SEARCH · Engineering Papers

Results for “machining processes”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 631 records · Page 35

All sources NASA NTRS DOE OSTI

NASA NTRS2022

Natural Language Processing Techniques for Intelligent Knowledge Management of Safety Reports

Safety, failure, and incident reports are common artifacts across various domains, including aviation and wildfire response. These reports are often mandatory to submit, resulting in the culmination of large repositories of text-based documents. Simultaneously, these reports and corresponding repositories are often only manually analyzed and queried by users via out-of-date search engines. As a consequence, we have been developing the Manager for Intelligent Knowledge Access (MIKA) toolkit, which uses natural language processing to improve information access and reuse. In this presentation, we discuss natural language processing techniques for knowledge discovery and apply these methods to a repository of aerial wildfire mishap reports. Two methods are used for knowledge discovery: topic modeling and named-entity recognition. We use topic modeling to identify hazards and perform a trend analysis to produce a data-driven risk matrix. A custom named-entity recognition model, build from fine tuning a pre-trained language model, is used to identify failure modes, failure causes, failure effects, control processes, and recommendations to aid in failure modes and effects analysis (FMEA). Throughout the presentation, we discuss and apply natural language processing techniques to better leverage the vast amount of information contained in report repositories.

Machine learning↗

NASA NTRS2022

Design of Materials with Alchemite

Machine learning models that establish the relationships between materials processing and properties can enable inverse design of materials through active learning. Alchemite is a commercial software that can perform inverse materials design on sparse data. Here we evaluate Alchemite’s performance on a dataset of shape memory alloys and a dataset of heat exchangers compared to baseline random forest models. Alchemite had higher accuracy when making predictions on sparse data and was more accurate or nearly as accurate as random forests on complete datasets while also quantifying uncertainty. The software was also used to suggest processing steps and design parameters to optimize properties and performance; however, physical validation of the suggested design parameters was beyond the scope of this work. Several useful design insights were gained about the impact of the design parameters on properties and performance including the importance of dopant choice and amount for shape memory alloys and the importance of height and weight on the thermal resistance of heat exchangers.

Machine learning↗

NASA NTRS2010

Corrosion-Resistant Container for Molten-Material Processing

In a carbothermal process, gaseous methane is passed over molten regolith, which is heated past its melting point to a temperature in excess of 1,625 C. At this temperature, materials in contact with the molten regolith (or regolith simulant) corrode and lose their structural properties. As a result, fabricating a crucible to hold the molten material and providing a method of contact heating have been problematic. Alternative containment approaches use a large crucible and limit the heat zone of the material being processed, which is inefficient because of volume and mass constraints. Alternative heating approaches use non-contact heating, such as by laser or concentrated solar energy, which can be inefficient in transferring heat and thus require higher power heat sources to accomplish processing. The innovation is a combination of materials, with a substrate material having high structural strength and stiffness and high-temperature capability, and a coating material with a high corrosion resistance and high-temperature capability. The material developed is a molybdenum substrate with an iridium coating. Creating the containment crucible or heater jacket using this material combination requires only that the molybdenum, which is easily processed by conventional methods such as milling, electric discharge machining, or forming and brazing, be fabricated into an appropriate shape, and that the iridium coating be applied to any surfaces that may come in contact with the corrosive molten material. In one engineering application, the molybdenum was fashioned into a container for a heat pipe. Since only the end of the heat pipe is used to heat the regolith, the container has a narrowing end with a nipple in which the heat pipe is snugly fit, and the external area of this nipple, which contacts the regolith to transfer heat into it, is coated with iridium. At the time of this reporting, no single material has been found that can perform the functions of this combination of materials, and other combinations of materials have not proven to be survivable to the corrosiveness of this environment. High-temperature processing of materials with similar constituencies as lunar regolith is fairly common. The carbo-thermal process is commonly used to make metallurgical-grade silicon for the semiconductor and solar-cell industries.

