Digital Twins: A Systematic Literature Review Based on Data Analysis and Topic Modeling

Abstract

The digital twin has recently become a popular topic in research related to manufacturing, such as Industry 4.0, the industrial internet of things, and cyber-physical systems. In addition, digital twins are the focus of several research areas: construction, urban management, digital transformation of the economy, medicine, virtual reality, software testing, and others. The concept is not yet fully defined, its scope seems unlimited, and the topic is relatively new; all this can present a barrier to research. The main goal of this paper is to develop a proper methodology for visualizing the digital-twin science landscape using modern bibliometric tools, text-mining and topic-modeling, based on machine learning models—Latent Dirichlet Allocation (LDA) and BERTopic (Bidirectional Encoder Representations from Transformers). The scope of the study includes 8693 publications on the topic selected from the Scopus database, published between January 1993 and September 2022. Keyword co-occurrence analysis and topic-modeling indicate that studies on digital twins are still in the early stage of development. At the same time, the core of the topic is growing, and some topic clusters are emerging. More than 100 topics can be identified; the most popular and fastest-growing topic is ‘digital twins of industrial robots, production lines and objects.’ Further efforts are needed to verify the proposed methodology, which can be achieved by analyzing other research fields.

Digital twins in the minerals industry – a comprehensive review

“…There had been increasing DT-related research across all industries involved during the second half of the 2010s led by manufacturing and aerospace applications. This trend is also consistent with statistics provided by the Scopus database shown in Figure 7 where a search query was made with ‘Digital Twin’ in the publication's title, abstract, and keyword search field (Kukushkin et al 2022). By cross-referencing literature reviews by Kritzinger et al (2018) and Li and Mahadevan (2017), it is clear that the number of publications on digital twins increased exponentially after 2017.…”

Scientometric Insights of Digital Twins in Industry 4.0

“…This dimension aims to encapsulate a critical aspect of the diversity within academic literature on this topic. Kukushkin et al [52] focuses on establishing an effective method for mapping out the scientific field of DTs through advanced bibliometric techniques, text mining, and topic modeling, utilizing ML models such as LDA and BERTopic. The study encompasses an analysis of 8,693 publications on DTs, gathered from the Scopus database, spanning from January 1993 to September 2022.…”

Digital Twin Application and Bibliometric Analysis for Digitization and Intelligence Studies in Geology and Deep Underground Research Areas

“…In their literature review, the evolution process of digital twins was divided into four stages: the digital twin concept was introduced and focus was directed on R&D in the first stage (Information Monitoring Model, 1985-2002, connected to a web browser and applied to multinational companies in the second stage (Digital Simulation, 2003-2014, connected to devices for effective virtual-physical data transmission in the third stage (IoT Implementation, 2014-2016), and real-time simulation, optimization, and defect detection using artificial intelligence and machine learning in the fourth stage (Decision-Making Tools, 2017-present) [11]. Kukushkin et al collected digital twin papers from 1993 to 2022 and analyzed LDA, machine learning-based topic modeling, and Bidirectional Encoder Representations from Transformers (BERTopic) [12]. Consequently, the digital twin papers' keywords were Industry 4.0, Internet of things, machine learning, simulation, cyber-physical systems, modeling, digitalization, deep learning, optimization, etc.…”

“…Consequently, the digital twin papers' keywords were Industry 4.0, Internet of things, machine learning, simulation, cyber-physical systems, modeling, digitalization, deep learning, optimization, etc. [12].…”

“…Because the total number of papers from 1984 to 2000 was 152, which is a small number, papers from that period with missing summaries were filled in after checking the original text. Semeraro et al analyzed digital twin papers from 2002 to 2020, and Kukushkin et al analyzed digital twin papers from 1993 to 2022[5,12]. However, this study includes the period of the third industrial revolution, the spread of digitalization that contributed to the establishment of digital twins.…”