Abstract

A digital twin is a computational model in cyberspace that is used to support engineering decisions. Maintaining high predictive capability of a digital twin model is of great concern to the engineers who make design decisions at the early stages of product development. In the work described in this paper, the predictive capability of the digital twin approach is improved by considering uncertainties in manufacturing and test conditions. The proposed digital twin approach can be used in a variety of product development settings. The proposed idea takes advantage of hybrid digital twin approaches, using both data-driven and physics-based approaches. The proposed approach is based on two techniques; (i) statistical model calibration and (ii) probabilistic element updating. In statistical model calibration, statistical parameters of input variables are estimated. Further, probabilistic analysis using estimated statistical parameters can predict possible critical elements. In probabilistic element updating procedures, the possible crack initiation and growth element is updated. The validity of the proposed method is demonstrated using a case study of an automotive sub-frame fatigue test. From the results, we conclude that the proposed digital twin approach can accurately estimate crack initiation and growth of an automotive structure under uncertain loading conditions and material properties.