bluuebunny/crossref_metadata_2025_split huggingface dataset:
Plackett‐Burman Designs
Authors: Tyssedal John 
Published: September 2014

Abstract
Plackett–Burman designs are important screening designs. In this article we explain how they can be constructed and how they can be classified according to projectivity of two‐level designs. We give a reasonably exhaustive list of existing methods for analyzing them and illustrate one of these methods by an example. Some techniques useful for constructing designs with good projective properties for various experimental situations are described at the end.
Factorial Designs, Resolution of
Authors: Romano Daniele 
Published: September 2014

AbstractAfter providing the definition of resolution for 2k−pdesigns we present two rationales that justify the practical importance of resolution as a criterion for selecting good fractional designs. Insight is given into designs of resolution III, IV, and V, i.e., the most widespread in practice. Then we describe how to determine the resolution of a design and a simple rule for finding half‐fractions of maximum resolution. Smaller fractions of maximum resolution can be found in specialized books referenced in the text. We also describe some circumstances when the resolution criterion needs to be supplemented or replaced by other user‐relevant criteria.
Sample‐Size Determination in Experimental Designs
Authors: Lenth Russell V. 
Published: September 2014

Abstract
Sample‐size determination is a procedure by which we decide how much data are required in order to achieve stated goals for a statistical study. The article focuses primarily on the two most popular paradigms (confidence interval width and power), and on the use of software to solve sample‐size problems. It discusses what situations are most pertinent with regard to sample‐size planning, the importance of eliciting a relevant effect size, and the use of pilot data. An illustration is given for a complex experiment. There is also information on the related issue of sample‐size allocation, and on sample‐size requirements for pilot studies.
Split Plot Designs in Industry
Authors: Goos Peter 
Published: September 2014

Abstract

Split‐plot designs are
nested designs
that are frequently used in industrial experimentation and result in correlated observations. A correct analysis of the data from split‐plot designs takes account of the correlation. However, as split‐plot designs are often used inadvertently, the typical split‐plot correlation structure is not recognized in many applications. The resulting statistical analysis is then inappropriate. In this contribution, the agricultural origin of split‐plot experimentation is discussed first. Design aspects are discussed as well as the classical analysis of variance (ANOVA) (
see
Analysis of Variance
) and industrial applications of split‐plot designs. It is explained that a generalized least‐squares analysis can be used for estimation and inference and different approaches to constructing split‐plot designs are discussed.
Bergman‐Hynén Method for Dispersion Effects
Authors: Arvidsson Martin , Bergman Bo 
Published: September 2014

Abstract
This paper presents the Bergman‐Hynén method for dispersion effect identification in unreplicated fractional factorials. In contrast with other methods the Bergman‐Hynén test statistic has a known reference distribution which allows use of formal hypothesis testing for the dispersion effect identification.
Ridge Analysis in Experimental Design
Authors: Draper Norman R. 
Published: September 2014

Abstract

Ridge analysis is a valuable tool for exploring second‐order surfaces that have been fitted to experimental data. Paths of absolute maxima (ridge of maxima), absolute minima (ridge of minima), or of intermediate turning values (ridges that are neither overall maxima nor minima but are best locally) can be located and followed. The paths can start from
any
selected location in the predictor space; often the coded origin of the experimental space would be chosen.
Attributes Sampling Schemes in International Standards
Authors: Balamurali Saminathan , Göb Rainer , Jun Chi‐Hyuck 
Published: September 2014

Abstract
Sampling tables for the inspection of attribute quality characteristics were developed during World War II by the United States Department of Defense. In 1950, these tables led to the first military standard MIL‐STD‐105A which evolved into the latest version MIL‐STD‐105E issued in 1989. Further national and international standards emerged as derivatives of MIL‐STD. On international level, the International Standards Organization (ISO) introduced the attributes sampling standard ISO 2859 which has been adopted as a national standard in many countries. The article analyzes the purpose, structure, and operation of MIL‐STD‐105E and of ISO 2859.
Engineering Process Control
Authors: He Bin , Adams Benjamin M. 
Published: September 2014

Abstract

This article addresses the similarities and distinctions between traditional statistical process control (SPC) methods (
see
Control Charts, Overview
) and engineering process control (EPC) methods (
see
Feedback Control
). Both techniques share a primary goal of reducing process variation while maintaining target values. The strategic and mathematical foundations for the techniques are, however, quite different. These differences have led to confusion surrounding the appropriate choice of control method when both SPC and EPC appear to be viable quality tools. This article provides a brief overview of SPC and EPC methods and seeks to clarify the relationship between these two complementary methods.
Assessment of Experimental Designs
Authors: Anderson‐Cook Christine 
Published: September 2014

