Multiple Dependent State Sampling Plan Based On Process Capability Index
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Multiple Dependent State Sampling Plan Based on Process Capability Index
This paper extends the idea of multiple dependent state sampling plans to the case of using process capability index when the quality characteristic of the product follows the normal distribution. The plan parameters are determined using the optimization process with minimum values of sample size so that the specified producer's risk and consumer's risk should be satisfied simultaneously for given values of acceptable quality level and limiting quality level in terms of fraction defective beyond two specification limits. The plan parameters are determined under symmetric and asymmetric cases of fraction defective. The advantage of the proposed plan is discussed over the single variable sampling plan. A real example is presented to illustrate the proposed plan in practice.
Mixed Multiple Dependent State Sampling Plans Based on Process Capability Index
In this paper, a mixed multiple dependent state sampling plan based on the process capability index is presented for a quality characteristic of interest follows a normal distribution. The parameters of the proposed plan are determined with regard to the producer's and consumer's risks for specified values of acceptable quality level and limiting quality level. Tables containing parameters of the proposed plan are provided for symmetry and asymmetry cases. The advantages of the proposed plan over the attribute plan in terms of the sample size are discussed. An industrial example is given to explain the proposed procedure.
Testing and Inspection Using Acceptance Sampling Plans
This book introduces a number of new sampling plans, such as time truncated life tests, skip sampling plans, resubmitted plans, mixed sampling plans, sampling plans based on the process capability index and plans for big data, which can be used for testing and inspecting products, from the raw-materials stage to the final product, in every industry using statistical process control techniques. It also presents the statistical theory, methodology and applications of acceptance sampling from truncated life tests. Further, it discusses the latest reliability, quality and risk analysis methods based on acceptance sampling from truncated life, which engineering and statisticians require in order to make decisions, and which are also useful for researchers in the areas of quality control, lifetime analysis, censored data analysis, goodness-of-fit and statistical software applications. In its nine chapters, the book addresses a wide range of testing/inspection sampling schemes for discrete and continuous data collected in various production processes. It includes a chapter on sampling plans for big data and offers several illustrative examples of the procedures presented. Requiring a basic knowledge of probability distributions, inference and estimation, and lifetime and quality analysis, it is a valuable resource for graduate and senior undergraduate engineering students, and practicing engineers, more specifically it is useful for quality engineers, reliability engineers, consultants, black belts, master black belts, students and researchers interested in applying reliability and risk and quality methods.