
Process Optimization
A Statistical Approach
Enrique del Castillo(Author)
Springer (Publisher)
Published on 29. November 2010
Book
Paperback/Softback
XVIII, 462 pages
978-1-4419-4396-5 (ISBN)
Description
PROCESS OPTIMIZATION: A Statistical Approach is a textbook for a course in experimental optimization techniques for industrial production processes and other "noisy" systems where the main emphasis is process optimization. The book can also be used as a reference text by Industrial, Quality and Process Engineers and Applied Statisticians working in industry, in particular, in semiconductor/electronics manufacturing and in biotech manufacturing industries.
More details
Series
Edition
1st ed. Softcover of orig. ed. 2007
Language
English
Place of publication
New York
United States
Target group
Primary & secondary/elementary & high school
Graduate
Illustrations
76 s/w Abbildungen
XVIII, 462 p. 76 illus.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 27 mm
Weight
727 gr
ISBN-13
978-1-4419-4396-5 (9781441943965)
DOI
10.1007/978-0-387-71435-6
Schweitzer Classification
Other editions
Additional editions

E-Book
09/2007
1st Edition
Springer
€53.49
Available for download

Book
08/2007
Springer
€53.49
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Content
Preliminaries.- An Overview of Empirical Process Optimization.- Elements of Response Surface Methods.- Optimization Of First Order Models.- Experimental Designs For First Order Models.- Analysis and Optimization of Second Order Models.- Experimental Designs for Second Order Models.- Statistical Inference in Process Optimization.- Statistical Inference in First Order RSM Optimization.- Statistical Inference in Second Order RSM Optimization.- Bias Vs. Variance.- Robust Parameter Design and Robust Optimization.- Robust Parameter Design.- Robust Optimization.- Bayesian Approaches in Process Optimization.- to Bayesian Inference.- Bayesian Methods for Process Optimization.- to Optimization of Simulation and Computer Models.- Simulation Optimization.- Kriging and Computer Experiments.- Appendices.- Basics of Linear Regression.- Analysis of Variance.- Matrix Algebra and Optimization Results.- Some Probability Results Used in Bayesian Inference.