Fuzzy-Control-Chart Methodology for Assessing Specification Compliance in Cervical Cytology Sampling

2021 ◽  
Vol 19 (11) ◽  
pp. 1858-1866
Author(s):  
Juan Miguel Cogollo-Florez ◽  
Myladis Cogollo ◽  
Monica Arteaga
Keyword(s):  
Fuzzy Control ◽  
Control Chart ◽  
Cervical Cytology

Fuzzy Control Chart A Better Alternative for Shewhart Average Chart

2006 ◽  
Vol 41 (3) ◽  
pp. 375-385 ◽  
Author(s):  
Alireza Faraz ◽  
M. Bameni Moghadam
Keyword(s):  
Fuzzy Control ◽  
Control Chart

scholarly journals A Fuzzy Control Chart Approach for Attributes and Variables

2018 ◽  
Vol 8 (5) ◽  
pp. 3360-3365 ◽  
Author(s):  
N. Pekin Alakoc ◽  
A. Apaydin
Keyword(s):  
Quality Control ◽  
Fuzzy Control ◽  
Control Chart ◽  
Control Charts ◽  
Fuzzy Numbers ◽  
Fuzzy Theory ◽  
New Approach ◽  
Shewhart Control Charts ◽  
Shewhart Control ◽  
Quality Control Chart

The purpose of this study is to present a new approach for fuzzy control charts. The procedure is based on the fundamentals of Shewhart control charts and the fuzzy theory. The proposed approach is developed in such a way that the approach can be applied in a wide variety of processes. The main characteristics of the proposed approach are: The type of the fuzzy control charts are not restricted for variables or attributes, and the approach can be easily modified for different processes and types of fuzzy numbers with the evaluation or judgment of decision maker(s). With the aim of presenting the approach procedure in details, the approach is designed for fuzzy c quality control chart and an example of the chart is explained. Moreover, the performance of the fuzzy c chart is investigated and compared with the Shewhart c chart. The results of simulations show that the proposed approach has better performance and can detect the process shifts efficiently.

Fuzzy U control chart based on fuzzy rules and evaluating its performance using fuzzy OC curve

2018 ◽  
Vol 30 (3) ◽  
pp. 232-247 ◽  
Author(s):  
Somayeh Fadaei ◽  
Alireza Pooya
Keyword(s):  
Fuzzy Control ◽  
Sample Size ◽  
Control Chart ◽  
Control Charts ◽  
Operating Characteristic ◽  
Fuzzy Rules ◽  
Type Ii ◽  
Type Ii Error ◽  
Content Type ◽  
Variable Sample Size

Purpose The purpose of this paper is to apply fuzzy spectrum in order to collect the vague and imprecise data and to employ the fuzzy U control chart in variable sample size using fuzzy rules. This approach is improved and developed by providing some new rules. Design/methodology/approach The fuzzy operating characteristic (FOC) curve is applied to investigate the performance of the fuzzy U control chart. The application of FOC presents fuzzy bounds of operating characteristic (OC) curve whose width depends on the ambiguity parameter in control charts. Findings To illustrate the efficiency of the proposed approach, a practical example is provided. Comparing performances of control charts indicates that OC curve of the crisp chart has been located between the FOC bounds, near the upper bound; as a result, for the crisp control chart, the probability of the type II error is of significant level. Also, a comparison of the crisp OC curve with OCavg curve and FOCα curve approved that the probability of the type II error for the crisp chart is more than the same amount for the fuzzy chart. Finally, the efficiency of the fuzzy chart is more than the crisp chart, and also it timely gives essential alerts by means of linguistic terms. Consequently, it is more capable of detecting process shifts. Originality/value This research develops the fuzzy U control chart with variable sample size whose output is fuzzy. After creating control charts, performance evaluation in the industry is important. The main contribution of this paper is to employs the FOC curve for evaluating the performance of the fuzzy control chart, while in prior studies in this area, the performance of fuzzy control chart has not been evaluated.

scholarly journals FUZZY CONTROL CHART FOR MONITORING MEAN AND RANGE OF UNIVARIATE PROCESSES

2019 ◽  
Vol 39 (2) ◽  
pp. 339-357
Author(s):  
Amanda dos Santos Mendes ◽  
Marcela A. G. Machado ◽  
Paloma M. S. Rocha Rizol
Keyword(s):  
Fuzzy Control ◽  
Control Chart

scholarly journals A Fuzzy EWMA Attribute Control Chart to Monitor Process Mean

Information ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 312 ◽  
Author(s):  
Muhammad Zahir Khan ◽  
Muhammad Farid Khan ◽  
Muhammad Aslam ◽  
Seyed Taghi Akhavan Niaki ◽  
Abdur Razzaque Mughal
Keyword(s):  
Fuzzy Control ◽  
Control Chart ◽  
Control Charts ◽  
Moving Average ◽  
Real Life ◽  
Fuzzy Data ◽  
Process Mean ◽  
Statistical Process ◽  
Control Charting ◽  
Real Life Data

Conventional control charts are one of the most important techniques in statistical process control which are used to assess the performance of processes to see whether they are in- or out-of-control. As traditional control charts deal with crisp data, they are not suitable to study unclear, vague, and fuzzy data. In many real-world applications, however, the data to be used in a control charting method are not crisp since they are approximated due to environmental uncertainties and systematic ambiguities involved in the systems under investigation. In these situations, fuzzy numbers and linguistic variables are used to grab such uncertainties. That is why the use of a fuzzy control chart, in which fuzzy data are used, is justified. As an exponentially weighted moving average (EWMA) scheme is usually used to detect small shifts, in this paper a fuzzy EWMA (F-EWMA) control chart is proposed to detect small shifts in the process mean when fuzzy data are available. The application of the newly developed fuzzy control chart is illustrated using real-life data.

Silk Cocoons Quality Characterization By Fuzzy Control Chart

2018 ◽  
Vol 11 (12) ◽  
Author(s):  
Niharendu Bikash Kar ◽  
Subhasis Das ◽  
Anindya Ghosh ◽  
Debamalya Banerjee
Keyword(s):  
Fuzzy Control ◽  
Control Chart

scholarly journals The Effect of Measurement Error on X̃-R̃ Fuzzy Control Charts

2012 ◽  
Vol 2 (1) ◽  
pp. 173-176 ◽  
Author(s):  
M. Moameni ◽  
A. Saghaei ◽  
M. Ghorbani Salanghooch
Keyword(s):  
Measurement Error ◽  
Fuzzy Control ◽  
Set Theory ◽  
Control Chart ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Control Charts ◽  
Manufacturing Processes ◽  
Covariate Model ◽  
Control Situations

Control charts are tools used for monitoring manufacturing processes. Fuzzy Set Theory has found its way in control charts and new types of fuzzy control charts, with different capabilities, has been introduced. In this paper, a process in which the result of the measuring of each piece is imprecise is studied, and a X̃-R̃ fuzzy control chart is used for monitoring. The aim is to study the effect of measurement error on the effectiveness of the fuzzy control chart to detect out of control situations. The model used in this research is a linear covariate model. ARL parameters are used to study the performance of the fuzzy control chart when the parameters of covariate model is increased or decreased.

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