Digital Twin in manufacturing: A categorical literature review and classification

By W. Kritzinger, M. Karner, Georg Traar, J. Henjes, Wilfried Sihn. 2018

2,708 citations recorded by the source corpus.

Abstract The Digital Twin (DT) is commonly known as a key enabler for the digital transformation, however, in literature is no common understanding concerning this term. It is used slightly different over the disparate disciplines. The aim of this paper is to provide a categorical literature review of the DT in manufacturing and to classify existing publication according to their level of integration of the DT. Therefore, it is distinct between Digital Model (DM), Digital Shadow (DS) and Digital Twin. The results are showing, that literature concerning the highest development stage, the DT, is scarce, whilst there is more literature about DM and DS.

View Paper: https://www.semanticscholar.org/paper/f2145771e7eae7d3744887b495c104ecb91b6e88

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Citations in the corpus

Differentiating Digital Twin from Digital Shadow: Elucidating a Paradigm Shift to Expedite a Smart, Sustainable Built Environment (2021, methodology): “, BIM) and DT as similar concepts or used them interchangeably [33,66,67].”
From BIM to digital twins: a systematic review of the evolution of intelligent building representations in the AEC-FM industry (2021, unknown): “…of the Digital Twin originated from the aerospace field (Shafto et al. 2010), and then expanded to industrial manufacturing (Negri et al. 2017, Kritzinger et al. 2018, Zhuang et al. 2018, Tchana et al. 2019, Zhang et al. 2019), has attracted increasing attention in the built environment…”
Introducing digital twins to agriculture (2021, background): “…will be some confusion in the coming years about what a DT is and when a technology can be considered a DT. Research has been done to classify technologies based on how close they are to becoming DT (Kritzinger et al., 2018), but it is still difficult to identify when a system can be called a DT.”
Digital-Twin-Enabled 6G: Vision, Architectural Trends, and Future Directions (2021, background): “Meanwhile, the authors in [6] reviewed digital twins for Industry 4.”
Digital Twins: State of the Art Theory and Practice, Challenges, and Open Research Questions (2020, background): “Some works focus on speciﬁc domains, such as manufacturing [54], aerospace [60] and production science [39], others are general [20, 73].”
Digital Twin in the IoT Context: A Survey on Technical Features, Scenarios, and Architectural Models (2020, background): “This effort covers different stages in the production and also different applications cases [27].”
Digital Twin: Enabling Technologies, Challenges and Open Research (2020, background): “[11] to produce a categorical review table of selected publications.”
City Digital Twin Potentials: A Review and Research Agenda (2021, background): “However, distinguishing digital twins from other types of models is crucial to realize the applicability of utilizing the completely mirrored digital twin for city-scale modeling; that is, the level of data integration, which varies according to the type of model, between the two counterparts (digit”
Digital twin paradigm: A systematic literature review (2021, background): “(Stark et al., 2017) characterizes the DT as (1) an unique instance of the universal Digital Master model of an asset, (2) its individual Digital Shadow and (3) an intelligent linkage (algorithm, simulation model, correlation, etc.) of the two elements above (Kritzinger et al., 2018).”
Construction with digital twin information systems (2020, background): “Although there is no commonly agreed conceptualization or definition of the term (Kritzinger et al., 2018; Gerber et al., 2019), numerous organizations have defined digital twins in terms of their functions and characteristics.”
A Survey on Digital Twin: Definitions, Characteristics, Applications, and Design Implications (2019, background): “, from 2001 to 2006 [41], from 2014 [37], from 2005 to 2016 [38]).”
Characterising the Digital Twin: A systematic literature review (2020, background): “5 Fidelity [33] [80] [108] [24] [85] [8] [56] [88] [68] [112] [99] [78] [32] [111] [36] [7] [71] [63] [61] [73] [20] [67] [6] [4] [48] [55] [95] [16] [92] [98] [87] [9] [66] [110] [18] [89] [12]”