfaces or adjacent base metals.

Tang, Wei↗
DOE OSTI2020

Multi-objective optimization of peel and shear strengths in ultrasonic metal welding using machine learning-based response surface methodology

Ultrasonic metal welding (UMW) is a solid-state joining technique with varied industrial applications. Despite of its numerous advantages, UMW has a relative narrow operating window and is sensitive to variations in process conditions. As such, it is imperative to quantitatively characterize the influence of welding parameters on the resulting joint quality. The quantification model can be subsequently used to optimize the parameters. Conventional response surface methodology (RSM) usually employs linear or polynomial models, which may not be able to capture the intricate, nonlinear input-output relationships in UMW. Furthermore, some UMW applications call for simultaneous optimization of multiple quality indices such as peel strength, shear strength, electrical conductivity, and thermal conductivity. To address these challenges, this paper develops a machine learning (ML)-based RSM to model the input-output relationships in UMW and jointly optimize two quality indices, namely, peel and shear strengths. The performance of various ML methods including spline regression, Gaussian process regression (GPR), support vector regression (SVR), and conventional polynomial regression models with different orders is compared. A case study using experimental data shows that GPR with radial basis function (RBF) kernel and SVR with RBF kernel achieve the best prediction accuracy. The obtained response surface models are then used to optimize a compound joint strength indicator that is defined as the average of normalized shear and peel strengths. In addition, the case study reveals different patterns in the response surfaces of shear and peel strengths, which has not been systematically studied in the literature. While developed for the UMW application, the method can be extended to other manufacturing processes.

42 ENGINEERING↗
DOE OSTI2016

Definition of Beam Diameter for Electron Beam Welding

It is useful to characterize the dimensions of the electron beam during process development for electron beam welding applications. Analysis of the behavior of electron beam welds is simplest when a single number can be assigned to the beam properties that describes the size of the beam spot; this value we generically call the “beam diameter”. This approach has worked well for most applications and electron beam welding machines with the weld dimensions (width and depth) correlating well with the beam diameter. However, in recent weld development for a refractory alloy, Ta-10W, welded with a low voltage electron beam machine (LVEB), it was found that the weld dimensions (weld penetration and weld width) did not correlate well with the beam diameter and especially with the experimentally determined sharp focus point. These data suggest that the presently used definition of beam diameter may not be optimal for all applications. The possible reasons for this discrepancy and a suggested possible alternative diameter definition is the subject of this paper.

42 ENGINEERING↗
DOE OSTI2014

Low-Cost Efficient Manufacturing of Pressurized Conformable Compressed Natural Gas Storage Tanks

The project goal was to combine welding and superplastic forming (SPF) processes to develop a conformal pressure vessel with >90% conformability, >12 MJ/kg energy density, and <$\$$1500 tank cost. PNNL investigated an extensive set of geometries and materials showing that reinforcement geometries based on the Schwarz-P minimal surface spanning a volume produced the highest flexibility in tank geometry, conformability, and nearly met the energy density target (11.9 vs. 12 MJ/kg) based on Titanium 6Al-4V material strength. The idealized geometry proved very challenging to achieve using welding and SPF of either tubing or sheet metal. PNNL demonstrated that full Schwarz-P shapes could be formed from titanium tubing using hot metal gas forming. However, that approach was discontinued because of the many components which required joining to assemble a tank structure. Superplastic blow-forming of a welded, multi-sheet pack was further developed using PNNL’s E-beam welding and SPF capabilities. Several sheet-pack designs were welded and formed, culminating in a small demonstration tank designed to withstand the design burst pressure. X-rays confirmed that the modeled internal structure was achieved during forming. During pressure tested the tank leaked (short of the required burst pressure) at thin spots in irregular E-beam welds connecting the outer pressure boundary to the inner reinforcing layers. However, the tank did not burst and the flat sides of the conformal tank were maintained, confirming the integrity of the internal reinforcements. Further refinement of the weld energy and speed plus the control of an automated welding machine would improve weld quality and increase the pressure to leak or burst.

