RESPONSE SURFACE METHODOLOGY

RESPONSE SURFACE METHODOLOGY

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RESPONSE SURFACE METHODOLOGY Process and Product Optimization Using Designed Experiments Third Edition RAYMOND H. MYERS Virginia Polytechnic University, Department of Statistics, Blacksburg, VA DOUGLAS C. MONTGOMERY Arizona State University, Department of Industrial Engineering, Tempe, AZ CHRISTINE M. ANDERSON-COOK Los Alamos National Laboratory, Los Alamos, NM

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CONTENTS Preface xi 1 Introduction 1 1.1 Response Surface Methodology, 1 1.1.1 Approximating Response Functions, 2 1.1.2 The Sequential Nature of RSM, 6 1.1.3 Objectives and Typical Applications of RSM, 8 1.1.4 RSM and the Philosophy of Quality Improvement, 9 1.2 Product Design and Formulation (Mixture Problems), 10 1.3 Robust Design and Process Robustness Studies, 10 1.4 Useful References on RSM, 11 2 Building Empirical Models 13 2.1 Linear Regression Models, 13 2.2 Estimation of the Parameters in Linear Regression Models, 14 2.3 Properties of the Least Squares Estimators and Estimation of s 2,22 2.4 Hypothesis Testing in Multiple Regression, 24 2.4.1 Test for Significance of Regression, 24 2.4.2 Tests on Individual Regression Coefficients and Groups of Coefficients, 27 2.5 Confidence Intervals in Multiple Regression, 31 2.5.1 Confidence Intervals on the Individual Regression Coefficients b, 32 2.5.2 A Joint Confidence Region on the Regression Coefficients b, 32 2.5.3 Confidence Interval on the Mean Response, 33 2.6 Prediction of New Response Observations, 35 2.7 Model Adequacy Checking, 36 v

vi CONTENTS 2.7.1 Residual Analysis, 37 2.7.2 Scaling Residuals, 38 2.7.3 Influence Diagnostics, 42 2.7.4 Testing for Lack of Fit, 44 2.8 Fitting a Second-Order Model, 47 2.9 Qualitative Regressor Variables, 55 2.10 Transformation of the Response Variable, 58 Exercises, 63 3 Two-Level Factorial Designs 73 3.1 Introduction, 73 3.2 The 2 2 Design, 74 3.3 The 2 3 Design, 86 3.4 The General 2 k Design, 96 3.5 A Single Replicate of the 2 k Design, 96 3.6 The Addition of Center Points to the 2 k Design, 109 3.7 Blocking in the 2 k Factorial Design, 114 3.7.1 Blocking in the Replicated Design, 115 3.7.2 Confounding in the 2 k Design, 116 3.8 Split-Plot Designs, 121 Exercises, 124 4 Two-Level Fractional Factorial Designs 135 4.1 Introduction, 135 4.2 The One-Half Fraction of the 2 k Design, 136 4.3 The One-Quarter Fraction of the 2 k Design, 148 4.4 The General 2 k2p Fractional Factorial Design, 154 4.5 Resolution III Designs, 158 4.6 Resolution IV and V Designs, 167 4.7 Fractional Factorial Split-Plot Designs, 168 4.8 Summary, 172 Exercises, 173 5 Process Improvement with Steepest Ascent 181 5.1 Determining the Path of Steepest Ascent, 182 5.1.1 Development of the Procedure, 182 5.1.2 Practical Application of the Method of Steepest Ascent, 184 5.2 Consideration of Interaction and Curvature, 189 5.2.1 What About a Second Phase?, 191 5.2.2 What Happens Following Steepest Ascent?, 192 5.3 Effect of Scale (Choosing Range of Factors), 193 5.4 Confidence Region for Direction of Steepest Ascent, 195 5.5 Steepest Ascent Subject to a Linear Constraint, 198 5.6 Steepest Ascent in a Split-Plot Experiment, 202 Exercises, 210

