Digital Twin Definitions and Categories: Comparison
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Please note this is a comparison between Version 2 by Sirius Huang and Version 1 by Rebecca Richstein.

During the course of its rapid expansion into various fields of application, many definitions of the Digital Twin emerged, tailored to its respective applications. Taxonomies can cluster the diversity and define application-specific archetypes. 

  • archetypes
  • design
  • digital twin
  • structural health monitoring
  • structural mechanics
  • taxonomy

1. Introduction

The Digital Twin (DT) is one of the major technology trends of the last decade [1]. Initially introduced in 2002 by Micheal Grieves [2] under the name “Conceptual Ideal for Product Life Cycle Management”, many interpretations have arisen, generating various definitions from diverse fields of application. As a result, the Digital Twin, with its characteristic features, is not clearly outlined. Consequently, new definitions are often created for new developments to focus on the relevant aspects of a particular application. The derivation of a generally valid definition fails due to the sheer variety in the different fields of application for Digital Twins.
Several approaches were pursued to address this problem. Jones et al. [3] present a systematic literature review to generate 13 characteristics and processes of the Digital Twin. Furthermore, gaps and future directions for research were identified, including “Digital Twin across the Product Life Cycle” or “Integration between Virtual Entities”. They highlight the importance of framing future Digital Twin use cases with a consolidated common understanding and terminology. Josifovska et al. [4] developed a reference framework for developing Digital Twins of physical entities which are part of a cyber-physical system (CPS). A key result is the main building blocks of a Digital Twin framework in CPSs with their properties and interrelations. Semeraro et al. [5] explore the main features of Digital Twins from a manufacturing perspective. One of the research challenges highlighted here is the missing architecture of Digital Twins, which leads to partial Digital Twin solutions using different technologies, interfaces, communication protocols and models for specific applications. Van der Valk et al. [6] generalize this approach further to derive an application-independent taxonomy. All studies emphasize the relevance of a shared understanding and the development of holistic solutions for implementing Digital Twins in various application areas.

