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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 OSTI2024

Dissolution of Nb(C,N) During Post-weld Heat-Treatment of Electric Resistance Welded X70 Line Pipe

The effect of peak temperature for post-weld heat treatment (PWHT) on the dissolution of Nb-rich carbonitrides, Nb(C,N), in the weldline of an electric resistance welded (ERW) X70 grade pipeline steel was investigated. Selected-area electron diffraction patterns and corresponding electron energy loss spectroscopy mappings revealed both Nb-rich carbonitrides and Ti-rich nitrides, (Ti,Nb)N, in the sample heat-treated to 1080 °C peak temperature. However, only Ti-rich nitrides were revealed in the sample heat-treated to 1220 °C peak temperature. During continuous heating to higher than the Ac3 temperature, i.e., near 1100 °C temperature, the dissolution of Nb(C,N) was observed in dilation and derivative of dilation. Notably, this experimental result agreed with the predictions by a DICTRA model. Furthermore, a phenomenological model was developed to explain the volume shrinkage during the dissolution of Nb(C,N) precipitates.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Solid-State Welding of the Nanostructured Ferritic Alloy 14YWT Using a Capacitive Discharge Resistance Welding Technique

Joining nanostructured ferritic alloys (NFAs) has proved challenging, as the nano-oxides that provide superior strength, creep resistance, and radiation tolerance at high temperatures tend to agglomerate, redistribute, and coarsen during conventional fusion welding. In this study, capacitive discharge resistance welding (CDRW)—a solid-state variant of resistance welding—was used to join end caps and thin-walled cladding tubes of the NFA 14YWT. The resulting solid-state joints were found to be hermetically sealed and were characterized across the weld region using electron microscopy (macroscopic, microscopic, and nanometer scales) and nanoindentation. Microstructural evolution near the weld line was limited to narrow (~50–200 μm) thermo-mechanically affected zones (TMAZs) and to a reduction in pre-existing component textures. Dispersoid populations (i.e., nano-oxides and larger oxide particles) appeared unchanged by all but the highest energy and power CDRW condition, with this extreme producing only minor nano-oxide coarsening (~2 nm → ~5 nm Ø). Despite a minimal microstructural change, the TMAZs were found to be ~10% softer than the surrounding base material. These findings are considered in terms of past solid-state welding (SSW) efforts—cladding applications and NFA-like materials in particular—and in terms of strengthening mechanisms in NFAs and the potential impacts of localized temperature–strain conditions during SSW.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A Mechanism for Carbon Depletion at Bondline of High-Frequency Electric-Resistance-Welded X70 Pipeline Steel

The bondline of electric-resistance-welded (ERW) linepipe steel, often etched white (i.e., ferrite) in optical microscopy, is generally believed to be carbon depleted. The mechanism for the carbon depletion, however, is not fully understood by researchers. To this end, here we used atom probe tomography (APT) to measure elemental segregation of the as-welded and post-weld heat-treated bondline regions of X70 linepipe welds. The thin vertical features at the bondline in the as-welded condition were identified as carbon-rich martensite-austenite (M-A) constituents, and the majority ferrite phase in the bondline was identified as carbon-depleted ferrite. Following the post-weld normalization, all alloying elements, except Nb and Mo, are homogenized across the bondline and heat-affected zone. The carbon depletion in the ERW bondline was accurately measured. A new mechanism for carbon depletion has been proposed using Scheil calculations of elemental partitioning during weld formation. Segregation of elements in the heat-affected zone was shown to follow the negligible partitioning local equilibrium (NPLE) kinetics for bainite transformation.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Microstructure and crystallographic texture of high frequency electric resistance welded X65 pipeline steel

Here, microstructure and crystallographic texture and their correlation with impact toughness of high-frequency electric resistance welded (HF-ERW) X65 Grade pipeline steel have been investigated. X65 base metal after HF-ERW has formed a typical hourglass-shaped weld joint with four different regions, including the thermomechanical-affected zone (TMAZ), fine-grained heat affected zone (FGHAZ), coarse-grained heat affected zone (CGHAZ), and bondline (Weld Zone). X65 base metal shows prominent (113) [110] and (112) [110] texture components due to thermomechanical-controlled processing. TMAZ depicted random texture with a minor intensity of Cube component (001)[110] due to the partial recrystallization during welding. In the FGHAZ and CGHAZ, near the bondline, the base metal texture has been converted to rotated Goss (110) [110], Goss (110) [001], and rotated cube (001) [110] due to the recrystallization of the austenite and the shear deformation in the austenite temperature range. A high density of {100} and {110} cleavage planes is observed in the CGHAZ, which is often found to be the fracture location for the impact toughness testing.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Postweld Heat Treatment on Toughness of Electric- Resistance Welded X70 Steel

