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Program helps process engineers set up manufacturing plans for machined parts. System allows one to develop and store library of similar parts characteristics, as related to particular facility. Information is then used in interactive system to help develop manufacturing plans that meet required standards.
A cost effective design and manufacturing process which would produce solar cell modules capable of meeting qualification test criteria was developed. Emphasis was placed on the development of an aluminum paste back contact process.
Silicon dendritic web, a single crystal ribbon shaped during growth by crystallographic forces and surface tension (rather than dies), is a highly promising base material for efficient low cost solar cells. The form of the product smooth, flexible strips 100 to 200 microns thick, conserves expensive silicon and facilitates automation of crystal growth and the subsequent manufacturing of solar cells. These characteristics, coupled with the highest demonstrated ribbon solar cell efficiency-15.5%-make silicon web a leading candidate to achieve, or better, the 1986 Low Cost Solar Array (LSA) Project cost objective of 50 cents per peak watt of photovoltaic output power. The main objective of the Web Program, technology development to significantly increase web output rate, and to show the feasibility for simultaneous melt replenishment and growth, have largely been accomplished. Recently, web output rates of 23.6 sq cm/min, nearly three times the 8 sq cm/min maximum rate of a year ago, were achieved. Webs 4 cm wide or greater were grown on a number of occassions.
Two main results are presented. The first deals with a simple method that determines the minority-carrier lifetime and the effective surface recombination velocity of the quasi-neutral base of silicon solar cells. The method requires the observation of only a single transient, and is amenable to automation for in-process monitoring in manufacturing. This method, which is called short-circuit current decay, avoids distortion in the observed transient and consequent inacccuracies that arise from the presence of mobile holes and electrons stored in the p/n junction spacecharge region at the initial instant of the transient. The second main result consists in a formulation of the relevant boundary-value problems that resembles that used in linear two-port network theory. This formulation enables comparisons to be made among various contending methods for measuring material parameters of p/n junction devices, and enables the option of putting the description in the time domain of the transient studies in the form of an infinite series, although closed-form solutions are also possible.
Topics covered include: Semiautonomous Avionics-and-Sensors System for a UAV; Biomimetic/Optical Sensors for Detecting Bacterial Species; System Would Detect Foreign-Object Damage in Turbofan Engine; Detection of Water Hazards for Autonomous Robotic Vehicles; Fuel Cells Utilizing Oxygen From Air at Low Pressures; Hybrid Ion-Detector/Data-Acquisition System for a TOF-MS; Spontaneous-Desorption Ionizer for a TOF-MS; Equipment for On-Wafer Testing From 220 to 325 GHz; Computing Isentropic Flow Properties of Air/R-134a Mixtures; Java Mission Evaluation Workstation System; Using a Quadtree Algorithm To Assess Line of Sight; Software for Automated Generation of Cartesian Meshes; Optics Program Modified for Multithreaded Parallel Computing; Programs for Testing Processor-in-Memory Computing Systems; PVM Enhancement for Beowulf Multiple-Processor Nodes; Ion-Exclusion Chromatography for Analyzing Organics in Water; Selective Plasma Deposition of Fluorocarbon Films on SAMs; Water-Based Pressure-Sensitive Paints; System Finds Horizontal Location of Center of Gravity; Predicting Tail Buffet Loads of a Fighter Airplane; Water Containment Systems for Testing High-Speed Flywheels; Vapor-Compression Heat Pumps for Operation Aboard Spacecraft; Multistage Electrophoretic Separators; Recovering Residual Xenon Propellant for an Ion Propulsion System; Automated Solvent Seaming of Large Polyimide Membranes; Manufacturing Precise, Lightweight Paraboloidal Mirrors; Analysis of Membrane Lipids of Airborne Micro-Organisms; Noninvasive Diagnosis of Coronary Artery Disease Using 12-Lead High-Frequency Electrocardiograms; Dual-Laser-Pulse Ignition; Enhanced-Contrast Viewing of White-Hot Objects in Furnaces; Electrically Tunable Terahertz Quantum-Cascade Lasers; Few-Mode Whispering-Gallery-Mode Resonators; Conflict-Aware Scheduling Algorithm; and Real-Time Diagnosis of Faults Using a Bank of Kalman Filters.
