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About The Importance of Autonomy and Digital Twins for the Future of Manufacturing
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Industrie 4.0 - the “brand” name of the German initiative driving the future of manufacturing - is one of several initiatives around the globe emphasizing the importance of industrial manufacturing for economy and society. Besides the socio-economical if not political question which has to be answered - including the question about the future of labor - there are a couple of substantial technical and technological questions that have to be taken care of as well.
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“In Table 1 seven articles developed ' ad-hoc ' systems to build DT: for the management of a didactic production line [26], to construct a MES in Android systems [43], to build user guides for safety reason [60,62], and to evaluate the performances of a cloud-based DT [74,75];”
From Review of digital twin applications in manufacturing (2019) · cites this paper as [62]
In the citing paper
“For this aspect, the articles considered can be divided into two different categories: (i) those that effectively use a cloud and build a cloud-based DT application, marked in the table as [27,34,37,43,47,48,54,56,62,67,74-76], (ii) those that construct the DT application in a virtual space with the use of a software highlighting the possibility to build it also in a cloud”
From Review of digital twin applications in manufacturing (2019) · cites this paper as [62]
In the citing paper
“Also, there are papers that mainly belong to the first group but also offer services of the second group ([39,40,50,70]), and vice versa ([56,61,62]).”
From Review of digital twin applications in manufacturing (2019) · cites this paper as [62]
In the citing paper
“It can also be implemented for its capability to represent and deal with a continuous flow of data [48] [49].”
From Digital Twin in the IoT Context: A Survey on Technical Features, Scenarios, and Architectural Models (2020) · cites this paper as [48]
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Research works printed but not linked: 2
- Siemens 2014. A worldwide production language, Siemens Industry Journal, 01 | 2014 | siemens.com/industry
- Tenorth, 2013, KnowRob – A Knowledge Processing Infrastructure for Cognition-enabled Robots, International Journal of Robotics Research (IJRR), 32
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Parameter tuning for dynamic digital twins in inverter‐dominated distribution grid
Song, X.-Y., Jiang, T., Schlegel, S., & Westermann, D. (2019) · IET Renewable Power Generation · doi:10.1049/iet-rpg.2019.0163 · Cited by 50
Diagnosis of PV faults using digital twin and convolutional mixer with LoRa notification system
Hong, Y.-Y., & Pula, R. A. (2023) · Energy Reports · doi:10.1016/j.egyr.2023.01.011 · Cited by 50
Moiceanu, G., & Paraschiv, G. (2022) · Sensors · doi:10.3390/s22041388 · Cited by 60
“Virtual models of physical objects created using digital twin can help simulate their behavior, thus understanding of their state, and estimating and analyzing the dynamic changes [17,19,20].”
BackgroundAtalay, M., Murat, U., Oksuz, B., Parlaktuna, A. M., Pisirir, E., & Testik, M. C. (2022) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2021.2022762 · Cited by 60
“Beyond the general manufacturing on the shop floor, DT applications can be related with three main areas, namely production planning and con-trol (Rosen et al. 2015), maintenance (D’Addona, Ullah, and Matarazzo 2017; Susto et al. 2015), and layout planning (Uhlemann, Lehmann, and Steinhilper 2017).”
BackgroundMethodInfluentialDigital Twin Models: Functions, Challenges, and Industry Applications
Rayhana, R., Bai, L., Xiao, G. G., Liao, M., & Liu, Z. (2024) · IEEE Journal of Radio Frequency Identification · doi:10.1109/jrfid.2024.3387996 · Cited by 68
“…[86], [87], object [59], [61], [63], [88], [89], [90], [91], [92], [93], process [64], [94], [95], [96], machine [97], [98], [99], [100], [101], device [63], [102], system [36], [37], [52], [54], [65], [83], asset [103], [104], [105], [106], [107], [108], [109] and entity [55], [65], [110], [111].”
