Robust engineering design optimization with non-uniform rational B-splines-based metamodels

Abstract

Non-uniform rational B-splines (NURBs) demonstrate properties that make them attractive as metamodels, or surrogate models, for engineering design purposes. Previous research has resulted in the development of algorithms capable of fitting NURBs-based metamodels to engineering design spaces, and optimizing these models. This article presents an approach to robust optimization that employs NURBs-based metamodels. This robust optimization technique exploits the unique structure of NURBs-based metamodels to derive a simple but effective robustness metric. An algorithm is demonstrated that uses this metric to weigh robustness against optimality, and visualizes the trade-offs between these metamodel properties. This approach is demonstrated with test problems of increasing dimensionality, including several practical design challenges.