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At least 433 records · Page 24

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NASA NTRS1984

An investigation into the injection molding of PMR-15 polyimide

The chemorheological behavior of the PRM-15 molding compounds were characterized, the range of suitable processing parameters for injection molding in a reciprocating screw injection molding machine was determined, and the effects of the injection molding processing parameters on the mechanical properties of molded PMR-15 parts were studied. The apparatus and procedures for measuring viscosity and for determining the physical response of the material during heating are described. Results show that capillary rheometry can be effectively used with thermosets if the equipment is designed to overcome some of the inherent problems of these materials. A uniform temperature was provided in the barrel by using a circulating hot oil system. Standard capillary rheometry methods can provide the dependence of thermoset apparent viscosity on shear rate, temperature, and time. Process conditions resulting in complete imidization should be carefully defined. Specification of controlled oven temperature is inadequate and can result in incomplete imidization. For completely imidized PMR-15 heat at 15 C/min melt flow without gas evolution occurs in the temperature range of 325 C to 400 C.

Colaluca, M. A.↗

NASA NTRS1992

An Introduction to Intelligent Processing Programs Developed by the Air Force Manufacturing Technology Directorate

The Air Force has numerous on-going manufacturing and integration development programs (machine tools, composites, metals, assembly, and electronics) which are instrumental in improving productivity in the aerospace industry, but more importantly, have identified strategies and technologies required for the integration of advanced processing equipment. An introduction to four current Air Force Manufacturing Technology Directorate (ManTech) manufacturing areas is provided. Research is being carried out in the following areas: (1) machining initiatives for aerospace subcontractors which provide for advanced technology and innovative manufacturing strategies to increase the capabilities of small shops; (2) innovative approaches to advance machine tool products and manufacturing processes; (3) innovative approaches to advance sensors for process control in machine tools; and (4) efforts currently underway to develop, with the support of industry, the Next Generation Workstation/Machine Controller (Low-End Controller Task).

Sampson, Paul G.↗

NASA NTRS2006

Identification Of Cells With A Compact Microscope Imaging System With Intelligent Controls

A Microscope Imaging System (CMIS) with intelligent controls is disclosed that provides techniques for scanning, identifying, detecting and tracking mic?oscopic changes in selected characteristics or features of various surfaces including, but not limited to, cells, spheres, and manufactured products subject to difficult-to-see imperfections. The practice of the present invention provides applications that include colloidal hard spheres experiments, biological cell detection for patch clamping, cell movement and tracking, as well as defect identification in products, such as semiconductor devices, where surface damage can be significant, but difficult to detect. The CMIS system is a machine vision system, which combines intelligent image processing with remote control capabilities and provides the ability to autofocus on a microscope sample, automatically scan an image, and perform machine vision analysis on multiple samples simultaneously.

McDowell, Mark↗

NASA NTRS2006

Operation of a Cartesian Robotic System in a Compact Microscope with Intelligent Controls

A Microscope Imaging System (CMIS) with intelligent controls is disclosed that provides techniques for scanning, identifying, detecting and tracking microscopic changes in selected characteristics or features of various surfaces including, but not limited to, cells, spheres, and manufactured products subject to difficult-to-see imperfections. The practice of the present invention provides applications that include colloidal hard spheres experiments, biological cell detection for patch clamping, cell movement and tracking, as well as defect identification in products, such as semiconductor devices, where surface damage can be significant, but difficult to detect. The CMIS system is a machine vision system, which combines intelligent image processing with remote control capabilities and provides the ability to autofocus on a microscope sample, automatically scan an image, and perform machine vision analysis on multiple samples simultaneously.

