Preface xvAcronyms xviNomenclature xxviiIntroduction xxxC H A P T E R 1Reliability Deployment 11.1. Product Reliability Realization 11.1.1. Product Quality, Robustness, and Reliability 21.1.2.

Realization Process of Product Reliability 21.1.3. Tools for Reliability Realization 31.1.4. Advanced Reliability Planning 41.1.

5. Reliability Lauded in VOC 61.1.6. Product Reliability Growth 71.2. Uncertainty of Measurement 71.2.

1. Accuracy: Precision and Trueness 71.2.2. Essential Elements of Measurement 91.3. Unreliability 101.3.

1. Physical and Functional Failures 101.3.2. Probability of Product Failure 111.3.3. Failure Measured by Time 111.

4. Design for Reliability 121.4.1. Reference Model and Simulation for System Identification 131.4.2. Boundary Diagram 131.

4.3. Parameter Diagram (P-Diagram) 141.4.4. Fishbone Diagram 171.4.5.

Variation Modes and Effect Analysis 181.5. Classification of DOE 191.5.1. Basic Types of DOE 191.5.2.

Acceptance Sampling and Test Plans 201.5.3. Statistical Tolerance Design 201.5.4. Manufacturing Reliability 21ContentsContentsviii1.5.

5. Supply Chain Reliability 211.5.6. Operational Reliability 211.5.7. Marketing Reliability 221.

5.8. Holistic Approach 221.6. Software for DOE 22References 24Problems 26C H A P T E R 2Full Factorial Design 2K 272.1. Why DOE 272.1.

1. Complete Search for Solution in the Multidomain 282.1.2. Tremendous Reduction in the Number of Experimental Tests 292.2. What Is DOE 292.2.

1. Application for DOE 302.2.2. Experimental Design Process 302.2.3. History of DOE 312.

3. Objective Function and Goals 322.3.1. Quantifiable and Achievable 322.3.2. RSM: Regression Using DOE 332.

4. Factors 352.4.1. Types of Factors 362.4.2. Effects of Factors and Their Interactions 362.

4.3. Selections--Doable and Controllable 372.4.4. Dimensionless Format 372.4.5.

Design Contrasts and Effects 382.4.6. Statistical Significance 392.5. Statistical Inference by Student''s t-Distribution 402.5.1.

t-Distribution 412.5.2. Null Hypothesis 422.5.3. Determine the Experimental Error 422.5.

4. Experimental Error with Replications by Sample Variance SampleStandard Deviation 432.5.5. Experimental Error with No Replications 442.5.6. Engineering Error 442.

5.7. Test for Outliers 442.5.8. Process Bias 452.5.9.

Process Stability 462.6. Full Factorial Design with Two Levels 46Contents ix2.6.1. 2 3 Factorial Design with No Replication 462.6.2.

2 K Factorial Design 502.6.3. Replication 512.6.4. Randomization 532.6.

5. Blocking 532.7. Diagnostic Checking 542.7.1. Checking Effectiveness 552.7.

2. Checking Adequacy 552.7.3. Model Validation 562.8. Statistical Inference by Normal Probability Plots 562.8.

1. Normal Distribution 562.8.2. Normal Probability Plots (Normal Quantile Plots) 582.8.3. Making Normal Probability Plots Using Excel 592.

8.4. Half-Normal Probability Plots 612.9. Full Factorial Design Examples 622.9.1. 2 4 Factorial Design--Case Study on Tasting Coffee 622.

9.2. 2 5 Factorial Design--Case Study on Finite Element Accuracy 642.9.3. 2 6 Factorial Design--Case Study on Simple-Stranded Wire Cable 68References 72Problems 73C H A P T E R 3Fractional Factorial Design 2RK-P 753.1. Fractional Factorial Design 753.

1.1. Fractional Factorial Notation 763.1.2. 2 IV4-1 Design 763.1.3.

Foldover Pair 793.2. Design Generators 803.3. Frequently Used Fractional Factorial Designs 823.3.1. 2 V5-1 Example--Time to Charge a Cell Phone 833.

3.2. 2 III6-3 Example: Case Study on Shrink of Optical-Fiber Cable 853.4. Foldover Design 873.4.1. 2 III7-4 Design 873.

