Influence of accelerated corrosion on Al/steel RSW joints by in situ compression tests
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Pan, Bo (ORCID:0000000281830967)↗
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Prediction of Aluminum to Steel RSW Joint Failure (CRADA 405)
General Motors, LLC has developed a resistance spot welding process to join aluminum sheet to steel for body-in-white manufacturing. Efforts to develop the process for any one joint configuration (combination of alloy and thicknesses) is intensive. To aid in process understanding and eventual reduction in process development efforts, an initiative was created to develop an finite element model of the various destructive test configurations (lap shear, cross-tension, and coach peel) to help predict the failure mode and load displacement curves under tensile testing. This report describes the finite element modeling approach, the means by which local material properties were determined, as well as model validation and refinement efforts.
36 MATERIALS SCIENCE↗
Review: magnetically assisted resistance spot welding
Currently, the use of advanced high strength steels (AHSSs) is the most cost effective means of reducing vehicle body weight and maintaining structural integrity at the same time. However, AHSSs present a big challenge to the traditional resistance spot welding (RSW) widely applied in automotive industries because the rapid heating and cooling procedures during RSW produce hardened weld microstructures, which lower the ductility and fatigue properties of welded joints and raise the probability of interfacial failure under external loads. Changing process parameters or post-weld heat treatment may reduce the weld brittleness, but those traditional quality control methods also increase energy consumption and prolong cycle time. In recent years, a magnetically assisted RSW (MA-RSW) method was proposed, in which an externally applied magnetic field would interact with the conduction current to produce a Lorentz force that would affect weld nugget formation. This paper is a review of an experimental MA-RSW platform, the mode of the external magnetic field and the mechanism that controls nugget shape, weld microstructures and joint performance. In conclusion, the advantages of the MA-RSW method in improving the weldability of AHSSs are given, a recent application of the MA-RSW process to light metals is described and the outlook for the MA-RSW process is presented.
36 MATERIALS SCIENCE↗
Focused acoustic beam imaging of grain structure and local Young's modulus with Rayleigh and surface skimming longitudinal waves
The interaction of a focused acoustic beam with materials generates Rayleigh surface waves (RSW) and surface skimming longitudinal waves (SSLW). Acoustic microscopic investigations have used the RSW amplitude and the velocity measurements, extensively for grain structure analysis. Although, the presence of SSLW has been recognized, it is rarely used in acoustic imaging. This paper presents an approach to perform microstructure imaging and local elastic modulus measurements by combining both RSW and SSLW. The acoustic imaging of grain structure was performed by measuring the amplitude of RSW and SSLW signal. The microstructure images obtained on the same region of the samples with RSW and SSLW are compared and the difference in the contrast observed is discussed based on the propagation characteristics of the individual surface waves. The velocity measurements are determined by two point defocus method. The surface wave velocities of RSW and SSLW of the same regions of the sample are combined and presented as average Young's modulus image.
36 MATERIALS SCIENCE↗
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↗
Machine Learning for Joint Quality Control
The use of lightweight material combinations has been highly demanded in manufacturing automotive structures. However, making robust dissimilar material joints of such lightweight materials is still challenging. A significant barrier to achieving high-quality and repeatable joint performance is a deficient understanding of the relationship between the welding process, joint attributes, and joint performance. In this context, welding factors refer to material, equipment, environment, and process parameters, while joint features comprise specific microstructural attributes of the weld such as nugget size, heat affected zone (HAZ) topology, intermetallic layer thickness, and sheet thickness reduction. Joint performance is quantified in terms of strength (e.g., tensile shear, coach peel, cross-tension), weld size, and hardness, among other factors. While there have been many attempts to establish this process-structure-property relationship by developing a model derived from the associated physics and first principles, the complexity of the joining processes compounded by the complex interactions with different materials in an automotive assembly line environment, has hindered the usefulness of such attempts. The complexity is further exacerbated using different stacking materials, especially comprising dissimilar material combinations. In practice, the common approach has been the laborious process of creating welds, characterizing them, and then physically testing them through experimentation. With the emergence of artificial intelligence (AI) methods, an alternative pathway to eliciting the desired process-structure-property relationship at an accelerated pace is to use a data-driven approach by employing machine-learning (ML) techniques. This approach is benefitted by the availability of large streams of data, generated through years of research and testing by original equipment manufacturers, in the form of material, process, environmental, equipment, microstructural, and bulk-scale performance information from multimodal, multiscale sensors making measurements from laboratory-scale to production-scale processes. During Phase I efforts, which ended in fiscal year (FY) 2021, the Oak Ridge National Laboratory and Pacific Northwest National Laboratory (ORNL/PNNL) team demonstrated the effectiveness of different ML/AI frameworks in modeling complex relationships between resistance spot welding (RSW) process parameters, weld attributes, and joint properties using a subset of data from General Motors (GM). In FY 2022, the project team further refined and expanded their respective ML models to analyze additional welds with new weld stack-ups and materials to enhance the ML model predictive capability. ORNL extended its unified deep neural networks (DNN) ML training and prediction framework with new data streams of process parameters, and PNNL extended its model describing RSW process parameters’ associations with weld attributes. In FY 2023, the project team completed the development of the AI/ML architecture for analyzing aluminum/steel joints manufactured by GM via RSW and transitioned into the inline welding quality monitoring task for steel/steel RSW joints provided by GM.
