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Fuzzy confidence regions for the Taguchi capability index

Author

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  • Zeinab Ramezani
  • Abbas Parchami
  • Mashaallah Mashinchi

Abstract

Most of the traditional methods for assessing the capability of manufacturing processes are dealing with crisp quality. In quality control, such as other statistical problems, we may confront imprecise concepts. One case is a situation in which specification limits (SLs) are imprecise. In this situation, the fuzzy process capability indices (PCIs) , and are necessary for measuring the fuzzy quality in an in-control process. These fuzzy capability indices are also helpful for comparing manufacturing processes with SLs. The fuzzy capability index is used to provide an assessment of the ability of the fuzzy process to be clustered around the target value. The emphasis on the use of over the other two fuzzy indices, and , is due to its definition that provides indications of both the process variability and deviation of process mean from a specified target. In this article, by using triangular fuzzy SLs, we present four approximate fuzzy confidence regions for the fuzzy PCI . A numerical example is given to show the performance of the method.

Suggested Citation

  • Zeinab Ramezani & Abbas Parchami & Mashaallah Mashinchi, 2011. "Fuzzy confidence regions for the Taguchi capability index," International Journal of Systems Science, Taylor & Francis Journals, vol. 42(6), pages 977-987.
    • Handle: RePEc:taf:tsysxx:v:42:y:2011:i:6:p:977-987
    DOI: 10.1080/00207720903267890
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    Cited by:

    1. Gholamreza Hesamian & Mohamad Ghasem Akbari, 2021. "A process capability index for normal random variable with intuitionistic fuzzy information," Operational Research, Springer, vol. 21(2), pages 951-964, June.

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