Model validation has become a primary means to evaluate accuracy and reliability of computational simulations in engineering design. Mathematical models enable engineers to establish what the most likely response of a system is. However, despite the enormous power of computational models, uncertainty is inevitable in all model-based engineering design problems, due to the variation in the physical system itself, or lack of knowledge, and the use of assumptions by model builders. Therefore, realistic mathematical models should contemplate uncertainties. Due to the uncertainties, the assessment of the validity of a modeling approach must be conducted based on stochastic measurements to provide designers with the confidence of using a model. In this paper, a generic model validation methodology via uncertainty propagation is presented. The approach reduces the number of physical testing at each design setting to one by shifting the evaluation effort to uncertainty propagation of the computational model. Response surface methodology is used to create metamodels as less costly approximations of simulation models for uncertainty propagation. The methodology is illustrated with the examination of the validity of a finite-element analysis model for predicting springback angles in a sample flanging process.

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