2,458 research outputs found

    Technology for large space systems: A special bibliography with indexes (supplement 04)

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    This bibliography lists 259 reports, articles, and other documents introduced into the NASA scientific and technical information system between July 1, 1980 and December 31, 1980. Its purpose is to provide information to the researcher, manager, and designer in technology development and mission design in the area of the Large Space Systems Technology Program. Subject matter is grouped according to systems, interactive analysis and design. Structural concepts, control systems, electronics, advanced materials, assembly concepts, propulsion, solar power satellite systems, and flight experiments

    The design and implementation of material and information flow for manufacturing systems

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    Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2000.Includes bibliographical references (p. 143-145).Production systems are characterized by complex interactions between elements, both human and mechanical, with the goal to accomplish certain high-level manufacturing objectives. In order to ensure that the decisions made and the actions taken during the design and implementation of production systems are aligned with all of the objectives, a structured approach must be followed. In developing this structured approach, the axiomatic design methodology is applied, which provides the means for creating a hierarchy of system design objectives (what to do) and solutions (how to do it). From this conceptual design process, a Production System Design and Implementation (PSDI) Path is presented here. The PSDI Path guides the design through a series of steps in creating a successful physical manufacturing system environment in terms of the original high-level objectives. Defining the material and information flow in the system is a critical part of the PSDI path. Based on the steps in the PSDI Path and the design hierarchy, a procedure for constructing the material and information flow in the production system is developed. To aid in the design of material and information flow in the manufacturing system, a manufacturing system modeling environment is developed as the tool for visualizing and communicating the flow in the manufacturing system design. KEYWORDS: Lean Manufacturing, Value Stream Management, Manufacturing System Design, Production System Design, Cellular Manufacturing, Axiomatic Design.by Brandon J. Carrus.S.M

    Extending Cyber-Physical Systems to Support Stakeholder Decisions Under Resource and User Constraints: Applications to Intelligent Infrastructure and Social Urban Systems

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    In recent years, rapid urbanization has imposed greater load demands on physical infrastructure while placing stressors (e.g., pollution, congestion, social inequity) on social systems. Despite these challenges, opportunities are emerging from the unprecedented proliferation of information technologies enabling ubiquitous sensing, cloud computing, and full-scale automation. Together, these advancements enable “intelligent” systems that promise to enhance the operation of the built environment. Even with these advancements, the ability of professionals to “sense for decisions” —data-driven decision processes based on sensed data that have quantifiable returns on investment—remains unrealized for an entire class of problems. In response, this dissertation builds a rigorous foundation enabling stakeholders to use sensor data to inform decisions in two applications: infrastructure asset management and community-engaged decision making. This dissertation aligns sensing strategies with decisions governing infrastructure management by extending the role of reliability methods to quantify system performance. First, the reliability index is used as a scalar measure of the safety (i.e., failure probability) that is extracted from monitoring data to assess structural condition relative to a failure limit state. As an example, long-term data collected from a wireless sensing network (WSN) installed on the Harahan Bridge (Memphis, TN) is used in a reliability framework to track the fatigue life of critical eyebar assemblies. The proposed reliability-based SHM framework is then generalized to formally and more broadly link SHM data with condition ratings (CRs) because inspector-assigned CRs remain the primary starting point for asset management decisions made in practice today. While reliability methods historically quantify safety with respect to a single failure limit state, this work demonstrates that there exist measurable reliability index values associated with “lower” limit states below failure that more richly characterize structural performance and rationally map to CR scales. Consequently, monitoring data can be used to assign CRs based on quantitative information encompassing the measurable damage domain, as opposed to relying on visual inspection. This work reflects the first-ever SHM framework to explicitly map monitoring data to actionable decisions and is validated using a WSN on the Telegraph Road Bridge (TRB) (Monroe, MI). A primary challenge faced by solar-powered WSNs is their stringent energy constraints. For decision-making processes relying on statistical estimation of performance, the utility of data should be considered to optimize the data collection process given these constraints. This dissertation proposes a novel stochastic data collection and transmission policy for WSNs that minimizes the variance of a measured process’ estimated parameters subject to constraints imposed by energy and data buffer sizes, stochastic models of energy and event arrivals, the value of measured data, and temporal death. Numerical results based on one-year of data collected from the TRB illustrate the gains achieved by implementing the optimal policy to obtain response data used to estimate the reliability index. Finally, this dissertation extends the work performed in WSN and sense-for-decision frameworks by exploring their role in community-based decision making. This work poses societal engagement as a necessary entry point to urban sensing efforts because members of under-resourced communities are vulnerable to lack of access to data and information. A novel, low-power WSN architecture is presented that functions as a user-friendly sensing solution that communities can rapidly deploy. Applying this platform, transformative work to “democratize” data is proposed in which members of vulnerable communities collect data and generate insights that inform their decision-making strategies.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162898/1/kaflanig_1.pd

