Towards traceable and trustworthy Digital Twins for quality control

DTs are simulation models that replicate physical systems in a virtual environment, dynamically updating the virtual model according to the observed state of its real counterpart to achieve physical control of the latter. DTs consist of a Physical to Virtual (P2V) and a Virtual to Physical (V2P) connection. DTs require complex modelling, often resorting to data-driven approaches. DTs allow for defects and systems fault prediction, enabling reliable predictive maintenance and process adjustment and control to be implemented [1]: DTs are essential for sustainability and digitalization [2]. The creation of DTs often neglects quality control measurements, resulting in their lack of traceability and inability to associate them with a confidence level in the prediction [2]. The evaluation of the measurement uncertainty will allow DTs’ application in the industrial context for quality control, defects and system faults prediction, statistical predictive defect correction and system maintenance within a traceable application framework. Available methods for DT’s uncertainty evaluation neglect coupling with the different parts of the DT, especially the closed-loop feedback control and the V2P connection [1,4]. Bayesian approaches will allow for rigorous management of such coupling effect also by non-parametric approaches. A rigorous definition of DT’s metrological characteristics is unavailable, and both accuracy and precision shall be defined, catering for the V2P closed-loop feedback control. This is being developed by the Trustworthy virtual experiments and digital twins (ViDiT) project, funded by the European Partnership on Metrology, tackling four complex applications: robot and machine tools, nanoindentation, primary electrical and cylindricity measurements.