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 language | English (US) |
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Article number | 031007 |
Journal | Journal of Verification, Validation and Uncertainty Quantification |
Volume | 6 |
Issue number | 3 |
DOIs | |
State | Published - Sep 2021 |
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Modeling and Simulation
- Computer Science Applications
- Computational Theory and Mathematics