Abstract
Collecting data as part of a designed experiment involves choices between different candidate designs. The criteria for choosing between these designs involves many competing objectives which frequently force us to make trade‐offs based on which of these ideal characteristics of the design we think are most important. Optimality criteria are single‐number summaries for the “goodness” of the design, which typically focus on the quality of estimation or prediction. In addition to these measures, the robustness of the design to model misspecification, problems with data collection during the experiment, or erroneous data are also important. A number of the characteristics of good designs are outlined and compared, and some graphical methods for better understanding the performance of a design are shown. While there is no single answer for how to choose a good design, an experimenter can make a better choice of which objectives are most important for a particular application by considering a variety of diverse criteria.
Integrating Computer and Physical Experiment Data
Authors: Williams Brian J. , Higdon David M. 
Published: September 2014

Abstract
The investigation of complex physical systems utilizing sophisticated computer models has become commonplace with the advent of modern computational facilities. In many applications, experimental data on the physical systems of interest is extremely expensive to obtain and hence is available in limited quantities. The mathematical systems implemented by the computer models often include parameters having uncertain values. This article provides an overview of statistical methodology for calibrating uncertain parameters to experimental data. This approach assumes that prior knowledge about such parameters is represented as a probability distribution, and the experimental data is used to refine our knowledge about these parameters, expressed as a posterior distribution. Uncertainty quantification for computer model predictions of the physical system are based fundamentally on this posterior distribution. Computer models are generally not perfect representations of reality for a variety of reasons, such as inadequacies in the physical modeling of some processes in the dynamic system. The statistical model includes components that identify and adjust for such discrepancies. A standard approach to statistical modeling of computer model output for unsampled inputs is introduced for the common situation where limited computer model runs are available. Extensions of the statistical methods to functional outputs are available and discussed briefly.
Process Capability, Error in Estimation of
Authors: Pearn Wen Lea , Wu Chien‐Wei 
Published: September 2014

Abstract
Process capability indices (PCIs) are widely used to provide numerical measures of process performance, which have received considerable research attention and increased usage in process assessments and purchasing decisions during the last decade. A substantial majority of capability research works that appeared in the literature do not take into account gauge measurement errors. However, the gauge measurement errors have a significant effect on estimating and assessing process capability since measurement systems are not 100% precise. An inaccurate measurement system can thwart all the benefits of improvement endeavors resulting in poor quality. Analyzing process capability without considering gauge capability may often lead to unreliable decisions. It could result in a serious loss to producers if gauge capability is not being considered in process capability estimation and testing. Since measurement errors unfortunately cannot be avoided, using appropriate confidence coefficients and power becomes an essential task. In this article, we summarize the issue on estimating and testing the process capability is the presence of measurement errors, provide a numerical example discussing the influence of measurement errors, and cite additional references for more discussions on gauge repeatability and reproducibility (GR&R) studies.
Process Capability Indices, Nonnormal
Authors: Chou Youn‐Min , Polansky Alan M. 
Published: September 2014

AbstractData from manufacturing processes may often come from nonnormal distributions. Process capability indices for nonnormal distributions have attracted much research in the last 20 years. This paper presents process capability indices for nonnormal distributions and reviews various approaches to deal with the problem of nonnormality in evaluating process capability.
Process Capability Indices, Nonparametric
Authors: Polansky Alan M. 
Published: September 2014

Abstract
The capability of a manufacturing process is a measure of the ability of the process to consistently produce items within engineering specifications. Most early attempts to assess the capability of a process were based on the assumption that the quality characteristic of interest follows a parametric distribution, usually normal. Several studies of process data indicate that many quality characteristics follow nonnormal distributions.Further, theoretical and empirical studies of measures of process capability indicate that violations of the assumption relating to the distribution of the quality characteristic can produce serious errors in conclusions about the capability of the process. This article reviews methods that have been developed to assess process capability within a nonparametric framework. Such methods require few specific assumptions about the distribution of the quality characteristic. The methods reviewed in this article include those based on nonparametric tolerance limits, nonparametric estimates of the process yield, and indices based on the percentiles of the process distribution.
Process Capability Indices, Multivariate
Authors: Fuchs Camil , Kenett Ron S. 
Published: September 2014

