Frontiers in Statistical Quality Control 5 (eBook, PDF)

Redaktion: Lenz, Hans-Joachim; Wilrich, Peter-Theodor

• Format: PDF

Produktbeschreibung

Like the preceding volumes, and met with a lively response, the present volume is collecting contributions stressed on methodology or successful industrial applications. The papers are classified under four main headings: sampling inspection, process quality control, data analysis and process capability studies and finally experimental design.

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Produktdetails

• Verlag: Physica-Verlag HD
• Seitenzahl: 297
• Erscheinungstermin: 7. März 2013
• Englisch
• ISBN-13: 9783642592393
• Artikelnr.: 53137973

Inhaltsangabe

1: Sampling Inspection.- An Adaptive Sampling System with a Fuzzy Controller.- Determination of Sample Sizes for Double Sampling Attribute Plans.- Unbiased Estimation of Generalized Moments of Process Curves.- On the Non-Robustness of Maximum-Likelihood Sampling Plans by Variables.- Normal Approximations to the Distribution Function of the Symmetric Beta Distribution.- 2: Process Quality Control.- A Mathematical Framework for Statistical Process Control.- A Review of Statistical and Fuzzy Quality Control Charts Based on Categorical Data.- Efficient Estimation of Control Chart Parameters.- Control Charts for Dependent and Multivariate Measurements.- On EWMA Charts for Time Series.- Statistical Process Control for Autocorrelated Processes: A Case-Study.- Quality Control of a Continuously Monitored Production Process.- Group Sequential Design with Delayed Observations for Selecting One of Two Processes in a Production System.- Design of the ($$ overline x $$, s) Control Chart Based on Kullback-Leibler Information.- 3: Data Analysis And Process Capability Studies.- Measurement Error Effects on the Performance of Process Capability Indices.- A New Approach for Describing and Controlling Process Capability.- On the Use of Field Failure Data for Repairable Systems to Identify Sources of Variation.- 4: Experimental Design.- Control Chart Method for Analyzing Staggered Nested Data.- Prediction Properties of the Process Variance Using the Combined Array.- Joint Analysis of Mean and Variance Function Based on Second Order Polynomials.- Dependability Improvement Through Unreplicated Experimental Designs.