Probabilistic Design for Optimization and Robustness for Engineers.pdf

Probabilistic Design for Optimization and Robustness for Engineers.pdf


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 Acknowledgements 1. Product development process 1.1 Introduction to new product development 1.2 Phases of new product development 1.3 Patterns of new product development 1.4 New product development and design for six sigma 1.5 Summary 1.6 Exercises 2. Statistical background for engineering design 2.1 Expectation 2.2 Statistical distributions 2.3 Probability plotting 2.4 Summary 2.5 Exercises 3. Introduction to variation in engineering design 3.1 Variation in engineering design 3.2 Propagation of error 3.3 Protecting designs against variation 3.4 Estimates of means and variances of functions of several variables 3.5 Statistical bias 3.6 Robustness 3.7 Summary 3.8 Exercises 4. Monte Carlo simulation 4.1 Determining variation of the inputs 4.2 Random number generators 4.3 Validation 4.4 Stratified sampling 4.5 Summary 4.6 Exercises 5. Modeling variation of complex systems 5.1 Approximating the mean, bias and variance 5.2 Estimating the parameters of non-normal distributions 5.3 Limitations of first order Taylor series approximation for variance 5.4 Effect of non-normal input distributions 5.5 Non-constant input standard deviation 5.6 Summary 5.7 Exercises 6. Desirability 6.1 Introduction 6.2 Requirements and scorecards 6.3 Desirability -- single requirement 6.4 Desirability -- multiple requirements 6.5 Desirability -- accounting for variation 6.6 Summary 6.7 Exercises 7. Optimization and sensitivity 7.1 Optimization procedure 7.2 Statistical outliers 7.3 Process capability 7.4 Sensitivity and cost reduction 7.5 Reservoir flow example 7.6 Summary 7.7 Exercises 8. Modeling system cost and multiple outputs 8.1 Optimizing for total system cost 8.2 Multiple outputs 8.3 Large scale systems 8.4 Summary 8.5 Exercises 9. Tolerance analysis 9.1 Introduction 9.2 Tolerance analysis methods 9.3 Tolerance allocation 9.4 Drift, shift and sorting 9.5 Non-normal inputs 9.6 Summary 9.7 Exercises 10. Empirical model development 10.1 Screening 10.2 Response surface 10.3 Taguchi 10.4 Summary 10.5 Exercises 11. Binary logistic regression and loss functions 11.1 Introduction 11.2 Binary logistic regression 11.3 Logistic regression and customer loss functions 11.4 Loss function with maximum (or minimum) response 11.5 Summary 11.6 Exercises 12. Verification and validation 12.1 Introduction 12.2 Engineering model V&V 12.3 Design verification methods and tools 12.4 Process validation procedure 12.5 Summary References Bibliography Answers to selected exercises Index


当当网购书 京东购书 卓越购书