The DOI matches its Crossref record; 43 of the 49 references it cites were found in a registry or an index.
Digital Twin in manufacturing: A categorical literature review and classification
Open accessAbstract
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.
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“a single process [34,36,44,45]”
From Review of digital twin applications in manufacturing (2019) · cites this paper as [36]
In the citing paper
“Dealing with a constant flux of data makes it also possible to apply Artificial Intelligence (AI) and Machine Learning (ML) techniques to IoT systems based on Digital Twin [51][52][53] [54].”
From Digital Twin in the IoT Context: A Survey on Technical Features, Scenarios, and Architectural Models (2020) · cites this paper as [53]
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Scalable Digital Twins for industry 4.0 digital services: a dataspaces approach
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“Manufacturing industries became built towards the customer experience, offering high customization levels (Aheleroff, Mostashiri, et al., 2021; Kritzinger et al., 2018) to meet individualization.”
BackgroundMethodInfluentialA subject-oriented reference model for Digital Twins
Bönsch, J., Elstermann, M., Kimmig, A., & Ovtcharova, J. (2022) · Computers & Industrial Engineering · doi:10.1016/j.cie.2022.108556 · Cited by 42
Overview of Tool Wear Monitoring Methods Based on Convolutional Neural Network
Wang, Q., Wang, H., Hou, L., & Yi, S. (2021) · Applied Sciences · doi:10.3390/app112412041 · Cited by 35
Industrializing precast productions
Mark, P., Lanza, G., Lordick, D., Albers, A., König, M., Borrmann, A., Stempniewski, L., Forman, P., Frey, A., Renz, R., et al. (2021) · Civil Engineering Design · doi:10.1002/cend.202100019 · Cited by 30
“representation of a physical product, where changes in the information world result in changes in the real world and vice versa.(55) Figure 12 shows the transfer of the basic principle of the digital twin to”
BackgroundDigital Twin-Driven Rapid Customized Design of Board-Type Furniture Production Line
Yan, D., Liu, Q., Leng, J., Zhang, D., Zhao, R., Zhang, H., & Wei, L. (2021) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4050617 · Cited by 34
A Framework for Using Data as an Engineering Tool for Sustainable Cyber-Physical Systems
Broo, D. G., & Schooling, J. (2021) · IEEE Access · doi:10.1109/access.2021.3055652 · Cited by 30
Li, X., Shen, Y., Cheng, H., Yuan, F., & Huang, L. (2022) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2022.3166794 · Cited by 35
“[22] classified existing publications according to the level of integration and discussed the key technologies and focused areas of DT in manufacturing.”
BackgroundSmart City Development with Digital Twin Technology
Hämäläinen, M. (2020) · Bled eConference · doi:10.18690/978-961-286-362-3.20 · Cited by 30
“One-directional data flow and connection is also referred as digital shadow (Kritzinger et al., 2018; Zheng, Yang, Chen, 2019).”
BackgroundA Review of Digital Twinning for Rotating Machinery
Inturi, V., Ghosh, B., Sabareesh, G. R., & Pakrashi, V. (2024) · Sensors · doi:10.3390/s24155002 · Cited by 33
“…a digital shadow (DS) ensures one-directional connectivity between the physical system and the digital counterpart so that any modification (operational and/or geometrical) in the physical system’s state would lead to the modification of the digital counterpart’s state in the virtual space [2].”
BackgroundOpportunities and Threats of Adopting Digital Twin in Construction Projects: A Review
Wang, M., Ashour, M., Mahdiyar, A., & Sabri, S. (2024) · Buildings · doi:10.3390/buildings14082349 · Cited by 29
Factories-in-Space for Servicing, Assembly, & Manufacturing
Malshe, H., Bapat, S., Vickers, J., & Malshe, A. P. (2023) · Manufacturing Letters · doi:10.1016/j.mfglet.2023.08.142 · Cited by 29
A Co-Simulation Virtual Reality Machinery Simulator for Advanced Precision Agriculture Applications
Cutini, M., Bisaglia, C., Brambilla, M., Bragaglio, A., Pallottino, F., Assirelli, A., Romano, E., Montaghi, A., Leo, E., Pezzola, M., et al. (2023) · Agriculture · doi:10.3390/agriculture13081603 · Cited by 29
Metcalfe, B., Boshuizen, H., Bulens, J., & Koehorst, J. (2023) · F1000Research · doi:10.12688/f1000research.137262.1 · Cited by 29
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“DT has been increasingly adopted in several fields, such as manufacturing and production engineering [18], [19], medicine [20], healthcare [21], systems engineering [22], and product design [23].”
