Evaluation of User Interfaces Description Languages to Augmented Reality

Cruz-Carrizales, Camilo-Andrés; Bautista, Luis; Pedraza, Gabriel

doi:10.19053/01211129.v30.n57.2021.13304

Cited by 1 publication

(1 citation statement)

References 26 publications

(36 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, the Augmented reality (AR) is a technology that overlays digital information onto the physical world, creating an interactive and immersive experience [28]. Unlike virtual reality, which creates a completely artificial environment, AR enhances the real world by adding digital elements to it.…”

Section: Related Workmentioning

confidence: 99%

Non-Fungible Token based Smart Manufacturing to scale Industry 4.0 by using Augmented Reality, Deep Learning and Industrial Internet of Things

Ahmed Khan,

Abubakar Ibrahim

2023

IJPCC

View full text Add to dashboard Cite

The recent revolution in Industry 4.0 (IR 4.0) has characterized the integration of advance technologies to bring the fourth industrial revolution to scale the manufacturing landscape. There are different key drivers for this revolution, in this research we have explored the following among them such as, Industrial Internet of Things (IIoT), Deep Learning, Blockchain and Augmented Reality. The emerging concept from blockchain namely “Non-Fungible Token” (NFT) relating to the uniqueness of digital assets has vast potential to be considered for physical assets identification and authentication in the IR 4.0 scenario. Similarly, the data acquired through the deployment of IIoT devices and sensors into smart industry spectrum can be transformed to generated robust analytics for different industry use-cases. The predictive maintenance is a major scenario in which early equipment failure detection using deep learning model on acquired data from IIoT devices has major potential for it. Similarly, the augmented reality can be able to provide real-time visualization within the factory environment to gather real-time insight and analytics from the physical equipment for different purposes. This research initially conducted a survey to analyse the existing developments in these domains of technologies to further widen its horizon for this research. This research developed and deployed a smart contract into an ethereum blockchain environment to simulate the use-case for NFT for physical assets and processes synchronization. The next phase was deploying deep learning algorithms on a dataset having data generated from IIoT devices and sensors. The Feedforward and Convolutional Neural Network were used to classify the target variables in relation with predictive maintenance failure analysis. Lastly, the research also proposed an AR based framework for the visualization ecosystem within the industry environment to effectively visualize and monitory IIoT based equipment’s for different industrial use-cases i.e., monitoring, inspection, quality assurance.

show abstract

Section: Related Workmentioning

confidence: 99%