Fuzzy X and R control charts: Fuzzy dominance approach

The statistical process control (SPC), an internationally recognized technique for improving products quality and productivity, has been widely employed throughout various industries. The SPC relies on the use of control charts to monitor a manufacturing process for identifying special causes in the process variation and signaling the necessity of a certain corrective action for the process. Since fuzzy data ubiquitously exist in the modern manufacturing process, for monitoring its sample average and variance, we propose the fuzzy X@? and R control charts, whose fuzzy control limits are obtained on the basis of the results of the resolution identity, a well-known theory in the fuzzy set field. By using the fuzzy dominance approach, which compares the fuzzy averages and variances to their respective fuzzy control limits, we are capable of determining whether the manufacturing process is needed to be adjusted or not. Finally, a practical manufacturing process illustrates the proposed methodologies to show the potential application in monitoring its average and variability while its fuzzy sample data are taken into consideration.

[1]  Tzvi Raz,et al.  On the construction of control charts using linguistic variables , 1990 .

[2]  Alex Ryer,et al.  Light measurement handbook , 2013 .

[3]  Chi-Bin Cheng,et al.  Fuzzy process control: construction of control charts with fuzzy numbers , 2005, Fuzzy Sets Syst..

[4]  Yufei Yuan Criteria for evaluating fuzzy ranking methods , 1991 .

[5]  Hiroshi Ohta,et al.  Control charts for process average and variability based on linguistic data , 1993 .

[6]  Cengiz Kahraman,et al.  An alternative approach to fuzzy control charts: Direct fuzzy approach , 2007, Inf. Sci..

[7]  Naotoshi Sugano,et al.  FUZZY SET THEORETICAL APPROACH TO ACHROMATIC RELEVANT COLOR ON THE NATURAL COLOR SYSTEM , 2006 .

[8]  R. Viertl,et al.  Testing hypotheses with fuzzy data: The fuzzy p-value , 2004 .

[9]  James R. Evans,et al.  The management and control of quality , 1989 .

[10]  Da Ruan,et al.  α‐Cut fuzzy control charts for linguistic data , 2004, Int. J. Intell. Syst..

[11]  Przemysław Grzegorzewski,et al.  Testing fuzzy hypotheses with vague data , 2002 .

[12]  Przemyslaw Grzegorzewski,et al.  Testing statistical hypotheses with vague data , 2000, Fuzzy Sets Syst..

[13]  W. Woodall,et al.  A probabilistic and statistical view of fuzzy methods , 1995 .

[14]  L. Zadeh Probability measures of Fuzzy events , 1968 .

[15]  Reinhard Viertl,et al.  Fuzzy information and imprecise probability , 2004 .

[16]  Lotfi A. Zadeh,et al.  The Concepts of a Linguistic Variable and its Application to Approximate Reasoning , 1975 .

[17]  Douglas C. Montgomery,et al.  Introduction to Statistical Quality Control , 1986 .

[18]  Roger M. Sauter,et al.  Introduction to Statistical Quality Control (2nd ed.) , 1992 .