    Orbiting experiment for study of extended weightlessness. Volume 5 - Program plans

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    Orbiting primate spacecraft project planning and documentation on systems design and engineering, experiment design, tests programs, and manufacturin

    Technology for the Future: In-Space Technology Experiments Program, part 2

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    The purpose of the Office of Aeronautics and Space Technology (OAST) In-Space Technology Experiments Program In-STEP 1988 Workshop was to identify and prioritize technologies that are critical for future national space programs and require validation in the space environment, and review current NASA (In-Reach) and industry/ university (Out-Reach) experiments. A prioritized list of the critical technology needs was developed for the following eight disciplines: structures; environmental effects; power systems and thermal management; fluid management and propulsion systems; automation and robotics; sensors and information systems; in-space systems; and humans in space. This is part two of two parts and contains the critical technology presentations for the eight theme elements and a summary listing of critical space technology needs for each theme

    Irish H & V News

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    Impact of manufacturing system design, organizational processes and leadership on manufacturing system change and implementation

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    Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, June 2001.Includes bibliographical references (p. 131-135).Manufacturing system design methodologies often ignore the importance of enterprise related issues that affect the implementation and improvement efforts. Systems Engineering provides a rigorous approach to system design and coupled with a decomposition approach can result in effective system design. Manufacturing system design must be linked to the strategy and objectives of the firm. The decomposition ensures that low-level design decisions are related to the higher-level objectives of the firm. Manufacturing system design is a complicated process that involves all sections of the manufacturing organization; systems engineering provides the rigor to guide the design and implementation process through various phases and ensures that the design is comprehensive. However, the manufacturing organization cannot function independent of the enterprise. Often projects aimed at implementing effective system designs fail as the organizational processes are not aligned and the system is not prepared for the change. Leadership owns the responsibility for aligning interfaces and processes to facilitate change. The thesis is aimed at providing a case study based illustration of the above discussion that highlights certain causes of poor systemic performance. Finally the thesis proposes a methodology that combines some of the pioneering research at the Production System design Laboratory in the area of manufacturing system design to the systems engineering approach and relates these to issues of strategy, organizational processes and alignment of enterprise interfaces.by Abhinav Shukla.S.M

    A framework to support automation in manufacturing through the study of process variability

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    In manufacturing, automation has replaced many dangerous, mundane, arduous and routine manual operations, for example, transportation of heavy parts, stamping of large parts, repetitive welding and bolt fastening. However, skilled operators still carry out critical manual processes in various industries such as aerospace, automotive and heavy-machinery. As automation technology progresses through more flexible and intelligent systems, the potential for these processes to be automated increases. However, the decision to undertake automation is a complex one, involving consideration of many factors such as return of investment, health and safety, life cycle impact, competitive advantage, and resources and technology availability. A key challenge to manufacturing automation is the ability to adapt to process variability. In manufacturing processes, human operators apply their skills to adapt to variability, in order to meet the product and process specifications or requirements. This thesis is focussed on understanding the ‎variability involved in these manual processes, and how it may influence the automation solution. ‎ Two manual industrial processes in polishing and de-burring of high-value components were observed to evaluate the extent of the variability and how the operators applied their skills to overcome it. Based on the findings from the literature and process studies, a framework was developed to categorise variability in manual manufacturing processes and to suggest a level of automation for the tasks in the processes, based on scores and weights given to the parameters by the user. The novelty of this research lies in the creation of a framework to categorise and evaluate process variability, suggesting an appropriate level of automation. The framework uses five attributes of processes; inputs, outputs, strategy, time and requirements and twelve parameters (quantity, range or interval of variability, interdependency, diversification, number of alternatives, number of actions, patterned actions, concurrency, time restriction, sensorial domain, cognitive requisite and physical requisites) to evaluate variability inherent in the process. The level of automation suggested is obtained through a system of scores and weights for each parameter. The weights were calculated using Analytical Hierarchical Process (AHP) with the help of three experts in manufacturing processes. Finally, this framework was validated through its application to two processes consisting of a lab-based peg-in-a-hole manual process and an industrial process on welding. In addition, the framework was further applied to three processes (two industrial processes and one process simulated in the laboratory) by two subjects for each process to verify the consistency of the results obtained. The results suggest that the framework is robust when applied by different subjects, presenting high similarity in outputs. Moreover, the framework was found to be effective when characterising variability present in the processes where it was applied. The framework was developed and tested in manufacturing of high value components, with high potential to be applied to processes in other industries, for instance, automotive, heavy machinery, pharmaceutical or electronic components, although this would need further investigation. Thus, future work would include the application of the framework in processes in other industries, hence enhancing its robustness and widening its scope of applicability. Additionally, a database would be created to assess the correlation between process variability and the level of automation
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