BackgroundDigital Twin of Shipbuilding Process in Shipyard 4.0
Iwańkowicz, R. R., & Rutkowski, R. (2023) · Sustainability · doi:10.3390/su15129733 · Cited by 52
Martínez-Olvera, C. (2022) · Automation · doi:10.3390/automation3010010 · Cited by 29
“This translates in an intensive bidirectional [59,60], standardized and/or automatized [61], real-time information flow [35] related to the current products, processes and resources [62] between the DT and the real manufacturing system [63,64], via the internet [65].”
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“In other words, they can be used as digital models of physical systems for various purposes, utilising real-time synchronisation of data sensed from the field level (Kritzinger et al. 2018).”
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Martínez, S., Salguero, A. F. M., Sanchez, S., Montes, A. M., Triana, J. M., Barbieri, G., Abolghasem, S., Luzuriaga, J. G. V., & Guevara, M. (2021) · Production & Manufacturing Research · doi:10.1080/21693277.2021.1964405 · Cited by 26
Machining Phenomenon Twin Construction for Industry 4.0: A Case of Surface Roughness
Ghosh, A. K., Ullah, A., Kubo, A., Akamatsu, T., & D'Addona, D. (2020) · Journal of Manufacturing and Materials Processing · doi:10.3390/jmmp4010011 · Cited by 29
“[29] reviewed various aspects (definition, construction, integration, key enabling technologies, and application) of DTs in manufacturing.”
BackgroundThe development of Pilot Production Environments based on Digital Twins and Virtual Dashboards
Lutters, E., & Damgrave, R. (2019) · Procedia CIRP · doi:10.1016/j.procir.2019.04.228 · Cited by 29
Towards Digital Twin Modeling and Applications for Permanent Magnet Synchronous Motors
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Elshabshiri, A., Ghanim, A., Hussien, A., Maksoud, A., & Mushtaha, E. S. N. (2024) · Journal of Building Engineering · doi:10.1016/j.jobe.2024.111541 · Cited by 28
Design and Implementation of Digital Twin Factory Synchronized in Real-Time Using MQTT
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“While digital twin systems offer numerous benefits, their practical implementation also presents several challenges [5].”
BackgroundAmaizu, G. C., Njoku, J. N., Lee, J., & Kim, D. (2024) · ICT Express · doi:10.1016/j.icte.2024.02.010 · Cited by 33
Kasper, L., Schwarzmayr, P., Birkelbach, F., Javernik, F., Schwaiger, M., & Hofmann, R. (2024) · Applied Energy · doi:10.1016/j.apenergy.2023.122192 · Cited by 28
Towards a Taxonomy of Digital Twin Evolution for Technical Sustainability
David, I., & Bork, D. (2023) · 2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C) · doi:10.1109/models-c59198.2023.00147 · Cited by 28
“1 Digital Twins are live virtual representations of physical systems with control capabilities over the said physical sys-tem [2].”
BackgroundMulti-agent digital twinning for collaborative logistics: Framework and implementation
Xu, L., Mak, S., Schoepf, S., Ostroumov, M., & Brintrup, A. M. (2025) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2025.100799 · Cited by 22
“First publicly introduced in 2002 by Michael Grieves [28], DTs have found applications in various domains, including aerospace [29], construction [30], manufacturing [31], supply chain [27], and logistics [32].”
BackgroundGuidelines for designing a digital twin for Li-ion battery: A reference methodology
Semeraro, C., Aljaghoub, H., Abdelkareem, M., Alami, A., Dassisti, M., & Olabi, A. (2023) · Energy · doi:10.1016/j.energy.2023.128699 · Cited by 28
Digital Twin for a Collaborative Painting Robot
Chancharoen, R., Chaiprabha, K., Wuttisittikulkij, L., Asdornwised, W., Saadi, M., & Phanomchoeng, G. (2022) · Italian National Conference on Sensors · doi:10.3390/s23010017 · Cited by 28
“and digital technologies enables the integration of the physical and digital worlds to create virtual products and processes, resulting in optimized factories and manufacturing processes [5].”
