A study of inert-gas welding process transferability of set-up parameters
Feasibility of transferring TIG weld settings and obtaining similar weld characteristics
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Feasibility of transferring TIG weld settings and obtaining similar weld characteristics
Test program and statistical analysis of setup parameter transferability for inert gas welding
The objectives of the Flat-plate Solar Array (FSA) Project Silicon Material Task are to evaluate technologies, new and old; to develop the most promising technologies; to establish practicality of the processes to meet production, energy use, and economic criteria; and to develop an information base on impurities in polysilicon and to determine their effects on solar cell performance. The approach involves determining process feasibility, setting milestones for the forced selection of the processes, and establishing the technical readiness of the integrated process.
Spring formation is a predicted consequence of the interaction of former Martian aquifers with structures common to Mars, including basin margins, Tharsis structures, and other structural deformation characteristics. The arid environment and high abundance of water soluble compounds in the crust will have likewise encouraged spring deposit formation at spring sites. Such spring deposits may be recognized from morphological criteria if the characteristics of formation and preservation are understood. An important first step in the current Mars exploration strategy [10] is the detection of sites where there is evidence for past or present near-surface water on Mars. This study evaluates the large-scale morphology of spring deposits and the physical processes of their formation, growth, and evolution in terms that relate to (1) their identification in image data, (2) their formation, evolution, and preservation in the environment of Mars, and (3) their potential as sites of long-term or late stage shallow groundwater emergence at the surface of Mars.
The optimum processing (likelihood functional) is found for a set of M images, each the sum of a member of a signal sequence due to an object to be detected and its parameters estimated, a sample function of a noise field, and a sample function of a common background field. The noise fields are independent, zero mean, white Gaussian fields, all independent of the background field. The latter is assumed to be either (1) completely unknown or of known mean and covariance functions with (2) a certain fluctuation property or (3) Gaussian. Three equivalent forms of the optimum processing are found: (1) a summation of generalized matched filterings of the images, (2) a summation of matched filtering of certain generalized differences of the images, and (3) a summation of 'estimator-correlator' type filterings. The detection performance and optimum signal/image selection under the Neyman-Pearson criterion is given, and is shown that optimum processor and signal design can completely eliminate any effect of the background on detectability.
On the surface, it appears that AS9100 has little to say about how to apply a Quality Management System (QMS) to major aerospace test programs (or even smaller ones). It also appears that there is little in the quality engineering Body of Knowledge (BOK) that applies to testing, unless it is nondestructive examination (NDE), or some type of lab or bench testing associated with the manufacturing process. However, if one examines: a) how the systems engineering (SE) processes are implemented throughout a test program; and b) how these SE processes can be mapped to the requirements of AS9100, a number of areas for involvement of the quality professional are revealed. What often happens is that quality assurance during a test program is limited to inspections of the test article; what could be considered a manufacturing al fresco approach. This limits the quality professional and is a disservice to the programs and projects, since there are a number of ways that quality can enhance critical processes, and support efforts to improve risk reduction, efficiency and effectiveness. The Systems Engineering (SE) discipline is widely used in aerospace to ensure the progress from Stakeholder Expectations (the President, Congress, the taxpayers) to a successful, delivered product or service. Although this is well known, what is not well known is that these same SE processes are implemented in varying complexity, to prepare for and implement test projects that support research, development, verification and validation, qualification, and acceptance test projects. Although the test organization's terminology may vary from the SE terminology, and from one test service provider to another, the basic process is followed by successful, reliable testing organizations. For this analysis, NASA Procedural Requirements (NPR) 7123.1, NASA Systems Engineering Processes and Requirements is used to illustrate the SE processes that are used for major aerospace testing. Many of these processes are also implemented for smaller test projects, and this set of processes will also look familiar to those who have participated in launch site activation and flight demonstrations.
During the early stages of the NASA Scientific Data Purchase (SDP) program, three approximately equal vertical stripes were observable in the IKONOS imagery of highly spatially uniform sites. Although these effects appeared to be less than a few percent of the mean signal, several investigators requested new imagery. Over time, Space Imaging updated its processing to minimize these artifacts. This however, produced differences in Space Imaging products derived from archive imagery processed at different times. Imagery processed before 2/22/01 is processed with one set of coefficients, while imagery processed after that date requires another set. Space Imaging produces its products from raw imagery, so changes in the ground processing over time can change the delivered digital number (DN) values, even for identical orders of a previously acquired scene. NASA Stennis initiated studies to investigate the magnitude and changes in these artifacts over the lifetime of the system and before and after processing updates.
