Business Reliability Growth for Automotive Engineering, Volume IV

• Record Nr.: UNINA9911070617903321
• Autore: Chiang Young J
• Pubbl/distr/stampa: Warrendale : , : SAE International, , 2026; ©2025
• ISBN: 1-4686-0771-5; 1-4686-0768-5
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (348 pages)
• Collana: Design of Experiments for Product Reliability Growth with Automotive Applications Series ; ; v.4
• Disciplina: 629.2
• Soggetti: Automobiles; Statistical methods
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Front Cover -- Half Title -- Title Page -- Copyright Page -- From the Publisher -- Table of Contents -- Preface -- Acronyms -- Nomenclature -- Chapter 16: Holistic Reliability -- 16.1 Systemic Approach to Reliability -- 16.2 Split-Plot Design -- 16.2.1 How to Generate a Split-Plot Design -- 16.2.2 Analysis of Variance (ANOVA) for Two-Factor Split-Plot Design -- 16.2.3 Analysis of Variance (ANOVA) for Split-Split-Plot Design -- 16.2.4 Split-Plot Response Surface -- 16.2.5 D-Optimal Split-Plot Designs -- 16.2.6 Fractional Factorial Design (2 K-P × 2 U-V) R -- 16.3 Strip-Plot Design -- 16.3.1 How to Build a Strip-Plot Design -- 16.3.2 Why Strip-Plot Design -- 16.3.3 Analysis of Variance (ANOVA) for a Strip-Plot Design -- 16.3.4 Strip Design Example-Open-Circuit Voltage (OCV) of Battery -- 16.3.5 Fractional Factorial Design of Two Levels in Uncoupled Strata -- 16.3.6 Fractional Factorial Design of Two Levels in Coupled Strata -- 16.4 Minimum Aberration Fractional Factorial Split-Plot Design -- 16.4.1 Simplified Analysis by Engineering Significance -- 16.4.2 Statistical Significance by Analysis of Variance (ANOVA) -- 16.5 Crossed-Nested Factorial Design with Two Fixed and One Random Variable -- 16.5.1 Fixed-Effect Factor and Random Effect Factor -- 16.5.2 Staggered-Nested Design with Three Factors -- 16.5.3 Crossed-Nested Design with Three Random Factors -- 16.5.4 Further Explanation of ; Staggered-Nested Design with Fixed Factors A and B -- 16.6 System Reliability by Mixed Weibull Statistics -- 16.6.1 Finite Mixed Weibull Distribution with Alternative Failures -- 16.6.2 Product Reliability with Components in Series -- 16.6.3 Product Reliability with Components in Parallel -- 16.6.4 Product Reliability with Series-Parallel Subsystems -- 16.6.5 Approximating Normal and Lognormal Distributions by Two-Parameter Weibull Statistics.; 16.7 Adaptive DOEs by Cross-Validation -- 16.7.1 Error Messages for Adaptive DOEs -- 16.7.2 Leave-One-Out Cross-Validation (LOOCV) -- 16.7.3 DOE with Dependent Variable Transformations by Leave-One-Out Cross-Validation (LOOCV) -- 16.7.4 Leave-One-Out Cross-Validation (LOOCV) with Adaptive Sampling -- 16.7.5 More Cross-Validation Techniques -- 16.8 Adaptive DOEs by Expected Improvement -- 16.8.1 Adaptive DOE Process Based on Expected Improvement -- 16.8.2 Probability of Improvement -- 16.8.3 Exploitation by Expected Improvement -- 16.8.4 Exploration versus Exploitation -- 16.8.5 Mutual Information -- 16.8.6 Probability in the Target Range -- 16.8.7 Conventional DOE or Adaptive DOE: Illustrated Using a Case Study -- 16.9 Fault Tree Analysis (FTA) -- 16.9.1 Events -- 16.9.2 Gates -- 16.9.2.1 AND and OR -- 16.9.2.2 Two-out-of-Three (2/3) Logic Gate -- 16.9.3 Transfer -- 16.9.4 Minimal Cut Sets -- 16.9.5 Bridge-Networked Logic Gate -- 16.9.6 Causes of Failure -- 16.9.7 Qualitative Fault Tree Analysis (FTA) -- 16.9.8 Quantitative Fault