Probabilistic Design for Optimization and Robustness for Engineers

Preface

Engineers spend years learning mathematical models to describe the behavior of systems. However, only a small portion of the engineering curriculum is dedicated to accounting for variation faced by product and process designers. Even here, the focus is usually limited to controlling manufacturing variation through tolerance analysis. Today, many engineering curricula offer elective courses in experimental design or robust design, but these courses focus more on system optimization and reducing variation in design through experimentation. This book presents the theory of modeling variation using physical models and presents methods for practical applications including making designs less sensitive to variation. This approach helps create designs that are easy to manufacture, with less design and manufacturing costs, and utilize more realistic tolerances. Methods are presented for determining nominal parameter settings that minimize output variation, determining the output variation caused by each input parameter, and minimizing total system costs, which includes the cost of non-conformance.

A challenge for this book is the lack of in-depth statistical training for many engineers. Many engineering curricula require a single course on probability or have no requirement at all. Stochastic modeling and optimization require some advanced statistical methods. Introductory chapters provide a logical roadmap to allow a complete understanding of the material without overwhelming ...