With the ever-expanding aviation industry, a need is arising for more rapid production of composite aircraft to meet increasing demand. State-of-the-art aircraft such as the Boeing 787showcase 50% composite material usage by weight, highlighting this emerging industry-wide adoption of the material system. Currently, many of the large structures associated with these aircraft are manufactured additively via Automated Fiber Placement (AFP). The AFP process shows great potential for efficient manufacturing, however unavoidable defects still occur because of tool surface geometry, placement errors, or poor process planning, resulting in decreased quality and throughput. Due to such effects, it is critical to incorporate design for manufacturing (DFM) principles to achieve the optimal manufacturing plan and resulting structure. This work will develop a methodology for incorporating design information into process planning metrics in an automated fashion to achieve an optimal set of process inputs.The analysis incorporates HyperX, Computer Aided Process Planning (CAPP) and Vericut Composite Programming (VCP). Safety margins from HyperX are imported into CAPP where AFP defects are mapped to the values. The resulting margins are then incorporated into the CAPP manufacturability algorithms, creating a design informed process planning analysis
To release open, standards-compliant processed data sets in the Open Science Data Repository (OSDR), the GeneLab Data Processing team works with the scientific community through the OSDR Analysis Working Groups to design and build open-source data processing pipelines. Once baselined internally, these pipelines are wrapped into workflows and published on the NASA GeneLab Data Processing public GitHub repository along with detailed instructions for installation and use. Each workflow must be approved through NASA's Software Release Authorization (SRA) process prior to publishing. However, the SRA process lacks sufficient documentation and clarity regarding which forms are applicable for new open-source software that utilizes publicly available 3rd party tools, and the SRA process can take several months to complete, making sharing software outside of NASA cumbersome and in contradiction with the concept of Open Science. Furthermore, the SRA process was designed as a one-size fits all approach and thus many of the questions asked are not applicable to our open-source workflows. Here we describe the software provided on the NASA GeneLab Data Processing GitHub repository, summarize our experiences with the SRA process to release these software, and propose a more stream-lined approach for review of open-source projects.
To release open, standards-compliant processed data sets in the Open Science Data Repository (OSDR), the GeneLab Data Processing team works with the scientific community through the OSDR Analysis Working Groups to design and build open-source data processing pipelines. Once baselined internally, these pipelines are wrapped into workflows and published on the NASA GeneLab Data Processing public GitHub repository along with detailed instructions for installation and use. Each workflow must be approved through NASA's Software Release Authorization (SRA) process prior to publishing. However, the SRA process lacks sufficient documentation and clarity regarding which forms are applicable for new open-source software that utilizes publicly available 3rd party tools, and the SRA process can take several months to complete, making sharing software outside of NASA cumbersome and in contradiction with the concept of Open Science. Furthermore, the SRA process was designed as a one-size fits all approach and thus many of the questions asked are not applicable to our open-source workflows. Here we describe the software provided on the NASA GeneLab Data Processing GitHub repository, summarize our experiences with the SRA process to release these software, and propose a more stream-lined approach for review of open-source projects.
Data sets selected for mini-batches and the software modifications required for processing these sets are described. Initial analysis was performed on minibatch field model recovery. Studies are being performed to examine the convergence of the solutions and the maximum expansion order the data will support in the constant and secular terms.
A Monte Carlo based LTA (laser transit anemometry) simulation system has been used to perform a detailed evaluation of a set of processing algorithms proposed by Mayo and Smart (1984) for the extraction of two-dimensional flow parameters from LTA data sets collected in a plane normal to the optical axis of the system. The present evaluation includes data ensembles containing 0.0, 5.0, 10.0, and 20.0 percent background noise levels in the constituent correlograms. The results of these evaluations indicate that for turbulence levels of up to 10.0 percent the processing system is able to extract the necessary flow parameters accurately from the LTA data sets. Mean velocity magnitude and flow angle are measurable to within 2.0 percent for turbulence intensity levels of up to 14.0 percent. Standard deviations are measureable to within 10.0 percent over a turbulence range of 3.0-10.0 percent at the same noise levels. These results indicate that the algorithms described have applications in fluid flow surveys.
