SEARCH · Search NASA

Results for “intelligent manufacturing”

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 37 records · Page 2

All sources NASA NTRS DOE OSTI

NASA NTRS2015

Embedded Thermal Control for Subsystems for Next Generation Spacecraft Applications

Thermal Fluids and Analysis Workshop, Silver Spring MD NCTS 21070-15. NASA, the Defense Department and commercial interests are actively engaged in developing miniaturized spacecraft systems and scientific instruments to leverage smaller cheaper spacecraft form factors such as CubeSats. This paper outlines research and development efforts among Goddard Space Flight Center personnel and its several partners to develop innovative embedded thermal control subsystems. Embedded thermal control subsystems is a cross cutting enabling technology integrating advanced manufacturing techniques to develop multifunctional intelligent structures to reduce Size, Weight and Power (SWaP) consumption of both the thermal control subsystem and overall spacecraft. Embedded thermal control subsystems permit heat acquisition and rejection at higher temperatures than state of the art systems by employing both advanced heat transfer equipment (integrated heat exchangers) and high heat transfer phenomena. The Goddard Space Flight Center Thermal Engineering Branch has active investigations seeking to characterize advanced thermal control systems for near term spacecraft missions. The embedded thermal control subsystem development effort consists of fundamental research as well as development of breadboard and prototype hardware and spaceflight validation efforts. This paper will outline relevant fundamental investigations of micro-scale heat transfer and electrically driven liquid film boiling. The hardware development efforts focus upon silicon based high heat flux applications (electronic chips, power electronics etc.) and multifunctional structures. Flight validation efforts include variable gravity campaigns and a proposed CubeSat based flight demonstration of a breadboard embedded thermal control system. The CubeSat investigation is technology demonstration will characterize in long-term low earth orbit a breadboard embedded thermal subsystem and its individual components to develop optimized operational schema.

Spacecraft Systems↗

NASA NTRS2016

Frontier In-Situ Resource Utilization for Enabling Sustained Human Presence on Mars

The currently known resources on Mars are massive, including extensive quantities of water and carbon dioxide and therefore carbon, hydrogen and oxygen for life support, fuels and plastics and much else. The regolith is replete with all manner of minerals. In Situ Resource Utilization (ISRU) applicable frontier technologies include robotics, machine intelligence, nanotechnology, synthetic biology, 3-D printing/additive manufacturing and autonomy. These technologies combined with the vast natural resources should enable serious, pre- and post-human arrival ISRU to greatly increase reliability and safety and reduce cost for human colonization of Mars. Various system-level transportation concepts employing Mars produced fuel would enable Mars resources to evolve into a primary center of trade for the inner solar system for eventually nearly everything required for space faring and colonization. Mars resources and their exploitation via extensive ISRU are the key to a viable, safe and affordable, human presence beyond Earth. The purpose of this paper is four-fold: 1) to highlight the latest discoveries of water, minerals, and other materials on Mars that reshape our thinking about the value and capabilities of Mars ISRU; 2) to summarize the previous literature on Mars ISRU processes, equipment, and approaches; 3) to point to frontier ISRU technologies and approaches that can lead to safe and affordable human missions to Mars; and 4) to suggest an implementation strategy whereby the ISRU elements are phased into the mission campaign over time to enable a sustainable and increasing human presence on Mars.

Moses, Robert W.↗

NASA NTRSDate not supplied

Power and Propulsion: Small Core Advanced Thermal Management Project Overview

This presentation is intended to provide an overview of the various efforts under the Advanced Air Transport Technology (AATT) project related to engine thermal management. Sustainable flight is the key motivator for all our efforts, and innovative thermal technologies play a crucial role in achieving this goal. The technologies described in this presentation can be applied to both traditional gas turbines and advanced cycles which utilize alternative fuels. Within our project, we utilize the capabilities of artificial intelligence (AI), machine learning (ML), and additive manufacturing. This ranges from using AI to generate heat exchanger fin topologies, to using ML for a reduction in computational cost which allows for a more thorough design exploration. Many times, the resulting topologies can only be realized through additive manufacturing techniques. Most of what’s presented is currently low TRL, but the intent is to achieve TRL 4 by the end of the project.

