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Deep Learning-based Computer Vision Algorithms, Immersive Analytics and Simulation Software, and Virtual Reality Modeling Tools in Digital Twin-driven Smart Manufacturing

Deep Learning-based Computer Vision Algorithms, Immersive Analytics and Simulation Software, and... The aim of this systematic review is to synthesize and analyze digital twin- and augmented reality-based cyber-physical production systems. With increasing evidence of digital twin simulation and modeling tools, there is an essential demand for comprehending whether digital twin-based cyber-physical production systems and industrial automation are instrumental in machining process performance of smart factories. In this research, prior findings were cumulated indicating that digital twinbased cyber-physical production systems deploy smart factory data across virtual environments. I carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout February 2022, with search terms including “digital twin” + “deep learning-based computer vision algorithms,” “immersive analytics and simulation software,” “virtual reality modeling tools,” and “smart manufacturing.” As I analyzed research published in 2022, only 154 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant data), results unsupported by replication, undetailed content, or papers having quite similar titles, I decided on 23, chiefly empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, Distiller SR, ROBIS, and SRDR. JEL codes: D53; E22; E32; E44; G01; G41 Keywords: digital twin; smart manufacturing; virtual reality modeling tool http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Economics, Management, and Financial Markets Addleton Academic Publishers

Deep Learning-based Computer Vision Algorithms, Immersive Analytics and Simulation Software, and Virtual Reality Modeling Tools in Digital Twin-driven Smart Manufacturing

Nancy Lyons
Economics, Management, and Financial Markets , Volume 17 (2): 15 – Jan 1, 2022
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15 pages

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Publisher
Addleton Academic Publishers
Copyright
© 2009 Addleton Academic Publishers
ISSN
1842-3191
eISSN
1938-212X
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Abstract

The aim of this systematic review is to synthesize and analyze digital twin- and augmented reality-based cyber-physical production systems. With increasing evidence of digital twin simulation and modeling tools, there is an essential demand for comprehending whether digital twin-based cyber-physical production systems and industrial automation are instrumental in machining process performance of smart factories. In this research, prior findings were cumulated indicating that digital twinbased cyber-physical production systems deploy smart factory data across virtual environments. I carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout February 2022, with search terms including “digital twin” + “deep learning-based computer vision algorithms,” “immersive analytics and simulation software,” “virtual reality modeling tools,” and “smart manufacturing.” As I analyzed research published in 2022, only 154 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant data), results unsupported by replication, undetailed content, or papers having quite similar titles, I decided on 23, chiefly empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, Distiller SR, ROBIS, and SRDR. JEL codes: D53; E22; E32; E44; G01; G41 Keywords: digital twin; smart manufacturing; virtual reality modeling tool

Journal

Economics, Management, and Financial Markets – Addleton Academic Publishers

Published: Jan 1, 2022

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