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The Finite Element Machine at the NASA Langley Research Center is a prototype computer designed to support parallel solutions to structural analysis problems. The hardware architecture and support software for the machine, initial solution algorithms and test applications, and preliminary results are described. Directions for future work are presented.
An ASEA IRB90 robotic manipulator with attached inspection cameras was moved through a Space Shuttle Payload Assist Module (PAM) Cradle under computer control. The Operator and Operator Control Station, including graphics simulation, gross-motion spatial planning, and machine vision processing, were located at JPL. The Safety and Support personnel, PAM Cradle, IRB90, and image acquisition system, were stationed at the Kennedy Space Center (KSC). Images captured at KSC were used both for processing by a machine vision system at JPL, and for inspection by the JPL Operator. The system found collision-free paths through the PAM Cradle, demonstrated accurate knowledge of the location of both objects of interest and obstacles, and operated with a communication delay of two seconds. Safe operation of the IRB90 near Shuttle flight hardware was obtained both through the use of a gross-motion spatial planner developed at JPL using artificial intelligence techniques, and infrared beams and pressure sensitive strips mounted to the critical surfaces of the flight hardward at KSC. The Demonstration showed that telerobotics is effective for real tasks, safe for personnel and hardware, and highly productive and reliable for Shuttle payload operations and Space Station external operations.
Multispectral data processing procedures are outlined beginning with the data display process used to accomplish data editing and proceeding through clustering, feature selection criterion for error probability estimation, and sample clustering and sample classification. The effective utilization of large quantities of remote sensing data by formulating a three stage sampling model for evaluation of crop acreage estimates represents an improvement in determining the cost benefit relationship associated with remote sensing technology.
Machine learning (ML) applications in Earth and environmental sciences (EES) have gained incredible momentum in recent years. However, these ML applications have largely evolved in ‘isolation’ from the mechanistic, process-based modelling (PBM) paradigms, which have historically been the cornerstone of scientific discovery and policy support. In this perspective, we assert that the cultural barriers between the ML and PBM communities limit the potential of ML, and even its ‘hybridization’ with PBM, for EES applications. Fundamental, but often ignored, differences between ML and PBM are discussed as well as their strengths and weaknesses in light of three overarching modelling objectives in EES, (1) nowcasting and prediction, (2) scenario analysis, and (3) diagnostic learning. The paper ponders over a ‘coevolutionary’ approach to model building, shifting away from a borrowing to a co-creation culture, to develop a generation of models that leverage the unique strengths of ML such as scalability to big data and high-dimensional mapping, while remaining faithful to process-based knowledge base and principles of model explainability and interpretability, and therefore, falsifiability.
The Kansas City Division of Allied Signal Inc., as part of the Integrated Flexible Manufacturing Program (IFMP), is developing an integrated manufacturing environment. Several systems are being developed to produce standards and automation tools for specific activities within the manufacturing environment. The Advanced Manufacturing Development System (AMDS) is concentrating on information standards (STEP) and product data transfer; the Expert Cut Planner system (XCUT) is concentrating on machining operation process planning standards and automation capabilities; the Advanced Numerical Control system (ANC) is concentrating on NC data preparation standards and NC data generation tools; the Inspection Planning and Programming Expert system (IPPEX) is concentrating on inspection process planning, coordinate measuring machine (CMM) inspection standards and CMM part program generation tools; and the Intelligent Scheduling and Planning System (ISAPS) is concentrating on planning and scheduling tools for a flexible manufacturing system environment. All of these projects are working together to address information exchange, standardization, and information sharing to support rapid prototyping in a Flexible Manufacturing System (FMS) environment.
NASA and Lockheed-Martin approached the FSW machine vendor community with a specification for longitudinal barrel production FSW weld machines and a shorter travel process development machine in June of 2000. This specification was based on three years of FSW process development on the Space Shuttle External Tank alloys, AL2 195-T8M4 and AL22 19-T87. The primary motivations for changing the ET longitudinal welds from the existing variable polarity Plasma Arc plasma weld process included: (1) Significantly reduced weld defect rates and related reduction in cycle time and uncertainty; (2) Many fewer process variables to control (5 vs. 17); (3) Fewer manufacturing steps; (4) Lower residual stresses and distortion; (5) Improved weld strengths, particularly at cryogenic temperatures; (6) Fewer hazards to production personnel. General Tool was the successful bidder. The equipment is at this writing installed and welding flight hardware. This paper is a means of sharing with the rest of the FSW community the unique features developed to assure NASA/L-M of successful production welds.
Pattern recognition techniques applied to process identification, examining capability of mode learning machines for on-line identification of both linear and nonlinear processes
NASA’s Air Traffic Management-Exploration (ATM-X) Urban Air Mobility (UAM) Airspace Subproject is conducting research that evolves UAM airspace towards a highly automated and operationally flexible system of the future (see https://www.nasa.gov/uam-overview/ for more information). The complexity of UAM airspace, and its evolution through a series of transformative epochs, requires a planning tool to effectively organize, integrate, and communicate the research that will guide the evolution of UAM operations in the National Airspace System (NAS). The planning tool, called the UAM airspace research roadmap (or just roadmap), is being developed as a new system engineering methodology leveraging model based system engineering (MBSE) and machine learning natural language processing (ML NLP, or just NLP) capabilities. This presentation gives an overview of the NLP application within this system engineering methodology and will describe how it is being used to meet the ATM-X UAM Airspace Subproject’s overarching research goals.
