Volume 20 , Issue 4 , PP: 119-127, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Uma G. 1 * , Nandhitha S. 2
Doi: https://doi.org/10.54216/IJNS.200409
In Statistical Quality Control (SQC), the judgement of the accepting the lot or rejecting the lot s carried out with the help of acceptance sampling plans in the inspection process of any manufacturing industry. Based on the predefined risk the output is attained with minimum inspection cost based on the optimum sample size. Generally Classical statistics is used based on the deterministic nature of the information and measurements. In some circumstances, the quality characteristics may not be certain enough leading to vagueness or impreciseness situation. Accordingly, in past few decades Fuzzy logic is one of the most popular techniques to model the uncertainty in the manufacturing industries. As an advent of technology and knowledge data era, an extension of Fuzzy, a new concept known as Neutrosophic Logic is in progress to apply to achieve these uncertainties. In this, such vagueness, imprecise is called as indeterminants. Thus, Neutrosophic Logic taken its role in Acceptance Sampling Plans with Probability distributions for various plan parameters such as AQL, LQL and Neutrosophic defection status are offered for Poisson distribution in the first time. The chief formulations of Acceptance Sampling plans for Single Sampling were derived based on Neutrosophic Statistics. As an advanced step the mixture of Acceptance Sampling plans with the shifting ruling for swapping from one plan to another plan are named as Sampling System and one such system is Quick Switching System the most widely applicable to safeguard from bad quality which give high level protection as well as to reduce the cost of inspection and time. In this study, Quick Switching System (QSS) with Single Sampling Plan (SSP) as reference plan is constructed based on Neutrosophic sets on Poisson distribution as baseline distribution. The procedures, OC Curves and tables have been redesigned and presented with numerical example.
Fuzzy logic , NS , SQC , QSS , SSP , OC , Poisson distribution , Classical Statistics.
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