CONTENTS vii 6 The Analysis of Second-Order Response Surfaces 219 6.1 Second-Order Response Surface, 219 6.2 Second-Order Approximating Function, 220 6.2.1 The Nature of the Second-Order Function and Second-Order Surface, 220 6.2.2 Illustration of Second-Order Response Surfaces, 222 6.3 A Formal Analytical Approach to the Second-Order Model, 223 6.3.1 Location of the Stationary Point, 223 6.3.2 Nature of the Stationary Point (Canonical Analysis), 224 6.3.3 Ridge Systems, 228 6.3.4 Role of Contour Plots, 232 6.4 Ridge Analysis of the Response Surface, 235 6.4.1 What is the Value of Ridge Analysis?, 236 6.4.2 Mathematical Development of Ridge Analysis, 237 6.5 Sampling Properties of Response Surface Results, 242 6.5.1 Standard Error of Predicted Response, 243 6.5.2 Confidence Region on the Location of the Stationary Point, 245 6.5.3 Use and Computation of the Confidence Region on the Location of the Stationary Point, 246 6.5.4 Confidence Intervals on Eigenvalues in Canonical Analysis, 250 6.6 Multiple Response Optimization, 253 6.7 Further Comments Concerning Response Surface Analysis, 264 Exercises, 265 7 Experimental Designs for Fitting Response Surfaces I 281 7.1 Desirable Properties of Response Surface Designs, 281 7.2 Operability Region, Region of Interest, and Model Inadequacy, 282 7.2.1 Model Inadequacy and Model Bias, 283 7.3 Design of Experiments for First-Order Models, 285 7.3.1 The First-Order Orthogonal Design, 286 7.3.2 Orthogonal Designs for Models Containing Interaction, 288 7.3.3 Other First-Order Orthogonal Designs The Simplex Design, 291 7.3.4 Another Variance Property Prediction Variance, 294 7.4 Designs for Fitting Second-Order Models, 296 7.4.1 The Class of Central Composite Designs, 297 7.4.2 Design Moments and Property of Rotatability, 302 7.4.3 Rotatability and the CCD, 306 7.4.4 More on Prediction Variance Scaled, Unscaled, and Estimated, 310 7.4.5 The Cuboidal Region and the Face-Centered Cube, 312 7.4.6 When is the Design Region Spherical?, 315 7.4.7 Summary Statements Regarding CCD, 316 7.4.8 The Box Behnken Design, 317 7.4.9 Other Spherical RSM Designs; Equiradial Designs, 321 7.4.10 Orthogonal Blocking in Second-Order Designs, 325 Exercises, 336

viii CONTENTS 8 Experimental Designs for Fitting Response Surfaces II 349 8.1 Designs that Require a Relatively Small Run Size, 350 8.1.1 The Hoke Designs, 350 8.1.2 Koshal Design, 352 8.1.3 Hybrid Designs, 354 8.1.4 The Small Composite Design, 358 8.1.5 Some Saturated or Near-Saturated Cuboidal Designs, 362 8.2 General Criteria for Constructing, Evaluating, and Comparing Experimental Designs, 362 8.2.1 Practical Design Optimality, 365 8.2.2 Use of Design Efficiencies for Comparison of Standard Second-Order Designs, 371 8.2.3 Graphical Procedure for Evaluating the Prediction Capability of an RSM Design, 374 8.3 Computer-Generated Designs in RSM, 386 8.3.1 Important Relationship between Prediction Variance and Design Augmentation for D-Optimality, 386 8.3.2 Illustrations Involving Computer-Generated Design, 390 8.4 Some Final Comments Concerning Design Optimality and Computer-Generated Design, 405 Exercises, 406 9 Advanced Topics in Response Surface Methodology 417 9.1 Effects of Model Bias on the Fitted Model and Design, 417 9.2 A Design Criterion Involving Bias and Variance, 420 9.2.1 The Case of a First-Order Fitted Model and Cuboidal Region, 423 9.2.2 Minimum Bias Designs for a Spherical Region of Interest, 429 9.2.3 Simultaneous Consideration of Bias and Variance, 430 9.2.4 How Important is Bias?, 431 9.3 Errors in Control of Design Levels, 432 9.4 Experiments with Computer Models, 435 9.5 Minimum Bias Estimation of Response Surface Models, 442 9.6 Neural Networks, 446 9.7 RSM for Non-Normal Responses Generalized Linear Models, 449 9.7.1 Model Framework: The Link Function, 449 9.7.2 The Canonical Link Function, 450 9.7.3 Estimation of Model Coefficients, 451 9.7.4 Properties of Model Coefficients, 452 9.7.5 Model Deviance, 453 9.7.6 Overdispersion, 454 9.7.7 Examples, 455 9.7.8 Diagnostic Plots and Other Aspects of the GLM, 462 9.8 Split-Plot Designs for Second-Order Models, 466 Exercises, 476