MethodMeta-model-based shop-floor digital twin architecture, modeling and application
Yang, X., Liu, X., Zhang, H., Fu, L., & Yu, Y. (2023) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2023.102595 · Cited by 58
From Digital Twins to Digital Twin Prototypes: Concepts, Formalization, and Applications
Barbie, A., & Hasselbring, W. (2024) · IEEE Access · doi:10.1109/access.2024.3406510 · Cited by 48
Zambrano, V., Mueller-Roemer, J., Sandberg, M., Talasila, P., Zanin, D., Larsen, P., Loeschner, E., Thronicke, W., Pietraroia, D., Landolfi, G., et al. (2022) · Array · doi:10.1016/j.array.2022.100176 · Cited by 48
“A DT can reflect the entire life cycle of a product [20,21], however, targeted domain-specific applications such as monitoring and prediction of crack growth are commonly seen as well [22].”
BackgroundOsho, J., Hyre, A., Pantelidakis, M., Ledford, A., Harris, G. A., Liu, J., & Mykoniatis, K. (2022) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2022.04.014 · Cited by 55
Mechanistic Mathematical Models as a Basis for Digital Twins.
Moser, A., Appl, C., Brüning, S., & Hass, V. (2020) · Advances in Biochemical Engineering/Biotechnology · doi:10.1007/10_2020_152 · Cited by 48
Cattaneo, L., & Macchi, M. (2019) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2019.10.016 · Cited by 48
Probabilistic machine learning based predictive and interpretable digital twin for dynamical systems
Tripura, T., Desai, A. S., Adhikari, S., & Chakraborty, S. (2023) · Computers & Structures · doi:10.1016/j.compstruc.2023.107008 · Cited by 47
Fantozzi, I. C., Santolamazza, A., Loy, G., & Schiraldi, M. M. (2025) · Future Internet · doi:10.3390/fi17010041 · Cited by 63
“Studies emphasize the challenges of creating accurate models, ensuring real-time data integration and managing the complexities of IT infrastructure required for effective DT functioning [20,21].”
Enhancing Cognition for Digital Twins
Eirinakis, P., Kalaboukas, K., Lounis, S., Mourtos, I., Rožanec, J., Stojanović, N., & Zois, G. (2020) · International Conference on Engineering, Technology and Innovation · doi:10.1109/ice/itmc49519.2020.9198492 · Cited by 47
“…©2020 IEEE DTs have found their place in Industry 4.0 and smart manufacturing mainly with cases that provide information continuity [13], data management [14] along the product lifecycle, as well as monitoring of physical twin assets [15] to even optimization of system behaviour…”
BackgroundInfluentialModeling of Digital Twin Workshop Based on Perception Data
Zhang, Q., Zhang, X., Xu, W., Liu, A.-M., Zhou, Z.-D., & Pham, D. (2017) · International Conference on Intelligent Robotics and Applications · doi:10.1007/978-3-319-65298-6_1 · Cited by 47
“Serviceability, intelligence and sustainable had become the development trend of manufacturing workshop [10].”
BackgroundTransformation of robotic workcells to digital twins
Erdős, G., Paniti, I., & Tipary, B. (2020) · CIRP Annals · doi:10.1016/j.cirp.2020.03.003 · Cited by 52
Riedelsheimer, T., Dorfhuber, L., & Stark, R. (2020) · Procedia CIRP · doi:10.1016/j.procir.2020.01.123 · Cited by 47
Olivotti, D., Dreyer, S., Lebek, B., & Breitner, M. H. (2019) · Information Systems and e-Business Management · doi:10.1007/s10257-018-0376-0 · Cited by 47
“Digital twins are realistic models representing machines with all their components, their current state and their interaction with the environment (Rosen et al. 2015; Gabor et al. 2016; Alam and El Saddik 2017).”
BackgroundDigital twins: Transforming the chemical process industry—A review
Pal, P. K., Hens, A., Behera, N., & Lahiri, S. K. (2025) · The Canadian Journal of Chemical Engineering · doi:10.1002/cjce.25611 · Cited by 58
Tlapa, D. A., Tortorella, G., Fogliatto, F., Kumar, M., Cawley, A. M. M., Vassolo, R., Enberg, L., & Baez-López, Y. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19159018 · Cited by 46
“In such a new paradigm, the use of simulation will be extended [161] to support the design and redesign of patient-centered healthcare settings.”
