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Internal Wire Guide For Gas/Tungsten-Arc Welding
Wire kept in shielding gas, preventing oxidation. Guide inside gas cup of gas/tungsten-arc welding torch feeds filler wire to weld pool along line parallel to axis of torch. Eliminates problem of how to place and orient torch to provide clearance for external wire guide.
An investigation into geometry and microstructural effects upon the ultimate tensile strengths of butt welds
A mathematical theory was evaluated empirically. This theory predicts weld ultimate tensile strength based on material properties and fusion line angles, mismatch, peaking, and weld widths. Welds were made on 1/4 and 1/2 in. aluminum 2219-T87, their geometries were measured, they were tensile tested, and these results were compared to theoretical predictions. Statistical analysis of results was performed to evaluate correlation of theory to results for many different categories of weld geometries.
Considerations on repeated repairing of weldments in Inconel 718 alloy
The effects of repeated weld repairs on the metallurgical characteristics, high cycle fatigue (HCF), and tensile properties of Inconel 718 butt weld joints were determined. A 1/4 in thick plate and a 1/2 in thick plate were used as well as tungsten inert gas welding, and Inconel 718 filler wire. Weld panels were subjected to 2, 6, and 12 repeated repairs and were made in a highly restrained condition. Post weld heat treatments were also conducted with the welded panel in the highly restrained condition. Results indicate that no significant metallurgical anomaly is evident as a result of up to twelve repeated weld repairs. No degradation in fatigue life is noted for up to twelve repeated repairs. Tensile results from specimens which contained up to twelve repeated weld repairs revealed no significant degradation in UTS and YS. However, a significant decrease in elongation is evident with specimens (solution treated and age hardened after welding) which contained twelve repeated repairs. The elongation loss is attributed to the presence of a severe notch on each side (fusion line) of the repair weld bead reinforcement.
Mineralogy of the Almahata Sitta Ureilite
Mineralogy & Petrography: Almahata Sitta, deriving from the asteroid 2008 TC3, is a coarse-grained- to porous, fine-grained, fragmental breccia with subrounded mineral fragments and olivine aggregates embedded in a cataclastic matrix of ureilitic material. Mineral fragments include polycrystalline olivine, low-calcium, pigeonite, and augite. Abundant carbonaceous aggregates containing graphite, microdiamonds and aliphatics. Kamacite, Cr-rich troilite, silica and schreibersite are abundant. The compositional range of the silicates is characteristic of the ureilites as a group, but unusually broad for an individual ureilite. The dense lithology is typical for ureilites, but the porous lithology is anomalous. In the porous lithology pore walls are largely coated by crystals of olivine. Classification: Almahata Sitta is an anomalous, polymict eucrite. Anomalous features include large compositional range of silicates, high abundance and large size of pores, crystalline pore wall linings, and fine-grained texture. Tomography reveals that the pores define thin, discontinuous "sheets" connected in three dimensions, suggesting that they outline grains that have been incompletely welded together. The crystals lining the pore walls are probably vapor phase deposits. Therefore Almahata Sitta may represent an agglomeration of coarse- to fine-grained, incompletely reduced pellets formed during impact, and subsequently welded together at high temperature.
Characterization of Residual Stress Effects on Fatigue Crack Growth of a Friction Stir Welded Aluminum Alloy
An on-line compliance-based method to account for residual stress effects in stress-intensity factor and fatigue crack growth property determinations has been evaluated. Residual stress intensity factor results determined from specimens containing friction stir weld induced residual stresses are presented, and the on-line method results were found to be in excellent agreement with residual stress-intensity factor data obtained using the cut compliance method. Variable stress-intensity factor tests were designed to demonstrate that a simple superposition model, summing the applied stress-intensity factor with the residual stress-intensity factor, can be used to determine the total crack-tip stress-intensity factor. Finite element, VCCT (virtual crack closure technique), and J-integral analysis methods have been used to characterize weld-induced residual stress using thermal expansion/contraction in the form of an equivalent delta T (change in local temperature during welding) to simulate the welding process. This equivalent delta T was established and applied to analyze different specimen configurations to predict residual stress distributions and associated residual stress-intensity factor values. The predictions were found to agree well with experimental results obtained using the crack- and cut-compliance methods.
