Past, present, and future research of digital twin for smart manufacturing

Author:

Son Yoo Ho1,Kim Goo-Young1,Kim Hyeon Chan1,Jun Chanmo2,Noh Sang Do1

Affiliation:

1. Department of Industrial Engineering, Sungkyunkwan University, Suwon-si 16419, Republic of Korea

2. Manufacturing Innovation Center, Production Engineering Research Institute, LG Electronics, Pyeongtaek-si 17709, Republic of Korea

Abstract

ABSTRACT In the era of the Fourth Industrial Revolution, there is a growing focus on digital twin (DT) in order to advance toward smart manufacturing. Thus, researchers have conducted numerous studies on DT and extensively developed related technologies. There are many studies that apply and analyse DT to actual manufacturing sites for the realization of a smart factory, but it is necessary to clearly consider which part of DT is applied and what function it performs in manufacturing. As such, this study analysed and classified prior literature based on various phases of product lifecycle management, an application field of DT in manufacturing, and the hierarchy level axis of Reference Architecture Model Industry 4.0, the target scope of DT. Accordingly, this study identified research trends in the past and present as well as analysed and identified the major functions of DT (prototyping, pilot testing, monitoring, improvement, and control). Through a gab study on the inadequate aspects of past and present researches, this study proposes directions for future studies on DT and a system architecture that can perform all the functions of DT.

Funder

MOTIE

KIAT

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computer Graphics and Computer-Aided Design,Human-Computer Interaction,Engineering (miscellaneous),Modeling and Simulation,Computational Mechanics

Reference164 articles.

1. Chatter model for enabling a digital twin in machining;Afazov;The International Journal of Advanced Manufacturing Technology,2020

2. Digital twin as a service (DTaaS) in industry 4.0: An architecture reference model;Aheleroff;Advanced Engineering Informatics,2021

3. The use of digital twin for predictive maintenance in manufacturing;Aivaliotis;International Journal of Computer Integrated Manufacturing,2019

4. Digital twin-driven supervised machine learning for the development of artificial intelligence applications in manufacturing;Alexopoulos;International Journal of Computer Integrated Manufacturing,2020

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