Sensitizing Rules for Fuzzy Control Charts
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32769
Sensitizing Rules for Fuzzy Control Charts

Authors: N. Pekin Alakoç, A. Apaydın

Abstract:

Quality control charts indicate out of control conditions if any nonrandom pattern of the points is observed or any point is plotted beyond the control limits. Nonrandom patterns of Shewhart control charts are tested with sensitizing rules. When the processes are defined with fuzzy set theory, traditional sensitizing rules are insufficient for defining all out of control conditions. This is due to the fact that fuzzy numbers increase the number of out of control conditions. The purpose of the study is to develop a set of fuzzy sensitizing rules, which increase the flexibility and sensitivity of fuzzy control charts. Fuzzy sensitizing rules simplify the identification of out of control situations that results in a decrease in the calculation time and number of evaluations in fuzzy control chart approach.

Keywords: Fuzzy set theory, Quality control charts, Run Rules, Unnatural patterns.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1328412

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3474

References:


[1] D.C. Montgomery, Introduction to Statistical Quality Control, 5th edition, John Wiley & Sons Inc., NY, USA, 1996.
[2] Western Electric Company, Statistical Quality Control handbook, 1th edition, AT & T Technologies, Indianapolis, Indiana, 1956.
[3] L.S. Nelson, "The Shewhart control chart-tests for special causes", Journal of Quality Technology, 16, 237-239 1984.
[4] L.S. Nelson, "Interpreting Shewhart x-bar control charts", Journal of Quality Technology, 17, 114-116, 1985.
[5] L.A. Zadeh, "Fuzzy sets", Information and Control, 8, 338-353, 1965.
[6] T. Raz and J.H. Wang, "Probabilistic and memberships a roaches in the construction of control charts for linguistic data", Production Planning and Control, 1, 147-157, 1990.
[7] J.H. Wang and T. Raz, "On the construction of control charts using linguistic variables", Intelligent Journal of Production Research, 28, 477-487, 1990.
[8] A. Kanagawa, F. Tamaki and H. Ohta, "Control charts for process average and variability based on linguistic data", Intelligent Journal of Production Research, 31 (4), 913-922, 1993.
[9] H. Taleb and M. Limam, "On fuzzy and probabilistic control charts", International Journal of Production Research, 40 12(15), 2849 - 2863, 2002.
[10] M. G├╝lbay, C. Kahraman and D. Ruan, "╬▒ - Cuts fuzzy control charts for linguistic data", International Journal of Intelligent Systems, 19, 1173-1196, 2004.
[11] C.B. Cheng, "Fuzzy process control: construction of control charts with fuzzy numbers", Fuzzy Sets and Systems, 154 2, 287-303, 2005.
[12] M. G├╝lbay and C. Kahraman, "An alternative approach to fuzzy control charts: direct fuzzy approach", Information Sciences, 77 (6), 1463-1480, 2007.
[13] O. Hryniewicz, "Statistics with fuzzy data in statistical quality control, Soft Computing - A Fusion of Foundations", Methodologies and Applications, 12 3, 229 - 234, 2007.
[14] V. Amirzadeh, M. Mashinchi and A. Parchami, "Construction of pcharts using degree of nonconformity", Information Sciences, 179 (1-2), 1501-60, 2009.
[15] A. Faraz and M.B. Moghadam, "Fuzzy control chart a better alternative for Shewhart average chart", Quality and Quantity, 41 3(11), 375-385, 2007.
[16] S. Senturk and N. Erginel, "Development of fuzzy X~ R~ − and X~ S~ − control charts using ╬▒ - cuts", Information Sciences, 179, 1542-1551, 2009.
[17] A. Faraz, R.B. Kazemzadeh, M.B. Moghadam and A. Bazdar, "Constructing a fuzzy Shewhart control chart for variables when uncertainty and randomness are combined", Journal of Quality & Quantity, 44 5, 905-914, 2009.
[18] A. Faraz and A.F. Shapiro, "An application of fuzzy random variables to control charts", Fuzzy Sets and Systems, vol. 161, pp. 2684-2694, 2010.
[19] M.H. Shu and H.C. Wu, "Fuzzy X and R control charts: Fuzzy dominance approach", Computers & Industrial Engineering, 613, 676- 686, 2011.
[20] B.H. Gwee, M.H. Lim and B.H. Soong, "Self-Adjusting Diagnostic System for the Manufacture of Crystal Resonators", Proceedings of IEEE Industry Application Society Annual Meeting, IAS-93, Toronto, Canada, 3, 2014-2020, 1993.
[21] C. Kahraman, E. Tolga and Z. Ulukan, "Using triangular fuzzy numbers in the tests of control charts for unnatural patterns", in: Proceedings of INRIA/IEEE Conference on Emerging Technologies and Factory Automation, Paris, France, 291-298, 1995.
[22] L.R. Wang and H. Rowlands, "A fuzzy logic application in SPC evaluation and control", in: Proceedings of IEEE International Conference on Emerging Technologies and Factory Automation, 1, 679- 684, 1999.
[23] H.M. Hsu and Y.K. Chen, "A fuzzy reasoning based diagnosis system for X control charts", Journal of Intelligent Manufacturing, 12, 2001.
[24] J.D.T. Tannock, "A fuzzy control charting method for individuals", International Journal of Production Research, 41 5, 2003.
[25] M .G├╝lbay and C. Kahraman, "Development of fuzzy process control charts and fuzzy unnatural pattern analyses", Computational Statistics and Data Analysis, 51, 434-451, 2006.
[26] M.H. Fazel Zarandi, A. Alaeddini and I.B. T├╝rksen, "A hybrid fuzzy adaptive sampling - Run rules for Shewhart control charts", Information Sciences, 178 4, 1152-1170, 2008.
[27] K. Demirli and S. Vijayakumar, "Fuzzy logic based assignable cause diagnosis using control chart patterns", Information Sciences, 180, 3258- 3272, 2010.
[28] G. Bortolan and R. Degani, "A review of some methods for ranking fuzzy numbers", Fuzzy Sets and Systems, 15, 1 - 19, 1985.
[29] X. Wang and E. E. Kerre, "Reasonable properties for the ordering of fuzzy quantities (I)", Fuzzy Sets and Systems, 118 (3), 375-385, 2001.
[30] X. Wang and E. E. Kerre, "Reasonable properties for the ordering of fuzzy quantities (II)", Fuzzy Sets and Systems, 118 (3), 387-405, 2001.