Actions for Robust optimization : world's best practices for developing winning vehicles

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Robust optimization : world's best practices for developing winning vehicles / Subir Chowdhury, Shin Taguchi

Author
Chowdhury, Subir
Published
Chichester, West Sussex, United Kingdom : John Wiley & Sons, Inc., [2016]
Physical Description
1 online resource
Additional Creators
Taguchi, Shin
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Contents
Machine generated contents note: 1.Introduction to Robust Optimization -- 1.1.What Is Quality as Loss? -- 1.2.What Is Robustness? -- 1.3.What Is Robust Assessment? -- 1.4.What Is Robust Optimization? -- 1.4.1.Noise Factors -- 1.4.2.Parameter Design -- 1.4.3.Tolerance Design -- 2.Eight Steps for Robust Optimization and Robust Assessment -- 2.1.Before Eight Steps: Select Project Area -- 2.2.Eight Steps for Robust Optimization -- 2.2.1.Step 1: Define Scope for Robust Optimization -- 2.2.2.Step 2: Identify Ideal Function/Response -- 2.2.2.1.Ideal Function: Dynamic Response -- 2.2.2.2.Nondynamic Responses -- 2.2.3.Step 3: Develop Signal and Noise Strategies -- 2.2.3.1.How Input M is Varied to Benchmark "Robustness" -- 2.2.3.2.How Noise Factors Are Varied to Benchmark "Robustness" -- 2.2.4.Step 4: Select Control Factors and Levels -- 2.2.4.1.Traditional Approach to Explore Control Factors -- 2.2.4.2.Exploration of Design Space by Orthogonal Array -- 2.2.4.3.Try to Avoid Strong Interactions between Control Factors -- 2.2.4.4.Orthogonal Array and its Mechanics -- 2.2.5.Step 5: Execute and Collect Data -- 2.2.6.Step 6: Conduct Data Analysis -- 2.2.6.1.Computations of S/N and β -- 2.2.6.2.Computation of S/N and β for L18 Data Sets -- 2.2.6.3.Response Table for S/N and β -- 2.2.6.4.Determination of Optimum Design -- 2.2.7.Step 7: Predict and Confirm -- 2.2.7.1.Confirmation -- 2.2.8.Step 8: Lesson Learned and Action Plan -- 2.3.Eight Steps for Robust Assessment -- 2.3.1.Step 1: Define Scope -- 2.3.2.Step 2: Identify Ideal Function/Response and Step 3: Develop Signal and Noise Strategies -- 2.3.3.Step 4: Select Designs for Assessment -- 2.3.4.Step 5: Execute and Collect Data -- 2.3.5.Step 6: Conduct Data Analysis -- 2.3.6.Step 7: Make Judgments -- 2.3.7.Step 8: Lesson Learned and Action Plan -- 2.4.As You Go through Case Studies in This Book -- 3.Implementation of Robust Optimization -- 3.1.Introduction -- 3.2.Robust Optimization Implementation -- 3.2.1.Leadership Commitment -- 3.2.2.Executive Leader and the Corporate Team -- 3.2.3.Effective Communication -- 3.2.4.Education and Training -- 3.2.5.Integration Strategy -- 3.2.6.Bottom Line Performance -- 4.Optimization of Vehicle Offset Crashworthy Design Using a Simplified Analysis Model / Chrysler -- 4.1.Executive Summary -- 4.2.Introduction -- 4.3.Stepwise Implementation of DFSS Optimization for Vehicle Offset Impact -- 4.3.1.Step 1: Scope Defined for Optimization -- 4.3.2.Step 2: Identify/Select Design Alternatives -- 4.3.3.Step 3: Identify Ideal Function -- 4.3.4.Step 4: Develop Signal and Noise Strategy -- 4.3.4.1.Input and Output Signal Strategy -- 4.3.5.Step 5: Select Control/Noise Factors and Levels -- 4.3.5.1.Simplified