Fuzzy quality and analysis on fuzzy probability
Fuzzy Sets and Systems - Special issue on fuzzy methodology in system failure engineering
Comparison of fuzzy numbers using a fuzzy distance measure
Fuzzy Sets and Systems - Fuzzy intervals
Fuzzy confidence interval for fuzzy process capability index
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Fuzzy estimation for process capability indices
Information Sciences: an International Journal
A genetic algorithm approach to determine the sample size for attribute control charts
Information Sciences: an International Journal
Development of fuzzy process accuracy index for decision making problems
Information Sciences: an International Journal
Confidence interval of generalized Taguchi index
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
| Hi-index | 12.05 |
Process capability indices (PCIs) can be viewed as the effective and excellent means of measuring product quality and process performance. They are very useful statistical analysis tools to summarize process dispersion and location by using process capability analysis (PCA). However, there are some limitations which prevent a deep and flexible analysis because of the crisp definition of PCA's parameters. In this paper, the fuzzy set theory is used to add more information and flexibility to PCA. For this aim, fuzzy process mean, @m@? and fuzzy variance, @s@?^2, which are obtained by using the fuzzy extension principle, are used. Then fuzzy specification limits (SLs) are used together with @m@? and @s@?^2 to produce fuzzy PCIs (FPCIs). The fuzzy formulations of the indices C"p, C"p"k, C"a, C"p"m, and C"p"m"k which are the most used traditional PCIs, are developed and a numerical example for each from an automotive company is given. The results show that fuzzy estimations of PCIs have much more treasure to evaluate the process performance when it is compared with the crisp case.