36 MATERIALS SCIENCE↗
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↗
Relationships between fluid mixing, biodiversity, and chemosynthetic primary productivity in Yellowstone hot springs
The factors that influence biodiversity and productivity of hydrothermal ecosystems are not well understood. Here we investigate the relationship between fluid mixing, biodiversity, and chemosynthetic primary productivity in three co-localized hot springs (RSW, RSN, and RSE) in Yellowstone National Park that have different geochemistry. All three springs are sourced by reduced hydrothermal fluid, but RSE and RSN receive input of vapour phase gas and oxidized groundwaters, with input of both being substantially higher in RSN. Metagenomic sequencing revealed that communities in RSN were more biodiverse than those of RSE and RSW in all dimensions evaluated. Microcosm activity assays indicate that rates of dissolved inorganic carbon (DIC) uptake were also higher in RSN than in RSE and RSW. Together, these results suggest that increased mixing of reduced volcanic fluid with oxidized fluids generates additional niche space capable of supporting increasingly biodiverse communities that are more productive. These results provide insight into the factors that generate and maintain chemosynthetic biodiversity in hydrothermal systems and that influence the distribution, abundance, and diversity of microbial life in communities supported by chemosynthesis. These factors may also extend to other ecosystems not supported by photosynthesis, including the vast subterranean biosphere and biospheres beneath ice sheets and glaciers.
59 BASIC BIOLOGICAL SCIENCES↗
Evaluation of Corrosion and Its Impact on the Mechanical Performance of Al–Steel Joints
Aluminum–steel joints are increasingly used in the automotive industry to meet the requirements for energy saving and emission reduction. Among various joining technologies, self-pierce riveting (SPR) and resistance spot welding (RSW) are two well-established technologies for fabricating dissimilar joints with stable and high mechanical performance. However, corrosion will occur in these joints inevitably due to different electrochemical properties, which can degrade the surface quality and the mechanical performance, such as strength. This paper presents a method of understanding the corrosion mechanisms in joining aluminum and steel. For this understanding, a hybrid method combining experimental observations, mechanical properties identification, and analytical approaches was used to assess the evolution of the impact of corrosion on the joining performance, such as traction separation curves. The study was conducted on common combinations used in the vehicles, e.g., a 1.2 mm thickness aluminum alloy (AA 6022) and 2.0 mm thickness hot deep galvanized steel (HDG HSLA 340) joined by SPR and RSW. After the fabrication of these joints, accelerated cyclic corrosion tests of up to 104 cycles were performed, which reproduced the environmental conditions to which a vehicle was exposed. By investigating the microstructural evolution within the joints, the corrosion mechanisms of SPR and RSW joints were revealed, including the initiation and propagation. Moreover, the intrinsic impact of the corrosion on the mechanical performance, including the strength, axial stiffness, and crashworthiness, was analyzed by performing a lap-shear test. It showed that as corrosion proceeds, the fracture modes and mechanical performance are affected significantly.
Chemistry↗
Characterization of Static Performance and Failure of Resistance Spot Welds of High-Strength and Press-Hardened Steels
The static performances and failure characteristics of resistance spot welds (RSW) of steel sheets were studied in this work. Both similar and dissimilar joints of press-hardened steel grade 22MnB5 and as-rolled high-strength steel grade 1000 were examined. From metallographic analyses and micro-hardness measurements, it was shown that the microstructures of base metal (BM), heat affected zone (HAZ) and fusion zone of steel joints were noticeably different. In particular, the HAZ close to BM or subcritical HAZ (SCHAZ) of 22MnB5 steel exhibited greatly decreased hardness due to occurring tempered martensite, in which carbide precipitation was clearly observed. Furthermore, tensile shear test and cross-tension test of the welded steel sheets were carried out. The determined maximum loads, energy absorbability and corresponding failure mechanisms were analyzed and compared. The RSW samples of similar steel grade 1000 provided the highest load-carrying capacity and absorbed energy in both tests. Due to the large hardness difference and soft zone in weldment of dissimilar steel specimen, failure loads and static absorbed energies were deteriorated up to 17 and 68%, respectively. The observed fracture occurrences of all investigated joints were pullout failure mode, in which BM and HAZ were the crack initiation sites of grades 1000 steel and 22MnB5 steel, respectively. The SCHAZ of welded boron steel considerably caused the failure onset in tension shear test, but seemed to have negligible effect on cross-tension test.
