نتایج جستجو برای: Fuzzy control charts

تعداد نتایج: 1414668  

Journal: :مدیریت صنعتی 0
مهدی عباسی عباس ابراهیمی علی جمالی

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...

Journal: :international journal of supply and operations management 2014
mohammad hossein zavvar sabegh ablofazl mirzazadeh saber salehian gerhard wilhelm weber

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...

Journal: :iranian journal of fuzzy systems 2006
mohammad hassan fazel zarandi ismail burhan turksen ali husseinizadeh kashan

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...

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 ...

Journal: :Informatica, Lith. Acad. Sci. 2017
Sevil Sentürk Jurgita Antucheviciene

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...

2013
N. Pekin Alakoç

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...

Journal: :international journal of industrial engineering and productional research- 0
rahebe keshavarzi unit 1,block 205, mabas apartments, pasdaran avenue, shiraz, iran mohammad hossein abooie department of industrial engineering, yazd university, yazd iran,

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...

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...

Journal: :مدیریت صنعتی 0
رضا اسماعیل پور دانشگاه گیلان محمد رحیم رمضانیان دانشگاه گیلان فاروق کاظم اف

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...

F. Momeni, S. Shokri,

Nonparametric control charts are presented in order to figure out the problem of detecting changes in the process median (or mean)‎, ‎or changes in the variability process where there is limited knowledge regarding the underlying process‎. ‎When observations are reported imprecise‎, ‎then it is impossible to use classical nonparametric control charts‎. ‎This paper is devoted to the problem of c...

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