Kuzma Kukushkin
@spbstu.ru
Peter the Great Saint-Petersburg Polytechnic University
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
A. Y. Yashin, A. M. Ponomarenko, N. S. Zhlitsov, K. A. Kukushkin, G. S. Kurskiev, V. B. Minaev, A. V. Petrov, Yu. V. Petrov, and N. V. Sakharov
Pleiades Publishing Ltd
Kuzma Kukushkin, Yury Ryabov, and Alexey Borovkov
MDPI AG
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.
Yulia Turovets, Konstantin Vishnevskiy, Maria Tokareva, and Kuzma Kukushkin
IOP Publishing
The technological foresight plays a significant role in promotion of the emerging technologies. The paper investigates technological prospects of priorities implementation indicated in the ‘Strategy for the Scientific and Technological Development of the Russian Federation’. More precisely, we examine the advanced digital and intelligent production technologies deployment by applying foresight methods and new solutions such as an advanced text-mining. The main goal of the study is to define a range of the most promising technologies with respect to markets and products within the priority. We identified five large cross-cutting areas manifesting physical and digital convergence. These clusters include computer modelling and flexible manufacturing systems from the production side, and sensor technologies, virtual/augmented reality, the Internet of Things from the IT side. The revealed technological trends correspond to global patterns and unveil a range of sectoral applications. Product development technologies are confessed to be the core of digital manufacturing. During the analysis we uncovered 350 prospective products and services that should be exposed to further expert evaluation. Our results offer a new insight on science and technology policy-making by adjusting technology development across industries. A suggested framework allows expanding current boundaries of forecasting activities at the national level in order to boost Russian scientific performance.