1st Edition

Six Sigma and Beyond Design of Experiments, Volume V

By D.H. Stamatis Copyright 2002
    656 Pages 322 B/W Illustrations
    by CRC Press

    I In this volume, the author demystifies the Design of Experiments (DOE). He begins with a clear explanation of the traditional experimentation process. He then covers the concept of variation and the importance of experimentation and follows through with applications. Stamatis also discusses full and fractional factorials. The strength of this volume lies in the fact that not only does it introduce the concept of robustness, it also addresses "Robust Designs" with discussions on the Taguchi methodology of experimentation. And throughout the author ties these concepts into the Six Sigma philosophy and shows readers how they use those concepts in their organizations.

    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

    Biography

    D.H. Stamatis

    "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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