Document Type : Original Article


1 Masters Student, Mashhad Ferdowsi University.

2 Associate professor, Ferdowsi University of Mashhad.

3 Associate Professor, Mashhad Ferdowsi University.


Statistical Quality Control is an important approach that getting help from statistical tools to illustrate the process. Shewhart control charts is one of the most important techniques of quality control, which is used to show the variance with reason. One type of control charts is control charts for attribute control of defects that can be used with variable sample size. Attribute is under Fuzzy Condition because of uncertainty in the defect of the product and making decision by the inspector. In this study, fuzzy rules are used in the design of fuzzy U control charts. This approach is performed for fuzzy control of the process. Judgments in control of classical charts process is not more than  two result, While in the design of fuzzy control charts using the fuzzy rules methods, there are intermediate levels of decision-making too. To check the validity of designed model, it is implemented in company imen khodro shargh for sewing quality characteristics. And the results were compared with the results of classical methods using operating characteristic curve and the results indicate better and faster performance fuzzy control charts to detect changes in the process.


1. Bilgic, T., & Turksen I.B. (1999). Measurement of Membership Functions:Theoretical and Empirical Work. Handbook of Fuzzy Sets and Systems Vol 1, Fundamentals Fuzzy Sets, Chapter 3. Kluwer, pp. 195-232.
2. Erginel, N., Sentürk, S., Kahraman, C., & Kaya. I. (2011). Evaluating the Packing Process in Food Industry Using Fuzzy and [Stilde] Control Charts. International Journal of Computational Intelligence Systems4 (4), 509-520.
3. 11. Gulbay.M, & Kahraman. C. (2006). Development of fuzzy process control charts and fuzzy unnatural pattern analyses. Computational Statistics & Data Analysis, 51, 434 – 451.
4. Gulbay.M, & Kahraman.C. (2007). An alternative approach to fuzzy control charts: Direct fuzzy approach. Information Sciences, 177, 1463–1480.
5. Gulbay.M, Kahraman.C, Ruan.D. (2004). a-Cut Fuzzy Control Charts for Linguistic Data. International journal of intelligent systems, Vol. 19, 1173–1195.
Grayson.J, Runger.G, & Montgomery. M.C. (1995). Average Run Length Performance of the u Chart with Control Limits Based on the Average Sample Size, Quality Engineering, 8(1), 117-127
6. rayson.J, Runger.G, Montgomery.M.C.(1995) Average Run Length Performance of the u Chart with Control Limits Based on the Average Sample Size, Quality Engineering, 8:1, 117-127
7. Kahraman.C, Gulbay.M, Erginel N. ,Senturk S.(2010). Fuzzy Statistical Process Control Techniques in Production Systems. Prod. Engr. & Manage., Studfuzz 252, pp. 431–456.
8. Hart M..K, Hart.R.F. (2007). Introduction to STATISTICAL PROCESS CONTROL TECHNIQUES. Statit Software, Inc., 1128 NE 2nd Street, Ste 108, Corvallis, Oregon 97330
9. Hsieh.K.L, Tong.L.I,. Wang.M.C.(2007). The application of control chart for defects and defect clustering in IC manufacturing based on fuzzy theory. Expert Systems with Applications, Vol. 32, pp. 765–776.
10. Hung Shu.M, Chung Wu.H. (2011). Fuzzy X and R control charts: Fuzzy dominance approach. Computers & Industrial Engineering, Vol. 61, pp. 676–685.
11. Ross, T. J. (2004), “Fuzzy Logic with Engineering Applications,” Prentice-Hall International Inc., New Jersey, USA.
12. Raz.T and Wang.J.H. (1990). Probabilistic and membership approaches in the construction of control charts for linguistic data. Production Planning & Control: The Management of Operations, Vol. 1, No. 3, pp. 147-157.
13. Senturk S., Erginel N. (2009). Development of fuzzy Xbar _R and Xbar _S control charts using a-cuts. Information Sciences, Vol. 179, pp. 1542–1551.
14. Kaya I., Kahraman C. (2011). Process capability analyses based on fuzzy measurements and fuzzy control charts. Expert Systems with Applications, Vol. 38, pp. 3172–3184.
15.  Sogandi F., Mousavi M., Ghanaatiyan.R.(2014).  An extension of fuzzy P control chart based on α-level fuzzy midrange. Advanced Computational Techniques in Electromagnetics, Volume 2014, Article ID acte-00177, 8 Pages.
16. Moheb Alizadeha H., Fatemi Ghomib.S.M.T. (2011). Fuzzy development of Mean and Range control charts using statistical properties of different representative values. Journal of Intelligent & Fuzzy Systems Vol. 22, PP 253-265
17. ZavvarSabegh.M, Mirzazadeh.A, Salehian.S, Weber.G.W.(2014). A Literature Review on the Fuzzy Control Chart; Classifications & Analysis. International Journal of Supply and Operations Management