Teams of scientists studied data flow concepts, static data flow machine architecture, and the VAL language. Each team mapped its application onto the machine and coded it in VAL. The principal findings of the study were: (1) Five of the seven applications used the full power of the target machine. The galactic simulation and multigrid fluid flow teams found that a significantly smaller version of the machine (16 processing elements) would suffice. (2) A number of machine design parameters including processing element (PE) function unit numbers, array memory size and bandwidth, and routing network capability were found to be crucial for optimal machine performance. (3) The study participants readily acquired VAL programming skills. (4) Participants learned that application-based performance evaluation is a sound method of evaluating new computer architectures, even those that are not fully specified. During the course of the study, participants developed models for using computers to solve numerical problems and for evaluating new architectures. These models form the bases for future evaluation studies.
The advanced inspection system is an autonomous control and analysis system that improves the inspection and remediation operations for ground and surface systems. It uses optical imaging technology with intelligent computer vision algorithms to analyze physical features of the real-world environment to make decisions and learn from experience. The advanced inspection system plans to control a robotic manipulator arm, an unmanned ground vehicle and cameras remotely, automatically and autonomously. There are many computer vision, image processing and machine learning techniques available as open source for using vision as a sensory feedback in decision-making and autonomous robotic movement. My responsibilities for the advanced inspection system are to create a software architecture that integrates and provides a framework for all the different subsystem components; identify open-source algorithms and techniques; and integrate robot hardware.
Different retrieval algorithms have been developed to process top-of-atmosphere (TOA) spectral radiance data provided by hyperspectral infrared sounder missions. Those algorithms are either optimal estimation method (OEM) based schemes with radiative transfer calculation involved in the retrieval process, or machine learning based methods that allow ultra-efficient data procession but lack of radiometric consistency validation based on the directly measured information. Combining both approaches leverages their respective technical advantages, leading to more accurate results. This study introduces a hyperspectral sounder fingerprinting algorithm to explore this hybrid approach. This approach involves the use of a spectral information-based classification method to identify an reference geophysical state and the corresponding radiative kernel. This enables the efficient retrieval of geophysical variables of interest through a radiative kernel-based linear inversion procedure. The fingerprinting method has been applied to analyze a decade-long hyperspectral sounder data record.
In experimental manufacturing process, numerically-controlled machine tool temporarily converts into inspection machine by installing electronic touch probes and specially-developed numerical-control software. Software drives probes in paths to and on newly machined parts and collects data on dimensions of parts.
Proposed system offers safety and large return on investment. System, operating by year 2000, employs machines and processes based on proven principles. According to concept, line of parallel machines, connected in groups of four to service modules, attacks face of coal seam. High-pressure water jets and central auger on each machine break face. Jaws scoop up coal chunks, and auger grinds them and forces fragments into slurry-transport system. Slurry pumped through pipeline to point of use. Concept for highly automated coal-mining system increases productivity, makes mining safer, and protects health of mine workers.
This report presents the results of an R&D effort to apply neural-net processed electronic holography to NDE of rotors. Electronic holography was used to generate characteristic patterns or mode shapes of vibrating rotors and rotor components. Artificial neural networks were trained to identify damage-induced changes in the characteristic patterns. The development and optimization of a neural-net training method were the most significant contributions of this work, and the training method and its optimization are discussed in detail. A second positive result was the assembly and testing of a fiber-optic holocamera. A major disappointment was the inadequacy of the high-speed-holography hardware selected for this effort, but the use of scaled holograms to match the low effective resolution of an image intensifier was one interesting attempt to compensate. This report also discusses in some detail the physics and environmental requirements for rotor electronic holography. The major conclusions were that neural-net and electronic-holography inspections of stationary components in the laboratory and the field are quite practical and worthy of continuing development, but that electronic holography of moving rotors is still an expensive high-risk endeavor.
Currently, the NASA Nebula Cloud Computing Platform is available to Agency personnel in a pre-release status as the system undergoes a formal operational readiness review. Over the past year, two projects within the Earth Science Office at NASA Marshall Space Flight Center have been investigating the performance and value of Nebula s "Infrastructure as a Service", or "IaaS" concept and applying cloud computing concepts to advance their respective mission goals. The Short-term Prediction Research and Transition (SPoRT) Center focuses on the transition of unique NASA satellite observations and weather forecasting capabilities for use within the operational forecasting community through partnerships with NOAA s National Weather Service (NWS). SPoRT has evaluated the performance of the Weather Research and Forecasting (WRF) model on virtual machines deployed within Nebula and used Nebula instances to simulate local forecasts in support of regional forecast studies of interest to select NWS forecast offices. In addition to weather forecasting applications, rapidly deployable Nebula virtual machines have supported the processing of high resolution NASA satellite imagery to support disaster assessment following the historic severe weather and tornado outbreak of April 27, 2011. Other modeling and satellite analysis activities are underway in support of NASA s SERVIR program, which integrates satellite observations, ground-based data and forecast models to monitor environmental change and improve disaster response in Central America, the Caribbean, Africa, and the Himalayas. Leveraging SPoRT s experience, SERVIR is working to establish a real-time weather forecasting model for Central America. Other modeling efforts include hydrologic forecasts for Kenya, driven by NASA satellite observations and reanalysis data sets provided by the broader meteorological community. Forecast modeling efforts are supplemented by short-term forecasts of convective initiation, determined by geostationary satellite observations processed on virtual machines powered by Nebula.
