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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

In-Situ Powder-Directed Energy Process Control for Additively Manufactured Multi-Layer, Functionally Graded Components

Coatings and cladding can experience damage in harsh environments: Erosion Oxidation Delamination External mechanical damage Radiation induced damage Functionally Graded Materials (FGMs) were primarily designed to reduce thermal stress in thermal barrier coatings (TBCs), but can produce coatings which resist a variety of material damage (corrosion, surface damage, diffusion) Powder-fed DED with multiple independent feeders allows for AM FGMs, and provides robust control over process parameters to optimize microstructure and performance. FGMs have a great opportunity for applications in energy applications as a strategy to increase plant efficiency and lifetime.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1973

Methods of Measurement for Semiconductor Materials, Process Control, and Devices

The development of methods of measurement for semiconductor materials, process control, and devices is reported. Significant accomplishments include: (1) Completion of an initial identification of the more important problems in process control for integrated circuit fabrication and assembly; (2) preparations for making silicon bulk resistivity wafer standards available to the industry; and (3) establishment of the relationship between carrier mobility and impurity density in silicon. Work is continuing on measurement of resistivity of semiconductor crystals; characterization of generation-recombination-trapping centers, including gold, in silicon; evaluation of wire bonds and die attachment; study of scanning electron microscopy for wafer inspection and test; measurement of thermal properties of semiconductor devices; determination of S-parameters and delay time in junction devices; and characterization of noise and conversion loss of microwave detector diodes.

Bullis, W. M.↗
DOE OSTI2017

Characterisation of strain-induced precipitation behaviour in microalloyed steels during thermomechanical controlled processing

The temperature at which thermomechanical controlled processing is undertaken strongly influences strain-induced precipitation (SIP) in microalloyed steels. In this study, the recrystallisation-precipitation-time-temperature curve was simulated to determine the full recrystallisation temperature, recrystallisation-stop temperature and the temperature where precipitation would occur at the shortest time. The calculated temperatures were verified by experimental testing for rolling between 1100 °C and 850 °C. On the basis of this a finishing deformation of 850 °C was chosen in order to maximise the precipitate number density formed in a fully unrecrystallised austenite. The orientation relationship between the SIP in austenite, and subsequent transformation to ferrite was identified by calculation from the coordinate transformation matrix, and by electron diffraction in the transmission electron microscope. The NbC formed as coherent/semi-coherent precipitates in the austenite, and remained coherent/semi-coherent in the ferrite, indicating a Kurdjumov-Sachs orientation relationship between the austenite and ferrite on transformation. - Highlights: •The austenite deformation temperature will influence strain-induced precipitation. •Precipitates are NbC, exhibiting an NaCl structure and lattice parameter 0.447 nm. •Fine NbC (< 10 nm) formed in austenite as coherent or semi-coherent precipitates. •Confirmed cube-on-cube orientation relationship between the NbC, the austenite and the ferrite.

36 MATERIALS SCIENCE↗
NASA NTRS1975

Contamination control in hybrid microelectronic modules. Part 3: Specifications for coating material and process controls

Resin systems for coating hybrids prior to hermetic sealing are described. The resin systems are a flexible silicone junction resin system and a flexible cycloaliphatic epoxy resin system. The coatings are intended for application to the hybrid after all the chips have been assembled and wire bonded, but prior to hermetic sealing of the package. The purpose of the coating is to control particulate contamination by immobilizing particles and by passivating the hybrid. Recommended process controls for the purpose of minimizing contamination in hybrid microcircuit packages are given. Emphasis is placed on those critical hybrid processing steps in which contamination is most likely to occur.

Himmel, R. P.↗
DOE OSTI2021

OWL Change Control Process

The Online Waste Library (OWL) provides a consolidated source of information on Department of Energy-managed radioactive waste likely to require deep geologic disposal. With the release of OWL Version 1.0 in fiscal year 2019 (FY2019), much of the FY2020 work involved developing the OWL change control process and the OWL release process. These two processes (in draft form) were put into use for OWL Version 2.0, which was released in early FY2021. With the knowledge gained, the OWL team refined and documented the two processes in two separate reports. This report focuses on the change control process and discusses the following: (1) definitions and system components; (2) roles and responsibilities; (3) origin of changes; (4) the change control process including the Change List, Task List, activity categories, implementation examples, and checking and review; and (5) the role of the re lease process in ensuring changes in the Change List are incorporated into a public release.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1990

The application of intelligent process control to space based systems

The application of Artificial Intelligence to electronic and process control can help attain the autonomy and safety requirements of manned space systems. An overview of documented applications within various industries is presented. The development process is discussed along with associated issues for implementing an intelligence process control system.

