In robust design of complex systems, metamodeling techniques are commonly used to replace expensive computer simulations. To improve the sampling efficiency, efforts have been made towards developing objective-oriented sequential sampling methods for deterministic problems. In this paper, an extended objective-oriented sequential sampling method is proposed for robust design, with an emphasis on those problems with uncertainty in design variables. The method involves quantitative assessment of the effects of metamodeling uncertainty on the robust responses, as well as a sequential strategy of choosing samples to adaptively improve the predicted robust response. To validate the benefits of the sequential strategy, two mathematical examples are illustrated first. This is followed by an automotive crashworthiness design example, a highly expensive and non-linear problem. Results show that the proposed method can mitigate the effect of both metamodeling uncertainty and design uncertainty, and more efficiently identify the robust solution compared to the one-stage sampling approach that is commonly used in practice.

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