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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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30 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

High Strength Steel-Aluminum Components by Vaporizing Foil Actuator Welding

This project aimed to address the challenge of effectively welding dissimilar materials—high-strength steel and high-strength aluminum for creating lightweight, multi-material automotive components. For automotive companies, reducing weight of a vehicle is critical task regulated by the government to solve the issue of greenhouse gas emissions. Production of lightweight cars and trucks can be achieved by substitution of current all-steel structures with multi-material lightweight structures that include high strength-to-weight-ration materials such as high-strength steels, aluminum alloys, magnesium alloys, titanium alloys, coupled with lightweight designs. This requires dissimilar metal welding, which is challenging for state of the art joining processes such as resistance spot welding. The cycle of melting-cooling-freezing during traditional welding that can easily ruin the designed outstanding properties of the advanced base metals, such as aluminum alloys, making the welded area much weaker than the base metals. To weld two different metals with great difference in melting points, such as aluminum and steel, it’s even more difficult or impossible because of the formation of brittle intermetallic compounds at the welded interface. In this project, a novel welding method, developed at OSU, was selected for validation and development. This novel technology enables welding by impact without melting and proves to be robust to join various dissimilar lightweight metals. Termed as vaporizing foil actuator welding or VFAW, the technology uses a thin aluminum foil that is rapidly vaporized by a high current pulse to produce an explosive-like pressure pulse to drive one metallic piece into another at the high speed required for impact welding. This project entailed development of the early-stage welding technology in terms of (a) the consumables, the welding apparatus and the power sources, (b) coupon scale screening of many material combinations including corrosion studies, (c) computational modeling and design of the welded interface as well as of the multi-material prototype component, and (d) mechanical testing for strength and durability at coupon scale and to a certain extent the prototype scale. The all-steel engine cradle of 2016 Chevrolet Cruze was chosen as the baseline prototype component. The target set for the project was to demonstrate a 20% weight reduction at a cost premium of less than $\$ $5/lb saved without compromising on baseline mechanical properties. At project completion, a 12% lighter prototype component was demonstrated with an estimated cost premium of $\$ $9.8/lb saved. Besides prototype level demonstration of the technology, this project also enabled elevation of the technology’s readiness level to where a hydraulically actuated welding head was developed and made ready for deployment at a research and development facility for Tier 1 automotive supplier.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Extending Ultrasonic Welding Techniques to New Material Pairs (FY 2023 Annual Progress Report)

Modern multimaterial vehicles require joining of various lightweight materials, such as aluminum (Al) and magnesium (Mg) alloys and carbon fiber reinforced polymers (CFRP), with advanced high-strength steels together to form a high-performance and lightweight body structure. A variety of joining methodologies (e.g., resistance spot welding, adhesive bonding, linear fusion welding, hemming, clinching, bolting, riveting) have been attempted by the automotive industry to join different materials. Often, these joining techniques are limited to only certain material combinations. For capital and operational cost, automobile original equipment manufacturers need to limit the number of joining technologies implemented on an assembly line.

36 MATERIALS SCIENCE↗
DOE OSTI2024

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↗
DOE OSTI2022

Physics-informed machine learning assisted uncertainty quantification for the corrosion of dissimilar material joints

Jointing techniques like the Self-Piercing Riveting (SPR), Resistance Spot Welding (RSW) and Rivet-Weld (RW) joints are used for mass production of dissimilar material joints due to their high performance, short cycle time, and adaptability. However, the service life and safety usage of these joints can be largely impacted by the galvanic corrosion due to the difference in equilibrium potentials between the metals with the presence of electrolyte. Here, in this paper, we focus on Al-Fe galvanic corrosion and develop physics-informed machine learning based surrogate model for statistical corrosion analysis, which enables the reliability analysis of dissimilar material joints under corrosion environment. In this study, a physics-based finite element (FE) corrosion model has been developed to simulate the galvanic corrosion between a Fe cathode and an Al anode. Geometric and environmental factors including crevice gap, roughness of anode, conductivity, and the temperature of the electrolyte are investigated. Further, a thorough Uncertainty Quantification (UQ) analysis is conducted for the overall corrosion behavior of the Fe-Al joints. It is found that the electrolyte conductivity has the largest effects on the material loss and needs to be managed closely for better corrosion control. This will help in designing and manufacturing joints with improved corrosion performance.

