Credibility Assessment of Machine Learning in a Manufacturing Process Application

Gregory A. Banyay, Clarence L. Worrell, Scott E. Sidener, Joshua S. Kaizer

    Research output: Contribution to journalArticlepeer-review

    1 Scopus citations


    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 coupled a purely data-based ML model with a physics-based adjustment that encoded subject matter expertise of the physical process. Establishing credibility of the resulting model provided the basis for eliminating a costly intermediate testing process that was previously used to determine the control variable setting.

    Original languageEnglish (US)
    Article number031007
    JournalJournal of Verification, Validation and Uncertainty Quantification
    Issue number3
    StatePublished - Sep 2021

    All Science Journal Classification (ASJC) codes

    • Statistics and Probability
    • Modeling and Simulation
    • Computer Science Applications
    • Computational Theory and Mathematics


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