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Digital Twin: Origin to Future

1
Material Research Institute, Athlone Institute of Technology, Athlone, Co., N37 HD68 Westmeath, Ireland
2
Confirm Centre for Smart Manufacturing, School of Engineering University of Limerick, Limerick, Co., V94 T9PX Limerick, Ireland
3
Software Research Institute, Athlone Institute of Technology, Athlone, Co., N37 HD68 Westmeath, Ireland
*
Author to whom correspondence should be addressed.
Academic Editor: Félix Jesús García Clemente
Appl. Syst. Innov. 2021, 4(2), 36; https://doi.org/10.3390/asi4020036
Received: 30 April 2021 / Revised: 19 May 2021 / Accepted: 21 May 2021 / Published: 24 May 2021
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. View Full-Text
Keywords: digital twin; Industry 4.0; digital model; system optimization; predictive maintenance digital twin; Industry 4.0; digital model; system optimization; predictive maintenance
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MDPI and ACS Style

Singh, M.; Fuenmayor, E.; Hinchy, E.P.; Qiao, Y.; Murray, N.; Devine, D. Digital Twin: Origin to Future. Appl. Syst. Innov. 2021, 4, 36. https://doi.org/10.3390/asi4020036

AMA Style

Singh M, Fuenmayor E, Hinchy EP, Qiao Y, Murray N, Devine D. Digital Twin: Origin to Future. Applied System Innovation. 2021; 4(2):36. https://doi.org/10.3390/asi4020036

Chicago/Turabian Style

Singh, Maulshree, Evert Fuenmayor, Eoin P. Hinchy, Yuansong Qiao, Niall Murray, and Declan Devine. 2021. "Digital Twin: Origin to Future" Applied System Innovation 4, no. 2: 36. https://doi.org/10.3390/asi4020036

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