Quality Improvement with Design of Experiments : A Response Surface Approach

I N Vuchkov

ISBN 10: 0792368274 / ISBN 13: 9780792368274

Edité par Springer Feb 2001, 2001

A propos de cet article

Neuware - This book presents a model-based approach to quality improvement through design of experiments. After a description of statistical methods for data analysis and design of experiments it addresses the following topics: Taguchi's approach to quality improvement; Reduction of errors transmitted from factors to the response; Robustness against errors in product/process parameters and external noise factors; Optimization procedures for product/process design; Quality improvement through mechanistic models; Quality improvement of products with both qualitative and quantitative factors; and Quality improvement based on replicated observations. The book provides systematic and detailed practical guidance to a model-based approach to quality engineering problems. All methods are illustrated by real-world examples that make them readily accessible to readers. All mathematical proofs are given in appendices to the relevant chapters. The book is written for a wide range of engineers, quality engineering professionals, engineering designers, engineering statisticians, and all those who want to apply design of experiments to solving quality improvement problems. The text is appropriate for undergraduate and graduate students in engineering and statistics.

N° de réf. du vendeur 9780792368274

Détails bibliographiques

• Titre: Quality Improvement with Design of Experiments : A Response Surface Approach
• Éditeur: Springer Feb 2001
• Date de parution: 2001
• Langue: anglais
• ISBN à 10 chiffres: 0792368274
• ISBN à 13 chiffres: 9780792368274
• Reliure: Buch
• État de l'article: Neu
• Dimensions: 241 L x 160 H x 33 P (en millimètres) 241 millimètres de largeur par 160 millimètres de hauteur par 33 millimètres de profondeur
• Poids: 951 grammes

A propos de ce titre

Synopsis

Residualplots 74 Normaland half-normal plots 77 2. 3. 10. TRANSFORMATIONS OF VARIABLES 80 2. 3. 11. WEIGHTED LEAST SQUARES 82 2. 4. Bibliography 84 Appendix A. 2. 1. Basic equation ofthe analysis ofvariance 84 Appendix A. 2. 2. Derivation of the simplified formulae (2. 1 0) and (2. 11) 85 Appendix A. 2. 3. Basic properties ofleast squares estimates 86 Appendix A. 2. 4. Sums ofsquares for tests for lack offit 88 Appendix A. 2. 5. Properties ofthe residuals 90 3. DESIGN OF REGRESSION EXPERIMENTS 96 3. 1. Introduction 96 3. 2. Variance-optimality of response surface designs 98 3. 3. Two Ievel full factorial designs 106 3. 3. 1. DEFINITIONS AND CONSTRUCTION 106 3. 3. 2. PROPERTIES OF TWO LEVEL FULL FACTORIAL DESIGNS 109 3. 3. 3. REGRESSION ANALYSIS OF DAT A OBT AlNED THROUGH TWO LEVEL FULL F ACTORIAL DESIGNS 113 Parameter estimation 113 Effects of factors and interactions 116 Statistical analysis of individual effects and test for lack of fit 118 3. 4. Two Ievel fractional factorial designs 123 3. 4. 1. CONSTRUCTION OF FRACTIONAL F ACTORIAL DESIGNS 123 3. 4. 2. FITTING EQUATIONS TO DATA OBTAlNED BY FRACTIONAL F ACTORIAL DESIGNS 130 3. 5. Bloclung 133 3. 6. Steepest ascent 135 3. 7. Second order designs 142 3. 7. 1. INTRODUCTION 142 3. 7. 2. COMPOSITE DESIGNS 144 Rotatable central composite designs 145 D-optimal composite designs 146 Hartley' s designs 146 3. 7. 3.

Présentation de l'éditeur

This book presents a model-based approach to quality improvement through design of experiments. After a description of statistical methods for data analysis and design of experiments it addresses the following topics:

• Taguchi's approach to quality improvement;
• Reduction of errors transmitted from factors to the response;
• Robustness against errors in product/process parameters and external noise factors;
• Optimization procedures for product/process design;
• Quality improvement through mechanistic models;
• Quality improvement of products with both qualitative and quantitative factors; and
• Quality improvement based on replicated observations.

The book provides systematic and detailed practical guidance to a model-based approach to quality engineering problems. All methods are illustrated by real-world examples that make them readily accessible to readers. All mathematical proofs are given in appendices to the relevant chapters.
The book is written for a wide range of engineers, quality engineering professionals, engineering designers, engineering statisticians, and all those who want to apply design of experiments to solving quality improvement problems. The text is appropriate for undergraduate and graduate students in engineering and statistics.