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Robust Optimization is a method to improve robustness using low-cost variations of a single, conceptual design. The benefits of Robust Optimization include faster product development cycles; faster launch cycles; fewer manufacturing problems; fewer field problems; lower-cost, higher performing products and processes; and lower warranty costs. All these benefits can be realized if engineering and product development leadership of automotive and manufacturing organizations leverage the power of using Robust Optimization as a competitive weapon.

Written by world renowned authors, Robust Optimization: World’s Best Practices for Developing Winning Vehicles, is a ground breaking book whichintroduces the technical management strategy of Robust Optimization. The authors discuss what the strategy entails, 8 steps for Robust Optimization and Robust Assessment, and how to lead it in a technical organization with an implementation strategy. Robust Optimization is defined and it is demonstrated how the techniques can be applied to manufacturing organizations, especially those with automotive industry applications, so that Robust Optimization creates the flexibility that minimizes product development cost, reduces product time-to-market, and increases overall productivity.

Key features:

Presents best practices from around the globe on Robust Optimization that can be applied in any manufacturing and automotive organization in the world
Includes 19 successfully implemented best case studies from automotive original equipment manufacturers and suppliers
Provides manufacturing industries with proven techniques to become more competitive in the global market
Provides clarity concerning the common misinterpretations on Robust Optimization

Robust Optimization: World’s Best Practices for Developing Winning Vehicles is a must-have book for engineers and managers who are working on design, product, manufacturing, mechanical, electrical, process, quality area; all levels of management especially in product development area, research and development personnel and consultants. It also serves as an excellent reference for students and teachers in engineering.

Subir Chowdhury has been a thought leader in quality management strategy and methodology for more than 20 years. Currently Chairman and CEO of ASI Consulting Group, LLC, he leads Six Sigma and Quality Leadership implementation, and consulting and training efforts. Subir's work has earned him numerous awards and recognition. The New York Times cited him as a "leading quality expert"; BusinessWeek hailed him as the "Quality Prophet." The Conference Board Review described him as "an excitable, enthusiastic evangelist for quality."
Subir has worked with many organizations across diverse industries including manufacturing, healthcare, food, and non-profit organizations. His client list includes major global corporations and industrial leaders such as American Axle, Berger Health Systems, Bosch, Caterpillar, Daewoo, Delphi Automotive Systems, Fiat-Chrysler Automotive, Ford, General Motors, Hyundai Motor Company, ITT Industries, Johns Manville, Kaplan Professional, Kia Motors, Leader Dogs for the Blind, Loral Space Systems, Make It Right Foundation, Mark IV Automotive, Procter & Gamble, State of Michigan, Thomson Multimedia, TRW, Volkswagen, Xerox, and more. Under Subir’s leadership, ASI Consulting Group has helped hundreds of clients around the world save billions of dollars in recovered productivity and increased revenues.
Subir is the author of 14 books, including the international bestseller The Power of Six Sigma (Dearborn Trade, 2001), which has sold more than a million copies worldwide and been translated into more than 20 languages. Design for Six Sigma (Kaplan Professional, 2002) was the first book to popularize the "DFSS" concept. With quality pioneer Dr. Genichi Taguchi, Subir co-authored of two technical bestsellers Robust Engineering (McGraw Hill, 1999) and Taguchi's Quality Engineering Handbook (Wiley, 2005).
His book, the critically acclaimed The Ice Cream Maker (Random House Doubleday, 2005) introduced LEO (Listen, Enrich, Optimize), a flexible management strategy that brings the concept of quality to every member of an organization. The book was formally recognized and distributed to every member of the 109^thCongress. The LEO process continues to be implemented in many organizations. His most recent book, The Power of LEO (McGraw-Hill, 2011) was an Inc. Magazine bestseller. A follow-up to The Ice Cream Maker, the book shows organizations how the LEO methodology can be integrated into a complete quality management system.

