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Methods are proposed for measuring the resistance and impedance of the welding circuit in contact machines; the machine may be switched on in short-circuit mode. Methods are outlined for measuring and calculating the basic electrical parameters.
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.
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.
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.
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.
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
Process produces continuous internal channel systems on a repeatable basis in 2014-T6 aluminum. Standard machining forms the initial channel, which is filled with tungsten carbide powder. TIG machine fusion welding completes formation of the channel. Chem-mill techniques enlarge it to the desired size.
Tool with stepped shoulders alines tubes for machining in preparation for welding. Alinement with machine tool axis accurate to within 5 mils (0.13mm) and completed much faster than visual setup by machinist.
The as-welded friction stir weld has a cross section that may act as a stress concentrator. The geometry associated with the stress concentration may reduce the weld strength and it makes the weld challenging to inspect with ultrasound. In some cases, the geometry leads to false positive nondestructive evaluation (NDE) indications and, in many cases, it requires manual blending to facilitate the inspection. This study will measure the stress concentration effect and develop an improved phased array ultrasound testing (PAUT) technique for friction stir welding. Post-welding, the friction stir weld (FSW) tool would be fitted with an end mill that would machine the weld smooth, trimmed shaved. This would eliminate the need for manual weld preparation for ultrasonic inspections. Manual surface preparation is a hand operation that varies widely depending on the person preparing the welds. Shaving is a process that can be automated and tightly controlled.
A regeneratively cooled thrust chamber has been designed and fabricated, consisting of an inner TD nickel liner which was spin formed, welded, and machined and an outer shell of electroformed nickel. Coolant channels were produced in the outer surface of the inner liner by the electric discharge machining process before electroforming the shell. Accessory manifolds and piping were attached by welding. Manufacturing processes employed are described.
Adaptive machining is method of machining large, somewhat flexible workpieces to close tolerances. Devised for machining precise weld lands on aft skirts of rocket nozzles, but underlying concept generally applicable to precise machining of any of large variety of workpieces deformed by thermal, gravitational, and/or machining forces. For example, in principle, method used to bore precise hole on unanchored end of long cantilever beam.