Propulsion↗

NASA NTRS1994

Intelligent robotics can boost America's economic growth

A case is made for strategic investment in intelligent robotics as a part of the solution to the problem of improved global competitiveness for U.S. manufacturing, a critical industrial sector. Similar cases are made for strategic investments in intelligent robotics for field applications, construction, and service industries such as health care. The scope of the country's problems and needs is beyond the capability of the private sector alone, government alone, or academia alone to solve independently of the others. National cooperative programs in intelligent robotics are needed with the private sector supplying leadership direction and aerospace and non-aerospace industries conducting the development. Some necessary elements of such programs are outlined. The National Aeronautics and Space Administration (NASA) and the Lyndon B. Johnson Space Center (JSC) can be key players in such national cooperative programs in intelligent robotics for several reasons: (1) human space exploration missions require supervised intelligent robotics as enabling tools and, hence must develop supervised intelligent robotic systems; (2) intelligent robotic technology is being developed for space applications at JSC (but has a strong crosscutting or generic flavor) that is advancing the state of the art and is producing both skilled personnel and adaptable developmental infrastructure such as integrated testbeds; and (3) a NASA JSC Technology Investment Program in Robotics has been proposed based on commercial partnerships and collaborations for precompetitive, dual-use developments.

Erickson, Jon D.↗

NASA NTRS1987

Applications of artificial intelligence V; Proceedings of the Meeting, Orlando, FL, May 18-20, 1987

The papers contained in this volume focus on current trends in applications of artificial intelligence. Topics discussed include expert systems, image understanding, artificial intelligence tools, knowledge-based systems, heuristic systems, manufacturing applications, and image analysis. Papers are presented on expert system issues in automated, autonomous space vehicle rendezvous; traditional versus rule-based programming techniques; applications to the control of optional flight information; methodology for evaluating knowledge-based systems; and real-time advisory system for airborne early warning.

Gilmore, John F.↗

NASA NTRS1992

Intelligent Processing Equipment Projects at DLA

The Defense Logistics Agency is successfully incorporating Intelligent Processing Equipment (IPE) into each of its Manufacturing Technology thrust areas. Several IPE applications are addressed in the manufacturing of two 'soldier support' items: combat rations and military apparel. In combat rations, in-line sensors for food processing are being developed or modified from other industries. In addition, many process controls are being automated to achieve better quality and to gain higher use (soldier) acceptance. IPE applications in military apparel include: in-process quality controls for identification of sewing defects, use of robots in the manufacture of shirt collars, and automated handling of garments for pressing.

Obrien, Donald F.↗

NASA NTRS1992

Intelligent Processing Equipment Research Supported by the National Science Foundation

The research in progress on processes, workstations, and systems has the goal of developing a high level of understanding of the issues involved. This will enable the incorporation of a level of intelligence that will allow the creation of autonomous manufacturing systems that operate in an optimum manner, under a wide range of conditions. The emphasis of the research has been on the development of highly productive and flexible techniques to address current and future problems in manufacturing and processing. Several of these projects have resulted in well-defined and established models that can now be implemented in the application arena in the next few years.

Rao, Suren B.↗

NASA NTRS1991

Joint U.S./Japan Conference on Adaptive Structures, 1st, Maui, HI, Nov. 13-15, 1990, Proceedings

The present volume of adaptive structures discusses the development of control laws for an orbiting tethered antenna/reflector system test scale model, the sizing of active piezoelectric struts for vibration suppression on a space-based interferometer, the control design of a space station mobile transporter with multiple constraints, and optimum configuration control of an intelligent truss structure. Attention is given to the formulation of full state feedback for infinite order structural systems, robustness issues in the design of smart structures, passive piezoelectric vibration damping, shape control experiments with a functional model for large optical reflectors, and a mathematical basis for the design optimization of adaptive trusses in precision control. Topics addressed include approaches to the optimal adaptive geometries of intelligent truss structures, the design of an automated manufacturing system for tubular smart structures, the Sandia structural control experiments, and the zero-gravity dynamics of space structures in parabolic aircraft flight.

Wada, Ben K.↗

NASA NTRS2020

Robotic Assembly Activities at NASA Langley Research Center

Over the past several decades, NASA Langley Research Center (LaRC) has developed a suite of hardware and software capabilities for robotic in-space assembly. Specific robots include the Lightweight Surface Manipulation System (LSMS), Tendon-Actuated Lightweight In-Space Manipulator (TALISMAN), NASA Intelligent Jigging and Assembly Robot (NINJAR), Strut Assembly, Manufacturing, Utility & Robotic Aid (SAMURAI), and most recently the Assemblers modular robots. Alongside the hardware, software tools such as the Autonomous Entity Operations Network (AEON) and the Baseline Environment for Autonomous Modeling (BEAM) have been developed to enable communication and simulation respectively. These tools have supported foundational research in single and multi-agent control, sensing and perception, trajectory generation, task allocation, and human-machine teaming. This talk will provide a broad overview of these capabilities and go into detail on recent developments made by the Assemblers project to create modular, reconfigurable robots for autonomous in-space assembly.