CONTENTS ix 10 Robust Parameter Design and Process Robustness Studies 483 10.1 Introduction, 483 10.2 What is Parameter Design?, 483 10.2.1 Examples of Noise Variables, 484 10.2.2 An Example of Robust Product Design, 485 10.3 The Taguchi Approach, 486 10.3.1 Crossed Array Designs and Signal-to-Noise Ratios, 486 10.3.2 Analysis Methods, 489 10.3.3 Further Comments, 494 10.4 The Response Surface Approach, 495 10.4.1 The Role of the Control Noise Interaction, 495 10.4.2 A Model Containing Both Control and Noise Variables, 499 10.4.3 Generalization of Mean and Variance Modeling, 502 10.4.4 Analysis Procedures Associated with the Two Response Surfaces, 506 10.4.5 Estimation of the Process Variance, 515 10.4.6 Direct Variance Modeling, 519 10.4.7 Use of Generalized Linear Models, 521 10.5 Experimental Designs for RPD and Process Robustness Studies, 525 10.5.1 Combined Array Designs, 525 10.5.2 Second-Order Designs, 527 10.5.3 Other Aspects of Design, 529 10.6 Dispersion Effects in Highly Fractionated Designs, 537 10.6.1 The Use of Residuals, 537 10.6.2 Further Diagnostic Information from Residuals, 538 10.6.3 Further Comments Concerning Variance Modeling, 544 Exercises, 548 11 Experiments with Mixtures 557 11.1 Introduction, 557 11.2 Simplex Designs and Canonical Mixture Polynomials, 560 11.2.1 Simplex Lattice Designs, 560 11.2.2 The Simplex-Centroid Design and Its Associated Polynomial, 567 11.2.3 Augmentation of Simplex Designs with Axial Runs, 569 11.3 Response Trace Plots, 576 11.4 Reparameterizing Canonical Mixture Models to Contain a Constant Term (b 0 ), 577 Exercises, 581 12 Other Mixture Design and Analysis Techniques 589 12.1 Constraints on the Component Proportions, 589 12.1.1 Lower-Bound Constraints on the Component Proportions, 590 12.1.2 Upper-Bound Constraints on the Component Proportions, 599

x CONTENTS 12.1.3 Active Upper- and Lower-Bound Constraints, 602 12.1.4 Multicomponent Constraints, 616 12.2 Mixture Experiments Using Ratios of Components, 617 12.3 Process Variables in Mixture Experiments, 621 12.3.1 Mixture-Process Model and Design Basics, 621 12.3.2 Split-Plot Designs for Mixture-Process Experiments, 629 12.3.3 Robust Parameter Designs for Mixture-Process Experiments, 636 12.4 Screening Mixture Components, 641 Exercises, 643 Appendix 1 Moment Matrix of a Rotatable Design 655 Appendix 2 Rotatability of a Second-Order Equiradial Design 661 References 665 Index 677

PREFACE This book deals with the exploration and optimization of response surfaces. This is a problem faced by experimenters in many technical fields, where, in general, the response variable of interest is y and there is a set of predictor variables x 1, x 2,..., x k. For example, y might be the viscosity of a polymer and x 1, x 2, and x 3 might be the reaction time, the reactor temperature, and the catalyst feed rate in the process. In some systems the nature of the relationship between y and the x s might be known exactly, based on the underlying engineering, chemical, or physical principles. Then we could write a model of the form y ¼ g(x 1, x 2,..., x k ) þ 1, where 1 represents the error in the system. This type of relationship is often called a mechanistic model. We consider the more common situation where the underlying mechanism is not fully understood, and the experimenter must approximate the unknown function g with an appropriate empirical model y ¼ f(x 1, x 2,..., x k ) þ 1. Usually the function f is a first-order or second-order polynomial. This empirical model is called a response surface model. Identifying and fitting an appropriate response surface model from experimental data requires some knowledge of statistical experimental design fundamentals, regression modeling techniques, and elementary optimization methods. This book integrates all three of these topics into what has been popularly called response surface methodology (RSM). We assume that the reader has some previous exposure to statistical methods and matrix algebra. Formal coursework in basic principles of experimental design and regression analysis would be helpful, but are not essential, because the important elements of these topics are presented early in the text. We have used this book in a graduate-level course on RSM for statisticians, engineers, and chemical/physical scientists. We have also used it in industrial short courses and seminars for individuals with a wide variety of technical backgrounds. This third edition is a substantial revision of the book. We have rewritten many sections to incorporate new material, ideas, and examples, and to more fully explain some topics that were only briefly mentioned in previous editions. We have also woven the computer more xi