McDowell, Mark↗

NASA NTRS2007

Tracking of Cells with a Compact Microscope Imaging System with Intelligent Controls

McDowell, Mark↗

NASA NTRS1986

Choosing a software design method for real-time Ada applications: JSD process inversion as a means to tailor a design specification to the performance requirements and target machine

The validity of real-time software is determined by its ability to execute on a computer within the time constraints of the physical system it is modeling. In many applications the time constraints are so critical that the details of process scheduling are elevated to the requirements analysis phase of the software development cycle. It is not uncommon to find specifications for a real-time cyclic executive program included to assumed in such requirements. It was found that prelininary designs structured around this implementation abscure the data flow of the real world system that is modeled and that it is consequently difficult and costly to maintain, update and reuse the resulting software. A cyclic executive is a software component that schedules and implicitly synchronizes the real-time software through periodic and repetitive subroutine calls. Therefore a design method is sought that allows the deferral of process scheduling to the later stages of design. The appropriate scheduling paradigm must be chosen given the performance constraints, the largest environment and the software's lifecycle. The concept of process inversion is explored with respect to the cyclic executive.

Withey, James V.↗

NASA NTRS2009

Porting Gravitational Wave Signal Extraction to Parallel Virtual Machine (PVM)

Laser Interferometer Space Antenna (LISA) is a planned NASA-ESA mission to be launched around 2012. The Gravitational Wave detection is fundamentally the determination of frequency, source parameters, and waveform amplitude derived in a specific order from the interferometric time-series of the rotating LISA spacecrafts. The LISA Science Team has developed a Mock LISA Data Challenge intended to promote the testing of complicated nested search algorithms to detect the 100-1 millihertz frequency signals at amplitudes of 10E-21. However, it has become clear that, sequential search of the parameters is very time consuming and ultra-sensitive; hence, a new strategy has been developed. Parallelization of existing sequential search algorithms of Gravitational Wave signal identification consists of decomposing sequential search loops, beginning with outermost loops and working inward. In this process, the main challenge is to detect interdependencies among loops and partitioning the loops so as to preserve concurrency. Existing parallel programs are based upon either shared memory or distributed memory paradigms. In PVM, master and node programs are used to execute parallelization and process spawning. The PVM can handle process management and process addressing schemes using a virtual machine configuration. The task scheduling and the messaging and signaling can be implemented efficiently for the LISA Gravitational Wave search process using a master and 6 nodes. This approach is accomplished using a server that is available at NASA Ames Research Center, and has been dedicated to the LISA Data Challenge Competition. Historically, gravitational wave and source identification parameters have taken around 7 days in this dedicated single thread Linux based server. Using PVM approach, the parameter extraction problem can be reduced to within a day. The low frequency computation and a proxy signal-to-noise ratio are calculated in separate nodes that are controlled by the master using message and vector of data passing. The message passing among nodes follows a pattern of synchronous and asynchronous send-and-receive protocols. The communication model and the message buffers are allocated dynamically to address rapid search of gravitational wave source information in the Mock LISA data sets.

Thirumalainambi, Rajkumar↗

NASA NTRS1983

On Why It Is Impossible to Prove that the BDX90 Dispatcher Implements a Time-sharing System

The Software Implemented Fault Tolerance SIFT system, is written in PASCAL except for about a page of machine code. The SIFT system implements a small time sharing system in which PASCAL programs for separate application tasks are executed according to a schedule with real time constraints. The PASCAL language has no provision for handling the notion of an interrupt such as the B930 clock interrupt. The PASCAL language also lacks the notion of running a PASCAL subroutine for a given amount of time, suspending it, saving away the suspension, and later activating the suspension. Machine code was used to overcome these inadequacies of PASCAL. Code which handles clock interrupts and suspends processes is called a dispatcher. The time sharing/virtual machine idea is completely destroyed by the reconfiguration task. After termination of the reconfiguration task, the tasks run by the dispatcher have no relation to those run before reconfiguration. It is impossible to view the dispatcher as a time-sharing system implementing virtual BDX930s running concurrently when one process can wipe out the others.

