Bibliometric Analysis of Digital Twin: Scientometrics and Future Research Trends

A digital twin (DT) is a precise digital replica of an object or the physical world in which real-time data is input to model, analyze, forecast, and improve key performance indicators. To explore the complex landscape of DT research, the current scientometric review focuses on four pivotal components: architecture, application, middleware, and tools. For the study, the Citespace visualization tool is employed and articles are sourced from the Scopus database, focusing on publications between 2010 and 2024. The initial search yielded 11247 publications, which, after applying filters dataset was reduced to a final dataset of 2214 relevant articles. The view in the current study is centered on the following: keyword co-occurrence network (KCN), country analysis, document co-citation network (DCN), and burst reference analysis. The goal of the current study is to explore the four important components: architecture, applications, middleware, and tools. Limited previous study has explored such components. The current work presents a statistical summary of the development of research on DTs, examining existing patterns and highlighting important scientific discoveries. Finding the most popular trends and patterns of DT technology around the world will assist future researchers gain important knowledge about new sectors and indicate areas that could benefit from more research and development.