ABSTRACT. The objective of this paper is to systematically review digital twin-based cyber-physical production systems in immersive 3D environments. The findings and analyses highlight that multi-source heterogeneous product lifecycle data, machining equipments and tools, and digital manufacturing technology can be harnessed in digital twin-driven applications. Throughout March 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “digital twin” + “cyber-physical production systems,” “immersive 3D environments,” “virtual modeling and simulation tools,” “spatial data visualization techniques,” and “remote sensing technologies.” As research published in 2022 was inspected, only 144 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, we selected 19 mainly empirical sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Dedoose, Distiller SR, and SRDR.
JEL codes: D53; E22; E32; E44; G01; G41

Keywords: digital twin; cyber-physical production system; immersive 3D environment

How to cite: Cug, J., Suler, P., and Taylor, E. (2022). “Digital Twin-based Cyber-Physical Production Systems in Immersive 3D Environments: Virtual Modeling and Simulation Tools, Spatial Data Visualization Techniques, and Remote Sensing Technologies,” Economics, Management, and Financial Markets 17(2): 82–96. doi: 10.22381/emfm17220225.

Received 26 March 2022 • Received in revised form 23 June 2022
Accepted 25 June 2022 • Available online 30 June 2022