John R Cooper↗

NASA NTRS2023

Machine Learning to Increase the Quality and Repeatability of 3D Printing - Workflow

The imprecise nature of three-dimensional (3D) printing limits the technology’s use beyond prototyping. For production of end-use parts, such as those for aerospace applications, improvements are needed to enhance quality and repeatability. Much of the difficulty in obtaining high quality printed parts lies in finding optimum printing parameters. Currently, this requires trial and error performed by an expert. Finding the optimum printing parameters is also obfuscated by the variation in optimum parameters throughout the part due to part geometry and printer effects. To allow for locally optimized printing parameters, one can envision a machine learning algorithm that could take in an object, predict the best printing parameters, and communicate these parameters to a printer. With this scenario in mind, we developed a tool that can predict and implement locally optimized printing parameters in 3D printing. This tool consists of elements designed to detect errors in a printed part, predict the probability of local flaws occurring at each point in the part, and select the optimal local parameters for the highest quality part given hardware limitations. The results of this work were highlighted in Advanced Materials Technologies. In this paper, we will discuss in greater depth the workflow and algorithms involved with this tool that were not detailed in the journal publication.

additive manufacturing↗

NASA NTRS1982

Advanced automation for space missions

A NASA/ASEE Summer Study conducted at the University of Santa Clara in 1980 examined the feasibility of using advanced artificial intelligence and automation technologies in future NASA space missions. Four candidate applications missions were considered: (1) An intelligent earth-sensing information system, (2) an autonomous space exploration system, (3) an automated space manufacturing facility, and (4) a self-replicating, growing lunar factory. The study assessed the various artificial intelligence and machine technologies which must be developed if such sophisticated missions are to become feasible by century's end.

Freitas, R. A., Jr.↗

NASA NTRS1991

A cost-effective intelligent robotic system with dual-arm dexterous coordination and real-time vision

Dexterous coordination of manipulators based on the use of redundant degrees of freedom, multiple sensors, and built-in robot intelligence represents a critical breakthrough in development of advanced manufacturing technology. A cost-effective approach for achieving this new generation of robotics has been made possible by the unprecedented growth of the latest microcomputer and network systems. The resulting flexible automation offers the opportunity to improve the product quality, increase the reliability of the manufacturing process, and augment the production procedures for optimizing the utilization of the robotic system. Moreover, the Advanced Robotic System (ARS) is modular in design and can be upgraded by closely following technological advancements as they occur in various fields. This approach to manufacturing automation enhances the financial justification and ensures the long-term profitability and most efficient implementation of robotic technology. The new system also addresses a broad spectrum of manufacturing demand and has the potential to address both complex jobs as well as highly labor-intensive tasks. The ARS prototype employs the decomposed optimization technique in spatial planning. This technique is implemented to the framework of the sensor-actuator network to establish the general-purpose geometric reasoning system. The development computer system is a multiple microcomputer network system, which provides the architecture for executing the modular network computing algorithms. The knowledge-based approach used in both the robot vision subsystem and the manipulation control subsystems results in the real-time image processing vision-based capability. The vision-based task environment analysis capability and the responsive motion capability are under the command of the local intelligence centers. An array of ultrasonic, proximity, and optoelectronic sensors is used for path planning. The ARS currently has 18 degrees of freedom made up by two articulated arms, one movable robot head, and two charged coupled device (CCD) cameras for producing the stereoscopic views, and articulated cylindrical-type lower body, and an optional mobile base. A functional prototype is demonstrated.

Marzwell, Neville I.↗

NASA NTRS2018

Introduction to NASA Goddard Workshop on Artificial Intelligence

Artificial Intelligence (AI) is a collection of advanced technologies that allows machines to think and act, both humanly and rationally, through sensing, comprehending, acting and learning. AI's foundations lie at the intersection of several traditional fields Philosophy, Mathematics, Economics, Neuroscience, Psychology and Computer Science. Although the inception of AI started in the 1950's, it has recently made a strong comeback in all aspects of society and all over the world; this is mainly due to the timely combination of increased data volumes, advanced and mature algorithms, and improvements in computing power and storage. Current AI applications include big data analytics, robotics, intelligent sensing, assisted decision making, and speech recognition just to name a few.This workshop will be investigating how AI technologies can be adapted or developed to address the following challenges: Discover events of interest and correlations in large amounts of science data; improve the outcomes of science modeling and data assimilation using improved data processing, integration, and analysis. Design advisors for mission planning and operations, including anomaly detection and spacecraft health monitoring. Develop tools for engineering support, including advanced manufacturing, orbit determination, new component design and system engineering. Customize intelligent user interfaces, including visual analytics and natural language processing.

