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Analyzing Attribute Control Charts for Defectives Based on Intuitionistic Fuzzy Sets

Yıl 2020, Cilt: 3 Sayı: 1, 122 - 128, 15.12.2020

Öz

Control charts (CCs) are one of the most used statistical quality control (SQC) techniques to determine the process' situation is under control or not. The CCs can be classified into two groups based on the quality characteristics such as “variable” or “attribute”. Two well--known attribute control charts (ACCs) named $p$ and $np$ control charts are designed to measure the defectives during the manufacturing stages. If the process is deal with the number of defectives, then $np$ control chart is used. Similarly, if the process deals with the defective rate, the $p$ control chart is used. In the traditional CCs, one of the most important issues is to represent the available data with the highest rate. Since the handled data may consist of uncertain information, ordinary $p$ and $np$ CCs have remained incapable of the ability to reflect the data. Moreover, the operators or the observers of the system can be hesitant while measuring these values during the data gathering process. Therefore, dealing with these problems can be realized by extending the ordinary CCs with useful tools. In the literature, classical fuzzy sets are used to extend $p$ an $np$ control charts. This paper aims to extend these CCs by using Intuitionistic fuzzy sets (IFSs). Comparing with the existed studies, the usage of IFSs enables to represent the hesitancy in their design stages. For this aim, two types of ACCs have been re-designed based on IFSs to improve their sensitiveness and flexibility. In this paper, the extensions of $p$ and $np$ control charts with IFs are proposed and the design of these CCs based on IFs has also been represented in detail. Additionally, control limits and center lines have been re-formulated by using IFs. Moreover, a descriptive example is introduced to analyze the applicability of the proposed method.

Destekleyen Kurum

TUBITAK

Proje Numarası

119K408

Teşekkür

This study is supported by The Scientific and Technological Research Council of Turkey (TUBITAK) under Project Number 119K408.

Kaynakça

  • 1 A.V. Feigenbaum, Total quality control, 3, Mc. Graw-Hill, New York, 1991.
  • 2 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.
  • 3 L. Zadeh Fuzzy sets, Inf. Sci., 8(3) (1965), 338–353.
  • 4 M.H. Shu, H.C. Wu, Monitoring imprecise fraction of nonconforming items using p control charts, J. Appl. Stat, 37(8) (2010), 1283-–1297.
  • 5 T.T. Huang, L.H. Chen, Y.W. Wang, Y.S. Su, Design of fuzzy quality control charts for attributes based on triangular fuzzy numbers, Sixth International Conference on Genetic and Evolutionary Computing, (2012), 449—452.
  • 6 N. Erginel, Fuzzy rule-based p and np control charts, J. Intell. Fuzzy Syst., 27(1) (2014), 159—171.
  • 7 F. Sogandi, S. Mousavi, R. Ghanaatiyan, An extension of fuzzy p–control chart based on α–level fuzzy midrange, Adv. Comput. Tech. Electromagn, Article ID acte–00177 (2014), 1–8.
  • 8 M. Gulbay, C. Kahraman, An alternative approach to fuzzy control charts: Direct fuzzy approach, Inform. Sci., 177(6) (2007), 1463–1480.
  • 9 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.
  • 10 N. Erginel, Fuzzy rule-based p ̃ and np ̃ control charts, Int J Intell Syst., 27(1) (2014), 159-–171.
  • 11 D.C. Montgomery, Introduction to statistical quality control, John Wiley & Sons, New Jersey, 2013.
  • 12 S. Ahmed, G. Kibria, K. Zaman, A new approach to constructing control chart for Inspecting Attribute Type Quality Parameters under limited sample information, International Conference on Industrial Engineering and Operations Management, (2019), 159–171.
  • 13 E. Haktanir, C. Kahraman, Defects control charts using interval–valued Pythagorean fuzzy sets, International Conference on Intelligent and Fuzzy Systems, (2020), 1396–1406.
  • 14 K.P. Lin, C.M. Yu, K.S. Chen, Production data analysis system using novel process capability indices–based circular economy, Ind. Manag, 119(8–9) (2019), 1655–1668.
  • 15 S.A. Mukhtar, N.E. Hoffman, G. MacQuillan, J.B. Semmens, The hospital mortality project: A tool for using administrative data for continuous clinical quality assurance, J. Healthc. Manag., 37(2) (2008), 9–18.
  • 16 V. Amirzadeh, M. Mashinchi, A. Parchami, Construction of p-charts using degree of nonconformity, Inform. Sci., 179(1–2) (2009), 150-–160.
  • 17 F. Bakadi, M. Rouai, A. Dekayir, E. Benyassine, Degradation study of an earthen historical rampart of Meknes City (Morocco) using ultrasonic non-destructive testing, Geotechnics for Sustainable Infrastructure Development, Springer, Singapore, 2020.
  • 18 S.S.A. Pandian, P. Puthiyanayagam, Triangular fuzzy multinomial control chart with variable sample size using α—cuts, Int. J. Eng. Sci. Technol. 5(3) (2013), 699-–707.
  • 19 C. Kahraman, O. Kabak, (Eds.), Fuzzy Shewhart control charts in fuzzy statistical decision-making, Fuzzy Statistical Decision-Making, Springer, 2016.
  • 20 S. Yanik, C. Kahraman, H. Yilmaz, Intelligent process control using control charts: control charts for attribute, Intelligent Systems Reference Library, Springer, 2016.
  • 21 M.H. Madadi, M. Mahmoudzadeh, A fuzzy development for attribute control chart with Monte Carlo simulation method, Manag Sci Lett., 7, 555–564, 2017.
  • 22 E. Sakthivel, K.K. Senthamarai, M. Logaraj, Application of fuzzy logic approach in statistical control charts, Global and Stochastic Analysis, 4(1), 139–147, 2016.
  • 23 N. Erginel, S. Senturk, G. Yildiz, Modeling attribute control charts by interval type–2 fuzzy sets, Soft Comput., 22 (2018), 5033–5041.
  • 24 K.T. Atanassov, Intuitionistic fuzzy sets, Fuzzy Sets and Systems, 20(1) (1986), 87—96.
  • 25 T. Zhao, J. Xiao, Type–2 intuitionistic fuzzy sets, Control Theory Technol., 29(9) (2012), 1215–1222.
Yıl 2020, Cilt: 3 Sayı: 1, 122 - 128, 15.12.2020

