Edge intelligence-driven digital twin of CNC system: Architecture and deployment

Abstract

• Propose a theoretical modeling approach based on the hierarchical structure of CNC systems. • Proposes an edge intelligence-driven digital twin architecture for CNC system. • Proposes a generic digital twin modeling method. • Proposes a novel task partitioning method to improve system throughput. • Proposes an adaptive model selection algorithm . In recent years, digital twin (DT) technology has gradually become the primary way to achieve the intelligence of CNC systems. However, with the development of next-generation information technologies such as artificial intelligence (AI) and its wide application in CNC systems, the limitation of computing power and network resources has become one of the urgent problems that must be solved by the DT of CNC systems. To address these problems, a theoretical modeling method for CNC systems based on its hierarchical structure is proposed first, and the edge intelligence (EI) technology is introduced to support the deployment of DT models. Meanwhile, a model partitioning method and a model selection algorithm are proposed to support real-time model response in the model deployment process. In addition, an application case of EI-driven DT of CNC system is given to diagnose and predict the tool wear during machining processes.