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Frontiers in Statistical Quality Control 6 : Frontiers in Statistical Quality Control - Hans-Joachim Lenz

Frontiers in Statistical Quality Control 6

Frontiers in Statistical Quality Control

By: Hans-Joachim Lenz (Editor), Peter-Theodor Wilrich (Editor)


Published: 30th January 2001
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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).

Sampling Inspection
Methodological Foundations of Statistical Lot Inspectionp. 3
Credit-based Accept-zero Sampling Schemes for the Control of Outgoing Qualityp. 25
Acceptance Sampling Plans by Attributes with Fuzzy Risks and Quality Levelsp. 36
Acceptance Sampling by Variables under Measurement Uncertaintyp. 47
Statistical Process Control
Simultaneous Shewhart-Type Charts for the Mean and the Variance of a Time Seriesp. 61
Application of ISO 3951 Acceptance Sampling Plans to the Inspection by Variables in Statistical Process Control (SPC)p. 80
Optimal Set-Up of a Manufacturing Process with Unequal Revenue from Oversized and Undersized Itemsp. 93
Frequency Distribution Supporting Recognition of Unnatural Patterns on Shewhart X-bar Chartp. 102
Monitoring Processes with Data Available in Tabular Form Using Multiple Correspondence Analysisp. 118
Monitoring a Proportion Using CUSUM and SPRT Control Chartsp. 155
The Effect of Non-Normality on the Performance of CUSUM Proceduresp. 176
Process Control for Non-Normal Populations Based on an Inverse Normalizing Transformationp. 194
On Nonparametric Multivariate Control Charts Based on Data Depthp. 207
Multivariate Process Monitoring for Nylon Fiber Productionp. 228
The Management of SPCp. 247
Data Analysis and Process Capability Studies
Application of Statistical Causal Analysis to Process Analysisp. 263
Detecting Changes in the Mean from Censored Lifetime Datap. 275
A Graphical Method to Control Process Capabilityp. 290
Confidence Limits for the Process Capability Index C[subscript pk] for Autocorrelated Quality Characteristicsp. 312
Experimental Design
Split-Plot Experimentation for Process and Quality Improvementp. 335
An Alternative Analysis Method of the ANOVA for p[superscript k] Unreplicated Fractional Factorial Experimentsp. 351
Modeling and Analysis of Dynamic Robust Design Experimentsp. 360
Table of Contents provided by Blackwell. All Rights Reserved.

ISBN: 9783790813746
ISBN-10: 3790813745
Series: Frontiers in Statistical Quality Control
Audience: General
Format: Paperback
Language: English
Number Of Pages: 375
Published: 30th January 2001
Publisher: Springer-Verlag Berlin and Heidelberg Gmbh & Co. Kg
Country of Publication: DE
Dimensions (cm): 23.39 x 15.6  x 2.06
Weight (kg): 0.55