The improvement in land cover classification achieved by merging microwave data with Landsat MSS data is examined. To produce a merged data set for analysis and comparison, a registration procedure by which a set of Seasat SAR digital data was merged with the MSS data is described. The Landsat MSS data and the merged Landsat/Seasat data sets were processed using conventional multichannel spectral pattern recognition techniques. An analysis of the classified data sets indicates that while Landsat data delineate different forest types (i.e., deciduous/coniferous) and allow some species separation, SAR data provide additional information related to plant canopy configuration and vegetation density as associated with varying water regimes, and therefore allow for further subdivision in the classification of forested wetlands of the coastal region of the southern United States.
Unmanned Aerial Vehicle (UAV) designs typically have aerodynamic configurations that result in complex aerodynamic and acoustic conditions, such as wing and propeller interaction. In response, the Aerodynamic and Acoustic Rotorprop Test (AART) Program was implemented, a primary objective of which was to determine the aerodynamics and acoustics related to an auxiliary propulsor mounted behind an isolated wing in the National Full-Scale Aerodynamics Complex (NFAC) 40-by 80-Foot Wind Tunnel. Three configurations (no wing, half wing, and full wing) were tested, with conditions including variation of the propeller speed, wind tunnel Mach number, and Yaw. The acoustic set up, processing, and analysis are discussed along with the known issues for this complex data set. The interaction of upstream bodies and the resulting substantial increase in acoustic emissions are discussed in detail.
Artificial neural networks have been used for a number of years to process holography-generated characteristic patterns of vibrating structures. This technology depends critically on the selection and the conditioning of the training sets. A scaling operation called folding is discussed for conditioning training sets optimally for training feed-forward neural networks to process characteristic fringe patterns. Folding allows feed-forward nets to be trained easily to detect damage-induced vibration-displacement-distribution changes as small as 10 nm. A specific application to aerospace of neural-net processing of characteristic patterns is presented to motivate the conditioning and optimization effort.
An artificial neural network is disclosed that processes holography generated characteristic pattern of vibrating structures along with finite-element models. The present invention provides for a folding operation for conditioning training sets for optimally training forward-neural networks to process characteristic fringe pattern. The folding pattern increases the sensitivity of the feed-forward network for detecting changes in the characteristic pattern The folding routine manipulates input pixels so as to be scaled according to the location in an intensity range rather than the position in the characteristic pattern.
Diffractive lenses have arrived. Literally hundreds of papers have been published and technology impact reports have been written about the exciting addition of a new tool for the lens designer. Sophisticated computer programs have been developed to aid in the optimization of these diffractive phase profiles for a wide variety of applications. Now, several fabrication methods are being pursued to produce these diffractive elements economically. The best known process is the etching of a multi-level relief grating, known as binary optics. This process uses sets of computer generated lithographic masks. Another, more recently developed method is Dry Photopolymer Embossing (DPE). This replication process uses master holograms. And now, diamond turning is being applied for the machining of these elements. Diamond turning is especially well suited for infrared optics. As any process has advantages and limitations, so has diamond turning. These advantages and limitations are discussed and general guidelines are presented to aid the designer and systems engineer in the project predesign stage.
Cognitive Agent Architecture (Cougaar) is a Java-based architecture for large-scale distributed agent-based applications. A Cougaar agent is an autonomous software entity with behaviors that represent a real-world entity (e.g., a business process). A Cougaar-based Model Driven Architecture approach, currently under development, uses a description of system's functionality (requirements) to automatically implement the system in Cougaar. The Communicating Sequential Processes (CSP) formalism is used for the formal validation of the generated system. Two main agent components, a blackboard and a plugin, are modeled as CSP processes. A set of channels represents communications between the blackboard and individual plugins. The blackboard is represented as a CSP process that communicates with every agent in the collection. The developed CSP-based Cougaar modeling framework provides a starting point for a more complete formal verification of the automatically generated Cougaar code. Currently it is used to verify the behavior of an individual agent in terms of CSP properties and to analyze the corresponding Cougaar society.