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dc.contributor.authorDemirel, Kenan Cem
dc.contributor.authorŞahin, Ahmet
dc.contributor.authorAlbey, Erinç
dc.date.accessioned2021-09-20T10:51:56Z
dc.date.available2021-09-20T10:51:56Z
dc.date.issued2020
dc.identifier.isbn978-303054594-9
dc.identifier.issn1865-0929en_US
dc.identifier.urihttp://hdl.handle.net/10679/7565
dc.identifier.urihttps://link.springer.com/chapter/10.1007/978-3-030-54595-6_6
dc.description.abstractIn this study we construct a decision support system (DSS), which utilizes the production process parameters to predict the quality characteristics of final products in two different manufacturing plants. Using the idea of regressor chains, an ensemble method is developed to attain the highest prediction accuracy. Collected data is divided into two sets, namely “normal” and “unusual”, using local outlier factor method. The prediction performance is tested separately for each set. It is seen that the ensemble idea shows its competence especially in situations, where collected data is classified as “unusual”. We tested the proposed method in two different real-life cases: textile manufacturing process and plastic injection molding process. Proposed DSS supports online decisions through live process monitoring screens and provides real time quality predictions, which help to minimize the total number of nonconforming products.en_US
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.relation.ispartofInternational Conference on Data Management Technologies and Applications DATA 2019: Data Management Technologies and Applications
dc.rightsrestrictedAccess
dc.titleA web-based decision support system for quality prediction in manufacturing using ensemble of regressor chainsen_US
dc.typeConference paperen_US
dc.publicationstatusPublisheden_US
dc.contributor.departmentÖzyeğin University
dc.contributor.authorID(ORCID 0000-0001-5004-0578 & YÖK ID 144710) Albey, Erinç
dc.contributor.ozuauthorAlbey, Erinç
dc.identifier.volume1255en_US
dc.identifier.startpage96en_US
dc.identifier.endpage114en_US
dc.identifier.doi10.1007/978-3-030-54595-6_6en_US
dc.subject.keywordsIndustry 4.0en_US
dc.subject.keywordsQuality predictionen_US
dc.subject.keywordsEnsemble methodsen_US
dc.subject.keywordsRegressor chainsen_US
dc.subject.keywordsDecision support systemen_US
dc.identifier.scopusSCOPUS:2-s2.0-85089312976
dc.contributor.ozugradstudentDemirel, Kenan Cem
dc.contributor.ozugradstudentŞahin, Ahmet
dc.contributor.authorMale3
dc.relation.publicationcategoryConference Paper - International - Institutional Academic Staff, PhD Student and Graduate Student


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