Le Moigne, Jacqueline↗

NASA NTRS2001

Advanced Engineering Environments: Implications for Aerospace Manufacturing

There are significant challenges facing today's aerospace industry. Global competition, more complex products, geographically-distributed design teams, demands for lower cost, higher reliability and safer vehicles, and the need to incorporate the latest technologies quicker all face the developer of aerospace systems. New information technologies offer promising opportunities to develop advanced engineering environments (AEEs) to meet these challenges. Significant advances in the state-of-the-art of aerospace engineering practice are envisioned in the areas of engineering design and analytical tools, cost and risk tools, collaborative engineering, and high-fidelity simulations early in the development cycle. These advances will enable modeling and simulation of manufacturing methods, which will in turn allow manufacturing considerations to be included much earlier in the system development cycle. Significant cost savings, increased quality, and decreased manufacturing cycle time are expected to result. This paper will give an overview of the NASA's Intelligent Synthesis Environment, the agency initiative to develop an AEE, with a focus on the anticipated benefits in aerospace manufacturing.

Thomas, D.↗

NASA NTRS1989

Vacuum mechatronics

The discipline of vacuum mechatronics is defined as the design and development of vacuum-compatible computer-controlled mechanisms for manipulating, sensing and testing in a vacuum environment. The importance of vacuum mechatronics is growing with an increased application of vacuum in space studies and in manufacturing for material processing, medicine, microelectronics, emission studies, lyophylisation, freeze drying and packaging. The quickly developing field of vacuum mechatronics will also be the driving force for the realization of an advanced era of totally enclosed clean manufacturing cells. High technology manufacturing has increasingly demanding requirements for precision manipulation, in situ process monitoring and contamination-free environments. To remove the contamination problems associated with human workers, the tendency in many manufacturing processes is to move towards total automation. This will become a requirement in the near future for e.g., microelectronics manufacturing. Automation in ultra-clean manufacturing environments is evolving into the concept of self-contained and fully enclosed manufacturing. A Self Contained Automated Robotic Factory (SCARF) is being developed as a flexible research facility for totally enclosed manufacturing. The construction and successful operation of a SCARF will provide a novel, flexible, self-contained, clean, vacuum manufacturing environment. SCARF also requires very high reliability and intelligent control. The trends in vacuum mechatronics and some of the key research issues are reviewed.

Hackwood, Susan↗

NASA NTRS1992

Assessment of Intelligent Processing Equipment in the National Aeronautics and Space Administration, 1991

Summarized here is an assessment of intelligent processing equipment (IPE) within NASA. An attempt is made to determine the state of IPE development and research in specific areas where NASA might contribute to the national capability. Mechanisms to transfer NASA technology to the U.S. private sector in this critical area are discussed. It was concluded that intelligent processing equipment is finding extensive use in the manufacture of space hardware, especially in the propulsion components of the shuttle. The major benefits are found in improved process consistency, which lowers cost as it reduces rework. Advanced feedback controls are under development and being implemented gradually into shuttle manufacturing. Implementation is much more extensive in new programs, such as in the advanced solid rocket motor and the Space Station Freedom.

Jones, C. S.↗

NASA NTRS1989

Modularity in robotic systems

Most robotic systems today are designed one at a time, at a high cost of time and money. This wasteful approach has been necessary because the industry has not established a foundation for the continued evolution of intelligent machines. The next generation of robots will have to be generic, versatile machines capable of absorbing new technology rapidly and economically. This approach is demonstrated in the success of the personal computer, which can be upgraded or expanded with new software and hardware at virtually every level. Modularity is perceived as a major opportunity to reduce the 6 to 7 year design cycle time now required for new robotic manipulators, greatly increasing the breadth and speed of diffusion of robotic systems in manufacturing. Modularity and its crucial role in the next generation of intelligent machines are the focus of interest. The main advantages that modularity provides are examined; types of modules needed to create a generic robot are discussed. Structural modules designed by the robotics group at the University of Texas at Austin are examined to demonstrate the advantages of modular design.

Tesar, Delbert↗

NASA NTRS1988

Using a simulation assistant in modeling manufacturing systems

Numerous simulation languages exist for modeling discrete event processes, and are now ported to microcomputers. Graphic and animation capabilities were added to many of these languages to assist the users build models and evaluate the simulation results. With all these languages and added features, the user is still plagued with learning the simulation language. Futhermore, the time to construct and then to validate the simulation model is always greater than originally anticipated. One approach to minimize the time requirement is to use pre-defined macros that describe various common processes or operations in a system. The development of a simulation assistant for modeling discrete event manufacturing processes is presented. A simulation assistant is defined as an interactive intelligent software tool that assists the modeler in writing a simulation program by translating the modeler's symbolic description of the problem and then automatically generating the corresponding simulation code. The simulation assistant is discussed with emphasis on an overview of the simulation assistant, the elements of the assistant, and the five manufacturing simulation generators. A typical manufacturing system will be modeled using the simulation assistant and the advantages and disadvantages discussed.

Schroer, Bernard J.↗