Complete the initial testing on friction stir welding machine to evaluate the hardware issues that caused inconsistent weld quality
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Chen, Jian [ORNL] (ORCID:0000000165321081)↗
SEARCH · Engineering Papers
Results for “WELDING MACHINE”
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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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↗
Enhance Resistance Spot Welding Quality Control: A Machine Learning Approach
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Zhang, Wei↗
Design, Fabrication and Testing of Surveillance Test Articles for MSR Materials Degradation Management
This report details the design, fabrication, and testing of surveillance test articles aimed at assessing material damage in reactor-relevant environments for effective degradation management. Two types of surveillance test articles with reduced sizes were developed based on design algorithms and finite element modeling: welded design and interlocking design. A furnace heating setup was adopted to apply multiple thermal cyclic loading profiles on the test articles with a temperature range of 500°C - 700°C, while the strain response was monitored using a digital image correlation technique. The testing results demonstrated the successful capturing of expected strain range for welded design while machining tolerance should be improved to engage strain coupling in the interlocking design. Mid-term (500 hours) and long-term (1500 hours) cyclic tests were conducted on welded test articles. A constant strain range of ~0.6% was observed at the specimen with testing under 500 hours, while a gradual decrease of strain at specimen was observed after 500 hours. Non-destructive evaluation through X-ray computed tomography confirmed the microcracks in the welds at specimen-driver joints after cyclic test that caused the strain change. Creep testing of the specimen after long-term cyclic test revealed a short creep life than expected. A multi-profile cyclic test was also conducted on a test article and demonstrated consistent strain response under different temperature ramp rates. The report also briefly discussed the challenges and future research efforts to advance test article development for material surveillance.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
A Mobile Hot Cell for Conditioning Disused Sealed Radioactive Sources for Storage or Transportation
An innovative Mobile Hot Cell (MHC) has been developed for conditioning Disused Sealed Radioactive Sources (DSRS) category 1 and 2 for storage or transportation. The MHC is designed to provide both Radiological and Biological containment with a maximum capacity of 1000 Ci Co60 or 5000Ci Cs137 source and can be transported via standard cargo containers. This project has been supported through the National Nuclear Safety Administration (NNSA) Offsite Source Recovery Program (OSRP). The project is intended for the international community rather than domestic although domestic use is a possibility. Many countries have significant stockpiles of these devices that are often stored in less-than-optimal circumstances. This necessitates that these devices be addressed expeditiously, and the sources secured. The MHC utilizes robotics, automation, and other non-traditional methods for disassembling, characterizing, and packaging these sources that have reached end of life or are otherwise not needed. These innovative approaches are necessary to facilitate an expedited timeline to efficiently and safely secure these sources in a non-proliferation effort. Conditioning efforts include disassembling the device such as a teletherapy head used for cancer treatment, or blood/research irradiators such that the radioactive sources may be removed safely. The sources are then characterized. Leak checks are performed, dimensions are verified, and serial numbers are confirmed. Upon completion, the sources are typically placed into a Standard Forms Capsule which is seal welded closed. It is leak tested and placed into a Long-Term Storage Shield (LTSS) which can either be secured for storage directly or loaded into an appropriate cask for transportation. Further innovations include multiple deployment scenarios that include a full deployment of MHC components, deployment of the MHC automation internal components to an existing hot cell, deployment of minimally required MHC components and incorporation of sand for shielding, and integration of the MHC for Silo Storage, or Bore Hole Storage efforts. The MHC has evolved from a very specific use case to a “Swiss Army Knife” type of a tool in that it can be readily adapted to a large variety of situations. Innovative approaches such as the use of robotics, Computer Numeric Control (CNC) machining centers, automated welding equipment, HDMI Cameras, and LED lighting are some of the developed technologies incorporated into the MHC design. Shielding is accomplished with a steel walled Base Box which is surrounded by four nesting doll shield shells which when combined limits the external dose rate to 5mr/hr when a 1000 Ci Co60 source is exposed inside.
99 - GENERAL AND MISCELLANEOUS↗
Detailed Design and Cost Estimation of a 300 MWe Oxy-Fuel sCO2 Turbine
The detailed design of a 300 MWe, utility scale oxy-fuel turbine has been completed for purposed operation in the sCO2 direct fired Allam-Fetvedt cycle, targeting near-zero emissions and a 50% LHV system efficiency. The turbine and its supporting plant aim to offer a lower levelized cost of energy than a natural gas combined cycle plant employing carbon capture. The oxy-fuel turbine conditions include an inlet temperature of 1150°C and inlet pressure of 305 bar, representing temperatures near that of a gas turbine simultaneously with pressures near an ultra-supercritical steam turbine. The combustor housing and turbine designs were completed according to the ASME BPVC; the turbine case specifically incorporates a multi-body design with inner high-pressure barrel case and low-pressure (30 bar) horizontally split outer case of low-chromium steel material. Lateral rotordynamic evaluation demonstrated acceptable vibration response for a range of imbalance conditions per API standards. The cooling flow required in the six-stage turbine flowpath for 30,000 hr. blade and stator lifetime is predicted through thermal and structural modeling of the first stage. The provided cost estimate of the turbine is formed through a combination of scaled up-costs from procured 10 MWe scale sCO2 turbomachinery hardware, and vendor provided budgetary quotes of larger components including the turbine case requiring casting, welding, and final machining processes. The performance and cost estimation of the oxy-fuel turbine predicted for the completed detailed design provides important information towards future development needs for market penetration of utility scale direct fired sCO2 power cycles.
