A Literature Review on the Fuzzy Control Chart; Classifications & Analysis

Document Type : Review Paper

Authors

1 Kharazmi University, Tehran, Iran

2 Middle East Technical University,Ankara, Turkey

Abstract

Quality control plays an important role in increasing the product quality. Fuzzy control charts are more sensitive than Shewhart control chart. Hence, the correct use of fuzzy control chart leads to producing better-quality products. This area is complex because it involves a large scope of industries, and information is not well organized. In this research, we provide a literature review of the control chart under a fuzzy environment with proposing several classifications and analysis. Moreover, our research considered both attribute and variable control chart by analyzing the related researches based on the content analysis method, to classify past and current developments in the fuzzy control chart. This work has included a distribution of articles according to the journal, the case studies related to fuzzy control chart, the percentage of types of fuzzy control charts used in the literature, performance evaluation of the fuzzy control chart and summary of key points of each review paper. Finally, this paper discusses some future research direction and our overviews. The results of this study can help researchers become familiar with well-known journals, fuzzy control charts used in sample case studies, and to extract key points of each paper in minimum time.

Keywords

References

Abdolshah M., Rosnah M. Y, Tang Sai Hong b, Md. Yusof., Naimi Sadigh I. (2011). Measuring process capability index Cpmk with fuzzy data and compare it with other fuzzy process capability indices. Expert Systems with Applications, Vol. 38, pp. 6452–6457.
Alaeddini A., Ghazanfari M., Amin Nayeri M. (2009). A hybrid fuzzy-statistical clustering approach for estimating the time of changes in fixed and variable sampling control charts. Information Sciences,Vol. 179. Pp. 1769–1784
Alipour H., Noorassana R. (2010). Fuzzy multivariate exponentially weighted moving average control chart. International Advanced Manufacturing Technology, Vol. 48, pp. 1001–1007.
Anagava A.K., Tamaki F. and Ohta H. (1993). Control charts for process average and variability based on linguistic data. International Journal of Production Research, Vol. 31, No. 4, pp. 913- 922.
Bradshaw C.W. (1983). A fuzzy set theoretic interpretation of economic control limits. European Journal of Operational Research, Vol. 13, pp. 403-408.
Chen. Y-K.Eh.C.Y (2004). An enhancement of DSI Xbar control charts using a fuzzy-genetic approach. International Advanced Manufacturing Technology, Vol. 24, pp. 32–40.
Chen.Y.K ,Chang.H, Chiu.FR. (2008). Optimization design of control charts based on minimax decision criterion and fuzzy process shifts. Expert Systems with Applications, Vol. 35, pp. 207– 213
Chena K.S., Chen T.W. (2008). Multi-process capability plot and fuzzy inference evaluation. Int. J. Production Economics, Vol. 111, pp. 70–79.
Cheng.C.B. (2005). Fuzzy process control: construction of control charts with fuzzy numbers. Fuzzy Sets and Systems, Vol. 154, pp. 287–303.
Cheng-C.C. Chun-M L· Hsiao-Y N. (2010). Measuring process capability index Cpm with fuzzy data. Qual Quant, Vol. 44, pp. 529–535.
Chih - H.W- Kuo.W (2007). Identification of control chart patterns using wavelet filtering and robust fuzzy clustering. Journal of Intelligent Manufacturing, Vol. 18, pp. 343–350.
Chung-H.C and Chao-Y.C. (2002). A Note on Selecting Target and Process Capability Index Based on Fuzzy Optimization. Economic Quality Control, ISSN 0940-5151, Vol. 17, No. 1, pp. 75–80.
Demirli K., Kumar S., Kumar V. (2010). Fuzzy logic based assignable cause diagnosis using control chart patterns. Information Sciences, Vol. 180, pp. 3258–3272.
