The proposed control chart is applied to real world data.
📌 3 Key Takeaways
🎯
Objective & Hypothesis:A control chart is a tool for representing and to monitoring a process.
📊
Key Findings & Data:Many papers about fuzzy control charts, which are constructed based on information techniques with type-1 fuzzy sets, exist in literature.
💡
Conclusion & Impact:The proposed control chart is applied to real world data.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters
Abstract
A control chart is a tool for representing and to monitoring a process. A control chart also detects shifts in a process and abnormal conditions in a process. If a process is monitored by c control charts, c control charts may not be applicable due to the uncertainty of the attribute data. Because process requires certain information. Many papers about fuzzy control charts, which are constructed based on information techniques with type-1 fuzzy sets, exist in literature. The fuzzy c control chart based on the fuzzy rule method is constructed in this paper for the first time. The proposed control chart is applied to real world data.
🔖
Cite This Article
ŞENTÜRK, S. (2022). CONSTRUCTION OF FUZZY C CONTROL CHARTS BASED ON FUZZY RULE METHOD. Anadolu Üniversitesi Bilim ve Teknoloji Dergisi :A-Uygulamalı Bilimler ve Mühendislik, Cilt: 18 - Sayı: 3, 563–572. https://ajindex.com/articles/438292
Rowlands H and Wang LR. An approach of fuzzy logic evaluation and control in SPC, Quality Reliability Engineering Intelligent,16, 2000, 91-98
El-Shal SM and Morris AS. A fuzzy rule-based algorithm to improve the performance of statistical process control in quality systems, Journal of Intelligent Fuzzy Systems, 9, 2000, 207-223
Zarandi MHF, Alaeddini A and Turksen IB. A hybrid fuzzy adaptive sampling - Run rules for Shewhart control charts, Information Sciences, 178, 2008, 1152-1170
Erginel N. Fuzzy individual and moving range control charts with ?-cuts, Journal of Intelligent & Fuzzy Systems, 19, 2008, 373-383
Şentürk S and Erginel N. Development of fuzzy X R? and X S ? control charts using ?-cuts, Information Sciences, 179, 2009, 1542-1551
Şentürk S. Fuzzy Regression Control Chart Based on ?-cut Approximation, International Journal of Computational Intelligence Systems, 3(1), 2010, 123-140
Erginel N, Şentürk S, Kahraman C and Kaya İ. Evaluating the packing process in food industry using fuzzy and control charts, International Journal of Computational Intelligence Systems, 4, 2011, 509-520
Raz T and Wang JH. Probabilistic and memberships approaches in the construction of control chart for linguistic data, Production Planning and Control, 1, 1990, 147
Wang JH and Raz T. On the construction of control charts using linguistic variables, Intelligent Journal of Production Research, 28,1990, 477-487
Kanagawa A, Tamaki F and Ohta H. Control charts for process average and variability based on linguistic data, Intelligent Journal of Production Research, 31(4),1993, 913-922
Gülbay M, Kahraman C and Ruan D. ?-cut Fuzzy control charts for linguistic data, International Journal of Intelligent Systems 19, 2004, 1173-1196
Gülbay M and Kahraman C. Development of fuzzy process control charts and fuzzy unnatural pattern analyses, Computational Statistics and Data Analysis, 51, 2006a, 434-451
Gülbay M and Kahraman C. An alternative approach to fuzzy control charts: Direct fuzzy approach, Information Sciences, 77 (6), 2006b, 1463-1480
Şentürk S, Erginel N, Kaya İ and Kahraman C. Design of Fuzzy Control Chart, Journal of Multiple Valued- Logic and Soft Computing, 2011, 459-473
Kaya İ and Kahraman C. Process capability analyses based on fuzzy measurement and fuzzy control charts, Expert systems with applications, 38, 2011, 3172-3184
Erginel N. Fuzzy Rule Based p-np control charts, Journal o