A Virtual Testing Framework for Engineering Product Development

Virtual testing is a new engineering development trend to design, evaluate, and test new engineered products. This research proposes a virtual testing framework for new product development using three successive steps: (i) statistical model calibration, (ii) hypothesis test for validity check and (iii) virtual qualification. Statistical model calibration first improves the predictive capability of a computational model over a calibration domain. Next, the hypothesis test is performed under limited observed data to see if a calibrated model is sufficiently predictive for virtual testing of a new design. A u-pooling metric is employed for the hypothesis test to measure the degree of mismatch between predicted and observed results while considering uncertainty in the u-pooling metric due to the lack of experimental data. The calibrated model can be rejected only when the measured metric of the calibrated model strongly suggest that the null hypothesis—the calibrated model is valid—is false. If the null hypothesis is accepted, the virtual qualification process can be executed with a qualified model for new product developments. The qualification process builds a design decision matrix to aid in rational decision-making on the product developments. A computational model of a tire tread block was used to demonstrate the effectiveness of the proposed framework.