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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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Auxiliary argon jets create more nearly complete nonoxidizing atmosphere. Pyramid-shaped cup directs stream of additional argon over weld. Gas supplements provided by automatic welding machine so oxidation more completely suppressed.
Air-powered grinder enables high-quality rework of welds. Tool removes excess material from back side of weld in preparation for reworking. Used on workpiece while workpiece still mounted in welding fixture or welding machine. Then rewelded in original position, so full process control maintained. Used for grinding material in spaces to which access limited, such as in pressure vessels and in hulls of ships. Also used to grind materials other than metals.
In a proposed improvement of tooling for friction stir welding, gimballed shoulders would supplant shoulders that, heretofore, have been fixedly aligned with pins. The proposal is especially relevant to self-reacting friction stir welding. Some definitions of terms, recapitulated from related prior NASA Tech Briefs articles, are prerequisite to a meaningful description of the proposed improvement. In friction stir welding, one uses a tool that includes (1) a rotating shoulder on top (or front) of the workpiece and (2) a pin that rotates with the shoulder and protrudes from the shoulder into the depth of the workpiece. In conventional friction stir welding, the main axial force exerted by the tool on the workpiece is reacted through a ridged backing anvil under (behind) the workpiece. When conventional friction stir welding is augmented with an auto-adjustable pin-tool (APT) capability, the depth of penetration of the pin into the workpiece is varied in real time by a position- or forcecontrol system that extends or retracts the pin as needed to obtain the desired effect. In self-reacting (also known as self-reacted) friction stir welding as practiced heretofore, there are two shoulders: one on top (or front) and one on the bottom (or back) of the workpiece. In this case, a threaded shaft protrudes from the tip of the pin to beyond the back surface of the workpiece. The back shoulder is held axially in place against tension by a nut on the threaded shaft. Both shoulders rotate with the pin and remain aligned coaxially with the pin. The main axial force exerted on the workpiece by the tool and front shoulder is reacted through the back shoulder and the threaded shaft into the friction-stir-welding machine head, so that a backing anvil is no longer needed. A key transmits torque between the bottom shoulder and the threaded shaft, so that the bottom shoulder rotates with the shaft. This concludes the prerequisite definitions of terms.
Magnesium has shown promise as a lighter-weight alternative to the aluminum alloys now used to make the main structural components of axially grooved heat pipes that contain ammonia as the working fluid. Magnesium heat-pipe structures can be fabricated by conventional processes that include extrusion, machining, welding, and bending. The thermal performances of magnesium heat pipes are the same as those of equal-sized aluminum heat pipes. However, by virtue of the lower mass density of magnesium, the magnesium heat pipes weigh 35 percent less. Conceived for use aboard spacecraft, magnesium heat pipes could also be attractive as heat-transfer devices in terrestrial applications in which minimization of weight is sought: examples include radio-communication equipment and laptop computers.
The integrally stiffened cylinder (ISC) process, pioneered by NASA Langley Research Center, offers significant savings for launch vehicle structures. The ISC process is a near net shape, advanced manufacturing method for fabrication of single-piece, thin-walled barrels with internal longitudinal stiffeners, eliminating the need for longitudinal welding and machining. A cost-benefit analysis of the ISC process estimated a 50 % reduction in manufacturing costs and a 10% weight savings over the current multi-piece, machined and welded construction approach for stiffened barrels. In 2017, commercial-scale manufacturing trials were pursued, leading to a successful manufacturing demonstration of 10-ft. diameter ISC barrels fabricated from Al alloy 2219. Some cracking was observed in the stiffeners during fabrication. Optical metallography was performed to evaluate the as-formed microstructures to identify potential causes of cracking, along with mechanical testing to evaluate formability. Results revealed that cracking in the stiffeners was attributed to three likely causes: 1) large Al-Cu stringer particles, 2) stick-slip friction between the mandrel and ISC due to non-optimized lubrication, and 3) differences in material flow rate between the wall and stiffener locations. Testing to gauge formability revealed a significant decrease in ductility in the longitudinal directional for the as-formed condition, attributed to reduced ductility due to the large Al-Cu stringer particles. These results provide guidance toward materials and processing modifications for future ISC forming trials.
Over the past decade, NASA Langley Research Center (LaRC) has led several manufacturing demonstration projects exploiting flow forming technology. The work has resulted in the commercial-scale manufacture of 10-ft. diameter single-piece, integrally stiffened cylinders. Near-net-shape flow forming offers simplified manufacturing schedules and cost savings through reduced part count. Reduction or elimination of machining, welding, and/or riveting can also lead to significant performance gains. NASA LaRC recently established a flow forming research facility to investigate new alloys and stiffener geometries for fuselage and launch vehicle cryotank applications. Candidate aluminum alloys and heat treatment combinations have been characterized through advanced mechanical testing and microscopy to maximize workability during flow forming trials. Elasto-plastic deformation simulations of the forming process have been performed using the finite element software DEFORM and correlated with forming trial results. The overall objective is to optimize structural performance through a combination of innovative materials, processes, and designs.
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Dry machining technique removes original surfaces to obtain contamination free welding surface. Device straddles edge being prepared and aligns with existing edges and adjacent surfaces of workpiece while sensing device regulates cut depth. Description of prototype is given.
Portable exhaust duct for machining and welding shops removes oil mist, dust, smoke, and fumes. Duct used with shop exhaust system, inlets of which placed at various convenient locations in shop floor. Flanged connector on underside of wheeled base links flexible tube to exhaust system under floor. Made especially for welding in room with low ceiling.
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.
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.
Single tool prepares a pipe end for precision welding by simultaneously performing internal machining, end facing, and bevel cutting to specification standards. The machining operation requires only one milling adjustment, can be performed quickly, and produces the high quality pipe-end configurations required to ensure precision-welded joints.
Computer welding skate for automatic gas tungsten- arc welding of contoured or double-contoured parts, providing control without touching work surface with transducers
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Operational and man-machine issues for welding underwater, in outer space, and at other remote sites are investigated, and recent process developments are described. Probable remote welding missions are classified, and the essential characteristics of fundamental remote welding tasks are analyzed. Various possible operational modes for remote welding fabrication are identified, and appropriate roles for humans and machines are suggested. Human operator performance in remote welding fabrication tasks is discussed, and recent advances in the development of remote welding systems are described, including packaged welding systems, stud welding systems, remotely operated welding systems, and vision-aided remote robotic welding and autonomous welding systems.
Electrical maintenance program for welding equipment and machine tools