Wakefield, G. Steve↗
DOE OSTI2016

Fermentation process using specific oxygen uptake rates as a process control

Specific oxygen uptake (OUR) is used as a process control parameter in fermentation processes. OUR is determined during at least the production phase of a fermentation process, and process parameters are adjusted to maintain the OUR within desired ranges. The invention is particularly applicable when the fermentation is conducted using a microorganism having a natural PDC pathway that has been disrupted so that it no longer functions. Microorganisms of this sort often produce poorly under strictly anaerobic conditions. Microaeration controlled by monitoring OUR allows the performance of the microorganism to be optimized.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1993

Process control and recovery in the Link Monitor and Control Operator Assistant

This paper describes our approach to providing process control and recovery functions in the Link Monitor and Control Operator Assistant (LMCOA). The focus of the LMCOA is to provide semi-automated monitor and control to support station operations in the Deep Space Network. The LMCOA will be demonstrated with precalibration operations for Very Long Baseline Interferometry on a 70-meter antenna. Precalibration, the task of setting up the equipment to support a communications link with a spacecraft, is a manual, time consuming and error-prone process. One problem with the current system is that it does not provide explicit feedback about the effects of control actions. The LMCOA uses a Temporal Dependency Network (TDN) to represent an end-to-end sequence of operational procedures and a Situation Manager (SM) module to provide process control, diagnosis, and recovery functions. The TDN is a directed network representing precedence, parallelism, precondition, and postcondition constraints. The SM maintains an internal model of the expected and actual states of the subsystems in order to determine if each control action executed successfully and to provide feedback to the user. The LMCOA is implemented on a NeXT workstation using Objective C, Interface Builder and the C Language Integrated Production System.

Lee, Lorrine↗
NASA NTRS2013

Closed-Loop Process Control for Electron Beam Freeform Fabrication and Deposition Processes

A closed-loop control method for an electron beam freeform fabrication (EBF(sup 3)) process includes detecting a feature of interest during the process using a sensor(s), continuously evaluating the feature of interest to determine, in real time, a change occurring therein, and automatically modifying control parameters to control the EBF(sup 3) process. An apparatus provides closed-loop control method of the process, and includes an electron gun for generating an electron beam, a wire feeder for feeding a wire toward a substrate, wherein the wire is melted and progressively deposited in layers onto the substrate, a sensor(s), and a host machine. The sensor(s) measure the feature of interest during the process, and the host machine continuously evaluates the feature of interest to determine, in real time, a change occurring therein. The host machine automatically modifies control parameters to the EBF(sup 3) apparatus to control the EBF(sup 3) process in a closed-loop manner.

Taminger, Karen M.↗
DOE OSTI2017

The Alliance of Advanced Process Control and Accountability – A Future Safeguards-By-Design Tool

For any chemical separation process producing a valuable product, a material balance is an important process control measurement. That is particularly true for the separation of actinides from irradiated nuclear fuel, not only for their intrinsic value but also because an incomplete material balance may indicate diversion for unauthorized use. The DOE Office of Nuclear Energy is currently carrying out at the Pacific Northwest National Laboratory an experimental measurement of how well and with what precision current technologies can implement near real-time actinide material balances. This measurement effort is called the CoDCon project. It involves the separation of a product with a 70/30 uranium/plutonium mass ratio. Initial tests will use dissolved fuel simulants prepared with pure uranium and plutonium nitrates at the same input ratios as irradiated fuel. Subsequent testing with actual irradiated fuel would be done to verify the results obtained with simulants. The experiments will use advanced on-line instrumentation supported by dynamic process models. Since accountability uncertainties could mask diversions, the aim of the project is not only to measure present-day capabilities but also, through sensitivity analyses, to identify those measurements with the greatest potential for overall material-balance improvements. The latter results will help identify priorities for future fuel cycle R&D programs. Advanced separations process control and material accountability technologies thus have a common goal: to provide the best tools available for safeguards-by-design [defined by the International Atomic Energy Agency (IAEA) as the integration of the design of a new nuclear facility through planning, construction, operation and decommissioning]. Since the potential domestic use of CoDCon results may be later than their possible foreign applications, arrangements may be feasible for possible bilateral or multinational cooperation in the CoDCon project.

