Online Credibility Assessment of Equipment Digital Twin for Discrete Manufacturing

The equipment digital twins (EDTs) for discrete manufacturing should be calibrated quickly to avoid irreversible physical damage to the equipment caused by biased control commands. Therefore, an online credibility assessment method for EDTs is urgently needed. However, existing assessment approaches consume too much time, and thus could not reveal dynamic faults in time. In this paper, the dynamic relationship between online evolution and actual applications of EDTs is investigated. Then, two steps are proposed to accelerate the assessment process significantly. One involves pre-constructing a performance-deviation-agent (PDA), and the other involves dynamically fitting the application-time-window (ATW) probability distribution. The methodology is applicable to discrete manufacturing processes. The dynamic credibility of EDT evolution process can be updated after every iteration of the model evolution. Sorting manufacturing equipment was used as a case study to demonstrate the effectiveness of this method. The time consumption was reduced by 90% compared with traditional assessment methods in the case. Note to Practitioners—The application of digital twins (DTs) for discrete manufacturing equipment typically aims to enhance the control effectiveness of the physical equipment. To ensure the effectiveness of control signals derived from DT-based simulations, the credibility of the equipment DTs must be known and guaranteed. This paper addresses the quick updating of the credibility of equipment DTs as they evolve according to real-time data collected from the equipment. The proposed methods are currently applicable to individual pieces of equipment with straightforward workflows. By adapting the training method for the error prediction model to scenarios involving multiple pieces of equipment and more complex workflows, the overall control efficacy within a workshop can be significantly improved.