###### Abstract
Leading industrial enterprises worldwide, such as Siemens and General Electric (GE), have made substantial investments in the field of digital twins, driving the deep integration and widespread application of this technology across various industries [9]. A digital twin is a technical framework that integrates multi-physical, multi-scale, and multi-disciplinary attributes. It possesses real-time synchronization capabilities, accurate mappings, and high-fidelity characteristics, enabling seamless interaction and fusion between the physical and digital worlds [10]. Digital twin technology ensures the smooth integration of the digital and physical domains through the use of digital model simulations, analyses, and data aggregation, further enhanced by the capabilities of artificial intelligence and big data analytics [11]. This technology not only facilitates the integration of the manufacturing information domain with the physical world but also has significant implications for the advancement of intelligent manufacturing. Its applications span numerous sectors, including industrial manufacturing [12], health management [13], energy management [14], smart cities [15], and aerospace [16], it enhances efficiency, boosts performance, and nurtures innovation. Digital twin technology is a fundamental component of smart manufacturing, enabling the creation of intricate virtual representations of production lines that enhance operational efficiency and product quality. These digital twins facilitate the simulation and optimization of manufacturing processes, effectively identifying potential issues and uncovering opportunities for improvement [17]. As a result, they empower manufacturers to make more informed decisions regarding production planning and resource allocation, as evidenced by successful applications across various industries [18]. The integration of sensor technology, the industrial internet, machine learning, and data analytics within digital twin technology establishes a closed-loop control system [19]. [PERSON] et al. [20] introduced a quality monitoring methodology for production lines based on digital twin principles, where parameters indicative of quality performance are mapped to a virtual environment and optimized. Similarly, [PERSON] et al. [21] developed a quality management framework for the final assembly line of aircraft using digital twin technology, employing data mining tools to analyze real-time quality data to enhance assembly line quality. This system supports real-time monitoring and the optimization of industrial processes, as demonstrated by its effective implementation in smart factory management and zero-carbon industrial parks [22]. Consequently, it significantly boosts production efficiency and elevates the level of intelligent operations. The application of digital twin technology in intelligent manufacturing has become increasingly widespread, encompassing key areas such as the development of theoretical models, exploration of practical applications, fault detection techniques, health monitoring systems, and advanced production controls [23]. [PERSON], [PERSON] et al. [24] from Beihang University introduced the concept of a digital twin workshop and established a five-dimensional model along with a standardized architecture. Concurrently, scholars such as [PERSON], [PERSON] et al. [25] have made significant contributions to digital twin modeling, technology development, and the construction of virtual machine tools. Digital twin technology collects key parameters such as machine performance, temperature, and pressure, employing sensors and data acquisition tools to facilitate real-time data retrieval from physical systems and perform parallel analysis on digital counterparts, simultaneously offering manufacturers instruments for immediate visualization and analysis [26]. This technology relies on modeling, simulation, and virtual reality technologies to dynamically replicate real-world elements and accomplish real-world regulation and control.