TDEA Thermoplastic Resistance Welding
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Allison Clark↗
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Modeling and Simulation of Thermoplastic Composite (AS4/PEEK) Resistance Welding With DIGIMAT
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Pierre-Yves Lavertu↗
Resistance Welding Simulation of a Thermoplastic Composite Lap Shear Coupon
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Crystallization kinetics↗
State-of-technology for joining TD-NiCr sheet
At the current state-of-technology there are many joining processes that can be used to make sound welds in TD-NiCr sheet. Some of these that are described in this report are electron beam welding, gas-tungsten arc welding, diffusion welding, resistance spot welding, resistance seam welding, and brazing. The strengths of the welds made by the various processes show considerable variation, especially at elevated temperatures. Most of the fusion welding processes tend to give weak welds at elevated temperatures (with the exception of fusion-type resistance spotwelds). However, solid-state welds have been made with parent metal properties. The process used for a specific application will be dictated by the specific joint requirements. In highly stressed joints at elevated temperatures, one of the solid-state processes, such as DFW, RSW (solid-state or fusion), and RSEW, offer the most promise.
Holko, K. H.↗
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↗
Tool For Robotic Resistive Roll Welding
Roll-welding attachment for robot simple, inexpensive device incorporating modified commercial resistance-welding gun. Modified welding gun easily attaches to end effector of robot. Robot applies welding force and moves electrode wheel along prescribed path. Resistance-welding current starts and stops automatically according to force exerted against workpiece. Used to apply brazing foil to workpiece.
Gilber, Jeffrey L.↗
Solid-state and fusion resistance spot welding of TD-NiCr sheet
By using specially processed TD-NiCr sheet in both 0.4-mm (0.015-in.) and 1.6-mm (0.062-in.) thicknesses and carefully selected welding procedures, solid state resistance spot welds were produced which, after postheating at 1200 C, were indistinguishable from the parent material. Stress-rupture shear tests of single-spot lap joints in 0.4-mm (0.015-in.) thick sheet showed that these welds were as strong as the parent material. Similar results were obtained in tensile-shear tests at room temperature and 1100 C and in fatigue tests. Conventional fusion spot welds in commercial sheet were unsatisfactory because of poor stress-rupture shear properties resulting from metallurgical damage to the parent material.
Moore, T. J.↗
The application of statistically designed experiments to resistance spot welding
State-of-the-art Resistance Spot Welding (RSW) equipment has the potential to permit realtime monitoring of operations through advances in computerized process control. In order to realize adaptive feedback capabilities, it is necessary to establish correlations among process variables, welder outputs, and weldment properties. The initial step toward achieving this goal must involve assessment of the effect of specific process inputs and the interactions among these variables on spot weld characteristics. This investigation evaluated these effects through the application of a statistically designed experiment to the RSW process. A half-factorial, Taguchi L sub 16 design was used to understand and refine a RSW schedule developed for welding dissimilar aluminum-lithium alloys of different thickness. The baseline schedule had been established previously by traditional trial and error methods based on engineering judgment and one-factor-at-a-time studies. A hierarchy of inputs with respect to each other was established, and the significance of these inputs with respect to experimental noise was determined. Useful insight was gained into the effect of interactions among process variables, particularly with respect to weldment defects. The effects of equipment related changes associated with disassembly and recalibration were also identified. In spite of an apparent decrease in equipment performance, a significant improvement in the maximum strength for defect-free welds compared to the baseline schedule was achieved.
Hafley, Robert A.↗
Predicting Nugget Size of Resistance Spot Welds Using Infrared Thermal Videos With Image Segmentation and Convolutional Neural Network
Resistance spot welding (RSW) is a widely adopted joining technique in automotive industry. Recent advancement in sensing technology makes it possible to collect thermal videos of the weld nugget during RSW using an infrared (IR) camera. The effective and timely analysis of such thermal videos has the potential of enabling in situ nondestructive evaluation (NDE) of the weld nugget by predicting nugget thickness and diameter. Deep learning (DL) has demonstrated to be effective in analyzing imaging data in many applications. However, the thermal videos in RSW present unique data-level challenges that compromise the effectiveness of most pre-trained DL models. We propose a novel image segmentation method for handling the RSW thermal videos to improve the prediction performance of DL models in RSW. The proposed method transforms raw thermal videos into spatial-temporal instances in four steps: video-wise normalization, removal of uninformative images, watershed segmentation, and spatial-temporal instance construction. The extracted spatial-temporal instances serve as the input data for training a DL-based NDE model. The proposed method is able to extract high-quality data with spatial-temporal correlations in the thermal videos, while being robust to the impact of unknown surface emissivity. Overall, our case studies demonstrate that the proposed method achieves better prediction of nugget thickness and diameter than predicting without the transformation.
