Research on statistical sampling models and plans based on OIML R87 for prepackages inspection

WenJun He; Kai Zhang; Kun Ma; JunJie Wang; JiYi Liu; LuLu Qin; ShuCheng Wang

doi:10.1117/12.2672268

28 March 2023 Research on statistical sampling models and plans based on OIML R87 for prepackages inspection

WenJun He, Kai Zhang, Kun Ma, JunJie Wang, JiYi Liu, LuLu Qin, ShuCheng Wang

Proceedings Volume 12597, Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022); 1259723 (2023) https://doi.org/10.1117/12.2672268
Event: Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022), 2022, Nanjing, China

Abstract

Metrological testing for quantity of product in prepackages is an important national legal measurement activity primarily aiming to ensure fair market trade and consumers’ interests in the world. International Recommendation OIML R87 is a model regulation and procedural guide widely used for the compliance testing of prepackage marked with a constant nominal quantity. OIML R87-2016 presents sampling procedures without replacement to revise the unreasonable one of OIML R87-2004. Single sampling plans for inspection having desired operating characteristics by mean and the numbers of inadequate prepackages with T-type errors were established. The paper discusses four statistical mathematical models based on the random sampling without replacement. The corresponding sampling plans can be concluded to meet the requirements of OIML R87-2016. The drawback of the sampling plans that sample size decrease suddenly and sharply with the lot size increasing was carried out. New sampling plans with appropriate limited condition are proposed by VBA program.

Citation Download Citation

WenJun He, Kai Zhang, Kun Ma, JunJie Wang, JiYi Liu, LuLu Qin, and ShuCheng Wang "Research on statistical sampling models and plans based on OIML R87 for prepackages inspection", Proc. SPIE 12597, Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022), 1259723 (28 March 2023); https://doi.org/10.1117/12.2672268

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