Engineering Papers⌕ Search

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.

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

At least 1,297 records · Page 72
All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

Friction stir welding process to repair voids in aluminum alloys

The present invention provides an in-process method to repair voids in an aluminum alloy, particularly a friction stir weld in an aluminum alloy. For repairing a circular void or an in-process exit hole in a weld, the method includes the steps of fabricating filler material of the same composition or compatible with the parent material into a plug form to be fitted into the void, positioning the plug in the void, and friction stir welding over and through the plug. For repairing a longitudinal void (30), the method includes machining the void area to provide a trough (34) that subsumes the void, fabricating filler metal into a strip form (36) to be fitted into the trough, positioning the strip in the trough, and rewelding the void area by traversing a friction stir welding tool longitudinally through the strip. The method is also applicable for repairing welds made by a fusing welding process or voids in aluminum alloy workpieces themselves.

Rosen, Charles D.↗
NASA NTRSDate not supplied

Surface Temperature and the Compositional Effects of Electron Bombardment on Dione

Saturn’s icy moons are composed of mostly water ice with minor “contaminants,” such as CO2 ice, and a dark component thought to be organics, hematite and/or metallic Fe [1]. The space weathering process of electron bombardment is expected to be particularly important on the surfaces of Saturn’s inner moons (Mimas, Tethys, Dione, and Rhea), as they orbit within the extended plasma sheet of Saturn’s magnetosphere. Terrains exhibiting thermal anomalies (i.e., colder temperatures in the day and warmer temperatures at night than surrounding areas) correspond to regions of high energy electron bombardment [2; 3]. Energetic electrons impact the surfaces, fracturing ice particles into sub-micron sized grains, and this process becomes more effective for increasing particle energies [4]. In addition to alteration of ices, the dark material may also be modified or generated within icy surfaces by interaction with solar UV radiation, cosmic rays, or by particles trapped in Saturn’s magnetic field [5]. CO2 could be sourced from irradiation of dark organic material [7]. Many of these alterations should be observable in Visible and Infrared Mapping Spectrometer (VIMS) spectra [8]. We used surface temperatures derived from Cassini’s Composite Infrared Spectrometer (CIRS) to help isolate areas where space weathering due to electron bombardment is more predominant and investigated the subtle changes in VIMS spectra using machine learning techniques. We present our results here for Dione. On ocean worlds, space weathering products would be present along with any potential indications of life. We will need to understand the manifestation of space weathering in the data to separate the two processes.

Cindy Lee Young↗
NASA NTRS2026

Smart Process Planning for Automated Fiber Placement

Many industries, including aerospace, automotive, wind energy, maritime, and sporting goods, rely on strong, lightweight materials called composites. These materials are made by layering fibers, which can come in the form of narrow strips or wider sheets, and setting them in a polymer matrix. One of the most advanced ways to make these parts is through automated fiber placement, where a machine lays down the fibers in precise patterns. This method can create very efficient and strong designs, but it is complex, expensive, and often depends heavily on the experience of skilled engineers. Today, the design, manufacturing, and inspection stages of composite production are usually handled separately. This separation means that important information, such as how a part will be built or what defects might occur, is not always shared between stages. As a result, parts may not be as lightweight, strong, or defect-free as possible, and the process can take longer and cost more. This research develops a smart process planning system that connects design, manufacturing, and inspection into one continuous process. Built as software that works with existing tools, the system can automatically plan how the fibers are placed, predicting and reducing defects while improving both manufacturability and strength. The system optimizes not only individual layers but also how defects are distributed across all layers, preventing them from stacking up in ways that weaken the final part. It also uses inspection results from completed parts to improve future designs, creating a feedback loop where each stage informs the others. The system was tested by designing a composite panel using this new approach and comparing it to a panel made with state-of-the-art manual planning methods. The results showed that the system could intentionally control where defects appeared and increase the efficiency of the planning process. By unifying design, manufacturing, and inspection, this research shows a way to make advanced composite manufacturing more efficient, consistent, and cost-effective. This approach lowers the barrier to using automated fiber placement and opens the door for its wider adoption not only in aerospace but also in industries such as automotive, wind energy, maritime, and sporting goods, where strong and lightweight structures are essential.

