INTERVAL ROBUST DESIGN ON QUALITY IMPROVEMENT FOR N ON-NORMAL AND CONTAMINATED RESPONSES

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dc.authorid0000-0001-9680-0085
dc.authorid0000-0002-3842-1009
dc.authorid0000-0002-8267-074X
dc.authorid0000-0002-7387-9701
dc.contributor.authorBaydar, Atakan
dc.contributor.authorZeybek, Melis
dc.contributor.authorKozan, Elif
dc.contributor.authorKozan, Agah
dc.date.accessioned2025-04-25T11:17:37Z
dc.date.available2025-04-25T11:17:37Z
dc.date.issued2024
dc.departmentEge Üniversitesi, Fen Fakültesi, İstatistik Bölümü
dc.description.abstractThe basis of robust parameter design is the creation of a design that can resist the negative effects caused by uncontrollable or difficult-to-control external and environmental factors, which affect the product parameters in achieving product design during product realization activities. Robustness is the ability of a product or process to be least affected by variabilities caused by external factors. The success of the response surface methodology generally depends on a model chosen to fit the data distribution. Making incorrect assumptions regarding data distribution when creating response surface models can affect the effectiveness of the quality improvement strategy used. Non-normal or contaminated data is a common phenomenon in quality improvement applications. Although non-normal data is common in robust parameter applications, it is often the case that users ignore the underlying distribution shape of the data at the modeling stage and use normal theory techniques naively. This study proposes a dual response surface approach based on robust confidence intervals for cases where the experimental data do not meet normality assumptions or have contaminated data distribution. A new dual response surface methodology is proposed based on modeling the MAD - t confidence interval, S-n - t confidence interval, and Q(n) - t confidence interval formulations with the response surface methodology. All the proposed methods make the process median unbiased for the mean using the skewness of the experimental data. Two well-known experimental design studies are used to demonstrate the procedure and its advantages.
dc.identifier.citationBaydar, A., Zeybek, M., Kozan, E., & Kozan, A. (2024). interval robust design on quality improvement for n on-normal and contaminated responses. International Journal of Industrial Engineering, 31(5), 1105-1116.
dc.identifier.doi10.23055/ijietap.2024.31.5.10045
dc.identifier.endpage1116
dc.identifier.issn10724761
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85207322877
dc.identifier.scopusqualityQ3
dc.identifier.startpage1105
dc.identifier.urihttps://doi.org/10.23055/ijietap.2024.31.5.10045
dc.identifier.urihttps://hdl.handle.net/11454/117160
dc.identifier.volume31
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorBaydar, Atakan
dc.institutionauthorZeybek, Melis
dc.institutionauthorKozan, Elif
dc.institutionauthorKozan, Agah
dc.institutionauthorid0000-0001-9680-0085
dc.institutionauthorid0000-0002-3842-1009
dc.institutionauthorid0000-0002-8267-074X
dc.institutionauthorid0000-0002-7387-9701
dc.language.isoen
dc.publisherUniversity Cincinnati Industrial Engineering
dc.relation.ispartofThe International Journal of Industrial Engineering-Theory Applications and Practice
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectMAD
dc.subjectResponse
dc.subjectResponse surface
dc.subjectRobust confidence interval
dc.subjectRobust estimator
dc.titleINTERVAL ROBUST DESIGN ON QUALITY IMPROVEMENT FOR N ON-NORMAL AND CONTAMINATED RESPONSES
dc.typeArticle

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