{"product_id":"probabilistic-design-for-optimization-and-robustness-for-engineers-isbn-9781118796191","title":"Probabilistic Design for Optimization and Robustness for Engineers","description":"\u003cp\u003e\u003ci\u003eProbabilistic Design for Optimization and Robustness\u003c\/i\u003e:\u003c\/p\u003e \u003cul\u003e \u003cli\u003ePresents the theory of modeling with variation using physical models and methods for practical applications on designs more insensitive to variation.\u003c\/li\u003e \u003cli\u003eProvides a comprehensive guide to optimization and robustness for probabilistic design.\u003c\/li\u003e \u003cli\u003eFeatures examples, case studies and exercises throughout.\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThe 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.\u003c\/p\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  \u003cp\u003e\u003cb\u003eBRYAN DODSON\u003c\/b\u003e, \u003ci\u003eExecutive Engineer, SKF, USA\u003c\/i\u003e \u003c\/p\u003e\u003cp\u003e\u003cb\u003ePATRICK C. HAMMETT\u003c\/b\u003e, \u003ci\u003eLead Faculty Six Sigma Program, Integrative Systems \u0026amp; Design, College of Engineering, University of Michigan, Ann Arbor, USA\u003c\/i\u003e \u003c\/p\u003e\u003cp\u003e\u003cb\u003eRENÉ KLERX\u003c\/b\u003e,\u003ci\u003e Principal Statistician, SKF, The Netherlands\u003c\/i\u003e    \u003c\/p\u003e\u003cp\u003e\u003cb\u003ePROBABILISTIC DESIGN FOR OPTIMIZATION AND ROBUSTNESS FOR ENGINEERS\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003cb\u003eHow to apply robust design to engineering design problems\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eUnlike the Taguchi approach to robustness, which requires experimentation, the approach described in this book takes advantage of engineering knowledge to create models for system variation. Probabilistic Design for Optimization and Robustness for Engineers illustrates how to use these variation models to optimize total system cost, including component cost, manufacturing cost, re-work cost, and scrap cost. The text begins with simple, single output systems, and proceeds to complex systems with multiple outputs and many inputs. This methodology works equally well for engineering designs, or process design, or process improvement. \u003c\/p\u003e\u003cp\u003eThis book: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eProvides a comprehensive guide to optimization and robustness for probabilistic design for engineers without a statistical background\u003c\/li\u003e \u003cli\u003eFeatures examples, case studies, and exercises that are applicable to a wide range of disciplines such as mechanical, electrical, chemical, aerospace, and industrial engineering\u003c\/li\u003e \u003cli\u003eDescribes how to derive an empirical model when the engineering model is unknown\u003c\/li\u003e \u003cli\u003eProvides a robustness roadmap when using engineering modeling software, such as finite element analysis\u003c\/li\u003e \u003cli\u003eDemonstrates the effective application of numerous tools and methods to develop robust designs including Total Desirability Index and Binary Logistic Regression for Customer Loss Functions\u003c\/li\u003e \u003cli\u003eIs supported by an accompanying website featuring interactive animations and templates that can be customized for design problems encountered in practice\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eProbabilistic Design for Optimization and Robustness for Engineers\u003c\/i\u003e is useful for practising engineers faced with the challenge of variation in design as well as senior and graduate level engineering and statistics students studying systems engineering or multi-disciplinary design. Simulations are also featured on the book's companion website, providing an excellent tool for instructors to use during lectures.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989856993509,"sku":"NP9781118796191","price":113.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781118796191.jpg?v=1761785694","url":"https:\/\/k12savings.com\/products\/probabilistic-design-for-optimization-and-robustness-for-engineers-isbn-9781118796191","provider":"K12savings","version":"1.0","type":"link"}