BackgroundAn Established Theory of Digital Twin Model for Tunnel Construction Safety Assessment
Zhao, Y., Wang, N., & Liu, Z.-S. (2022) · Applied Sciences · doi:10.3390/app122312256 · Cited by 28
Nassehi, A., Colledani, M., Kádár, B., & Lutters, E. (2022) · CIRP Annals · doi:10.1016/j.cirp.2022.05.002 · Cited by 38
Udugama, I. A., Bayer, C., Baroutian, S., Gernaey, K., Yu, W., & Young, B. (2022) · Education for Chemical Engineers · doi:10.1016/j.ece.2022.03.003 · Cited by 28
Digital twin in hydrocarbon industry
Sircar, A., Nair, A., Bist, N., & Yadav, K. (2022) · Petroleum Research · doi:10.1016/j.ptlrs.2022.04.001 · Cited by 38
Revisiting Digital Twins: Origins, Fundamentals and Practices
Zhou, J.-H., Zhang, S., & Gu, M. (2022) · Frontiers of Engineering Management · doi:10.48550/arxiv.2203.12867 · Cited by 28
“In response to the misuse and abuse of the term DT, this paper profoundly traces the source of DT by consulting a large number of first-hand materials, corrects such claim that Digital Twins originated from the mirrored space model (MSM), and corrects the misstatement that DT first appeared in the technical report on…”
BackgroundMethodInfluentialDigital Twin in the Architecture, Engineering, and Construction Industry: A Bibliometric Review
Almatared, M., Liu, H.-X., Tang, S.-X., Sulaiman, M., Lei, Z., & Li, H.-X. (2022) · Construction Research Congress 2022 · doi:10.1061/9780784483961.070 · Cited by 28
An approach for Industrie 4.0-compliant and data-sovereign Digital Twins
Jacoby, M., Volz, F., Weißenbacher, C., Stojanović, L., & Uslander, T. (2021) · at - Automatisierungstechnik · doi:10.1515/auto-2021-0074 · Cited by 28
“the context of product life-cycle management [1], it is get-”
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Introduction of a comprehensive Structure Model for the Digital Twin in Manufacturing
Lechler, T., Fuchs, J., Sjarov, M., Brossog, M., Selmaier, A., Faltus, F., Donhauser, T., & Franke, J. E. (2020) · IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) · doi:10.1109/etfa46521.2020.9212030 · Cited by 47
“introduced three levels in 2018, which differ in the gradations from manual to automatic according to the degree of automation regarding their data flow: (a) Digital Model, (b) Digital Shadow and (c) Digital Twin [3].”
BackgroundConsilvio, A., Solís-Hernández, J., Jimenez–Redondo, N., Sanetti, P., Papa, F., & Mingolarra-Garaizar, I. (2020) · Sustainability · doi:10.3390/su12062544 · Cited by 33
“…representations of physical assets are built by using Building Information Modeling (BIM) [41,42], while digital twins of complex systems and processes are built by other instruments, such as agent-based models, discrete event simulations, Petri nets (PN), and Bayesian Networks (BN) [43,44].”
MethodDigital Twins for Industry 4.0: A Review
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Yuan, J., Ma, J., Tian, Z., & Man, K. L. (2024) · IEEE Open Journal of Power Electronics · doi:10.1109/ojpel.2024.3422021 · Cited by 37
“4 shows the classification framework that categorizes different types of DT systems as Digital Model (DM), Digital Shadow (DS), and Digital Twin [41].”
BackgroundDigital twins in manufacturing: An assessment of key features
Assad, A., da Silva, E. R. D. R., Deschamps, F., & de Lima, E. P. (2021) · Procedia CIRP · doi:10.1016/j.procir.2020.05.222 · Cited by 28
Leveraging the Asset Administration Shell for Agent-Based Production Systems
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A case-study in the introduction of a digital twin in a large-scale smart manufacturing facility
O’Sullivan, J., O'Sullivan, D., & Bruton, K. (2020) · Procedia Manufacturing · doi:10.1016/j.promfg.2020.10.212 · Cited by 28
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Reinpold, L. M., Wagner, L. P., Gehlhoff, F., Ramonat, M., Kilthau, M., Gill, M. S., Reif, J. T., Henkel, V., Scholz, L., & Fay, A. (2025) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-023-02278-y · Cited by 27
“In this work, the definitions introduced by Tao et al. (2018) and Kritzinger et al. (2018) are referred to and used as a guideline in order to establish a common understanding of this concept.”