Engin O., Celik A., Kaya I. (2008). A fuzzy approach to define sample size for attributes control chart in multistage processes: An application in engine valve manufacturing process. Applied Soft Computing, Vol. 8 , pp. 1654–1663.
Erginal N., Senturk S., Kahraman C., Kaya I. (2011). Evaluating the Packing Process in Food Industry Using Fuzzy and [Stilde] Control Charts. International Journal of Computational Intelligence Systems, Vol. 4, No. 4, pp. 509-520.
Ertugrul I., Aytac E. (2009). Construction of quality control charts by using probability and fuzzy approaches and an application in a textile company. Journal of Intelligent Manufacturing, Vol. 20, pp. 139–149.
Faraz A., Baradaran Kazemzadeh R., Bameni Moghadam M., Bazdar A.A. (2010). Constructing a fuzzy Shewhart control chart for variables when uncertainty and randomness are combined. Qual Quant, Vol. 44, pp. 905–914.
Faraz A., Shapiro A.F. (2010). An application of fuzzy random variables to control charts. Fuzzy Sets and Systems, Vol. 161, pp. 2684–2694.
Fazel Zarandi M.H., Alaeddini A., Turksen I.B. (2008). A hybrid fuzzy adaptive sampling – Run rules for Shewhart control charts. Information Sciences, Vol. 178, pp. 1152–1170.
Fazel Zarandi M.H., Turksen I.B. and. Kashan H. (2006). Fuzzy Control Charts for Variable and Attribute Quality Characteristic. Iranian Journal of Fuzzy Systems, Vol. 3, No. 1, pp. 31-44
Fonseca D.J., Elam M.E. and Tibbs L. (2007). Fuzzy Short-Run Control Charts. Math ware & Soft Computing, Vol. 14, pp. 81-101.
Franceschine F. and Romano D. (1999). Control chart for linguistic variables: A method based on the use of linguistic quantifiers. International Journal of Production Research, Vol. 37, No. 16, pp. 3791- 3800.
Ghobadi S., Noghondarian K., Noorossana R., Mirhosseini S.M.S. (2012). Developing a multivariate approach to monitor fuzzy quality profiles. Qual Quant, Springer science Business Media Dordrecht, Vol. 48, Issue. 2, pp. 817-836.
Gulbay M., Kahraman C. (2006). Development of fuzzy process control charts and fuzzy unnatural pattern analyses. Computational Statistics & Data Analysis, Vol. 51, pp. 434–451.
Gulbay M., Kahraman C. (2007). An alternative approach to fuzzy control charts: Direct fuzzy approach. Information Sciences, Vol. 177, pp. 1463–1480.
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.
Hsu.H.M and Chen.Y.K (2001). A fuzzy reasoning based diagnosis system for Xbar control charts. Journal of Intelligent Manufacturing, Vol. 12, pp. 57-64
Hsu.B.M,Shu.M.H, (2008). Fuzzy inference to assess manufacturing process capability with imprecise data. European journal of operational research, Vol. 186, Issue. 2, pp. 652–670.
Hungshu.M- Lamnguyen.T. MinshuT. (2013). Fuzzy MaxGWMA chart for identifying abnormal variations of on-line manufacturing processes with imprecise information. Expert Systems with Applications, xxx xxx–xxx(article in press).
Hungshu.M and. Hsien.C .W. (2010). Monitoring imprecise fraction of nonconforming items using p control charts. Journal of Applied Statistics, Vol. 37, No. 8, pp. 1283–1297.
Intaramo R. (2012). Development of Fuzzy Extreme Value Theory Control Charts Using α-cuts for Skewed Populations. Applied Mathematical Sciences, Vol. 6, No. 117, pp. 5811 – 5834.
Kaya I., Kahraman C. (2010). A new perspective on fuzzy process capability indices: Robustness. Expert Systems with Applications, Vol. 37, pp. 4593–4600.
Kaya I., Kahraman C. (2011). Process capability analyses with fuzzy parameters. Expert Systems with Applications, Vol. 38, pp. 11918–11927.
Kaya I., Kahraman C. (2011). Fuzzy process capability indices with asymmetric tolerances. Expert Systems with Applications, Vol. 38, pp. 14882–14890.
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.
Latva.K-Kayra. (2001). Fuzzy Logic and SPC. Springer, pp. 197-210.
