Statistical Data Mining for Efficient Quality Control in Manufacturing

Abdul Rauf Khan, Henrik Schiøler, Torben Steen Knudsen, Murat Kulahci

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Extensive use of machines, flexible/re-configurable manufacturing and transition towards the fully automated factories call for intelligent use of information recorded during the manufacturing process. Modern manufacturing processes produce Terabytes of information during different stages of the process e.g sensor measurements, machine readings etc, and the major contributor of these big data sets are different quality control processes. In this article we will present methodology to extract valuable insight from manufacturing data. The proposed methodology is based on comparison of probabilities and extension of likelihood principles in statistics as a performance function for Genetic Algorithm.
Original languageEnglish
Title of host publicationProceedings of the 20th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2015)
PublisherIEEE
Publication date2015
Pages1-4
ISBN (Print)978-1-4673-7929-8
DOIs
Publication statusPublished - 2015
Event20th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2015) - Luxembourg, Luxembourg
Duration: 8 Sep 201511 Sep 2015
Conference number: 20
http://www.etfa2015.org/

Conference

Conference20th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2015)
Number20
CountryLuxembourg
CityLuxembourg
Period08/09/201511/09/2015
Internet address

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