42 ENGINEERING↗
DOE OSTI2021

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↗
DOE OSTI2022

A Multi-Scale Computational Platform for Predictive Modeling of Corrosion in Al-Steel Joints (Final Report)

The research team proposed to develop innovative multi-scale models to predict corrosion and the resulting mechanical performances in aluminum-steel joints. The methods of joining considered are resistance spot welding, self-piercing riveting, and rivet-welding, all suitable for mass production applications. The multi-scale models integrate high throughput first-principle calculations based on density functional theory (DFT), high throughput calculation of phase diagrams (CALPHAD) modeling, and finite element method (FEM) simulations. These models are to be validated through laboratory experiments. Furthermore, the models are available as open source so as to enable scientists and engineers in the community to adapt and contribute to the development and application. The approaches rely on the research team’s extensive experience on the prediction of properties of individual phases at finite temperatures and variable compositions through DFT calculations, and our broad expertise on dissimilar material joining and their corrosion. The proposed computational framework enables high throughput computations for improved predictions of corrosion and the associated mechanical performance in dissimilar material joints, resulting in significant reduction in computational time needed by the current state-of-the-art methods. With the participation of researchers from three universities, an auto manufacturer, two manufacturing technology/equipment suppliers, and a software developer/vendor, the interdisciplinary research team applies the technical development on both phase-based modeling and laboratory experiments into the automobile body joining processes for validation and technology demonstration. The global cost of corrosion was estimated at about 3.4% of the global GDP in 2013. By using available corrosion control practices, it is estimated a saving between 15-35% of the cost of corrosion. In the U.S., more than $276 billion is spent repairing corrosion damage. Prediction of the corrosion and its impact on performance of the dissimilar material joints is critical for reducing the massive number of the current corrosion-based recalls for automobiles. Thus, the project goal is to develop models to enable predictive maintenance and end-of-life planning of multi-metal joints with risk of corrosion under different conditions such as exposure to high temperatures in summer and salt solutions in winter, quantified through its pH. An academia-industry consortium led by the University of Michigan and including Pennsylvania State University, University of Illinois Urbana-Champaign, University of Georgia, General Motors Company, Livermore Software Technology Corporation, and Optimal Process Technologies, LLC. created multi-scale models for prediction of corrosion in aluminum-steel joint structures such of them used in vehicle subassemblies – chassis and transmission systems. Starting from the first principle calculations, the team developed mathematical and data-driven models to predict the metallic components, which are formed during joining of two metals, for example aluminum and steel - a lightweight multilateral system which is currently used in more than 60% car bodies. These models were used for simulating chemical reactions that are happening when the joining metallic components are exposed to high temperatures and different pH values. The team was able to predict how the corrosion installs on the metallic components and how they lead to a sudden failure of components in cars. Newly developed machine learning algorithms combining Science, Technology, Engineering and Math disciplines, advanced finite element simulation and experimental validations have been integrated in a platform for prediction of the corrosion evolution and prediction the failure of joints under mechanical loadings and fatigue. Moreover, based on machine learning and inverse analysis, the team proposed solutions for designing new metallic alloys less susceptible to corrosion when joining multi-material assembles. An average of 4% error compared with experiments was achieved for the most common joints that are used in vehicle subassemblies.

36 MATERIALS SCIENCE↗
DOE OSTI2024

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↗
DOE OSTI2021

USAMP Low-Cost Mg Sheet Component Development and Demonstration Project

The overarching objective of this USAMP project was to develop and demonstrate door panels made from magnesium (Mg) sheet with a cost penalty over conventional steel stampings of no more than $\$5.50$/kg saved. The technical approach integrated experiments with advanced computational tools based on Integrated Computational Materials Engineering (ICME) methods to develop new alloy chemistries and their thermomechanical processing that promise improved formability and lower forming temperatures. A penultimate task before finally forming the stampings was to incorporate actual microstructure into models that would enable formability simulations. This approach would, for the first time, account for individual magnesium grains moving in an anisotropic fashion unlike that for aluminum or steel that have isotropic properties upon which the current simulation tools are based. In separate activities, new coatings and lubricants to facilitate forming and improved corrosion protection and joining strategies, were developed to ensure that the door could be produced with stated product requirements. A technical cost model, which included parts production, assembly, and paint for a door specifically designed for Mg sheet, showed the cost penalty to be between $\$4.26$ to $\$6.60$/kg saved, which enveloped the project’s cost targets. The cost of the coated Mg sheet was identified as the key driver for the cost penalty. The mass of the Mg-intensive door was 7.9 kg, which was 54% less than the baseline steel door.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Low-Cost Magnesium Alloy Sheet Component Development and Demonstration Project