InfluentialDing, P.-P., Liu, S., Chiong, R., Dhakal, S., Chen, D., Li, D.-B., Ma, H.-L., & Chung, S.-H. (2026) · Computers in industry (Print) · doi:10.1016/j.compind.2025.104398 · Cited by 27
Modern trends and industrial use cases of digital twin technology with 3D behavioral representation
Macháček, Z., Hercík, R., Vaclavik, A., Zemanek, J., Hameed, I. A., & Koziorek, J. (2025) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-025-02709-y · Cited by 32
“Sepasgozar ( 2021 ); Kritzinger et al. (2018); Ladj et al. ( 2020 ) The digi - tal shadow reflects the real-time state of the system but does not interact with the physical machine, which means it cannot influence or control it.”
Hu, W., Ou, Y., Liu, H., Ni, P., & Chang, C. (2025) · Building and Environment · doi:10.1016/j.buildenv.2025.113997 · Cited by 27
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Harnessing the Future: Exploring Digital Twin Applications and Implications in Renewable Energy
Semeraro, C., Aljaghoub, H., Al-Ali, H., Abdelkareem, M., & Olabi, A. (2025) · Energy Nexus · doi:10.1016/j.nexus.2025.100415 · Cited by 27
Digital Twins in Critical Infrastructure
Λαμπρόπουλος, Γ., Larrucea, X., & Colomo‐Palacios, R. (2024) · Information · doi:10.3390/info15080454 · Cited by 40
Digital Twin Evolution for Sustainable Smart Ecosystems
Michael, J., David, I., & Bork, D. (2024) · ACM/IEEE International Conference on Model Driven Engineering Languages and Systems · doi:10.1145/3652620.3688343 · Cited by 27
“While evolution has a substantial body of knowledge in model-driven software engineering [6, 11], hybrid cyber-physical components of smart ecosystems, such as digital twins [14], give rise to challenges traditional software engineering techniques fall short of addressing.”
BackgroundGasiyarov, V., Radionov, A., Loginov, B., Karandaev, A., Gasiyarova, O., & Khramshin, V. (2023) · Journal of Manufacturing and Materials Processing · doi:10.3390/jmmp7010041 · Cited by 27
“In digital shadows, state observers are characterized by a one-way data transfer from the object to the model [16 – 18].”
Digital transformation in manufacturing industry – A comprehensive insight
Duraivelu, K. (2022) · Materials Today: Proceedings · doi:10.1016/j.matpr.2022.07.409 · Cited by 27
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“According to [16], three designations are possible depending on the integration level between the physical asset and its digital corresponding.”
BackgroundDigital Twins in Simulative Applications: A Taxonomy
van der Valk, H., Hunker, J., Rabe, M., & Otto, B. (2020) · Online World Conference on Soft Computing in Industrial Applications · doi:10.1109/wsc48552.2020.9384051 · Cited by 27
“Following Kritzinger et al. (2018), a Digital Twin is a virtual representation with a fully integrated data flow between the virtual and the physical part, whereas a Digital Shadow only possesses an integrated data flow in one direction.”
MethodPrototype of a cyber-physical façade system
Böke, J., Knaack, U., & Hemmerling, M. (2020) · Journal of Building Engineering · doi:10.1016/j.jobe.2020.101397 · Cited by 27
“These in clude, for ex am ple, the in ter con nec tion to a dig i tal twin de scribed by Ne gri et al. [ 10 ] and Kritzinger et al. [ 11 ], which en ables both mon i tor ing and op ti mi sa tion of pro duc tion processes via dig i tal sim u - la tions.”