Öz

Proje Numarası

119K408

Kaynakça

  • 1 A.V. Feigenbaum, Total quality control, 3, Mc. Graw-Hill, New York, 1991.
  • 2 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.
  • 3 L. Zadeh Fuzzy sets, Inf. Sci., 8(3) (1965), 338–353.
  • 4 M.H. Shu, H.C. Wu, Monitoring imprecise fraction of nonconforming items using p control charts, J. Appl. Stat, 37(8) (2010), 1283-–1297.
  • 5 T.T. Huang, L.H. Chen, Y.W. Wang, Y.S. Su, Design of fuzzy quality control charts for attributes based on triangular fuzzy numbers, Sixth International Conference on Genetic and Evolutionary Computing, (2012), 449—452.
  • 6 N. Erginel, Fuzzy rule-based p and np control charts, J. Intell. Fuzzy Syst., 27(1) (2014), 159—171.
  • 7 F. Sogandi, S. Mousavi, R. Ghanaatiyan, An extension of fuzzy p–control chart based on α–level fuzzy midrange, Adv. Comput. Tech. Electromagn, Article ID acte–00177 (2014), 1–8.
  • 8 M. Gulbay, C. Kahraman, An alternative approach to fuzzy control charts: Direct fuzzy approach, Inform. Sci., 177(6) (2007), 1463–1480.
  • 9 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.
  • 10 N. Erginel, Fuzzy rule-based p ̃ and np ̃ control charts, Int J Intell Syst., 27(1) (2014), 159-–171.
  • 11 D.C. Montgomery, Introduction to statistical quality control, John Wiley & Sons, New Jersey, 2013.
  • 12 S. Ahmed, G. Kibria, K. Zaman, A new approach to constructing control chart for Inspecting Attribute Type Quality Parameters under limited sample information, International Conference on Industrial Engineering and Operations Management, (2019), 159–171.
  • 13 E. Haktanir, C. Kahraman, Defects control charts using interval–valued Pythagorean fuzzy sets, International Conference on Intelligent and Fuzzy Systems, (2020), 1396–1406.
  • 14 K.P. Lin, C.M. Yu, K.S. Chen, Production data analysis system using novel process capability indices–based circular economy, Ind. Manag, 119(8–9) (2019), 1655–1668.
  • 15 S.A. Mukhtar, N.E. Hoffman, G. MacQuillan, J.B. Semmens, The hospital mortality project: A tool for using administrative data for continuous clinical quality assurance, J. Healthc. Manag., 37(2) (2008), 9–18.
  • 16 V. Amirzadeh, M. Mashinchi, A. Parchami, Construction of p-charts using degree of nonconformity, Inform. Sci., 179(1–2) (2009), 150-–160.
  • 17 F. Bakadi, M. Rouai, A. Dekayir, E. Benyassine, Degradation study of an earthen historical rampart of Meknes City (Morocco) using ultrasonic non-destructive testing, Geotechnics for Sustainable Infrastructure Development, Springer, Singapore, 2020.
  • 18 S.S.A. Pandian, P. Puthiyanayagam, Triangular fuzzy multinomial control chart with variable sample size using α—cuts, Int. J. Eng. Sci. Technol. 5(3) (2013), 699-–707.
  • 19 C. Kahraman, O. Kabak, (Eds.), Fuzzy Shewhart control charts in fuzzy statistical decision-making, Fuzzy Statistical Decision-Making, Springer, 2016.
  • 20 S. Yanik, C. Kahraman, H. Yilmaz, Intelligent process control using control charts: control charts for attribute, Intelligent Systems Reference Library, Springer, 2016.
  • 21 M.H. Madadi, M. Mahmoudzadeh, A fuzzy development for attribute control chart with Monte Carlo simulation method, Manag Sci Lett., 7, 555–564, 2017.
  • 22 E. Sakthivel, K.K. Senthamarai, M. Logaraj, Application of fuzzy logic approach in statistical control charts, Global and Stochastic Analysis, 4(1), 139–147, 2016.
  • 23 N. Erginel, S. Senturk, G. Yildiz, Modeling attribute control charts by interval type–2 fuzzy sets, Soft Comput., 22 (2018), 5033–5041.
  • 24 K.T. Atanassov, Intuitionistic fuzzy sets, Fuzzy Sets and Systems, 20(1) (1986), 87—96.
  • 25 T. Zhao, J. Xiao, Type–2 intuitionistic fuzzy sets, Control Theory Technol., 29(9) (2012), 1215–1222.
Toplam 25 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Articles
Yazarlar

