@article { author = {Chen, S. M. and Hung, T. M.}, title = {What can fuzziness do for capability analysis based on fuzzy data}, journal = {Scientia Iranica}, volume = {28}, number = {2}, pages = {1049-1064}, year = {2021}, publisher = {Sharif University of Technology}, issn = {1026-3098}, eissn = {2345-3605}, doi = {10.24200/sci.2019.52104.2537}, abstract = {An advantage of process incapability index over the classical process capability index is that it provides uncontaminated separation between process accuracy and process precision. However, the value of the index is hard to be obtained accurately when process parameters cannot be determined precisely. In such case fuzzy set theory can be applied to get more flexible and more sensitive information. In this article a fuzzy process incapability index is proposed when the specification limits are assumed to be type-2 fuzzy. When the process mean and variance are unknown and when the data collected is fuzzy, three fuzzy estimators are considered. A simulation study is conducted based on a TFT-LCD fuzzy process. By employing the total integral value method to the membership function of a fuzzy index, the comparisons of quality among different processes become easier.}, keywords = {Fuzzy set,lower membership function,upper membership function,process incapability index}, url = {https://scientiairanica.sharif.edu/article_21609.html}, eprint = {https://scientiairanica.sharif.edu/article_21609_92ef95499d68d210795599c0aab134fb.pdf} }