{"product_id":"robust-optimization-9781119212126","title":"Robust Optimization","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eRobust 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.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e\u003c\/b\u003eWritten by world renowned authors, \u003ci\u003eRobust Optimization: World's Best Practices for Developing Winning Vehicles, \u003c\/i\u003eis 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 an\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003ePreface xxi\u003c\/p\u003e \u003cp\u003eAcknowledgments xxv\u003c\/p\u003e \u003cp\u003eAbout the Authors xxvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction to Robust Optimization 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 What Is Quality as Loss? 2\u003c\/p\u003e \u003cp\u003e1.2 What Is Robustness? 4\u003c\/p\u003e \u003cp\u003e1.3 What Is Robust Assessment? 5\u003c\/p\u003e \u003cp\u003e1.4 What Is Robust Optimization? 5\u003c\/p\u003e \u003cp\u003e1.4.1 Noise Factors 8\u003c\/p\u003e \u003cp\u003e1.4.2 Parameter Design 9\u003c\/p\u003e \u003cp\u003e1.4.3 Tolerance Design 13\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Eight Steps for Robust Optimization and Robust Assessment 17\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Before Eight Steps: Select Project Area 18\u003c\/p\u003e \u003cp\u003e2.2 Eight Steps for Robust Optimization 19\u003c\/p\u003e \u003cp\u003e2.2.1 Step 1: Define Scope for Robust Optimization 19\u003c\/p\u003e \u003cp\u003e2.2.2 Step 2: Identify Ideal Function\/Response 20\u003c\/p\u003e \u003cp\u003e2.2.2.1 Ideal Function: Dynamic Response 20\u003c\/p\u003e \u003cp\u003e2.2.2.2 Nondynamic Responses 21\u003c\/p\u003e \u003cp\u003e2.2.3 Step 3: Develop Signal and Noise Strategies 23\u003c\/p\u003e \u003cp\u003e2.2.3.1 How Input M is Varied to Benchmark “Robustness” 23\u003c\/p\u003e \u003cp\u003e2.2.3.2 How Noise Factors Are Varied to Benchmark “Robustness” 23\u003c\/p\u003e \u003cp\u003e2.2.4 Step 4: Select Control Factors and Levels 32\u003c\/p\u003e \u003cp\u003e2.2.4.1 Traditional Approach to Explore Control Factors 32\u003c\/p\u003e \u003cp\u003e2.2.4.2 Exploration of Design Space by Orthogonal Array 33\u003c\/p\u003e \u003cp\u003e2.2.4.3 Try to Avoid Strong Interactions between Control Factors 33\u003c\/p\u003e \u003cp\u003e2.2.4.4 Orthogonal Array and its Mechanics 36\u003c\/p\u003e \u003cp\u003e2.2.5 Step 5: Execute and Collect Data 38\u003c\/p\u003e \u003cp\u003e2.2.6 Step 6: Conduct Data Analysis 38\u003c\/p\u003e \u003cp\u003e2.2.6.1 Computations of S\/N and β 39\u003c\/p\u003e \u003cp\u003e2.2.6.2 Computation of S\/N and β for L18 Data Sets 43\u003c\/p\u003e \u003cp\u003e2.2.6.3 Response Table for S\/N and β 43\u003c\/p\u003e \u003cp\u003e2.2.6.4 Determination of Optimum Design 48\u003c\/p\u003e \u003cp\u003e2.2.7 Step 7: Predict and Confirm 49\u003c\/p\u003e \u003cp\u003e2.2.7.1 Confirmation 50\u003c\/p\u003e \u003cp\u003e2.2.8 Step 8: Lesson Learned and Action Plan 50\u003c\/p\u003e \u003cp\u003e2.3 Eight Steps for Robust Assessment 52\u003c\/p\u003e \u003cp\u003e2.3.1 Step 1: Define Scope 52\u003c\/p\u003e \u003cp\u003e2.3.2 Step 2: Identify Ideal Function\/Response and Step 3: Develop Signal and Noise Strategies 52\u003c\/p\u003e \u003cp\u003e2.3.3 Step 4: Select Designs for Assessment 52\u003c\/p\u003e \u003cp\u003e2.3.4 Step 5: Execute and Collect Data 52\u003c\/p\u003e \u003cp\u003e2.3.5 Step 6: Conduct Data Analysis 52\u003c\/p\u003e \u003cp\u003e2.3.6 Step 7: Make Judgments 53\u003c\/p\u003e \u003cp\u003e2.3.7 Step 8: Lesson Learned and Action Plan 53\u003c\/p\u003e \u003cp\u003e2.4 As You Go through Case Studies in This Book 55\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Implementation of Robust Optimization 57\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 