A more automated process to produce wind tunnel models using existing facilities is discussed. A process was sought to more rapidly determine the aerodynamic characteristics of advanced aircraft configurations. Such aerodynamic characteristics are determined from theoretical analyses and wind tunnel tests of the configurations. Computers are used to perform the theoretical analyses, and a computer aided manufacturing system is used to fabricate the wind tunnel models. In the past a separate set of input data describing the aircraft geometry had to be generated for each process. This process establishes a common data base by enabling the computer aided manufacturing system to use, via a software interface, the geometric input data generated for the theoretical analysis. Thus, only one set of geometric data needs to be generated. Tests reveal that the process can reduce by several weeks the time needed to produce a wind tunnel model component. In addition, this process increases the similarity of the wind tunnel model to the mathematical model used by the theoretical aerodynamic analysis programs. Specifically, the wind tunnel model can be machined to within 0.008 in. of the original mathematical model. However, the software interface is highly complex and cumbersome to operate, making it unsuitable for routine use. The procurement of an independent computer aided design/computer aided manufacturing system with the capability to support both the theoretical analysis and the manufacturing tasks was recommended.
Safely launching and landing spacecraft, especially reusable spacecraft, requires detailed inspections of the launch vehicle to identify potential manufacturing and service-induced flaws. As an automated full-structure ultrasonic scanning technology, Acoustic Steady-State Excitation Spatial Spectroscopy (ASSESS) can rapidly and non-intrusively detect a wide variety of defects in applications where identifying subtle flaws is paramount. ASSESS is a non-destructive evaluation technology that leverages continuous excitation and laser Doppler vibrometry to make fast, high-quality inspections at stand-off distances. The ASSESS technology has undergone extensive laboratory testing and several field tests specific to oil and gas and additive manufacturing applications. We seek a commercialization partner to fund aerospace application-specific R&D and product development through a Cooperative Research and Development Agreement (CRADA).
A lithium ion-conductive solid electrolyte including a freestanding inorganic vitreous sheet of sulfide-based lithium ion conducting glass is capable of high performance in a lithium metal battery. Such an electrolyte is also manufacturable, and readily adaptable for battery cell and cell component manufacture, in a cost-effective, scalable manner using an automated machine based system, apparatus and methods based on inline spectrophotometry to assess and inspect the quality of such vitreous solid electrolyte sheets and associated components. Suitable manufacturing methods can involve providing a sulfur precursor, providing a boron precursor material having lithium as a second constituent, combining the sulfur and boron precursor materials to form a precursor mixture, melting the mixture, and cooling the melt to form a solid lithium ion conducting glass. The glass may have a Li+ conductivity of at least 10 −5 S/cm. The boron precursor material may be synthesized by reducing boron oxide to boron metal by heating the boron oxide in direct contact with lithium metal.
Automation and quality control techniques for 2S and PS sensor modules for the CMS Outer Tracker for the HL-LHC era have been developed at the Silicon Detector facility at Fermilab. Examples include a laser scanning procedure to check manufacturing specifications for the 2S sensor modules, and an automated gluing process to accurately assemble PS sensor modules. Processes were verified and documented for the production stage of the Outer Tracker.
Advances in robotics technology that will be necessary for the NASA Space Station to achieve its optimal level of automation are examined. The present state and emerging trends in teleoperator systems, hybrid teleoperated robots, and autonomous robots are reviewed, and scenarios of assembly, inspection, satellite servicing, and manufacturing are presented in order to illustrate potential uses of automation and robotics on the Space Station. Individual important technology development areas for Space Station robotics are surveyed, including end effectors and mechanization, control systems, telepresence and human factors, preception, manipulation in space, operation planning and data bases, and hardware maintenance.
A smart thermostat is an internet-connected device that controls home heating, ventilation, and air-conditioning (HVAC) equipment and can automatically adjust temperature set points to optimize performance and achieve energy savings. Smart thermostat features often include two way communication, occupancy detection (such as geofencing and occupancy sensors), schedule learning, and seasonal optimization algorithms. Smart thermostats can control most conventional HVAC systems, including central air conditioners, heat pumps, and forced air furnaces. Several types of residential utility programs offer smart thermostats as replacements measures. Working with smart thermostat vendors, utilities can offer separate optimization programs to produce energy savings beyond those achieved by installing a smart thermostat. From an evaluation perspective, smart thermostat programs have several noteworthy features. First, the energy savings from a smart thermostat may change over the life of the device. As a smart thermostat is connected to the internet, original equipment manufacturers can update the thermostat software to improve the thermostat's energy efficiency. Likewise, users can adjust the thermostat settings and schedules over time in response to changes in weather, thermal comfort, energy prices, or preferences for energy efficiency. Additionally, many thermostat manufacturers offer seasonal optimization programs that recommend changes or make minor, automated adjustments to the thermostat settings to improve energy efficiency. These opt-in programs are now standard offerings for many smart thermostat manufacturers and provided at no additional cost to users. The potential for software updates and continuous optimization