Robust Design For Quality Engineering And Six Sigma

by Park, Sung H.; Antony, Jiju

• ISBN13: 9789812778673
• ISBN10: 9812778675
• Format: Hardcover
• Copyright: 2008-09-26
• Publisher: World Scientific Pub Co Inc

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, manager, 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."--BOOK JACKET.

Table of Contents

Preface	p. v
Introduction of Quality Engineering	p. 1
Quality	p. 1
Taguchi's approach to quality engineering	p. 3
Stages of new product development	p. 11
Quality management and Six Sigma	p. 13
Analysis of Quality Information and Quality Improvement Effort	p. 17
Assessment of process capability	p. 17
Signal-to-noise ratio	p. 27
Factor-finding methods for quality problems	p. 34
Multiple regression analysis	p. 40
Procedure for quality problem-solving	p. 51
A strategy for quality improvement by team effort	p. 53
Exercises	p. 57
Fundamentals of Designing Experiments	p. 61
Framework of experimental design	p. 61
One-factor-at-a-time experiment	p. 67
Two-factor factorial design	p. 70
Classification of experimental designs	p. 85
The role of experimental design	p. 87
History of experimental design and advancement of robust design	p. 89
Exercises	p. 91
Orthogonal Array Experiments	p. 95
Structure and use of two-level orthogonal arrays	p. 95
Structure and use of three-level orthogonal arrays	p. 108
Linear graphs	p. 117
Column-merging method	p. 124
Classification of orthogonal arrays	p. 131
Dummy-level technique	p. 133
Exercises	p. 139
Parameter Design for Continuous Data	p. 145
Structure of parameter design	p. 145
Steps of parameter design	p. 149
Pareto analysis of variation	p. 151
Experiments involving larger-the-better characteristics	p. 164
Experiments involving nominal-is-best characteristics	p. 169
Exercises	p. 176
Parameter Design for Discrete Data	p. 181
Two-class discrete data: SN ratio analysis	p. 182
Two-class discrete data: 0/1 data direct analysis	p. 187
Omega method for estimation from 0/1 data	p. 190
Multi-class discrete data: scoring method	p. 193
Multi-class discrete data: accumulating analysis	p. 198
Exercises	p. 206
Alternative Parameter Design and Other Considerations	p. 209
Parameter design by combined array	p. 209
Combined array approach for two-level factors	p. 214
Combined array approach for three-level factors	p. 220
Estimation using nonlinear regression	p. 226
Simultaneous optimization for multiple characteristics	p. 233
Exercises	p. 240
Parameter Design for Dynamic Characteristics	p. 243
Dynamic characteristics	p. 243
Factorial experiments	p. 245
Orthogonal array experiment I	p. 251
Orthogonal array experiment II	p. 257
Exercises	p. 263
Tolerance Design	p. 267
Introduction	p. 267
Determination of tolerances	p. 269
Orthogonal polynomials	p. 276
Tolerance design by factorial experiments	p. 288
Tolerance design using orthogonal arrays	p. 295
Exercises	p. 301
Robust Response Surface Design and Analysis	p. 305
Response surface methodology and its roles in quality improvement	p. 305
Analysis of a second-order model	p. 309
Response surface designs for fitting second-order models	p. 316
Desirable properties of response surface designs	p. 319
Robust response surface designs	p. 325
Optimization of multiresponse experiments	p. 332
Parameter design in response surface analysis	p. 338
Exercises	p. 342
Six Sigma for Management Innovation	p. 345
What is Six Sigma?	p. 345
Why is Six Sigma fascinating?	p. 347
Key concepts of management	p. 349
Measurement of process performance	p. 353
Six Sigma framework	p. 358
DMAIC process and project team activities	p. 366
Further Issues for the Implementation of Six Sigma	p. 375
Data Technology	p. 375
Knowledge-based digital Six Sigma	p. 378
Six Sigma for service industry	p. 387
Black belt training	p. 395
A practical framework for Six Sigma implementation	p. 399
Keys for Six Sigma success	p. 404
Design for Six Sigma	p. 407
DFSS Framework	p. 407
Case study of DMADOV process	p. 417
Case study of DMAIC process	p. 423
Case study of product design through RSM	p. 429
Robust Design and Implementation of Six Sigma	p. 439
Barriers and benefits of robust design	p. 439
Case study of robust design in fiber optic sensor development	p. 443
A new dimension of Six Sigma: Samsung DFSS	p. 455
Case study of Six Sigma implementation	p. 466
Practical questions in implementing Six Sigma	p. 474
Appendices	p. 483
Table of Acronyms	p. 483
Standard normal distribution table	p. 486
t-distribution table of t[subscript 1-alpha]([phi])	p. 487
x[superscript 2]-distribution table of x[superscript 2 subscript 1-alpha]([phi])	p. 488
F-distribution table of F[subscript 1-alpha]([phi]sb1],[phi subscript 2])	p. 489
Omega transformation table	p. 493
Devibel table	p. 497
Orthogonal arrays and linear graphs	p. 501
Constants for X - R control chart	p. 526
GE Quality 2000: A dream with a great plan	p. 527
References	p. 531
Index	p. 539
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