Dean V. Neubauer

Acceptance Sampling in Quality Control

Introduction Acceptance Quality Control Acceptance Control and Process Control Process Quality Control Background of Acceptance Quality Control NEW! Top 10 Reasons for Acceptance Sampling Probability and the Operating Characteristic Curve Probability Random Samples and Random Numbers Counting Possibilities Probability Calculus Operating Characteristic Curve Probability Functions Probability Distributions Measures of Distribution Functions Hypergeometric Distribution Binomial Distribution Poisson Distribution NEW! f-Binomial Distribution Negative Binomial Distribution Exponential and Continuous Distributions Weibull Distribution Normal Distribution Summary of Distributions Tables of Distributions NEW! Summary Useful Approximations Tests of Fit Concepts and Terminology NEW! Average Run Length of Type B Plans Sample Size and Lot Size Effect of Inspection Error Rectification Curtailment Tolerance and Confidence Intervals Levels and Risks Choosing Quality Levels Classification of Defects Measures of Sampling Plans: Terminology Graphs of Measures Specifying a Plan Single Sampling by Attributes Operation Selection Measures Double and Multiple Sampling by Attributes Operation Selection Measures Further Considerations Sequential Sampling by Attributes Operation Selection Measures Sequential Sampling for Defects per Unit Variables Sampling for Process Parameter Single Sampling for Process Parameter Acceptance Control Charts Sequential Plans for Process Parameter (sigma Known) Sequential Plans for Process Parameter (sigma Unknown) Cumulative Sum Charts Bulk Sampling Construction of the Sample Estimation Sampling Plans Simple Random Sampling of a Unique Lot (Components of Variance Unknown) Sampling from Stream of Lots Sampling by Variables for Proportion Nonconforming Specification Limits Assumptions and Theory Operation Selection Measures M Method Plans Based on Sample Range Double Sampling by Variables Tolerance Intervals and Variables Plans for Percent Nonconforming Sequential Plans for Proportion Nonconforming Further Considerations Attributes Sampling Schemes NEW! Sampling Schemes NEW! Quick Switching Systems NEW! Tightened-Normal-Tightened (TNT) Plans NEW! MIL-STD-105E and Derivative Standards Operation Selection Measures NEW! Scheme Properties Implementation of MIL-STD-105E Matching Individual Sampling Plans to MIL-STD-105E System Performance NEW! MIL-STD-105 Derivatives Further Considerations Variables Sampling Schemes NEW! MIL-STD-414 Operation Selection Measures Implementation of Form 2 Implementation of Form 1 Implementation of Plans for Range and Variability Known Match between MIL-STD-414 and MIL-STD-105E NEW! Conversion of MIL-STD-414 to ANSI/ASQ Z1.9 NEW! MIL-STD-414 Derivatives NEW! ANSI/ASQ Z1.9 NEW! ISO 3951-1 Further Considerations Special Plans and Procedures No-Calc Plans Lot Plot Plans Narrow-Limit Gauging Mixed Variables-Attributes Plan NEW! MIL-STD-414 Dependent Mixed Plans Series of Lots: Rectification Schemes Single-Sampling AOQL Plan Dodge-Romig Sampling Scheme Anscombe Rectifying Inspection Procedure NEW! Credit-Based Schemes Continuous Sampling Plans Dodge Continuous Plans Multilevel Plans Tightened Multilevel Plans Block Continuous Plans MIL-STD-1235B Cumulative Results Plans Skip-Lot Sampling Plans Chain Sampling Plans Deferred Sentencing Schemes Demerit Rating Plan Cumulative Results Criterion (CRC) Plan Compliance Sampling Lot Sensitive Sampling (LSP) Plan TNT Scheme Quick Switching System (QSS) NEW! MIL-STD-1916 Simplified Grand Lot Procedure NEW! Nomograph for Samples Having Zero Defectives NEW! Accept on Zero (AOZ) Plans Reliability Sampling Censored Sampling Variables Plans for Life Testing and Reliability Handbook H-108 Technical Report TR 7 Administration of Acceptance Sampling Selection and Implementation of a Sampling Procedure Determining Quality Levels Economic Considerations Mandatory Standards Computer Programs A Basic Principle of Administration Appendix Answers to Problems Author Index Subject Index References and Problems appear at the end of each chapter.

Success Through Quality

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Statistical Standards and ASTM, Part 2

ABSTRACT In this article we review standards published by ASTM International originating from Committee E11 on Quality and Statistics. This article will discuss the suite of standards that are created and supported by ASTM Subcommittee E11.30 on Statistical Quality Control. We end with a short outline of current and future work.

