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
Uncertain probabilities II: the continuous case
Soft Computing - A Fusion of Foundations, Methodologies and Applications
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
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
A neural network applied to estimate process capability of non-normal processes
Expert Systems with Applications: An International Journal
Interval estimation of capability index Cpmk for manufacturing processes with asymmetric tolerances
Computers and Industrial Engineering
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 accuracy index for decision making problems
Information Sciences: an International Journal
A new perspective on fuzzy process capability indices: Robustness
Expert Systems with Applications: An International Journal
Fuzzy process capability analyses with fuzzy normal distribution
Expert Systems with Applications: An International Journal
Process capability analyses based on fuzzy measurements and fuzzy control charts
Expert Systems with Applications: An International Journal
Fuzzy confidence regions for the Taguchi capability index
International Journal of Systems Science
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. Traditional PCIs are generally used for a process which has a symmetric tolerance when the target value (T) locates on the midpoint of the specification interval (m). When this is not the case (Tm), there are serious disadvantages in the casual use and interpretation of traditional PCIs. To overcome these problems, PCIs with asymmetric tolerances have been developed and applied successfully. Although PCIs are very usable statistics, they have some limitations which prevent a deep and flexible analysis because of the crisp definitions for specification limits (SLs), mean, and variance. In this paper, the fuzzy set theory is used to add more information and flexibility to PCIs with asymmetric tolerances. For this aim, fuzzy process mean, @m@? and fuzzy variance, @s@?^2, which are obtained by using the fuzzy extension principle, are used together with fuzzy specification limits (SLs) and target value (T) to produce fuzzy PCIs with asymmetric tolerances. The fuzzy formulations of the indices C~"p"k^'',C~"p"m^*,C~"p"m"k^'', which are the most used PCIs with asymmetric tolerances, are developed. Then a real case application from an automotive company is given. The results show that fuzzy estimations of PCIs with asymmetric tolerances include more information and flexibility to evaluate the process performance when it is compared with the crisp case.