Marshall, Michael [Southwest Research Institute, S↗
A Semi-supervised Hybrid Machine Learning Framework for the Qualification of Resistance Spot Welds
• Industries requiring high structural integrity, including automotive, aerospace, and construction, place considerable significance on weld quality classification. • The inspection normally involves human expertise through predefined quality metrics that are subjective, error-prone, and time-intensive • The challenge to classification model development is the scarcity of labeled data and imbalanced distributions in the data that are labeled. • This work develops a new hybrid methodology that achieves clustering using KMeans++ together with supervised classification to overcome these challenges. • The ensemble-based classifiers were identified as optimal, with accuracy enhancements of up to 8% using the pseudo-labeled dataset. • The work provides practical insight into feature engineering and machine learning integration in industrial quality assurance applications.
Rogers, Jeremy K. [Savannah River National Laborat↗
Manufacturing Full-Scale High Gradient Copper Accelerators: Electron Beam Welding and Allied Processes
This DOE ARDAP-funded study examines the technical and business feasibility of manufacturing high-gradient normal conducting RF (NCRF) copper accelerating structures using electron beam welding (EBW) instead of conventional high-temperature brazing. The core motivation is material performance: brazing softens copper significantly, while hard copper alloys have demonstrated ~75% higher operational gradients in SLAC tests, making cold-joining techniques highly attractive. EBW, applied to split-cell (half or quadrant) structure designs, preserves copper hardness away from the weld joint and simplifies machining — but industrial process optimization remains immature and a substantial learning curve is expected. The business case was modeled for two scenarios: a greenfield EBW linac company (> $10M upfront costs, viable above ~30 units/year) and an EBW division added to an existing accelerator firm to reduce risk and upfront costs, at the expense of being less optimal structure for the higher volume production. The study concludes that without a significant increase in demand, private investment alone cannot sustain this capability, and recommends federal support through R&D grants, procurement incentives, and CAPEX cost-sharing to incubate domestic EBW-NCRF manufacturing — with the existing-company model.
43 PARTICLE ACCELERATORS↗
High plasticity in refractory composite fabrication by ultrasonic additive manufacturing
Refractory metal composites are desirable for use in extreme environments that require materials with high specific strengths and resilience to external environments such as that found in nuclear and aerospace. However, due to the high melting temperature of refractories, liquid state joining processes such as welding remains difficult. Ultrasonic additive manufacturing (UAM) provides a potential route for processing refractory composites because it is a solid-state (i.e., no-melting) process and allows for intermittent machining operations to be performed between welds. Further, to demonstrate refractory composite fabrication, this study utilized UAM to machine a cavity to locate and sequester a Mo foil in a Zircaloy-4 (Zry-4) baseplate and additively build over the top with Zry-4 foils, thereby embedding the Mo in Zry-4 matrix. Significant deformation of the Zry-4 microstructure was observed: this deformation caused adiabatic heating and subsequent dynamic recrystallization through the transformation from α→β and then back to α as the material cooled. Flexural testing of the Zr–Mo composite revealed no delamination or failure, but the strength was not as expected, falling lower than a cold-worked Zry-4 sample. Finite element analysis supported that some bonding must have existed between the Zry-4 and Mo. There was indeed an interdiffusion zone at the Zry-4 foil–Mo foil interface, observing a metastable body-centered cubic β-Zr lathe. It was determined that sufficient strain energy was present to encourage the nucleation of the β-Zr grain along α-Zr grains. Future work is warranted to investigate UAM for refractory composite fabrication.
36 MATERIALS SCIENCE↗
MCPC Friction Stir Welding (FSW) Process Data
Processing parameters and machine log data for the MCPC LDRD Agile investment is collected material samples processed. This dataset captures the selected processing parameters, machine logs captured during material processing, and descriptions of how characterization samples were extracted from processed plates of material. The collect characterization data is captured in other datasets.
