Root cause analysis of failures and quality deviations in manufacturing using machine learning

Today root causes of failures and quality deviations in manufacturing are usually identified using existing on-site expert knowledge about causal relationships between process steps and the nature of failures and deviations. Automatization of identification and back tracking of root causes for said failures and deviations would benefit companies both in that knowledge can be transferred between factories and that knowledge will be preserved for future use. We propose a machine learning framework using Bayesian networks to model the causal relationships between manufacturing stages using expert knowledge, and demonstrate the usefulness of the framework on two simulated manufacturing processes.