Design of Control Charts for Number of Defects Based on Pythagorean Fuzzy Sets
Year 2020,
Volume: 3 Issue: 1, 115 - 121, 15.12.2020
İhsan Kaya
,
Ali Karaşan
,
Esra İlbahar
,
Beyza Cebeci
Abstract
Control charts (CCs) are completely useful techniques to monitor the process’ stability and control. If the process is stable and in statistical control, it only displays a variation that is inherent to the process. So, usage of them is completely critical for the process’ improvements. Two types of attribute control charts (ACCs) are related to the total count of defects that are the designs of the non--measurable characteristics and they are called $u$ and $c$ control charts. If the process related to evaluations about defects includes some uncertainty, the ACCs are insufficient to reflect the available data in design stages. The fuzzy set theory (FST) is a tool for representing uncertainty by assigning membership functions. In case of quality engineers' intuitive behaviours, ordinary fuzzy sets are incapable of the representation of the data. In this type of data, Pythagorean fuzzy sets (PFSs), which is an extension of FST is a proper way of representing not only uncertainty in the data but also hesitancy of the quality engineers. Through that, in this paper, the design of control charts for the number of defects based on PFSs has been proposed. An extension of $u$ and $c$ control charts based on PFSs are constructed and the design of these control charts has been detailed. Moreover, a descriptive example is introduced to check the applicability of the proposed method.
Supporting Institution
TUBITAK
Thanks
This study is supported by The Scientific and Technological Research Council of Turkey (TUBITAK) under Project Number 119K408.
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Year 2020,
Volume: 3 Issue: 1, 115 - 121, 15.12.2020
İhsan Kaya
,
Ali Karaşan
,
Esra İlbahar
,
Beyza Cebeci
References
- 1 L. Zadeh, Fuzzy sets, Inf. Sci., 8(3) (1965), 338–35.
- 2 H. E. Teksen, A. S. Anagün, Interval type-2 fuzzy c-control charts using ranking methods, Hacettepe Journal of Mathematics and Statistics, 48(2) (2019), 510-520.
- 3 S. Senturk, Construction of fuzzy c control charts based on fuzzy rule method, Anadolu University Journal of Science and Technology–A Applied Sciences and Engineering, 18(3), 563–572, 2017.
- 4 M. H. Shu, C. C. Chiu, T. L.Nguyen, B. M. Hsu, W. I. Hsiao, T. H. Lam, Monitoring welding discontinuities with fuzzy control chart, 893, Advanced Materials Research, 2014.
- 5 A. Aslangiray, G. Akyüz, Fuzzy control charts: An application in a textile company, Istanbul University Management Faculty Journal, 43(1) (2014), 70–89.
- 6 S. Fadaei, A. Pooya, Fuzzy U control chart based on fuzzy rules and evaluating its performance using fuzzy OC curve, The TQM Journal, 30(3) (2018), 232–247.
- 7 M. Gulbay, C. Kahraman, An alternative approach to fuzzy control charts: Direct fuzzy approach, Inform. Sci., 177(6) (2007), 1463–1480.
- 8 M.F. Zarandi, A. Alaeddini, I.B. Turksen, A hybrid fuzzy adaptive sampling—run rules for Shewhart control charts, Inform. Sci., 178(4) (2008), 1152–1170.
- 9 R.R. Yager, Pythagorean fuzzy subsets, Joint IFSA world congress and NAFIPS annual meeting (IFSA/NAFIPS), (2013), 57–61.
- 10 E. Haktanır, C. Kahraman, A novel interval-valued Pythagorean fuzzy QFD method and its application to solar photovoltaic technology development, Comput. Ind. Eng., 132 (2019), 361–372.
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- 12 O. Engin, A. Celik, I. Kaya, A fuzzy approach to define sample size for attributes control chart in multistage processes: An application in engine valve manufacturing process, Appl. Soft Comput., 8(4) (2008), 1654—1663.
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