Computer-Aided Process Planning↗
NASA NTRSDate not supplied

Using Machine Learning to Estimate Surface-Level SO2 Concentrations from Satellite-Based Measurements

Sulfur dioxide (SO2) is a criteria air pollutant due to its contributions to aerosol formation, rainfall acidification, and harm to human health. The placement of air quality monitoring sites is typically biased towards urban areas, leaving large areas with very limited monitoring data. The Ozone Monitoring Instrument (OMI) has been used to provide estimates of SO2 vertical column densities (VCDs) globally at spatial resolution of 10s of kms once per day. OMI SO2 VCDs have been previously used to estimate surface SO2 concentrations using chemical transport model (CTM) simulations. The CTMs use estimated emissions and assimilated meteorological data, and simulate the chemical and physical processes that determine the vertical profile of SO2, which can be used to derive a ratio between the surface concentrations and VCDs. These models are complex, computationally expensive, and have large uncertainties in the simulated surface-to-VCD ratio due to biases in emissions and relatively coarse resolution. Machine learning techniques are comparatively easier to use, much less computationally expensive to use after training, and can produce more accurate estimations of surface concentrations than the CTM-based method. The interpretation of machine learning models often poses challenges, and in some cases, non-physical variables unrelated to SO2 are used as predictors. In this work, we create an artificial neural network (ANN) to relate OMI retrievals and archived GEOS-FP boundary layer heights to surface SO2 concentrations from the ChinaHighAirPollutants ChinaHighSO2 dataset (CHAP; Wei et al., 2023) on a seasonal average timescale from 2013-2018. Our model only utilizes five variables that are directly relevant to the satellite retrieval, lifetime, and spatial distribution of SO2. The model was trained on 16 seasons (four of each) with independent validation (one of each season) and testing datasets (one of each season) to avoid overfitting. Our ANN generates surface SO2 concentrations that are sensitive (slope = 0.51) and consistent (r = 0.74) with the CHAP data, but are underpredicted by an average of 1.2 ppbv with a mean absolute error of 2.2 ppbv. These results are better than recent studies utilizing the CTM method. To our knowledge, this is the best performing machine learning model that only uses physical variables to predict surface SO2. Our work demonstrates that a carefully constructed, simple ML model can accurately estimate surface-based SO2 concentrations from satellite VCD measurements, and this technique has future promise to expend to newer, higher resolution satellites and other air pollutants.

SO2, air quality, OMI, machine learning↗
NASA NTRS1993

Restraint Age Forming Of Machined Panels

Panels bent permanently without cracking. In restraint age forming, panel wrapped onto mandrel having necessary curved contour, restrained on mandrel clamps and vacuum, and heat treated. When panel released after end of heat treatment, retains contour of mandrel except for small springback. Process repeatable: panels subsequently processed under same mechanical and thermal conditions on same contour emerge with same final contour.

Wood, Rebecca↗
NASA NTRS1988

Automatic mathematical modeling for real time simulation system

A methodology for automatic mathematical modeling and generating simulation models is described. The models will be verified by running in a test environment using standard profiles with the results compared against known results. The major objective is to create a user friendly environment for engineers to design, maintain, and verify their model and also automatically convert the mathematical model into conventional code for conventional computation. A demonstration program was designed for modeling the Space Shuttle Main Engine Simulation. It is written in LISP and MACSYMA and runs on a Symbolic 3670 Lisp Machine. The program provides a very friendly and well organized environment for engineers to build a knowledge base for base equations and general information. It contains an initial set of component process elements for the Space Shuttle Main Engine Simulation and a questionnaire that allows the engineer to answer a set of questions to specify a particular model. The system is then able to automatically generate the model and FORTRAN code. The future goal which is under construction is to download the FORTRAN code to VAX/VMS system for conventional computation. The SSME mathematical model will be verified in a test environment and the solution compared with the real data profile. The use of artificial intelligence techniques has shown that the process of the simulation modeling can be simplified.

Wang, Caroline↗
NASA NTRS1989

Agglomeration of ceramic powders

A research program directed at a critical comparison of numerical models for power agglomeration with experimental observations is currently underway. Central to this program is the quantitative characterization of the distribution of mass within an agglomerate as a function of time. Current experiments are designed to restrict agglomeration to a surface, which is oriented perpendicular to the force of gravity. These experiments are discussed with reference to: their significance to ceramic processing; artifacts which may be avoided in microgravity experiments; and the comparison of information available in real space (from optical microscopy) to that in reciprocal space (from light scattering). The principle machine requirement appears to be a need to obtain information at small scattering angles.