36 MATERIALS SCIENCE↗
Microstructure and mechanical properties of resistance-spot-welded joints for A5052 aluminum alloy and DP 600 steel
The microstructure and mechanical properties of resistance-spot-welded A5052 aluminum alloy and DP 600 dual-phase steel joint were studied. The fusion zone (FZ) and heat-affected zone (HAZ) of DP 600 exhibited lath martensite and ferrite-martensite structures, respectively. The microstructure of FZ and HAZ in the A5052 side was composed of cellular crystals and the boundary region of FZ exhibited a columnar crystal morphology. A Fe{sub 2}Al{sub 5} intermetallic compound (IMC) layer with 3.3 μm thickness was found adjacent to the DP 600 side, whereas a needle-shaped Fe{sub 4}Al{sub 13} IMC layer with length of 0.67 μm to 15.8 μm was found adjacent to the aluminum alloy side. The maximum tensile shear load of the A5052/DP 600 joint was 5.5 KN, with a corresponding molten nugget diameter of 6.3 mm. The fracture morphology of the optimized A5052/DP 600 joint was mainly an elongated dimple fracture accompanied by cleavage fracture. - Highlights: •A5052 and DP 600 with large gaps in properties were investigated by RSW. •The microstructures of RSW joints in DP 600/A5052 were examined detailedly. •The micro/macro-characteristics and strength relations of joints were analyzed.
36 MATERIALS SCIENCE↗
Nonlinear photoconductivities and quantum geometry of chiral multifold fermions
Here, chiral multifold fermions are quasiparticles that appear only in chiral crystals such as transition metal silicides in the cubic B20 structure (i.e., the CoSi family), and they may show exotic physical properties. Here we study the injection and shift photoconductivities and also the related geometrical quantities for several types of chiral multifold fermions, including spin-1/2 as well as pseudospin-1 and -3/2 fermions, dubbed as Kramers Weyl, triple-point, and Rarita-Schwinger-Weyl (RSW) fermions, respectively. We utilize the minimal symmorphic model to describe the triple-point fermions (TPF). We also consider the more realistic model Hamiltonian for the CoSi family including both linear and quadratic terms. We find that injection currents due to circularly polarized light are quantized as a result of the Chern numbers carried by the multifold fermions within the linear models. Surprisingly, we discover that in the TPF model, the linear shift conductivities, responsible for the shift current generation by linearly polarized light, are proportional to the pseudo spin-orbit coupling and independent of photon frequency. In contrast, for the RSW and Kramer Weyl fermions, the linear shift conductivity is linearly proportional to photon frequency. The numerical results agree with the power-counting analysis for quadratic Hamiltonians. The frequency independence of the linear shift conductivity could be attributed to the strong resonant symplectic Christoffel symbols of the flat bands. Moreover, the calculated symplectic Christoffel symbols show significant peaks at the nodes, suggesting that the shift currents are due to the strong geometrical response near the topological nodes.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
UIR-Net: Object Detection in Infrared Imaging of Thermomechanical Processes in Automotive Manufacturing
Thermomechanical processes (TMPs) such as resistance spot welding (RSW) and hot stamping are widely used in automotive manufacturing. Recent advancement in sensing technology has led to an increasing adoption of thermographic cameras to capture the infrared (IR) radiation of a metal part (or component of a part) during its thermomechanical processing or immediately after the process when the part is still hot. Detecting the object(s) of interest from raw IR images is an essential step in analyzing these data. Deep learning (DL) has been a recent success for object detection (OD), but the application of DL-based OD for industrial IR images in manufacturing is largely lagging behind. The major contribution of this work, which is also the distinction from previous OD studies, is the capability of building the OD model with unlabeled IR images, i.e., imaging data without accurate information indicating the object position. Here, the architecture of Unsupervised IR Image Net (UIR-Net) is designed to accommodate the unique characteristics of IR images from TMPs in manufacturing. This study presents a novel method for OD in unlabeled IR images from TMPs. The proposed method, called UIR-Net, consists of two components: label generation and DL model construction. Two case studies from automotive manufacturing, RSW and hot stamping, are reported to demonstrate the feasibility and effectiveness of the proposed method.
42 ENGINEERING↗
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↗
Online Weld Quality Inspection System with Infrared Thermography
Today’s passenger vehicle on average contains 4,000 to 6,000 resistance spot welds (RSW). All US automakers perform destructive teardown evaluation to ensure the quality of the spot welds due to their criticality in crash resistance and performance of vehicles. The destructive teardown evaluation is labor intensive and costly. Sponsored by DOE VT Office, ORNL recently developed a prototype system (hardware and software) to non-destructively evaluate the quality of RSW based on infrared (IR) thermography. Highly cost competitive, ORNL’s system has been tested extensively in the laboratory with initial successful production environment validation. Ford, Honda, GM, Chrysler and major Tier One suppliers all have shown keen interest in this new technology. ORNL’s Technology Transfer Office licensed this technology to a small business (APLAIR Manufacturing Solutions) in TN. The licensee planned to develop a new hardware system based on ORNL’s IR NDE technology for real-time online weld inspection. In this CRADA project, ORNL was tasked to assist the licensee to transfer the technology and develop the new product suitable for automotive body-in-white production environment, making the technology ready for ‘real-world’ auto-body assembly line production environment.
47 OTHER INSTRUMENTATION↗
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.
99 GENERAL AND MISCELLANEOUS↗
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.
42 ENGINEERING↗