BackgroundZhu, F., Chen, J., Wen, J., Yang, Y., Yi, C., Tie, Y., Zhang, P., Cai, J., Niyato, D. T., & Guizani, M. (2026) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2026.3665395 · Cited by 26
“In this stage, the DT world developed beyond solely monitoring to serve as the intelligent guidance platform, directly outputting the optimal decisions based on the real-time physical data to guide actions in the real-world [62].”
Sharma, H., & Kaur, S. (2025) · IEEE Access · doi:10.1109/access.2025.3598130 · Cited by 27
Smart Manufacturing for High-Performance Materials: Advances, Challenges, and Future Directions
Jose, S. A., Tonner, A., Feliciano, M., Roy, T., Shackleford, A., & Menezes, P. L. (2025) · Materials · doi:10.3390/ma18102255 · Cited by 37
Real-time process safety and systems decision-making toward safe and smart chemical manufacturing
Braniff, A., Akundi, S. S., Liu, Y., Dantas, B., Niknezhad, S. S., Khan, F., Pistikopoulos, E. N., & Tian, Y. (2025) · Digital Chemical Engineering · doi:10.1016/j.dche.2025.100227 · Cited by 30
Digital Twins in Supply Chain Operations Bridging the Physical and Digital Worlds using AI.
Jaramillo, M. T. E. (2024) · Journal of Electrical Systems · doi:10.52783/jes.5434 · Cited by 26
A quantitative digital twin maturity model for underground infrastructure based on D-ANP
Li, T., Rui, Y., Zhao, S., Zhang, Y., Zhu, H.-H., & Li, X.-J. (2024) · Tunnelling and Underground Space Technology · doi:10.1016/j.tust.2024.105612 · Cited by 26
AI Lifecycle Zero-Touch Orchestration within the Edge-to-Cloud Continuum for Industry 5.0
Alberti, E., Álvarez-Napagao, S., Anaya, V., Barroso, M., Barrué, C., Beecks, C., Bergamasco, L., Chala, S. A., Giménez-Ábalos, V., Grass, A., et al. (2024) · Syst. · doi:10.3390/systems12020048 · Cited by 26
Leutert, F., Bohlig, D., Kempf, F., Schilling, K., Mühlbauer, M., Ayan, B., Hulin, T., Stulp, F., Albu-Schäffer, A., Kutscher, V., et al. (2024) · Acta Astronautica · doi:10.1016/j.actaastro.2024.01.019 · Cited by 26
“By using the data, digital twins are more and more applied in production by enabling advanced production control and optimisation [63].”
MethodFrom digital twin paradigm to digital water services
Ciliberti, F. G., Berardi, L., Laucelli, D., Ariza, A. D., Enriquez, L. V., & Giustolisi, O. (2023) · Journal of Hydroinformatics · doi:10.2166/hydro.2023.237 · Cited by 28
Digital twins in the minerals industry – a comprehensive review
Qu, J., Kizil, M. S., Yahyaei, M., & Knights, P. F. (2023) · Mining Technology Transactions of the Institutions of Mining and Metallurgy · doi:10.1080/25726668.2023.2257479 · Cited by 25
“Meanwhile, around 30% of the publications from the minerals industry that are listed in this paper had either discussed or implemented a true digital twin as de fi ned by Kritzinger et al. (2018).”
BackgroundMethodResultInfluentialProbabilistic residual strength assessment of smart composite aircraft panels using guided waves
Giannakeas, I. N., Mazaheri, F., Bacarreza, O., Khodaei, Z. S., & Aliabadi, M. H. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2023.109338 · Cited by 30
Low-Cost Digitalization Solution through Scalable IIoT Prototypes
Vuković, M., Jorg, O., Hosseinifard, M., & Fantoni, G. (2022) · Applied Sciences · doi:10.3390/app12178571 · Cited by 26
Eleftheriou, O., & Anagnostopoulos, C. (2022) · Digital Twin · doi:10.12688/digitaltwin.17581.1 · Cited by 26
Phanden, R. K., Aditya, S., Sheokand, A., Goyal, K. K., Gahlot, P., & Jacsó, Á. (2021) · Materials Today Proceedings · doi:10.1016/j.matpr.2021.12.217 · Cited by 30
PhysiNet: A combination of physics‐based model and neural network model for digital twins
Sun, C., & Shi, V. G. (2021) · International Journal of Intelligent Systems · doi:10.1002/int.22798 · Cited by 30
Gou, N., Cheng, K., & Huo, D. (2021) · Machines · doi:10.3390/machines9050085 · Cited by 29
On the Engineering of IoT-Intensive Digital Twin Software Systems
Rivera, L. F. Z., Müller, H. A., Villegas, N. M., Tamura, G., & Jiménez, M. (2020) · International Conference on Software Engineering · doi:10.1145/3387940.3392195 · Cited by 27
“Despite the increasing acceptance and proliferation of DTs [4, 5, 12, 21], further research on how to model and engineer DT systems is still required [1, 4, 14].”
