A framework of model validation and virtual product qualification with limited experimental data based on statistical inference

Virtual testing is a recent engineering development trend to design, evaluate, and test new engineered products. This research proposes a framework of virtual testing based on statistical inference for new product development comprising of 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 in a calibration domain. Next, the hypothesis test is performed with limited observed data to see if a calibrated model is sufficiently predictive for virtual testing of a new product design. An area metric and the u-pooling method are employed for the hypothesis test to measure the degree of mismatch between predicted and observed results while considering statistical uncertainty in the area metric due to the lack of experimental data. Once the calibrated model becomes valid, 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 for product design alternatives. The effectiveness of the proposed framework is demonstrated through the case study of a tire tread block.