4.2. 2 IV8-4 as Foldover Design from 2 III7-4 on an Additional Factor 893.4.3. Foldover Design Example: Gasket Sealing 913.4.4.

Applying 2 IV8-4 Directly without Foldover 953.5. Fractional Factorial Design with Many Factors 963.5.1. Placket-Burman Designs 96Contentsx3.5.2.

2 III15-11 Example: Case Study on Angular Rigidity of AutomotiveSide Door Hinges 973.5.3. 2 III16-11 Example: Case Study on Sealing Tire Air 102References 110Problems 111C H A P T E R 4General Factorial Design LK-P 1154.1. RSM and F-Distribution 1154.1.1.

High-Level Factorial Design 1164.1.2. F-Distribution 1164.2. ANOVA for LK Design 1194.2.1.

I 1J1M1 Design and 33 Design 1194.2.2. Null Hypothesis and F-Test 1254.2.3. Coded Variables for Three-Level Factorial Designs 1264.2.

4. 3 3 Example--Pull Strength of Seat-Belt Knuckle 1274.2.5. 3 K Design 1294.3. Predictive Equation by Multifactorial DOE Regression 1304.3.

1. ANOVA and Regression 1304.3.2. Least-Squares Regression for ANOVA with 33 Factor Design 1304.3.3. Decomposition of Interactions in Factorial Design 3 3 1344.

4. Generic Concept of Multiple Linear Regression 1364.4.1. Response Surface Regression by Linear and Quadratic Terms 1404.4.2. Example: Case Study on Shrinkage of Optical-Fiber Cable (Continuedfrom Section 3.

3.2) 1404.5. Checking Adequacy of Predictive Equations by Regression 1424.5.1. Correlation and Adjusted Correlation 1434.5.

2. Checking Lack of Fit 1444.6. Product Variability, Process Capability, and Gage R&R 1454.6.1. Measured Variability 1464.6.

2. Process Capability Indices 1474.6.3. Sample Size Determination for the Estimation of PCIs 1494.6.4. ANOVA for Two Factors 1504.

6.5. Gage R&R Based on ANOVA 1524.7. Fractional Factorial Design with Three Levels 1554.7.1. Interactions in 3K and 3K-P Factorial Designs 1564.

7.2. 3 III3-1 1574.7.3. 3 IV4-1 158Contents xi4.7.4.

3 III4-2 1604.7.5. 3 III5-2 1614.7.6. 3 III9-6 1624.7.

7. 3 III11-8 1634.7.8. 3 III13-10 1654.8. DOE with Definitely Known Inferential Mechanisms 1654.8.

1. Regression Based on Second-Ordered Response Surface Model 1674.8.2. Regression Based on Observance of Inferential Mechanism 170References 171Problems 172C H A P T E R 5Composite Designs 1755.1. Composite Factorial Designs 1755.2.

Central Composite Designs 1765.2.1. Face-Centered Design (FCD) 1775.2.2. Spherical Composite Design-Circumscribed (SCD-C) 1785.2.

3. Spherical Composite Design-Inscribed (SCD-I) 1815.2.4. Study on Warpage of Injection-Molded Plastics Using SphericalComposite Design 1815.2.5. Between-Face-and-Spherical CD 1835.

2.6. Box-Behnken Design 1835.2.7. Small CD 1855.2.8.

Incomplete Small CD 1865.3. Design with Mixed Two-Level and Three-Level Factors 1865.3.1. 2 1×31 1865.3.2.

2 1×37-5 (L18) with Resolution III 1875.3.3. 2 11-9×312-10 (L36) with Resolution III 1885.3.4. Orthogonal Array CD (OACD) 1895.4.

Design with Mixed Two-Level and Four-Level Factors 1925.4.1. 4 1x22 (Full-Factorial Design) 1925.4.2. (4 1×23)IV 1935.4.

3. (4 2×23) IV 1945.4.4. (4 1×24)III 1955.4.5. 4 4-2IV with Example on Voiding on Ball Grid Arrays 1965.