Cawley, James D.↗
NASA NTRS1992

Mechanical properties of coated titanium Beta-21S after exposure to air at 700 and 800 C

Mechanical properties of Beta-21S (Ti-15Mo-3Al-2.7Nb-0.2Si, wt percent) with glass, aluminide, and glass-on-aluminide coatings less than 3-micron thick were studied. Coatings were deposited by sol-gel processing or electron-beam evaporation onto 4.5-mil (113-micron) thick Beta-21S sheet from which, after oxidizing in air at 700 or 800 C, tensile test specimens were machined. Plastic elongation was the most severely degraded of the tensile properties; the glass-on-aluminide coatings were the most effective in preventing degradation. It was found that oxygen trapping by forming oxides in the coating, and reactions between the coatings and the Beta-21S alloy played significant roles.

Wiedemann, Karl E.↗
NASA NTRS2003

Applications of Hilbert Spectral Analysis for Speech and Sound Signals

A new method for analyzing nonlinear and nonstationary data has been developed, and the natural applications are to speech and sound signals. The key part of the method is the Empirical Mode Decomposition method with which any complicated data set can be decomposed into a finite and often small number of Intrinsic Mode Functions (IMF). An IMF is defined as any function having the same numbers of zero-crossing and extrema, and also having symmetric envelopes defined by the local maxima and minima respectively. The IMF also admits well-behaved Hilbert transform. This decomposition method is adaptive, and, therefore, highly efficient. Since the decomposition is based on the local characteristic time scale of the data, it is applicable to nonlinear and nonstationary processes. With the Hilbert transform, the Intrinsic Mode Functions yield instantaneous frequencies as functions of time, which give sharp identifications of imbedded structures. This method invention can be used to process all acoustic signals. Specifically, it can process the speech signals for Speech synthesis, Speaker identification and verification, Speech recognition, and Sound signal enhancement and filtering. Additionally, as the acoustical signals from machinery are essentially the way the machines are talking to us. Therefore, the acoustical signals, from the machines, either from sound through air or vibration on the machines, can tell us the operating conditions of the machines. Thus, we can use the acoustic signal to diagnosis the problems of machines.

Huang, Norden E.↗
NASA NTRS2004

Transitioning from Software Requirements Models to Design Models

The Scenario Creation and Simulation Process (SCASP) includes the following steps: 1) Write Requirements; 2) Write Use Cases; 3) Prioritize Use Cases; 4) Write Nominal Scenarios; 5) Identify Relationships; 6) Refine/Generalize Scenarios; 7) Transform to State Machines. SCASP provides thorough simulation of use cases before design/implementation, resulting in: 1) Reduced cost; 2) Fewer misunderstandings; 3) Reuse of executable form of use cases. SCASP gives systematic guidelines on how to 1) Separate concerns in use case descriptions; 2) Elicit non-nominal scenarios (alternatives, exceptions, concurrent scenarios, etc.); 3) Transform those scenarios automatically into a set of concurrent state machines; 4) Execute those state machines, i.e., scenario simulation.

Whittle, Jon↗
NASA NTRS2009

Proceedings of the First NASA Formal Methods Symposium

Topics covered include: Model Checking - My 27-Year Quest to Overcome the State Explosion Problem; Applying Formal Methods to NASA Projects: Transition from Research to Practice; TLA+: Whence, Wherefore, and Whither; Formal Methods Applications in Air Transportation; Theorem Proving in Intel Hardware Design; Building a Formal Model of a Human-Interactive System: Insights into the Integration of Formal Methods and Human Factors Engineering; Model Checking for Autonomic Systems Specified with ASSL; A Game-Theoretic Approach to Branching Time Abstract-Check-Refine Process; Software Model Checking Without Source Code; Generalized Abstract Symbolic Summaries; A Comparative Study of Randomized Constraint Solvers for Random-Symbolic Testing; Component-Oriented Behavior Extraction for Autonomic System Design; Automated Verification of Design Patterns with LePUS3; A Module Language for Typing by Contracts; From Goal-Oriented Requirements to Event-B Specifications; Introduction of Virtualization Technology to Multi-Process Model Checking; Comparing Techniques for Certified Static Analysis; Towards a Framework for Generating Tests to Satisfy Complex Code Coverage in Java Pathfinder; jFuzz: A Concolic Whitebox Fuzzer for Java; Machine-Checkable Timed CSP; Stochastic Formal Correctness of Numerical Algorithms; Deductive Verification of Cryptographic Software; Coloured Petri Net Refinement Specification and Correctness Proof with Coq; Modeling Guidelines for Code Generation in the Railway Signaling Context; Tactical Synthesis Of Efficient Global Search Algorithms; Towards Co-Engineering Communicating Autonomous Cyber-Physical Systems; and Formal Methods for Automated Diagnosis of Autosub 6000.

