Robust yield test for a normal production process

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

Robust yield test for a normal production process. / Iranmanesh, Hamideh; Jabbari Nooghabi, Mehdi; Parchami, Abbas.

In: Quality Engineering, Vol. 36, No. 2, 2024, p. 273-286.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Iranmanesh, H, Jabbari Nooghabi, M & Parchami, A 2024, 'Robust yield test for a normal production process', Quality Engineering, vol. 36, no. 2, pp. 273-286. https://doi.org/10.1080/08982112.2023.2202727

APA

Iranmanesh, H., Jabbari Nooghabi, M., & Parchami, A. (2024). Robust yield test for a normal production process. Quality Engineering, 36(2), 273-286. https://doi.org/10.1080/08982112.2023.2202727

Vancouver

Iranmanesh H, Jabbari Nooghabi M, Parchami A. Robust yield test for a normal production process. Quality Engineering. 2024;36(2):273-286. https://doi.org/10.1080/08982112.2023.2202727

Author

Iranmanesh, Hamideh ; Jabbari Nooghabi, Mehdi ; Parchami, Abbas. / Robust yield test for a normal production process. In: Quality Engineering. 2024 ; Vol. 36, No. 2. pp. 273-286.

Bibtex

@article{8744b667610c48859e7391d32aa9073d,
title = "Robust yield test for a normal production process",
abstract = "Testing the performance of a production process is a very serious and important topic in statistical quality control. This article presents a robust yield test to investigate the performance of an industrial production process in the presence of outliers. For this purpose, a new robust estimator of Spk is introduced to test the production yield for any normal distribution in the presence of various numbers of outliers. Moreover, a Monte Carlo simulation method to estimate the decision-making components is proposed for testing the production yield based on the yield index Spk by normal data. Meanwhile, this article discusses how well the proposed Monte Carlo method can be used for some non-normal data. Numerical computations of the simulation and real data analyses are provided to explain the proposed method.",
author = "Hamideh Iranmanesh and {Jabbari Nooghabi}, Mehdi and Abbas Parchami",
year = "2024",
doi = "10.1080/08982112.2023.2202727",
language = "English",
volume = "36",
pages = "273--286",
journal = "Quality Engineering",
issn = "0898-2112",
publisher = "Taylor & Francis",
number = "2",

}

RIS

TY - JOUR

T1 - Robust yield test for a normal production process

AU - Iranmanesh, Hamideh

AU - Jabbari Nooghabi, Mehdi

AU - Parchami, Abbas

PY - 2024

Y1 - 2024

N2 - Testing the performance of a production process is a very serious and important topic in statistical quality control. This article presents a robust yield test to investigate the performance of an industrial production process in the presence of outliers. For this purpose, a new robust estimator of Spk is introduced to test the production yield for any normal distribution in the presence of various numbers of outliers. Moreover, a Monte Carlo simulation method to estimate the decision-making components is proposed for testing the production yield based on the yield index Spk by normal data. Meanwhile, this article discusses how well the proposed Monte Carlo method can be used for some non-normal data. Numerical computations of the simulation and real data analyses are provided to explain the proposed method.

AB - Testing the performance of a production process is a very serious and important topic in statistical quality control. This article presents a robust yield test to investigate the performance of an industrial production process in the presence of outliers. For this purpose, a new robust estimator of Spk is introduced to test the production yield for any normal distribution in the presence of various numbers of outliers. Moreover, a Monte Carlo simulation method to estimate the decision-making components is proposed for testing the production yield based on the yield index Spk by normal data. Meanwhile, this article discusses how well the proposed Monte Carlo method can be used for some non-normal data. Numerical computations of the simulation and real data analyses are provided to explain the proposed method.

U2 - 10.1080/08982112.2023.2202727

DO - 10.1080/08982112.2023.2202727

M3 - Journal article

VL - 36

SP - 273

EP - 286

JO - Quality Engineering

JF - Quality Engineering

SN - 0898-2112

IS - 2

ER -

ID: 346487808