Automatic discovery of the root causes for quality drift in high dimensionality manufacturing processes

Authors:
Lior Rokach;Dan Hutter
Affiliations:
Department of Information Systems Engineering, Ben-Gurion University of the Negev, Beersheba, Israel 84105;Department of Information Systems Engineering, Ben-Gurion University of the Negev, Beersheba, Israel 84105
Venue:
Journal of Intelligent Manufacturing
Year:
2012

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Abstract

A new technique for finding the root cause for problems in a manufacturing process is presented. The new technique is designated to continuously and automatically detect quality drifts on various manufacturing processes and then induce the common root cause. The proposed technique consists of a fast, incremental algorithm that can process extremely high dimensional data and handle more than one root-cause at the same time. Application of such a methodology consists of an on-line machine learning system that investigates and monitors the behavior of manufacturing product routes.