Spring Mass Model Creation and Validation -- 4.3.5.2.Control Variable Selection -- 4.3.5.3.Control Factor Level Application for Spring Stiffness Updates -- 4.3.6.Step 6: Execute and Conduct Data Analysis -- 4.3.7.Step 7: Validation of Optimized Model -- 4.4.Conclusion -- 4.4.1.Acknowledgments -- 4.5.References -- 5.Optimization of the Component Characteristics for Improving Collision Safety by Simulation / Isuzu Advanced Engineering Center -- 5.1.Executive Summary -- 5.2.Introduction -- 5.3.Simulation Models -- 5.4.Concept of Standardized S/N Ratios with Respect to Survival Space -- 5.5.Results and Consideration -- 5.6.Conclusion -- 5.6.1.Acknowledgment -- 5.7.Reference -- 6.Optimization of Small DC Motors Using Functionality for Evaluation / Jidosha Denki Kogyo Co. -- 6.1.Executive Summary -- 6.2.Introduction -- 6.3.Functionality for Evaluation in Case of DC Motors -- 6.4.Experiment Method and Measurement Data -- 6.5.Factors and Levels -- 6.6.Data Analysis -- 6.7.Analysis Results -- 6.8.Selection of Optimal Design and Confirmation -- 6.9.Benefits Gained -- 6.10.Consideration of Analysis for Audible Noise -- 6.11.Conclusion -- 6.11.1.The Importance of Functionality for Evaluation -- 6.11.2.Evaluation under the Unloaded (Idling) Condition -- 6.11.3.Evaluation of Audible Noise (Quality Characteristic) -- 6.11.4.Acknowledgment -- 7.Optimal Design for a Double-Lift Window Regulator System Used in Automobiles / Ohi Seisakusho Co -- 7.1.Executive Summary -- 7.2.Introduction -- 7.3.Schematic Figure of Double-Lift Window Regulator System -- 7.4.Ideal Function -- 7.5.Noise Factors -- 7.6.Control Factors -- 7.7.Conventional Data Analysis and Results -- 7.8.Selection of Optimal Condition and Confirmation Test Results -- 7.9.Evaluation of Quality Characteristics -- 7.10.Concept of Analysis Based on Standardized S/N Ratio -- 7.11.Analysis Results Based on Standardized S/N Ratio -- 7.12.Comparison between Analysis Based on Standardized S/N Ratio and Analysis Based on Conventional S/N Ratio -- 7.13.Conclusion -- 7.13.1.Acknowledgments -- 7.14.Further Reading -- 8.Optimization of Next-Generation Steering System Using Computer Simulation / Nissan Motor Co -- 8.1.Executive Summary -- 8.2.Introduction -- 8.3.System Description -- 8.4.Measurement Data -- 8.5.Ideal Function -- 8.6.Factors and Levels -- 8.6.1.Signal and Response -- 8.6.2.Noise Factors -- 8.6.3.Indicative Factor -- 8.6.4.Control Factors -- 8.7.Pre-analysis for Compounding the Noise Factors -- 8.8.Calculation of Standardized S/N Ratio -- 8.9.Analysis Results -- 8.10.Determination of Optimal Design and Confirmation -- 8.11.Tuning to the Targeted Value -- 8.12.Conclusion -- 8.12.1.Acknowledgment -- 9.Future Truck Steering Effort Robustness / General Motors Corporation -- 9.1.Executive Summary -- 9.2.Background -- 9.2.1.Methodology -- 9.2.2.Hydraulic Power-Steering Assist System -- 9.2.3.Valve Assembly Design -- 9.2.4.Project Scope -- 9.3.Parameter Design -- 9.3.1.Ideal Steering Effort Function -- 9.3.2.Control Factors -- 9.3.3.Noise Compounding Strategy and Input Signals -- 9.3.4.Standardized S/N Post-Processing -- 9.3.5.Quality Loss Function -- 9.4.Acknowledgments -- 9.5.References -- 10.Optimal