Computer Aided Engineering (CAE) based optimization has a long tradition in engineering. The goal of optimization is often the reduction of material consumption while pushing the design performance to the boundaries of allowable stresses, deformations or other critical design responses. At the same time safety margins should be reduced, products should remain cost-effective and over-engineering be avoided. Of course a product should perform effectively in the real world, with the variety of manufacturing, assembly and environmental conditions which may be expected to occur. In the virtual world we can investigate the impact of such variations through, for example, stochastic analyses leading to CAE-based robustness evaluation. If CAE-based optimization and robustness evaluation is combined, we are entering the area of Robust Design Optimization (RDO), which may also be called “Design for Six Sigma” (DFSS) or just “Robust Design” (RD).

The main idea behind such methodologies is that uncertainties are considered in the design process. These uncertainties may have different sources: for example, variations in loading conditions, tolerances of the geometrical dimensions and material properties caused by production or deterioration. Some of these uncertainties may have a significant impact on design performance and must therefore be considered in the design optimization procedure.

Contents

Introduction 
Global Sensitivity Analysis
Multidisciplinary Deterministic Optimization 
Robustness Evaluation 
Robust Design Optimization 
Illustrative Example: Robust Design of a Steel Hook
Summary and Conclusions