Robust design for quality engineering and six sigma

This book is written primarily for engineers and researchers who use statistical robust design for quality engineering and Six Sigma, and for statisticians who wish to know about the wide range of applications of experimental design in industry. It is a valuable guide and reference material for students, managers, quality improvement specialists and other professionals interested in Taguchi's robust design methods as well as the implementation of Six Sigma. This book can also be useful to those who would like to learn about the role of Robust Design within the Six Sigma (Improve phase) methodology and Design for Six Sigma (DFSS) (Optimize) methodology. It combines classical experimental design methods with those of Taguchi's robust designs, demonstrating their prowess in DFSS and suggesting new directions for the development of statistical design and analysis.

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

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