Table of Contents
Preface v
Chapter 1 Introduction of Quality Engineering 1
1.1 Quality 1
1.2 Taguchi's approach to quality engineering 3
1.3 Stages of new product development 11
1.4 Quality management and Six Sigma 13
Chapter 2 Analysis of Quality Information and Quality Improvement Effort 17
2.1 Assessment of process capability 17
2.2 Signal-to-noise ratio 27
2.3 Factor-finding methods for quality problems 34
2.4 Multiple regression analysis 40
2.5 Procedure for quality problem-solving 51
2.6 A strategy for quality improvement by team effort 53
Exercises 57
Chapter 3 Fundamentals of Designing Experiments 61
3.1 Framework of experimental design 61
3.2 One-factor-at-a-time experiment 67
3.3 Two-factor factorial design 70
3.4 Classification of experimental designs 85
3.5 The role of experimental design 87
3.6 History of experimental design and advancement of robust design 89
Exercises 91
Chapter 4 Orthogonal Array Experiments 95
4.1 Structure and use of two-level orthogonal arrays 95
4.2 Structure and use of three-level orthogonal arrays 108
4.3 Linear graphs 117
4.4 Column-merging method 124
4.5 Classification of orthogonal arrays 131
4.6 Dummy-level technique 133
Exercises 139
Chapter 5 Parameter Design for Continuous Data 145
5.1 Structure of parameter design 145
5.2 Steps of parameter design 149
5.3 Pareto analysis of variation 151
5.4 Experiments involving larger-the-better characteristics 164
5.5 Experiments involving nominal-is-best characteristics 169
Exercises 176
Chapter 6 Parameter Design for Discrete Data 181
6.1 Two-class discrete data: SN ratio analysis 182
6.2 Two-class discrete data: 0/1 data directanalysis 187
6.3 Omega method for estimation from 0/1 data 190
6.4 Multi-class discrete data: scoring method 193
6.5 Multi-class discrete data: accumulating analysis 198
Exercises 206
Chapter 7 Alternative Parameter Design and Other Considerations 209
7.1 Parameter design by combined array 209
7.2 Combined array approach for two-level factors 214
7.3 Combined array approach for three-level factors 220
7.4 Estimation using nonlinear regression 226
7.5 Simultaneous optimization for multiple characteristics 233
Exercises 240
Chapter 8 Parameter Design for Dynamic Characteristics 243
8.1 Dynamic characteristics 243
8.2 Factorial experiments 245
8.3 Orthogonal array experiment I 251
8.4 Orthogonal array experiment II 257
Exercises 263
Chapter 9 Tolerance Design 267
9.1 Introduction 267
9.2 Determination of tolerances 269
9.3 Orthogonal polynomials 276
9.4 Tolerance design by factorial experiments 288
9.5 Tolerance design using orthogonal arrays 295
Exercises 301
Chapter 10 Robust Response Surface Design and Analysis 305
10.1 Response surface methodology and its roles in quality improvement 305
10.2 Analysis of a second-order model 309
10.3 Response surface designs for fitting second-order models 316
10.4 Desirable properties of response surface designs 319
10.5 Robust response surface designs 325
10.6 Optimization of multiresponse experiments 332
10.7 Parameter design in response surface analysis 338
Exercises 342
Chapter 11 Six Sigma for Management Innovation 345
11.1 What is Six Sigma? 345
11.2 Why is Six Sigma fascinating? 347
11.3 Key concepts of management 349
11.4 Measurement of process performance 353
11.5 Six Sigma framework 358
11.6 DMAIC process and project team activities 366
Chapter 12 Further Issues for the Implementation of Six Sigma 375
12.1 Data Technology 375
12.2 Knowledge-based digital Six Sigma 378
12.3 Six Sigma for service industry 387
12.4 Black belt training 395
12.5 A practical framework for Six Sigma implementation 399
12.6 Keys for Six Sigma success 404
Chapter 13 Design for Six Sigma 407
13.1 DFSS Framework 407
13.2 Case study of DMADOV process 417
13.3 Case study of DMAIC process 423
13.4 Case study of product design through RSM 429
Chapter 14 Robust Design and Implementation of Six Sigma 439
14.1 Barriers and benefits of robust design 439
14.2 Case study of robust design in fiber optic sensor development 443
14.3 A new dimension of Six Sigma: Samsung DFSS 455
14.4 Case study of Six Sigma implementation 466
14.5 Practical questions in implementing Six Sigma 474
Appendices 483
Table of Acronyms 483
Appendix A Standard normal distribution table 486
Appendix B t-distribution table of t[subscript 1-alpha]([phi]) 487
Appendix C x[superscript 2]-distribution table of x[superscript 2 subscript 1-alpha]([phi]) 488
Appendix D F-distribution table of F[subscript 1-alpha]([phi]sb1],[phi subscript 2]) 489
Appendix E Omega transformation table 493
Appendix F Devibel table 497
Appendix G Orthogonal arrays and linear graphs 501
Appendix H Constants for X - R control chart 526
Appendix I GE Quality 2000: A dream with a great plan 527
References 531
Index 539
