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Executive routine primitives and process control for spaceborne computer functional design specification - Vol. 3
Abstract Manufacturing plastic parts with secondary feedstocks has risen to the forefront of importance in recent years. However, the variation in molecular weight and rheology of secondary feedstock can lead to inconsistent part quality. This work evaluates the effectiveness of a novel closed‐loop adaptive process control system that adjusts nozzle pressure in response to in‐mold pressure data. Five different recycled polypropylene blends, with a broad distribution of flow properties, were evaluated to determine the effectiveness of the control system at reducing processing variation. The experimental results show that the process control strategy reduced the variation within the mold, as seen by in‐mold pressure curves and calculated in‐mold viscosity values. Additionally, the parameters that control the automated process adjustments were investigated, showing the importance of optimization. The analysis of the correlation between in‐mold rheology and mechanical properties showed a slight variation in the mechanical properties and parts weight with a coefficient of variation of under 5%. Overall, the results demonstrate the ability of pressure‐controlled molding and automated viscosity adjustment to reduce the variability when molding a secondary feedstock. Highlights Pressure‐controlled injection molding of recycled polypropylene. Automated closed‐loop adaptive process control methodology. Methodology resulted in a reduction in pressure variation during molding. Changes in mechanical properties and in‐mold viscosity were investigated. Results show the potential of pressure‐controlled molding at reducing variation.
A method of applying open-operating-system standard interfaces to the NASA User Interface Language (UIL) has been devised. UIL is a computing language that can be used in monitoring and controlling automated processes: for example, the Timeliner computer program, written in UIL, is a general-purpose software system for monitoring and controlling sequences of automated tasks in a target system. In providing the major elements of connectivity between UIL and the target system, the present method offers advantages over the prior method. Most notably, unlike in the prior method, the software description of the target system can be made independent of the applicable compiler software and need not be linked to the applicable executable compiler image. Also unlike in the prior method, it is not necessary to recompile the source code and relink the source code to a new executable compiler image. Abstraction of the description of the target system to a data file can be defined easily, with intuitive syntax, and knowledge of the source-code language is not needed for the definition.
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.
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.
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.
Operators of critical processes, such as nuclear power production, must contend with highly complex systems, procedures, and regulations. Developing human-machine interfaces (HMIs) that better support operators is a high priority for ensuring the safe and reliable operation of critical processes. Human factors engineering (HFE) provides a rich and mature set of tools for evaluating the performance of HMIs, but the set of tools for developing and designing HMIs is still in its infancy. Here we propose that Microsoft Windows Presentation Foundation (WPF) is well suited for many roles in the research and development of HMIs for process control.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
The objective of this work was to determine the feasibility of using on-line multivariate statistical process control (MSPC) for safeguards applications in natural uranium conversion plants. Multivariate statistical process control is commonly used throughout industry for the detection of faults. For safeguards applications in uranium conversion plants, faults could include the diversion of intermediate products such as uranium dioxide, uranium tetrafluoride, and uranium hexafluoride. This study was limited to a 100 metric ton of uranium (MTU) per year natural uranium conversion plant (NUCP) using the wet solvent extraction method for the purification of uranium ore concentrate. A key component in the multivariate statistical methodology is the Principal Component Analysis (PCA) approach for the analysis of data, development of the base case model, and evaluation of future operations. The PCA approach was implemented through the use of singular value decomposition of the data matrix where the data matrix represents normal operation of the plant. Component mole balances were used to model each of the process units in the NUCP. However, this approach could be applied to any data set. The monitoring framework developed in this research could be used to determine whether or not a diversion of material has occurred at an NUCP as part of an International Atomic Energy Agency (IAEA) safeguards system. This approach can be used to identify the key monitoring locations, as well as locations where monitoring is unimportant. Detection limits at the key monitoring locations can also be established using this technique. Several faulty scenarios were developed to test the monitoring framework after the base case or normal operating conditions of the PCA model were established. In all of the scenarios, the monitoring framework was able to detect the fault. Overall this study was successful at meeting the stated objective.