ASN-minimax double sampling plans by variables for two-sided specification limits when the standard deviation is known

Sampling plans are a useful tool to decide whether large-size lots should be accepted or rejected. In this paper we introduce double sampling plans by variables for a normally distributed characteristic with known standard deviation and two-sided specification limits. These plans fulfill the classical two-points-condition on the operating characteristic (OC) and feature minimal maximal average sample number (ASN).     

Designing of a variables two-plan system by minimizing the average sample number

This article proposes a variables two-plan sampling system called tightened-normal-tightened (TNT) sampling inspection scheme where the quality characteristic follows a normal distribution or a lognormal distribution and has an upper or a lower specification limit. The TNT variables sampling inspection scheme will be useful when testing is costly and destructive. The advantages of the variables TNT scheme over variables single and double sampling plans and attributes TNT scheme are discussed. Tables are also constructed for the selection of parameters of known and unknown standard deviation variables TNT schemes for a given acceptable quality level (AQL) and limiting quality level (LQL). The problem is formulated as a nonlinear programming where the objective function to be minimized is the average sample number and the constraints are related to lot acceptance probabilities at AQL and LQL under the operating characteristic curve.     

ASN-minimax double sampling plans for variables

X be a continuous quality characteristic, with one-sided lower specification limit, having either normal distribution with known σ or exponential distribution. We report on an algorithm allowing the calculation of the so-called ASN-Minimax plan. This plan has minimal maximal average sample size among all double sampling plans for variables that obey the classical two-points-condition on the operating characteristic. We give examples and tables of the ASN-Minimax plans in the normal as well as in the exponential case. Received: March 20, 2000; revised version: January 22, 2001     

Optimal Designing of Variables Sampling Plan for Resubmitted Lots

This article proposes a variables sampling plan that can be applied for sampling inspection of resubmitted lots when the quality characteristic of interest follows the normal distribution. Resubmission of lots for inspection is allowed in some situations where the original inspection results are suspected or when the supplier or producer is allowed to opt for resampling as per the provisions of the contract, etc. The advantages of this proposed variables sampling plan over the existing single sampling variables plan are discussed. Tables are also constructed for the selection of optimal parameters of known and unknown standard deviation variables resampling scheme for specified two points on the operating characteristic curve, namely, the acceptable quality level and the limiting quality level along with the producer and consumer's risks. The optimization problem is formulated as a nonlinear programming where the objective function to be minimized is the average sample number and the constraints are related to lot acceptance probabilities at acceptable quality level and limiting quality level under the operating characteristic curve.     

Some sampling plans with identical operating characteristic curves

The properties of three sampling inspection plans with identical operating characteristic curves are discussed. Relative merits and demerits are studied and an eficient plan is identiJied. Tables yielding parametric values for designing plans for various entry parameters are provided.     

Chain sampling plan for variables inspection

SUMMARY This paper extends the concept of chain sampling to variables inspection when the standard deviation of the normally distributed characteristic is known. A discussion of the shape of the known sigma single-sampling variables plan is given. The chain sampling plan for variables inspection will be useful when testing is costly or destructive.     

Optimal designing of a new mixed variable lot-size chain sampling plan based on the process capability index

In this paper, a new mixed sampling plan based on the process capability index (PCI) C_pk is proposed and the resultant plan is called mixed variable lot-size chain sampling plan (ChSP). The proposed mixed plan comprises of both attribute and variables inspections. The variable lot-size sampling plan can be used for inspection of attribute quality characteristics and for the inspection of measurable quality characteristics, the variables ChSP based on PCI will be used. We have considered both symmetric and asymmetric fraction non conforming cases for the variables ChSP. Tables are developed for determining the optimal parameters of the proposed mixed plan based on two points on the operating characteristic (OC) approach. In order to construct the tables, the problem is formulated as a non linear programming where the average sample number function is considered as an objective function to be minimized and the lot acceptance probabilities at acceptable quality level and limiting quality level under the OC curve are considered as constraints. The practical implementation of the proposed mixed sampling plan is explained with an illustrative real time example. Advantages of the proposed sampling plan are also discussed in terms of comparison with other existing sampling plans.     

A New Design on Attributes Single Sampling Plans

In this article, two new approaches are introduced to design attributes single plans, and the corresponding models are constructed separately. For Approach I, an algorithm is proposed to design sampling plans by setting a goal function to fulfill the two-point conditions on the operating characteristic curve. For Approach II, the plan parameters are solved by a nonlinear optimization model which minimizes the integration of the probability of acceptance in the interval from the producer's risk quality to the consumer's risk quality. Then numerical examples and discussions based on numerical computation results are given to illustrate the approaches, and tables of the designed plans under various conditions are provided. Moreover, a fact is given to be proved that there is a relation between the conventional design and the new approaches.     

Exact calculation of the Dodge-Romig LTPD single sampling plans for inspection by variables

In this paper we shall deal with the acceptance sampling plans when the remainder of rejected lots is inspected. We shall consider two types of LTPD plans- for inspection by variables and for inspection by variables and attributes (all items from the sample are inspected by variables, remainder of rejected lots is inspected by attributes). We shall report on an algorithm allowing the calculation of these plans when the non-central t distribution is used for the operating characteristic. The calculation is considerably difficult, algorithm for non-central t distribution takes several minutes. For the calculation we shall use an original method.     

Acceptance Sampling Plans from Truncated Life Tests Based on the Birnbaum–Saunders Distribution for Percentiles

Time to failure due to fatigue is one of the common quality characteristics in material engineering applications. In this article, acceptance sampling plans are developed for the Birnbaum–Saunders distribution percentiles when the life test is truncated at a pre-specified time. The minimum sample size necessary to ensure the specified life percentile is obtained under a given customer's risk. The operating characteristic values (and curves) of the sampling plans as well as the producer's risk are presented. The R package named spbsq is developed to implement the developed sampling plans. Two examples with real data sets are also given as illustration.     