57\u003c\/p\u003e \u003cp\u003e3.2 Robust Optimization Implementation 57\u003c\/p\u003e \u003cp\u003e3.2.1 Leadership Commitment 58\u003c\/p\u003e \u003cp\u003e3.2.2 Executive Leader and the Corporate Team 58\u003c\/p\u003e \u003cp\u003e3.2.3 Effective Communication 60\u003c\/p\u003e \u003cp\u003e3.2.4 Education and Training 61\u003c\/p\u003e \u003cp\u003e3.2.5 Integration Strategy 62\u003c\/p\u003e \u003cp\u003e3.2.6 Bottom Line Performance 62\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART ONE VEHICLE LEVEL OPTIMIZATION 63\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Optimization of Vehicle Offset Crashworthy Design Using a Simplified AnalysisModel 65\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eChrysler LLC, USA\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Executive Summary 65\u003c\/p\u003e \u003cp\u003e4.2 Introduction 66\u003c\/p\u003e \u003cp\u003e4.3 Stepwise Implementation of DFSS Optimization for Vehicle Offset Impact 67\u003c\/p\u003e \u003cp\u003e4.3.1 Step 1: Scope Defined for Optimization 67\u003c\/p\u003e \u003cp\u003e4.3.2 Step 2: Identify\/Select Design Alternatives 67\u003c\/p\u003e \u003cp\u003e4.3.3 Step 3: Identify Ideal Function 68\u003c\/p\u003e \u003cp\u003e4.3.4 Step 4: Develop Signal and Noise Strategy 69\u003c\/p\u003e \u003cp\u003e4.3.4.1 Input and Output Signal Strategy 69\u003c\/p\u003e \u003cp\u003e4.3.5 Step 5: Select Control\/Noise Factors and Levels 70\u003c\/p\u003e \u003cp\u003e4.3.5.1 Simplified Spring Mass Model Creation and Validation 70\u003c\/p\u003e \u003cp\u003e4.3.5.2 Control Variable Selection 72\u003c\/p\u003e \u003cp\u003e4.3.5.3 Control Factor Level Application for Spring Stiffness Updates 73\u003c\/p\u003e \u003cp\u003e4.3.6 Step 6: Execute and Conduct Data Analysis 73\u003c\/p\u003e \u003cp\u003e4.3.7 Step 7: Validation of Optimized Model 74\u003c\/p\u003e \u003cp\u003e4.4 Conclusion 77\u003c\/p\u003e \u003cp\u003e4.4.1 Acknowledgments 77\u003c\/p\u003e \u003cp\u003e4.5 References 77\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Optimization of the Component Characteristics for Improving Collision Safety by Simulation 79\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eIsuzu Advanced Engineering Center, Ltd, Japan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Executive Summary 79\u003c\/p\u003e \u003cp\u003e5.2 Introduction 80\u003c\/p\u003e \u003cp\u003e5.3 Simulation Models 81\u003c\/p\u003e \u003cp\u003e5.4 Concept of Standardized S\/N Ratios with Respect to Survival Space 82\u003c\/p\u003e \u003cp\u003e5.5 Results and Consideration 86\u003c\/p\u003e \u003cp\u003e5.6 Conclusion 94\u003c\/p\u003e \u003cp\u003e5.6.1 Acknowledgment 94\u003c\/p\u003e \u003cp\u003e5.7 Reference 94\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART TWO SUBSYSTEMS LEVEL OPTIMIZATION BY ORIGINAL EQUIPMENT MANUFACTURERS (OEMs) 95\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Optimization of Small DC Motors Using Functionality for Evaluation 97\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eNissan Motor Co., Ltd, Japan and Jidosha Denki Kogyo Co., Ltd, Japan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Executive Summary 97\u003c\/p\u003e \u003cp\u003e6.2 Introduction 98\u003c\/p\u003e \u003cp\u003e6.3 Functionality for Evaluation in Case of DC Motors 98\u003c\/p\u003e \u003cp\u003e6.4 Experiment Method and Measurement Data 99\u003c\/p\u003e \u003cp\u003e6.5 Factors and Levels 100\u003c\/p\u003e \u003cp\u003e6.6 Data Analysis 101\u003c\/p\u003e \u003cp\u003e6.7 Analysis Results 104\u003c\/p\u003e \u003cp\u003e6.8 Selection of Optimal Design and Confirmation 104\u003c\/p\u003e \u003cp\u003e6.9 Benefits Gained 107\u003c\/p\u003e \u003cp\u003e6.10 Consideration of Analysis for Audible Noise 108\u003c\/p\u003e \u003cp\u003e6.11 Conclusion 110\u003c\/p\u003e \u003cp\u003e6.11.1 The Importance of Functionality for Evaluation 110\u003c\/p\u003e \u003cp\u003e6.11.2 Evaluation under the Unloaded (Idling) Condition 110\u003c\/p\u003e \u003cp\u003e6.11.3 Evaluation of Audible Noise (Quality Characteristic) 111\u003c\/p\u003e \u003cp\u003e6.11.4 Acknowledgment 111\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Optimal Design for a Double-Lift Window Regulator System Used in Automobiles 113\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eNissan Motor Co., Ltd, Japan and Ohi Seisakusho Co., Ltd, Japan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Executive Summary 113\u003c\/p\u003e \u003cp\u003e7.2 Introduction 114\u003c\/p\u003e \u003cp\u003e7.3 Schematic Figure of Double-Lift Window Regulator System 114\u003c\/p\u003e \u003cp\u003e7.4 Ideal Function 114\u003c\/p\u003e \u003cp\u003e7.5 Noise Factors 116\u003c\/p\u003e \u003cp\u003e7.6 Control Factors 117\u003c\/p\u003e \u003cp\u003e7.7 Conventional Data Analysis and Results 119\u003c\/p\u003e \u003cp\u003e7.8 Selection of Optimal Condition and Confirmation Test Results 120\u003c\/p\u003e \u003cp\u003e7.9 Evaluation of Quality Characteristics 122\u003c\/p\u003e \u003cp\u003e7.10 Concept of Analysis Based on Standardized S\/N Ratio 124\u003c\/p\u003e \u003cp\u003e7.11 Analysis Results Based on Standardized S\/N Ratio 125\u003c\/p\u003e \u003cp\u003e7.12 Comparison between Analysis Based on Standardized S\/N Ratio and Analysis Based on Conventional S\/N Ratio 127\u003c\/p\u003e \u003cp\u003e7.13 Conclusion 132\u003c\/p\u003e \u003cp\u003e7.13.1 Acknowledgments 132\u003c\/p\u003e \u003cp\u003e7.14 Further Reading 132\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Optimization of Next-Generation Steering System Using Computer Simulation 133\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eNissan Motor Co., Ltd, Japan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Executive Summary 133\u003c\/p\u003e \u003cp\u003e8.2 Introduction 134\u003c\/p\u003e \u003cp\u003e8.3 System Description 134\u003c\/p\u003e \u003cp\u003e8.4 Measurement Data 135\u003c\/p\u003e \u003cp\u003e8.5 Ideal Function 136\u003c\/p\u003e \u003cp\u003e8.6 Factors and Levels 136\u003c\/p\u003e \u003cp\u003e8.6.1 Signal and Response 136\u003c\/p\u003e \u003cp\u003e8.6.2 Noise Factors 136\u003c\/p\u003e \u003cp\u003e8.6.3 Indicative Factor 137\u003c\/p\u003e \u003cp\u003e8.6.4 Control Factors 137\u003c\/p\u003e \u003cp\u003e8.7 Pre-analysis for Compounding the Noise Factors 137\u003c\/p\u003e \u003cp\u003e8.8 Calculation of Standardized S\/N Ratio 138\u003c\/p\u003e \u003cp\u003e8.9 Analysis Results 141\u003c\/p\u003e \u003cp\u003e8.10 Determination of Optimal Design and Confirmation 141\u003c\/p\u003e \u003cp\u003e8.11 Tuning to the Targeted Value 142\u003c\/p\u003e \u003cp\u003e8.12 Conclusion 144\u003c\/p\u003e \u003cp\u003e8.12.1 Acknowledgment 145\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Future Truck Steering Effort Robustness 147\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eGeneral Motors Corporation, USA\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Executive Summary 147\u003c\/p\u003e \u003cp\u003e9.2 Background 148\u003c\/p\u003e \u003cp\u003e9.2.1 Methodology 148\u003c\/p\u003e \u003cp\u003e9.2.2 Hydraulic Power-Steering Assist System 149\u003c\/p\u003e \u003cp\u003e9.2.3 Valve Assembly Design 152\u003c\/p\u003e \u003cp\u003e9.2.4 Project Scope 153\u003c\/p\u003e \u003cp\u003e9.3 Parameter Design 154\u003c\/p\u003e \u003cp\u003e9.3.1 Ideal Steering Effort Function 154\u003c\/p\u003e \u003cp\u003e9.3.2 Control Factors 157\u003c\/p\u003e \u003cp\u003e9.3.3 Noise Compounding Strategy and Input Signals 157\u003c\/p\u003e \u003cp\u003e9.3.4 Standardized S\/N Post-Processing 159\u003c\/p\u003e \u003cp\u003e9.3.5 Quality Loss Function 