scholarly journals Virtual Reality Digital Twin for a Smart Factory

Author(s):  
Gicu-Călin Deac ◽  
◽  
Crina-Narcisa Georgescu ◽  
Cicerone Laurentiu Popa ◽  
Costel Emil Cotet

This paper describes authors’ research in developing collaborative virtual reality applications as an interface for monitoring big data by creating a digital twin of the factory and sync the movement of virtual machines with the real ones. The platform allows an interactive reading of the sensor telemetry and processes data, maintenance information and access to a large technical library. For data acquisition and reports, a novel image data method was used. The data values that are encoded as pixel colors of images, using different encoding methods for each data type (text, integer, float, Boolean) are also encrypted using an image as a symmetric encryption key and are stored in the cloud in a time base folder structure, assuring a better data compression, security and speed, compared with the existing solutions based on JSON and NoSQL databases. The platform allows the remote access from the VR environment to the machines consoles and allows parametrization and remote commands.

2021 ◽  
Vol 4 (2) ◽  
pp. 36
Author(s):  
Maulshree Singh ◽  
Evert Fuenmayor ◽  
Eoin Hinchy ◽  
Yuansong Qiao ◽  
Niall Murray ◽  
...  

Digital Twin (DT) refers to the virtual copy or model of any physical entity (physical twin) both of which are interconnected via exchange of data in real time. Conceptually, a DT mimics the state of its physical twin in real time and vice versa. Application of DT includes real-time monitoring, designing/planning, optimization, maintenance, remote access, etc. Its implementation is expected to grow exponentially in the coming decades. The advent of Industry 4.0 has brought complex industrial systems that are more autonomous, smart, and highly interconnected. These systems generate considerable amounts of data useful for several applications such as improving performance, predictive maintenance, training, etc. A sudden influx in the number of publications related to ‘Digital Twin’ has led to confusion between different terminologies related to the digitalization of industries. Another problem that has arisen due to the growing popularity of DT is a lack of consensus on the description of DT as well as so many different types of DT, which adds to the confusion. This paper intends to consolidate the different types of DT and different definitions of DT throughout the literature for easy identification of DT from the rest of the complimentary terms such as ‘product avatar’, ‘digital thread’, ‘digital model’, and ‘digital shadow’. The paper looks at the concept of DT since its inception to its predicted future to realize the value it can bring to certain sectors. Understanding the characteristics and types of DT while weighing its pros and cons is essential for any researcher, business, or sector before investing in the technology.


2007 ◽  
Vol 61 ◽  
pp. 379-391 ◽  
Author(s):  
Ralf A. Kockro ◽  
Axel Stadie ◽  
Eike Schwandt ◽  
Robert Reisch ◽  
Cleopatra Charalampaki ◽  
...  

Author(s):  
Vuthea Chheang ◽  
Patrick Saalfeld ◽  
Fabian Joeres ◽  
Christian Boedecker ◽  
Tobias Huber ◽  
...  

2020 ◽  
Author(s):  
Xavier Martinez ◽  
Marc Baaden

AbstractMotivated by the current Covid-19 pandemic that has spurred a substantial flow of structural data we describe how molecular visualization experiences can be used to make these datasets accessible to a broad audience. Using a variety of technology vectors related to the cloud, 3D- and virtual reality gear, we examine how to share curated visualizations of structural biology, modeling and/or bioinformatics datasets for interactive and collaborative exploration. We discuss F.A.I.R. as overarching principle for sharing such visualizations. We provide four initial example scenes related to recent Covid-19 structural data together with a ready-to-use (and share) implementation in the UnityMol software.SynopsisVisualization renders structural molecular data accessible to a broad audience. We describe an approach to share molecular visualization experiences based on FAIR principles. Our workflow is exemplified with recent Covid-19 related data.


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