Shin Taguchi is Chief Technical Officer (CTO)for ASI Consulting Group, LLC. He is a Master Black Belt in Six Sigma and Design for Six Sigma (DFSS) and was one of the world authorities in developing the DFSS program at ASI-CG, an internationally recognized training and consulting organization, dedicated to improving the competitive position of industries. He is the son of Dr. Genichi Taguchi, developer of new engineering approaches for robust technology that have saved American industry billions of dollars.
Over the last thirty years, Shin has trained more than 60,000 engineers around the world in quality engineering, product/process optimization, and robust design techniques, Mahalanobis-Taguchi System, known as Taguchi Methods^TM. Some of the many clients he has helped to make products and processes Robust include: Ford Motor Company, General Motors, Delphi Automotive Systems, Fiat-Chrysler Automotive, ITT, Kodak, Lexmark, Goodyear Tire & Rubber, General Electric, Miller Brewing, The Budd Company, Westinghouse, NASA, Texas Instruments, Xerox, Hyundai Motor Company, TRW and many others. In 1996, Shin developed and started to teach a Taguchi Certification Course. Over 360 people have graduated to date from this ongoing 16-day master certification course.
Shin is a Fellow of the Royal Statistical Society in London, and is a member of the Institute of Industrial Engineering (IIE) and the American Society for Quality (ASQ); Shin is a member of the Quality Control Research Group of the Japanese Standards Association (JSA) and Quality Engineering Society of Japan. He is an editor of the Quality Engineering Forum Technical Journal and was awarded the Craig Award for the best technical paper presented at the annual conference of the ASQ. Shin has been featured in the media through a number of national and international forums, including Fortune Magazine and Actionline (a publication of AIAG). Shin co-authored "Robust Engineering" published by McGraw Hill in 1999. He has given presentations and workshops at numerous conferences, including ASQ, ASME, SME, SAE, and IIE. He is also a Master Black Belt for Design for Six Sigma (DFSS).