Laviolette M. Seaman J.W., Barret J.J.D., and Woodall W.H. (1995). A probabilistic and statistical view of fuzzy methods. American society for Quality control, Technimetrics, Vol.37, No. 3, pp. 249-261.
Lenz H.J. & Wilrich P.T. (1997). Frontiers in statistical view of fuzzy methods. Springerverlagh, pp. 83-90.
Moameni M., Saghaei A., Ghorbani Salanghooch M. (2012). The Effect of Measurement Error on X̃-R̃Fuzzy Control Charts. Engineering, Technology & Applied Science Research, Vol. 2, pp. 173-176.
Moheb Alizadeh H., Arshadi Khamseh A.R. and Fatemi Ghomi S.M.T. (2010). Fuzzy Development of Multivariate Variable Control Charts Using the Fuzzy Likelihood Ratio Test. SCIENTIA IRANICA, Vol. 17, No. 2, pp. 150-163.
Pandurajan & Varadharajan R. (2011). Construction of α - cut fuzzy and Xbar-R and Xbar-S Control Charts Using Fuzzy Trapezoidal Number. International Journal of Research and Reviews in Applied Sciences, Vol. 9, pp. 100–111.
Parchami A., Mashinchi M., Yavari A.R. and Maleki H.R. (2005). Process Capability Indices as Fuzzy Numbers. Austrian Journal of Statistics, Vol. 34, No. 4, pp. 391–402.
Parkinson W.J., Smith R.E., Mortensen F.N. and Wantuck P.J. (2002). Fuzzy SPC Filter for a Feed-Forward Control System for a Three-phase Oil Field Centrifuge, automation congress. Proceeding of the 5th biannual world, Vol. 13, pp. 59-64.
Raz.T and Wang.J.H.(1990). On the construction of control charts using linguistic variables. International Journal of Production Research, Vol. 28, No. 3, pp. 477-487.
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.
Renkuan.G, Dunne T. (2005)., Grey Predictive Process Control Charts. The 4th International Conference on Quality and Reliability, 9-11th, Beijing, China.
Saric.I and and imen.O. (2011). A New Diagnosis System Based on Fuzzy Reasoning to detect Mean and/or Variance Shifts In a Process. International Journal of Innovative Computing. Information and Control ISSN 1349-4198, Vol. 7, No. 12, pp. 6935-6948.
Selva S., Pandian A., Puthiyanayagam P. (2013). Process Capability A nalysis based on range with Triangular Fuzzy Numbers. International Journal of Applied Mathematics & Statistical Sciences (IJAMSS) ISSN 2319-3972, Vol. 2, Issue. 1, pp. 1-12.
Spiridonica A.M., Pislaru M., Ciobanu R.C. (2010). A Fuzzy Approach Regarding the Optimization of Statistical Process Control through Shewhart Control Charts. 10th International Conference on Development and Application Systems, Suceava, Romania, pp. 27-29.
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.
Senturk S. (2010). Fuzzy Regression Control Chart Based on α-cut Approximation. International Journal of Computational Intelligence Systems, Vol.3, No. 1, pp. 123-140.
Shu.M.H,Hsien-C.W. (2011). Fuzzy X and R control charts: Fuzzy dominance approach. Computers & Industrial Engineering, Vol. 61, pp. 676–685.
Talebs H. and Limam M. (2002). On fuzzy and probabilistic control charts. International Journal of Production Research, Vol. 40, No. 12, pp. 2849-2863.
Taleb H., Limam M., Hirota K. (2006). Multivariate Fuzzy Multinomial Control Charts. Quality Technology & Quantitative Management, Vol. 3, No. 4, pp. 437-453.
Tannock J.D.T. (2003). A fuzzy control charting method for individuals. International Journal of Production Research, Vol. 41, No. 5, pp. 1017–1032.
Taylan O. and Darrab I.A. (2012). Fuzzy control charts for process quality improvement and product assessment in tip shear carpet industry. Journal of Manufacturing Technology Management, Vol. 23, No. 3, pp. 402-420.
Wang.R.C and Chen.CH. (1995). Economic statistical np-control chart designs based on fuzzy optimization. International Journal of Quality & Reliability Management, Vol. 12, No. 1, pp. 82-92.
Zadeh L.A. (1965). Fuzzy sets. Information and control, Vol. 8, pp. 338-353.
 
International Journal of Supply and Operations Management
Volume 1, Issue 2 - Serial Number 2
August 2014
Pages 167-189

Files

Share

How to cite

Statistics