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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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Composite-material (matrix/fiber) structural components manufactured at moderate to high rates in production-line-style processes, according to proposal. In method, production lines largely automated and takes advantage of fact matrix resins cured by electron beams in addition to heat. Net result reductions in production times and costs.
High area output rates and continuous, automated growth are two key technical requirements for the growth of low-cost silicon ribbons for solar cells. By means of computer-aided furnace design, silicon dendritic web output rates as high as 27 sq cm/min have been achieved, a value in excess of that projected to meet a $0.50 per peak watt solar array manufacturing cost. The feasibility of simultaneous web growth while the melt is replenished with pelletized silicon has also been demonstrated. This step is an important precursor to the development of an automated growth system. Solar cells made on the replenished material were just as efficient as devices fabricated on typical webs grown without replenishment. Moreover, web cells made on a less-refined, pelletized polycrystalline silicon synthesized by the Battelle process yielded efficiencies up to 13% (AM1).
The U.S. Army Aviation Systems Command (AVSCOM), through the Propulsion Directorate at NASA LRC, has recently sponsored projects to advance the manufacturing process for spiral bevel gears. This type of gear is a critical component in rotary-wing propulsion systems. Two successfully completed contracted projects are described. The first project addresses the automated inspection of spiral bevel gears through the use of coordinate measuring machines. The second project entails the computer-numerical-control (CNC) conversion of a spiral bevel gear grinding machine that is used for all aerospace spiral bevel gears. The results of these projects are described with regard to the savings effected in manufacturing time.
The U.S. Army Aviation Systems Command (AVSCOM), through the Propulsion Directorate at NASA Lewis Research Center, has recently sponsored projects to advance the manufacturing process for spiral bevel gears. This type of gear is a critical component in rotary-wing propulsion systems. Two successfully completed contracted projects are described. The first project addresses the automated inspection of spiral bevel gears through the use of coordinate measuring machines. The second project entails the computer-numerical-control (CNC) conversion of a spiral bevel gear grinding machine that is used for all aerospace spiral bevel gears. The results of these projects are described with regard to the savings effected in manufacturing time.
In the alloy design and development process, a wealth of atomically resolved structural high-resolution transmission electron microscopy (HRTEM) images are produced. Identifying the different nano-precipitate phases and tracking their evolution under various compositions and during manufacturing or post-processing requires hundreds of HRTEM images and thousands of precipitates. The nanoscopic phase information labeling and analysis purely relies on humans are prohibitively costly and time-consuming, sometimes not reliable because of the lack of authoritative knowledge. Here, in this work, we develop a novel unsupervised machine learning approach coupled with adaptive computer vision techniques with features in the Fourier space to automatically determine the number of phases and segment/quantify the phases with nanoscale resolution, allowing for quantitative correlation between nanostructure formation, processing and functional properties. To automate the phase extraction/quantification and ascertain its applicability, we have applied the developed framework to the HRTEM images from several alloy systems, processing conditions, image magnifications, and phase types and morphologies (precipitates, nano-twins, stacking faults, crystalline matrix, and amorphous structures) for verification. This study paves the road for compression, visualization, and translation of raw image structural data into physically relevant information in real-time with minimal human supervision. It shows the promise of enabling high-throughput materials characterization for the acceleration of alloy manufacturing and design.
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Machine learning (ML) methods are progressively gaining acceptance in the electron microscopy community for de-noising, semantic segmentation, and dimensionality reduction of data post-acquisition. The introduction of the application programming interfaces (APIs) by major instrument manufacturers now allows the deployment of ML workflows in microscopes, not only for data analytics but also for real-time decision-making and feedback for microscope operation. However, the number of use cases for real-time ML remains remarkably small. Furthermore, we discuss some considerations in designing ML-based active experiments and pose that the likely strategy for the next several years will be human-in-the-loop automated experiments (hAE). In this paradigm, the ML learning agent directly controls beam position and image and spectroscopy acquisition functions, and a human operator monitors experiment progression in real and feature space of the system and tunes the policies of the ML agent to steer the experiment toward specific objectives.
With the transition toward continuous bioprocessing, process analytical technology (PAT) is becoming necessary for rapid and reliable in-process monitoring during biotherapeutics manufacturing. Bioprocess 4.0 is looking to build end-to-end bioprocesses that include PAT-enabled real-time process control. This is especially important for drug product quality attributes that can change during bioprocessing, such as protein N-glycosylation, a critical quality attribute for most monoclonal antibody (mAb) therapeutics. Glycosylation of mAbs is known to influence their efficacy as therapeutics and is regulated for a majority of mAb products on the market today. Currently, there is no method to truly measure N-glycosylation using on-line PAT, hence making it impractical to design upstream process control strategies. We recently described the N-GLYcanyzer workflow: an integrated PAT unit that measures mAb N-glycosylation within 3 hours of automated sampling from a bioreactor. Here, we integrated Agilent's Instant Procainamide (InstantPC) based chemistry workflow into the N-GLYcanyzer PAT unit to allow for nearly 10× faster near real-time analysis of mAb glycoforms. Furthermore, our methodology is explained in detail to allow for replication of the PAT workflow as well as present a case study demonstrating the use of this PAT to autonomously monitor a mammalian cell perfusion process at the bench scale to gain increased knowledge of mAb glycosylation dynamics during continuous biologics manufacturing using Chinese hamster ovary (CHO) cells.