İhsan Kaya

Ali Karaşan 0000-0002-5571-6554

Esra İlbahar

Beyza Cebeci

Proje Numarası 119K408
Yayımlanma Tarihi 15 Aralık 2020
Kabul Tarihi 29 Eylül 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 3 Sayı: 1

Kaynak Göster

APA Kaya, İ., Karaşan, A., İlbahar, E., Cebeci, B. (2020). Analyzing Attribute Control Charts for Defectives Based on Intuitionistic Fuzzy Sets. Conference Proceedings of Science and Technology, 3(1), 122-128.
AMA Kaya İ, Karaşan A, İlbahar E, Cebeci B. Analyzing Attribute Control Charts for Defectives Based on Intuitionistic Fuzzy Sets. Conference Proceedings of Science and Technology. Aralık 2020;3(1):122-128.
Chicago Kaya, İhsan, Ali Karaşan, Esra İlbahar, ve Beyza Cebeci. “Analyzing Attribute Control Charts for Defectives Based on Intuitionistic Fuzzy Sets”. Conference Proceedings of Science and Technology 3, sy. 1 (Aralık 2020): 122-28.
EndNote Kaya İ, Karaşan A, İlbahar E, Cebeci B (01 Aralık 2020) Analyzing Attribute Control Charts for Defectives Based on Intuitionistic Fuzzy Sets. Conference Proceedings of Science and Technology 3 1 122–128.
IEEE İ. Kaya, A. Karaşan, E. İlbahar, ve B. Cebeci, “Analyzing Attribute Control Charts for Defectives Based on Intuitionistic Fuzzy Sets”, Conference Proceedings of Science and Technology, c. 3, sy. 1, ss. 122–128, 2020.
ISNAD Kaya, İhsan vd. “Analyzing Attribute Control Charts for Defectives Based on Intuitionistic Fuzzy Sets”. Conference Proceedings of Science and Technology 3/1 (Aralık 2020), 122-128.
JAMA Kaya İ, Karaşan A, İlbahar E, Cebeci B. Analyzing Attribute Control Charts for Defectives Based on Intuitionistic Fuzzy Sets. Conference Proceedings of Science and Technology. 2020;3:122–128.
MLA Kaya, İhsan vd. “Analyzing Attribute Control Charts for Defectives Based on Intuitionistic Fuzzy Sets”. Conference Proceedings of Science and Technology, c. 3, sy. 1, 2020, ss. 122-8.
Vancouver Kaya İ, Karaşan A, İlbahar E, Cebeci B. Analyzing Attribute Control Charts for Defectives Based on Intuitionistic Fuzzy Sets. Conference Proceedings of Science and Technology. 2020;3(1):122-8.