Probabilistic Design for Optimization and Robustness for Engineers -- Contents -- Preface -- Acknowledgments -- 1 New product development process -- 1.1 Introduction -- 1.2 Phases of new product development -- 1.2.1 Phase I-concept planning -- 1.2.2 Phase II-product planning -- 1.2.3 Phase III-product engineering design and verification -- 1.2.4 Phase IV-process engineering -- 1.2.5 Phase V-manufacturing validation and ramp-up -- 1.3 Patterns of new product development -- 1.4 New product development and Design for Six Sigma -- 1.4.1 DfSS core objectives -- 1.4.2 DfSS methodology -- 1.4.3 Embedded DfSS -- 1.5 Summary -- Exercises -- 2 Statistical background for engineering design -- 2.1 Expectation -- 2.2 Statistical distributions -- 2.2.1 Normal distribution -- 2.2.2 Lognormal distribution -- 2.2.3 Weibull distribution -- 2.2.4 Exponential distribution -- 2.3 Probability plotting -- 2.3.1 Probability plotting-lognormal distribution -- 2.3.2 Probability plotting-normal distribution -- 2.3.3 Probability plotting-Weibull distribution -- 2.3.4 Probability plotting-exponential distribution -- 2.3.5 Probability plotting with confidence limits -- 2.4 Summary -- 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 -- 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 -- 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 Nonconstant input standard deviation -- 5.6 Summary -- Exercises -- 6 Desirability -- 6.1 Introduction -- 6.2 Requirements and scorecards -- 6.2.1 Types of requirements -- 6.2.2 Design scorecard -- 6.3 Desirability-single requirement -- 6.3.1 Desirability-one-sided limit -- 6.3.2 Desirability-two-sided limit -- 6.3.3 Desirability-nonlinear function -- 6.4 Desirability-multiple requirements -- 6.4.1 Maxi-min total desirability index -- 6.5 Desirability-accounting for variation -- 6.5.1 Determining desirability-using expected yields -- 6.5.2 Determining desirability-using non-mean responses -- 6.6 Summary -- Exercises -- 7 Optimization and sensitivity -- 7.1 Optimization procedure -- 7.2 Statistical outliers -- 7.3 Process capability -- 7.4 Sensitivity and cost reduction -- 7.4.1 Reservoir flow example -- 7.4.2 Reservoir flow initial solution -- 7.4.3 Reservoir flow initial solution verification -- 7.4.4 Reservoir flow optimized with normal horsepower distribution -- 7.4.5 Reservoir flow optimized with normal horsepower distribution verification -- 7.4.6 Reservoir flow horsepower variation sensitivity -- 7.4.7 Reservoir flow horsepower lognormal probability plot -- 7.4.8 Reservoir flow horsepower Cpk optimization using a lognormal distribution -- 7.5 Summary -- Exercises -- 8 Modeling system cost and multiple outputs -- 8.1 Optimizing for total system cost -- 8.2 Multiple outputs -- 8.2.1 Optimization -- 8.2.2 Computing nonconformance -- 8.3 Large-scale systems -- 8.4 Summary -- Exercises -- 9 Tolerance analysis -- 9.1 Introduction -- 9.2 Tolerance analysis methods -- 9.2.1 Historical tolerancing -- 9.2.2 Worst-case tolerancing -- 9.2.3 Statistical tolerancing -- 9.3 Tolerance allocation -- 9.4 Drift, shift, and sorting.

9.5 Non-normal inputs -- 9.6 Summary -- Exercises -- 10 Empirical model development -- 10.1 Screening -- 10.2 Response surface -- 10.2.1 Central composite designs -- 10.3 Taguchi -- 10.4 Summary -- Exercises -- 11 Binary logistic regression -- 11.1 Introduction -- 11.2 Binary logistic regression -- 11.2.1 Types of logistic regression -- 11.2.2 Binary versus ordinary least squares regression -- 11.2.3 Binary logistic regression and the logit model -- 11.2.4 Binary logistic regression with multiple predictors -- 11.2.5 Binary logistic regression and sample size planning -- 11.2.6 Binary logistic regression fuel door example -- 11.2.7 Binary logistic regression-significant binary input -- 11.2.8 Binary logistic regression-nonsignificant binary input -- 11.2.9 Binary logistic regression-continuous input -- 11.2.10 Binary logistic regression-multiple inputs -- 11.3 Logistic regression and customer loss functions -- 11.4 Loss function with maximum (or minimum) response -- 11.5 Summary -- Exercises -- 12 Verification and validation -- 12.1 Introduction -- 12.2 Engineering model V&V2 -- 12.3 Design verification methods and tools -- 12.3.1 Design verification reviews -- 12.3.2 Virtual prototypes and simulation -- 12.3.3 Physical prototypes and early production builds -- 12.3.4 Confirmation testing comparing alternatives -- 12.3.5 Confirmation tests comparing the design to acceptance criteria -- 12.4 Process validation procedure -- 12.5 Summary -- References -- Bibliography -- Answers to selected exercises -- Index -- EULA.