Overview

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.

Full Product Details

Author:   Enrique del Castillo
Publisher:   Springer-Verlag New York Inc.
Imprint:   Springer-Verlag New York Inc.
Edition:   2007 ed.
Volume:   105
Dimensions:   Width: 15.50cm , Height: 2.60cm , Length: 23.50cm
Weight:   0.889kg
ISBN:  

9780387714349

ISBN 10:   0387714340
Pages:   462
Publication Date:   06 August 2007
Audience:   College/higher education ,  Professional and scholarly ,  Postgraduate, Research & Scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Out of stock  
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Table of Contents

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.

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