Preface xxi

Acknowledgments xxv

About the Authors xxvii

1 Introduction to Robust Optimization 1

1.1 What Is Quality as Loss? 2

1.2 What Is Robustness? 4

1.3 What Is Robust Assessment? 5

1.4 What Is Robust Optimization? 5

1.4.1 Noise Factors 8

1.4.2 Parameter Design 9

1.4.3 Tolerance Design 13

2 Eight Steps for Robust Optimization and Robust Assessment 17

2.1 Before Eight Steps: Select Project Area 18

2.2 Eight Steps for Robust Optimization 19

2.2.1 Step 1: Define Scope for Robust Optimization 19

2.2.2 Step 2: Identify Ideal Function/Response 20

2.2.2.1 Ideal Function: Dynamic Response 20

2.2.2.2 Nondynamic Responses 21

2.2.3 Step 3: Develop Signal and Noise Strategies 23

2.2.3.1 How Input M is Varied to Benchmark “Robustness” 23

2.2.3.2 How Noise Factors Are Varied to Benchmark “Robustness” 23

2.2.4 Step 4: Select Control Factors and Levels 32

2.2.4.1 Traditional Approach to Explore Control Factors 32

2.2.4.2 Exploration of Design Space by Orthogonal Array 33

2.2.4.3 Try to Avoid Strong Interactions between Control Factors 33

2.2.4.4 Orthogonal Array and its Mechanics 36

2.2.5 Step 5: Execute and Collect Data 38

2.2.6 Step 6: Conduct Data Analysis 38

2.2.6.1 Computations of S/N and β 39

2.2.6.2 Computation of S/N and β for L18 Data Sets 43

2.2.6.3 Response Table for S/N and β 43

2.2.6.4 Determination of Optimum Design 48

2.2.7 Step 7: Predict and Confirm 49

2.2.7.1 Confirmation 50

2.2.8 Step 8: Lesson Learned and Action Plan 50

2.3 Eight Steps for Robust Assessment 52

2.3.1 Step 1: Define Scope 52

2.3.2 Step 2: Identify Ideal Function/Response and Step 3: Develop Signal and Noise Strategies 52

2.3.3 Step 4: Select Designs for Assessment 52

2.3.4 Step 5: Execute and Collect Data 52

2.3.5 Step 6: Conduct Data Analysis 52

2.3.6 Step 7: Make Judgments 53

2.3.7 Step 8: Lesson Learned and Action Plan 53

2.4 As You Go through Case Studies in This Book 55

3 Implementation of Robust Optimization 57

3.1 Introduction 57

3.2 Robust Optimization Implementation 57

3.2.1 Leadership Commitment 58

3.2.2 Executive Leader and the Corporate Team 58

3.2.3 Effective Communication 60

3.2.4 Education and Training 61

3.2.5 Integration Strategy 62

3.2.6 Bottom Line Performance 62

PART ONE VEHICLE LEVEL OPTIMIZATION 63

4 Optimization of Vehicle Offset Crashworthy Design Using a Simplified AnalysisModel 65
Chrysler LLC, USA

4.1 Executive Summary 65

4.2 Introduction 66

4.3 Stepwise Implementation of DFSS Optimization for Vehicle Offset Impact 67

4.3.1 Step 1: Scope Defined for Optimization 67

4.3.2 Step 2: Identify/Select Design Alternatives 67

4.3.3 Step 3: Identify Ideal Function 68

4.3.4 Step 4: Develop Signal and Noise Strategy 69

4.3.4.1 Input and Output Signal Strategy 69

4.3.5 Step 5: Select Control/Noise Factors and Levels 70

4.3.5.1 Simplified Spring Mass Model Creation and Validation 70

4.3.5.2 Control Variable Selection 72

4.3.5.3 Control Factor Level Application for Spring Stiffness Updates 73

4.3.6 Step 6: Execute and Conduct Data Analysis 73

4.3.7 Step 7: Validation of Optimized Model 74

4.4 Conclusion 77

4.4.1 Acknowledgments 77

4.5 References 77

5 Optimization of the Component Characteristics for Improving Collision Safety by Simulation 79
Isuzu Advanced Engineering Center, Ltd, Japan

5.1 Executive Summary 79

5.2 Introduction 80

5.3 Simulation Models 81

5.4 Concept of Standardized S/N Ratios with Respect to Survival Space 82

5.5 Results and Consideration 86

5.6 Conclusion 94

5.6.1 Acknowledgment 94

5.7 Reference 94

PART TWO SUBSYSTEMS LEVEL OPTIMIZATION BY ORIGINAL EQUIPMENT MANUFACTURERS (OEMs) 95

6 Optimization of Small DC Motors Using Functionality for Evaluation 97
Nissan Motor Co., Ltd, Japan and Jidosha Denki Kogyo Co., Ltd, Japan

6.1 Executive Summary 97

6.2 Introduction 98

6.3 Functionality for Evaluation in Case of DC Motors 98

6.4 Experiment Method and Measurement Data 99

6.5 Factors and Levels 100

6.6 Data Analysis 101

6.7 Analysis Results 104

6.8 Selection of Optimal Design and Confirmation 104

6.9 Benefits Gained 107

6.10 Consideration of Analysis for Audible Noise 108

6.11 Conclusion 110

6.11.1 The Importance of Functionality for Evaluation 110

6.11.2 Evaluation under the Unloaded (Idling) Condition 110

6.11.3 Evaluation of Audible Noise (Quality Characteristic) 111

6.11.4 Acknowledgment 111

7 Optimal Design for a Double-Lift Window Regulator System Used in Automobiles 113
Nissan Motor Co., Ltd, Japan and Ohi Seisakusho Co., Ltd, Japan