Developing a variables two-plan sampling system for product acceptance determination

In this paper, a variables tightened-normal-tightened (TNT) two-plan sampling system based on the widely used capability index C_pk is developed for product acceptance determination when the quality characteristic of products has two-sided specification limits and follows a normal distribution. The operating procedure and operating characteristic (OC) function of the variables TNT two-plan sampling system, and the conditions for solving plan parameters are provided. The behavior of OC curves for the variables TNT sampling system under various parameters is also studied, and compared with the variables single tightened inspection plan and single normal inspection plan.     

Double and group acceptance sampling plan for truncated life test based on inverse log-logistic distribution

This paper introduces a double and group acceptance sampling plans based on time truncated lifetimes when the lifetime of an item follows the inverse log-logistic (ILL) distribution with known shape parameter. The operating characteristic function and average sample number (ASN) values of the double acceptance sampling inspection plan are provided. The values of the minimum number of groups and operating characteristic function for various quality levels are obtained for a group acceptance sampling inspection plan. A comparative study between single acceptance sampling inspection plan and double acceptance sampling inspection plan is carried out in terms of sample size. One simulated example and four real-life examples are discussed to show the applicability of the proposed double and group acceptance sampling inspection plans for ILL distributed quality parameters.     

Inverse-gaussian acceptance sampling plans by variables

Variable sampling plans to control fraction defective are obtained using the Inverse-Gaussian (IG) distribution. OC curves are obtained and impact of sample size and specification limits on these curves are discussed. Simulation studies are used to investigate sensitivity of the sampling plans under the more commonly used normal distribution.     

A variables repetitive group sampling plan under failure-censored reliability tests for Weibull distribution

We propose a variables repetitive group sampling plan under type-II or failure-censored life testing when the lifetime of a part follows a Weibull distribution with a known shape parameter. The acceptance criteria do not involve unknown scale parameter differently from the existing plans. To determine the design parameters of the proposed plan, the usual approach of using two points on the operating characteristic curve is adopted and an optimization problem is formulated so as to minimize the average number of failures observed. Tables for design parameters are constructed when the quality of parts is represented by the unreliability or the ratio of the mean lifetime to the specified life. It is found that the proposed sampling plan can reduce the sample size significantly than do the single sampling plan.     

Determination of Single Sampling Plans by Attributes Under the Conditions of Zero-Inflated Poisson Distribution

When the manufacturing process is well monitored, occurrence of nondefects would be a frequent event in sampling inspection. The appropriate probability distribution of the number of defects is a zero-inflated Poisson (ZIP) distribution. In this article, determination of single sampling plans (SSPs) by attributes using unity values is considered, when the number of defects follows a ZIP distribution. The operating characteristic (OC) function of the sampling plan is derived. Plan parameters are obtained for some sets of values of (p₁, α, p₂, β). Numerical illustrations are given to describe the determination of SSP under ZIP distribution and to study its performance in comparison with Poisson SSP.     

Autoregressive processes applied to acceptance sampling by varlables

The effect of serial correlation on acceptance sampling plans by variables has been examined in this paper assuming the quality measurements follow an AR(p) process. The effect of serial correlation can be examined by comparing OC curves, sample size and producer's risks, ∝, with that of the independent case when the process standard deviation, σ, is known. When σ is unknown and for large n, sampling plans can be constructed using the central limit theorem. However, for σ unknown and for small n, there is no satisfactory method of obtaining sampling plans.     

Improved attribute acceptance sampling plans based on maxima nomination sampling

This paper demonstrates the use of maxima nomination sampling (MNS) technique in design and evaluation of single AQL, LTPD, and EQL acceptance sampling plans for attributes. We exploit the effect of sample size and acceptance number on the performance of our proposed MNS plans using operating characteristic (OC) curve. Among other results, we show that MNS acceptance sampling plans with smaller sample size and bigger acceptance number perform better than commonly used acceptance sampling plans for attributes based on simple random sampling (SRS) technique. Indeed, MNS acceptance sampling plans result in OC curves which, compared to their SRS counterparts, are much closer to the ideal OC curve. A computer program is designed which can be used to specify the optimum MNS acceptance sampling plan and to show, visually, how the shape of the OC curve changes when parameters of the acceptance sampling plan vary. Theoretical results and numerical evaluations are given.     

Procedures and tables for the construction and selection of conditional double sampling plans

Procedures and tables for the selection of conditional double sampling plans for various entry parameters are given. A search procedure is also developed to determine the parameters of the plans when two points on the operating characteristic curve are specified.     

One sided acceptance sampling by variables: the case of the laplace distribution

The theory of acceptance sampling by variables is well known when the underlying distribution is normal. When the normality assumption is not true, using the usual normal case method can be quite misleading. In this paper we deal with the Laplace distribution for both the standard deviation known and then unknown. We establish a decision rule for accepting a lot of product containing a defective proportion p. We determine the density function of the decision rule statistic, for small and large sample sizes. We give some practical ways to choose the sample size and the acceptance constant to obtain a desired operating characteristic curve     

Reliability sampling plans for the two-parameter exponential distribution under progressive censoring

This paper presents reliability sampling plans for the two-parameter exponential distribution under progressive censoring. These sampling plans are quite useful to practitioners, because they provide savings in resources and in total test time. Furthermore, they off er the flexibility to remove functioning test specimens from further testing at various stages of the experimentation. In the construction of these sampling plans, the operating characteristic curve is derived using the exact distributional properties of maximum likelihood estimators. An example is given to illustrate the application of the proposed sampling plans.