Boyer, R. S.↗

NASA NTRSDate not supplied

Fast Assessment of Metal Performance through Dislocation Physics and Machine Learning

The microstructure of metals is key to their mechanical properties. The types, density, composition and morphology of crystal defects all have pronounced impact on the properties. Changes to the microstructure occurring during processing and use can be very striking. The emerging technology additive manufacturing (AM) has the potential to improve performance by allowing optimized designs, but the process and environments can lead to unusual microscale features whose properties must be understood and characterized to enable higher technological readiness levels and application. Experimentally, an extensive evaluation of mechanical properties of 3D printed metals is a challenge, and anomalous effects related to the AM process add complexity. We present a new machine learning (ML) model predicting mechanical response based on dislocation mediated plasticity simulations. A large set of 3D discrete dislocation dynamics simulations with wide ranges of loading conditions is transformed to preprocessed data ready for training with the ML model. The trained model can predict the mechanical response of Mo30W for a given microstructure evolution, providing key information essential for optimization of AM processing.

Jaehyun Cho↗

NASA NTRSDate not supplied

Fast Assessment of Metal Performance through Dislocation Physics and Machine Learning

Jaehyun Cho↗

NASA NTRS1982

Forest Fire Observation

Conditions on the perimeter of a forest fire can be obtained by use of airborne remote sensing techniques demonstrated by Ames Research Center. An Ames U-2 high-altitude survey aircraft served as an aerial fire observation system. Equipped with two types of sensors, the U-2 produces real-time infrared images of fireground scenes. Information acquired by the U-2's scanners defines the fire boundary and aids fire management decisions by showing the size, shape and direction of burn and the locations of hot spots in the fire zone. U-2 sends sensor date in digital form to an antenna at Ames Research Center. There the data is computer processed into images which are overlaid on U.S. Geological Survey topographical maps of the fire area. Maps are then transmitted by telecopy machine directly to fire control center. Whole process takes less than 10 minutes and the U-2 can provide information for up to five hours. Ames antenna can pick up signals from the U-2 anywhere within a 300 mile radius from Ames.

Source record↗

NASA NTRS1974

Turbomachinery design described by similarity considerations

Expressions of operating conditions related to the design of turbomachinery are examined. The dimensionless field of turbomachinery design is discussed. The relationships of dimensionless design parameters such as diameter ratios, flow coefficients, head coefficients, and design choices regarding the fluid and the machine are explained. The core of the design process is said to relate such sets of design parameters to the dimensionless form of the machine. Mathematical models are provided to support the theoretical considerations.

Wislicenus, G. F.↗

NASA NTRS2008

Automated Data Assimilation and Flight Planning for Multi-Platform Observation Missions

This is a progress report on an effort in which our goal is to demonstrate the effectiveness of automated data mining and planning for the daily management of Earth Science missions. Currently, data mining and machine learning technologies are being used by scientists at research labs for validating Earth science models. However, few if any of these advanced techniques are currently being integrated into daily mission operations. Consequently, there are significant gaps in the knowledge that can be derived from the models and data that are used each day for guiding mission activities. The result can be sub-optimal observation plans, lack of useful data, and wasteful use of resources. Recent advances in data mining, machine learning, and planning make it feasible to migrate these technologies into the daily mission planning cycle. We describe the design of a closed loop system for data acquisition, processing, and flight planning that integrates the results of machine learning into the flight planning process.