7.1 Executive Summary 113

7.2 Introduction 114

7.3 Schematic Figure of Double-Lift Window Regulator System 114

7.4 Ideal Function 114

7.5 Noise Factors 116

7.6 Control Factors 117

7.7 Conventional Data Analysis and Results 119

7.8 Selection of Optimal Condition and Confirmation Test Results 120

7.9 Evaluation of Quality Characteristics 122

7.10 Concept of Analysis Based on Standardized S/N Ratio 124

7.11 Analysis Results Based on Standardized S/N Ratio 125

7.12 Comparison between Analysis Based on Standardized S/N Ratio and Analysis Based on Conventional S/N Ratio 127

7.13 Conclusion 132

7.13.1 Acknowledgments 132

7.14 Further Reading 132

8 Optimization of Next-Generation Steering System Using Computer Simulation 133
Nissan Motor Co., Ltd, Japan

8.1 Executive Summary 133

8.2 Introduction 134

8.3 System Description 134

8.4 Measurement Data 135

8.5 Ideal Function 136

8.6 Factors and Levels 136

8.6.1 Signal and Response 136

8.6.2 Noise Factors 136

8.6.3 Indicative Factor 137

8.6.4 Control Factors 137

8.7 Pre-analysis for Compounding the Noise Factors 137

8.8 Calculation of Standardized S/N Ratio 138

8.9 Analysis Results 141

8.10 Determination of Optimal Design and Confirmation 141

8.11 Tuning to the Targeted Value 142

8.12 Conclusion 144

8.12.1 Acknowledgment 145

9 Future Truck Steering Effort Robustness 147
General Motors Corporation, USA

9.1 Executive Summary 147

9.2 Background 148

9.2.1 Methodology 148

9.2.2 Hydraulic Power-Steering Assist System 149

9.2.3 Valve Assembly Design 152

9.2.4 Project Scope 153

9.3 Parameter Design 154

9.3.1 Ideal Steering Effort Function 154

9.3.2 Control Factors 157

9.3.3 Noise Compounding Strategy and Input Signals 157

9.3.4 Standardized S/N Post-Processing 159

9.3.5 Quality Loss Function 165

9.4 Acknowledgments 172

9.5 References 172

10 Optimal Design of Engine Mounting System Based on Quality Engineering 173
Mazda Motor Corporation, Japan

10.1 Executive Summary 173

10.2 Background 174

10.3 Design Object 174

10.4 Application of Standard S/N Ratio Taguchi Method 175

10.5 Iterative Application of Standard S/N Ratio Taguchi Method 178

10.6 Influence of Interval of Factor Level 181

10.7 Calculation Program 184

10.8 Conclusions 185

10.8.1 Acknowledgments 186

10.9 References 186

11 Optimization of a Front-Wheel-Drive Transmission for Improved Efficiency and Robustness 187
Chrysler Group, LLC, USA and ASI Consulting Group, LLC, USA

11.1 Executive Summary 187

11.2 Introduction 188

11.3 Experimental 189

11.3.1 Ideal Function and Measurement 189

11.4 Signal Strategy 190

11.5 Noise Strategy 191

11.6 Control Factor Selection 192

11.7 Orthogonal Array Selection 193

11.8 Results and Discussion 196

11.8.1 S/N Calculations 196

11.8.2 Graphs of Runs 200

11.8.3 Response Plots 201

11.8.4 Confirmation Run 201

11.8.5 Verification of Results 203

11.9 Conclusion 206

11.9.1 Acknowledgments 207

11.10 References 207

12 Fuel Delivery System Robustness 209
Ford Motor Company, USA

12.1 Executive Summary 209

12.2 Introduction 210

12.2.1 Fuel System Overview 210

12.2.2 Conventional Fuel System 211

12.2.3 New Fuel System 211

12.3 Experiment Description 211

12.3.1 Test Method 211

12.3.2 Ideal Function 211

12.4 Noise Factors 213

12.4.1 Control Factors 213

12.4.2 Fixed Factors 214

12.5 Experiment Test Results 214

12.6 Sensitivity (β) Analysis 214

12.7 Confirmation Test Results 217

12.7.1 Bench Test Confirmation 217

12.7.1.1 Initial Fuel Delivery System 217

12.7.1.2 Optimal Fuel Delivery System 218

12.7.2 Vehicle Verification 218

12.7.2.1 Initial Fuel Delivery System 219

12.7.2.2 Optimal Fuel Delivery System 219

12.8 Conclusion 220

13 Improving Coupling Factor in Vehicle Theft Deterrent Systems Using Design for Six Sigma (DFSS) 223
General Motors Corporation, USA

13.1 Executive Summary 223

13.2 Introduction 224

13.3 Objectives 225

13.4 The Voice of the Customer 225

13.5 Experimental Strategy 225

13.5.1 Response 225

13.5.2 Noise Strategy 226

13.5.3 Control Factors 226

13.5.4 Input Signal 227

13.6 The System 227

13.7 The Experimental Results 228

13.8 Conclusions 229

13.8.1 Summary 233

13.8.2 Acknowledgments 234

PART THREE SUBSYSTEMS LEVEL OPTIMIZATION BY SUPPLIERS 235

14 Magnetic Sensing System Optimization 237
ALPS Electric, Japan

14.1 Executive Summary 237

14.1.1 The Magnetic Sensing System 238

14.2 Improvement of Design Technique 239

14.2.1 Traditional Design Technique 239

14.2.2 Design Technique by Quality Engineering 239

14.3 System Design Technique 241

14.3.1 Parameter Design Diagram 241

14.3.2 Signal Factor, Control Factor, and Noise Factor 242

14.3.3 Implementation of Parameter Design 244

14.3.4 Results of the Confirmation Experiment 244

14.4 Effect by Shortening of Development Period 246

14.5 Conclusion 246

14.5.1 Acknowledgments 247

14.6 References 247

15 Direct Injection Diesel Injector Optimization 249
Delphi Automotive Systems, Europe and Delphi Automotive Systems, USA

