Review
Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues

https://doi.org/10.1016/j.rcim.2019.101837Get rights and content

Highlights

This paper reviews the current status and advancement of Digital Twin-driven smart manufacturing, with highlights on the following aspects:

  • Presented the connotation of Digital Twin-driven smart manufacturing and its potential impacts.

  • Proposed a reference model for constructing a Digital Twin, comprising of an information model, data processing and industrial communication technologies.

  • Discussed seven crucial research issues for developing Digital Twin applications for smart manufacturing.

Abstract

This paper reviews the recent development of Digital Twin technologies in manufacturing systems and processes, to analyze the connotation, application scenarios, and research issues of Digital Twin-driven smart manufacturing in the context of Industry 4.0. To understand Digital Twin and its future potential in manufacturing, we summarized the definition and state-of-the-art development outcomes of Digital Twin. Existing technologies for developing a Digital Twin for smart manufacturing are reviewed under a Digital Twin reference model to systematize the development methodology for Digital Twin. Representative applications are reviewed with a focus on the alignment with the proposed reference model. Outstanding research issues of developing Digital Twins for smart manufacturing are identified at the end of the paper.

Introduction

Digital Twin has gained significant impetus as a breakthrough technological development that has the potential to transform the landscape of manufacturing today and tomorrow [1]. Digital Twin [2], acting as a mirror of the real world, provides a means of simulating, predicting and optimizing physical manufacturing systems and processes. Using Digital Twin, together with intelligent algorithms, organizations can achieve data-driven operation monitoring and optimization [3], develop innovative product and services [4], and diversify value creation and business models [5].

Though studies have reported the potential application scenarios of Digital Twin in manufacturing, we identified that current approaches to the implementation of Digital Twin in manufacturing lack a thorough understanding of Digital Twin concept, framework, and development methods, which impedes the development of genuine Digital Twin applications for smart manufacturing. In this study, we discussed the connotations of Digital Twin-driven smart manufacturing in the context of Industry 4.0. The objectives and the contributions of this paper are to provide comprehensive discussions on the impact, reference model, application scenarios and research issues of Digital Twin for achieving smart manufacturing.

The remainder of the paper starts with tracing the vision of Digital Twin and the development to date based on studies from the literature (see Section 2). This is followed by an in-depth discussion on the connotation of Digital Twin-driven smart manufacturing in Section 3, highlighting how Digital Twin will transform the future manufacturing landscape. Section 4 details a Digital Twin reference model and enabling technologies for developing a Digital Twin-driven smart manufacturing solution. An overview of existing Digital Twin applications and some typical application scenarios are presented in Section 5. Section 6 discusses the critical research issues for future research. Section 7 concludes the research work.

Section snippets

Digital Twin overview

This section traces the history of the Digital Twin concept, clarifies its relations with several other tropical concepts in the manufacturing domain, summarizes its research and development progress, and highlights the research gaps.

Digital Twin-driven smart manufacturing

Manufacturing is becoming smart at all levels from the physical device, through factory management, to production networks, gaining abilities to learn, configure and execute with cognitive intelligence. This section outlines the trend of smart manufacturing and discusses the connotation of Digital Twin-driven smart manufacturing, highlighting the impact that Digital Twin may have for future manufacturing.

Digital Twin reference model

Digital Twin reflects the two-way dynamic mapping between a physical object and its virtual model in the cyberspace [24]. A Digital Twin presents a middleware architecture that abstracts its physical counterpart for high-level engineering management systems to make near real-time decisions [25]. Fig. 4 shows a Digital Twin reference model. At the technical core, the development of Digital Twin needs three components: (1) an information model that abstracts the specifications of a physical

Application scenarios

Although Digital Twin is a relatively new concept, some practical applications of Digital Twin have already been developed and reported in the literature. This section briefs the current status of Digital Twin applications. First, an overview of existing Digital Twin applications is provided, and the current status of Digital Twin applications is discussed. Second, three representative Digital Twin applications are introduced to demonstrate the advantages and potential of Digital Twin.

Research issues

Based on the discussions in the above sections, we summarize the following key research issues for advancing the research of Digital Twin-driven smart manufacturing.

Research issue 1: architecture pattern for a Digital Twin

There exist two system architecture patterns, namely server-based and edge-based. In server-based architecture, the data acquired from a physical device is routed back to a centralized server that performs the data analysis and Digital Twin construction. This pattern provides

Conclusions

This paper presents the current status and advancement of Digital Twin-driven smart manufacturing. The core concept, reference model, enabling technologies, application scenarios, and research issues of Digital Twin-driven smart manufacturing are discussed in detail.

