Sequential inspection sampling plan based on Burr-Ⅻ amputated life testing with numerical illustrations and industrial applications

It is often desired for practical reasons to cease a life test at a predetermined period $ {{𝓽}}_{0} $. In this study, we provide sequential inspection sampling plan (SISP) for amputated life tests underlying the Burr Ⅻ (BTXII) distribution. We considered the $ \rho ^{{\rm{th}}}$ percentile lifetime of a product as the quality parameter. The sequential sampling plan is a dynamic and efficient technique to quality control and statistical decision-making. Unlike fixed sample plans, which require a predetermined number of samples before deciding, sequential plan enables ongoing analysis as a batch is examined. This flexible plan enables decisions- such as rejecting, accepting or continuing the sampling process - to be made after each sample, which is cumulative data for the number of nonconforming items. Acceptance, rejection limit lines, and optimal sample size at levels of manufacturer and customer errors were analyzed. A technique is provided for calculating the operation characteristic (OC) function and average sample number (ASN) in the suggested SISP. The effectiveness of the SISP was compared to the single, double, and repeating sampling strategies. In comparison to the single, double, and repetitive acceptance sampling plans (RASPs) the proposed sequential sampling acceptance strategy requires fewer sample resources on average for amputated life testing. The suggested method's uses are demonstrated with illustrated instances. In industrial applications, two actual sets of data are employed to demonstrate the SISP's flexibility.

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Publication Details

Journal
AIMS Mathematics
Published
2025-01-01
DOI
https://doi.org/10.3934/math.2025669
Citations
1
Primary Topic
Advanced Statistical Process Monitoring
Type
article
Field-Weighted Citation Impact
1.40
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article

Sequential inspection sampling plan based on Burr-Ⅻ amputated life testing with numerical illustrations and industrial applications

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1 citations
AIMS Mathematics
Advanced Statistical Process Monitoring
1.40
article

Sequential inspection sampling plan based on Burr-Ⅻ amputated life testing with numerical illustrations and industrial applications

Mohammad Abiad, Yusra Tashkandy, Md. Mahabubur Rahman, Oluwafemi Samson Balogun, Haitham M. Yousof, Mahmoud Ebrahim Bakr, Basma Ahmed, Anoop Kumar, Mohammad Yusuf
article en
1 citations

Abstract

It is often desired for practical reasons to cease a life test at a predetermined period $ {{𝓽}}_{0} $. In this study, we provide sequential inspection sampling plan (SISP) for amputated life tests underlying the Burr Ⅻ (BTXII) distribution. We considered the $ \rho ^{{\rm{th}}}$ percentile lifetime of a product as the quality parameter. The sequential sampling plan is a dynamic and efficient technique to quality control and statistical decision-making. Unlike fixed sample plans, which require a predetermined number of samples before deciding, sequential plan enables ongoing analysis as a batch is examined. This flexible plan enables decisions- such as rejecting, accepting or continuing the sampling process - to be made after each sample, which is cumulative data for the number of nonconforming items. Acceptance, rejection limit lines, and optimal sample size at levels of manufacturer and customer errors were analyzed. A technique is provided for calculating the operation characteristic (OC) function and average sample number (ASN) in the suggested SISP. The effectiveness of the SISP was compared to the single, double, and repeating sampling strategies. In comparison to the single, double, and repetitive acceptance sampling plans (RASPs) the proposed sequential sampling acceptance strategy requires fewer sample resources on average for amputated life testing. The suggested method's uses are demonstrated with illustrated instances. In industrial applications, two actual sets of data are employed to demonstrate the SISP's flexibility.

AIMS MathematicsVol. 10(6)
Zero hunger
Openalex Percentile: Top 15%
Advanced Statistical Process Monitoring
1.40
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