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Statistical Robust Design: An Industrial Perspective [Kõva köide]

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(Tetra Pak Packaging Solutions, Sweden)
  • Formaat: Hardback, 256 pages, kõrgus x laius x paksus: 236x156x20 mm, kaal: 454 g
  • Ilmumisaeg: 28-Mar-2014
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 111862503X
  • ISBN-13: 9781118625033
Teised raamatud teemal:
Statistical Robust Design: An Industrial PerspectiveSuurem pilt
  • Formaat: Hardback, 256 pages, kõrgus x laius x paksus: 236x156x20 mm, kaal: 454 g
  • Ilmumisaeg: 28-Mar-2014
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 111862503X
  • ISBN-13: 9781118625033
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781118625033.html
Märksõnad:
A UNIQUELY PRACTICAL APPROACH TO ROBUST DESIGN FROM A STATISTICAL AND ENGINEERING PERSPECTIVE

Variation in environment, usage conditions, and the manufacturing process has long presented a challenge in product engineering, and reducing variation is universally recognized as a key to improving reliability and productivity. One key and cost-effective way to achieve this is by robust design making the product as insensitive as possible to variation.

With Design for Six Sigma training programs primarily in mind, the author of this book offers practical examples that will help to guide product engineers through every stage of experimental design: formulating problems, planning experiments, and analysing data. He discusses both physical and virtual techniques, and includes numerous exercises and solutions that make the book an ideal resource for teaching or self-study.





Presents a practical approach to robust design through design of experiments. Offers a balance between statistical and industrial aspects of robust design. Includes practical exercises, making the book useful for teaching. Covers both physical and virtual approaches to robust design. Supported by an accompanying website www.wiley/com/go/robust featuring MATLAB scripts and solutions to exercises. Written by an experienced industrial design practitioner.