Stern, Theodore G.↗

NASA NTRS1999

Textural-Contextual Labeling and Metadata Generation for Remote Sensing Applications

Despite the extensive research and the advent of several new information technologies in the last three decades, machine labeling of ground categories using remotely sensed data has not become a routine process. Considerable amount of human intervention is needed to achieve a level of acceptable labeling accuracy. A number of fundamental reasons may explain why machine labeling has not become automatic. In addition, there may be shortcomings in the methodology for labeling ground categories. The spatial information of a pixel, whether textural or contextual, relates a pixel to its surroundings. This information should be utilized to improve the performance of machine labeling of ground categories. Landsat-4 Thematic Mapper (TM) data taken in July 1982 over an area in the vicinity of Washington, D.C. are used in this study. On-line texture extraction by neural networks may not be the most efficient way to incorporate textural information into the labeling process. Texture features are pre-computed from cooccurrence matrices and then combined with a pixel's spectral and contextual information as the input to a neural network. The improvement in labeling accuracy with spatial information included is significant. The prospect of automatic generation of metadata consisting of ground categories, textural and contextual information is discussed.

Kiang, Richard K.↗

NASA NTRSDate not supplied

Creating Benchmark Data for Artificial Intelligence and Machine Learning Space Biology Research

To identify an appropriate AI/ML approach for a specific problem, the best practice is to measure algorithm performance through the benchmarking process. A scientific benchmark consists of an AI-ready dataset and a reference implementation on a specific scientific question. The NASA Science Mission Directorate (SMD) has started the “Benchmark Initiative for AI/ML to create scientific benchmark datasets in three applications: 1) scientific benchmarking, which finds the best algorithm for a specific problem; 2) application benchmarking, which measures algorithm performance against a set of parameters; and 3) system benchmarking, which evaluates performance of hardware and software architecture. Currently, there are no standardized datasets available to benchmark AI/ML algorithms in the domain of space biology. In this work, we constructed two AI/ML-ready biological datasets from experiments in space-flown mice: cellular imaging and RNA-seq. First, radiation-exposed immune cells harbor DNA damage foci that can be fluorescently marked to visualize the amount of damage following exposure to ionizing radiation. However, such large datasets are difficult to analyze visually, due to imaging inconsistencies and human bias, and classical image processing approaches can fail on imaging artifacts. AI/ML are therefore exciting alternative, providing the speed of machines and the accuracy of humans. We have made this dataset available at https://registry.opendata.aws/bps_microscopy/. Second, high-throughput nucleic acid sequencing (DNA-seq, RNA-seq) has become widespread in biomedical research due to the growing availability and affordability of these assays. However, most sequencing datasets suffer from high dimensionality and low sample count. In this work, we used a generative adversarial network to synthesize a standardized, AI-ready, publicly available benchmark dataset for space biology RNA-seq data with sufficient space-flown and ground control mouse liver samples from NASA GeneLab. This dataset is available at https://registry.opendata.aws/bps_rnaseq/. These datasets are now fully open the Space Biology community to test their favorite AI/ML approaches.

James Casaletto↗

NASA NTRS1977

A technique for real-time data preprocessing

A processing system is presented that implements simultaneously the efficiency of the special-purpose processor and the total applicability of the general-purpose computer - characteristics commonly thought of as being mutually exclusive. The solution adopted is that of specializing the machine by programming the hardware structure, rather than by adding software systems to it. Data are organized in circulating pages which form a plurality of local dynamic memories for each process. Programs are made up of modules, each describing a transient special-purpose machine. Applications to real-time processing of radar signals are referred to.

Schaffner, M. R.↗

NASA NTRS1984

Operating systems

A counter operating system creates a hierarchy of levels of abstraction, so that at a given level all details concerning lower levels can be ignored. This hierarchical structure separates functions according to their complexity, characteristic time scale, and level of abstraction. The lowest levels include the system's hardware; concepts associated explicitly with the coordination of multiple tasks appear at intermediate levels, which conduct 'primitive processes'. Software semaphore is the mechanism controlling primitive processes that must be synchronized. At higher levels lie, in rising order, the access to the secondary storage devices of a particular machine, a 'virtual memory' scheme for managing the main and secondary memories, communication between processes by way of a mechanism called a 'pipe', access to external input and output devices, and a hierarchy of directories cataloguing the hardware and software objects to which access must be controlled.