165\u003c\/p\u003e \u003cp\u003e9.4 Acknowledgments 172\u003c\/p\u003e \u003cp\u003e9.5 References 172\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Optimal Design of Engine Mounting System Based on Quality Engineering 173\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMazda Motor Corporation, Japan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Executive Summary 173\u003c\/p\u003e \u003cp\u003e10.2 Background 174\u003c\/p\u003e \u003cp\u003e10.3 Design Object 174\u003c\/p\u003e \u003cp\u003e10.4 Application of Standard S\/N Ratio Taguchi Method 175\u003c\/p\u003e \u003cp\u003e10.5 Iterative Application of Standard S\/N Ratio Taguchi Method 178\u003c\/p\u003e \u003cp\u003e10.6 Influence of Interval of Factor Level 181\u003c\/p\u003e \u003cp\u003e10.7 Calculation Program 184\u003c\/p\u003e \u003cp\u003e10.8 Conclusions 185\u003c\/p\u003e \u003cp\u003e10.8.1 Acknowledgments 186\u003c\/p\u003e \u003cp\u003e10.9 References 186\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Optimization of a Front-Wheel-Drive Transmission for Improved Efficiency and Robustness 187\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eChrysler Group, LLC, USA and ASI Consulting Group, LLC, USA\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Executive Summary 187\u003c\/p\u003e \u003cp\u003e11.2 Introduction 188\u003c\/p\u003e \u003cp\u003e11.3 Experimental 189\u003c\/p\u003e \u003cp\u003e11.3.1 Ideal Function and Measurement 189\u003c\/p\u003e \u003cp\u003e11.4 Signal Strategy 190\u003c\/p\u003e \u003cp\u003e11.5 Noise Strategy 191\u003c\/p\u003e \u003cp\u003e11.6 Control Factor Selection 192\u003c\/p\u003e \u003cp\u003e11.7 Orthogonal Array Selection 193\u003c\/p\u003e \u003cp\u003e11.8 Results and Discussion 196\u003c\/p\u003e \u003cp\u003e11.8.1 S\/N Calculations 196\u003c\/p\u003e \u003cp\u003e11.8.2 Graphs of Runs 200\u003c\/p\u003e \u003cp\u003e11.8.3 Response Plots 201\u003c\/p\u003e \u003cp\u003e11.8.4 Confirmation Run 201\u003c\/p\u003e \u003cp\u003e11.8.5 Verification of Results 203\u003c\/p\u003e \u003cp\u003e11.9 Conclusion 206\u003c\/p\u003e \u003cp\u003e11.9.1 Acknowledgments 207\u003c\/p\u003e \u003cp\u003e11.10 References 207\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Fuel Delivery System Robustness 209\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eFord Motor Company, USA\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Executive Summary 209\u003c\/p\u003e \u003cp\u003e12.2 Introduction 210\u003c\/p\u003e \u003cp\u003e12.2.1 Fuel System Overview 210\u003c\/p\u003e \u003cp\u003e12.2.2 Conventional Fuel System 211\u003c\/p\u003e \u003cp\u003e12.2.3 New Fuel System 211\u003c\/p\u003e \u003cp\u003e12.3 Experiment Description 211\u003c\/p\u003e \u003cp\u003e12.3.1 Test Method 211\u003c\/p\u003e \u003cp\u003e12.3.2 Ideal Function 211\u003c\/p\u003e \u003cp\u003e12.4 Noise Factors 213\u003c\/p\u003e \u003cp\u003e12.4.1 Control Factors 213\u003c\/p\u003e \u003cp\u003e12.4.2 Fixed Factors 214\u003c\/p\u003e \u003cp\u003e12.5 Experiment Test Results 214\u003c\/p\u003e \u003cp\u003e12.6 Sensitivity (β) Analysis 214\u003c\/p\u003e \u003cp\u003e12.7 Confirmation Test Results 217\u003c\/p\u003e \u003cp\u003e12.7.1 Bench Test Confirmation 217\u003c\/p\u003e \u003cp\u003e12.7.1.1 Initial Fuel Delivery System 217\u003c\/p\u003e \u003cp\u003e12.7.1.2 Optimal Fuel Delivery System 218\u003c\/p\u003e \u003cp\u003e12.7.2 Vehicle Verification 218\u003c\/p\u003e \u003cp\u003e12.7.2.1 Initial Fuel Delivery System 219\u003c\/p\u003e \u003cp\u003e12.7.2.2 Optimal Fuel Delivery System 219\u003c\/p\u003e \u003cp\u003e12.8 Conclusion 220\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Improving Coupling Factor in Vehicle Theft Deterrent Systems Using Design for Six Sigma (DFSS) 223\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eGeneral