## 2 Methods  
Drawing upon the five-dimensional framework of digital twins--comprising the physical object, virtual model, twin data, service system, and connectivity, as articulated by Professor [PERSON]. [34]--this study presents a construction framework for a digital twin production line, as illustrated in Figure 1. This research focuses on the processing, refinement, inspection, and management of automotive wheel components, aiming to enhance the automotive wheel production line by advancing it toward a more sophisticated paradigm of intelligent manufacturing through the development of a precise digital twin model. The technological process of the intelligent automobile wheel production line consists of several key components: An industrial robot, operating according to a preprogrammed sequence, accurately grasps the tool unit and swiftly replaces the gripper. Simultaneously, the automated 3D warehouse within the storage unit retrieves the blank hub, preparing it for further processing. The industrial robot then transports the hub to the processing unit for CNC machining. Once the CNC machining is complete, the parts are conveyed to the grinding unit for a meticulous polishing process. Following image detection, the finished  
Figure 1: Five-dimensional conceptual model of digital twin production line. products are transported to the sorting unit, where they are categorized into either the qualified or unqualified areas based on established quality standards and inspection results. This study examines the complexities of an intelligent manufacturing production line specifically designed for automotive wheels. It seamlessly integrates a diverse array of core components, including industrial robotic execution units, CNC machining centers, detection units, sorting units, storage units, tool units, and grinding units, all of which ultimately converge into a master control unit. Furthermore, the system harmoniously incorporates advanced technologies such as the industrial internet, PLC control, virtual debugging, machine vision recognition, industrial network communication protocols, the intricate programming logic of industrial robots, the MES management system, and artificial intelligence technology, thereby fully realizing the intelligence of automobile wheel production. Figure 2 illustrates the model of each unit within the intelligent manufacturing line for automotive wheels. The critical aspect of developing a digital twin system for an intelligent automotive wheel production line is the precise modeling of each equipment unit, along with the integrated development of the entire digital twin production line. OPC UA technology facilitates the accurate acquisition and seamless integration of multi-source heterogeneous data. Furthermore, the virtual and physical production lines can achieve real-time synchronization. Deep learning and knowledge graph technologies have been integrated and applied to the fault diagnosis and maintenance strategies of the production line, accompanied by a real-time visual display of production operation data. The specific implementation method is as follows:  
Figure 2: Models of each unit in an intelligent manufacturing production line for automobile wheels: (**a**) master control unit; (**b**) tool unit; (**c**) robotic execution unit; (**d**) CNC machining units; (**e**) grinding unit; (**f**) sorting unit; (**g**) detection unit; (**h**) storage units; (**i**) vehicle logo unit; (**j**) wheel hubs; (**k**) RFID read/write head controller. 1. Establish a digital twin model for each individual piece of equipment and develop a comprehensive digital twin of the entire production line. 2. Using OPC UA technology, real-time data from the physical production line is collected and efficiently transmitted to both the digital twin of the production line and the application service layer. 3. The communication interface and variable data connection technology facilitate seamless synchronous operation between the virtual workshop and the physical workshop. 4.