Oza, Nikunj↗

NASA NTRSDate not supplied

Modeling Ring-Opening Polymerization with Machine-Learned Committors

Many important industrial and biological processes depend on ring-opening polymerization (ROP), including large-scale production polymers, such as nylon 6, and a variety of biodegradable polymers and high-performance resins. Large-scale simulations (>200K atoms) of ROP were performed using a deep neural network-based method that captured the effect of the local chemical environment of the reactive sites using training data from quantum chemical methods. The utility of this method was demonstrated by modeling the cationic ROP of bis-benzoxazine, a promising resin for use in advanced high-temperature application composites (>200°C). Density functional theory calculations were used to map out the various ring opening and polymerization reaction paths that occur during the thermal processing of bis-benzoxazine. The probabilities of these reactions, as a function of local atomic configurations, were used to construct committor functions using a neural network approach. The learned committor functions were then used to determine when a particular reaction occurred within the REACTER framework for reactive molecular dynamics simulations. The experimentally observed exotherm that occurs during bis-benzoxazine ROP was captured in an effective manner in the simulations by imposing a local heating of the system that corresponds to the enthalpy of reaction for each reaction. The dependence of the final polymer morphology on the ROP reaction temperature was analyzed at the molecular scale. The results presented in this work indicate that machine-learned committor functions are a promising approach for incorporating high-fidelity reactivity criteria that capture the effect of local chemical environments into large-scale polymerization simulations.

machine learning, polymer simulations, molecular d↗

NASA NTRS2007

Tracking of cells with a compact microscope imaging system with intelligent controls

A Microscope Imaging System (CMIS) with intelligent controls is disclosed that provides techniques for scanning, identifying, detecting and tracking microscopic changes in selected characteristics or features of various surfaces including, but not limited to, cells, spheres, and manufactured products subject to difficult-to-see imperfections. The practice of the present invention provides applications that include colloidal hard spheres experiments, biological cell detection for patch clamping, cell movement and tracking, as well as defect identification in products, such as semiconductor devices, where surface damage can be significant, but difficult to detect. The CMIS system is a machine vision system, which combines intelligent image processing with remote control capabilities and provides the ability to auto-focus on a microscope sample, automatically scan an image, and perform machine vision analysis on multiple samples simultaneously.

McDowell, Mark↗

NASA NTRSDate not supplied

Interpretable Machine Learning Models for Autonomous Characterization of Analogue Ocean World Seawater Chemistry and Biosignature Potential Using Isotope Ratio Data

Background: Future missions to ocean worlds, such as Enceladus and Europa, will attempt to characterize the subsurface seawater chemistry and assess the potential for life. Such missions will be equipped with capabilities to precisely measure volatile isotopes in plumes, atmospheres, and exospheres. Motivation: While large isotopic fractionations can indicate a biological source, there are signatures resulting from abiotic geochemical processes that mimic isotopic biosignatures. While machine learning (ML) has the potential to disentangle competing effects and biotic mimicry, high-dimensional isotope ratio mass spectrometry (IRMS) data is likely to contain noise/irrelevant features and involve complex statistical interactions that make human inference and interpretation difficult. Further, ML predictions with as far-reaching implications as an extraterrestrial biosignature on an ocean world requires the use of interpretable models (i.e., not “black box” models) with physically and mathematically meaningful feature spaces along with false positive diagnostics. Methods: We use volatile CO2 IRMS data of analogue ocean world seawaters to validate an ML approach to provide biogeochemical context for biosignature detection. We employ a feature selection method called nearest-neighbor projected distance regression (NPDR) that detects statistical interactions and helps elucidate the mechanisms of the Random Forest classification models. Results: We train and validate predictive ML models on volatile CO2 IRMS data of analogue ocean world seawaters to predict major salt components (e.g., MgSO4, NaHCO3), pH, ionic strength, and the presence of biosignatures. Features derived from IRMS measurements are augmented with extracted time-series features. Our results show high test accuracy and interpretability, which is increased by interaction network visualization, sample-wise variable importance scores, and single-sample class probability estimates. We demonstrate an ML mission software solution that triggers autonomous data transmission and biogeochemical sample prediction.

geochemistry↗

NASA NTRS1988

A representational framework and user-interface for an image understanding workstation