Abstract
Process capability is determined by comparing the actual performance of a process with required specifications. Several indices have been proposed to report the capability of a process in the univariate case. When performance is tracked in several dimensions, an extension of these indices is required. We present several multivariate capability indices, and extend the idea of multivariate tolerance regions for assessing the capability of a process.
Process Capability Indices, Comparison of
Authors: Spiring Fred 
Published: September 2014

Abstract

The most common process capability indices are examined from several perspectives. A brief historical review outlines recent developments from simple variability measures to today's more sophisticated indices. The behavior of the indices
C

pk

,
C

pm

, and
C

pmk

is compared with
C

p

for processes that are not necessarily centered at target, illustrating the relationships among the various indices. A weight function is presented that allows the functional form of a variety of indices to be examined. A discussion of process yield and the resulting interpretation from a quality loss perspective is presented and the differing philosophies represented by the competing indices are highlighted.
Yield Surface Modeling
Authors: Maass Eric Charles 
Published: September 2014

Abstract

Yield Surface Modeling™ (YSM) has been used to perform multiple‐response optimization for over 60 new products with high yields and first‐pass successes. The approach expands on response surface modeling (RSM), using the Generation of System Moments method to forecast the mean and variance, and then the process capability index (
C

pk

), and yield for each response—effectively replacing the expected value of the response with the expected value of the
C

pk

and yield for each combination of settings of factor levels. Composite
C

pk

and composite yield surfaces are generated for those combinations of settings of factor levels. The method is illustrated with a case study for an integrated alternator regulator (IAR) (integrated circuit). The strengths and limitations of the approach are discussed, including the value of the sensitivity analyses that come from the use of the Generation of System Moments method.
Control Charts and Process Capability
Authors: Lovelace Cynthia R. 
Published: September 2014

Abstract

Statistical process control (SPC) charts play a very important role in the determination of process capability. It is important that SPC be achieved
via
the use of appropriate control charts before process capability indices are calculated, so that the indices reflect the long‐term capability of a process to produce product within specification. A significant portion of the theoretical research dedicated to process capability indices has focused on those index estimators that utilize a pooled estimator of process variance, but data collected in subsamples from an in‐control SPC chart, most likely an

X

−
R
or

X

−
s
chart, is preferred in practice for index estimation to insure process stability. This article is focused on the significant work published to date on process capability indices estimated using subsamples, including confidence bounds and hypothesis testing procedures for the primary indices
C

p

,
C

pk

, and
C

pm

.
Measurement Systems Analysis, Overview
Authors: Michelson Diane K. 
Published: September 2014

Abstract

Measurement systems analysis is a way to quantify the variability of a metrology process. The fundamental technique is the gauge
repeatability and reproducibility
(GR&R) study, which yields an estimate of . The estimate is then used to determine whether or not the metrology system is capable of measuring the process under study. The stability of the measurement system is also determined.
Gauge Repeatability and Reproducibility (
R
&
R
), Variance Components in
Authors: Borror Connie M. , Burdick Richard K. , Montgomery Douglas C. 
Published: September 2014

Abstract

Traditionally, a standard gauge repeatability and reproducibility (R&R) study consisted of a two‐factor crossed design with operators and parts; with each operator measuring each part say
r
times. There are numerous situations where there are more than two factors, the design is not crossed but possibly nested, or the appropriate design is crossed and nested. In this article, variance components for gauge R&R studies are presented for these more complex experimental situations.
Measurement Systems Analysis, Capability Measures for
Authors: Larsen Greg A. 
Published: September 2014

Abstract
There are two “families” of measurement capability metrics in common use. There are those that compare measurement variation to total or part variation, and there are those that compare to the tolerance or specification width. These capability metrics are all functions of the variance components in the statistical model used to describe the response of interest. In addition to these capability metrics, it is also often useful to estimate the misclassification rates that arise from using the measurement system to discriminate between “good” and “bad” items being tested. An additional capability metric is obtained from the ratio of the estimated misclassification rates to the rates that would occur purely due to random chance. This section will define several capability measures, how they are related to one another, and give some common criteria for capability. Additional topics discuss the effect of the unit of measure and present a situation where none of the aforementioned metrics are particularly useful in quantifying the capability of a measurement process.
Gauge Repeatability and Reproducibility (
R
&
R
) Studies, Destructive Testing
Authors: Trip Albert , Mast Jeroen 
Published: September 2014

