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Probabilistic Design for Optimization and Robustness for Engineers

Book Description

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.

    Table of Contents

    1. Preface
    2. Acknowledgments
    3. 1 New product development process
      1. 1.1 Introduction
      2. 1.2 Phases of new product development
      3. 1.3 Patterns of new product development
      4. 1.4 New product development and Design for Six Sigma
      5. 1.5 Summary
      6. Exercises
    4. 2 Statistical background for engineering design
      1. 2.1 Expectation
      2. 2.2 Statistical distributions
      3. 2.3 Probability plotting
      4. 2.4 Summary
      5. Exercises
      6. Notes
    5. 3 Introduction to variation in engineering design
      1. 3.1 Variation in engineering design
      2. 3.2 Propagation of error
      3. 3.3 Protecting designs against variation
      4. 3.4 Estimates of means and variances of functions of several variables
      5. 3.5 Statistical bias
      6. 3.6 Robustness
      7. 3.7 Summary
      8. Exercises
      9. Notes
    6. 4 Monte Carlo simulation
      1. 4.1 Determining variation of the inputs
      2. 4.2 Random number generators
      3. 4.3 Validation
      4. 4.4 Stratified sampling
      5. 4.5 Summary
      6. Exercises
      7. Notes
    7. 5 Modeling variation of complex systems
      1. 5.1 Approximating the mean, bias, and variance
      2. 5.2 Estimating the parameters of non-normal distributions
      3. 5.3 Limitations of first-order Taylor series approximation for variance
      4. 5.4 Effect of non-normal input distributions
      5. 5.5 Nonconstant input standard deviation
      6. 5.6 Summary
      7. Exercises
      8. Notes
    8. 6 Desirability
      1. 6.1 Introduction
      2. 6.2 Requirements and scorecards
      3. 6.3 Desirability—single requirement
      4. 6.4 Desirability—multiple requirements
      5. 6.5 Desirability—accounting for variation
      6. 6.6 Summary
      7. Exercises
      8. Notes
    9. 7 Optimization and sensitivity
      1. 7.1 Optimization procedure
      2. 7.2 Statistical outliers
      3. 7.3 Process capability
      4. 7.4 Sensitivity and cost reduction
      5. 7.5 Summary
      6. Exercises
      7. Notes
    10. 8 Modeling system cost and multiple outputs
      1. 8.1 Optimizing for total system cost
      2. 8.2 Multiple outputs
      3. 8.3 Large-scale systems
      4. 8.4 Summary
      5. Exercises
      6. Notes
    11. 9 Tolerance analysis
      1. 9.1 Introduction
      2. 9.2 Tolerance analysis methods
      3. 9.3 Tolerance allocation
      4. 9.4 Drift, shift, and sorting
      5. 9.5 Non-normal inputs
      6. 9.6 Summary
      7. Exercises
      8. Notes
    12. 10 Empirical model development
      1. 10.1 Screening
      2. 10.2 Response surface
      3. 10.3 Taguchi
      4. 10.4 Summary
      5. Exercises
      6. Notes
    13. 11 Binary logistic regression
      1. 11.1 Introduction
      2. 11.2 Binary logistic regression
      3. 11.3 Logistic regression and customer loss functions
      4. 11.4 Loss function with maximum (or minimum) response
      5. 11.5 Summary
      6. Exercises
      7. Notes
    14. 12 Verification and validation
      1. 12.1 Introduction
      2. 12.2 Engineering model V&V
      3. 12.3 Design verification methods and tools
      4. 12.4 Process validation procedure
      5. 12.5 Summary
      6. Notes
    15. References
    16. Bibliography
    17. Answers to selected exercises
    18. Index
    19. End User License Agreement