03 NATURAL GAS↗
DOE OSTI2015

The use of the taguchi method and a neural-genetic approach to optimize the quality of a pulsed Nd:YAG laser welding process

In the production process of lithium-ion secondary batteries, the lap-joint quality of the safety vent and the cathode lead influences the product quality and production efficiency. A pulsed Nd:YAG laser welding machine was employed herein. The welding parameters that influence the pulsed Nd:YAG laser welding quality was evaluated by measuring the tensile-shear strength. In this study, the Taguchi method was used to perform the initial optimization of the process parameters. A neural network (NN) with the Levenberg–Marquardt back-propagation algorithm was adopted to develop the nonlinear relationship between factors and the response. Then, a genetic algorithm based on a well-trained NN model was applied to determine the optimal factor settings. Experimental results illustrated the proposed approach.

42 ENGINEERING↗
DOE OSTI2012

APPLICATION FO FLOW FORMING FOR USE IN RADIOACTIVE MATERIAL PACKAGING DESIGNS

This paper reports on the development and testing performed to demonstrate the use of flow forming as an alternate method of manufacturing containment vessels for use in radioactive material shipping packaging designs. Additionally, ASME Boiler and Pressure Vessel Code, Section III, Subsection NB compliance along with the benefits compared to typical welding of containment vessels will be discussed. SRNL has completed fabrication development and the testing on flow formed containment vessels to demonstrate the use of flow forming as an alternate method of manufacturing a welded 6-inch diameter containment vessel currently used in the 9975 and 9977 radioactive material shipping packaging. Material testing and nondestructive evaluation of the flow formed parts demonstrate compliance to the minimum material requirements specified in applicable parts of ASME Boiler and Pressure Vessel Code, Section II. Destructive burst testing shows comparable results to that of a welded design. The benefits of flow forming as compared to typical welding of containment vessels are significant: dimensional control is improved due to no weld distortion; less final machining; weld fit-up issues associated with pipes and pipe caps are eliminated; post-weld non-destructive testing (i.e., radiography and die penetrant tests) is not necessary; and less fabrication steps are required. Results presented in this paper indicate some of the benefits in adapting flow forming to design of future radioactive material shipping packages containment vessels.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2015

FSW of Aluminum Tailor Welded Blanks across Machine Platforms

Development and characterization of friction stir welded aluminum tailor welded blanks was successfully carried out on three separate machine platforms. Each was a commercially available, gantry style, multi-axis machine designed specifically for friction stir welding. Weld parameters were developed to support high volume production of dissimilar thickness aluminum tailor welded blanks at speeds of 3 m/min and greater. Parameters originally developed on an ultra-high stiffness servo driven machine where first transferred to a high stiffness servo-hydraulic friction stir welding machine, and subsequently transferred to a purpose built machine designed to accommodate thin sheet aluminum welding. The inherent beam stiffness, bearing compliance, and control system for each machine were distinctly unique, which posed specific challenges in transferring welding parameters across machine platforms. This work documents the challenges imposed by successfully transferring weld parameters from machine to machine, produced from different manufacturers and with unique control systems and interfaces.

aluminum↗
DOE OSTI2022

A New Perspective of Post-Weld Baking Effect on Al-Steel Resistance Spot Weld Properties through Machine Learning and Finite Element Modeling

The root cause of post-weld baking on the mechanical performance of Al-steel dissimilar resistance spot welds (RSWs) has been determined by machine learning (ML) and finite element modeling (FEM) in this study. A deep neural network (DNN) model was constructed to associate the spot weld performance with the joint attributes, stacking materials, and other conditions, using a comprehensive experimental dataset. The DNN model positively identified that the post-weld baking reduces the joint performance, and the extent of degradation depends on the thickness of stacking materials. A three-dimensional finite element (FE) model was then used to investigate the root cause and the mechanism of the baking effect. It revealed that the formation of high thermal stresses during baking, from the mismatch of thermal expansion between steel and Al alloy, causes damage and cracking of the brittle intermetallic compound (IMC) formed at the interface of the weld nugget during welding. This in turn reduces the joint performance by promoting undesirable interfacial fracture when the welds were subjected to externally applied loads. The FEM model further revealed that increase in structural stiffness, because of increase in steel sheet thickness, reduces the thermal stresses at the interface caused by the thermal expansion mismatch and consequently lessens the detrimental effect of post-weld baking on the joint performance.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Critical processes and parameters in the development of accident tolerant fuels drop-in capsule irradiation tests

