Process quality recheck for Gamma quality characteristic from supplier products: a case study on radio-frequency power

Meng, Fanbing; Yang, Jun; Li, Qi

doi:10.1080/00207543.2022.2049910

Cited by 1 publication

(3 citation statements)

References 48 publications

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“…[5][6][7][8][9][10][11][12][13] More recent studies are considered by the references. [14][15][16][17] To model PCIs with uncertainty, Yalçin and Kaya 14 incorporated the fuzzy set theory. Haktanır and Kahraman 15 developed PCI models using penthagorean fuzzy sets, which is a new extension of intuitionistic fuzzy sets.…”

Section: Introductionmentioning

confidence: 99%

“…Haktanır and Kahraman 15 developed PCI models using penthagorean fuzzy sets, which is a new extension of intuitionistic fuzzy sets. Meng et al 16 studied PCIs with non-normal data such as gamma quality characteristic data. Yang et al 17 considered PCI models based on the truncated normal distribution.…”

Section: Introductionmentioning

confidence: 99%

“…For more details, see the references 5–13 . More recent studies are considered by the references 14–17 . To model PCIs with uncertainty, Yalçin and Kaya 14 incorporated the fuzzy set theory.…”

Section: Introductionmentioning

confidence: 99%

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Confidence intervals of the process capability index Cpc$C_{pc}$ revisited via modified bootstrap technique and ROC curves

Ouyang

Dey²,

Byun

et al. 2023

Quality & Reliability Eng

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We consider the process capability index (PCI), a widely used quality‐related statistic used to assess the quality of products and performance of monitored processes in various industries. It is widely known that the conventional PCIs perform well when the quality process being monitored has a normal distribution. Unfortunately, using the indices to evaluate a non‐normally distributed process often leads to inaccurate results. In this article, we consider a new PCI, , that can be used in both normal and non‐normal scenarios. The objective of this article is threefold: (i) We provide a corrected form of the confidence interval for . (ii) We compare the performance of three nonparametric bootstrap confidence intervals (BCIs) for . Specifically, the standard bootstrap, percentile bootstrap, and bias‐corrected percentile bootstrap. Under various distributional assumptions such as the normal, chi‐square, Student t, Laplace, and two‐parameter exponential distributions, the estimated coverage probabilities and average width of the confidence intervals and BCIs for are compared. (iii) The power of the respective bootstrap approaches is evaluated by using the equivalence relation between confidence interval construction and two‐sided hypothesis testing. We also provide the receiver operating characteristic curves to evaluate their performance. Finally, as an illustrative example, an actual data set related to groove dimensions (in inches) measured from a manufacturing process of ignition keys is re‐analyzed to illustrate the utility of the proposed methods.

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