Denning, P. J.↗

NASA NTRSDate not supplied

Machine Learning Application in Aircraft Engine Conceptual Design

In the current competitive environment, the successful creation and application of machine learning (ML) technologies have become crucial across multiple industries. This study outlines the process of creating and implementing ML models for conceptualizing and evaluating aircraft engines. These models use supervised deep-learning algorithms to analyze patterns within an open-source repository containing data on both production and research conventional turbofan engines. Key focus areas include crucial engine parameters such as thrust-specific fuel consumption (TSFC), engine weight, engine diameter, and turbomachinery stage counts. While developing ML models is fundamental, ensuring their seamless deployment is equally important. To address this, a conversational AI chatbot is constructed using natural language processing (NLP) techniques to facilitate the deployment of these ML models. The comprehensive workflow includes several key stages: gathering and enhancing engine data, training and cross validating the ML models, testing and evaluating their performance, and finally, deploying, monitoring, and updating the ML models. By following this systematic approach, the aim is to streamline the development and deployment process of ML models tailored for aircraft engine conceptual design.

Aircraft Engine↗

NASA NTRS2011

Method and system for fault accommodation of machines

A method for multi-objective fault accommodation using predictive modeling is disclosed. The method includes using a simulated machine that simulates a faulted actual machine, and using a simulated controller that simulates an actual controller. A multi-objective optimization process is performed, based on specified control settings for the simulated controller and specified operational scenarios for the simulated machine controlled by the simulated controller, to generate a Pareto frontier-based solution space relating performance of the simulated machine to settings of the simulated controller, including adjustment to the operational scenarios to represent a fault condition of the simulated machine. Control settings of the actual controller are adjusted, represented by the simulated controller, for controlling the actual machine, represented by the simulated machine, in response to a fault condition of the actual machine, based on the Pareto frontier-based solution space, to maximize desirable operational conditions and minimize undesirable operational conditions while operating the actual machine in a region of the solution space defined by the Pareto frontier.

Goebel, Kai Frank↗

NASA NTRS1988

Vulnerability-attention analysis for space-related activities

Techniques for representing and analyzing trouble spots in structures and processes are discussed. Identification of vulnerable areas usually depends more on particular and often detailed knowledge than on algorithmic or mathematical procedures. In some cases, machine inference can facilitate the identification. The analysis scheme proposed first establishes the geometry of the process, then marks areas that are conditionally vulnerable. This provides a basis for advice on the kinds of human attention or machine sensing and control that can make the risks tolerable.

Ford, Donnie↗

NASA NTRS2003

Metal Cutting Theory and Friction Stir Welding Tool Design

Friction Stir Welding (FSW) is a relatively new industrial process that was invented at The Weld Institute (TWI, United Kingdom) and patented in 1992 under research funded by in part by the National Aeronautics and Space Administration (NASA). Often quoted advantages of the process include good strength and ductility along with minimization of residual stress and distortion. Less well advertised are the beneficial effects of this solid state welding process in the field of occupational and environmental safety. It produces superior weld products in difficult to weld materials without producing any toxic fumes or solid waste that must be controlled as hazardous waste. In fact, it reduces noise pollution in the workspace as well. In the early days of FSW, most welding was performed on modified machine tools, in particular on milling machines with modified milling cutters. In spite of the obvious milling heritage of the process, the techniques and lessons learned from almost 250 years of successful metalworking with milling machines have not been applied in the field of modern Friction Stir Welding. The goal of the current research was to study currently successful FSW tools and parameterize the process in such a way that the design of new tools for new materials could be accelerated. Along the way, several successful new tooling designs were developed for current issues at the Marshall Space Flight Center with accompanying patent disclosures

Payton, Lewis N.↗

NASA NTRS1993

The WINCOF-I code: Detailed description

The performance of an axial-flow fan-compressor unit is basically unsteady when there is ingestion of water along with the gas phase. The gas phase is a mixture of air and water vapor in the case of a bypass fan engine that provides thrust power to an aircraft. The liquid water may be in the form of droplets and film at entry to the fan. The unsteadiness is then associated with the relative motion between the gas phase and water, at entry and within the machine, while the water undergoes impact on material surfaces, centrifuging, heat and mass transfer processes, and reingestion in blade wakes, following peal off from blade surfaces. The unsteadiness may be caused by changes in atmospheric conditions and at entry into and exit from rain storms while the aircraft is in flight. In a multi-stage machine, with an uneven distribution of blade tip clearance, the combined effect of various processes in the presence of steady or time-dependent ingestion is such as to make the performance of a fan and a compressor unit time-dependent from the start of ingestion up to a short time following termination of ingestion. The original WINCOF code was developed without accounting for the relative motion between gas and liquid phases in the ingested fluid. A modification of the WINCOF code was developed and named WINCOF-1. The WINCOF-1 code can provide the transient performance of a fan-compressor unit under a variety of input conditions.