In this report the effect of postweld heat treatment (PWHT) on Charpy V-notch impact toughness of a highfrequency electric-resistance welded (HF-ERW) grade X70 pipeline steel is investigated. PWHT thermal cycles are simulated using the Gleeble on the as-welded specimens. Microstructure, along with crystallographic texture and microhardness, is characterized in specimens that are impact tested at –5°C (23°F), –30°C (–22°F), and –45°C (–49°F). The impact toughness values show a wide scatter band and decrease with the increasing peak temperature of the PWHT. For higher peak temperatures, the microstructure of the heat-affected zone (HAZ) gradually changed from equiaxed ferrite to bainitic ferrite. Furthermore, the prior austenite grain size (PAGS) increases with the increasing peak temperature. The density of high-angle grain boundaries decreases, and the fraction of cleavage planes {100} parallel to the impact fracture plane increases for higher peak temperatures of the PWHT

36 MATERIALS SCIENCE↗
DOE OSTI2025

Dissolution of Nb(C,N) and (Ti,V)N in the Coarse-Grained Heat-Affected Zone of Electric Resistance Welded X65 Line Pipe

Nb-rich and Ti/V-rich precipitates were introduced into the X65 linepipe via thermomechanical controlled processing. Their dissolution kinetics were investigated in the coarse-grained heat-affected zone after electric resistance welding. Nb-rich precipitates were found to dissolve completely, whereas Ti/V-rich precipitates were found to dissolve partially, because of slower dissolution kinetics. In conclusion, a complete dissolution of Nb-rich precipitates was associated with significant austenite grain coarsening, which promoted bainite as a quench product, which might have reduced the impact toughness.

microstructure↗
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 OSTI2021

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↗
DOE OSTI2022

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↗
DOE OSTI2022

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↗
DOE OSTI2021

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↗
DOE OSTI2024

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↗
DOE OSTI2021

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↗
DOE OSTI2024

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↗
DOE OSTI2021

Hydrogen-assisted fracture resistance of pipeline welds in gaseous hydrogen

Fracture resistance of pipeline welds from a range of strength grades and welding techniques was measured in air and 21 MPa hydrogen gas, including electric resistance weld of X52, friction stir weld of X100 and gas metal arc welds (GMAW) of X52, X65 and X100. Welds exhibited a decrease in fracture resistance in hydrogen compared to complementary tests in air. A general trend was observed that fracture resistance in 21 MPa hydrogen gas decreased with increasing yield strength. To accommodate material constraints, two different fracture coupon geometries were used in this study, which were shown to yield similar fracture resistance values in air and 21 MPa hydrogen gas; values using different coupons resulted in less than 15% difference. In addition, fracture coupons were removed from controlled locations in select welds to examine the potential influence of orientation and residual stress. The two orientations examined in the X100 GMAW exhibited negligible differences in fracture resistance in air and, similarly, negligible differences in hydrogen. Residual stress exhibited a modest influence on fracture resistance; however, a consistent trend was not observed between tests in air and hydrogen, suggesting further studies are necessary to better understand the influence of residual stress. A comparison of welds and base metals tested in hydrogen gas showed similar susceptibility to hydrogen-assisted fracture. The overall dominant factor in determining the susceptibility to fracture resistance in hydrogen is the yield strength.

08 HYDROGEN↗
DOE OSTI2024

Enhanced joining strength in additive-manufactured polylactic-acid structures fused by embedded heated metallic meshes

Additively manufactured thermoplastic polymers, such as polylactic acid (PLA), hold significant promise for sustainable engineering structures, including wind turbine blades. Upscaling these structures beyond the limitations of 3D printer build volumes is a challenge; fusion joining presents a potential solution. This paper introduces a displacement-controlled resistance welding process for PLA, as an alternative to the typical force-controlled methods. Here, we investigated the bonding quality of resistance-welded and adhesive-bonded PLA beams through three-point bending and measured the surface deformations using digital image correlation. Different metal meshes (30%/0.11 mm Ni—Cu, 34%/0.07 mm Ni—Cu, and 36%/0.25 mm Co—Ni) served as heating elements. The process parameters were varied for the 34%/0.07 mm Ni—Cu mesh to identify an optimum set of parameters. Results showed that this optimized displacement-controlled welding achieved 94% of the original strength of monolithic samples. This indicates that the new welding process not only ensures high-quality bonding and fine surface finishing but also promotes sustainability, recyclability, and economic efficiency in various polymer and composite structural applications.

Additive manufacturing↗
DOE OSTI2021

Autonomous nondestructive evaluation of resistance spot welded joints

The application of non-destructive evaluation approaches has attracted strong interests in modern automotive industries. Here, we present an autonomous deep-computing framework to analyze raw videos from infrared systems and to predict weld nugget shape and size with unprecedented accuracy and speed. In a comprehensive training and testing experiment with 90 videos (seven sets of welding material stack-ups), a new method was developed to assemble sufficient datasets for neural network training. Our framework successfully predicts all the nugget shapes with F1 scores that range from 0.84 to 0.92. The total training time on Nvidia DGX station takes less than 10 min for each set of welding material stack-up. The real inference time of an individual dataset (with 30 video frames) takes about 0.005 s. The procedure and methods developed in the study can be applied to other image-based weld property prediction, as well as other manufacturing processes. Furthermore, our well-trained neural networks take limited memory resources (2.3 MB) and are suitable for embedded microprocessors for in-situ welding quality control as edge computing within an intelligent welding framework.

42 ENGINEERING↗