Root Cause Analysis of Failures and Quality Deviations in Manufacturing Using Machine Learning

A. Lokrantz, E. Gustavsson, M. Jirstrand. In proceedings of the 51st CIRP Conference on Manufacturing Systems, Stockholm, Sweden, 16-18 May 2018.


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.


This research was supported by the project Root Cause Analysis of Quality Deviations in Manufacturing using Machine Learning (RCA-ML) in the funding program The smart digital factory (DNR 2016-04472), administered by VINNOVA, the Swedish Government Agency for Innovation Systems.

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