and the evolving nature of user interactions mean future energy savings may differ from first-year savings and the energy savings of smart thermostats may need to be evaluated more than once. Second, smart thermostats often have small unit energy savings relative to a home's total energy consumption, especially in comparison to whole- home retrofit programs. This can make it difficult to detect the smart thermostat savings in billing or advanced metering infrastructure (AMI) meter consumption data. For example, as cooling loads in many regions average about 20% of annual electricity consumption, smart thermostat savings of 10% of cooling energy use would equate to a 2% reduction in home electricity consumption. Evaluators should use regression analysis of whole-home billing consumption or advanced metering infrastructure (AMI) meter consumption data to evaluate smart thermostat savings because, as explained at greater length below , these data are usually available to evaluators and regression can control for the impacts of weather and other potentially confounding factors on a home's energy consumption. Finally, as with other energy efficiency programs, participation in smart thermostat programs is self-selective. As discussed at greater length below , smart thermostat participants tend to be, among other things, younger, higher-income, and more likely to adopt electric vehicles (EVs) and internet connected devices than nonparticipants. These differences are often unobservable to the evaluator and correlated with a home's energy consumption, creating the potential for bias in estimating savings. Due to the small unit savings of thermostats, errors and biases from self-selection that may not be very consequential when evaluating a whole- home retrofits (e.g., ±2% of home electricity consumption) can have a major impact when evaluating the savings and cost-effectiveness of smart thermostat programs. A percentage point change in the estimated savings could affect the cost-effectiveness of a program. This means it is important for evaluators to assess and to minimize the potential for error from selection bias in estimating smart thermostat program savings. The Uniform Methods Project provides model protocols for determining energy savings and demand reductions that result from specific energy efficiency measures implemented through state and utility programs. In most cases, the measure protocols are based on a particular option identified by the International Performance Verification and Measurement Protocol ; however, this work provides a more detailed approach to implementing that option. Each chapter is written by technical experts in collaboration with their peers, reviewed by industry experts, and subject to public review and comment. The UMP protocols can be used by utilities, program administrators, public utility commissions, evaluators, and other stakeholders for both program planning and evaluation.
The potential for site-specific, process-parameter control is an attribute of additive manufacturing (AM) that makes it highly attractive as a manufacturing process. The research interest in the functionally grading material properties of numerous AM processes has been high for years. However, one of the issues that slows developmental progress in this area is process planning. It is not uncommon for manual programming methods and bespoke solutions to be utilized for site-specific control efforts. This article presents the development of slicing software that contains a fully automated process planning approach for enabling through-thickness, process-parameter control for a range of AM processes. The technique includes the use of parent and child geometries for controlling the locations of site-specific parameters, which are overlayed onto unmodified toolpaths, i.e., a vector-based planning approach is used in which additional information, such as melt pool size for large-scale metal AM processes, is assigned to the vectors. This technique has the potential for macro- and micro-structural modifications to printed objects. A proof-of-principle experiment is highlighted in which this technique was used to generate dynamic bead geometries that were deposited to induce a novel surface embossing effect, and additional software examples are presented that highlight software support for more complex objects.
Additive manufacturing (AM) enables the fabrication of complex geometries, yet its application to thermosets remains limited by post-processing requirements. Frontal ring-opening metathesis polymerization (FROMP) offers a promising alternative, enabling energy-efficient, in situ curing of freestanding thermoset structures. This study presents a real-time process monitoring and automated control system for direct ink writing (DIW) of FROMP thermosets. By integrating thermochromic leuco dyes and computer vision, we enable real-time polymerization front tracking, allowing autonomous printing parameter adjustments for consistent geometries across resin formulations. The system’s accuracy was validated against manual tracking, demonstrating precise front velocity detection. Its adaptability was confirmed by printing freestanding mechanical springs with different resins, achieving consistent geometries and mechanical properties despite front velocity variations. These findings highlight the potential of automated DIW control for scalable, repeatable, and material-agnostic 3D printing of thermosets.
A lithium ion-conductive solid electrolyte including a freestanding inorganic vitreous sheet of sulfide-based lithium ion conducting glass is capable of high performance in a lithium metal battery by providing a high degree of lithium ion conductivity while being highly resistant to the initiation and/or propagation of lithium dendrites. Such an electrolyte is also itself manufacturable, and readily adaptable for battery cell and cell component manufacture, in a cost-effective, scalable manner. An automated machine based system, apparatus and methods assessing and inspecting the quality of such vitreous solid electrolyte sheets, electrode sub-assemblies and lithium electrode assemblies can be based on spectrophotometry and can be performed inline with fabricating the sheet or web (e.g., inline with drawing of the vitreous Li ion conducting glass) and/or with the manufacturing of associated electrode sub-assemblies and lithium electrode assemblies and battery cells.
