ABSTRACT. In this article, previous research findings were cumulated, indicating that digital twin and Web3 technologies, industrial Artificial Intelligence of Things, 3D image modeling and intelligent data processing tools, and deep and machine learning algorithms can be deployed in interoperable extended reality environments. Throughout May 2023, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “Internet of Things reinforcement learning-based autonomous manufacturing operations in the industrial metaverse” + “simulated augmented reality and virtual immersive technologies,” “artificial vision and cloud-based digital twin production systems,” and “3D image modeling and intelligent data processing tools.” As research published in 2022 and 2023 was inspected, only 167 articles satisfied the eligibility criteria, and 20 mainly empirical sources were selected. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: Abstrackr, BIBOT, CASP, JBI SUMARI, Litstream, and MMAT.
JEL codes: D53; E22; E32; E44; G01; G41

Keywords: simulated augmented reality; digital twin production; 3D image modeling; Internet of Things; autonomous manufacturing; industrial metaverse

How to cite: Manole, C., Kalgı, M. E., Samoilă, A., and Alpopi, Ș. G. (2023). “Simulated Augmented Reality and Virtual Immersive Technologies, Artificial Vision and Cloud-based Digital Twin Production Systems, and 3D Image Modeling and Intelligent Data Processing Tools for Internet of Things Reinforcement Learning-based Autonomous Manufacturing Operations in the Industrial Metaverse,” Economics, Management, and Financial Markets 18(4): 9–23. doi: 10.22381/emfm18420231.

Received 14 June 2023 • Received in revised form 21 December 2023
Accepted 27 December 2023 • Available online 30 December 2023