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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
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

Development of Surface Sampling Techniques for the Canister Deposition Field Demonstration (FY22 Update)

This report describes the proposed surface sampling techniques and plan for the multi-year Canister Deposition Field Demonstration (CDFD). The CDFD is primarily a dust deposition test that will use three commercial 32PTH2 NUHOMS welded stainless steel storage canisters in Advanced Horizontal Storage Modules, with planned exposure testing for up to 10 years at an operating ISFSI site. One canister will be left at ambient condition, unheated; the other two will have heaters to achieve canister surface temperatures that match, to the degree possible, spent nuclear fuel (SNF) loaded canisters with heat loads of 10 kW and 40 kW. Surface sampling campaigns for dust analysis will take place on a yearly or bi-yearly basis. The goal of the planned dust sampling and analysis is to determine important environmental parameters that impact the potential occurrence of stress corrosion cracking on SNF dry storage canisters. Specifically, measured dust deposition rates and deposited particle sizes will improve parameterization of dust deposition models employed to predict the potential occurrence and timing of stress corrosion cracks on the stainless steel SNF canisters. The size, morphology, and composition of the deposited dust and salt particles will be quantified, as well as the soluble salt load per unit area and the rate of deposition, as a function of canister surface temperature, location, time, and orientation. Previously, a preliminary sampling plan was developed, identifying possible sampling locations on the canister surfaces and sampling intervals; possible sampling methods were also described. Further development of the sampling plan has commenced through three different tasks. First, canister surface roughness, a potentially important parameter for air flow and dust deposition, was characterized at several locations on one of the test canisters. Second, corrosion testing to evaluate the potential lifetime and aging of thermocouple wires, spot welds, and attachments was initiated. Third, hand sampling protocols were developed, and initial testing was carried out. The results of those efforts are presented in this report. The information obtained from the CDFD will be critical for ongoing efforts to develop a detailed understanding of the potential for stress corrosion cracking of SNF dry storage canisters.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

High-speed synchrotron X-ray imaging of directed energy deposition of titanium: effects of processing parameters on the formation of entrapped-gas pores

Laser based directed energy deposition (DED) is a competitive method for repairing and remanufacturing metallic parts used in numerous industries including aerospace and biomedical. However, the numerous dynamic phenomena associated with the DED process often result in defects such as entrapped-gas pores, lack of fusion, and undesirable anisotropic properties. The entrapped-gas pore, being one of the most common issues, not only influences melt-pool dynamics but also reduces the fabrication quality and mechanical properties of parts fabricated by the DED process. To reduce and further understand this issue, the real-time observation of the pore formation process needs to be studied first. To directly observe the phenomena in the melt pool, high-speed techniques are needed because rapid solidification leads to rapid pore formation and movement. In-situ high-speed X-ray has been proven to be an effective method in investigating the melt pool dynamics and pore formation mechanisms in the laser powder bed fusion process, in which the fabrication process is quite different from that in DED. Here, the high-speed X-ray method is extended to study the formation of entrapped-gas pores. The real-time formation and quantitative analysis of pores under each set of processing parameters (particle velocity, laser power, and spot welding dwelling time of stationary laser) in the DED process are investigated. We found that the DED with a higher particle velocity (3.19 m/s) produced a smaller average pore size of 27.8 µm and a lower pore area fraction of 0.52%. The DED under lower laser power (156 W) generated a smaller average pore size of 20.3 µm and a lower pore area fraction of 1.94%. The shorter dwelling time (10 ms) benefited the decrease of both average pore size and pore area fraction.

Defect↗
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 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 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

