{"product_id":"probabilistic-design-for-optimization-and-robustness-for-engineers-9781118796191","title":"Probabilistic Design for Optimization and","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eProbabilistic 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.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface ix\u003c\/p\u003e \u003cp\u003eAcknowledgments xi\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 New product development process 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.2 Phases of new product development 2\u003c\/p\u003e \u003cp\u003e1.2.1 Phase I—concept planning 3\u003c\/p\u003e \u003cp\u003e1.2.2 Phase II—product planning 4\u003c\/p\u003e \u003cp\u003e1.2.3 Phase III—product engineering design and verification 6\u003c\/p\u003e \u003cp\u003e1.2.4 Phase IV—process engineering 9\u003c\/p\u003e \u003cp\u003e1.2.5 Phase V—manufacturing validation and ramp-up 10\u003c\/p\u003e \u003cp\u003e1.3 Patterns of new product development 11\u003c\/p\u003e \u003cp\u003e1.4 New product development and Design for Six Sigma 13\u003c\/p\u003e \u003cp\u003e1.4.1 DfSS core objectives 13\u003c\/p\u003e \u003cp\u003e1.4.2 DfSS methodology 15\u003c\/p\u003e \u003cp\u003e1.4.3 Embedded DfSS 16\u003c\/p\u003e \u003cp\u003e1.5 Summary 17\u003c\/p\u003e \u003cp\u003eExercises 17\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Statistical background for engineering design 19\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Expectation 19\u003c\/p\u003e \u003cp\u003e2.2 Statistical distributions 24\u003c\/p\u003e \u003cp\u003e2.2.1 Normal distribution 24\u003c\/p\u003e \u003cp\u003e2.2.2 Lognormal distribution 27\u003c\/p\u003e \u003cp\u003e2.2.3 Weibull distribution 30\u003c\/p\u003e \u003cp\u003e2.2.4 Exponential distribution 32\u003c\/p\u003e \u003cp\u003e2.3 Probability plotting 34\u003c\/p\u003e \u003cp\u003e2.3.1 Probability plotting—lognormal distribution 35\u003c\/p\u003e \u003cp\u003e2.3.2 Probability plotting—normal distribution 36\u003c\/p\u003e \u003cp\u003e2.3.3 Probability plotting—Weibull distribution 37\u003c\/p\u003e \u003cp\u003e2.3.4 Probability plotting—exponential distribution 39\u003c\/p\u003e \u003cp\u003e2.3.5 Probability plotting with confidence limits 40\u003c\/p\u003e \u003cp\u003e2.4 Summary 43\u003c\/p\u003e \u003cp\u003eExercises 44\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Introduction to variation in engineering design 46\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Variation in engineering design 46\u003c\/p\u003e \u003cp\u003e3.2 Propagation of error 47\u003c\/p\u003e \u003cp\u003e3.3 Protecting designs against variation 48\u003c\/p\u003e \u003cp\u003e3.4 Estimates of means and variances of functions of several variables 51\u003c\/p\u003e \u003cp\u003e3.5 Statistical bias 59\u003c\/p\u003e \u003cp\u003e3.6 Robustness 59\u003c\/p\u003e \u003cp\u003e3.7 Summary 60\u003c\/p\u003e \u003cp\u003eExercises 61\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Monte Carlo simulation 63\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Determining variation of the inputs 63\u003c\/p\u003e \u003cp\u003e4.2 Random number generators 64\u003c\/p\u003e \u003cp\u003e4.3 Validation 66\u003c\/p\u003e \u003cp\u003e4.4 Stratified sampling 70\u003c\/p\u003e \u003cp\u003e4.5 Summary 74\u003c\/p\u003e \u003cp\u003eExercises 75\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Modeling variation of complex systems 76\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Approximating