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Computer modeling and simulation have significantly facilitated the efficiency of product design and development in modern engineering, especially in the automotive industry. For the design and optimization of car models, optimization algorithms usually work better if the initial searching points are within or close to a feasible domain. Therefore, finding a feasible design domain in advance is beneficial. A data mining technique, Iterative Dichotomizer 3 (ID3), is exploited in this paper to identify sets of reduced feasible design domains from the original design space. Within the reduced feasible domains, optimal designs can be efficiently obtained while releasing computational burden in iterations. A mathematical example is used to illustrate the proposed method. Then an industrial application about automotive structural optimization is employed to demonstrate the proposed methodology. The results show the proposed method’s potential in practical engineering.

To reduce the computational time of the iterations in robust design, meta-models are frequently utilized to approximate time-consuming computer aided engineering models. However, the bias of meta-model uncertainty largely affects the robustness of the prediction results, this uncertainty need to be addressed before design optimization. In this paper, an efficient uncertainty quantification method considering both model and parameter uncertainties is proposed. Firstly, the uncertainty of parameters are characterized by statistical distributions. The Bayesian inference is then performed to improve the predictive capabilities of the surrogate models, meanwhile, the model uncertainty can also be quantified in the form of variance. Monte Carlo sampling is finally utilized to quantify the compound uncertainties of model and parameter. Furthermore, the proposed uncertainty quantification method is used for robust design.