Development of a Digital Twin Monitoring Framework for Blade Polishing Robot Process Based on Cloud–Edge–Device

Digital twins are digital representations of physical entities that enable real-time updates through data transmission between the physical and virtual domains. Based on a cloud–edge–device framework, this paper investigates methods for real-time data transmission, processing, and storage during the polishing process of a belt grinding robot. On this basis, a digital twin monitoring framework is constructed for blade-specific belt grinding robots. First, a virtual robot model was constructed using a joint modeling workflow in SolidWorks 2025 and 3ds Max 2025, incorporating a lightweight high-fidelity mesh processing algorithm based on the QEM method. Second, a data acquisition and transmission architecture was proposed for the belt grinding robot, enabling data reading, writing, and real-time monitoring during machining, as well as establishing a cloud–edge–device database. Finally, a cloud–edge–device digital twin monitoring framework for the blade belt grinding robot was developed, based on real-time monitoring of grinding process data. This work establishes a foundational data acquisition and visualization platform for the blade grinding robot, providing the necessary cyber-physical infrastructure, which future predictive models can develop and validate.