The existing refrigeration system for OpenStudio was originally designed for a prototype supermarket model. To support the development of new modular space types, it is crucial to refactor the system. This process includes refining the design by utilizing the manufacturer's existing data, along with incorporating the latest data from manufacturers, to enhance the system’s adaptability and functionality. This repository currently contains the refrigeration system database and Python-based automation for generating OpenStudio-compatible refrigeration JSON files.
Innovations improve technology costs through various kinds of engineering advancements, including changes to materials choices and device or process designs. Understanding how these innovations relate to cost change can reveal aspects of the process of technology evolution, yet developing such understanding is often not possible with a strictly quantitative approach due to data limitations. In this paper we develop a hybrid quantitative-qualitative framework for relating specific innovations to cost change by using the variables in a quantitative technology cost change model as an organizing principle. We demonstrate this framework by applying it to the cost decline in photovoltaic (PV) systems over the last five decades. This framework generates new understanding of a set of innovations that contributed to PV modules’ sustained cost decline and the more modest trends observed in balance-of-system (BOS) costs. The results show the great diversity of innovations that affected PV costs, drawing on wide-ranging fields of expertise within scientific research and practice. We find that there are differences in the characteristics of innovations that reduced the cost of PV modules compared to innovations influencing BOS costs. Numerous module innovations reduced costs by advancing manufacturing tools and processes that improved material quality. Many BOS innovations reduced costs through a combination of component design changes, integration, automation, digitalization, and standardization. Overall, most innovations in our sample affected PV hardware. However, some also target ‘soft technologies’ such as task durations through innovations like fast-track permitting, which require improved collaboration and process streamlining. This framework also provides insight into the nature of knowledge spillovers between technologies. Both module and BOS hardware innovations show the benefits of PV’s position within an ‘ecosystem’ of continuously advancing technologies in many industries, in particular semiconductors and electronics, and also point to the importance of public institutions for accelerating testing, permitting, and training.
The test and checkout philosophy of the test program for the Skylab ATM module and the overall test flow including in-process, post-manufacturing, vibration, thermal vacuum, and prelaunch checkout activities are described. Capabilities and limitations of the test complex and its use of automation are discussed. Experiences with the organizational principle of using a dedicated test team for all checkout activities are reported. Material on the development of the ATM subsystems, the experimental program and the requirements of the scientific community, and the integration and verification of the complex systems/subsystems of the ATM are presented. The performance of the ATM test program in such areas as alignment, systems and subsystems, contamination control, and experiment operation is evaluated. The conclusions and recommendations resulting from the ATM test program are enumerated.
The accurate measure of material thickness is important for many non-destructive evaluation applications. Factors such as corrosion damage can jeopardize structural integrity through material thinning and process control considerations often mandate strict limits on material dimensions. Access to the material under test can be limited to a single side and large areas may need to be examined in a small time period. In an effort to enhance the effectiveness of material thickness measurements a flux focusing eddy current probe has been developed at NASA Langley Research Center. The probe provides an accurate measure of the thickness of conducting materials from a single sided measurement. It is straight forward to use and can be easily automated for production line testing. The probe also requires only minimal instrumentation and power so that extremely portable units can be manufactured at a low cost. This new eddy current probe has been used to accurately measure the thickness of aluminum alloy plates with a resolution of greater than 0.001 in. (25 microns). Simulated corrosion damage has also been detected on both single layer and multi-layer samples. The present work will explain the output voltage dependence of the device as a function of material thickness and present experimental results for thickness gauging and corrosion detection.