the mean, bias, and variance 77\u003c\/p\u003e \u003cp\u003e5.2 Estimating the parameters of non-normal distributions 81\u003c\/p\u003e \u003cp\u003e5.3 Limitations of first-order Taylor series approximation for variance 84\u003c\/p\u003e \u003cp\u003e5.4 Effect of non-normal input distributions 91\u003c\/p\u003e \u003cp\u003e5.5 Nonconstant input standard deviation 93\u003c\/p\u003e \u003cp\u003e5.6 Summary 93\u003c\/p\u003e \u003cp\u003eExercises 95\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Desirability 98\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 98\u003c\/p\u003e \u003cp\u003e6.2 Requirements and scorecards 99\u003c\/p\u003e \u003cp\u003e6.2.1 Types of requirements 100\u003c\/p\u003e \u003cp\u003e6.2.2 Design scorecard 101\u003c\/p\u003e \u003cp\u003e6.3 Desirability—single requirement 103\u003c\/p\u003e \u003cp\u003e6.3.1 Desirability—one-sided limit 104\u003c\/p\u003e \u003cp\u003e6.3.2 Desirability—two-sided limit 106\u003c\/p\u003e \u003cp\u003e6.3.3 Desirability—nonlinear function 107\u003c\/p\u003e \u003cp\u003e6.4 Desirability—multiple requirements 109\u003c\/p\u003e \u003cp\u003e6.4.1 Maxi-min total desirability index 114\u003c\/p\u003e \u003cp\u003e6.5 Desirability—accounting for variation 115\u003c\/p\u003e \u003cp\u003e6.5.1 Determining desirability—using expected yields 115\u003c\/p\u003e \u003cp\u003e6.5.2 Determining desirability—using non-mean responses 116\u003c\/p\u003e \u003cp\u003e6.6 Summary 118\u003c\/p\u003e \u003cp\u003eExercises 118\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Optimization and sensitivity 123\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Optimization procedure 123\u003c\/p\u003e \u003cp\u003e7.2 Statistical outliers 128\u003c\/p\u003e \u003cp\u003e7.3 Process capability 129\u003c\/p\u003e \u003cp\u003e7.4 Sensitivity and cost reduction 133\u003c\/p\u003e \u003cp\u003e7.4.1 Reservoir flow example 134\u003c\/p\u003e \u003cp\u003e7.4.2 Reservoir flow initial solution 135\u003c\/p\u003e \u003cp\u003e7.4.3 Reservoir flow initial solution verification 136\u003c\/p\u003e \u003cp\u003e7.4.4 Reservoir flow optimized with normal horsepower distribution 138\u003c\/p\u003e \u003cp\u003e7.4.5 Reservoir flow optimized with normal horsepower distribution verification 140\u003c\/p\u003e \u003cp\u003e7.4.6 Reservoir flow horsepower variation sensitivity 141\u003c\/p\u003e \u003cp\u003e7.4.7 Reservoir flow horsepower lognormal probability plot 143\u003c\/p\u003e \u003cp\u003e7.4.8 Reservoir flow horsepower \u003ci\u003eC\u003c\/i\u003e\u003csub\u003epk\u003c\/sub\u003e optimization using a lognormal distribution 144\u003c\/p\u003e \u003cp\u003e7.5 Summary 149\u003c\/p\u003e \u003cp\u003eExercises 150\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Modeling system cost and multiple outputs 153\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Optimizing for total system cost 153\u003c\/p\u003e \u003cp\u003e8.2 Multiple outputs 158\u003c\/p\u003e \u003cp\u003e8.2.1 Optimization 159\u003c\/p\u003e \u003cp\u003e8.2.2 Computing nonconformance 159\u003c\/p\u003e \u003cp\u003e8.3 Large-scale systems 164\u003c\/p\u003e \u003cp\u003e8.4 Summary 166\u003c\/p\u003e \u003cp\u003eExercises 167\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Tolerance analysis 170\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 170\u003c\/p\u003e \u003cp\u003e9.2 Tolerance analysis methods 174\u003c\/p\u003e \u003cp\u003e9.2.1 Historical