With the rapid growth of integrating information technologies and operation technologies in the industry, significant efforts have been made to make manufacturing smart. As a core element of future manufacturing, Digital Twin-driven

References (85)

  • A. Gandomi et al.

    Beyond the hype: big data concepts, methods, and analytics

    Int. J. Inf. Manage.

    (2015)
  • C. Weber et al.

    M2DDM - a maturity model for data-driven manufacturing

    Procedia CIRP

    (2017)
  • L. Hu et al.

    Modeling of cloud-based digital twins for smart manufacturing with MT connect

    Procedia Manuf.

    (2018)
  • Y. Zheng et al.

    An application framework of digital twin and its case study

    J. Ambient Intell. Humaniz. Comput.

    (2018)
  • M. Ayani et al.

    Digital Twin: applying emulation for machine reconditioning

    Procedia CIRP

    (2018)
  • A. Padovano et al.

    A digital twin based service oriented application for a 4.0 knowledge navigation in the smart factory

    IFAC-PapersOnLine

    (2018)
  • J. Wang et al.

    Digital twin for rotating machinery fault diagnosis in smart manufacturing

    Int. J. Prod. Res.

    (2018)
  • C. Liu et al.

    A systematic development method for cyber-physical machine tools

    J. Manuf. Syst.

    (2018)
  • Y.Y. Cai et al.

    Sensor data and information fusion to construct Digital-Twins virtual machine tools for cyber-physical manufacturing

    Procedia Manuf.

    (2017)
  • G.N. Schroeder et al.

    Digital twin data modeling with AutomationML and a communication methodology for data exchange

    IFAC-PapersOnLine

    (2016)
  • M. Helu et al.

    A standards-based approach for linking as-planned to as-fabricated product data

    CIRP Ann.

    (2018)
  • T. Petković et al.

    Human intention estimation based on hidden markov model motion validation for safe flexible robotized warehouses

    Robot. Comput. Integr. Manuf.

    (2019)
  • I. Graessler et al.

    Intelligent control of an assembly station by integration of a digital twin for employees into the decentralized control system

    Procedia Manuf.

    (2018)
  • C. Liu et al.

    MTConnect-based cyber-physical machine Tool: a case study

    Procedia CIRP

    (2018)
  • E.E.H. Glaessgen et al.

    The digital twin paradigm for future NASA and US air force vehicles, in: 53rd AIAA/ASME/ASCE/AHS/ASC structures

  • F. Tao et al.

    Digital twin-driven product design, manufacturing and service with big data

    Int. J. Adv. Manuf. Technol.

    (2018)
  • E.J. Tuegel et al.

    Reengineering aircraft structural life prediction using a digital twin

    Int. J. Aerospace Eng.

    (2011)
  • F. Tao et al.

    Digital twin shop-floor: a new shop-floor paradigm towards smart manufacturing

    IEEE Access

    (2017)
  • ISO, ISO/AWI 23247 - Digital Twin manufacturing framework (Under development),...
  • B. Bagheri et al.

    Big future for cyber-physical manufacturing systems

    Design World

    (2015)
  • Digital twin market worth 15.66 billion USD by 2023, (n.d.)....
  • Predix platform | GE Digital, (n.d.). https://www.ge.com/digital/iiot-platform(accessed April 22,...
  • Digital twin | Siemens, (n.d.)....
  • ABB Ability, (n.d.). https://new.abb.com/abb-ability(accessed April 24,...
  • Overview of azure digital twins | Microsoft Docs, Microsoft. (n.d.)....
  • Smart manufacturing operations planning and control program | NIST, (n.d.)....
  • E. Wenger

    Etienne, Artificial Intelligence and Tutoring Systems : Computational and Cognitive Approaches to the Communication of Knowledge

    (1987)
  • A. Bauer et al.

    Human–robot collaboration: a survey

    Int. J. Humanoid Rob.

    (2008)
  • A. Kumar

    From mass customization to mass personalization: a strategic transformation

    Int. J. Flexible Manuf. Syst.

    (2007)
  • W.Z. Bernstein et al.

    Contextualising manufacturing data for lifecycle decision-making

    Int. J. Product Lifecycle Manag.

    (2018)
  • ISO, ISO 10303-1: iIndustrial automation systems and integration-product data representation and exchange-part 1:...
  • ISO, ISO 14649-1: iIndustrial automation systems and integration - Physical device control - Data data model for...
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