A Review of Industrial Digital Twin Technology Research: Progress, Challenges and Future Directions

With the rapid development of industrial Internet, Internet of Things (IoT), and big data technologies, digital twin technology has emerged as a pivotal tool in enhancing industrial productivity, optimizing resource allocation, and driving the evolution of smart manufacturing systems. This paper provides a comprehensive review of the current state of research on industrial digital twins, exploring their applications across various sectors such as production processes, equipment management, and quality control. The review highlights the technological advances that have enabled the widespread adoption of digital twins, including virtual modeling, real-time data transmission and processing, and data fusion. It also discusses the key challenges faced by industries in implementing digital twins, such as data accuracy and integration, the complexity of virtual model construction, and the need for standardization and cross-platform integration. Additionally, the paper addresses the ongoing development trends in the field, including the increasing integration of digital twins with artificial intelligence, machine learning, and cloud computing. Finally, the paper provides an outlook on the future directions of digital twin research, identifying areas for further innovation and application, and offers recommendations for overcoming current limitations to support wider industrial adoption. By summarizing the progress and challenges, this review aims to provide theoretical insights and practical guidance for advancing industrial digital twin technology.