Digital twins-based smart manufacturing system design in Industry 4.0 : A review
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 119-137 |
Journal / Publication | Journal of Manufacturing Systems |
Volume | 60 |
Online published | 25 May 2021 |
Publication status | Published - Jul 2021 |
Link(s)
Abstract
A smart manufacturing system (SMS) is a multi-field physical system with complex couplings among various components. Usually, designers in various fields can only design subsystems of an SMS based on the limited cognition of dynamics. Conducting SMS designs concurrently and developing a unified model to effectively imitate every interaction and behavior of manufacturing processes are challenging. As an emerging technology, digital twins can achieve semi-physical simulations to reduce the vast time and cost of physical commissioning/reconfiguration by the early detection of design errors/flaws of the SMS. However, the development of the digital twins concept in the SMS design remains vague. An innovative Function-Structure-Behavior-Control-Intelligence-Performance (FSBCIP) framework is proposed to review how digital twins technologies are integrated into and promote the SMS design based on a literature search in the Web of Science database. The definitions, frameworks, major design steps, new blueprint models, key enabling technologies, design cases, and research directions of digital twins-based SMS design are presented in this survey. It is expected that this survey will shed new light on urgent industrial concerns in developing new SMSs in the Industry 4.0 era.
Research Area(s)
- Cyber-physical systems, Digital twins, Function-structure-behavior-control-intelligence-performance, Manufacturing system design, Smart manufacturing
Citation Format(s)
Digital twins-based smart manufacturing system design in Industry 4.0 : A review. / Leng, Jiewu; Wang, Dewen; Shen, Weiming et al.
In: Journal of Manufacturing Systems, Vol. 60, 07.2021, p. 119-137.Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review