Interpretable Machine learning for Quality Engineering in Manufacturing - Importance Measures that Reveal Insights on Errors

Abstract

This paper addresses the use of machine learning and techniques of interpretable machine learning to improve quality in manufacturing processes. It proposes analysis methods constitute novel importance measures that support quality engineers in the analysis of production errors. We illustrate and test the proposed methods on synthetic as well as on real-world data from German manufacturer.