A High Separation Factor for 165Er from Ho for Targeted Radionuclide Therapy

Background: Radionuclides emitting Auger electrons (AEs) with low (0.02–50 keV) energy, short (0.0007–40 µm) range, and high (1–10 keV/µm) linear energy transfer may have an important role in the targeted radionuclide therapy of metastatic and disseminated disease. Erbium-165 is a pure AE-emitting radionuclide that is chemically matched to clinical therapeutic radionuclide 177Lu, making it a useful tool for fundamental studies on the biological effects of AEs. This work develops new biomedical cyclotron irradiation and radiochemical isolation methods to produce 165Er suitable for targeted radionuclide therapeutic studies and characterizes a new such agent targeting prostate-specific membrane antigen. Methods: Biomedical cyclotrons proton-irradiated spot-welded Ho(m) targets to produce 165Er, which was isolated via cation exchange chromatography (AG 50W-X8, 200–400 mesh, 20 mL) using alpha-hydroxyisobutyrate (70 mM, pH 4.7) followed by LN2 (20–50 µm, 1.3 mL) and bDGA (50–100 µm, 0.2 mL) extraction chromatography. The purified 165Er was radiolabeled with standard radiometal chelators and used to produce and characterize a new AE-emitting radiopharmaceutical, [165Er]PSMA-617. Results: Irradiation of 80–180 mg natHo targets with 40 µA of 11–12.5 MeV protons produced 165Er at 20–30 MBq·µA−1·h−1. The 4.9 ± 0.7 h radiochemical isolation yielded 165Er in 0.01 M HCl (400 µL) with decay-corrected (DC) yield of 64 ± 2% and a Ho/165Er separation factor of (2.8 ± 1.1) · 105. Radiolabeling experiments synthesized [165Er]PSMA-617 at DC molar activities of 37–130 GBq·µmol−1. Conclusions: A 2 h biomedical cyclotron irradiation and 5 h radiochemical separation produced GBq-scale 165Er suitable for producing radiopharmaceuticals at molar activities satisfactory for investigations of targeted radionuclide therapeutics. This will enable fundamental radiation biology experiments of pure AE-emitting therapeutic radiopharmaceuticals such as [165Er]PSMA-617, which will be used to understand the impact of AEs in PSMA-targeted radionuclide therapy of prostate cancer.

165Er↗
NASA NTRS1962

DEVELOPMENT OF A LIGHT-WEIGHT REGENERATIVELY COOLED THRUST CHAMBER WITH BRAZED RIBS AND BRAZED OUTER WIRE TRAP

Design and fabrication procedures are discussed of a brazed-ribtype thrust chamber fabricated from stainless steel or inconel for a Sooo-lb thrust 20: 1 expansion-area-ratio regeneratively cooled liquidpropellant rocket engine which is designated as the JPL 6K Engine and operates at a chamber pressure of 150 psia. Hydrazine is used as the fuel and regenerative coolant and nitrogen tetroxide as the oxidizer. The nozzle-throat diameter is 5.42 in. The thrust chamber consists of a sheet-metal gas-side liner with longitudinal ribs spotwelded and brazed to the outer surface. Wire is wrapped over the ribs circumferentially, spot-welded to the ribs, and brazed. The liner, ribs, and wire form the coolant-passage walls. There are three basic advantages to this type of thrust chamber: light weight, ease of fabrication, and comparatively low cost. The total dry weight of a thrust chamber of this type and size made of 347 stainless steel is 46.2 lb, and the wet weight is 56.3 Ib. A total of 87 static engine tests were made on 19 of 23 thrust chambers fabricated by this technique. Four of the chambers tested had an expansion-area ratio of 20:1, and 15, which were shorter but had the same throat diameter, had an expansion-area ratio of 3.5:l. Accumulated test time was 1926 sec, including 558 sec regenerative cooling with hydrazine and 1368 sec cooling with water. One 20:l thrust chamber was subjected to 16 separate engine tests, with an accumulated test time of 399 sec, and was still in very good operating condition after the tests were completed. With water flowing through the coolant passages at 10 lb/sec, the measured nominal pressure drop between coolant manifolds of a 20:l thrust chamber wrapped with flat wire was 17.2 psi.

Thrust Chamber↗
NASA NTRS1967

Micromanipulation tool is easily adapted to many uses

A special micromanipulation tool equipped with a plunger mounted in a small tube can be easily adapted to such work operations as cutting, precision clamping, and spot welding of microscopic filaments or other parts. This tool is valuable where extreme steadiness of high magnification is required.

