A modeling and adaptive evolution method for simulation parameters of digital twin shop floor

Digital twin (DT) model can accurately predict the future state of the shop floor and promptly identify potential problems, abnormal situations, or optimization opportunities. However, traditional production simulation method without considering the temporal characteristics of entities’ attributes. In the life cycle of physical entities, its attributes’ change will increase the DT simulation parameters’ error. Therefore, deep learning algorithms are used to model and evolve the simulation parameters of the digital twin shop floor (DTSF) to improve simulation accuracy. Firstly, the interaction mechanism between deep learning and discrete event simulation is designed. Then, a sequential regression variational autoencoder (SRVAE) is proposed to model the DT temporal parameters. Furthermore, the online instructor algorithm is proposed to update SRVAE through online data. This approach improves the simulation accuracy of DTSF while allowing its parameters to be self-maintained. And the effectiveness of the proposed method is verified by a case study.