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Full Description
In the 1920's, Walter Shewhart visualized that the marriage of statistical methods and manufacturing processes would produce reliable and consistent quality products. Shewhart (1931) conceived the idea of statistical process control (SPC) and developed the well-known and appropriately named Shewhart control chart. However, from the 1930s to the 1990s, literature on SPC schemes have been "captured" by the Shewhart paradigm of normality, independence and homogeneous variance. When in fact, the problems facing today's industries are more inconsistent than those faced by Shewhart in the 1930s. As a result of the advances in machine and sensor technology, process data can often be collected on-line. In this situation, the process observations that result from data collection activities will frequently not be serially independent, but autocorrelated. Autocorrelation has a significant impact on a control chart: the process may not exhibit a state of statistical control when in fact, it is in control. As the prevalence of this type of data is expected to increase in industry (Hahn 1989), so does the need to control and monitor it. Equivalently, literature has reflected this trend, and research in the area of SPC with autocorrelated data continues so that effective methods of handling correlated data are available. This type of data regularly occurs in the chemical and process industries, and is pervasive in computer-integrated manufacturing environments, clinical laboratory settings and in the majority of SPC applications across various manufacturing and service industries (Alwan 1991).
Contents
1: Sampling Inspection.- Methodological Foundations of Statistical Lot Inspection.- Credit-based Accept-zero Sampling Schemes for the Control of Outgoing Quality.- Acceptance Sampling Plans by Attributes with Fuzzy Risks and Quality Levels.- Acceptance Sampling by Variables under Measurement Uncertainty.- 2: Statistical Process Control.- Simultaneous Shewhart-Type Charts for the Mean and the Variance of a Time Series.- Application of ISO 3951 Acceptance Sampling Plans to the Inspection by Variables in Statistical Process Control (SPC).- Optimal Set-Up of a Manufacturing Process with Unequal Revenue from Oversized and Undersized Items.- Frequency Distribution Supporting Recognition of Unnatural Patterns on Shewhart X-bar Chart 102.- Monitoring Processes with Data Available in Tabular Form Using Multiple Correspondence Analysis 118.- Monitoring a Proportion Using CUSUM and SPRT Control Charts.- The Effect of Non-Normality on the Performance of CUSUM Procedures.- Process Control for Non-Normal Populations Based on an Inverse Normalizing Transformation.- On Nonparametric Multivariate Control Charts Based on Data Depth.- Multivariate Process Monitoring for Nylon Fiber Production.- The Management of SPC.- 3: Data Analysis and Process Capability Studies.- Application of Statistical Causal Analysis to Process Analysis.- Detecting Changes in the Mean from Censored Lifetime Data.- A Graphical Method to Control Process Capability.- Confidence Limits for the Process Capability Index Cpk for Autocorrelated Quality Characteristics.- 4: Experimental Design.- Split-Plot Experimentation for Process and Quality Improvement.- An Alternative Analysis Method to the ANOVA for pk Unreplicated Fractional Factorial Experiments.- Modeling and Analysis of Dynamic Robust Design Experiments.