Shlichta, P. J.↗
NASA NTRS1972

Development of dispersion-strengthened Ni-Cr-ThOz alloys for the space shuttle thermal protection system

Manufacturing processes were developed for TD-NiCr providing small sheet (45 x 90 cm), and larger sheet (60 x 150 cm) and foil. The alternate alloy, DS-NiCr, was produced by pack-chromizing Ni-ThO2 sheet. Formability criteria are being established for basic sheet forming processes, which are brake forming, corrugation forming, joggling, dimpling, and beading. Resistance spot welding (fusion and solid state), resistance seam welding, solid state diffusion welding, and brazing are included in the joining programs. Major emphasis is centered on an Al-modified Ni-Cr-ThO2 alloy development. These alloys, containing 3 to 5% Al, form the protective Al2O3 scale. This enhances oxidation resistance under reentry conditions. Both TD-NiCrAl and DS-NiCrAl alloys are included. A tentative composition of Ni-16Cr-3.5Al-2ThO2 was selected based on oxidation resistance and fabricability.

Blankenship, C. P.↗
NASA NTRS1972

Development of dispersion-strengthened Ni-Cr-ThO2 alloys for the space shuttle thermal protection system.

An improved manufacturing process has been developed for TD-NiCr that provides sheet of improved quality, better gage control, and reproducible properties. A standard manufacturing process has been developed for the alternate alloy, DS-NiCr, produced by pack-chromizing Ni-2ThO2 sheet. Extension of forming and joining technology for TD-NiCr is in process. Formability criteria are being established for basic sheet forming processes related to the manufacture of Shuttle TPS panels. They include brake forming, corrugation forming, joggling, dimpling, and beading. Joining processes applicable to TPS panels are being optimized with emphasis on establishment of joint efficiencies. Resistance spot welding (fusion and solid state), resistance seam welding, solid-state diffusion welding, and brazing are included in the joining programs. Development of improved manufacturing technology for TD-NiCr fasteners is also being accomplished. Mechanical and physical properties of TD-NiCr are being characterized to provide design-allowable data.

Blankenship, C. P.↗
NASA NTRS1972

Comparative cut-bar thermal conductivity apparatus.

Construction details, error analysis, and typical data for a rapid comparative device for thermal conductivity measurements are presented. The apparatus consists of a comparative cut-bar arrangement contained in a bell-jar. The cylinder shaped specimen is placed between two ARMCO-iron meter bars of the same diameter. The upper bar is equipped with a heater, while the lower one is terminated in a heat sink. The temperature gradient along the bars is measured with a series of copper/constantan microthermocouples, spot welded to them. The assembly is held together by loading the bars to a desired pressure, via a pneumatic cylinder. Contact resistance at the boundaries between the specimen and the meter bars is minimized by using a Hg/In/Ga alloy film at the interface and sufficient loading force. The gradients determined for the meter bars then may be extrapolated to the boundaries yielding the temperature drop along the specimen. Using two different length specimens, the magnitude of the temperature drop across the interface can be computed, or the true gradient along the specimen can be obtained from the difference in overall temperature drops and lengths.

Gaal, P. S.↗
NASA NTRS1973

Flat-band assembly for toroidal transformer cores

Toroidal transformer cores are often banded together by means of strap. Spot welds secure strap. Proper tension is obtained by use of special fixture in conjunction with winding of wire which is placed temporarily on core; winding is excited by dc current to hold core halves together magnetically during alignment.

Mclyman, W. T.↗
NASA NTRS1975

Results of aerodynamic heat transfer tests of a 0.0175-scale model of the Rockwell International Space Shuttle Orbiter 139 (model number 22-0) in the NASA/Ames 3.5-foot hypersonic wind tunnel (test OH6)

The results of a hypersonic wind tunnel test program conducted using a 0.0175 scale thin-skin thermocouple model of the Space Shuttle Orbiter to obtain aerodynamic heat transfer data on the Orbiter under simulated reentry conditions were presented. The test program was conducted at a Mach number of 7.3 and a freestream Reynolds number ranging between 1.0 and 6.0 million/foot. The model was tested for angles of attack ranging between 10 deg and 30 deg and a sideslip angle of 0 deg. The model was constructed of 15-5 PH stainless steel with the instrumented areas machined to a nominal skin thickness of 0.030 in. The model instrumentation consisted of 288 iron-constantan thermocouples spot welded to the skin inner surface, but only 75 of these were used in this test program. A high-speed, analog-to-digital data acquisition system was used to record data on magnetic tape.

Dye, W. H.↗