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

5 Scopus citations

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 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
Volume6
Issue number3
DOIs
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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