42 ENGINEERING↗
Understanding formation mechanisms of intermetallic compounds in dissimilar Al/steel joint processed by resistance spot welding
Here, this paper confirmed the formation mechanism of intermetallic compounds (IMCs) in Al/steel resistance spot welds with transmission electron microscopy, electron backscatter diffraction, nanoindentation and thermodynamic calculations. In particular, the formation of AlFe with BCC_B2 structure, which is not commonly seen in welds, was identified. The formation mechanism of IMCs at the high welding energy region is described as follows. Al 13 Fe 4 first nucleates from the Al side, followed by Al 5 Fe 2 growth with Fe atoms accumulating at the Al 13 Fe 4 grain boundaries. Then, Al 5 Fe 2 grains grow continuously to coarse columnar grains, and AlFe forms at the interface of Al 5 Fe 2 grains and the ferrite phase. Lastly, the needle-like Al 13 Fe 4 forms in the cooling process. At the middle welding energy region, only equiaxed Al 5 Fe 2 and small Al 13 Fe 4 grains are formed at the interface because of the lower diffusion rates of Al and Fe, hence postponing the growth of Al 5 Fe 2 and Al 13 Fe 4 . At the low welding energy region, only sporadic Al 5 Fe 2 and Al 13 Fe 4 grains are formed surrounded by the Al phase.
42 ENGINEERING↗
Metallography of Battery Resistance Spot Welds
Li-ion cells provide an energy dense solution for systems that require rechargeable electrical power. However, these cells can undergo thermal runaway, the point at which the cell becomes thermally unstable and results in hot gas, flame, electrolyte leakage, and in some cases explosion. The heat and fire associated with this type of event is generally violent and can subsequently cause damage to the surrounding system or present a dangerous risk to the personnel nearby. The space flight environment is especially sensitive to risks particularly when it involves potential for fire within the habitable volume of the International Space Station (ISS). In larger battery packs such as Robonaut 2 (R2), numerous Li-ion cells are placed in parallel-series configurations to obtain the required stack voltage and desired run-time or to meet specific power requirements. This raises a second and less obvious concern for batteries that undergo certification for space flight use: the joining quality at the resistance spot weld of battery cells to component wires/leads and battery tabs, bus bars or other electronic components and assemblies. Resistance spot welds undergo materials evaluation, visual inspection, conductivity (resistivity) testing, destructive peel testing, and metallurgical examination in accordance with applicable NASA Process Specifications. Welded components are cross-sectioned to ensure they are free of cracks or voids open to any exterior surface. Pore and voids contained within the weld zone but not open to an exterior surface, and are not determined to have sharp notch like characteristics, shall be acceptable. Depending on requirements, some battery cells are constructed of aluminum canisters while others are constructed of steel. Process specific weld schedules must be developed and certified for each possible joining combination. The aluminum canisters' positive terminals were particularly difficult to weld due to a bi-metal strip that comes ultrasonically pre-welded by the manufacturer. This was further complicated as the maximum electrode force was limited to low-electrode force to prevent deflection of the aluminum can during welding. Other Li-ion cells are comprised of smaller diameter cylindrical steel canisters which are inherently capable of handling greater force from the electrodes. Allowing higher-electrode forces aids greatly in insuring a consistent resistance network for the weld. Overall lessons learned: developing good jigs is critical to insure the parts and electrodes are planer to one another and the location of the weld sites remains accurate and repeatable; maintaining strict control over materials is critical--materials must be of a specific hardness and chemical composition to insure that a weld schedule is repeatable; accuracy of the die used to stamp the projections is critical and worth the investment; and proper seasoning of the electrodes is critical to producing consistent welds--once the electrodes have been properly seasoned, cleaning/dressing should be avoided until it is absolutely necessary.
Martinez, J. E.↗
Resistance spot welding of dispersion-strengthened nickel alloys
To develop easily-applied production method for resistance spot welding use unrecrystallized sheet material, develop welding schedule that will produce a solid-state spot weld without recrystallizing sheet, and postheat to produce grain growth across weld line during recrystallization of sheet material.
Moore, T. J.↗
Learning the Temporal Effect in Infrared Thermal Videos With Long Short-Term Memory for Quality Prediction in Resistance Spot Welding
With the advances of sensing technology, in-situ infrared thermal videos can be collected from Resistance Spot Welding (RSW) processes. Each video records the formulation process of a weld nugget. The nugget evolution creates a “temporal effect” across the frames, which can be leveraged for real-time, nondestructive evaluation (NDE) of the weld quality. Currently, quality prediction with imaging data mainly focuses on optical feature extraction with Convolutional Neural Network (CNN) but does not make the most of such temporal effect. In this study, pixels corresponding to critical locations on the weld nugget surface are extracted from a video to form multivariate time series (MTS). Multivariate Adaptive Regression Splines (MARS) is used in MTS processing to remove noisy signals related to uninformative frames. A Stacked Long Short-Term Memory (LSTM) model is developed to learn from the processed MTS and then predicts weld nugget size and thickness in real-time NDE. Results from a case study on RSW of Boron steel demonstrates the improvement in prediction accuracy and computational time with the proposed method, as compared to CNN-based weld quality prediction.
