Analyzing of process capability indices under uncertain information and hesitancy by using Pythagorean fuzzy sets

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dc.contributor.authorKaya, I.
dc.contributor.authorYalcin, S.
dc.date.accessioned2024-03-13T10:33:08Z
dc.date.available2024-03-13T10:33:08Z
dc.date.issued2023
dc.departmentİstanbul Beykent Üniversitesien_US
dc.description.abstractProcess capability analysis (PCA) is a completely effective statistical tool for ability of a process to meet predetermined specification limits (SLs). Unfortunately, especially the real case problems include many uncertainties, it is one of the critical necessities to define the parameters of PCIs by using crisp numbers. So, the results obtained may be incorrect, if the PCIs are calculated without taking into account the uncertainty. To overcome this problem, the fuzzy set theory (FST) has been successfully used to design of PCA. We also know that fuzzy set extensions have an important role in modelling the case that include uncertainty, incomplete and inconsistent information and they are more powerful than traditional FST to model uncertainty. Defining of main parameters of PCIs such as SLs, mean (& mu;) and variance (o-2) by using the flexible of fuzzy set extensions rather than precise values due to uncertainty, time, cost, inspectors hesitancy and the results based on fuzzy sets for PCIs contain more, flexible and sensitive information. In this study, two of well-known PCIs called Cp and Cpk have been re-designed at the first time by using one of fuzzy set extensions named Pythagorean fuzzy sets (PFSs). Defining PCIs with more than one membership function instead of an only one membership function is enabling to evaluate the process more broadly more flexibility. For this aim, the main C ?sp, parameters of PCIs have been defined and analyzed by using PFSs. Finally, four new PCIs based on PFSs such as C ?spk, process and capability for gears have been analyzed. It is shown that the flexibility of the PFSs on PCIs enables the PCA to give more realistic, more sensitive, and more comprehensive results.en_US
dc.identifier.doi10.22111/IJFS.2023.7632
dc.identifier.endpage99en_US
dc.identifier.issn1735-0654
dc.identifier.issn2676-4334
dc.identifier.issue7en_US
dc.identifier.scopus2-s2.0-85176727107en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage85en_US
dc.identifier.urihttps://doi.org/10.22111/IJFS.2023.7632
dc.identifier.urihttps://hdl.handle.net/20.500.12662/3797
dc.identifier.volume20en_US
dc.identifier.wosWOS:001048560900006en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherUniv Sistan & Baluchestanen_US
dc.relation.ispartofIranian Journal Of Fuzzy Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectProcess capability analysisen_US
dc.subjectprocess capability indicesen_US
dc.subjectthe fuzzy set theoryen_US
dc.subjectPythagorean fuzzy setsen_US
dc.titleAnalyzing of process capability indices under uncertain information and hesitancy by using Pythagorean fuzzy setsen_US
dc.typeArticleen_US

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