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At least 289 records · Page 16
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NASA NTRSDate not supplied

Probing the Sun with Imaging Spectrographs

EUV and X-ray images of the Sun have revolutionized our understanding of our closest star. With them, we can probe the morphology and temperature structure of the solar atmosphere and see how they evolve as a function of space and time. However, image data cannot be used to determine line-of-sight velocities, abundances, or densities. This information is required to calculate the energy budget of eruptive events, provide boundary conditions for global solar models, and explore fundamental processes occurring in the solar atmosphere. For those diagnostics, we require spectroscopy. Because the structures on the Sun are extended sources, most modern-day spectrographs observe the Sun through long narrow slits. Two-dimensional, spectrally pure solar images with velocity, abundance, and density information are built up by stepping the slit over regions of interest. This mode of operation implies that two-dimensional information is highly limited by cadence and the temporal evolution and spatial structure of these parameters can never be truly separated. Both spatial and spectral information can be obtained in a single snapshot with slitless spectrographs, which were often used in the 1950-1970s, but were abandoned due to the difficulty of unfolding the overlapping spatial and spectral information. Thanks to advances in computer processing speeds and machine learning algorithms, there have been several techniques developed to complete the spatial/spectral unfolding, unlocking the full capability of slitless spectrographs for solar observations. The goal of this talk is to give a broad overview of the capability of such instruments and demonstrate their usefulness in the next decade of solar observatories and beyond.

Amy Winebarger↗
NASA NTRSDate not supplied

Artificial Intelligence Enhancements to Imagery for Space Operations

Philosophy classes still ponder the question asked by Dr. George Berkely, an Anglican Bishop and philosopher in the 1600’s-- “If a tree falls in a forest and no one is around to hear it, does it make a sound?” With that in mind, I ask the following—If a still image or motion imagery from a space mission cannot be found during a search, does it exist? Since the beginning of spaceflight, imagery has been a key form of data collected. Whether for mere curiosity (what does Earth look like from Space?), or for operational reasons (did the solar panel deploy?), or for engineering purposes (what was that object that floated away from the spacecraft?), imagery has been included in space missions. To be useful, though, the image or motion imagery must be accessible and accessed when needed. During the analog era, that typically meant captions and numbers associated with the physical media. With “born digital” imagery, it is possible to add metadata to the image data file. This metadata might include the date and time of capture, mission, camera, exposure data, and similar data fields. Many modern cameras embed some basic metadata into the image file at the moment of capture. The reality, though, is even with today’s born-digital enhancements with embedded metadata at the time of capture, reviewing and cataloging still and motion imagery is very labor intensive. Humans review the imagery for sensitive content (privacy concerns, imagery containing proprietary data/subject matter), and to identify imagery containing crew members or imagery that should be reviewed for engineering or scientific reasons. All this review and manual data entry is very time-consuming. Many improvements in Artificial Intelligence (AI), Machine Learning, and processing power now make it possible to identify persons, objects, motion, color, audio with sensitive content, and other details after or while the imagery is captured.

Rodney Grubbs↗
NASA NTRSDate not supplied

Probing the Sun with Imaging Spectrographs

EUV and X-ray images of the Sun have revolutionized our understanding of our closest star. With them, we can probe the morphology and temperature structure of the solar atmosphere and see how they evolve as a function of space and time. However, image data cannot be used to determine line-of-sight velocities, abundances, or densities. This information is required to calculate the energy budget of eruptive events, provide boundary conditions for global solar models, and explore fundamental processes occurring in the solar atmosphere. For those diagnostics, we require spectroscopy. Because the structures on the Sun are extended sources, most modern-day spectrometers observe the Sun through long narrow slits. Two-dimensional, spectrally pure solar images with velocity, abundance, and density information are built up by stepping the slit over regions of interest. This method implies that two-dimensional information is highly limited by cadence and the temporal evolution and spatial structure of these parameters can never be truly separated. Both spatial and spectral information can be obtained in a single snapshot with slitless spectrometers, which were often used in the 1950-1970s, but were abandoned due to the difficulty of unfolding the overlapping spatial and spectral information. Thanks to advances in computer processing speeds and machine learning algorithms, there have been several techniques developed to complete the spatial/spectral unfolding, unlocking the full capability of slitless spectrometers for solar observations. The goal of this talk is to give an overview of the capability of such instruments, including recent results from a sounding rocket flight, and demonstrate their usefulness in the next decade of solar observatories and beyond.