Lumetta, Gregg J.↗
DOE OSTI2016

SU-D-201-04: Evaluation of Elekta Agility MLC Performance Using Statistical Process Control

Purpose: to evaluate the performance and stability of the Elekta Agility MLC model using an automated quality control (QC) test in combination with statistical process control tools. Methods: Leaf positions were collected daily for 11 Elekta units over 5–19 months using the automated QC test, which analyzes 23 MV images to determine the location of MLC leaves relative to the radiation isocenter. The leaf positions are measured at 5 nominal positions, and images are acquired at collimator 0° and 180° to capture all MLC leaves in the field-of-view. Leaf positioning accuracy was assessed using individual and moving range control charts. Control limits were recomputed following MLC recalibration (occurred 1–2 times for 4 units). Specification levels of ±0.5, ±1 and ±1.5mm were tested. The mean and range of duration between out-of-control and out-of-specification events were determined. Results: Leaf position varied little over time, as confirmed by very tight individual control limits (mean ±0.19mm, range 0.09–0.44). Mean leaf position error was −0.03mm (range −0.89–0.83). Due to sporadic out-of-control events, the mean in-control duration was 3.3 days (range 1–23). Data stayed within ±1mm specification for 205 days on average (range 3–372) and within ±1.5mm for the entire date range. Measurements stayed within ±0.5mm for 1 day on average (range 0–17); however, our MLC leaves were not calibrated to this level of accuracy. Conclusion: The Elekta Agility MLC model was found to perform with high stability, as evidenced by the tight control limits. The in-specification durations support the current recommendation of monthly MLC QC tests with a ±1mm tolerance. Future work is on-going to determine if Agility performance can be optimized further using high-frequency QC test results to drive recalibration frequency. Factors that can affect leaf positioning accuracy, including beam spot motion, leaf gain calibration, drifting leaves, and image artifacts, are under investigation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Machine Learning-Based Process Control for Injection Molding of Recycled Polypropylene

The increased interest in artificial intelligence in manufacturing has driven the adoption of machine learning to optimize processes and improve efficiency. A key challenge in injection molding is the variability of recycled materials, which affects part quality and processing stability. This study presents a novel closed-loop process control approach for injection molding, leveraging machine learning to adaptively predict processing inputs and quality outcomes. The methodology was tested on five blends of recycled polypropylene (rPP), using artificial neural networks (ANNs), linear regression, and polynomial regression to model the relationships between material properties and process parameters. The dataset was split 80/20 into training and testing sets. The ANN model was implemented using TensorFlow and Keras, with six hidden layers of 32 neurons per layer, ReLU activation, and an Adam optimizer. Empirical tuning and early stopping were used to optimize performance and prevent overfitting. Predictions were evaluated based on mean absolute error (MAE), mean squared error (MSE), and percentage error. The results showed that yield stress, ultimate elongation, and part weight were accurately predicted within a 5% error for linear and polynomial regression models and within a 10% error for the ANN. However, modulus predictions were less reliable, with errors of ~11% for ANN and linear regression and ~40% for polynomial regression, reflecting the inherent variability of this property in rPP blends. Predictions of processing inputs had errors ranging from 3% to 25%, depending on the model and response variable. No single modeling approach was consistently superior across all responses, highlighting the complexity of the relationship between material properties, process parameters, and quality metrics. Overall, the work demonstrates that closed-loop process control, powered by machine learning, can effectively predict key quality parameters in injection molding of recycled materials. The proposed approach can improve process stability and material utilization, facilitating increased adoption of sustainable materials.

Krantz, Joshua↗
DOE OSTI2016

SU-F-T-241: Reduction in Planning Errors Via a Process Control Developed Using the Eclipse Scripting API

Purpose: Errors found during initial physics plan checks frequently require replanning and reprinting, resulting decreased departmental efficiency. Additionally, errors may be missed during physics checks, resulting in potential treatment errors or interruption. This work presents a process control created using the Eclipse Scripting API (ESAPI) enabling dosimetrists and physicists to detect potential errors in the Eclipse treatment planning system prior to performing any plan approvals or printing. Methods: Potential failure modes for five categories were generated based on available ESAPI (v11) patient object properties: Images, Contours, Plans, Beams, and Dose. An Eclipse script plugin (PlanCheck) was written in C# to check errors most frequently observed clinically in each of the categories. The PlanCheck algorithms were devised to check technical aspects of plans, such as deliverability (e.g. minimum EDW MUs), in addition to ensuring that policy and procedures relating to planning were being followed. The effect on clinical workflow efficiency was measured by tracking the plan document error rate and plan revision/retirement rates in the Aria database over monthly intervals. Results: The number of potential failure modes the PlanCheck script is currently capable of checking for in the following categories: Images (6), Contours (7), Plans (8), Beams (17), and Dose (4). Prior to implementation of the PlanCheck plugin, the observed error rates in errored plan documents and revised/retired plans in the Aria database was 20% and 22%, respectively. Error rates were seen to decrease gradually over time as adoption of the script improved. Conclusion: A process control created using the Eclipse scripting API enabled plan checks to occur within the planning system, resulting in reduction in error rates and improved efficiency. Future work includes: initiating full FMEA for planning workflow, extending categories to include additional checks outside of ESAPI via Aria database queries, and eventual automated plan checks.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2014