2. Digital Twin Definitions and Categories

The concept of the Digital Twin goes back to a presentation in 2002 held by Michael Grieves at the University of Michigan [2]. The presentation slides, shown in the context of establishing a centre for Product Life Cycle Management (PLM), initially called the concept of the future Digital Twin the “Conceptual Ideal for PLM”. The concept already contained the three central building blocks to define a Digital Twin: a physical instance, a virtual instance, and a data flow from the physical to the virtual instance and vice versa. In 2014, Grieves published his white paper “Digital Twin: Manufacturing Excellence through Virtual Factory Replication” [2], in which he postulated the previously published concept as the “Digital Twin” concerning recent technological advances [2].
A key feature of Grieves’ definition [2] is the bi-directional coupling between real and virtual instances, see Figure 1. This coupling feature is adopted in many definitions of Digital Twins [23,24][7][8]. Fuller et al. [23][7] distinguish the terms “Digital Model” and “Digital Shadow” from the term Digital Twin based on the extent of the coupling. The model describes a digital version of a planned or existing component, which coexists without coupling to an existing real component. If the information flows from the component to the model, the model will become a digital shadow. However, the digital shadow does not provide feedback to the real component. The defined Digital Twin will only be obtained if the flow of information is designed bi-directionally. The coupling factor and the feedback components make this definition particularly interesting for control applications and structural monitoring.
Designs 08 00008 g001
Figure 1. Feature of bi-directional coupling between a Digital Twin and a physical object according to Grieves [2].
In addition to the extent of the coupling, other aspects and perspectives have also shaped the definition of the Digital Twin. In the context of simulation technology [8][9] and Internet of Things [25][10] applications, the focus is particularly on interconnected, holistic modelling. Glaessgen and Stargel [13][11] describe the central feature as a holistic simulation in the form of a “multiphysics, multiscale, probabilistic simulation” that combines sensors, flight and fleet data along with a maintenance history. Other papers [26,27,28][12][13][14] also share the idea of Digital Twins as holistic simulation models that reveal all functional properties of a physical system. Roßmann and Schluse [29][15] define the term “Experimental Digital Twin” as an exact representation of an Industry 4.0 component with its structures, models and data, interfaces and communication capabilities. All-encompassing twins are highly attractive, but their implementation is not feasible or even sensible for many applications due to their complexity.
Another industry that shapes the definitions of Digital Twins is production engineering and manufacturing [7][16]. Here, the focus lies on the accuracy of mapping a specific process, enabling all information to be recorded and displayed at any time [30,31][17][18]. The Scientific Society for Production Technology in Germany therefore calls the Digital Twin a “supplier of an as identical as possible image […] via a process model” [30][17]. Here, the term digital shadow is used for a “real-time capable evaluation basis of all relevant data”.
To sum up, wone can state that each industry and application defines its own requirements and characteristics of the Digital Twin. No appropriate classification or definition has yet been made for the field of structural mechanics. Due to the increasing number of existing definitions, an overview and organization of the definitions and application areas of the Digital Twin are required. This has been performed in various reviews [3,5,32][3][5][19]. Jones et al. [3] reviewed 92 publications to derive 13 characteristics of the concept of a Digital Twin. Enders and Hoßbach [33][20] analyze Digital Twin applications across different industries and propose a classification scheme with six dimensions to describe the founded applications. In the context of so-called cyber-physical systems (CPSs), Josifovska et al. [4] develop a reference framework and specify the main building blocks of a Digital Twin in terms of structure and interrelations. All these approaches allow for categorizing and structuring the most diverse Digital Twin approaches and applications. Van der Valk et al. [6] generalize this approach further. Their aim is the derivation of an independent taxonomy as presented in Table 1. Based on this taxonomy, five archetypes of Digital Twins are presented, supported by literature reviews and interviews with various industry experts.
Table 1.
Taxonomy of Digital Twins according to van der Valk et al. [6].
Meta-Dimension Dimension Characteristics    
Data Collection Data Aquisition Automated Semi-manual  
Data Source Multiple Source Single Source  
Synchronization With Without  
Data Input Raw Data Preprocessed Data  
Data Handling Data Gouvern. Rules Applied Rules Not Applied  
Data Link Bi-Directional One-Directional  
Interface HMI M2M  
Interoperability None Via Translator Fully
Purpose Processing Transfer Repository
Conceptual Scope Accuracy Identical Partial  
Conceptual Elem. Independent Bound  
Time of Creation Digital First Physical First Simultaneously
The presented definitions of the Digital Twin can be roughly summarized; overall they form a blurry and partly contradictory picture for the Digital Twin. Many reviews have therefore taken on the task of creating overviews and clustering aspects. One of these efforts is the taxonomy for Digital Twins according to van der Valk et al. [6]. Based on such fundamentals, application-specific generic archetypes can now be formed, which exhibit relevant characteristics. 

References

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  16. Ciminio, C.; Negri, E.; Fumagalli, L. Review of digital twin applications in manufacturing. Comput. Ind. 2019, 113, 103130.
  17. Bauernhansl, T.; Krüger, J.; Reinhart, G.; Schuh, G. WGP-Standpunkt Industrie 4.0; Abele, E., Ed.; Scientific Society for Production Engineering: Darmstadt, Germany, 2016; Available online: https://www.google.co.th/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwiMm-y49t6DAxUE2TgGHcIzDAkQFnoECAsQAQ&url=https%3A%2F%2Fwww.ipa.fraunhofer.de%2Fcontent%2Fdam%2Fipa%2Fde%2Fdocuments%2FPresse%2FPresseinformationen%2F2016%2FJuni%2FWGP_Standpunkt_Industrie_40.pdf&usg=AOvVaw0dWTcKleEnFAt84WuBQlCA&opi=89978449 (accessed on 30 March 2023).
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