Amy Winebarger↗
NASA NTRSDate not supplied

Probing the Sun with Imaging Spectrographs

EUV and X-ray images of the Sun have revolutionized our understanding of our closest star. With them, we can probe the morphology and temperature structure of the solar atmosphere and see how they evolve as a function of space and time. However, image data cannot be used to determine line-of-sight velocities, abundances, or densities. This information is required to calculate the energy budget of eruptive events, provide boundary conditions for global solar models, and explore fundamental processes occurring in the solar atmosphere. For those diagnostics, we require spectroscopy. Because the structures on the Sun are extended sources, most modern-day spectrometers observe the Sun through long narrow slits. Two-dimensional, spectrally pure solar images with velocity, abundance, and density information are built up by stepping the slit over regions of interest. This method implies that two-dimensional information is highly limited by cadence and the temporal evolution and spatial structure of these parameters can never be truly separated. Both spatial and spectral information can be obtained in a single snapshot with slitless spectrometers, which were often used in the 1950-1970s, but were abandoned due to the difficulty of unfolding the overlapping spatial and spectral information. Thanks to advances in computer processing speeds and machine learning algorithms, there have been several techniques developed to complete the spatial/spectral unfolding, unlocking the full capability of slitless spectrometers for solar observations. The goal of this talk is to give an overview of the capability of such instruments, including recent results from a sounding rocket flight, and demonstrate their usefulness in the next decade of solar observatories and beyond.

Amy Winebarger↗
NASA NTRS2023

Automated Fiber Placement Through Thickness Defect Stacking Optimization

In its 2022 commercial market outlook, Boeing forecasted an 80% increase in the global fleet through 2041 compared to 2019 pre-pandemic levels. This sharp rise in demand will drive pressure onto airframe manufacturers to ramp up production and find more efficient ways to design and manufacture airplanes. Complicating this challenge is the industry’s recent transformation from traditional metal-based airframes towards hybrid composite-metal aircraft. While composites have been used in aviation for decades, aircraft manufacturers are still struggling to design and manufacture quality parts at a high rate. Automated Fiber Placement (AFP) is one of the main manufacturing techniques used to produce large-scale composite parts. After a design has been created, a manufacturing strategy has to be developed based on the working material, part geometry, and machine capabilities. This process planning stage is essential to the AFP workflow and currently requires a high level of manual input from an experienced process planner. In an effort to automate and optimize this stage, the Computer Aided Process Planning (CAPP) module was developed. CAPP assists process planners in identifying optimal starting point location and layup strategy for each ply of a laminate. This Ply-Level Optimization (PLO) phase operates on the quantification of ply quality through predictable geometry-based defects such as gaps, overlaps, angle deviation, and steering. As you move from PLO to Laminate-Level Optimization (LLO) the design space grows exponentially, emphasizing the need for automated optimization. The work presented in this thesis expands CAPP’s functionality by comparing the planned fiber paths through the thickness of the laminate to mitigate stacked area defects and achieve an optimal laminate-level manufacturing strategy. Within CAPP, predicted gap and overlap defects are imported from Vericut Composites Programming (VCP) and then discretized to streamline the through-thickness comparison. Two objective functions are used to score different combinations of ply layup strategies based on defect stacking both globally and locally. Four combinatorial optimization algorithms were coupled with these objective functions to investigate the laminate-level manufacturing strategy design space and converge on the optimal plan. These algorithms were evaluated based on accuracy and efficiency through virtual testing on a complex tool surface. A separate LLO approach was developed to achieve near-optimal laminates in significantly less time. The end result is a software package which greatly reduces the required input from process planners, shortening the design-build cycle time and improving part quality.

