Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/4767
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dc.contributor.authorAtik, F.-
dc.contributor.authorSarucan, A.-
dc.date.accessioned2023-11-11T09:03:39Z-
dc.date.available2023-11-11T09:03:39Z-
dc.date.issued2023-
dc.identifier.isbn9783031397738-
dc.identifier.issn2367-3370-
dc.identifier.urihttps://doi.org/10.1007/978-3-031-39774-5_52-
dc.identifier.urihttps://hdl.handle.net/20.500.13091/4767-
dc.descriptionIntelligent and Fuzzy Systems - Intelligence and Sustainable Future Proceedings of the INFUS 2023 Conference -- 22 August 2023 through 24 August 2023 -- -- 299549en_US
dc.description.abstractIn order to effectively prevent corrosion and ensure a long service life, the galvanizing (alkaline zinc plating) process is a frequently used process in the industrial field. The galvanizing process, which has a wide range of applications, especially in the automotive industry, the construction sector and the white goods industry, offers various benefits, especially the production of corrosion-resistant and long-lasting products to reduce costs. As a process with a large number of parameters, Galvanized coating can be made more efficient for industrial enterprises. In order to optimize the process, Taguchi method and fuzzy logic were used in the study. 8 factors and 4 performance characteristics that are effective in the process were tried to be improved with 27 experiments determined according to the Taguchi method. In order to find the optimal levels of the parameters, the signal-to-noise (S/N) ratio was determined and the best combination of factors was determined. In order to test the effectiveness and efficiency of this approach, verification experiments were conducted and the results were presented. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.en_US
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofLecture Notes in Networks and Systemsen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAlkaline zinc coatingen_US
dc.subjectFuzzy Logicen_US
dc.subjectTaguchien_US
dc.subjectAutomotive industryen_US
dc.subjectComputer circuitsen_US
dc.subjectConstruction industryen_US
dc.subjectCorrosion resistanceen_US
dc.subjectCorrosion resistant coatingsen_US
dc.subjectTaguchi methodsen_US
dc.subjectZinc coatingsen_US
dc.subjectAlkaline zinc coatingen_US
dc.subjectAlkalinesen_US
dc.subjectFuzzy-Logicen_US
dc.subjectGalvanisingen_US
dc.subjectLong service lifeen_US
dc.subjectModel-based OPCen_US
dc.subjectOptimisationsen_US
dc.subjectPlating processen_US
dc.subjectTaguchien_US
dc.subjectTaguchi's methodsen_US
dc.subjectFuzzy logicen_US
dc.titleOptimization of Alkaline Zinc Plating Process in a Company Using Taguchi Model Based on Fuzzy Logicen_US
dc.typeConference Objecten_US
dc.identifier.doi10.1007/978-3-031-39774-5_52-
dc.identifier.scopus2-s2.0-85171977815en_US
dc.departmentKTÜNen_US
dc.identifier.volume758 LNNSen_US
dc.identifier.startpage462en_US
dc.identifier.endpage470en_US
dc.institutionauthor-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.authorscopusid58613900700-
dc.authorscopusid54405086400-
dc.identifier.scopusqualityQ4-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairetypeConference Object-
crisitem.author.dept02.09. Department of Industrial Engineering-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
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