Criteria for evaluating fuzzy ranking methods
Fuzzy Sets and Systems
A probabilistic and statistical view of fuzzy methods
Technometrics
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
Joint monitoring of process mean and variance
Proceedings of second world congress on Nonlinear analysts
Fuzzy Sets and Systems
Fuzzy Probability and Statistics (Studies in Fuzziness and Soft Computing)
Fuzzy Probability and Statistics (Studies in Fuzziness and Soft Computing)
An alternative approach to fuzzy control charts: Direct fuzzy approach
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Ranking L-R fuzzy number based on deviation degree
Information Sciences: an International Journal
Fuzzy process control: construction of control charts with fuzzy numbers
Fuzzy Sets and Systems
Using one EWMA chart to jointly monitor the process mean and variance
Computational Statistics
A multivariate control chart for simultaneously monitoring process mean and variability
Computational Statistics & Data Analysis
Analyzing fuzzy risk based on a new fuzzy ranking method between generalized fuzzy numbers
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
Ranking fuzzy numbers based on the areas on the left and the right sides of fuzzy number
Computers & Mathematics with Applications
An approach for ranking of fuzzy numbers
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
A note on ranking generalized fuzzy numbers
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
The exponentially-weighted-moving-average (EWMA) control chart was developed to detect small shifts in mean and variability of a key quality characteristic in a manufacturing process. To improve its performance, several modified statistical schemes on manipulation of random samples of real data (precise numbers) collected from the quality characteristic have been proposed. Among them, the recently recommended control chart named the maximum generally weighted moving average (MaxGWMA) is found superior in recognition of its outstanding diagnostic abilities at warning abnormal-manufacturing variations swiftly. In this paper, based on the well-known fuzzy set theory, we develop a Fuzzy-MaxGWMA (F-MaxGWMA) chart, an extension of the MaxGWMA chart, to well accommodate the fuzzy environment where both the randomness and fuzziness of imprecise sample data (fuzzy numbers) are taken into consideration. Moreover, for identifying assignable variations of the on-line manufacturing process with fuzzy data, an index-of-optimism criterion is implemented to instantaneously monitoring as well as classifying the process conditions into multi-intermittent states between in control and out of control. It can overcome the constraints of binary classifications of the process condition used by the MaxGWMA chart when fuzzy data inevitably appear in practical manufacturing processes. Finally, a realistic example to control the coating thickness of an industrial cutting-tool manufacturing process is illustrated to demonstrate the adaptability and effectiveness of this newly extended approach.