BackgroundMachine Learning for Digital Twins to Predict Responsiveness of Cyber-Physical Energy Systems
Snijders, R., Pileggi, P., Broekhuijsen, J., Verriet, J., Wiering, M., & Kok, K. (2020) · Workshop on Modeling and Simulation of Cyber-Physical Energy Systems (MSCPES) · doi:10.1109/mscpes49613.2020.9133695 · Cited by 41
“The Digital Twin, despite there being no clear consensus on a definition in industry or academia [14], is a fit-for-purpose model with reasoning logic synchronized with the operating physical system to help the system be effective.”
BackgroundDigital Twin for automated guided vehicles fleet management
Alexandru, M., Dragoș, C., & Bălă-Constantin, Z. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.01.172 · Cited by 26
Challenges in Automated Commercial Aircraft Production
Gehlhoff, F., Nabizada, H., Weigand, M., Beers, L., Ismail, O., Wenzel, A., Fay, A., Nyhuis, P., Lagutin, W., & Röhrig, M. (2022) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2022.04.219 · Cited by 26
Panagou, S., Fruggiero, F., Lerra, M., Vecchio, C. D., Menchetti, F., Piedimonte, L., Natale, O. R., & Passariello, S. (2022) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2022.04.182 · Cited by 26
A meta-model for modular composition of tailored human digital twins in production
Montini, E., Bettoni, A., Ciavotta, M., Carpanzano, E., & Pedrazzoli, P. (2021) · Procedia CIRP · doi:10.1016/j.procir.2021.11.116 · Cited by 26
Stief, P., Dantan, J., Etienne, A., & Siadat, A. (2018) · doi:10.1016/j.procir.2018.02.026 · Cited by 26
“A classification of available literature on digital twins in manufacturing can be found in [37].”
BackgroundArefi, A., Vilas, C., Delele, M., Foerst, P., Gruber, S., Kaveh, M., Khoshnam, F., Hashim, N., Ali, M. M., Zohrabi, S., et al. (2025) · Journal of Food Engineering · doi:10.1016/j.jfoodeng.2025.112770 · Cited by 26
Ghasemzadeh, K., Jafari, M., Torabi, T., Amiri, A., & Iulianelli, A. (2026) · Advanced Membranes · doi:10.1016/j.advmem.2026.100219 · Cited by 25
Exploring Challenges and Opportunities in Manufacturing and Intelligence for Future Robotics
Choi, J. Y., Ahn, S., Kim, D., Heo, J., Yun, W.-J., Hong, S., Bae, S., & Ahn, S. (2025) · International Journal of Precision Engineering and Manufacturing · doi:10.1007/s12541-025-01318-2 · Cited by 33
“Digital twins have emerged as a cornerstone technology for advancing robotics, particularly in the field of humanoid and industrial applications [137], by enabling the creation of high-fidelity virtual replicas of physical systems [ 138].”
Li, H., & Hong, T.-Z. (2025) · Building Simulation · doi:10.1007/s12273-025-1290-2 · Cited by 25
“To address this lack of clarity, Kritzinger et al. (2018) proposed to categorize DTs into three distinct categories based on their level of data integration: digital model, digital shadow, and digital twin.”
Composable digital twins on Digital Twin as a Service platform
Talasila, P., Gomes, C., Vosteen, L. B., Iven, H., Leucker, M., Gil, S., Mikkelsen, P. H., Kamburjan, E., & Larsen, P. G. (2024) · International Conference on Advances in System Simulation · doi:10.1177/00375497241298653 · Cited by 25
“Some of the existing literature proposes three subcategories of DTs 15 : (i) Digital Model , in which physical object (PO) and digital object (DO) have no automated exchange of data, (ii) Digital Shadow , where PO emits data to the DO automatically and finally (iii) Digital Twin , in which case…”
Could digital twins be the next revolution in healthcare?