Implementation of Statistical Process Control: Evaluating the Mechanical Performance of a Candidate Silicone Elastomer Docking Seal

The National Aeronautics and Space Administration has been developing a novel docking system to meet the requirements of future exploration missions to low-Earth orbit and beyond. A dynamic gas pressure seal is located at the main interface between the active and passive mating components of the new docking system. This seal is designed to operate in the harsh space environment, but is also to perform within strict loading requirements while maintaining an acceptable level of leak rate. In this study, a candidate silicone elastomer seal was designed, and multiple subscale test articles were manufactured for evaluation purposes. The force required to fully compress each test article at room temperature was quantified and found to be below the maximum allowable load for the docking system. However, a significant amount of scatter was observed in the test results. Due to the stochastic nature of the mechanical performance of this candidate docking seal, a statistical process control technique was implemented to isolate unusual compression behavior from typical mechanical performance. The results of this statistical analysis indicated a lack of process control, suggesting a variation in the manufacturing phase of the process. Further investigation revealed that changes in the manufacturing molding process had occurred which may have influenced the mechanical performance of the seal. This knowledge improves the chance of this and future space seals to satisfy or exceed design specifications.

docking seal↗
DOE OSTI2024

Development of algorithms for augmenting and replacing conventional process control using reinforcement learning

Here, this work seeks to allow for the online operation and training of model-free reinforcement learning (RL) agents but limit the risk to system equipment and personnel. The parallel implementation of RL alongside more conventional process control (CPC) allows for the RL algorithm to learn from CPC. The past performance of both methods are assessed on a continuous basis allowing for a transition from CPC to RL and, if needed, transitioning back to CPC from RL. This allows for the RL algorithm to slowly and safely assume control of the process without significant degradation in control performance. It is shown that the RL can derive a near optimal policy even when coupled with a suboptimal CPC. It is also demonstrated that the coupled RL-CPC algorithm learns at a faster rate than traditional RL methods of exploration while the algorithm’s performance does not deteriorate below CPC, even when exposed to an unknown operating condition.

30 DIRECT ENERGY CONVERSION↗
NASA NTRS2003

Supplier Outreach and Process Control (SOPC) and Supplier Rating Initiative (SRI)

The viewgraph presentation presents an overview of NASA's Supplier Outreach and Process Control (SOPC) and Supplier Risk Initiatives. The discussion of the SOPC examines its importance, current groups who are involved, provides a mission statement, and describes outreach activities and how suppliers are selected. The discussion of the Supplier Risk Initiative examines the variety of ways that integrity, availability, and assurance factor in to supplier risk and describes a new supplier rating program.

quality control↗
DOE OSTI2016

TU-FG-201-05: Varian MPC as a Statistical Process Control Tool

Purpose: Quality assurance in radiotherapy requires the measurement of various machine parameters to ensure they remain within permitted values over time. In Truebeam release 2.0 the Machine Performance Check (MPC) was released allowing beam output and machine axis movements to be assessed in a single test. We aim to evaluate the Varian Machine Performance Check (MPC) as a tool for Statistical Process Control (SPC). Methods: Varian’s MPC tool was used on three Truebeam and one EDGE linac for a period of approximately one year. MPC was commissioned against independent systems. After this period the data were reviewed to determine whether or not the MPC was useful as a process control tool. Analyses on individual tests were analysed using Shewhart control plots, using Matlab for analysis. Principal component analysis was used to determine if a multivariate model was of any benefit in analysing the data. Results: Control charts were found to be useful to detect beam output changes, worn T-nuts and jaw calibration issues. Upper and lower control limits were defined at the 95% level. Multivariate SPC was performed using Principal Component Analysis. We found little evidence of clustering beyond that which might be naively expected such as beam uniformity and beam output. Whilst this makes multivariate analysis of little use it suggests that each test is giving independent information. Conclusion: The variety of independent parameters tested in MPC makes it a sensitive tool for routine machine QA. We have determined that using control charts in our QA programme would rapidly detect changes in machine performance. The use of control charts allows large quantities of tests to be performed on all linacs without visual inspection of all results. The use of control limits alerts users when data are inconsistent with previous measurements before they become out of specification. A. Carver has received a speaker’s honorarium from Varian.

07 ISOTOPE AND RADIATION SOURCES↗