Motors Corporation, USA\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Executive Summary 223\u003c\/p\u003e \u003cp\u003e13.2 Introduction 224\u003c\/p\u003e \u003cp\u003e13.3 Objectives 225\u003c\/p\u003e \u003cp\u003e13.4 The Voice of the Customer 225\u003c\/p\u003e \u003cp\u003e13.5 Experimental Strategy 225\u003c\/p\u003e \u003cp\u003e13.5.1 Response 225\u003c\/p\u003e \u003cp\u003e13.5.2 Noise Strategy 226\u003c\/p\u003e \u003cp\u003e13.5.3 Control Factors 226\u003c\/p\u003e \u003cp\u003e13.5.4 Input Signal 227\u003c\/p\u003e \u003cp\u003e13.6 The System 227\u003c\/p\u003e \u003cp\u003e13.7 The Experimental Results 228\u003c\/p\u003e \u003cp\u003e13.8 Conclusions 229\u003c\/p\u003e \u003cp\u003e13.8.1 Summary 233\u003c\/p\u003e \u003cp\u003e13.8.2 Acknowledgments 234\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART THREE SUBSYSTEMS LEVEL OPTIMIZATION BY SUPPLIERS 235\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Magnetic Sensing System Optimization 237\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eALPS Electric, Japan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Executive Summary 237\u003c\/p\u003e \u003cp\u003e14.1.1 The Magnetic Sensing System 238\u003c\/p\u003e \u003cp\u003e14.2 Improvement of Design Technique 239\u003c\/p\u003e \u003cp\u003e14.2.1 Traditional Design Technique 239\u003c\/p\u003e \u003cp\u003e14.2.2 Design Technique by Quality Engineering 239\u003c\/p\u003e \u003cp\u003e14.3 System Design Technique 241\u003c\/p\u003e \u003cp\u003e14.3.1 Parameter Design Diagram 241\u003c\/p\u003e \u003cp\u003e14.3.2 Signal Factor, Control Factor, and Noise Factor 242\u003c\/p\u003e \u003cp\u003e14.3.3 Implementation of Parameter Design 244\u003c\/p\u003e \u003cp\u003e14.3.4 Results of the Confirmation Experiment 244\u003c\/p\u003e \u003cp\u003e14.4 Effect by Shortening of Development Period 246\u003c\/p\u003e \u003cp\u003e14.5 Conclusion 246\u003c\/p\u003e \u003cp\u003e14.5.1 Acknowledgments 247\u003c\/p\u003e \u003cp\u003e14.6 References 247\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Direct Injection Diesel Injector Optimization 249\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eDelphi Automotive Systems, Europe and Delphi Automotive Systems, USA\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Executive Summary 249\u003c\/p\u003e \u003cp\u003e15.2 Introduction 250\u003c\/p\u003e \u003cp\u003e15.2.1 Background 250\u003c\/p\u003e \u003cp\u003e15.2.2 Problem Statement 250\u003c\/p\u003e \u003cp\u003e15.2.3 Objectives and Approach to Optimization 251\u003c\/p\u003e \u003cp\u003e15.3 Simulation Model Robustness 253\u003c\/p\u003e \u003cp\u003e15.3.1 Background 253\u003c\/p\u003e \u003cp\u003e15.3.2 Approach to Optimization 257\u003c\/p\u003e \u003cp\u003e15.3.3 Results 257\u003c\/p\u003e \u003cp\u003e15.4 Parameter Design 257\u003c\/p\u003e \u003cp\u003e15.4.1 Ideal Function 257\u003c\/p\u003e \u003cp\u003e15.4.2 Signal and Noise Strategies 258\u003c\/p\u003e \u003cp\u003e15.4.2.1 Signal Levels 258\u003c\/p\u003e \u003cp\u003e15.4.2.2 Noise Strategy 258\u003c\/p\u003e \u003cp\u003e15.4.3 Control Factors and Levels 259\u003c\/p\u003e \u003cp\u003e15.4.4 Experimental Layout 259\u003c\/p\u003e \u003cp\u003e15.4.5 Data Analysis and Two-Step Optimization 259\u003c\/p\u003e \u003cp\u003e15.4.6 Confirmation 263\u003c\/p\u003e \u003cp\u003e15.4.7 Discussions on Parameter Design Results 264\u003c\/p\u003e \u003cp\u003e15.4.7.1 Technical 264\u003c\/p\u003e \u003cp\u003e15.4.7.2 Economical 264\u003c\/p\u003e \u003cp\u003e15.5 Tolerance Design 268\u003c\/p\u003e \u003cp\u003e15.5.1 Signal Point by Signal Point Tolerance Design 269\u003c\/p\u003e \u003cp\u003e15.5.1.1 Factors and Experimental Layout 269\u003c\/p\u003e \u003cp\u003e15.5.1.2 Analysis of Variance (ANOVA) for Each Injection Point 269\u003c\/p\u003e \u003cp\u003e15.5.1.3 Loss Function 269\u003c\/p\u003e \u003cp\u003e15.5.2 Dynamic Tolerance Design 270\u003c\/p\u003e \u003cp\u003e15.5.2.1 Dynamic Analysis of Variance 271\u003c\/p\u003e \u003cp\u003e15.5.2.2 Dynamic Loss Function 273\u003c\/p\u003e \u003cp\u003e15.6 Conclusions 275\u003c\/p\u003e \u003cp\u003e15.6.1 Project Related 275\u003c\/p\u003e \u003cp\u003e15.6.2 Recommendations for Taguchi Methods 277\u003c\/p\u003e \u003cp\u003e15.6.3 Acknowledgments 278\u003c\/p\u003e \u003cp\u003e15.7 Reference and Further Reading 278\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 General Purpose Actuator Robust Assessment and Benchmark Study 279\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eRobert Bosch, LLC, USA\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Executive Summary 279\u003c\/p\u003e \u003cp\u003e16.2 Introduction 280\u003c\/p\u003e \u003cp\u003e16.3 Objectives 280\u003c\/p\u003e \u003cp\u003e16.3.1 Robust Assessment Measurement Method 281\u003c\/p\u003e \u003cp\u003e16.3.1.1 Test Equipment 281\u003c\/p\u003e \u003cp\u003e16.3.1.2 Data Acquisition 284\u003c\/p\u003e \u003cp\u003e16.3.1.3 Data Analysis Strategy 285\u003c\/p\u003e \u003cp\u003e16.4 Robust Assessment 286\u003c\/p\u003e \u003cp\u003e16.4.1 Scope and P-Diagram 286\u003c\/p\u003e \u003cp\u003e16.4.2 Ideal Function 286\u003c\/p\u003e \u003cp\u003e16.4.3 Signal and Noise Strategy 290\u003c\/p\u003e \u003cp\u003e16.4.4 Control Factors 291\u003c\/p\u003e \u003cp\u003e16.4.5 Raw Data 291\u003c\/p\u003e \u003cp\u003e16.4.6 Data Analysis 291\u003c\/p\u003e \u003cp\u003e16.5 Conclusion 296\u003c\/p\u003e \u003cp\u003e16.5.1 Acknowledgments 297\u003c\/p\u003e \u003cp\u003e16.6 Further Reading 297\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Optimization of a Discrete Floating MOS Gate Driver 299\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eDelphi-Delco Electronic Systems, USA\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Executive Summary 299\u003c\/p\u003e \u003cp\u003e17.2 Background 300\u003c\/p\u003e \u003cp\u003e17.3 Introduction 302\u003c\/p\u003e \u003cp\u003e17.4 Developing the “Ideal” Function 302\u003c\/p\u003e \u003cp\u003e17.5 Noise Strategy 305\u003c\/p\u003e \u003cp\u003e17.6 Control Factors and Levels 305\u003c\/p\u003e \u003cp\u003e17.7 Experiment Strategy and Measurement System 306\u003c\/p\u003e \u003cp\u003e17.8 Parameter Design Experiment Layout 306\u003c\/p\u003e \u003cp\u003e17.9 Results 307\u003c\/p\u003e \u003cp\u003e17.10 Response Charts 307\u003c\/p\u003e \u003cp\u003e17.11 Two-Step Optimization 311\u003c\/p\u003e \u003cp\u003e17.12 Confirmation 312\u003c\/p\u003e \u003cp\u003e17.13 Conclusions 312\u003c\/p\u003e \u003cp\u003e17.13.1 Acknowledgments 314\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Reformer Washcoat Adhesion on Metallic Substrates 315\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eDelphi Automotive Systems, USA\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Executive Summary 315\u003c\/p\u003e \u003cp\u003e18.2 Introduction 316\u003c\/p\u003e \u003cp\u003e18.3 Experimental Setup 317\u003c\/p\u003e \u003cp\u003e18.3.1 The Ideal Function 318\u003c\/p\u003e \u003cp\u003e18.3.2 P-Diagram 318\u003c\/p\u003e \u003cp\u003e18.3.3 Control Factors 319\u003c\/p\u003e \u003cp\u003e18.3.3.1 Alloy Composition 319\u003c\/p\u003e \u003cp\u003e18.3.3.2 Washcoat Composition 320\u003c\/p\u003e \u003cp\u003e18.3.3.3 Slurry Parameters 320\u003c\/p\u003e \u003cp\u003e18.3.3.4 Cleaning Procedures 320\u003c\/p\u003e \u003cp\u003e18.3.3.5 Preparation 320\u003c\/p\u003e \u003cp\u003e18.4 Control Factor Levels 320\u003c\/p\u003e \u003cp\u003e18.5 Noise Factors 320\u003c\/p\u003e \u003cp\u003e18.5.1 Signal Factor 320\u003c\/p\u003e \u003cp\u003e18.5.2 Unwanted Outputs 320\u003c\/p\u003e \u003cp\u003e18.6 Description