Determination of Production Defects in Iron and Steel Sector by Data Mining

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dc.contributor.authorAkinci, I.B.
dc.contributor.authorErsoz, F.
dc.date.accessioned2024-09-29T16:20:50Z
dc.date.available2024-09-29T16:20:50Z
dc.date.issued2019
dc.departmentKarabük Üniversitesien_US
dc.description3rd International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2019 -- 11 October 2019 through 13 October 2019 -- Ankara -- 156063en_US
dc.description.abstractThe studies related to the production industry are limited in the world and in our country. Especially in iron and steel sector, quality levels of different types of products need to be monitored. The studies show that with the emphasis on the quality levels of iron and steel products, the product life span is prolonged, and price and sales superiority is provided in the products. Accordingly, the market value of the products increases and there is a minimum loss of product. The primary purpose of the enterprises that realize the importance of quality work and improvements is to support quality production by preventing or reducing defects in production. Therefore, scientific studies in this sector should be focused on. Data mining and techniques, which is one of the scientific methods in this sector, have been used effectively in institutional and large enterprises. Data mining makes a significant contribution to business managers and includes the processes of finding and modeling meaningful relationships among the meaningless large data stacks in the enterprise. At this point, it is possible to define data mining as a set of techniques and concepts that generate new information for decision-making processes. In this study, firstly the data mining process is defined, data mining studies applied to certain quality improvement problems in manufacturing sector are examined and the optimization of process and quality parameters from quality improvement problems is emphasized. In the application part, data mining techniques are used to determine the variables and levels that cause production defects in an industrial enterprise. To achieve this aim, K-Means algorithm, which is one of the multivariate statistical methods, was examined by clustering analysis and the results obtained were supported by discriminant analysis. As a result of the analyzes, the products produced by the industrial enterprise were classified according to the production defects. © 2019 IEEE.en_US
dc.identifier.doi10.1109/ISMSIT.2019.8932741
dc.identifier.isbn978-172813789-6
dc.identifier.scopus2-s2.0-85078037882en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1109/ISMSIT.2019.8932741
dc.identifier.urihttps://hdl.handle.net/20.500.14619/9364
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof3rd International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2019 - Proceedingsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectcluster analysisen_US
dc.subjectdata miningen_US
dc.subjectdefect analysisen_US
dc.subjectdiscriminant analysisen_US
dc.subjectproductionen_US
dc.titleDetermination of Production Defects in Iron and Steel Sector by Data Miningen_US
dc.typeConference Objecten_US

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