Design of Engine Mounting System Based on Quality Engineering / Mazda Motor Corporation -- 10.1.Executive Summary -- 10.2.Background -- 10.3.Design Object -- 10.4.Application of Standard S/N Ratio Taguchi Method -- 10.5.Iterative Application of Standard S/N Ratio Taguchi Method -- 10.6.Influence of Interval of Factor Level -- 10.7.Calculation Program -- 10.8.Conclusions -- 10.8.1.Acknowledgments -- 10.9.References -- 11.Optimization of a Front-Wheel-Drive Transmission for Improved Efficiency and Robustness / Consulting Group -- 11.1.Executive Summary -- 11.2.Introduction -- 11.3.Experimental -- 11.3.1.Ideal Function and Measurement -- 11.4.Signal Strategy -- 11.5.Noise Strategy -- 11.6.Control Factor Selection -- 11.7.Orthogonal Array Selection -- 11.8.Results and Discussion -- 11.8.1.S/N Calculations -- 11.8.2.Graphs of Runs -- 11.8.3.Response Plots -- 11.8.4.Confirmation Run -- 11.8.5.Verification of Results -- 11.9.Conclusion -- 11.9.1.Acknowledgments -- 11.10.References -- 12.Fuel Delivery System Robustness / Ford Motor Company -- 12.1.Executive Summary -- 12.2.Introduction -- 12.2.1.Fuel System Overview -- 12.2.2.Conventional Fuel System -- 12.2.3.New Fuel System -- 12.3.Experiment Description -- 12.3.1.Test Method -- 12.3.2.Ideal Function -- 12.4.Noise Factors -- 12.4.1.Control Factors -- 12.4.2.Fixed Factors -- 12.5.Experiment Test Results -- 12.6.Sensitivity (β) Analysis -- 12.7.Confirmation Test Results -- 12.7.1.Bench Test Confirmation -- 12.7.1.1.Initial Fuel Delivery System -- 12.7.1.2.Optimal Fuel Delivery System -- 12.7.2.Vehicle Verification -- 12.7.2.1.Initial Fuel Delivery System -- 12.7.2.2.Optimal Fuel Delivery System -- 12.8.Conclusion -- 13.Improving Coupling Factor in Vehicle Theft Deterrent Systems Using Design for Six Sigma (DFSS) / General Motors Corporation -- 13.1.Executive Summary -- 13.2.Introduction -- 13.3.Objectives -- 13.4.The Voice of the Customer -- 13.5.Experimental Strategy -- 13.5.1.Response -- 13.5.2.Noise Strategy -- 13.5.3.Control Factors -- 13.5.4.Input Signal -- 13.6.The System -- 13.7.The Experimental Results -- 13.8.Conclusions -- 13.8.1.Summary -- 13.8.2.Acknowledgments -- 14.Magnetic Sensing System Optimization / ALPS Electric -- 14.1.Executive Summary -- 14.1.1.The Magnetic Sensing System -- 14.2.Improvement of Design Technique -- 14.2.1.Traditional Design Technique -- 14.2.2.Design Technique by Quality Engineering -- 14.3.System Design Technique -- 14.3.1.Parameter Design Diagram -- 14.3.2.Signal Factor, Control Factor, and Noise Factor -- 14.3.3.Implementation of Parameter Design -- 14.3.4.Results of the Confirmation Experiment -- 14.4.Effect by Shortening of Development Period -- 14.5.Conclusion -- 14.5.1.Acknowledgments -- 14.6.References -- 15.Direct Injection Diesel Injector Optimization / Delphi Automotive Systems -- 15.1.Executive Summary -- 15.2.Introduction -- 15.2.1.Background -- 15.2.2.Problem Statement -- 15.2.3.Objectives and Approach to Optimization -- 15.3.Simulation Model Robustness -- 15.3.1.Background -- 15.3.2.Approach to Optimization -- 15.3.3.Results -- 15.4.Parameter Design -- 15.4.1.Ideal Function -- 15.4.2.Signal and Noise Strategies -- 15.4.2.1.Signal Levels -- 