Abstract

The fundamental problem of gauge capability studies is that an object can be measured only once. After this measurement the object is different, sometimes emphatically so because the measurement is destructive. A standard gauge repeatability and reproducibility (R&R) study, to determine the precision of the measurement, is designed for nondestructive measurements, and assuming temporal stability of the objects. If temporal stability is beyond assumption, or if measurements can only be obtained by damaging the object—both types are called
destructive measurements
—then additional assumptions are needed to estimate the measurement precision. Several possibilities are given and reviewed. The assumptions are essentially attempts to model object‐to‐object variation, aiming at separating this from measurement precision. The failure of these attempts will show up in larger than necessary estimates of the measurement variation.
Process Capability Indices, Alternatives to
Authors: Post Richard I. 
Published: September 2014

Abstract
Since we (the statistical community) are to blame for the introduction of process capability indices (PCIs) and their ensuing use and abuse in the industrial world, it is now up to us to provide an alternative to PCIs that is both statistically sound and relatively simple to understand. The problems with the present capability indices have been well documented elsewhere (e.g., Gunter 1, Kotz 2, Journal of Quality Technology (JQT) 24 3) and these will not be addressed here. Instead, the dual roles of specifications and PCIs in process and product development and in the manufacturing environment will be discussed. A basic approach to meet most of the goals of PCIs without some of their problems will then be introduced. Incorporated into the measures will be the concepts of confidence level and sample size. An example of this procedure is included. The relatively simple assumptions of this approach can then be modified to meet requirements where PCIs are presently being applied. These measures are intended to be a foundation rather than a total solution to all the needs of PCI users. However, these techniques will provide a direction for future work.
Interlaboratory Comparisons
Authors: Guthrie William F. 
Published: September 2014

AbstractAn interlaboratory comparison for a measurement procedure is an exercise carried out by a group of laboratories to compare their performance or assess a measurement standard. Interlaboratory comparisons are typically used for one of three main purposes, to assess the random variation in measurement results across a population of laboratories, to determine the systematic differences in results among a fixed set of laboratories, or to determine the value of a physical property of an artifact or a population of artifacts. There are several design issues to consider when planning an interlaboratory study. These include selection of participating laboratories, number and stability of artifacts, time frame for the comparison, and numbers of replicate measurements made in each laboratory. The analysis of interlaboratory data ranges from simple, but informative, graphical analyses of the differences between laboratories to methods for constructing approximate confidence intervals for linear combinations of variance components. Probabilistic intervals for the physical property of an artifact or population of artifacts can also be obtained using maximum likelihood, generalized pivot quantities, or regression methods that handle random effects between laboratories and nonconstant measurement variation within each laboratory.
Calibration, issues in
Authors: Bzik Thomas J. 
Published: September 2014

Abstract
Calibration establishes and standardizes common units of measurement for commerce and scientific study. Through calibration the measured item or sample is related to items or samples with known properties, the standards. Statistical methods are typically used to identify and quantify the form of the relationship between sample measurements and one or more standards. They are also used to evaluate the accuracy and precision of the current as well as alternative measurement methods. Statistical tools are also used to determine the optimal frequency of recalibration as well as the frequency with which calibration should be checked. Calibration models can take the form of linear relationships or a nonlinear relationship. Two‐variable calibration models are often monotonic in their form. While many calibration models often involve only two variables, they can be multivariate in nature. Keeping bias out of calibration models requires not only selecting the correct model form but also ensuring that the model is fit, using an appropriate optimization criteria. Calibration models typically provide very high explanatory powers as compared to other typical scientific modeling applications such as plant or process optimization studies. Calibration models also typically yield extremely good levels of statistical significance. Current common statistical toolsets used in calibration studies are often not well structured for handling the actual, and sometimes complex, error structures that occur in routine application of calibration.
Life Cycle Costs and Reliability Engineering
Authors: Elsayed Elsayed A. 
Published: September 2014

Abstract
This chapter presents the life cycle cost (LCC) and relates it to product or system reliability. It discusses LCC through the product life phases beginning with the design phase and ending with warranty and field operation phase. It demonstrates how decisions in every phase directly or indirectly affect the LCC over the product life. Determinations of optimal process parameters or decision that minimize the LCC are demonstrated through examples in different phases of the product life cycle.
Multiattribute Warranty Policies
Authors: Chun Young H. 
Published: September 2014

Abstract
Warranties have been important elements in business planning and decision making for manufacturers of products and producers of services, and this will continue to be the case in years to come. In this article, we introduce various types of single‐attribute and multiattribute warranty policies, and review different modeling approaches with an emphasis on two‐attribute warranty plans. Some suggestions for future research are also provided.