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Summary
Summary
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.
Table of Contents
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 |