15.4.2.2.Noise Strategy -- 15.4.3.Control Factors and Levels -- 15.4.4.Experimental Layout -- 15.4.5.Data Analysis and Two-Step Optimization -- 15.4.6.Confirmation -- 15.4.7.Discussions on Parameter Design Results -- 15.4.7.1.Technical -- 15.4.7.2.Economical -- 15.5.Tolerance Design -- 15.5.1.Signal Point by Signal Point Tolerance Design -- 15.5.1.1.Factors and Experimental Layout -- 15.5.1.2.Analysis of Variance (ANOVA) for Each Injection Point -- 15.5.1.3.Loss Function -- 15.5.2.Dynamic Tolerance Design -- 15.5.2.1.Dynamic Analysis of Variance -- 15.5.2.2.Dynamic Loss Function -- 15.6.Conclusions -- 15.6.1.Project Related -- 15.6.2.Recommendations for Taguchi Methods -- 15.6.3.Acknowledgments -- 15.7.Reference and Further Reading -- 16.General Purpose Actuator Robust Assessment and Benchmark Study / Robert Bosch -- 16.1.Executive Summary -- and Contents note continued: 16.2.Introduction -- 16.3.Objectives -- 16.3.1.Robust Assessment Measurement Method -- 16.3.1.1.Test Equipment -- 16.3.1.2.Data Acquisition -- 16.3.1.3.Data Analysis Strategy -- 16.4.Robust Assessment -- 16.4.1.Scope and P-Diagram -- 16.4.2.Ideal Function -- 16.4.3.Signal and Noise Strategy -- 16.4.4.Control Factors -- 16.4.5.Raw Data -- 16.4.6.Data Analysis -- 16.5.Conclusion -- 16.5.1.Acknowledgments -- 16.6.Further Reading -- 17.Optimization of a Discrete Floating MOS Gate Driver / Delphi-Delco Electronic Systems -- 17.1.Executive Summary -- 17.2.Background -- 17.3.Introduction -- 17.4.Developing the "Ideal" Function -- 17.5.Noise Strategy -- 17.6.Control Factors and Levels -- 17.7.Experiment Strategy and Measurement System -- 17.8.Parameter Design Experiment Layout -- 17.9.Results -- 17.10.Response Charts -- 17.11.Two-Step Optimization -- 17.12.Confirmation -- 17.13.Conclusions -- 17.13.1.Acknowledgments -- 18.Reformer Washcoat Adhesion on Metallic Substrates / Delphi Automotive Systems -- 18.1.Executive Summary -- 18.2.Introduction -- 18.3.Experimental Setup -- 18.3.1.The Ideal Function -- 18.3.2.P-Diagram -- 18.3.3.Control Factors -- 18.3.3.1.Alloy Composition -- 18.3.3.2.Washcoat Composition -- 18.3.3.3.Slurry Parameters -- 18.3.3.4.Cleaning Procedures -- 18.3.3.5.Preparation -- 18.4.Control Factor Levels -- 18.5.Noise Factors -- 18.5.1.Signal Factor -- 18.5.2.Unwanted Outputs -- 18.6.Description of Experiment -- 18.6.1.Furnace -- 18.6.2.Orthogonal Array and Inner Array -- 18.6.3.Signal-to-Noise and Beta Calculations -- 18.6.4.Response Tables -- 18.7.Two Step Optimization and Prediction -- 18.7.1.Optimum Design -- 18.7.2.Predictions -- 18.8.Confirmation -- 18.8.1.Design Improvement -- 18.9.Measurement System Evaluation -- 18.10.Conclusion -- 18.11.Supplemental Background Information -- 18.12.Acknowledgment -- 18.13.Reference and Further Reading -- 19.Making Better Decisions Faster: Sequential Application of Robust Engineering to Math-Models, CAE Simulations, and Accelerated Testing / Robert Bosch Corporation -- 19.1.Executive Summary -- 19.2.Introduction -- 19.2.1.Thermal Equivalent Circuit [—] Detailed -- 19.2.2.Thermal Equivalent Circuit [—] Simplified -- 19.2.3.Closed Form Solution -- 19.3.Objective -- 19.3.1.Thermal Robustness Design Template -- 19.3.2.Critical Design