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    Credibility Assessment of Machine Learning in a Manufacturing Process Application 

    Source: Journal of Verification, Validation and Uncertainty Quantification:;2021:;volume( 006 ):;issue: 003:;page 031007-1
    Author(s): Banyay, Gregory A.; Worrell, Clarence L.; Sidener, Scott E.; Kaizer, Joshua S.
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: We present a framework for establishing credibility of a machine learning (ML) model used to predict a key process control variable setting to maximize product quality in a component manufacturing application. Our model ...
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    DSpace software copyright © 2002-2015  DuraSpace
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