Design of Experiments

ISBN-13: 9781574443141 / Angielski / Twarda / 2002 / 656 str.

D. H. Stamatis

Opis

Demystifies the Design of Experiments (DOE). This book covers the concept of variation and the importance of experimentation and follows through with applications. It also discusses full and fractional factorials. It introduces the concept of robustness, and addresses robust design with discussions on the Taguchi methodology of experimentation.

Recenzje

"The text is … well written, and the author's enthusiasm and extensive design expertise shines through. … This book is a worthwhile addition to the bookshelf of engineers or quality professionals who use or intend to use experimental design."
- Technometrics, Vol. 46, No. 4, November 2004


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Spis treści

TRADITIONAL EXPERIMENTAL DESIGN
Introduction
Fundamental concepts
Anatomy of an experiment
Principles of conduct
Variation
General types of designs
Logic of hypothesis testing
Experimental error
Expected values
Degrees of freedom
Coding and data analysis
Interaction
Fixed, Random, Mixed designs
EMS rules
Example
References
Selected bibliography
The planning and managing the "process" of experimentation
Plan
Do
Study
Act
Getting started with experimental design
Considerations of experimental designs
Statistical fundamentals
Measures of location
Measures of dispersion
Shape of distributions
Structure and form of experimental designs
Validity of experimentation
Design types
References
Selected bibliography
Analysis of existing data
Variance and covariance
Simple regression
Test for significance
Multiple regression
Calculating of the squared multiple correlation coefficient
References
Selected bibliography
Analysis of means
Statistical hypothesis/null hypothesis
Sample size considerations
Analysis of "means" (ANOM)
Sources of variation analysis (SVA)
Other "means" tests
Estimation error and confidence intervals
Independent samples
Dependent samples
Selected bibliography
Analysis of variance (ANOVA)
Assumptions of analysis of variance
Common designs for experiments
Complete randomization for background conditions
The one way analysis of variance
Two way analysis
Randomization block design for background conditions
Latin square design for background condition
Other designs
Types of ANOVA
After ANOVA, What?
Means effects
After ANOVA, What?
Homogeneity test
Recommendations
Examples
References
Selected bibliography
Factorial designs
Special vocabulary
A factorial experiment model
Factorial experiment assumptions
The nature of factorial analysis of variance
Advantages of factorial analysis of variance
Fractional factorial designs
References
Selected bibliography
Full factorial Experiments
Key vocabulary of terms
Notation
One factor situation
Two level factorial designs
Two factor situation
Three factor situation
Generalized 2k designs
Conduct experiments
Analysis of 2k factorials
Example
Run
Graphical aids for analysis
Judging the importance of location effects
Graphical assessment of effects
Judging the importance of variance effects
Judging the importance of differences of proportions
Selected bibliography
Model Building - Utility of models with experimental design
Single factor model
Two factor models
Generalized interactive models
Model checking
Residuals
Curvature checking with 2k designs
Selected bibliography
Fractional factorial experiments
Confounding and resolution
Catalog of fractional factorial designs
Randomization, replication and repetition
Analysis of fractional factorial designs
Worksheets for different designs
Two level fractional factorial screening designs
Eight run Plackett-Burman Designs
Interpretation
Combining designs
Worksheets for screening designs
Missing data
Revealing the confounding of fractional factorial experiments
Setting preferred designs
References
Selected Bibliography
Three level designs
3k factorial experiments
Examples of complexity for 32 and 33 designs
3k designs
The 33 design
Analysis of 3k designs
Yate's algorithm for the 3k design
Central composite design
Key items in factorial designs
References
Selected bibliography
Special topics in design of experiments
Covariance analysis
Evolutionary operation (EVOP)
Response surface methodology
Sequential on line optimization
Analysis of attribute data
Randomized Incomplete block designs - restriction on experimentation
References
Selected bibliography
ROBUST PARAMETER DESIGN
Introduction to Taguchi and Parameter Design
Introduction
Taguchi Design
The research process
A comparison between the typical steps in industrial experimentation and the Taguchi approach
References
Selected Bibliography
A new attitude and Approach
Orthogonal arrays
Average quality function
Quality characteristics and the loss function
Selected bibliography
Orthogonal arrays and linear graphs
The 23 layout
Definition of orthogonality
Weighing problem
Orthogonal array L8
Reasons for using Orthogonal arrays
Three level orthogonal arrays
The L9 orthogonal array
Linear graphs
Multilevel arrangements in 2 level series Orthogonal arrays
Preparation for a 4 level columns
Discussion
Warning about the L8, L18 and L27 OAs
References
Selected bibliography
Parameter design
The signal to noise ratio
Strategies dealing with noise factors
Behavior of the signal to noise ratio
Classified attribute analysis
Comparing mean analysis and signal to noise analysis
Robustness and the ideal function
Dynamic characteristics and ideal function
What are dynamic characteristics?
Ideal function
References
Selected bibliography
Taguchi and ANOVA
The role of ANOVA
ANOVA terms, notations and development
Definitions
Tolerance design
The relationship between tolerance design and loss function
Tolerance design process
Selected bibliography
Case studies
Parameter design - Die casting process
Process optimization - Clutch plate rust inhibition
Appendix A: Orthogonal Arrays and linear graphs
Appendix B: Technical discussions
Appendix C: Annotated computer program
Appendix E: Forms
Glossary
Selected Bibliography

O autorze

D.H. Stamatis