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Probabilistic Design for Optimization and Robustness for Engineers [Kõva köide]

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  • Formaat: Hardback, 272 pages, kõrgus x laius x paksus: 236x160x19 mm, kaal: 481 g
  • Ilmumisaeg: 26-Sep-2014
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1118796195
  • ISBN-13: 9781118796191
Teised raamatud teemal:
Probabilistic Design for Optimization and Robustness for EngineersSuurem pilt
  • Formaat: Hardback, 272 pages, kõrgus x laius x paksus: 236x160x19 mm, kaal: 481 g
  • Ilmumisaeg: 26-Sep-2014
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1118796195
  • ISBN-13: 9781118796191
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781118796191.html
Märksõnad:
Engineers Dodson and Hammett and statistician Klerx present the theory of modeling variation using physical models, and present methods for practical applications, including making designs less sensitive to variation. They present methods for determining nominal parameter settings that minimize output variation, for determining the output variation caused by each input parameter, and for minimizing non-conformance cost and total system costs. Understanding that most engineering curricula require little or no statistics, they introduce the statistics needed here in beginning chapters. Annotation ©2015 Ringgold, Inc., Portland, OR (protoview.com)

Probabilistic Design for Optimization and Robustness:

  • Presents the theory of modeling with variation using physical models and methods for practical applications on designs more insensitive to variation.
  • Provides a comprehensive guide to optimization and robustness for probabilistic design.
  • Features examples, case studies and exercises throughout.

The methods presented can be applied to a wide range of disciplines such as mechanics, electrics, chemistry, aerospace, industry and engineering. This text is supported by an accompanying website featuring videos, interactive animations to aid the readers understanding.