15.1 Executive Summary 249

15.2 Introduction 250

15.2.1 Background 250

15.2.2 Problem Statement 250

15.2.3 Objectives and Approach to Optimization 251

15.3 Simulation Model Robustness 253

15.3.1 Background 253

15.3.2 Approach to Optimization 257

15.3.3 Results 257

15.4 Parameter Design 257

15.4.1 Ideal Function 257

15.4.2 Signal and Noise Strategies 258

15.4.2.1 Signal Levels 258

15.4.2.2 Noise Strategy 258

15.4.3 Control Factors and Levels 259

15.4.4 Experimental Layout 259

15.4.5 Data Analysis and Two-Step Optimization 259

15.4.6 Confirmation 263

15.4.7 Discussions on Parameter Design Results 264

15.4.7.1 Technical 264

15.4.7.2 Economical 264

15.5 Tolerance Design 268

15.5.1 Signal Point by Signal Point Tolerance Design 269

15.5.1.1 Factors and Experimental Layout 269

15.5.1.2 Analysis of Variance (ANOVA) for Each Injection Point 269

15.5.1.3 Loss Function 269

15.5.2 Dynamic Tolerance Design 270

15.5.2.1 Dynamic Analysis of Variance 271

15.5.2.2 Dynamic Loss Function 273

15.6 Conclusions 275

15.6.1 Project Related 275

15.6.2 Recommendations for Taguchi Methods 277

15.6.3 Acknowledgments 278

15.7 Reference and Further Reading 278

16 General Purpose Actuator Robust Assessment and Benchmark Study 279
Robert Bosch, LLC, USA

16.1 Executive Summary 279

16.2 Introduction 280

16.3 Objectives 280

16.3.1 Robust Assessment Measurement Method 281

16.3.1.1 Test Equipment 281

16.3.1.2 Data Acquisition 284

16.3.1.3 Data Analysis Strategy 285

16.4 Robust Assessment 286

16.4.1 Scope and P-Diagram 286

16.4.2 Ideal Function 286

16.4.3 Signal and Noise Strategy 290

16.4.4 Control Factors 291

16.4.5 Raw Data 291

16.4.6 Data Analysis 291

16.5 Conclusion 296

16.5.1 Acknowledgments 297

16.6 Further Reading 297

17 Optimization of a Discrete Floating MOS Gate Driver 299
Delphi-Delco Electronic Systems, USA

17.1 Executive Summary 299

17.2 Background 300

17.3 Introduction 302

17.4 Developing the “Ideal” Function 302

17.5 Noise Strategy 305

17.6 Control Factors and Levels 305

17.7 Experiment Strategy and Measurement System 306

17.8 Parameter Design Experiment Layout 306

17.9 Results 307

17.10 Response Charts 307

17.11 Two-Step Optimization 311

17.12 Confirmation 312

17.13 Conclusions 312

17.13.1 Acknowledgments 314

18 Reformer Washcoat Adhesion on Metallic Substrates 315
Delphi Automotive Systems, USA

18.1 Executive Summary 315

18.2 Introduction 316

18.3 Experimental Setup 317

18.3.1 The Ideal Function 318

18.3.2 P-Diagram 318

18.3.3 Control Factors 319

18.3.3.1 Alloy Composition 319

18.3.3.2 Washcoat Composition 320

18.3.3.3 Slurry Parameters 320

18.3.3.4 Cleaning Procedures 320

18.3.3.5 Preparation 320

18.4 Control Factor Levels 320

18.5 Noise Factors 320

18.5.1 Signal Factor 320

18.5.2 Unwanted Outputs 320

18.6 Description of Experiment 322

18.6.1 Furnace 322

18.6.2 Orthogonal Array and Inner Array 323

18.6.3 Signal-to-Noise and Beta Calculations 323

18.6.4 Response Tables 323

18.7 Two Step Optimization and Prediction 323

18.7.1 Optimum Design 329

18.7.2 Predictions 329

18.8 Confirmation 329

18.8.1 Design Improvement 329

18.9 Measurement System Evaluation 334

18.10 Conclusion 334

18.11 Supplemental Background Information 336

18.12 Acknowledgment 340

18.13 Reference and Further Reading 340

19 Making Better Decisions Faster: Sequential Application of Robust Engineering to Math-Models, CAE Simulations, and Accelerated Testing 341
Robert Bosch Corporation, USA

