Abstract
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.
Highlights
Statistical process control includes a set of upcoming problems solving tools which is useful for creating stability in the process by decreasing the variability
Some models of the fuzzy control charts are to be used for monitoring the processes with impreciseness or fuzzy data, and there is no limit in the amount of impreciseness or error
The purpose of this paper is to study the effect of measurement error on X -Rfuzzy control charts [1], when the measured values by different experts are different
Summary
Abstract— 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. A process in which the result of the measuring of each piece is imprecise is studied, and a X -Rfuzzy 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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