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    Constructing Robust and Reliable Health Indices and Improving the Accuracy of Remaining Useful Life Prediction 

    Source: Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems:;2022:;volume( 005 ):;issue: 002:;page 21009-1
    Author(s): Wei, Yupeng; Wu, Dazhong; Terpenny, Janis
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: A system health index is a measurement of the health condition of complex systems. However, most of the health indices are developed based on strong assumptions. Consequently, existing health indices are not capable of ...
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    Economic Benefit Analysis of Cloud Based Design, Engineering Analysis, and Manufacturing 

    Source: Journal of Manufacturing Science and Engineering:;2015:;volume( 137 ):;issue: 004:;page 40903
    Author(s): Wu, Dazhong; Terpenny, Janis; Gentzsch, Wolfgang
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: From a business perspective, cloud computing has revolutionized the information and communication technology (ICT) industry by offering scalable and ondemand ICT services as well as innovative pricing plans such as payperuse ...
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    Ontology Based Knowledge Representation for Obsolescence Forecasting 

    Source: Journal of Computing and Information Science in Engineering:;2013:;volume( 013 ):;issue: 001:;page 14501
    Author(s): Zheng, Liyu; Nelson, III ,Raymond; Terpenny, Janis; Sandborn, Peter
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: The impact and pervasiveness of diminishing manufacturing sources and material shortages (DMSMS) obsolescence are increasing due to rapidly advancing technologies which shorten the procurement lives of hightech parts. For ...
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    Immersive Distributed Design Through Real-Time Capture, Translation, and Rendering of Three-Dimensional Mesh Data1 

    Source: Journal of Computing and Information Science in Engineering:;2017:;volume( 017 ):;issue: 003:;page 31010
    Author(s): Lesniak, Kevin; Terpenny, Janis; Tucker, Conrad S.; Anumba, Chimay; Bilén, Sven G.
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: With design teams becoming more distributed, the sharing and interpreting of complex data about design concepts/prototypes and environments have become increasingly challenging. The size and quality of data that can be ...
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    Immersive Distributed Design Through Real-Time Capture, Translation, and Rendering of Three-Dimensional Mesh Data1 

    Source: Journal of Computing and Information Science in Engineering:;2017:;volume( 017 ):;issue: 003:;page 31010
    Author(s): Lesniak, Kevin; Terpenny, Janis; Tucker, Conrad S.; Anumba, Chimay; Bilén, Sven G.
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: With design teams becoming more distributed, the sharing and interpreting of complex data about design concepts/prototypes and environments have become increasingly challenging. The size and quality of data that can be ...
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    A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests 

    Source: Journal of Manufacturing Science and Engineering:;2017:;volume( 139 ):;issue: 007:;page 71018
    Author(s): Wu, Dazhong; Jennings, Connor; Terpenny, Janis; Gao, Robert X.; Kumara, Soundar
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Manufacturers have faced an increasing need for the development of predictive models that predict mechanical failures and the remaining useful life (RUL) of manufacturing systems or components. Classical model-based or ...
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    Cloud-Based Parallel Machine Learning for Tool Wear Prediction 

    Source: Journal of Manufacturing Science and Engineering:;2018:;volume( 140 ):;issue: 004:;page 41005
    Author(s): Wu, Dazhong; Jennings, Connor; Terpenny, Janis; Kumara, Soundar; Gao, Robert X.
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: The emergence of cloud computing, industrial internet of things (IIoT), and new machine learning techniques have shown the potential to advance prognostics and health management (PHM) in smart manufacturing. While model-based ...
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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