19.1 Executive Summary 341

19.2 Introduction 342

19.2.1 Thermal Equivalent Circuit – Detailed 343

19.2.2 Thermal Equivalent Circuit – Simplified 343

19.2.3 Closed Form Solution 343

19.3 Objective 345

19.3.1 Thermal Robustness Design Template 345

19.3.2 Critical Design Parameters for Thermal Robustness 345

19.3.3 Cascade Learning (aka Leveraged Knowledge) 346

19.3.4 Test Taguchi Robust Engineering Methodology 346

19.4 Robust Optimization 347

19.4.1 Scope and P-Diagram 347

19.4.2 Ideal Function 347

19.4.3 Signal and Noise Strategy 349

19.4.4 Input Signal 350

19.4.5 Control Factors and Levels 350

19.4.6 Math-Model Generated Data 351

19.4.7 Data Analysis 351

19.4.8 Thermal Robustness (Signal-to-Noise) 354

19.4.9 Subsystem Thermal Resistance (Beta) 356

19.4.10 Prediction and Confirmation 357

19.4.11 Verification 362

19.5 Conclusions 364

19.5.1 Acknowledgments 365

19.6 Futher Reading 366

20 Pressure Switch Module Normally Open Feasibility Investigation and Supplier Competition 367
Robert Bosch, LLC, USA

20.1 Executive Summary 367

20.2 Introduction 368

20.2.1 Current Production Pressure Switch Module – Detailed 368

20.2.2 Current Production (N.C.) Switching Element – Detailed 369

20.3 Objective 370

20.4 Robust Assessment 370

20.4.1 Scope and P-Diagram 370

20.4.2 Ideal Function 371

20.4.3 Noise Strategy 372

20.4.4 Testing Criteria 372

20.4.5 Control Factors and Levels 373

20.4.6 Test Data 374

20.4.7 Data Analysis 375

20.4.8 Prediction and Confirmation 379

20.4.9 Verification 383

20.5 Summary and Conclusions 383

20.5.1 Acknowledgments 385

PART FOUR MANUFACTURING PROCESS OPTIMIZATION 387

21 Robust Optimization of a Lead-Free Reflow Soldering Process 389
Delphi Delco Electronics Systems, USA and ASI Consulting Group, LLC, USA

21.1 Executive Summary 389

21.2 Introduction 390

21.3 Experimental 391

21.3.1 Robust Engineering Methodology 391

21.3.2 Visual Scoring 394

21.3.3 Pull Test 396

21.4 Results and Discussion 396

21.4.1 Visual Scoring Results 396

21.4.2 Pull Test Results 400

21.4.3 Next Steps 401

21.5 Conclusion 401

21.5.1 Acknowledgment 402

21.6 References 402

22 Catalyst Slurry Coating Process Optimization for Diesel Catalyzed Particulate Traps 403
Delphi Energy and Chassis Systems, USA

22.1 Executive Summary 403

22.2 Introduction 404

22.3 Project Description 405

22.4 Process Map 406

22.4.1 Initial Performance 406

22.5 First Parameter Design Experiment 406

22.5.1 Function Analysis 407

22.5.2 Ideal Function 409

22.5.3 Measurement System Evaluation 409

22.5.4 Parameter Diagram 411

22.5.5 Factors and Levels 411

22.5.6 Compound Noise Strategy 412

22.5.7 Parameter Design Experiment Layout (1) 412

22.5.8 Means Plots 414

22.5.9 Means Tables 414

22.5.10 Two-Step Optimization and Prediction 415

22.5.11 Predicted Performance Improvement Before and After 416

22.6 Follow-up Parameter Design Experiment 416

22.6.1 Parameter Design Experiment Layout (2) 417

22.6.2 Means Plots for Signal-to-Noise Ratios 417

22.6.3 Confirmation Results in Tulsa 417

22.6.4 Noise Factor Q Affect on Slurry Coating 417

22.7 Transfer to Florange 419

22.7.1 Ideal Function and Parameter Diagram 421

22.7.2 Parameter Design Experiment Layout (3) 421

22.7.3 Means Plots for Signal-to-Noise Ratios 423

22.7.4 Prediction and Confirmation 423

22.7.5 Process Capability 423

22.8 Conclusion 424

22.8.1 The Team 424

Index 427

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Robust Optimization World's Best Practices for Developing Winning Vehicles

Robust Optimization World's Best Practices for Developing Winning Vehicles

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