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Risk-Based Design - 3 Day Course

Course Description

Designing products and systems in the presence of uncertainty is a challenge due to uncertain factors in manufacturing and operational processes and the difficulties of precisely predicting system performance and reliability, estimating the consequences of failure, and managing risk. This three-day course presents techniques that engineers can use to model risk, make good decisions, and optimize designs when uncertainty cannot be ignored. The course topics include modeling uncertainty, estimating probability distributions, evaluating and comparing the risk of design alternatives, decision-making under uncertainty, and design optimization under uncertainty.

Who Should Take the Course

Engineers concerned with the design of products and systems.

What the Student Will Learn

How to design products and systems that ensure high reliability and quality; how to optimize performance and reliability.

Included Materials

Attendees will receive a copy of course notes and sample code

Dr. Byeng D. Youn

Picture of Dr. Byeng D. Youn

is an assistant professor in the Department of Mechanical Engineering at the University of Maryland. He earned his Ph.D. from the University of Iowa. His expertise includes system risk assessment and design (multiphysics, multicomponent, and multiscale systems); prognostic health monitoring; Verification & Validation (V&V); Bayesian Information Toolkit (BIT); and Bayesian reliability analysis and design. Dr. Youn's current research includes risk-based design, prognostics, stochastic defect mechanics, and bioinspired design and has led to several notable awards including an ASME DETC Black & Decker Best Paper Award (2001) and ISSMO/Springer Prize for a Young Scientist (2004).

Dr. Jeffrey W. Herrmann

Picture of Dr. Jeffrey W. Herrmann

is an associate professor at the University of Maryland, where he holds a joint appointment with the Department of Mechanical Engineering and the Institute for Systems Research. Dr. Herrmann earned his B.S. in applied mathematics from Georgia Institute of Technology and received his Ph.D. in industrial and systems engineering from the University of Florida. His publications cover topics in process planning, production scheduling, design for manufacturing, and reliability estimation. His current research interests include reliability-based design optimization and engineering design decision-making.

Course Outline

  1. Introduction
    • Why and where is risk important?
    • How does design affect risk?
  2. Risk Measurement
    • How to model uncertainty in physical variables?
    • How do I measure the risk of a design?
    • How do I compare the risk of different design alternatives?
    • Model validation
  3. Design Selection
    • Design sensitivity analysis of risk
    • How do I select a design alternative in the presence of risk and other objectives (criteria)?
  4. Design Optimization
    • How can I find an optimal design in the presence of risk and other objectives (criteria)?
  5. Case Studies in Risk-Based Design