of Experiment 322\u003c\/p\u003e \u003cp\u003e18.6.1 Furnace 322\u003c\/p\u003e \u003cp\u003e18.6.2 Orthogonal Array and Inner Array 323\u003c\/p\u003e \u003cp\u003e18.6.3 Signal-to-Noise and Beta Calculations 323\u003c\/p\u003e \u003cp\u003e18.6.4 Response Tables 323\u003c\/p\u003e \u003cp\u003e18.7 Two Step Optimization and Prediction 323\u003c\/p\u003e \u003cp\u003e18.7.1 Optimum Design 329\u003c\/p\u003e \u003cp\u003e18.7.2 Predictions 329\u003c\/p\u003e \u003cp\u003e18.8 Confirmation 329\u003c\/p\u003e \u003cp\u003e18.8.1 Design Improvement 329\u003c\/p\u003e \u003cp\u003e18.9 Measurement System Evaluation 334\u003c\/p\u003e \u003cp\u003e18.10 Conclusion 334\u003c\/p\u003e \u003cp\u003e18.11 Supplemental Background Information 336\u003c\/p\u003e \u003cp\u003e18.12 Acknowledgment 340\u003c\/p\u003e \u003cp\u003e18.13 Reference and Further Reading 340\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Making Better Decisions Faster: Sequential Application of Robust Engineering to Math-Models, CAE Simulations, and Accelerated Testing 341\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eRobert Bosch Corporation, USA\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Executive Summary 341\u003c\/p\u003e \u003cp\u003e19.2 Introduction 342\u003c\/p\u003e \u003cp\u003e19.2.1 Thermal Equivalent Circuit – Detailed 343\u003c\/p\u003e \u003cp\u003e19.2.2 Thermal Equivalent Circuit – Simplified 343\u003c\/p\u003e \u003cp\u003e19.2.3 Closed Form Solution 343\u003c\/p\u003e \u003cp\u003e19.3 Objective 345\u003c\/p\u003e \u003cp\u003e19.3.1 Thermal Robustness Design Template 345\u003c\/p\u003e \u003cp\u003e19.3.2 Critical Design Parameters for Thermal Robustness 345\u003c\/p\u003e \u003cp\u003e19.3.3 Cascade Learning (aka Leveraged Knowledge) 346\u003c\/p\u003e \u003cp\u003e19.3.4 Test Taguchi Robust Engineering Methodology 346\u003c\/p\u003e \u003cp\u003e19.4 Robust Optimization 347\u003c\/p\u003e \u003cp\u003e19.4.1 Scope and P-Diagram 347\u003c\/p\u003e \u003cp\u003e19.4.2 Ideal Function 347\u003c\/p\u003e \u003cp\u003e19.4.3 Signal and Noise Strategy 349\u003c\/p\u003e \u003cp\u003e19.4.4 Input Signal 350\u003c\/p\u003e \u003cp\u003e19.4.5 Control Factors and Levels 350\u003c\/p\u003e \u003cp\u003e19.4.6 Math-Model Generated Data 351\u003c\/p\u003e \u003cp\u003e19.4.7 Data Analysis 351\u003c\/p\u003e \u003cp\u003e19.4.8 Thermal Robustness (Signal-to-Noise) 354\u003c\/p\u003e \u003cp\u003e19.4.9 Subsystem Thermal Resistance (Beta) 356\u003c\/p\u003e \u003cp\u003e19.4.10 Prediction and Confirmation 357\u003c\/p\u003e \u003cp\u003e19.4.11 Verification 362\u003c\/p\u003e \u003cp\u003e19.5 Conclusions 364\u003c\/p\u003e \u003cp\u003e19.5.1 Acknowledgments 365\u003c\/p\u003e \u003cp\u003e19.6 Futher Reading 366\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Pressure Switch Module Normally Open Feasibility Investigation and Supplier Competition 367\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eRobert Bosch, LLC, USA\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e20.1 Executive Summary 367\u003c\/p\u003e \u003cp\u003e20.2 Introduction 368\u003c\/p\u003e \u003cp\u003e20.2.1 Current Production Pressure Switch Module – Detailed 368\u003c\/p\u003e \u003cp\u003e20.2.2 Current Production (N.C.) Switching Element – Detailed 369\u003c\/p\u003e \u003cp\u003e20.3 Objective 370\u003c\/p\u003e \u003cp\u003e20.4 Robust Assessment 370\u003c\/p\u003e \u003cp\u003e20.4.1 Scope and P-Diagram 370\u003c\/p\u003e \u003cp\u003e20.4.2 Ideal Function 371\u003c\/p\u003e \u003cp\u003e20.4.3 Noise Strategy 372\u003c\/p\u003e \u003cp\u003e20.4.4 Testing Criteria 372\u003c\/p\u003e \u003cp\u003e20.4.5 Control Factors and Levels 373\u003c\/p\u003e \u003cp\u003e20.4.6 Test Data 374\u003c\/p\u003e \u003cp\u003e20.4.7 Data Analysis 375\u003c\/p\u003e \u003cp\u003e20.4.8 