5. Reliability and Confidence Level 1975.5.1. Confidence Interval 1985.5.2. Sampling CI in t-Distribution 1985.

5.3. CI of Sample Population by DOE-F 200Contentsxii5.5.4. CI of Sample Population by DOE-F with Confirmation Runs 2005.5.5.

Factorial Design 21×37-5 (L18 ) for Designing Magneto-Rheological Dampers 2005.6. Identification of Statistic Distribution 2055.6.1. Bootstrap Plot for a Statistic 2055.6.2.

Bootstrap Plot of Model-Averaged CIs for a DOE-t 2085.6.3. Identification of Statistical Distribution by Parametric Method Using Minitab 2085.7. Nested Design 2105.7.1.

Two-Stage Nested Design: B Nested in A 2105.7.2. Three-Stage Nested Design: B Nested in A and C Nested in B 2155.7.3. Predictive Equation for Nested Designs 2195.8.

Crossed Design 219References 219Problems 221C H A P T E R 6Optimal Designs 2236.1. Information-Based Optimality 2236.1.1. Information Matrix and Maximum Likelihood Method 2246.1.2.

Opportunities to Use Optimal Designs 2276.1.3. How to Select the Optimal Design 2276.1.4. Comparison of Different Optimal Designs 2276.1.

5. Residual Maximum Likelihood (REML) 2286.2. D-Optimality 2286.2.1. D-Optimal Design for Linear Regression Model 2286.2.

2. D-Optimal Design for Quadratic Regression with Constraints 2346.2.3. D-Optimal Design with Block Factors 2356.2.4. D-Optimal Design for Nonlinear Regression Models 2386.

3. A-Optimality 2386.4. I-Optimal Design and FDS Plots 2396.4.1. I-Efficiency 2396.4.

2. FDS Plots 2396.4.3. I-Optimality versus D-Optimality 2416.5. G-Optimal Design 2446.6.

Construction of Optimal ExperimentalDesigns 2446.6.1. Balance and Orthogonality of Optimal Experimental Designs 2446.6.2. Construction Algorithms 2456.6.

3. Generating an Optimal Design Using Commercial Codes 245Contents xiii6.7. Statistical Power Evaluation of Experimental Designs 2456.7.1. Operating Characteristic Curve 2466.7.

2. Statistical Power 2466.7.3. Statistical Power of Parametric Estimation in DOE 2476.7.4. Noncentrality Parameter 2476.

7.5. Noncentrality Parameters Based on ANOVA 2486.7.6. Noncentrality Parameters for a Two-Level Factorial Design 2496.7.7.

Statistical Power Evaluation for D-Optimal Design 2506.8. Definitive Screening Designs 2536.8.1. Definitive Screening Design Matrices 2536.8.2.

Conference Matrix 2556.8.3. Room for Improvement 2566.8.4. DSD with Added Two-Level Categorical Factors 2566.9.

Pareto Optimality 2566.9.1. Pareto Frontier 2576.9.2. Pareto Efficiency 2586.9.

3. Marginal Rate of Substitution and Utility Function 2596.10. Multiobjective Optimization Based on DOE 2626.10.1. Multiobjective PSO with Pareto Optimality 2626.10.

2. Example Problem for PSO with Pareto Optimality 2656.10.3. Evolutionary Multiobjective Design with Pareto Optimality 2696.10.4. Gray Relational Analysis 2696.

11. Distance-Based Optimality 2696.11.1. U-Optimality in Euclidean Space 2706.11.2. Distance-Based Maximum Likelihood Estimation 2716.

11.3. S-Optimality 2736.11.4. Distance-Based Measurements for Autonomous Vehicles 2736.12. Time-Based Optimality 2756.

12.1. Time-Based Optimality for Cost Function of ChargingPlug-in Electric Vehicles 2756.12.2. Time-Based Optimality for Profit Function of Charging PEVs 2796.12.3.

Just-in-Time Strategy for Production Workflow 2806.12.4. Time-Efficient Optimal Torque Split Strategy for Electric Vehicles withMultiple Motors 2806.13. Topological Optimization 2806.13.1.

Evolutionary Design Change by Finite Element Methods (FEM) 2826.13.2. Shape Design by Pare.