Preface ix
Acknowledgments xi
1 New product development process 1(18)
1.1 Introduction
1(1)
1.2 Phases of new product development
2(9)
1.2.1 Phase I—concept planning
3(1)
1.2.2 Phase II—product planning
4(2)
1.2.3 Phase III—product engineering design and verification
6(3)
1.2.4 Phase IV—process engineering
9(1)
1.2.5 Phase V—manufacturing validation and ramp-up
10(1)
1.3 Patterns of new product development
11(2)
1.4 New product development and Design for Six Sigma
13(4)
1.4.1 DfSS core objectives
13(2)
1.4.2 DfSS methodology
15(1)
1.4.3 Embedded DfSS
16(1)
1.5 Summary
17(1)
Exercises
17(2)
2 Statistical background for engineering design 19(27)
2.1 Expectation
19(5)
2.2 Statistical distributions
24(10)
2.2.1 Normal distribution
24(3)
2.2.2 Lognormal distribution
27(3)
2.2.3 Weibull distribution
30(2)
2.2.4 Exponential distribution
32(2)
2.3 Probability plotting
34(9)
2.3.1 Probability plotting—lognormal distribution
35(1)
2.3.2 Probability plotting—normal distribution
36(1)
2.3.3 Probability plotting—Weibull distribution
37(2)
2.3.4 Probability plotting—exponential distribution
39(1)
2.3.5 Probability plotting with confidence limits
40(3)
2.4 Summary
43(1)
Exercises
44(2)
3 Introduction to variation in engineering design 46(17)
3.1 Variation in engineering design
46(1)
3.2 Propagation of error
47(1)
3.3 Protecting designs against variation
48(3)
3.4 Estimates of means and variances of functions of several variables
51(8)
3.5 Statistical bias
59(1)
3.6 Robustness
59(1)
3.7 Summary
60(1)
Exercises
61(2)
4 Monte Carlo simulation 63(13)
4.1 Determining variation of the inputs
63(1)
4.2 Random number generators
64(2)
4.3 Validation
66(4)
4.4 Stratified sampling
70(4)
4.5 Summary
74(1)
Exercises
75(1)
5 Modeling variation of complex systems 76(22)
5.1 Approximating the mean, bias, and variance
77(4)
5.2 Estimating the parameters of non-normal distributions
81(3)
5.3 Limitations of first-order Taylor series approximation for variance
84(7)
5.4 Effect of non-normal input distributions
91(2)
5.5 Nonconstant input standard deviation
93(1)
5.6 Summary
93(2)
Exercises
95(3)
6 Desirability 98(25)
6.1 Introduction
98(1)
6.2 Requirements and scorecards
99(4)
6.2.1 Types of requirements
100(1)
6.2.2 Design scorecard
101(2)
6.3 Desirability—single requirement
103(6)
6.3.1 Desirability—one-sided limit
104(2)
6.3.2 Desirability—two-sided limit
106(1)
6.3.3 Desirability—nonlinear function
107(2)
6.4 Desirability—multiple requirements
109(6)
6.4.1 Maxi-min total desirability index
114(1)
6.5 Desirability—accounting for variation
115(3)
6.5.1 Determining desirability—using expected yields
115(1)
6.5.2 Determining desirability—using non-mean responses
116(2)
6.6 Summary
118(1)
Exercises
118(5)
7 Optimization and sensitivity 123(30)
7.1 Optimization procedure
123(5)
7.2 Statistical outliers
128(1)
7.3 Process capability
129(4)
7.4 Sensitivity and cost reduction
133(16)
7.4.1 Reservoir flow example
134(1)
7.4.2 Reservoir flow initial solution
135(1)
7.4.3 Reservoir flow initial solution verification
136(2)
7.4.4 Reservoir flow optimized with normal horsepower distribution
138(2)
7.4.5 Reservoir flow optimized with normal horsepower distribution verification
140(1)
7.4.6 Reservoir flow horsepower variation sensitivity
141(2)
7.4.7 Reservoir flow horsepower lognormal probability plot
143(1)
7.4.8 Reservoir flow horsepower Cpk optimization using a lognormal distribution
144(5)
7.5 Summary
149(1)
Exercises
150(3)
8 Modeling system cost and multiple outputs 153(17)
8.1 Optimizing for total system cost
153(5)
8.2 Multiple outputs
158(6)
8.2.1 Optimization
159(1)
8.2.2 Computing nonconformance
159(5)
8.3 Large-scale systems
164(2)
8.4 Summary
166(1)
Exercises
167(3)
9 Tolerance analysis 170(15)
9.1 Introduction
170(4)
9.2 Tolerance analysis methods
174(4)
9.2.1 Historical tolerancing
174(1)
9.2.2 Worst-case tolerancing
175(1)
9.2.3 Statistical tolerancing
175(3)
9.3 Tolerance allocation
178(1)
9.4 Drift, shift, and sorting
179(3)
9.5 Non-normal inputs
182(1)
9.6 Summary
182(1)
Exercises
182(3)
10 Empirical model development 185(17)
10.1 Screening
185(8)
10.2 Response surface
193(7)
10.2.1 Central composite designs
194(6)
10.3 Taguchi
200(1)
10.4 Summary
200(1)
Exercises
201(1)
11 Binary logistic regression 202(23)
11.1 Introduction
202(3)
11.2 Binary logistic regression
205(12)
11.2.1 Types of logistic regression
205(1)
11.2.2 Binary versus ordinary least squares regression
206(2)
11.2.3 Binary logistic regression and the logit model
208(3)
11.2.4 Binary logistic regression with multiple predictors
211(1)
11.2.5 Binary logistic regression and sample size planning
211(1)
11.2.6 Binary logistic regression fuel door example
212(1)
11.2.7 Binary logistic regression—significant binary input
213(1)
11.2.8 Binary logistic regression—nonsignificant binary input
214(1)
11.2.9 Binary logistic regression—continuous input
214(1)
11.2.10 Binary logistic regression—multiple inputs
215(2)
11.3 Logistic regression and customer loss functions
217(3)
11.4 Loss function with maximum (or minimum) response
220(3)
11.5 Summary
223(1)
Exercises
223(2)
12 Verification and validation 225(14)
12.1 Introduction
225(3)
12.2 Engineering model V&V
228(2)
12.3 Design verification methods and tools
230(3)
12.3.1 Design verification reviews
230(1)
12.3.2 Virtual prototypes and simulation
231(1)
12.3.3 Physical prototypes and early production builds
232(1)
12.3.4 Confirmation testing comparing alternatives
232(1)
12.3.5 Confirmation tests comparing the design to acceptance criteria
233(1)
12.4 Process validation procedure
233(5)
12.5 Summary
238(1)
References 239(3)
Bibliography 242(4)
Answers to selected exercises 246(5)
Index 251
BRYAN DODSON, Executive Engineer, SKF, USA

PATRICK C. HAMMETT, Lead Faculty Six Sigma Program, Integrative Systems & Design, College of Engineering, University of Michigan, Ann Arbor, USA

RENÉ KLERX, Principal Statistician, SKF, The Netherlands