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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
NASA NTRS1972

Pattern recognition systems and procedures

The objectives of the pattern recognition tasks are to develop (1) a man-machine interactive data processing system; and (2) procedures to determine effective features as a function of time for crops and soils. The signal analysis and dissemination equipment, SADE, is being developed as a man-machine interactive data processing system. SADE will provide imagery and multi-channel analog tape inputs for digitation and a color display of the data. SADE is an essential tool to aid in the investigation to determine useful features as a function of time for crops and soils. Four related studies are: (1) reliability of the multivariate Gaussian assumption; (2) usefulness of transforming features with regard to the classifier probability of error; (3) advantage of selecting quantizer parameters to minimize the classifier probability of error; and (4) advantage of using contextual data. The study of transformation of variables (features), especially those experimental studies which can be completed with the SADE system, will be done.

Nelson, G. D.↗
NASA NTRS2003

Intelligent Machines in the 21st Century: Automating the Processes of Inference and Inquiry

The last century saw the application of Boolean algebra toward the construction of computing machines, which work by applying logical transformations to information contained in their memory. The development of information theory and the generalization of Boolean algebra to Bayesian inference have enabled these computing machines. in the last quarter of the twentieth century, to be endowed with the ability to learn by making inferences from data. This revolution is just beginning as new computational techniques continue to make difficult problems more accessible. However, modern intelligent machines work by inferring knowledge using only their pre-programmed prior knowledge and the data provided. They lack the ability to ask questions, or request data that would aid their inferences. Recent advances in understanding the foundations of probability theory have revealed implications for areas other than logic. Of relevance to intelligent machines, we identified the algebra of questions as the free distributive algebra, which now allows us to work with questions in a way analogous to that which Boolean algebra enables us to work with logical statements. In this paper we describe this logic of inference and inquiry using the mathematics of partially ordered sets and the scaffolding of lattice theory, discuss the far-reaching implications of the methodology, and demonstrate its application with current examples in machine learning. Automation of both inference and inquiry promises to allow robots to perform science in the far reaches of our solar system and in other star systems by enabling them to not only make inferences from data, but also decide which question to ask, experiment to perform, or measurement to take given what they have learned and what they are designed to understand.

Knuth, Kevin H.↗
NASA NTRSDate not supplied

A Gaussian Process Enhancement to Linear Parameter Varying Models

Simulation and analysis for modern engineering systems now routinely requires the merging of multiple disciplines, physical-domains, time-scales, and data sets — all at ever increasing levels. These capabilities are especially needed in the domain of Advanced Air Mobility, where rapidly emerging vehicle designs are significantly more complex, while having to be both cost-effective and safe. To meet these engineering challenges, machine learning methods are an attractive option for merging models and data across multiple areas while providing uncertainty quantification and maintaining computational efficiency. This paper examines the use of Gaussian process machine learning to generalize and enhance the commonly used class of quasi-Linear Parameter Varying models for fast full-envelope simulation while also supporting control system design and analysis with model uncertainty. Gaussian process machine learning is selected because it: can fuse multiple data sets, enables an easy trade-off between data fitting and smoothing, provides model uncertainty quantification, scales well with increasing complexity, and does not generally require starting from a large training data set. To demonstrate the benefits of the approach, a robust stability analysis with Gaussian process uncertainty is shown for a NASA reference design of an electric quad-rotor air-taxi concept vehicle with motor parameter uncertainty.

Gaussian Process↗
NASA NTRS1991

Application of machine learning and expert systems to Statistical Process Control (SPC) chart interpretation

Statistical Process Control (SPC) charts are one of several tools used in quality control. Other tools include flow charts, histograms, cause and effect diagrams, check sheets, Pareto diagrams, graphs, and scatter diagrams. A control chart is simply a graph which indicates process variation over time. The purpose of drawing a control chart is to detect any changes in the process signalled by abnormal points or patterns on the graph. The Artificial Intelligence Support Center (AISC) of the Acquisition Logistics Division has developed a hybrid machine learning expert system prototype which automates the process of constructing and interpreting control charts.