tolerancing 174\u003c\/p\u003e \u003cp\u003e9.2.2 Worst-case tolerancing 175\u003c\/p\u003e \u003cp\u003e9.2.3 Statistical tolerancing 175\u003c\/p\u003e \u003cp\u003e9.3 Tolerance allocation 178\u003c\/p\u003e \u003cp\u003e9.4 Drift, shift, and sorting 179\u003c\/p\u003e \u003cp\u003e9.5 Non-normal inputs 182\u003c\/p\u003e \u003cp\u003e9.6 Summary 182\u003c\/p\u003e \u003cp\u003eExercises 182\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Empirical model development 185\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Screening 185\u003c\/p\u003e \u003cp\u003e10.2 Response surface 193\u003c\/p\u003e \u003cp\u003e10.2.1 Central composite designs 194\u003c\/p\u003e \u003cp\u003e10.3 Taguchi 200\u003c\/p\u003e \u003cp\u003e10.4 Summary 200\u003c\/p\u003e \u003cp\u003eExercises 201\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Binary logistic regression 202\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 202\u003c\/p\u003e \u003cp\u003e11.2 Binary logistic regression 205\u003c\/p\u003e \u003cp\u003e11.2.1 Types of logistic regression 205\u003c\/p\u003e \u003cp\u003e11.2.2 Binary versus ordinary least squares regression 206\u003c\/p\u003e \u003cp\u003e11.2.3 Binary logistic regression and the logit model 208\u003c\/p\u003e \u003cp\u003e11.2.4 Binary logistic regression with multiple predictors 211\u003c\/p\u003e \u003cp\u003e11.2.5 Binary logistic regression and sample size planning 211\u003c\/p\u003e \u003cp\u003e11.2.6 Binary logistic regression fuel door example 212\u003c\/p\u003e \u003cp\u003e11.2.7 Binary logistic regression—significant binary input 213\u003c\/p\u003e \u003cp\u003e11.2.8 Binary logistic regression—nonsignificant binary input 214\u003c\/p\u003e \u003cp\u003e11.2.9 Binary logistic regression—continuous input 214\u003c\/p\u003e \u003cp\u003e11.2.10 Binary logistic regression—multiple inputs 215\u003c\/p\u003e \u003cp\u003e11.3 Logistic regression and customer loss functions 217\u003c\/p\u003e \u003cp\u003e11.4 Loss function with maximum (or minimum) response 220\u003c\/p\u003e \u003cp\u003e11.5 Summary 223\u003c\/p\u003e \u003cp\u003eExercises 223\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Verification and validation 225\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 225\u003c\/p\u003e \u003cp\u003e12.2 Engineering model V\u0026amp;V 228\u003c\/p\u003e \u003cp\u003e12.3 Design verification methods and tools 230\u003c\/p\u003e \u003cp\u003e12.3.1 Design verification reviews 230\u003c\/p\u003e \u003cp\u003e12.3.2 Virtual prototypes and simulation 231\u003c\/p\u003e \u003cp\u003e12.3.3 Physical prototypes and early production builds 232\u003c\/p\u003e \u003cp\u003e12.3.4 Confirmation testing comparing alternatives 232\u003c\/p\u003e \u003cp\u003e12.3.5 Confirmation tests comparing the design to acceptance criteria 233\u003c\/p\u003e \u003cp\u003e12.4 Process validation procedure 233\u003c\/p\u003e \u003cp\u003e12.5 Summary 238\u003c\/p\u003e \u003cp\u003eReferences 239\u003c\/p\u003e \u003cp\u003eBibliography 242\u003c\/p\u003e \u003cp\u003eAnswers to selected exercises 246\u003c\/p\u003e \u003cp\u003eIndex 251\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49406924882263,"sku":"9781118796191","price":64.76,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781118796191.jpg?v=1730497578","url":"https:\/\/bookcurl.com\/products\/probabilistic-design-for-optimization-and-robustness-for-engineers-9781118796191","provider":"Book Curl","version":"1.0","type":"link"}