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
An alternative approach to fuzzy control charts: Direct fuzzy approach
Information Sciences: an International Journal
Fuzzy process capability analyses: An application to teaching processes
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Fuzzy theory and technology with applications
Expert Systems with Applications: 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 control charts and fuzzy unnatural pattern analyses
Computational Statistics & Data Analysis
Development of fuzzy process accuracy index for decision making problems
Information Sciences: an International Journal
Fuzzy process capability indices with asymmetric tolerances
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Process performance can be analyzed by using process capability indices (PCIs), which are summary statistics to depict the process location and dispersion successfully. Although they are very usable statistics, they have some limitations which prevent a deep and flexible analysis because of the crisp measurements and specification limits (SLs). If the specification limits or measurements are expressed by linguistic variables, traditional PCIs cause some misleading results. In this paper, the fuzzy set theory is used to add more information and flexibility to process capability analyses (PCA). For this aim, linguistic definition of the quality characteristic measurements are converted to fuzzy numbers and fuzzy PCIs are produced based on these measurements and fuzzy specification limits (SLs). Also fuzzy control charts are derived for fuzzy measurements of the related quality characteristic. They are used to increase the accuracy of PCA by determining whether or not the process is in statistical control. The fuzzy formulation of the indices C"p and C"p"k, which are the most used two traditional PCIs, are produced when SLs and measurements are both triangular (TFN) and trapezoidal fuzzy numbers (TrFN). The proposed methodologies are applied in a piston manufacturer in Konya's Industrial Area, Turkey.