A fatigue study was undertaken to determine how much electrical-discharge-machine (EDM) processing affected the fatigue life of balance materials: EDM and regular milling-machine (MM) samples were compared. Simulation of a typical balance stress configuration was devised for the fatigue testing in order to obtain results more closely related to balance situations. The fatigue testing of the EDM and MM specimens has indicated that the EDM technique does indeed reduce the fatigue life of 15-5PH steel, the first balance material tested. This conclusion was based on comparisons of the specimen fatigue lives with theoretical and manufacturer's data. Hence the EDM surface effects are detrimental to the fatigue life of this balance material.
The fatigue properties of modern powder metallurgy disk alloys can vary because of the different steps of materials and component processing and machining. Among these variables, the effects of nonmetallic inclusions introduced during the powder atomization and handling processes have been shown to significantly degrade low-cycle fatigue life. The levels of inclusion contamination have, therefore, been reduced to less than 1 part per million in state-of-the-art nickel disk powder-processing facilities. Yet the large quantities of compressor and turbine disks weighing from 100 to over 1000 lb have enough total volume and surface area for these rare inclusions to still be present and limit fatigue life. The objective of this study was to investigate the effects on fatigue life of these inclusions, as part of the Crack Resistant Disk Materials task within the Ultra Safe Propulsion Project. Inclusions were carefully introduced at elevated levels in a nickel-base disk superalloy, U720, produced using powder metallurgy processing. Multiple strain-controlled fatigue tests were then performed on extracted test specimens at 650 C. Analyses were performed to compare the low-cycle fatigue lives and failure initiation sites as functions of inclusion content and fatigue conditions. Powder of the nickel-base superalloy U720 was atomized in argon at Special Metals Corporation, Inc., using production-scale high-cleanliness powder-processing facilities and handling practices. The powder was then passed through a 270-mesh screen. One portion of this powder was set aside for subsequent consolidation without introduced inclusions. Two other portions of this powder were seeded with alumina inclusions. Small, polycrystalline soft (Type 2) inclusions of about 50 mm diameter were carefully prepared and blended into one powder lot, and larger hard (Type 1) inclusions of about 150 mm mean diameter were introduced into the other seeded portion of powder. All three portions of powder were then sealed in separate containers, hot isostatically pressurized, extruded, forged into subscale disks, and heat treated. Low-cycle-fatigue specimens were then extracted, machined, and tested. Fatigue tests were performed at 650 C in closed-loop servohydraulic testing machines using induction heating and axial extensometers. All tests were continued to failure, and fractographic evaluations were performed on all specimens to determine the crack initiation sites. A large majority of the failures in specimens with introduced inclusions occurred at cracks initiating from inclusions at the specimen surface, as shown for each type of inclusion in the following bar chart. The inclusions significantly reduced fatigue life from unseeded material levels, as shown in the bar chart. These effects were found to depend on the strain range, strain ratio, and inclusion size. Tests at lower strain ranges and higher strain ratios resulted in larger effects of inclusions on life. Inclusion effects on life were thereby maximized in tests at the lowest strain range of 0.6 percent and the most positive strain ratio of 0.5. Under these conditions, small Type 2 inclusions reduced life substantially-- about 20 times, whereas large Type 1 inclusions dramatically reduced life 100 times. These results clearly demonstrate that it is essential to include the effects of inclusions for realistic predictions of disk fatigue life. Important issues, including temperature dependence, crack initiation versus propagation, surface treatments, realistic disk features and machining, and realistic disk spin testing will be addressed to accurately model inclusion effects on disk fatigue life. Fatigue life varied from well over 105 cycles for no inclusions to a little over 103 cycles for 100-micrometer inclusions. A single crack initiating at a surface-connected seeded inclusion caused failure in each case.
Telerobotics studies remote control of distant robots by a human operator using supervisory or direct control. Even if the robot manipulators has vision or other senses, problems arise involving control, communications, and delay. The communication delays that may be expected with telerobots working in space stations while being controlled from an Earth lab have led to a number of experiments attempting to circumvent the problem. This delay in communication is a main motivating factor in moving from well understood instantaneous hands-on manual control to less well understood supervisory control; the ultimate step would be the realization of a fully autonomous robot. The 3-D model control plays a crucial role in resolving many conflicting image processing problems that are inherent in resolving in the bottom-up approach of most current machine vision processes. The 3-D model control approach is also capable of providing the necessary visual feedback information for both the control algorithms and for the human operator.