The progress and trend of digital twin research over the last 20 years: A bibliometrics-based visualization analysis

Digital Twin (DT) is an increasingly popular technology in both academia and industry due to its potential to facilitate the realization of advanced concepts such as digitization, intelligence, and service. This paper uses bibliometrics and visual methods to analyze the progress and trends of DT research. Data was collected from the Web of Science Core Collection, comprising 10,840 papers published from 2003 to 2023. Co-citation analysis, co-word analysis, and cluster analysis were employed, with the aid of software such as CiteSpace and VOSviewer, to construct a knowledge domain graph. Upon a meticulous examination of the DT research field, this study categorically delineates the evolution of DT across four levels: Basic Technology, Application Development, Specific Implementation, and Auxiliary Technology. These insights not only facilitate a profound comprehension of the trajectory and burgeoning horizons of DT but also serve as a beacon to guide future research endeavors. Such scholarly work is indispensable for the enhancement and refinement of DT, ensuring its robust evolution in the dynamic landscape of industrial digitization.