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Visualisation of the digital twin data in manufacturing by using augmented reality

Zhu, Zexuan, Liu, Chao and Xu, Xun 2019. Visualisation of the digital twin data in manufacturing by using augmented reality. Procedia CIRP 81 , pp. 898-903. 10.1016/j.procir.2019.03.223

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Abstract

With the wave of Industry 4.0, Digital Twin is attracting more and more attention world-wide. The term might have been coined some time ago, today the concept is increasingly being used in the field of smart manufacturing. Digital Twin provides advantages in different fields of manufacturing, such as production and design, remote diagnostics and service. Digital Twin relies on the continuously accumulated data and real-time presentation of the collected data to simultaneously update and modify with its physical counterpart. However, presenting a huge amount of collected data and information in a Digital Twin in an intuitive manner remains a challenge. Currently, augmented reality (AR) has been widely implemented in the manufacturing environment, such as product design, data management, assembly instructions, and equipment maintenance. By integrating graphics, audios and real-world objects, AR allows the users to visualise and interact with Digital Twin data at a new level. It gives the opportunity to provide intuitive and continual visualisation of the Digital Twin data. In this paper, an AR application that uses Microsoft HoloLens to visualise the Digital Twin data of a CNC milling machine in a real manufacturing environment is presented. The developed application allows the operator to monitor and control the machine tool at the same time, but also enables to interact and manage the Digital Twin data simultaneously, which provides an intuitive and consistent human machine interface to improve the efficiency during the machining process. The proposed application paves the way for further development of intelligent control process through AR devices in the future.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: Elsevier
ISSN: 2212-8271
Date of First Compliant Deposit: 25 June 2019
Date of Acceptance: 22 March 2019
Last Modified: 26 Jun 2019 13:09
URI: http://orca.cf.ac.uk/id/eprint/123709

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