Problems in image understanding involve a wide variety of data (e.g., image arrays, edge maps, 3-D shape models) and processes or algorithms (e.g., convolution, feature extraction, rendering). The underlying structure of an Image Understanding Workstation designed to support mulitple levels and types of representations for both data and processes is described, also the user-interface. The Image Understanding Workstation consists of two parts: the Image Understanding (IU) Framework, and the user-interface. The IU Framework is the set of data and process representations. It includes multiple levels of representation for data such as images (2-D), sketches (2-D), surfaces (2 1/2 D), and models (3-D). The representation scheme for processes characterizes their inputs, outputs, and parameters. Data and processes may reside on different classes of machines. The user-interface to the IU Workstation gives the user convenient access for creating, manipulating, transforming, and displaying image data. The user-interface follows the structure of the IU Framework and gives the user control over multiple types of data and processes. Both the IU Framework and user-interface are implemented on a LISP machine.

Schroeder, Joyce D.↗

NASA NTRS2003

Carbon-Based Wear Coatings: Properties and Applications

The technical function of numerous engineering systems - such as vehicles, machines, and instruments - depends on the processes of motion and on the surface systems. Many processes in nature and technology depend on the motion and dynamic behavior of solids, liquids, and gases. Smart surface systems are essential because of the recent technological push toward higher speeds, loads, and operating temperatures; longer life; lighter weight and smaller size (including nanotechnology); and harsh environments in mechanical, mechatronic, and biomechanical systems. If proper attention is not given to surface systems, then vehicles, machines, instruments, and other technical systems could have short lives, consume excessive energy, experience breakdowns, result in liabilities, and fail to accomplish their missions. Surface systems strongly affect our national economy and our lifestyles. At the NASA Glenn Research Center, we believe that proper attention to surface systems, especially in education, research, and application, could lead to economic savings of between 1.3 and 1.6 percent of the gross domestic product. Wear coatings and surface systems continue to experience rapid growth as new coating and surface engineering technologies are discovered, more cost-effective coating and surface engineering solutions are developed, and marketers aggressively pursue, uncover, and exploit new applications for engineered surface systems in cutting tools and wear components. Wear coatings and smart surface systems have been used widely in industrial, consumer, automotive, aerospace, and biomedical applications. This presentation expresses the author's views of and insights into smart surface systems in wear coatings. A revolution is taking place in carbon science and technology. Diamond, an allotrope of carbon, joins graphite, fullerenes, and nanotubes as its major pure carbon structures. It has a unique combination of extreme properties: hardness and abrasion resistance; adhesion and friction; thermal conductivity; chemical and thermal inertness; corrosion and wear resistance; radiation resistance and biocompatibility; electronic, acoustic, and electrochemical characteristics; and environmental compatibility. These properties make diamond attractive for a wide range of diverse applications. In particular, chemical-vapor-deposited (CVD) diamond coatings offer a broad potential, since size and cost are not as limiting. The production of large, superhard diamond films or sheets at low cost make designer materials possible. This presentation is divided into two sections: properties and applications of hard coatings. The first section is concerned with the fundamental properties of the surfaces of CVD diamonds and related materials. The surface properties of hard coatings with favorable coefficients of friction (less than or equal to 0.1) and dimensional wear coefficients (less than or equal to 10(exp -6) cubic millimeters/N.m) in specific environments are discussed. The second section is devoted to applications. Examples of actual, successful applications and of potential challenging applications of the coatings.such as CVD diamond, diamondlike carbon, and cubic boron nitride-are described. Cutting tools coated with CVD diamond are of immediate commercial interest. Other applications, such as microelectromechanical systems (MEMS), valves, and bearings of CVD diamond, are being developed, but at a slow pace. There is a continually growing interest in commercializing diamondlike carbon for wear parts applications, such as biomedical parts and implants, forming dies, transport guides, magnetic tapes and disks, valves, and gears. Cubic boron nitride films are receiving attention because they can be used on tools to machine ferrous materials or on wear parts in sliding contact with ferrous materials.

Miyoshi, Kazuhisa↗