The overarching objective of this USAMP project was to develop and demonstrate door panels made from magnesium (Mg) sheet with a cost penalty over conventional steel stampings of no more than $5.50/kg saved. The technical approach integrated experiments with advanced computational tools based on Integrated Computational Materials Engineering (ICME) methods to develop new alloy chemistries and their thermomechanical processing that promise improved formability and lower forming temperatures. A penultimate task before finally forming the stampings was to incorporate actual microstructure into models that would enable formability simulations. This approach would, for the first time, account for individual magnesium grains moving in an anisotropic fashion unlike that for aluminum or steel that have isotropic properties upon which the current simulation tools are based. In separate activities, new coatings and lubricants to facilitate forming and improved corrosion protection and joining strategies, were developed to ensure that the door could be produced with stated product requirements. A technical cost model, which included parts production, assembly, and paint for a door specifically designed for Mg sheet, showed the cost penalty to be between 4.26 USD to 6.60 USD/kg saved, which enveloped the project’s cost targets. The cost of the coated Mg sheet was identified as the key driver for the cost penalty. The mass of the Mg-intensive door was 7.9 kg, which was 54% less than the baseline steel door.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Determination of the Solid-State Resistance-Weldability of Additively Manufactured 304L Stainless Steel

• A scoping study was undertaken at Savannah River National Laboratory (SRNL) to determine the solidstate resistance-weldability of additively manufactured 304L stainless steel. • Additive manufacturing of pressure-containing boundaries is of interest to numerous industries. • The approach investigated in this study was a low energy, solid state spot weld (pinch weld). • This general approach has been routinely used for conventionally prepared tubing and is now being expanded to additively manufactured components. • This work aimed to understand the impact of AM on the bond quality and weld geometry of pinch welds.

Rogers, Jeremy K. [Savannah River National Laborat↗
DOE OSTI2023

In situ synchrotron X-ray diffraction analysis of ultrasonically assisted microstructure refinement during laser melting process

This work investigates the effect of ultrasonic treatment (UST) on the microstructure of AA4043 alloy during laser melting process. Here, the study utilizes in situ synchrotron X-ray diffraction (SXRD) and postmortem electron backscatter diffraction (EBSD) techniques to examine the ultrasonic grain refinement mechanism. The SXRD diffraction patterns exhibit higher continuity of major diffraction rings in the UST-test pattern, indicating a more refined grain structure. The presence of new diffraction spots in the post-test diffraction pattern with UST suggests the breakdown of epitaxial growth of columnar dendrites during solidification. Thermal profiles determined by analyzing the lattice parameter evolution show a faster cooling rate with UST. The inverse pole figures and aspect ratio distribution from EBSD reveal that the UST samples exhibit a refined, more equiaxed grain structure along the weld centerline. UST sample also develops more fine-sized low angle grain boundaries (LAGBs). These features offer better hot-cracking resistance in aluminum alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Microstructural Evolution During a Solid State Tube Pinch Weld of Type 304l Stainless Steel

Microstructure development is examined for a specialized spot weld that is used as a solid-state closure process for austenitic stainless steel tubing, referred to as pinch welding. In order to elucidate the microstructural evolution of the weld, a series of test welds were made at nominal conditions using tubing and production like components. These pinch welds normally terminate after twelve cycles of a 60 Hz AC weld process. In this study, production tubes were welded from one to twelve cycles and the microstructure and weld variables after each individual weld cycle number were characterized using radiography and optical metallography. Two electrochemical etchants were used to highlight different microstructural features. The study revealed that: (1) this type pinch weld is largely complete after about six cycles of 60 Hz AC current, half the weld time utilized; (2) the resistance, deformation, and closure length approach “steady-state” conditions after six cycles; and (3) both oxalic and nitric acid electrolytic etchants are useful for highlighting specific microstructural attributes of type 304 L stainless steel. Finally, two distinct microstructural regions can be identified for these welds: the edge of the weld, which is driven by concentrated deformation, recrystallization, and grain growth, and the center region, which is more typical of forge welding and micro-asperity breakdown followed by diffusion and grain-growth.

36 MATERIALS SCIENCE↗