Shewhart, Mark↗
NASA NTRS2022

Advanced Lightweight Metallic Fuselage Project Manufacturing Trade Study

Recent advances in large-scale flow forming of integrally stiffened cylinders (ISCs) have motivated evaluation of available technologies for rapid manufacturing of metallic fuselages. The current state-of-the-art in flow forming of ISCs produces 10-ft. diameter, 5-ft. long barrels with integral longitudinal blade stiffeners, and these single-piece ISCs are produced in approximately 1.5 hours. While other manufacturing processes are required to incorporate additional structural elements (ASE) such as circumferential ring frames, reinforcements around window and door cut-outs, and floors to the ISCs to complete the fuselage structure, flow forming technology may assist the aerospace industry in meeting manufacturing rate demands. In order to evaluate this technology, a fuselage manufacturing demonstration article (MDA) fabricated from two ISCs is scheduled for fabrication and delivery to NASA Langley Research Center (LaRC) by the end of 2022. In this study, eight manufacturing technologies were assessed to downselect candidate manufacturing processes for adding ASE to complete the MDA. A literature review and evaluation of contractor-produced panels covering a spectrum of welding and additive manufacturing (AM) processes were conducted at NASA LaRC. The analytical hierarchy process (AHP) was used to evaluate figures of merit (FOMs) for selecting manufacturing process(es) to integrate ASE with the ISCs to form a fuselage MDA. The AHP results revealed that scalability, structural performance, and distortion control were the most valued criteria for downselecting the manufacturing process to construct the internal stiffening structures. Based on the FOMs, this study concluded that a welding process is best suited for integrating the majority of the ASE, namely the circumferential ring frames. All of the welding processes received higher scores than AM processes due to higher maturity, higher structural performance, fewer post-processing requirements in machining and heat treating, and faster deposition rates. Among the welding processes, cold metal transfer (CMT) welding was ranked the most favorable process for assembling the MDA, with the other welding processes (laser welding (LW), friction stir welding (FSW), and refill friction stir spot welding (RFSSW)) scoring slightly lower. This was a consequence of the perceived maturity of the CMT welding process and its relatively high structural performance and low distortion resulting from the low heat input. Among the AM processes, CMT AM showed the greatest promise due to benefits derived from its scalability, lower 1st order process complexity, and low distortion. The AM processes may have potential for select applications, such as adding structural reinforcements around window and door cut-outs, but are not considered optimal for integrating the entire MDA.

Manufacturing↗
NASA NTRS1994

A new machine classification method applied to human peripheral blood leukocytes

Human beings judge images by complex mental processes, whereas computing machines extract features. By reducing scaled human judgments and machine extracted features to a common metric space and fitting them by regression, the judgments of human experts rendered on a sample of images may be imposed on an image population to provide automatic classification.

Rorvig, Mark E.↗
NASA NTRS1987

Solution of large linear systems of equations on the massively parallel processor

The Massively Parallel Processor (MPP) was designed as a special machine for specific applications in image processing. As a parallel machine, with a large number of processors that can be reconfigured in different combinations it is also applicable to other problems that require a large number of processors. The solution of linear systems of equations on the MPP is investigated. The solution times achieved are compared to those obtained with a serial machine and the performance of the MPP is discussed.

Ida, Nathan↗
NASA NTRS1979

Holding fixture for variable-contour parts

Array of vacuum cups on spindles holds parts for safe machining and other processings. Variable-contour part resting on fixture is held firmly enough for machining, coating, or other mechanical treatment.

Haynie, C. C.↗
NASA NTRS2003

Effect of Molding and Machining on Neoflon CTFE M400H Polychlorotrifluoroethylene Rod Stock and Valve Seat Properties

Since 1997 numerous fires have been reported to the Food and Drug Administration involving cylinder valves installed on medical use oxygen cylinders sold and operated within the United States. All of the cylinder valves in question had polychlorotrifluoroethylene (PCTFE) valve seats. Subsequent failure analysis showed that the main seat was the primary source of ignition. A review of the incidents involving cylinder valve fires indicated three possible ignition mechanisms: contaminant promotion, flow friction, and resonance. However, gas purity analysis showed that uncombusted, residual oxygen was within specification. Infrared and energy dispersive spectroscopy further showed that no contaminants or organic compounds were present in the remaining, uncombusted valve seat material or on seat plug surfaces. Therefore, contaminant-promoted ignition did not appear to be responsible for the failures. Observations of extruded material along the outer edge of the coined or loaded seat area produced by cylinder overuse or poppet overload led to concerns that accelerated gas flow across a deformed seat surface could generate enough localized heating to ignite the polymeric seat. Low molecular weight or highly amorphous quick-quenched PCTFE grades might be expected to be especially prone to this type of deformation. Such a failure mechanism has been described as flow friction; however, the corresponding mechanistic parameters are poorly understood. Subsequent revelation of low-temperature dimensional instability by thermomechanical analysis (TMA) in a variety of PCTFE sheet and rod stock samples led to new concerns that PCTFE valve seats could undergo excessive expansion or contraction during service. During expansion, additional extrusion and accompanying flow friction could occur. During contraction, a gap between the seal and adjacent metal surfaces could form. Gas flowing past the gap could, in turn, lead to resonance heating and subsequent ignition as described in ASTM Guide for Evaluation Nonmetallic Materials for Oxygen Service (G 63). Attempts to uncover the origins of the observed dimensional instability were hindered by uncertainties about resin grade, process history, and post-process heat history introduced by machining, annealing, and sample preparation. An approach was therefore taken to monitor property changes before and after processing and machining using a single, well-characterized lot of Neoflon CTFE.1 M400H resin. A task group consisting of the current PCTFE resin supplier, two molders, and four valve seat manufacturers was formed, and phased testing on raw resin, intermediate rod stock, and finished valve seats initiated. The effect of processing and machining on the properties of PCTFE rod stock and oxygen gas cylinder valve seats was then determined. Testing focused on two types of extruded rod stock and one type of compression-molded rod stock. To accommodate valve seat manufacturer preferences for certain rod stock diameters, two representative diameters were used (4.8 mm (0.1875 in.) and 19.1 mm (0.75 in.)). To encompass a variety of possible sealing configurations, seven different valve seat types with unique geometries or machining histories were tested. The properties investigated were dimensional stability as determined by TMA, specific gravity, differential scanning calorimetry (DSC), compressive strength, zero strength time, and intrinsic viscosity. Findings are discussed in the context of polymer structure-process-property relationships whenever possible.