Denney, Ewen↗
NASA NTRS2013

Silicon Micromachining for Terahertz Component Development

Waveguide component technology at terahertz frequencies has come of age in recent years. Essential components such as ortho-mode transducers (OMT), quadrature hybrids, filters, and others for high performance system development were either impossible to build or too difficult to fabricate with traditional machining techniques. With micromachining of silicon wafers coated with sputtered gold it is now possible to fabricate and test these waveguide components. Using a highly optimized Deep Reactive Ion Etching (DRIE) process, we are now able to fabricate silicon micromachined waveguide structures working beyond 1 THz. In this paper, we describe in detail our approach of design, fabrication, and measurement of silicon micromachined waveguide components and report the results of a 1 THz canonical E-plane filter.

Filter↗
NASA NTRS2015

Automatic Speech Acquisition and Recognition for Spacesuit Audio Systems

NASA has a widely recognized but unmet need for novel human-machine interface technologies that can facilitate communication during astronaut extravehicular activities (EVAs), when loud noises and strong reverberations inside spacesuits make communication challenging. WeVoice, Inc., has developed a multichannel signal-processing method for speech acquisition in noisy and reverberant environments that enables automatic speech recognition (ASR) technology inside spacesuits. The technology reduces noise by exploiting differences between the statistical nature of signals (i.e., speech) and noise that exists in the spatial and temporal domains. As a result, ASR accuracy can be improved to the level at which crewmembers will find the speech interface useful. System components and features include beam forming/multichannel noise reduction, single-channel noise reduction, speech feature extraction, feature transformation and normalization, feature compression, and ASR decoding. Arithmetic complexity models were developed and will help designers of real-time ASR systems select proper tasks when confronted with constraints in computational resources. In Phase I of the project, WeVoice validated the technology. The company further refined the technology in Phase II and developed a prototype for testing and use by suited astronauts.

Ye, Sherry↗
NASA NTRS2017

Prediction of Cognitive States During Flight Simulation Using Multimodal Psychophysiological Sensing

The Commercial Aviation Safety Team found the majority of recent international commercial aviation accidents attributable to loss of control inflight involved flight crew loss of airplane state awareness (ASA), and distraction was involved in all of them. Research on attention-related human performance limiting states (AHPLS) such as channelized attention, diverted attention, startle/surprise, and confirmation bias, has been recommended in a Safety Enhancement (SE) entitled "Training for Attention Management." To accomplish the detection of such cognitive and psychophysiological states, a broad suite of sensors was implemented to simultaneously measure their physiological markers during a high fidelity flight simulation human subject study. Twenty-four pilot participants were asked to wear the sensors while they performed benchmark tasks and motion-based flight scenarios designed to induce AHPLS. Pattern classification was employed to predict the occurrence of AHPLS during flight simulation also designed to induce those states. Classifier training data were collected during performance of the benchmark tasks. Multimodal classification was performed, using pre-processed electroencephalography, galvanic skin response, electrocardiogram, and respiration signals as input features. A combination of one, some or all modalities were used. Extreme gradient boosting, random forest and two support vector machine classifiers were implemented. The best accuracy for each modality-classifier combination is reported. Results using a select set of features and using the full set of available features are presented. Further, results are presented for training one classifier with the combined features and for training multiple classifiers with features from each modality separately. Using the select set of features and combined training, multistate prediction accuracy averaged 0.64 +/- 0.14 across thirteen participants and was significantly higher than that for the separate training case. These results support the goal of demonstrating simultaneous real-time classification of multiple states using multiple sensing modalities in high fidelity flight simulators. This detection is intended to support and inform training methods under development to mitigate the loss of ASA and thus reduce accidents and incidents.

Harrivel, Angela R.↗
NASA NTRSDate not supplied

Transitioning TEMPO Data for Air Quality Management Applications at the NASA SPoRT Center