AFP↗
NASA NTRSDate not supplied

Probing the Sun with Imaging Spectrographs

EUV and X-ray images of the Sun have revolutionized our understanding of our closest star. With them, we can probe the structure of the solar atmosphere and see how they evolve as a function of space and time. However, image data cannot be used to determine line-of-sight velocities, abundances, or densities. This information is required to calculate the energy budget of eruptive events, provide boundary conditions for global solar models, and explore fundamental processes occurring in the solar atmosphere. For those diagnostics, we require spectroscopy. Because the structures on the Sun are extended sources, most modern-day spectrometers observe the Sun through long narrow slits. Two-dimensional, spectrally pure solar images with velocity, abundance, and density information are built up by stepping the slit over regions of interest. This method implies that two-dimensional information is highly limited by cadence and the temporal evolution and spatial structure of these parameters can never be truly separated. Both spatial and spectral information can be obtained in a single snapshot with slitless spectrometers, which were often used in the 1950-1970s, but were abandoned due to the difficulty of unfolding the overlapping spatial and spectral information. Thanks to advances in computer processing speeds and machine learning algorithms, there have been several techniques developed to complete the spatial/spectral unfolding, unlocking the full capability of slitless spectrometers for solar observations. The goal of this talk is to give an overview of the capability of such instruments and demonstrate their usefulness in the next decade of solar observatories and beyond.

Amy Winebarger↗
NASA NTRSDate not supplied

3D Scanning Laboratory

The 3D Scanning Lab uses a number of technologies to capture 3D surface data. Those technologies are structured light scanning, discrete point measurement photogrammetry and Light Detecting and Ranging(LIDAR). Structured light is a non-contact optical technique used to capture as built surface geometries quickly and accurately. LIDAR is a laser scanning technique used to capture 3D surfaces. And photogrammetry uses a certified DSLR camera to measure discrete registered and non-registered points in 3D space. MSFC’s 3D Scanning Team uses 3D scanning to provide hardware inspections, reverse engineered CAD models, manufacturing/process development and digital assemblies. The inspection process compares the captured data to CAD, providing a color plot detailing the deviations of the scan data to the CAD model. Dimensional measurements and GD&T can also be interrogated. Inspection can provide scan-to-scan data comparisons which is ideal for assessing tested hardware, e.g. comparing data captured before and after hardware testing. Reverse engineering is used to develop CAD models from scan data. This is useful for heritage hardware studies, developing building or structural models and inputs for simulation studies and design efforts. The lab uses the 3D surface scan data to drive manufacturing/machining processes, termed match machining. This technique has been used to modify nozzles, injectors, additive manufactured parts, composite barrel sections, etc. Parts of an assembly can be scanned and assembled in a virtual environment providing an accurate 3D representation of the assembled hardware in its as-scanned state. This can provide detailed information of internal components that would otherwise not be accessible.

Advanced Manufacturing↗
NASA NTRSDate not supplied

Developing Natural Language Processing and Supervised Learning Techniques to Classify Mars Tasks

As NASA's Human Research Program (HRP) prepares for long-duration Mars missions, understanding astronaut tasks is crucial. This study, conducted at NASA Glenn Research Center (GRC), employed Natural Language Processing (NLP) and machine learning techniques to analyze and classify Mars tasks. A list of 1,058 Mars tasks was provided by HRP experts including binary labeling of 18 Human System Task Categories (HSTCs). We developed an NLP model using Google's BERT language model to capture the semantic and syntactic nuances of these tasks. Supervised training was initially applied to a subset of the NLP-analyzed tasks to assess the model's effectiveness in classifying the remaining tasks. Incorporating HSTC descriptions significantly enhanced the classification accuracy for 9 out of the 18 HSTCs and reduced training time. To address the issue of severe class imbalance in the HSTC data, we introduced innovative weighting and sampling techniques for data augmentation. We then fine-tune BERT to implement a pairwise relatedness scoring method, allowing us to cluster tasks based on their relatedness and similarity, getting a step closer to labeling the tasks without supervision. In this presentation we guide you through data preprocessing, deciphering key syntax components using BERT, and performing supervised classification of the Mars tasks. This work showcases the potential use of advanced NLP techniques to analyze Mars missions to be incorporated into various crew health and performance analyses.