Waller, Jess M.↗
NASA NTRS1989

Three-Dimensional Robotic Vision System

Stereoscopy and motion provide clues to outlines of objects. Digital image-processing system acts as "intelligent" automatic machine-vision system by processing views from stereoscopic television cameras into three-dimensional coordinates of moving object in view. Epipolar-line technique used to find corresponding points in stereoscopic views. Robotic vision system analyzes views from two television cameras to detect rigid three-dimensional objects and reconstruct numerically in terms of coordinates of corner points. Stereoscopy and effects of motion on two images complement each other in providing image-analyzing subsystem with clues to natures and locations of principal features.

Nguyen, Thinh V.↗
NASA NTRS1990

Intensity dependent spread theory

The Intensity Dependent Spread (IDS) procedure is an image-processing technique based on a model of the processing which occurs in the human visual system. IDS processing is relevant to many aspects of machine vision and image processing. For quantum limited images, it produces an ideal trade-off between spatial resolution and noise averaging, performs edge enhancement thus requiring only mean-crossing detection for the subsequent extraction of scene edges, and yields edge responses whose amplitudes are independent of scene illumination, depending only upon the ratio of the reflectance on the two sides of the edge. These properties suggest that the IDS process may provide significant bandwidth reduction while losing only minimal scene information when used as a preprocessor at or near the image plane.

Holben, Richard↗
NASA NTRS1967

Process sequence produces strong, lightweight reflectors of excellent quality

Large compound curved surfaces for collecting and concentrating radiation are fabricated by the use of several common machining and forming processes. Lightweight sectors are assembled into large reflectors. With this concept of fabrication, integrally stiffened reflective sectors up to 25 square feet in area have been produced.

Reader, A. F.↗
NASA NTRS1985

The Factory of the Future

A brief history of aircraft production techniques is given. A flexible machining cell is then described. It is a computer controlled system capable of performing 4-axis machining part cleaning, dimensional inspection and materials handling functions in an unmanned environment. The cell was designed to: allow processing of similar and dissimilar parts in random order without disrupting production; allow serial (one-shipset-at-a-time) manufacturing; reduce work-in-process inventory; maximize machine utilization through remote set-up; maximize throughput and minimize labor.

Byman, J. E.↗
NASA NTRS1988

Machine Tool Software

A NASA-developed software package has played a part in technical education of students who major in Mechanical Engineering Technology at William Rainey Harper College. Professor Hack has been using (APT) Automatically Programmed Tool Software since 1969 in his CAD/CAM Computer Aided Design and Manufacturing curriculum. Professor Hack teaches the use of APT programming languages for control of metal cutting machines. Machine tool instructions are geometry definitions written in APT Language to constitute a "part program." The part program is processed by the machine tool. CAD/CAM students go from writing a program to cutting steel in the course of a semester.

Source record↗
NASA NTRS2021

Machine Learning to Predict Joint Performance in Epoxy Composites Based on Process Parameters

Polymer matrix composites are gaining popularity in the aerospace industry due to their high specific strength, fatigue properties, and processability. However, based on current FAA certification guidelines, manufacturers utilizing current state-of-the-art composites made with adhesive bonds commonly install redundant fasteners to guarantee the strength of these adhesively bonded composite parts. The number of fasteners in a single-aisle commercial transport aircraft is typically on the order of 105, which reduces manufacturing rate, increases cost tremendously, and reduces the advantage of the specific strength composites provide. Due to this, the Adhesive Free Bonding of Composites (AERoBOND) project at NASA Langley Research Center has developed a novel assembly process to manufacture complex composite parts without the use of adhesives and fasteners. However, optimization of the process is currently challenging due to the complex and interdependent process parameters. To assist with the optimization, four machine learning algorithms utilizing gradient boosting decision trees were created to provide predictions for the mechanical and characterization properties of the composite parts. Approximately 200 random states from each algorithm were tested, and the models from each state were isolated and analyzed based on their accuracy, a validation process, and their feature importance. This analysis concluded that the models created from the machine learning algorithms could accelerate a parametric study for the AERoBOND process by rapidly optimizing process parameters to achieve desired performance characteristics.

Brennen Michael Middleton↗