Guo, Shenghan↗
Corrosion behavior in aluminum/galvanized steel resistance spot welds and self-piercing riveting joints in salt spray environment
Here, this paper underlined the corrosion behavior of aluminum alloy/galvanized steel joints fabricated by resistance spot welding (RSW) and self-piercing riveting (SPR) exposed to different salt-spray cycles. It was found that the crevice generated by different joining methods has an important impact on corrosion behavior. Compared with the RSW joint, less corrosion happened on the coupled regions in SPR joints because of the higher local pH value resulted from the smaller crevice. The crevice, however, has less impact on the types of corrosion products. Galvanic corrosion happened in the coupled region for both RSW and SPR joints. The Pourbaix diagram explained the stable corrosion products of Al and Zn—Fe alloy, which are Al 2 O 3 , ZnO, and Fe 3 O 4 . Additional corrosion products observed from the experiment included α-Al(OH) 3 , Ca 4 Al 2 (CO 3 )(OH) 12 (H 2 O) 5 , Zn 5 (OH) 8 Cl 2 (H 2 O), Zn 5 (CO 3 ) 2 (OH) 6 , and Fe 2 (OH) 2 (CO 3 ), which can be understood by considering the metastable corrosion products, Pourbaix diagram, and possible chemical reactions.
36 MATERIALS SCIENCE↗
Adaptively remeshed multiphysical modeling of resistance forge welding with experimental validation of residual stress fields and measurement processes
Welding processes used in the production of pressure vessels impart residual stresses in the manufactured component. Computational modeling is critical to predicting these residual stress fields and understanding how they interact with notches and flaws to impact pressure vessel durability. Here, in this work, we present a finite element model for a resistance forge weld and validate it using laboratory measurements. Extensive microstructural changes, near-melt temperatures, and large localized deformations along the weld interface pose significant challenges to Lagrangian finite element modeling. The proposed modeling approach overcomes these roadblocks in order to provide a high-fidelity simulation that can predict the residual stress state in the manufactured pressure vessel; a rich microstructural constitutive model accounts for material recrystallization dynamics, a frictional-to-tied contact model is coordinated with the constitutive model to represent interfacial bonding, and adaptive remeshing is employed to alleviate severe mesh distortion. An interrupted-weld approach is applied to the simulation to facilitate comparison to displacement measures. Several techniques are employed for residual stress measurement in order to validate the finite element model: neutron diffraction, the contour method, and the slitting method. Model-measurement comparisons are supplemented with detailed simulations that reflect the configurations of the residual-stress measurement processes themselves. The model results show general agreement with experimental measurements, and we observe some similarities in the features around the weld region. Factors that contribute to model-measurement differences are identified. Finally, we conclude with some discussion of the model development and residual stress measurement strategies, including how to best leverage the efforts put forth here for other weld problems.
36 MATERIALS SCIENCE↗
Resistance pressure weld for nuclear reactor fuel rod tube end plug
A fuel rod for a nuclear reactor, including a cladding tube having a first end with an annular end face, a second end with an annular end face, and a cylindrical body portion extending therebetween, and a first tube end plug including a front portion, an annular lip with an annular end face, and a substantially straight cylindrical body portion extending therebetween, wherein the surface area of the annular end face of the first end of the cladding tube and the annular end face of the annular lip of the first tube end plug are substantially equal, and the annular end face of the first end of the cladding tube and the annular end face of the annular lip of the first tube end plug are connected by a resistance pressure weld.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
MaterialsMap: A CALPHAD-based tool to design composition pathways through feasibility map for desired dissimilar materials, demonstrated with resistance spot welding joining of Ag-Al-Cu
Assembly of dissimilar metals can be achieved by different methods, for example, casting, welding, and additive manufacturing (AM). However, undesired phases formed in liquid-phase assembling processes due to solute segregation during solidification diminish mechanical and other properties of the processed parts. In the present work, an open-source software named MaterialsMap, has been developed based on the CALculation of Phase Diagrams (CALPHAD) approach. The primary objective of MaterialsMap is to facilitate the design of an optimal composition pathway for assembling dissimilar alloys with liquid-phases based on the formation of desired and undesired phases along the pathway. In MaterialsMap, equilibrium thermodynamic calculations are used to predict equilibrium phases formed at slow cooling rate, while Scheil-Gulliver simulations are employed to predict non-equilibrium phases formed during rapid cooling. Additionally, by combining these two simulations, MaterialsMap offers a thorough guide for understanding phase formation in various manufacturing processes, assisting users in making informed decisions during material selection and production. As a demonstration of this approach, a compositional pathway was designed from pure Al to pure Cu through Ag using MaterialsMap. The design was experimentally verified using resistance spot welding (RSW).
36 MATERIALS SCIENCE↗
Electronic Force Gage for Welders
Welding force monitored in process. Electronic force gage uses strain gage on deformable member. Oscilloscope trace of welding force photographed and compared with standard trace during calibration and troubleshooting of resistance welding equipment. Adaptable to small scale resistance welding in electronics industry.
Bradley, J. W.↗