The goal of the Accident Tolerant Fuel (ATF) program is to develop the next generation of Light Water Reactor (LWR) fuels with improved performance, reliability, and safety characteristics during normal operations and accident conditions and with reduced waste generation. An irradiation test series has been defined to assess the performance of proposed ATF concepts under normal LWR operating conditions. The Phase I ATF irradiation test series is planned to be performed as a series of drop-in capsule tests to be irradiated in the Advanced Test Reactor (ATR) operated by the Idaho National Laboratory (INL). Design, analysis, and fabrication processes for ATR drop-in capsule experiment preparation are presented in this paper to demonstrate the importance of special design considerations, parameter sensitivity analysis, and precise fabrication and inspection techniques for figure innovative materials used in ATF experiment assemblies. A Taylor Series Method sensitivity analysis approach was used to identify the most critical variables in cladding and rodlet stress, temperature, and pressure calculations for design analyses. The results showed that internal rodlet pressure calculations are most sensitive to the fission gas release rate uncertainty while temperature calculations are most sensitive to cladding I.D. and O.D. dimensional uncertainty. The analysis showed that stress calculations are most sensitive to rodlet internal pressure uncertainties, however the results also indicated that the inside radius, outside radius, and internal pressure were all magnified as they propagate through the stress equation. This study demonstrates the importance for ATF concept development teams to provide the fabricators as much information as possible about the material properties and behavior observed in prototype testing, mock-up fabrication and assembly, and chemical and mechanical testing of the materials that may have been performed in the concept development phase. Special handling, machining, welding, and inspection of materials, if known, should also be communicated to the experiment fabrication and inspection team.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Machine Learning for Automated Weld Quality Monitoring and Control (CRADA 461)

The emergence of Artificial Intelligence (AI) and Machine Learning (ML) methods may provide an alternative pathway to elicit the desired process-structure-property relationship. To achieve such relationship at an accelerated pace the use of data driven machine learning techniques are employed. This CRADA investigates the use and effectiveness of applying AI/ML techniques to a large dataset with the intention of creating a data-driven model that will predict processing parameters required to join lightweight materials at optimal performance.

97 MATHEMATICS AND COMPUTING↗
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 OSTI2013

Nuclear Fabrication Consortium

This report summarizes the activities undertaken by EWI while under contract from the Department of Energy (DOE) Office of Nuclear Energy (NE) for the management and operation of the Nuclear Fabrication Consortium (NFC). The NFC was established by EWI to independently develop, evaluate, and deploy fabrication approaches and data that support the re-establishment of the U.S. nuclear industry: ensuring that the supply chain will be competitive on a global stage, enabling more cost-effective and reliable nuclear power in a carbon constrained environment. The NFC provided a forum for member original equipment manufactures (OEM), fabricators, manufacturers, and materials suppliers to effectively engage with each other and rebuild the capacity of this supply chain by : Identifying and removing impediments to the implementation of new construction and fabrication techniques and approaches for nuclear equipment, including system components and nuclear plants. Providing and facilitating detailed scientific-based studies on new approaches and technologies that will have positive impacts on the cost of building of nuclear plants. Analyzing and disseminating information about future nuclear fabrication technologies and how they could impact the North American and the International Nuclear Marketplace. Facilitating dialog and initiate alignment among fabricators, owners, trade associations, and government agencies. Supporting industry in helping to create a larger qualified nuclear supplier network. Acting as an unbiased technology resource to evaluate, develop, and demonstrate new manufacturing technologies. Creating welder and inspector training programs to help enable the necessary workforce for the upcoming construction work. Serving as a focal point for technology, policy, and politically interested parties to share ideas and concepts associated with fabrication across the nuclear industry. The report the objectives and summaries of the Nuclear Fabrication Consortium projects. Full technical reports for each of the projects have been submitted as well.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