GenAI↗
NASA NTRS2007

Fabrication in Space - What Materials are Needed?

In order to sustain life on the moon, and especially on Mars, the inhabitants must be self-sufficient. As on Earth, electronic and mechanical systems will break down and must be repaired. It is not realistic to "send" parts to the moon or Mars in an effort to replace failed ones or have spares for all components. It will be important to have spares on hand and even better would be to have the capability to fabricate parts in situ. The In Situ Fabrication and Repair (ISFR) team is working to develop the Arcam Electron Beam Melting (EBM) machine as the manufacturing process that will have the capability to produce repair parts, as well as new designs, and tooling on the lunar surface and eventually on Mars. What materials will be available for the inhabitants to use? What materials would be most useful? The EBM process is versatile and can handle a multitude of materials. These include titanium, stainless steels, aluminums, inconels, and copper alloys. Research has shown what parts have failed during past space missions and this data has been compiled and assessed. The EBM machine is fully capable of processing these materials of choice. Additionally, the long-term goal is to use the lunar regolith as a viable feedstock. Preliminary work has been performed to assess the feasibility of using raw lunar regolith as a material source or use a binder combined with the regolith to achieve a good melt.

Good, J↗
NASA NTRS2005

Automated Rapid Prototyping of 3D Ceramic Parts

An automated system of manufacturing equipment produces three-dimensional (3D) ceramic parts specified by computational models of the parts. The system implements an advanced, automated version of a generic rapid-prototyping process in which the fabrication of an object having a possibly complex 3D shape includes stacking of thin sheets, the outlines of which closely approximate the horizontal cross sections of the object at their respective heights. In this process, the thin sheets are made of a ceramic precursor material, and the stack is subsequently heated to transform it into a unitary ceramic object. In addition to the computer used to generate the computational model of the part to be fabricated, the equipment used in this process includes: 1) A commercially available laminated-object-manufacturing machine that was originally designed for building woodlike 3D objects from paper and was modified to accept sheets of ceramic precursor material, and 2) A machine designed specifically to feed single sheets of ceramic precursor material to the laminated-object-manufacturing machine. Like other rapid-prototyping processes that utilize stacking of thin sheets, this process begins with generation of the computational model of the part to be fabricated, followed by computational sectioning of the part into layers of predetermined thickness that collectively define the shape of the part. Information about each layer is transmitted to rapid-prototyping equipment, where the part is built layer by layer. What distinguishes this process from other rapid-prototyping processes that utilize stacking of thin sheets are the details of the machines and the actions that they perform. In this process, flexible sheets of ceramic precursor material (called "green" ceramic sheets) suitable for lamination are produced by tape casting. The binder used in the tape casting is specially formulated to enable lamination of layers with little or no applied heat or pressure. The tape is cut into individual sheets, which are stacked in the sheet-feeding machine until used. The sheet-feeding machine can hold enough sheets for about 8 hours of continuous operation.

McMillin, Scott G.↗
NASA NTRS1995

Eutectic-Free Superalloy Made By Directional Solidification

By suitable control of thermal conditions in directional-solidification process, supperalloy structural and machine components (e.g., turbine blades) cast with microstructures enhancing resistance to fatigue. Specific version of process and thermal conditions chosen to reduce micro-segregation during solidification and to minimize or eliminate script carbide and eutectic-phase inclusions, which are brittle inclusions found to decrease resistance to fatigue.