Parameters for Thermal Robustness -- 19.3.3.Cascade Learning (aka Leveraged Knowledge) -- 19.3.4.Test Taguchi Robust Engineering Methodology -- 19.4.Robust Optimization -- 19.4.1.Scope and P-Diagram -- 19.4.2.Ideal Function -- 19.4.3.Signal and Noise Strategy -- 19.4.4.Input Signal -- 19.4.5.Control Factors and Levels -- 19.4.6.Math-Model Generated Data -- 19.4.7.Data Analysis -- 19.4.8.Thermal Robustness (Signal-to-Noise) -- 19.4.9.Subsystem Thermal Resistance (Beta) -- 19.4.10.Prediction and Confirmation -- 19.4.11.Verification -- 19.5.Conclusions -- 19.5.1.Acknowledgments -- 19.6.Further Reading -- 20.Pressure Switch Module Normally Open Feasibility Investigation and Supplier Competition / Robert Bosch -- 20.1.Executive Summary -- 20.2.Introduction -- 20.2.1.Current Production Pressure Switch Module [—] Detailed -- 20.2.2.Current Production (N.C.) Switching Element [—] Detailed -- 20.3.Objective -- 20.4.Robust Assessment -- 20.4.1.Scope and P-Diagram -- 20.4.2.Ideal Function -- 20.4.3.Noise Strategy -- 20.4.4.Testing Criteria -- 20.4.5.Control Factors and Levels -- 20.4.6.Test Data -- 20.4.7.Data Analysis -- 20.4.8.Prediction and Confirmation -- 20.4.9.Verification -- 20.5.Summary and Conclusions -- 20.5.1.Acknowledgments -- 21.Robust Optimization of a Lead-Free Reflow Soldering Process / ASI Consulting Group -- 21.1.Executive Summary -- 21.2.Introduction -- 21.3.Experimental -- 21.3.1.Robust Engineering Methodology -- 21.3.2.Visual Scoring -- 21.3.3.Pull Test -- 21.4.Results and Discussion -- 21.4.1.Visual Scoring Results -- 21.4.2.Pull Test Results -- 21.4.3.Next Steps -- 21.5.Conclusion -- 21.5.1.Acknowledgment -- 21.6.References -- 22.Catalyst Slurry Coating Process Optimization for Diesel Catalyzed Particulate Traps / Delphi Energy and Chassis Systems -- 22.1.Executive Summary -- 22.2.Introduction -- 22.3.Project Description -- 22.4.Process Map -- 22.4.1.Initial Performance -- 22.5.First Parameter Design Experiment -- 22.5.1.Function Analysis -- 22.5.2.Ideal Function -- 22.5.3.Measurement System Evaluation -- 22.5.4.Parameter Diagram -- 22.5.5.Factors and Levels -- 22.5.6.Compound Noise Strategy -- 22.5.7.Parameter Design Experiment Layout (1) -- 22.5.8.Means Plots -- 22.5.9.Means Tables -- 22.5.10.Two-Step Optimization and Prediction -- 22.5.11.Predicted Performance Improvement Before and After -- 22.6.Follow-up Parameter Design Experiment -- 22.6.1.Parameter Design Experiment Layout (2) -- 22.6.2.Means Plots for Signal-to-Noise Ratios -- 22.6.3.Confirmation Results in Tulsa -- 22.6.4.Noise Factor Q Affect on Slurry Coating -- 22.7.Transfer to Florange -- 22.7.1.Ideal Function and Parameter Diagram -- 22.7.2.Parameter Design Experiment Layout (3) -- 22.7.3.Means Plots for Signal-to-Noise Ratios -- 22.7.4.Prediction and Confirmation -- 22.7.5.Process Capability -- 22.8.Conclusion -- 22.8.1.The Team.
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ISBN
1119212081 (Adobe PDF)
111921209X electronic bk.
1119212146 (ePub)
9781119212089 (Adobe PDF)
9781119212096 electronic bk.
9781119212140 (ePub)
9781119212126 (cloth)
Bibliography Note
Includes bibliographical references and index.
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