xii PREFACE tightly into the presentation, relying on JMP 7 and Design-Expert Version 7 for much of the computing, but also continuing to employ SAS for a few applications. Chapters 1 through 4 contain the preliminary material essential to studying RSM. Chapter 1 is an introduction to the general field of RSM, describing typical applications such as (a) finding the levels of process variables that optimize a response of interest or (b) discovering what levels of these process variables will result in a product satisfying certain requirements or specifications on responses such as yield, molecular weight, purity, or viscosity. Chapter 2 is a summary of regression methods useful in response surface work, focusing on the basic ideas of least squares model fitting, diagnostic checking, and inference for the linear regression model. Chapters 3 and 4 describe two-level factorial and fractional factorial designs. These designs are essential for factor screening or identifying the correct set of process variables to use in the RSM study. They are also basic building blocks for many of the response surface designs discussed later in the text. Chapter 5 presents the method of steepest ascent, a simple but powerful optimization procedure used at the early stages of RSM to move the process from a region of relatively poor performance to one of greater potential. Chapter 6 introduces the analysis and optimization of a second-order response surface model. Both graphical and numerical techniques are presented. This chapter also includes techniques for the simultaneous optimization of several responses, a common problem in the application of RSM. Chapters 7 and 8 present detailed information on the choice of experimental designs for fitting response surface models. Chapter 7 is devoted to standard designs, including the central composite and Box Behnken designs, and the important topic of blocking a response surface design. Chapter 8 covers small response surface designs, design optimality criteria, the use of computer-generated designs in RSM, and methods for evaluation of the prediction properties of response surface models constructed from various designs. We focus on variance dispersion graphs and fraction of design space plots, which are very important ways to summarize prediction properties. Chapter 9 contains more advanced RSM topics, including the use of mean square error as a design criterion, the effect of errors in controllable variables, RSM experiments for computer models, neural networks and RSM, split-plot type designs in a response surface setting, and the use of generalized linear models in the analysis of response surface experiments. Chapter 10 describes how the problem of robust parameter design originally proposed by Taguchi can be efficiently solved in the RSM framework. We show how RSM not only makes the original problem posed by Taguchi easier to solve, but also provides much more information to the analyst about process or system performance. This chapter also contains much information on robust parameter design and process robustness studies. Chapters 11 and 12 present techniques for designing and analyzing experiments that involve mixtures. A mixture experiment is a special type of response surface experiment in which the design factors are the components or ingredients of a mixture, and the response depends on the proportions of the ingredients that are present. Extensive sets of end-of-chapter problems are provided, along with a reference section. The previous two editions of the text were written to emphasize methods that are useful in industry and that we have found useful in our own consulting experience. We have continued that applied focus in this new edition, though much new material has been added. We develop enough of the underlying theory to allow the reader to gain an understanding of the assumptions and conditions necessary to successfully apply RSM. We are grateful to many individuals that have contributed meaningfully to this book. In particular, Dr. Bradley Jones, Mr. Pat Whitcomb, Dr. Geoff Vining, Dr. Soren Bisgaard, Dr. Connie Borror, Dr. Scott Kowalski, Dr. Dennis Lin, Dr. George Runger,

PREFACE xiii and Dr. Enrique Del Castillo made many useful suggestions. Dr. Matt Carlyle and Dr. Enrique Del Castillo also provided some figures that were most helpful. We also thank the many classes of graduate students that have studied from the book and the instructors that have used the book. They have made many helpful comments and suggestions to improve the clarity of the presentation. We have tried to incorporate many of their suggestions. We also thank John Wiley & Sons for permission to use and adapt copyrighted material. Blacksburg, Virginia Tempe, Arizona Los Alamos, New Mexico March, 2008 RAYMOND H. MYERS DOUGLAS C. MONTGOMERY CHRISTINE M. ANDERSON-COOK