Schmidt, Deborah Dianne↗
NASA NTRS1968

A Computer Program for Simplifying Incompletely Specified Sequential Machines Using the Paull and Unger Technique

This report presents a description of a computer program mechanized to perform the Paull and Unger process of simplifying incompletely specified sequential machines. An understanding of the process, as given in Ref. 3, is a prerequisite to the use of the techniques presented in this report. This process has specific application in the design of asynchronous digital machines and was used in the design of operational support equipment for the Mariner 1966 central computer and sequencer. A typical sequential machine design problem is presented to show where the Paull and Unger process has application. A description of the Paull and Unger process together with a description of the computer algorithms used to develop the program mechanization are presented. Several examples are used to clarify the Paull and Unger process and the computer algorithms. Program flow diagrams, program listings, and a program user operating procedures are included as appendixes.

Ebersole, M. M.↗
NASA NTRS1973

Digital communications study

Research is reported dealing with problems of digital data transmission and computer communications networks. The results of four individual studies are presented which include: (1) signal processing with finite state machines, (2) signal parameter estimation from discrete-time observations, (3) digital filtering for radar signal processing applications, and (4) multiple server queues where all servers are not identical.

Boorstyn, R. R.↗
NASA NTRSDate not supplied

Microstructural Characterization of Laser Powder Bed Fusion (L-PBF) Additively Manufactured Inconel 718 for Aerospace Application

Alloy 718 (Inconel 718) is used for aerospace applications because of its excellent corrosion resistance and mechanical properties. This alloy is particularly applicable in manufacturing components subjected to high temperatures in rocket engines, aero-engines, and gas turbines. Properties for this alloy when processing on systems from similar and different Laser Powder Bed Fusion (L-PBF) machines provide subtle differences due to process parameters, feedstock, and machine configurations. A series of sixteen L-PBF AM Inconel 718 geometric feature build plates have been evaluated for microstructure using optical microscopy. This study presents the details of the microstructure analysis concerning geometry and different machine platforms. Microstructural investigations of these samples included average grain width measurement for all the X-Y and Y-Z build layers and are accompanied by process parameters and powder characterization. The present work concludes with a discussion on the importance of captured differences among builds to understand the practical limitations among AM platforms.

Additive Manufacturing↗
NASA NTRS1986

Concurrent Cholesky factorization of positive definite banded Hermitian matrices

First, the Cholesky factorization is extended to cover uniformly partitioned banded positive definite matrices of rank n which may be real symmetric or Hermitian. Then, two stratagems are given for the use of the algorithm in concurrent machines where the number of processing elements is less than required to factor the matrix in as few serial steps as possible, and where uniformly high efficiency is expected from all processing elements. Expressions are given for the efficiency factor e appearing in the speed-up expression q = eN, and these are specialized for the N node hypercube machine as a function of partition size s, the number N of processing elements of the hypercube machine, and the cost mu of interelement transmission relative to computation. It is shown that the efficiency factor e is inversely proportional to mu/s, and that e is almost independent of N when N is large and mu/s = 0. The task is completed in n/s serial steps with no limit on n. The half bandwidth b of the matrix is 2 Ns.

Utku, S.↗
NASA NTRS1979

Development of a beam builder for automatic fabrication of large composite space structures

The composite material beam builder which will produce triangular beams from pre-consolidated graphite/glass/thermoplastic composite material through automated mechanical processes is presented, side member storage, feed and positioning, ultrasonic welding, and beam cutoff are formed. Each process lends itself to modular subsystem development. Initial development is concentrated on the key processes for roll forming and ultrasonic welding composite thermoplastic materials. The construction and test of an experimental roll forming machine and ultrasonic welding process control techniques are described.

Bodle, J. G.↗
NASA NTRS1980

A framework for automated decision making and problem solving

Problems can be subdivided into two main categories: well structured problems and ill structured problems. The first require routine repetitive decisions which are generally amenable to programmable decision processes. The second require novel nonprogrammable decision processes. The decision making processes can be subdivided into those representative of those done by humans and those done by machine. Many of such decision processes require a combination of humans d machines. Automated decision making and problem solving technologies are expected to have their greatest potential impact in the space program.

Heer, E.↗