###### Abstract  
This study addresses the challenges associated with virtual-real interactions, the limitations of one-dimensional data presentation, restricted real-time functionalities, and the lack of effective models for monitoring production line status. It specifically investigates intelligent production lines for automotive wheels as the focal point of the research. This study explores the construction methodology and the application of intelligent production lines through the utilization of digital twin technology. A hierarchical design approach is employed, integrating industrial Internet of Things (IoT) technology to create a comprehensive digital twin system. This system consists of four layers: the physical production line layer, the data acquisition and processing layer, the digital twin production line layer, and the application service layer. Precise mapping from the physical production line to the digital twin model is achieved using the advanced 3D modeling and simulation software, PQ Factory, while real-time data collection and transmission are facilitated through the standardized OPC UA protocol. The effectiveness of the system is substantiated through a detailed case study. The findings demonstrate that the intelligent production line system, which leverages digital twin technology for automotive wheels, enables real-time monitoring of the production process and provides innovative solutions, along with a robust theoretical framework for comprehensive analysis, diagnosis, evaluation, optimization, prediction, and decision making in the production of automotive wheels. intelligent manufacturing; digital twins; automobile wheel hub; intelligent production line; real-time monitoring; PQ factory +
Footnote †: journal: Applied sciences  
0000-0001-6000-0001-6000][PERSON]. [PERSON] Research on the Construction of Automobile Wheel Intelligent Production Line Based on Digital Twin  
15, 867115871 analytics [3]. The integration of digital tools and data significantly optimizes production processes, resulting in substantial improvements in efficiency. The primary objective of intelligent manufacturing is to establish a flexible and responsive production system capable of rapidly adapting to market fluctuations, addressing individual customer requirements, and delivering high-quality products and services at a lower cost [4]. The concept of the digital twin has been documented in the international scientific literature for approximately two decades, highlighting its status as a relatively novel area of research. This field integrates advanced nonlinear multiscale and multiphysics models into a cohesive system, simulating the realistic responses of physical entities to varying environmental conditions. The initial reference to digital twinning can be traced back to 2002, when [PERSON] proposed the idea that a digital informational representation of a physical system could exist as an independent entity. Initially applied within the context of product lifecycle management (PLM) [5; 6], this concept has since expanded to encompass lean principles, as well as the roles of product manufacturers and consumer demands throughout the entire product lifecycle, which includes the stages of design, production, maintenance, and disposal [7]. The United States Air Force Research Laboratory, in collaboration with NASA, introduced the concept of a digital twin in 2011 for the development of future aircraft. This concept is defined as a highly integrated simulation model that encompasses multiple physical phenomena, scales, and probabilities, specifically tailored to aircraft or systems. It utilizes physical models, sensor data, and historical data to replicate the functionality, real-time status, and evolutionary trends of the corresponding entity, thereby drawing significant attention to the digital twin [8]. Since 2015, digital twin technology has experienced rapid development, and its scope of application has been expanding.