The TEMPO mission has been observing air pollutants every hour during the daytime across its Field of Regard (FoR) covering greater North America since First Light on August 2, 2023. The highly anticipated public release of TEMPO data occurred on May 20, 2024, consisting of level 2 and level 3 trace gas data products of nitrogen dioxide, formaldehyde, and ozone. The NASA SPoRT Center is developing value-added products and tools to support the TEMPO mission and Early Adopters program with special attention on stakeholder from air agencies. One component of our work is focused on evaluating the TEMPO products over stakeholder target areas using Pandora and surface monitor observations to characterize the uncertainties and develop best practices for processing and analyzing TEMPO data. Methods for oversampling TEMPO data to 1 km resolution are being applied over the target areas to resolve fine-scale emission sources and pollutant gradients. Machine learning techniques using TEMPO, surface monitor, and model data to estimate surface-level nitrogen dioxide concentrations are being developed over the target areas. A SPoRT viewer for TEMPO has been launched for providing visualizations of the TEMPO products and an ArcGIS dashboard is being designed for enabling air agency stakeholders to efficiently analyze TEMPO data. Training materials including user guides are being developed to ensure the effective and sustained use of TEMPO data at our stakeholder agencies. One major goal of our TEMPO initiatives at SPoRT is to better enable the inclusion of TEMPO data in air quality management applications such as exceptional event demonstrations through our close engagement with stakeholders. This talk will provide an update on our SPoRT activities and showcase use cases of TEMPO data for monitoring different emission sources including wildland fire smoke.

Air Quality↗
NASA NTRS2000

Level 1 Processing of MODIS Direct Broadcast Data From Terra

In February 2000, an effort was begun to adapt the Moderate Resolution Imaging Spectroradiometer (MODIS) Level 1 production software to process direct broadcast data. Three Level 1 algorithms have been adapted and packaged for release: Level 1A converts raw (level 0) data into Hierarchical Data Format (HDF), unpacking packets into scans; Geolocation computes geographic information for the data points in the Level 1A; and the Level 1B computes geolocated, calibrated radiances from the Level 1A and Geolocation products. One useful aspect of adapting the production software is the ability to incorporate enhancements contributed by the MODIS Science Team. We have therefore tried to limit changes to the software. However, in order to process the data immediately on receipt, we have taken advantage of a branch in the geolocation software that reads orbit and altitude information from the packets themselves, rather than external ancillary files used in standard production. We have also verified that the algorithms can be run with smaller time increments (2.5 minutes) than the five-minute increments used in production. To make the code easier to build and run, we have simplified directories and build scripts. Also, dependencies on a commercial numerics library have been replaced by public domain software. A version of the adapted code has been released for Silicon Graphics machines running lrix. Perhaps owing to its origin in production, the software is rather CPU-intensive. Consequently, a port to Linux is underway, followed by a version to run on PC clusters, with an eventual goal of running in near-real-time (i.e., process a ten-minute pass in ten minutes).

Lynnes, Christopher↗
NASA NTRS2018

Advanced Analytics and Big Earth Data

NASA's Earth Science Data Systems process, archive and distribute petabytes of Earth Observation data to a variety of end users. These end users will face dramatically increased data size in the near future, bringing about new challenges and opportunities in analyzing those data. One area of particular ferment currently is Machine Learning. Many Machine Learning methods are black boxes, limiting direct insight into the data's properties. However, they can be used for a variety of data enhancement purposes, such as parameter retrieval, data fusion and image classification and segmentation. The Earth Observing System Data and Information System is also evolving to host large data volumes in the cloud, enabling data proximal analysis. As part of this effort, an Analytics framework is being developed to support and enhance user analysis of the data. By using standards based services in the framework, diverse user communities can be served, while also allowing inter-system collaboration in the analysis process.

Cloud Computing↗
NASA NTRS1987

Nondestructive evaluation of structural ceramics by photoacoustic microscopy

A photoacoustic microscopy (PAM) digital imaging system was developed and utilized to characterize silicon nitride material at the various stages of the ceramic fabrication process. Correlation studies revealed that photoacoustic microscopy detected failure initiating defects in substantially more specimens than microradiography and ultrasonic techniques. Photoacoustic microscopy detected 10 to 100 micron size surface and subsurface pores and inclusions, respectively, up to 80 microns below the interrogating surface in machined sintered silicon nitride. Microradiography detected 50 micron diameter fracture controlling pores and inclusions. Subsurface holes were detected up to a depth of 570 microns and 1.00 mm in sintered silicon nitride and silicon carbide, respectively. Seeded voids of 20 to 30 micron diameters at the surface and 50 microns below the interrogating surface were detected by photoacoustic microscopy and microradiography with 1 percent X-ray thickness sensitivity. Tight surface cracks of 96 micron length x 48 micron depth were detected by photoacoustic microscopy. PAM volatilized and removed material in the green state which resulted in linear shallow microcracks after sintering. This significantly limits the use of PAM as an in-process NDE technique.

Khandelwal, Pramod K.↗