###### Abstract
In the realm of intelligent production lines, digital twin technology not only facilitates the creation of virtual models prior to the establishment of new or revamped production lines but also ensures the comprehensive integration of all elements, processes, and business data. This technology encompasses simulation, predictive simulation, and equipment diagnostics throughout the production process [27]. Furthermore, it demonstrates significant potential in monitoring line operations and maintenance through real-time data acquisition and simulation analysis, enabling the diagnosis of line health, the identification of faults, and the prediction of maintenance timing. [PERSON] [28] proposed an intelligent monitoring framework for product line reconstruction and the manual assembly of customized products based on digital twins, addressing issues related to misassembly and inefficiency. [PERSON] [29] introduced a digital twin architecture aimed at optimizing productivity within controlled-environment agriculture. Despite its immense potential, the implementation of digital twin technology faces challenges in integrating various technologies, adhering to stringent professional standards, and ensuring seamless data acquisition. The integration of technologies such as modeling, simulation, data acquisition, transmission, and artificial intelligence necessitates a high level of enterprise digitization. At the operational and maintenance levels, professional quality and expertise are crucial; highly skilled personnel are required, and the successful completion of projects often depends on cross-departmental collaboration [30]. Furthermore, acquiring data on physical entities, processing data fusion, and developing mapping mechanisms and interaction methods between virtual and real environments remain promising areas for further research. The collaborative integration of digital twin technology within the manufacturing industry has led to significant advancements in three-dimensional visual monitoring for intelligent production systems. This is evidenced by its successful implementation across various sectors, including automotive, aerospace, and mechanical manufacturing [31]. A major challenge in this context is the development of geometric, physical, logical, and data models that accurately represent the actual production line. Concurrently, it is essential to collect production data to facilitate real-time monitoring and synchronization between the physical and digital environments, thereby providing technical support for intelligent monitoring [32]. This study focuses on the application of digital twin technology in intelligent manufacturing production lines. It examines the role of sensors and data acquisition tools in collecting real-time data from physical systems and operational processes while simultaneously simulating the behavior of these systems through digital models. This dual capability facilitates real-time visualization and analytical functions [33]. Furthermore, the research explores the theoretical foundations of digital twin technology, its potential applications, and its significance in intelligent production. The objective of this paper is to develop a digital twin model that emphasizes critical condition monitoring elements within the manufacturing processes of intelligent production lines. Additionally, it investigates the architecture of a custom intelligent digital twin system specifically designed for automobile wheel production lines, ultimately providing innovative solutions and theoretical frameworks to enhance the intelligent manufacturing of automobile wheels.