Tree Analysis (FTA) -- 16.9.9 Sensitivity Analysis of a Fault Tree via DOEs -- References -- Problems -- Chapter 17: Supply Chain Reliability -- 17.1 Supply Chain Management (SCM) -- 17.1.1 Design for Supply Chain Reliability -- 17.1.2 Automotive Supply Chain Reliability -- 17.2 Operating Profit of a Four-Echelon Supply Chain -- 17.3 Supply Chain Operation through DOEs -- 17.3.1 Demand Management and Inventory Management -- 17.3.2 DOEs for a Supply Chain Node -- 17.3.3 Fill Rate -- 17.3.3.1 Predictive Equations for Fill Rate -- 17.3.3.2 Diagnostic Checking for Fill Rate -- 17.3.4 On-Hand Inventory (OHI) -- 17.3.4.1 Response Surface Regression for OHI -- 17.3.4.2 Predictive Equations for OHI -- 17.3.4.3 Diagnostic Checking for OHI -- 17.3.5 Inventory Cost -- 17.3.5.1 Response Surface Regression for Inventory Cost.; 17.3.5.2 Predictive Equations for Inventory Cost -- 17.3.5.3 Diagnostic Checking for Inventory Cost -- 17.4 Supplier Selection by DOEs -- 17.4.1 Wear of Piston Kit -- 17.4.2 Mixed Lubrication Regime and Friction in Sliding Mode -- 17.4.3 Depth of Wear in Dry Sliding Mode -- 17.4.4 Reliability Demonstration Test -- 17.4.5 Reliability Growth Tests Using Fractional Factorial Design 2 IV 8-4 -- 17.4.6 Predictive Equation for Depth of Wear -- 17.4.7 Diagnostic Checking -- 17.4.8 Conclusion from Case Study of Piston Kit Wear -- 17.5 Multivariate Statistical Process Control -- 17.5.1 Hotelling's T 2 for Two Variables -- 17.5.2 Hotelling's T 2 for Multiple Variables -- 17.6 Supply Chain Reliability Attributed to ­Network Mechanism -- 17.6.1 Network Reliability by DOEs -- 17.6.2 Monte Carlo Simulations for System Identification of a Large Supply Chain -- 17.7 Risk Assessment of Supply Chains Using ­Dynamic Fault Tree Analysis (DFTA) -- 17.7.1 Main-Backup Supply Chain -- 17.7.2 Mutual-Assistance Supply Chain -- 17.8 Automotive Supply Chain Management (SCM) Process and Documentation -- 17.8.1 Advanced Product Quality Planning (APQP) -- 17.8.2 Production Part Approval Process (PPAP) -- References -- Problems -- Chapter 18: Operational Reliability in Production Engineering -- 18.1 Operational Reliability in Production Process -- 18.1.1 Cost of Unreliability -- 18.1.2 Flexible Production -- 18.1.3 Improving Production Yield by Simulations Based on DOEs -- 18.2 Indices of Production Reliability -- ; 18.2.1 Mean Time to Failure (MTTF) -- 18.2.2 Mean Time between Failures (MTBF) -- 18.2.3 Mean Down Time (MDT) -- 18.2.4 Mean Time between Replacements (MTBR) -- 18.2.5 Mean Time between Maintenance (MTBM) -- 18.2.6 Mean Time between Failures with Scheduled Replacements (MTBF-SR) -- 18.2.7 Availability and Unavailability -- 18.2.8 Failure Frequencies.; 18.2.9 Functional Redundancy -- 18.2.10 Derating -- 18.3 Quality Function Deployment (QFD) Enhanced by DOEs -- 18.3.1 Quality House -- 18.3.2 DOEs for Quality Functional Deployment -- 18.4 Design for Testability (DFT) and Closed-Loop Control -- 18.4.1 Testability Analysis -- 18.4.2 Testability Metrics in FMEA and Quality Measure of Built-in Tests (BITs) -- 18.4.3 Closed-Loop Control via DOEs -- 18.5 Total Productive Maintenance (TPM) -- 18.5.1 Corrective Maintenance (CM) -- 18.5.2 Preventive Maintenance (PM) -- 18.5.3 Condition-Based Preventive Maintenance (PM) -- 18.5.4 Predictive Maintenance -- 18.5.5 Adaptive Maintenance -- 18.5.6 Overall Equipment Efficiency (OEE) -- 18.5.7 Total Effective Equipment Performance (TEEP) -- 18.5.8 