El-Warrak, L. D. O., & de Farias, C. M. (2024) · European Journal of Public Health · doi:10.1093/eurpub/ckae191 · Cited by 25
“A DT can be understood as technology capable of mapping the real world to the digital world through interaction between the two in real time [13–16].”
A Survey on Extended Reality, Digital Twins, and Metaverse Applications in Power Systems
Menezes, C. R., França, B. W., & Lopes, Y. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3450813 · Cited by 29
“In this context, [28] proposes a distinction between digital models, digital shadows, and DTs.”
BackgroundWerner, A., Schuseil, F., Hämmerle, M., Schaper, S., & Hölzle, K. (2024) · International Journal of Production Research · doi:10.1080/00207543.2024.2366997 · Cited by 25
“Kritzinger et al. (2018) state, that the Digital Shadow is a model fed by a one-way data flow from the state of an existing physical object.”
Digital twins' readiness and its impacts on supply chain transparency and sustainable performance
Patil, A., Srivastava, S., Paul, S. K., & Dwivedi, A. (2024) · Industrial Management & Data Systems · doi:10.1108/imds-10-2023-0767 · Cited by 36
Kim, M.-G., & Bartos, M. (2023) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2023.105868 · Cited by 25
Digital twin testbed and practical applications in production logistics with real-time location data
Hauge, J. B., Zafarzadeh, M., Jeong, Y.-H., Li, Y., Khilji, W. A., Larsen, C., & Wiktorsson, M. (2021) · International Journal of Industrial Engineering and Management · doi:10.24867/ijiem-2021-2-282 · Cited by 25
“For this type, the term digital shadow (DS) is also in use [23].”
BackgroundVision on a Digital Twin of the Road-Tire-Vehicle System for Future Mobility
Kaliske, M., Behnke, R., & Wollny, I. (2021) · Tire Science and Technology · doi:10.2346/tire.21.190223 · Cited by 25
“3, according to [ 1 ].”
Development of a Digital Twin for Aviation Research
Meyer, H., Zimdahl, J., Kamtsiuris, A., Meissner, R., Raddatz, F., Haufe, S., & Bässler, M. (2020) · doi:10.25967/530329 · Cited by 25
“[7] gives an overview of the various possible applications of a digital twin.”
BackgroundMiehe, R., Horbelt, J., Baumgarten, Y., & Bauernhansl, T. (2020) · CIRP journal of manufacturing science and technology · doi:10.1016/j.cirpj.2020.08.006 · Cited by 25
Horváthová, M., Lacko, R., & Hajduová, Z. (2019) · Kvalita Inovácia Prosperita · doi:10.12776/qip.v23i2.1211 · Cited by 25
“Many other applications of digital twins are mentioned in Kritzinger et al. (2018), Negri, Fumagalli and Macchi (2017), Padovano et al. (2018).”
Background
Metadata
- Authors
- Kritzinger, W., Karner, M., Traar, G., Henjes, J., & Sihn, W.
- Affiliations
- Fraunhofer Austria (AT)
- Corresponding author
- Kritzinger, Werner
- Venue
- IFAC-PapersOnLine
- Publisher
- Elsevier BV
- Published
- 2018
- Volume
- 51
- Issue
- 11
- Pages
- 1016-1022
- ISSN
- 2405-8963 (ISSN-L)
- OpenAlex
- W2890904471
- Cited by
- 3,612
- References
- 49
- Citations by year
- 2026: 761 · 2025: 639 · 2024: 576 · 2023: 577 · 2022: 437 · 2021: 356 · 2020: 194 · 2019: 71 · 2018: 1
- FWCI
- 128.89
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- IFAC-PapersOnLine (publishedVersion)
- Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) (submittedVersion)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- Funders
- European Commission
- Horizon 2020 Framework Programme
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Flexible and Reconfigurable Manufacturing Systems
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Applied Advanced Technologies
- Keywords
- digital twin, manufacturing industry, digital models, digital shadow, digital transformation, terminology, levels of integration
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