316 Stainless Steel↗
Analysis of attenuation data from the decommissioned ZIon unit 1 reactor pressure vessel beltline weld
In order to examine the attenuation of radiation damage through the thickness of an irradiated reactor pressure vessel (RPV), four segments were acquired from the Zion Unit 1 power plant RPV after the plant was decommissioned. The Zion Unit 1 RPV Beltline Weld Segment 1 was cut into seven blocks, consisting of five base metal and two beltline welds from the high fluence region of the segment. Through-wall test specimens were machined and tested. Specimens included those used for Charpy impact, Master Curve fracture toughness testing, and chemical analysis. The observed through-thickness ductile-to-brittle transition temperatures in the beltline weld deviated significantly from the expected behavior based on the attenuation of fast fluence as a function of depth into the RPV. Beginning at the inside surface, the 41-J Charpy transition temperature was either flat or slightly increasing until the ¾ -T location. The results of a simple, model-based analysis of the Zion beltline weld material that included the irradiation conditions and material chemistry were generally consistent with industry trend curves and the standard attenuation model, rather than the observed data. Although there was no archive material from the RPV available to permit measurement of the unirradiated properties, fracture toughness specimens fabricated from archive surveillance weld were used to obtain an estimate of the initial through-thickness values of the Charpy transition temperature. The Charpy shifts obtained using this approach were similarly in disagreement with the predictions of the US NRC Regulatory Guide 1.99, Rev. 2. However, testing of irradiated Charpy specimens taken from the RPV following post-irradiation annealing (10 hr. at 500 °C) provided a quite different estimate of the unirradiated properties which improved the agreement between the inferred through-thickness Charpy shifts and exponential attenuation model included in Regulatory Guide 1.99/2. In conclusion, the analysis of the Zion data and data obtained in previous post-mortem examinations of decommissioned RPVs indicates that more work is needed to understand the through-thickness properties of RPV materials in order to properly assess through-wall damage attenuation.
Charpy impact↗
Accurate and efficient predictions of keyhole dynamics in laser materials processing using machine learning-aided simulations
The keyhole phenomenon has been widely observed in laser materials processing, including laser welding, remelting, cladding, drilling, and additive manufacturing. Keyhole-induced defects, primarily pores, dramatically affect the performance of final products, impeding the broad use of these laser-based technologies. The formation of these pores is typically associated with the dynamic behavior of the keyhole. So far, the accurate characterization and prediction of keyhole features, particularly keyhole depth, as a function of time, has been a challenging task. In situ characterization of keyhole dynamic behavior using the synchrotron X-ray technique is informative but complicated and expensive. Current simulations are generally hindered by their poor accuracy and generalization abilities in predicting keyhole depths due to the lack of accurate laser absorptance data. In this study, we develop a machine learning-aided simulation method that accurately predicts keyhole dynamics, especially in keyhole depth fluctuations, over a wide range of processing parameters. In two case studies involving titanium and aluminum alloys, we achieve keyhole depth prediction with a mean absolute percentage error of 10 %, surpassing those simulated using the ray-tracing method with an error margin of 30 %, while also reducing computational time. This exceptional fidelity and efficiency empower our model to serve as a cost-effective alternative to synchrotron experiments. Our machine learning-aided simulation method is affordable and readily deployable for a large variety of materials, opening new doors to eliminate or reduce defects for a wide range of laser materials processing techniques.
Computational fluid dynamics↗
Harvesting Reactor Pressure Vessel Beltline Material from the Decommissioned Zion Nuclear Power Plant Unit 1
The decommissioning of the Zion Nuclear Power Plant (NPP) provided a unique opportunity to harvest and study service-aged reactor pressure vessel (RPV) beltline materials. This work, conducted through the U.S. Department of Energy’s Light Water Reactor Sustainability (LWRS) Program, aims to improve the understanding of radiation-induced embrittlement to support extended nuclear plant operations. Material segments containing the Linde 80 flux, wire heat 72105 (WF-70) beltline weld and the A533B Heat B7835-1 base metal, obtained from the intermediate shell region with a peak fluence of 0.7 × 10 19 n/cm 2 (E > 1.0 MeV), were extracted, cut into blocks, and machined into test specimens for mechanical and microstructural characterization. The segmentation process involved oxy-propane torch-cutting, followed by precision machining using wire saws and electrical discharge machining (EDM). A chemical composition analysis confirmed the expected variations in alloying elements, with copper levels being notably higher in the weld metal. The harvested specimens enable a detailed evaluation of through-wall embrittlement gradients, a comparison with the existing surveillance data, and the validation of predictive embrittlement models. This study provides critical data for assessing long-term reactor vessel integrity, informing aging-management strategies, and supporting regulatory decisions to extend the life of nuclear plants. This article is a revised and expanded version of a paper entitled, “Current Status of the Characterization of RPV Materials Harvested from the Decommissioned Zion Unit 1 Nuclear Power Plant”, PVP2017-65090, which was accepted and presented at the ASME 2017 Pressure Vessels and Piping Conference, Waikoloa, HI, USA, 16–20 July 2017.