Developing Controlled Process Flow -- 18.5.9 Production Process Improvement by DOEs -- 18.6 DOEs for Project Management -- 18.6.1 Project Management by DOEs -- 18.6.2 Engineering Changes -- 18.6.3 Process Flow and Control -- 18.6.4 Value Stream Mapping (VSM) -- 18.7 Operational Arbitration by Kappa Coefficients and DOEs -- 18.7.1 Kappa Coefficient -- 18.7.2 Cohen's Kappa Coefficient -- 18.7.3 Fleiss' Kappa Coefficient -- 18.7.4 Operational Arbitration by DOE -- 18.8 Design for Six Sigma (DFSS) -- 18.8.1 Define -- 18.8.2 Measure -- 18.8.3 Analyze -- 18.8.4 Design -- 18.8.5 Verify -- 18.8.6 Implementation of Kaizen-Five Steps (DMAIC Process) -- 18.8.7 Lockheed's Design for Six Sigma (DFSS) -- 18.9 Design for Manufacture and Assembly (DFMA) -- 18.9.1 Simplifying the Design and Reducing the Number of Parts -- 18.9.2 Standardizing and Using Common Parts and Materials -- 18.9.3 Design for Ease of Fabrication -- 18.9.4 Design within Process Capabilities and without Un-Needed Surface Finish -- 18.9.5 Foolproof -- 18.9.6 Production-Oriented Product Engineering (POPE) -- 18.9.7 Minimizing Flexible Parts and Interconnections.; 18.9.8 Utilizing Simple Patterns of Movement -- 18.9.9 Using Effective and Efficient Joining -- 18.9.10 Modular Products -- 18.9.11 Design for Automated Production -- References -- Problems -- Chapter 19: Operational Reliability of EVs -- 19.1 Automotive Electrification -- 19.1.1 Lithium-ion (Li-ion) Batteries (LIBs) -- 19.1.2 Li-Air Batteries -- 19.1.3 Hydrogen Fuel Cells -- 19.1.4 Autonomous and Automated Vehicles -- 19.1.5 Transport Infrastructures -- 19.2 Battery Electric Vehicles (BEVs) -- 19.2.1 Travel with BEVs -- 19.2.2 State of Charge (SOC) -- 19.2.3 State of Health (SOH) -- 19.2.4 Battery Capacity -- 19.2.5 Operation of a Battery-Powered Vehicle -- 19.3 Lithium-ion (Li-ion) Batteries (LIBs) -- 19.3.1 Electric Resistance of Graphite/NMC Li-Ion Cells -- 19.3.2 Temperature Rise of LIBs at Discharge -- 19.3.3 Solid Electrolytes for LIBs -- 19.3.4 Packing Cathodes for LIBs -- 19.3.5 Composite Carbon Cathodes for a Solid-State LIB -- 19.3.6 Loss of Mobile Li-Ions to Side Reactions in SEIs -- 19.3.7 Range Extension of EV -- 19.3.8 Test Standards and Regulations for Reliability of Automotive LIBs -- 19.4 Integrity of Battery Separators -- 19.4.1 Heterogeneity in Composite Separators -- 19.4.2 DOEs to Detect Irregular Transport of Ions -- 19.5 Automotive Battery Packaging -- 19.5.1 Electrical Interconnection -- 19.5.2 Thermal Management and Pressure Venting -- 19.5.3 Structural Integrity-Fastening -- 19.5.4 Structural Integrity-Sealing -- 19.5.5 Structural Integrity-Crush/Crashworthiness -- 19.6 Li-Air Batteries -- 19.6.1 Mechanism of Li-Air Batteries -- 19.6.2 Solid Electrolytes for Li-Air Batteries -- ; 19.7 Sodium-ion Batteries (SIBs) -- 19.7.1 Cathodes -- 19.7.2 Anodes -- 19.7.3 Solid Electrolytes -- 19.7.4 Merits of Solid-State SIBs -- 19.8 HFCVs -- 19.8.1 Proton Exchange Membrane Fuel Cell (PEMFC) -- 19.8.2 Supply Chain of Hydrogen.; 19.8.3 Physical Storage Vessels for Compressed Hydrogen.
• Sommario/riassunto: " In a world where every business process is under pressure to perform faster, safer, and more reliably, this book delivers a powerful roadmap for sustained operational excellence.Centered on the proven methodology of Design of Experiments (DOE), it shows how organizations can move beyond reactive problem-solving to systematic reliability growth.