This book's state of the art perspective will be of benefit to practitioners of robust design in industry, consultants providing training in Design for Six Sigma, and quality engineers. It will also be a valuable resource for specialized university courses in statistics or quality engineering.
Preface ix
1 What is robust design?
1(10)
1.1 The importance of small variation
1(1)
1.2 Variance reduction
2(2)
1.3 Variation propagation
4(1)
1.4 Discussion
5(3)
1.4.1 Limitations
6(1)
1.4.2 The outline of this book
7(1)
Exercises
8(3)
2 DOE for robust design, part 1
11(16)
2.1 Introduction
11(2)
2.1.1 Noise factors
11(1)
2.1.2 Control factors
12(1)
2.1.3 Control-by-noise interactions
12(1)
2.2 Combined arrays: An example from the packaging industry
13(8)
2.2.1 The experimental array
15(1)
2.2.2 Factor effect plots
15(2)
2.2.3 Analytical analysis and statistical significance
17(3)
2.2.4 Some additional comments on the plotting
20(1)
2.3 Dispersion effects
21(2)
Exercises
23(2)
Reference
25(2)
3 Noise and control factors
27(26)
3.1 Introduction to noise factors
27(6)
3.1.1 Categories of noise
28(5)
3.2 Finding the important noise factors
33(7)
3.2.1 Relating noise to failure modes
33(1)
3.2.2 Reducing the number of noise factors
34(6)
3.3 How to include noise in a designed experiment
40(8)
3.3.1 Compounding of noise factors
40(5)
3.3.2 How to include noise in experimentation
45(3)
3.3.3 Process parameters
48(1)
3.4 Control factors
48(1)
Exercises
49(2)
References
51(2)
4 Response, signal, and P diagrams
53(16)
4.1 The idea of signal and response
53(2)
4.1.1 Two situations
54(1)
4.2 Ideal functions and P diagrams
55(8)
4.2.1 Noise or signal factor
56(1)
4.2.2 Control or signal factor
56(2)
4.2.3 The scope
58(5)
4.3 The signal
63(2)
4.3.1 Including a signal in a designed experiment
64(1)
Exercises
65(4)
5 DOE for robust design, part 2
69(32)
5.1 Combined and crossed arrays
69(5)
5.1.1 Classical DOE versus DOE for robust design
69(1)
5.1.2 The structure of inner and outer arrays
70(4)
5.2 Including a signal in a designed experiment
74(15)
5.2.1 Combined arrays with a signal
74(7)
5.2.2 Inner and outer arrays with a signal
81(8)
5.3 Crossed arrays versus combined arrays
89(5)
5.3.1 Differences in factor aliasing
91(3)
5.4 Crossed arrays and split-plot designs
94(4)
5.4.1 Limits of randomization
94(1)
5.4.2 Split-plot designs
95(3)
Exercises
98(1)
References
99(2)
6 Smaller-the-better and larger-the-better
101(30)
6.1 Different types of responses
101(1)
6.2 Failure modes and smaller-the-better
102(4)
6.2.1 Failure modes
102(1)
6.2.2 STB with inner and outer arrays
103(3)
6.2.3 STB with combined arrays
106(1)
6.3 Larger-the-better
106(2)
6.4 Operating window
108(5)
6.4.1 The window width
110(3)
Exercises
113(2)
References
115(2)
Regression for robust design
117(1)
7.1 Graphical techniques
117(3)
7.2 Analytical minimization of (g'(z))2
120(1)
7.3 Regression and crossed arrays
121(7)
7.3.1 Regression terms in the inner array
127(1)
Exercises
128(3)
8 Mathematics of robust design
131(24)
8.1 Notational system
131(1)
8.2 The objective function
132(12)
8.2.1 Multidimensional problems
136(2)
8.2.2 Optimization in the presence of a signal
138(1)
8.2.3 Matrix formulation
139(2)
8.2.4 Pareto optimality
141(3)
8.3 ANOVA for robust design
144(8)
8.3.1 Traditional ANOVA
144(2)
8.3.2 Functional ANOVA
146(3)
8.3.3 Sensitivity indices
149(3)
Exercises
152(1)
References
153(2)
9 Design and analysis of computer experiments
155(22)
9.1 Overview of computer experiments
156(5)
9.1.1 Robust design
157(4)
9.2 Experimental arrays for computer experiments
161(6)
9.2.1 Screening designs
161(2)
9.2.2 Space filling designs
163(2)
9.2.3 Latin hypercubes
165(1)
9.2.4 Latin hypercube designs and alphabetical optimality criteria
166(1)
9.3 Response surfaces
167(4)
9.3.1 Local least squares
168(1)
9.3.2 Kriging
169(2)
9.4 Optimization
171(4)
9.4.1 The objective function
171(2)
9.4.2 Analytical techniques or Monte Carlo
173(2)
Exercises
175(1)
References
176(1)
10 Monte Carlo methods for robust design
177(18)
10.1 Geometry variation
177(2)
10.1.1 Electronic circuits
179(1)
10.2 Geometry variation in two dimensions
179(13)
10.3 Crossed arrays
192(3)
11 Taguchi and his ideas on robust design
195(14)
11.1 History and origin
195(2)
11.2 The experimental arrays
197(3)
11.2.1 The nature of inner arrays
197(2)
11.2.2 Interactions and energy thinking
199(1)
11.2.3 Crossing the arrays
200(1)
11.3 Signal-to-noise ratios
200(3)
11.4 Some other ideas
203(5)
11.4.1 Randomization
203(1)
11.4.2 Science versus engineering
204(1)
11.4.3 Line fitting for dynamic models
204(2)
11.4.4 An aspect on the noise
206(1)
11.4.5 Dynamic models
207(1)
Exercises
208(1)
References
208(1)
Appendix A Loss functions
209(10)
A.1 Why Americans do not buy American television sets
209(2)
A.2 Taguchi's view on loss function
211(1)
A.3 The average loss and its elements
211(3)
A.4 Loss functions in robust design
214(1)
Exercises
215(2)
References
217(2)
Appendix B Data for chapter 2
219(4)
Appendix C Data for chapter 5
223(8)
Appendix D Data for chapter 6
231(2)
Index 233
Magnus Arnér, Tetra Pak Packaging Solutions, Sweden.