RMDTs: Process-oriented function-triggered robotic milling digital twin system for service-expansion

Abstract

Recently, with the rapid development of information sensing and artificial intelligence, there exists a giant demand for high-performance machining of large complex components, especially for milling robot with both heavy-loaded cutting ability and flexible posture under extreme constraints. For both digital twin and robotic intelligent manufacturing, it has a broadly interdisciplinary prospect around the frontier basic science including sensing, data, information and AI. However, there exists a lacking of academic research and engineering exploration on the theory framework, function module, and service application of digital twin systems in robotic manufacturing. In this paper, a robotic milling digital twin system (RMDTs) based on process orientation, function triggering, and service extension is innovatively proposed. Furthermore, a dual-loop framework of RMDTs is established. The predictive simulation foreknowledge and intelligent decision-making are introduced, which enriches the outer loop of RMDTs. Furthermore, process data and its flow form of robotic milling is developed to construct the function modules of digital twin system. Finally, the proposed RMDTs has been validated in service expansion of three typical cases, with the excellent performance on motion performance and machining quality of robotic milling. The proposed framework and function modules of RMDTs display the potential to break through the extremely constrained conditions and flexible machining technology in robotic milling, supporting to develop the intelligent set of large-sized robotic machining equipment on the complicated curved marine propellers.