During the manufacture of tri-structural isotropic (TRISO)-coated nuclear fuel particles, the potential exists for the formation of internal fissure defects in the uranium oxycarbide (UCO) kernels. These fissures result in a defective fuel particle that can fracture during subsequent fuel processing. Therefore, it is necessary to detect the presence of fissured kernels in a batch to determine if the batch meets specification prior to blending with other batches and upgrading processes. Previous attempts at identifying fissures involved manual inspection of micrographs of UCO fuel kernel cross-sections. This process is tedious, time-consuming and may introduce counting errors making it a good candidate for automation. This work presents a method for the automated detection of fissures in UCO kernels. Image segmentation is used for the extraction of relevant features in the micrographs which then serve as the input to a convolutional neural network used to automatically distinguish between fissured and non-fissured kernels.
Fabrication of parts exhibiting multi-functionality has recently been complemented by hybrid polymer extrusion additive manufacturing in combination with wire embedding technology. While much mechanical characterization has been performed on parts produced with fused deposition modeling, limited characterization has been performed when combined electrical and thermal loads are applied to 3D printed multi-material parts. As such, this work describes the design, fabrication, and testing of 3D printed thermoplastic coupons containing embedded copper wires that carried current. An automated fabrication process was used employing a hybrid additive manufacturing machine that dispensed polycarbonate thermoplastic and embedded bare copper wires. Testing included AC and DC hipot testing as well as thermal testing on as-fabricated and heat treated coupons to determine the effect of porosity in the substrate. The heat-treated parts contained reduced amounts of porosity, as corroborated through scanning electron microscopy, which led to 50 % increased breakdown strength and 30 to 40 % increased heat dissipation capabilities. The results of this research are describing a set of design protocol that can be used as a guideline for 3D printed embedded electronics to predict the electrical and thermal behavior.
With the increase in electricity supply from clean energy sources, electrochemical reduction of carbon dioxide (CO 2 ) has received increasing attention as an alternative source of carbon-based fuels. As CO 2 reduction is becoming a stronger alternative for the clean production of chemicals, the need to model, optimize and control the electrochemical reduction of the CO 2 process becomes inevitable. However, on one hand, a first-principles model to represent the electrochemical CO 2 reduction has not been fully developed yet because of the complexity of its reaction mechanism, which makes it challenging to define a precise state-space model for the control system. On the other hand, the unavailability of efficient concentration measurement sensors continues to challenge our ability to develop feedback control systems. Gas chromatography (GC) is the most common equipment for monitoring the gas product composition, but it requires a period of time to analyze the sample, which means that GC can provide only delayed measurements during the operation. Moreover, the electrochemical CO 2 reduction process is catalyzed by a fast-deactivating copper catalyst and undergoes a selectivity shift from the product-of-interest at the later stages of experiments, which can pose a challenge for conventional control methods. To this end, machine learning (ML) techniques provide a potential approach to overcome those difficulties due to their demonstrated ability to capture the dynamic behavior of a chemical process from data. Motivated by the above considerations, we propose a machine learning-based modeling methodology that integrates support vector regression and first-principles modeling to capture the dynamic behavior of an experimental electrochemical reactor; this model, together with limited gas chromatography measurements, is employed to predict the evolution of gas-phase ethylene concentration. The model prediction is directly used in a proportional-integral (PI) controller that manipulates the applied potential to regulate the gas-phase ethylene concentration at energy-optimal set-point values computed by a real-time process optimizer (RTO). Specifically, the RTO calculates the operation set-point by solving an optimization problem to maximize the economic benefit of the reactor. Finally, suitable compensation methods are introduced to further account for the experimental uncertainties and handle catalyst deactivation. The proposed modeling, optimization, and control approaches are the first demonstration of active control for a CO 2 electrolyzer and contribute to the automation and scale-up efforts for electrified manufacturing of fuels and chemicals starting from CO 2 .
Tool condition monitoring (TCM) is an essential research area for the optimization and automation of metal machining processes, and could help manufacturers reduce costs, production time, machine downtime, energy use, and part scrappage. However, TCM systems developed in prior studies have struggled to reach the high level of generalizability which is necessary for industrial applications. This study addresses TCM system generalizability to new machining conditions, how variations in machining and environmental conditions may be used to improve model generalizability, and ensemble machine learning techniques for TCM. Further, milling tool life experiments were conducted using various machining conditions, and the processes' sound, spindle power, and axial load signals were collected. Different machine learning models were evaluated for the prediction of tool wear levels, including four individual models and five ensemble models. Changes in cutting speed were found to display a large effect on model performance, while the chip load showed some effect, and the feed rate had little effect. A simulated noise data augmentation technique for model improvement is applied within TCM for the first time, and resulted in increased model generalizability and reduced overfitting. Across several performance metrics the extremely randomized trees ensemble machine learning model generally performed the best for this application, achieving a leave-one-group-out cross validation accuracy score of 92.4 %, a 10-fold cross validation score of 98.9 %, and an averaged accuracy across 11 generalizability tests of 87.3 %.