nowadays statistical control process plays an important role in quality control of products. so wide variety of methods are utilized to do so. but since the most percentage of available information in the discrete control charts are verbal terms, fuzzy and vague ,in most cases it is difficult for us to refine them into the quantitative data. thus in this article we are to change these verbal da...

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 th...

Statistical process control ($SPC$), an internationally recognized technique for improving product quality and productivity, has been widely employed in various industries. $SPC$ relies on the use of control charts to monitor a manufacturing process for identifying causes of process variation and signaling the necessity of corrective action for the process. Fuzzy data exist ubiquitously in the ...

process capability indices (pcis) can be used as an effective tool for measuring product quality and process performance. in classic quality control there are some limitations which prevent a deep and flexible analysis because of the crisp definition of pca‟s parameters. fuzzy set theory can be used to add more flexibility to process capability analyses. in this study, the fuzzy x ba and mrx ba...

the classical control charts by using precise and specified data divide the production process into two groups: in-control or out of control, while the fuzzy sets with the definition continuous and cohesive membership functions and also using ambiguous and vague fuzzy numbers (triangular and trapezoid), it introduces different levels of decisions for the decision makers. in this paper, using th...

this paper addresses the design of control charts for both variable ( x chart) andattribute (u and c charts) quality characteristics, when there is uncertainty about the processparameters or sample data. derived control charts are more flexible than the strict crisp case, dueto the ability of encompassing the effects of vagueness in form of the degree of expert’spresumption. we extend the use o...

Many papers exist on ordinary fuzzy control charts in literature in order to consider the vagueness and uncertainty in observation data. These are on both variable and attribute control charts. Several extensions of fuzzy sets have appeared in literature since ordinary fuzzy sets emerged. Type-2 fuzzy sets are one of these extensions. Type-2 fuzzy sets take into account the imprecision of membe...

Abstract: Two approaches for constructing control charts to monitor multivariate attribute processes when data set is presented in linguistic form are suggested. Two monitoring statistics 2 f T and are developed based on fuzzy and probability theories. The first is similar to the Hotelling’s statistic and is based on representative values of fuzzy sets. The distribution of statistic, being a li...