Prediction and Confirmation 379\u003c\/p\u003e \u003cp\u003e20.4.9 Verification 383\u003c\/p\u003e \u003cp\u003e20.5 Summary and Conclusions 383\u003c\/p\u003e \u003cp\u003e20.5.1 Acknowledgments 385\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART FOUR MANUFACTURING PROCESS OPTIMIZATION 387\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Robust Optimization of a Lead-Free Reflow Soldering Process 389\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eDelphi Delco Electronics Systems, USA and ASI Consulting Group, LLC, USA\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e21.1 Executive Summary 389\u003c\/p\u003e \u003cp\u003e21.2 Introduction 390\u003c\/p\u003e \u003cp\u003e21.3 Experimental 391\u003c\/p\u003e \u003cp\u003e21.3.1 Robust Engineering Methodology 391\u003c\/p\u003e \u003cp\u003e21.3.2 Visual Scoring 394\u003c\/p\u003e \u003cp\u003e21.3.3 Pull Test 396\u003c\/p\u003e \u003cp\u003e21.4 Results and Discussion 396\u003c\/p\u003e \u003cp\u003e21.4.1 Visual Scoring Results 396\u003c\/p\u003e \u003cp\u003e21.4.2 Pull Test Results 400\u003c\/p\u003e \u003cp\u003e21.4.3 Next Steps 401\u003c\/p\u003e \u003cp\u003e21.5 Conclusion 401\u003c\/p\u003e \u003cp\u003e21.5.1 Acknowledgment 402\u003c\/p\u003e \u003cp\u003e21.6 References 402\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 Catalyst Slurry Coating Process Optimization for Diesel Catalyzed Particulate Traps 403\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eDelphi Energy and Chassis Systems, USA\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e22.1 Executive Summary 403\u003c\/p\u003e \u003cp\u003e22.2 Introduction 404\u003c\/p\u003e \u003cp\u003e22.3 Project Description 405\u003c\/p\u003e \u003cp\u003e22.4 Process Map 406\u003c\/p\u003e \u003cp\u003e22.4.1 Initial Performance 406\u003c\/p\u003e \u003cp\u003e22.5 First Parameter Design Experiment 406\u003c\/p\u003e \u003cp\u003e22.5.1 Function Analysis 407\u003c\/p\u003e \u003cp\u003e22.5.2 Ideal Function 409\u003c\/p\u003e \u003cp\u003e22.5.3 Measurement System Evaluation 409\u003c\/p\u003e \u003cp\u003e22.5.4 Parameter Diagram 411\u003c\/p\u003e \u003cp\u003e22.5.5 Factors and Levels 411\u003c\/p\u003e \u003cp\u003e22.5.6 Compound Noise Strategy 412\u003c\/p\u003e \u003cp\u003e22.5.7 Parameter Design Experiment Layout (1) 412\u003c\/p\u003e \u003cp\u003e22.5.8 Means Plots 414\u003c\/p\u003e \u003cp\u003e22.5.9 Means Tables 414\u003c\/p\u003e \u003cp\u003e22.5.10 Two-Step Optimization and Prediction 415\u003c\/p\u003e \u003cp\u003e22.5.11 Predicted Performance Improvement Before and After 416\u003c\/p\u003e \u003cp\u003e22.6 Follow-up Parameter Design Experiment 416\u003c\/p\u003e \u003cp\u003e22.6.1 Parameter Design Experiment Layout (2) 417\u003c\/p\u003e \u003cp\u003e22.6.2 Means Plots for Signal-to-Noise Ratios 417\u003c\/p\u003e \u003cp\u003e22.6.3 Confirmation Results in Tulsa 417\u003c\/p\u003e \u003cp\u003e22.6.4 Noise Factor Q Affect on Slurry Coating 417\u003c\/p\u003e \u003cp\u003e22.7 Transfer to Florange 419\u003c\/p\u003e \u003cp\u003e22.7.1 Ideal Function and Parameter Diagram 421\u003c\/p\u003e \u003cp\u003e22.7.2 Parameter Design Experiment Layout (3) 421\u003c\/p\u003e \u003cp\u003e22.7.3 Means Plots for Signal-to-Noise Ratios 423\u003c\/p\u003e \u003cp\u003e22.7.4 Prediction and Confirmation 423\u003c\/p\u003e \u003cp\u003e22.7.5 Process Capability 423\u003c\/p\u003e \u003cp\u003e22.8 Conclusion 424\u003c\/p\u003e \u003cp\u003e22.8.1 The Team 424\u003c\/p\u003e \u003cp\u003eIndex 427\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49528849695063,"sku":"9781119212126","price":38.95,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119212126.jpg?v=1731873268","url":"https:\/\/bookcurl.com\/products\/robust-optimization-9781119212126","provider":"Book Curl","version":"1.0","type":"link"}