A Fuzzy Control Chart Approach for Attributes and Variables

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 ha

QUALITY INTERVAL ACCEPTANCE SINGLE SAMPLING PLAN WITH FUZZY PARAMETER USING POISSON DISTRIBUTION

ABSTRACT The purpose of this paper is to present the Quality Interval acceptance single sampling plan when the fraction of nonconforming items is a fuzzy number and being modeled based on the fuzzy Poisson distribution. A new procedure for implementing Fuzzy logic in Quality Interval acceptance sampling plan has also been carried out

Gráficos difusos versus gráficos tradicionales para el control de procesos por atributos

Control charts are the main tool in the process control. They have been widely used and are still used in most manufacturing processes. However, proposals have emerged that seek to improve their performance, mainly about topics which involve the vagueness and uncertainty of the data. In this sense, fuzzy control charts area an important alternative for improve the performance of control charts. We present a 2017 comparison of Shewhart control charts (traditional) and fuzzy control charts for attributes with the aim of establishing similarities and differences between the two methodologies. We develop a numerical example for a c traditional chart and fuzzy charts these built with different methods: Fuzzy mode, fuzzy median and fuzzy midrange and the direct fuzzy approach, then we do the comparison used the rules for evaluation of patterns of not natural behavior in the control charts. From the results of the comparison carried out in this research, it is concluded that when using the evaluation rules for both graphs with the data used, no differences are obtained in the results.Los gráficos de control son una buena herramienta controlar procesos. Han sido ampliamente utilizados —y aún ahora se utilizan— en la mayoría de los procesos manufactureros. Sin embargo, se han presentado propuestas orientadas a mejorar el desempeño de los mismos, principalmente en los aspectos referentes a la incertidumbre y ambigüedad existente en los datos. En este sentido, los gráficos de control difusos son una alternativa valiosa para mejorar el desempeño de los gráficos tradicionales. Se presenta, entonces, una comparación de los gráficos de control Shewhart (tradicionales) y los gráficos de control difusos por atributos, con el objetivo de establecer las similitudes y diferencias existentes entre las dos metodologías. De esta manera, se desarrolla un ejemplo numérico de un gráfico tradicional c y gráficos difusos construidos a partir de las siguientes técnicas de transformación: moda difusa, mediana difusa, rango medio difuso y enfoque difuso directo. Para realizar una comparación se utilizaron las reglas de evaluación de patrones de comportamiento no natural en un gráfico de control. A partir de los resultados de la comparación realizada en esta investigación se concluye que al utilizar las reglas de evaluación en ambos gráficos con los mismos datos no se obtienen diferencias en los resultados