NERVA materials development
Materials development topics include: development of analysis techniques to adjust heterogeneous data; determination of thermal conductivity for AISI 347 stainless steel and elastic moduli and Poisson's ratio for Inconel 718 and Ti 5Al-2.5Sn; embrittlement effects of 1400 psi gaseous hydrogen for alloy 718 and Ti 5Al-2.5Sn; cryogenic radiation damage of Ti 5Al-2.5Sn; and evaluation of prepreg, impregnation, and fabric materials for optimum fibrous graphite properties. Component support topics include: tensile design allowable development of Ti 5Al-2.5Sn for turbopump applications; evaluation of fatigue, fracture toughness, and stress corrosion properties of AA 7039-T63 for pressure vessel applications; development of AISI 347 sheet tensile and creep properties for nozzle applications; evaluation of orbital weld techniques for aluminum line fabrication; material selection of shield materials; development of high load friction and wear properties of hard chrome/gold plate combinations; and evaluation of weld processes for NASS duct coolant channel fabrication.
Width Criterion For Weld-Seam-Tracking Data
Image-processing algorithm in "through-torch-vision" (T3V) system developed to guide gas/tungsten arc welding robot along weld seam modified, according to proposal, reducing incidence of inaccurate tracking of weld seam. Developmental system intended to provide closed-loop control of motion of welding robot along weld seam on basis of lines in T3V image identified by use of image-processing algorithm and assumed to coincide with edges of weld seam. Use of width criterion prevents tracking of many false pairs of lines, with consequent decrease in incidence of inaccurate tracking and increase in confidence in weld-tracking capability of robotic welding system.
NDE for the ARES I Upper Stage Common Bulkhead
The current design of the ARES 1 Upper Stage uses a common bulkhead to separate the liquid hydrogen and liquid oxygen tanks. The bulkhead consists of aluminum face sheets bonded to a Phenolic honeycomb core. The face sheets, or domes, are friction stir welded to Y-rings that connect the bulkhead to the barrel sections of the liquid hydrogen and liquid oxygen tanks. Load between the Y-rings is carried by an externally attached bolting ring. The development of nondestructive evaluation methods for the ARES I Upper Stage Common Bulkhead are outlined in this presentation. Methods for inspecting the various components of the bulkhead are covered focusing in on the dome skins, core-to-dome bond lines and friction stir welds as well as structural details like the fastener holes. Thermography, shearography and ultrasonic methods are discussed for the bond lines. Eddy current methods are discussed for the fastener holes and dome skins. A combination of phased array ultrasound, liquid penetrant and radiography are to being investigated for use on the friction stir welds. Keywords: Composite materials, NDE, Cryogenic structures
Simulating Welding-Robot Trajectories For Previewing
New method devised for generating welding-tool paths; parts of off-line-programmed test trajectories of computer-controlled welding robot. Reduces time necessary to set up simulation of welding process and eliminates some errors by reducing amount of repetition. Provides visual feedback similar to graphical simulation used in off-line-programming process after development of model.
Prevention of rust in the ID weld and heat affected zone of CRES 347 NERVA lines. NERVA program
Rust prevention in stainless steel fluid transmission lines of nuclear engine for rocket vehicles
Processing Welding Images For Robot Control
Image data from two distinct windows used to locate weld features. Analyzer part of vision system described in companion article, "Image Control in Automatic Welding Vision System" (MFS-26035). Horizontal video lines define windows for viewing unwelded joint and weld pool. Data from picture elements outside windows not processed. Widely-separated local features carry no significance, but closely spaced features indicate welding feature. Image processor assigns confidence level to group of local features according to spacing and pattern.
Three-Dimensional Coaxial Weld Monitoring
Optical system for coaxial-viewing welding torch enables perception or measurement of depth. Light from welding area passes through beam splitter into two optical trains forming two images, each viewed along line making small angle with axis of torch. Two lines of sight intersect at weld pool. Parallax between two views provides sensation of depth over entire field view.