harvesting beltline material↗
Direct mechanistic connection between acoustic signals and melt pool morphology during laser powder bed fusion
Various nondestructive diagnostic techniques have been proposed for in situ process monitoring of laser powder bed fusion (LPBF), including melt pool pyrometry, whole-layer optical imaging, acoustic emission, atomic emission spectroscopy, high speed melt pool imaging, and thermionic emission. Correlations between these in situ monitoring signals and defect formation have been demonstrated with acoustic signals having been shown to predict pore formation with especially high confidence in recent machine learning studies. Here, in this work, time-resolved acoustic data are collected in both the conduction and keyhole welding regimes of LPBF-processed Ti-6Al-4V alloy. A non-dimensionalized Strouhal number analysis, used in whistle aeroacoustics, is applied to demonstrate that the acoustic signals recorded in the keyhole regimes can be directly associated with the vapor depression morphology. This mechanistic understanding developed from whistle aeroacoustics shows that acoustic monitoring during the LPBF process can provide a direct probe into the vapor depression dynamics and defect occurrence, especially in the keyhole regimes relevant to printing and defect formation.
36 MATERIALS SCIENCE↗
Characterization and Quantification of Radiation-Induced Clusters/Precipitates in RPV Steels Using STEM-EDS and Machine Learning
Over the operational lifespan of a nuclear reactor, reactor pressure vessel (RPV) steels are subjected to significant neutron irradiation, resulting in complex microstructural changes and the consequent degradation of mechanical properties. Various physically motivated correlation models have been developed to predict neutron irradiation-induced embrittlement of RPVs under different irradiation conditions. However, the efficient and accurate characterizations and quantification of radiation-induced clusters in RPVs are still challenging, which will affect the precision of the predictive models for embrittlement of RPV components. In the DOE Visiting Faculty Program (VFP) research work at Oak Ridge National Lab (ORNL), I integrate machine learning to aid Scanning Transmission Electron Microscopy – Energy Dispersive X-ray Spectroscopy (STEM-EDS) analyses, which improve the characterization and quantification of radiation-induced clusters in RPV steels, thereby enabling more accurate predictions of material behavior under irradiation. The surveillance base- and welded- RPV steels were annealed at various temperatures of 340 °C, 450 °C and 500 °C for up to 168 hours, respectively. Afterwards, I have characterized radiation-induced clusters using advanced STEM-EDS techniques and subsequently applying machine learning algorithms to analyze and refine STEM-EDS datasets, enhancing the quantification of clusters compositions and distributions. In the end, an efficient workflow for integrating STEM-EDS data analysis with machine learning to address challenges including noise reduction has been developed. The completion of this VFP work will support bridge critical gaps in the accurate quantification of radiation-induced clusters in RPV steels using STEM-EDS and support the development of more precise models for predicting RPV embrittlement in the Light Water Reactor Sustainability program supported by Department of Energy and enhancing the collaboration between ORNL and Alred University. The outcome of the VFP project will leverage a few research papers submission to peer-reviewed journals in the relevant scientific field and a few oral presentations at national and international conferences.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Electro-Thermo-Mechanical (ETM) Study on Submarine Dynamic Power Cables (SDPC) for Offshore Wind Transmission
Understanding the failure mechanisms of submarine dynamic power cables (SDPC) is critical for innovative design to meet the 2035 cost reduction target of U.S. DOE Floating Offshore Wind Shot. This is important because the current design suffers a significant failure rate in the field. This project conducted a systematic electro-thermo-mechanical (ETM) experimental study on the power cores extracted from a 15kV power cable with three cores of copper conductor, ethylene propylene rubber (EPR) insulation, and continuously corrugated welded aluminum armor (CCWA). An ETM testing system was developed by integrating a high voltage (HV) amplifier, two ceramic heaters, and a rod-plate transverse compression setup into a mechanical testing machine. Increasing temperature from room temperature (RT, 22 degree C) to 90 degree C resulted in the 67% decrease in the failure mechanical load as defined by the dielectric breakdown. Under creep mechanical loading, the dielectric breakdown time was decreased by 70% for a given mechanical load when the specimen temperature increased from RT to 90oC. The failure strain in both monotonic and creep mechanical loading modes was related to the maximum mechanical load applied. Although the dielectric breakdown occurred in the ETM test, the post-test measurement revealed an impressive recovery of electric resistance. The failure analysis on cross section of tested specimens indicated a sizable gap across insulation layer near the area between copper conductor and loading rod, which apparently resulted from online dielectric failure and offline elastic recovery of components including conductor and insulation layer.
17 WIND ENERGY↗