Comparing Acceptance Sampling Standards, Part 2

ABSTRACT In this first part of a two-part series, common attribute sampling standards are discussed and compared. Military standard 105 (MIL-STD-105) is the premier attribute sampling standard that has led to a number of derivative standards in recent decades that are discussed here. While these standards share a lot of common ground, there are some differences among them. While military standards, such as MIL-STD-105, are no longer supported by the U.S. Department of Defense, there are many comparable standards to use which are supported by various standards organizations. In this paper, the reader will be introduced to these derivative standards and how they compare to each other and to MIL-STD-105. In the second part of this series, MIL-STD-414 and its derivative standards will be discussed for the inspection of variables data. Their ties to MIL-STD-105E and its derivatives will also be used to tie together all the standards in this article as well.

Chemical Process Performance Evaluation

Chapter 11 provides techniques for warranty analysis including data mining, reliability estimation from warranty claim data, warranty repair modeling, field failure monitoring, and warranty cost reduction. Chapters of this book are arranged according to the order of the product life cycle stages. Some of the techniques and methodologies described in each chapter may well serve in more than one product life cycle stage. For instance, the techniques described in Chapters 7 and 8 on accelerated life tests and degradation tests are useful in design and verification as well as testing stages. The author has done a great job in explaining the practical and state-ofthe-art techniques to access and enhance reliability throughout the product life cycle. This book deliberates on a wide range of topics in reliability engineering. Practical examples and exercises, mostly from the automotive industry, are used to illustrate the ideas and methodologies. Readers of this book are expected to have some knowledge of basic statistical inference, statistical modeling, and probability theory. This book will be of practical use for a variety of engineers, including reliability engineers, quality engineers, test engineers, systems engineers, or design engineers, who are working in different stages of the product life cycle. It will also serve well as a textbook or a reference book to students in a course on reliability, quality, or industrial engineering.

Statistics for the Sciences

The book starts very promisingly, with a description of graphical methods that are useful for the analysis of extremes, as well as several case studies taken from geology, meteorology, and insurance. These examples provide a convincing motivation for the importance of studying extremes. The same case studies are used throughout the text to demonstrate results, which would in theory provide a nice continuity. Unfortunately, the discussion of the case studies, beyond their initial presentation, is severely lacking in context and depth. For example, in Chapter 6, “Case Studies,” three case studies are presented. Although the chapter is replete with analysis and plots, it reads more like a litany of methods than actual in-depth case studies. The authors do not provide interpretation of the results, nor do they discuss what can be learned from the different methods of analysis and the discrepancies between them. Indeed, throughout the book copious plots are presented, but with minimal interpretation or discussion. I found myself skipping over most of the figures, because there is very little guidance on how to read them, which features are the most interesting and pertinent, and so forth—serious flaws in an applied treatment of any area of statistics. This was one of the more frustrating and disappointing aspects of the book, especially given the authors’ claim that it can be used as a textbook for advanced undergraduate or beginning graduate students. On that note, the book’s usefulness as a class text is limited. There are no exercises. Nor do I think the level appropriate for the stated audience, at least not at American universities. The authors write at a much higher level of mathematical sophistication than most beginning students in the U.S. are prepared to handle. As already mentioned, the examples are not adequately explained. Moreover, the writing is very dense and at times hard to follow. It does not help that the authors sometimes refer to a result “that was shown above” or “that will be seen below” without giving the details on where the result can be found. As a minor quibble, the URL for the website connected with the book, as given in the Preface, is simply incorrect. I was able to locate the page with very little effort, but of course the correct URL should have been listed to begin with. The page contains programs that were used to analyze the examples, and as such is useful for practitioners. Despite its failings as a course text, however, Statistics of Extremes: Theory and Applications is a potentially valuable resource for researchers interested in extreme value theory. The book is comprehensive, covering both early history and modern developments, and explores simple (e.g., Pareto, Weibull, Gumbel) models for tail behaviors as well as more complex models for extremes in regression analysis, multivariate data, and time series. The authors point out unanswered questions and major directions of current research, up to and including some newly developed Bayesian approaches, and provide an extensive bibliography, with many references from the past 5 years. In short, then, this book should be a useful reference for researchers wishing to learn more about the analysis of extreme data, including as it does a wealth of information about the topic in one relatively self-contained volume. I cannot, however, recommend it for use as a text for students.