Registration techniques play a central role in applications of image processing to computer vision, medical imaging, and automatic target tracking. Feature-based techniques such as scale-invariant feature transform (SIFT) and speeded up robust features (SURF) are commonly used to register images derived from a single modality. However, SIFT and SURF struggle to register images from different modalities because the features tend to manifest rather differently and at sometimes very different length-scales. The most successful methods that have been developed to register multi-modal data use information-theoretic approaches. These methods play a key part in nondestructive evaluation scenarios where data that is collected by sensors of different modalities must be registered to be fused. In this paper, automated registration based on normalized mutual information is applied to align data derived from ultrasonic and radiographic inspections of (i) additively manufactured titanium alloy test coupons, and (ii) thin, lithium metal pouch-cell batteries. The quality of the registration is quantified in terms of computational resources and spatial accuracy. In the first case the X-ray computed tomography (XCT) data is captured on a region corresponding to a small subset of the ultrasonic data, while in the case of the lithium batteries the digital radiography (DR) captures a larger region of interest than the ultrasonic data. In both cases the radiographic data resolution is much higher than for ultrasound, but interestingly, in both cases the accuracy of the registration is approximately equal to two-to-three-pixel lengths in the ultrasonic images.
The widespread adoption of networked devices, sophisticated automation, and data-driven processes in the industry - also known as Industry 4.0 - has boosted the quantity and quality of manufacturing products. With these benefits, however, comes a substantial increase in the attack surface of these systems. In addition to affecting the readiness and the quality of critical products, the attacks against manufacturing processes and systems carry the potential to have severe physical consequences, including human injury and death. In this paper we present the results of a remote network-based control injection attack on a CNC mill. Specifically, we focus on the impact of this type of the attack on the movement of CNC mill during operation. Evaluating the physical effect of these attacks on a workpiece, we provide machine agnostic, affordable, and scalable solution for their monitoring. We then demonstrate a simple threshold-based method for the detection of these attacks and evaluate the effectiveness of detection.
Owing to their remarkable properties and ordered architecture, vertically aligned CNT forests (VACNT) hold promises for countless applications ranging from energy storage to multifunctional fiber production. Over the last three decades, studies of carbon nanotube (CNT) growth have provided invaluable insight into the CNT nucleation and growth mechanism and enabled critical advancements in nanotechnology. Nevertheless, the ability to fully harness the exceptional VACNT properties in commercial devices is bottlenecked by compatibility of current CNT synthesis processes with large-scale manufacturing. Metal foil substrates present an economic alternative to traditional insulating substrates, like silicon or quartz, and are compatible with both roll-to-roll and automated, large-scale, batch processes. Compared to CNT growth on Si substrates, metal foils present additional challenges, such as increased roughness and reactivity at high temperatures. Nonetheless, many successful VACNT growths on conducting metal foils have been reported, which demonstrate the feasibility of metal substrate use for large-volume production of high-quality CNTs. Here, in this review, we discuss advancements in the field of VACNT growth on metal foil and specifically examine different choices for metal substrates, barrier and catalyst layers, deposition and growth methods, resulting CNT characteristics, and highlight a number of applications that benefit from CNT growth on metal substrates and transition to large-scale manufacturing.
A survey of line pilots' attitudes about flight deck automation was conducted by the Royal Air Force Institute of Aviation Medicine (RAF IAM, Farnborough, UK) under the sponsorship of the United Kingdom s Civil Aviation Authority and in cooperation with IATA (the International Air Transport Association). Survey freehand comments given by pilots operating 13 types of commercial transports across five manufacturers (Airbus, Boeing, British Aerospace, Lockheed, and McDonnell-Douglas) and 57 air carriers/organizations were analyzed by NASA. These data provide a "lessons learned" knowledge base which may be used for the definition of guidelines for flight deck automation and its associated crew interface within the High Speed Research Program. The aircraft chosen for analysis represented a progression of levels of automation sophistication and complexity, from "Basic" types (e.g., B727, DC9), through "Transition" types (e.g., A300, Concorde), to two levels of glass cockpits (e.g., Glass 1: e.g., A310; Glass 2: e.g., B747-400). This paper reports the results of analyses of comments from pilots flying commercial transport types having the highest level of automation sophistication (B757/B767, B747-400, and A320). Comments were decomposed into five categories relating to: (1) general observations with regard to flight deck automation; comments concerning the (2) design and (3) crew understanding of automation and the crew interface; (4) crew operations with automation; and (5) personal factors affecting crew/automation interaction. The goal of these analyses is to contribute to the definition of guidelines which may be used during design of future aircraft flight decks.
A robust approach for real-time analysis of the scanning transmission electron microscopy (STEM) data streams, based on ensemble learning and iterative training (ELIT) of deep convolutional neural networks, is implemented on an operational microscope, enabling the exploration of the dynamics of specific atomic configurations under electron beam irradiation via an automated experiment in STEM. Combined with beam control, this approach allows studying beam effects on selected atomic groups and chemical bonds in a fully automated mode. Here, in this study, we demonstrate atomically precise engineering of single vacancy lines in transition metal dichalcogenides and the creation and identification of topological defects in graphene. The ELIT-based approach facilitates direct on-the-fly analysis of the STEM data and engenders real-time feedback schemes for probing electron beam chemistry, atomic manipulation, and atom by atom assembly.