Digital twin architecture for data-guided intelligent production platforms

Due to the rapid increase in constantly changing client requirements, companies are undergoing rapid transformation and product life cycles are becoming shorter, making typical methods in modeling and simulation unsuitable for dealing with this situation. Meanwhile, utilization of digital twins in intelligent production domain can improve making accuracy, reduce production costs and control energy use by simulating the realistic states of production platforms in real time. Therefore, a universal data-guided architecture is proposed for automatically generating virtual models as the foundation for intelligent production digital twins. The novelty of the presented architecture lies in its data-guided method, which leverages advances in techniques of process mining and machine learning, as well as progressive model enhancement and verification. The objective of this architecture is to reduce and completely specify, or even remove, the demand for specialist knowledge when eliciting the appropriate virtual models. Finally, the proposed architecture is described using application example.