Metallographic Preparation of Space Shuttle Reaction Control System Thruster Electron Beam Welds for Electron Backscatter Diffraction
A Space Shuttle Reaction Control System (RCS) thruster failed during a firing test at the NASA White Sands Test Facility (WSTF), Las Cruces, New Mexico. The firing test was being conducted to investigate a previous electrical malfunction. A number of cracks were found associated with the fuel closure plate/injector assembly (Fig 1). The firing test failure generated a flight constraint to the launch of STS-133. A team comprised of several NASA centers and other research institutes was assembled to investigate and determine the root cause of the failure. The JSC Materials Evaluation Laboratory was asked to compare and characterize the outboard circumferential electron beam (EB) weld between the fuel closure plate (Titanium 6Al-4V) and the injector (Niobium C-103 alloy) of four different RCS thrusters, including the failed RCS thruster. Several metallographic challenges in grinding/polishing, and particularly in etching were encountered because of the differences in hardness, ductility, and chemical resistance between the two alloys and the bimetallic weld. Segments from each thruster were sectioned from the outboard weld. The segments were hot-compression mounted using a conductive, carbon-filled epoxy. A grinding/polishing procedure for titanium alloys was used [1]. This procedure worked well on the titanium; but a thin, disturbed layer was visible on the niobium surface by means of polarized light. Once polished, each sample was micrographed using bright field, differential interference contrast optical microscopy, and scanning electron microscopy (SEM) using a backscatter electron (BSE) detector. No typical weld anomalies were observed in any of the cross sections. However, areas of large atomic contrast were clearly visible in the weld nugget, particularly along fusion line interfaces between the titanium and the niobium. This prompted the need to better understand the chemistry and microstructure of the weld (Fig 2). Energy Dispersive X-Ray Spectroscopy (EDS) was used to confirm the chemical composition of the variations in contrast in these areas. Niobium alloys generally require exposure to more aggressive chemical reagents than titanium alloys for etching because of niobium s chemical resistance; therefore, the titanium portion of the sample was etched first. A five second immersion in Kroll s reagent revealed a general microstructure on the titanium portion of the sample; however, the titanium heat affected zone closest to the weld, was over-etched due to higher concentrations of refined grains and an increase in eta-phase. The Kroll s etchant also revealed some microstructure in the weld nugget itself; the niobium portion of the sample remained unetched.
Creep performance and microstructure of grade 91 steel weldments with integrated welding and thermal processing
Ferritic-Martensitic steel welds typically require post weld heat treatment (PWHT) to restore toughness and high temperature performance. This off-line thermal process reduces disparities between weld and base metal, but can cause distortion, cracking, or simply be impractical due to assembly size and joint non-uniformity. Here we show integrated welding and thermal processing applied to modified 9Cr-1Mo (Grade 91) steel, favored for advanced power generation applications, performed in real time through the addition of a secondary heat source near the primary weld head. Optimal integrated processing reduces weld fusion and heat affected zone hardness by 125 HV, approaching performance of conventional 730 °C, 60 min PWHT processing. Microstructures and mechanical performance are compared for mechanized GTAW welds, with equivalent lifetimes noted in cross-weld creep rupture tests up to 234 MPa at 550 °C, and up to 104 MPa at 650 °C. The integrated process was validated on a Grade 91 pressure vessel with multipass cold wire feed GTAW. After 550 °C, 71.4 bar thermomechanical cyclic testing, the maximum weld hardness is <350 HV.
Low-Temperature Friction-Stir Welding of 2024 Aluminum
Solid state friction-stir welding (FSW) has been demonstrated to involve dynamic recrystallization producing ultra-fine, equiaxed grain structures to facilitate superplastic deformation as the welding or joining mechanism. However, the average residual, equiaxed, grain size in the weld zone has ranged from roughly 0.5 micron to slightly more than 10 micron, and the larger weld zone grain sizes have been characterized as residual or static grain growth as a consequence of the temperatures in the weld zone (where center-line temperatures in the FSW of 6061 Al have been shown to be as high as 480C or -0.8 T(sub M) where T(sub M) is the absolute melting temperature)). In addition, the average residual weld zone grain size has been observed to increase near the top of the weld, and to decrease with distance on either side of the weld-zone centerline, an d this corresponds roughly to temperature variations within the weld zone. The residual grain size also generally decreases with decreasing FSW tool rotation speed. These observations are consistent with the general rules for recrystallization where the recrystallized grain size decreases with increasing strain (or deformation) at constant strain rate, or with increasing strain-rate, or with increasing strain rate at constant strain; especially at lower ambient temperatures, (or annealing temperatures). Since the recrystallization temperature also decreases with increasing strain rate, the FSW process is somewhat complicated because the ambient temperature, the frictional heating fraction, and the adiabatic heating fraction )proportional to the product of strain and strain-rate) will all influence both the recrystallization and growth within the FSW zone. Significantly reducing the ambient temperature of the base metal or work pieces to be welded would be expected to reduce the residual weld-zone grain size. The practical consequences of this temperature reduction would be the achievement of low temperature welding. This study compares the residual grain sizes and microstructures in 2024 Al friction-stir welded at room temperature (about 30C and low temperature (-30C).
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.
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.