Augmenting statistical quality control with machine learning techniques: an overview
by Aikaterini Fountoulaki; Nikos Karacapilidis; Manolis Manatakis
International Journal of Business and Systems Research (IJBSR), Vol. 5, No. 6, 2011

Abstract: This paper attempts to provide practical insights to issues related to the enrichment of statistical quality control (SQC) systems with machine learning (ML). It reports on ML techniques that have already augmented the major SQC methods, comments on their advantages and disadvantages and identifies areas of improvement that could delineate future work directions. Three major SQC methods are considered: acceptance sampling, statistical process control and experimental design. The work reported in this paper reveals that ML techniques can significantly augment SQC systems.

Online publication date: Wed, 22-Apr-2015

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