Murthy, S. N. B.↗

NASA NTRS2023

Machine Learning Framework for Hazard Extraction and Analysis of Trends (HEAT) in Wildfire Response

This research proposes a natural language processing enabled risk analysis framework, named Hazard Extraction andAnalysis of Trends (HEAT), and applies the framework to the ICS-209-PLUS data set of wildfire incident responseforms. The HEAT framework produces safety- and risk- relevant analyses, consisting of: (1) a set of hazards extractedfrom text data, (2) a primary analysis using hazard-relevant metrics, such as rate and severity, to form an FMEA-styletable and risk matrix, (3) a time series analysis of metric trends, and (4) a secondary analysis examining potentialpredictors for hazards. Results from HEAT provide quantitative risk-relevant information for high-level hazards doc-umented in existing-state operations. Because of the generalizability of the steps and limited data requirements, HEATcan be applied to any dataset containing narrative text, thus providing a framework for data-driven machine learning-enabled quantitative risk analysis across a variety of domains. To demonstrate HEAT in a case study, we apply theframework to the ICS-209-PLUS dataset of wildland fire incident response forms. Hazards identified in wildfire re-sponse arise from environmental conditions, the mission, and the wildland urban interface. The resulting risk matrixidentifies evacuations as high-risk hazards, while all other identified hazards are medium or serious risk.

natural language processing↗

NASA NTRS1970

Syntax-directed documentation for PL360

PL360 is a phrase-structured programming language which provides the facilities of a symbolic machine language for the IBM 360 computers. An automatic process, syntax-directed documentation, is described which acquires programming documentation through the syntactical analysis of a program, followed by the interrogation of the originating programmer. This documentation can be dispensed through reports of file query replies when other programmers later need to know the program structure and its details. A key principle of the programming documentation process is that it is managed solely on the basis of the syntax of programs.

Mills, H. D.↗

NASA NTRS1977

Infrared image construction with computer-generated reflection holograms

Computer-generated reflection holograms hold substantial promise as a means of carrying out complex machining, marking, scribing, welding, soldering, heat treating, and similar processing operations simultaneously and without moving the work piece or laser beam. In the study described, a photographically reduced transparency of a 64 x 64 element Lohmann hologram was used to make a mask which, in turn, was used (with conventional photoresist techniques) to produce a holographic reflector. Images from a commercial CO2 laser (150W TEM(00)) and the holographic reflector are illustrated and discussed.

Angus, J. C.↗

NASA NTRS1986

Producing Silicon Carbide/Silicon Nitride Fibers

Manufacturing process makes CxSiyNz fibers. Precursor fibers spun from extruding machine charged with polycarbosilazane resin. When pyrolyzed, resin converted to cross-linked mixture of silicon carbide and silicon nitride, still in fiber form. CxSiyNz fibers promising substitutes for carbon fibers in high-strength, low-weight composites where high electrical conductivity unwanted.

Source record↗

NASA NTRS1989

Laboratory process control using natural language commands from a personal computer

PC software is described which provides flexible natural language process control capability with an IBM PC or compatible machine. Hardware requirements include the PC, and suitable hardware interfaces to all controlled devices. Software required includes the Microsoft Disk Operating System (MS-DOS) operating system, a PC-based FORTRAN-77 compiler, and user-written device drivers. Instructions for use of the software are given as well as a description of an application of the system.

Will, Herbert A.↗

NASA NTRS1990

Shape Gauge Measures Surfaces

Tedious measurements and calculations performed quickly and accurately. Optical/mechanical/electronic system acts as shape gauge by taking measurements of machine-tool motions or complicated contours of objects, then processing measurement data into maps or profiles indicative of shapes. Transducers of shape gauge mechanical/electronic or optomechanical/electronic components that move along path on surface and measure roundness, perpendicular deviation, or slope of surface. Because of ease and speed, many intersecting profiles measured on surface.

Veale, Ralph C.↗