PROBABILISTIC DESIGN FOR OPTIMIZATION AND ROBUSTNESS FOR ENGINEERS

How to apply robust design to engineering design problems

Unlike 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.

This book:

• Provides a comprehensive guide to optimization and robustness for probabilistic design for engineers without a statistical background
• Features examples, case studies, and exercises that are applicable to a wide range of disciplines such as mechanical, electrical, chemical, aerospace, and industrial engineering
• Describes how to derive an empirical model when the engineering model is unknown
• Provides a robustness roadmap when using engineering modeling software, such as finite element analysis
• Demonstrates the effective application of numerous tools and methods to develop robust designs including Total Desirability Index and Binary Logistic Regression for Customer Loss Functions
• Is supported by an accompanying website featuring interactive animations and templates that can be customized for design problems encountered in practice

Probabilistic Design for Optimization and Robustness for Engineers 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.