BackgroundMethodBalta, E. C., Tilbury, D., & Barton, K. (2019) · 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE) · doi:10.1109/coase.2019.8843166 · Cited by 46
Afzal, M., Li, R. Y. M., Shoaib, M., Ayyub, M. F., Tagliabue, L. C., Bilal, M., Ghafoor, H., & Manta, O. P. (2023) · Sustainability · doi:10.3390/su152316436 · Cited by 74
“The DT has been characterized in multiple ways, such as a “digital representation” [93], “realistic model” [94], “virtual prototype” [66], “structure of inter-connected digital replicas” [4], and “dynamic virtual model” [95] that captures the characteristics and behavior patterns of a system in the…”
BackgroundMethodPeng, Y., Zhang, X.-L., Song, Y.-C., & Liu, D.-T. (2019) · International Instrumentation and Measurement Technology Conference · doi:10.1109/i2mtc.2019.8827160 · Cited by 44
A digital twin framework for the simulation and optimization of production systems
Ricondo, I., Porto, A., & Ugarte, M. (2021) · Procedia CIRP · doi:10.1016/j.procir.2021.11.128 · Cited by 44
A standards-based approach for linking as-planned to as-fabricated product data
Helu, M., Joseph, A., & Thomas Hedberg, Jr. (2018) · Cirp Annals-manufacturing Technology · doi:10.1016/j.cirp.2018.04.039 · Cited by 44
“[3] discuss applications of the digital twin to Helu et al.”
BackgroundApplication of Digital Twins in Port System
Yao, H., Wang, D., Su, M., & Qi, Y. (2021) · Journal of Physics: Conference Series · doi:10.1088/1742-6596/1846/1/012008 · Cited by 44
Sun, Z., Zhang, R., & Zhu, X. (2024) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2024.02.016 · Cited by 49
Digital twin challenges in biodiversity modelling
Trantas, A., Plug, R., Pileggi, P., & Lazovik, E. (2023) · Ecological Informatics · doi:10.1016/j.ecoinf.2023.102357 · Cited by 43
Digital Twin-Based Health Care Facilities Management
Madubuike, O. C., & Anumba, C. (2023) · Journal of computing in civil engineering · doi:10.1061/jccee5.cpeng-4842 · Cited by 43
González-Barrio, H., Calleja-Ochoa, A., de Lacalle, L. D., & Lamikiz, A. (2022) · Mechanical systems and signal processing · doi:10.1016/j.ymssp.2022.109337 · Cited by 43
“The concept “ digital twin ” is the aim of developing a more autonomous and competitive process [24].”
The Hitchhiker’s Guide to Fused Twins: A Review of Access to Digital Twins In Situ in Smart Cities
Grübel, J., Thrash, T., Aguilar, L., Gath-Morad, M., Chatain, J., Sumner, R. W., Hölscher, C., & Schinazi, V. R. (2022) · Remote Sensing · doi:10.3390/rs14133095 · Cited by 57
“Ideally, the model underlying a DT is complex enough to enable a system to continuously react to dynamic changes in physical space [144], including unforeseen/unexpected”
BackgroundDIGITAL TWINS - DEFINITIONS, CLASSES AND BUSINESS SCENARIOS FOR DIFFERENT INDUSTRY SECTORS
Wilking, F., Schleich, B., & Wartzack, S. (2021) · Proceedings of the Design Society · doi:10.1017/pds.2021.129 · Cited by 43
“Many other publications such as (Kritzinger et al. , 2018; Rosen et al. , 2015) tried to establish application specific definitions to describe explicit situations in which a Digital Twin is providing benefits, e. g. for production.”
BackgroundDigital Twin Technologies for Turbomachinery in a Life Cycle Perspective: A Review
Xie, R., Chen, M., Liu, W., Jian, H., & Shi, Y. (2021) · Sustainability · doi:10.3390/su13052495 · Cited by 59
“Simultaneously, the discretized model will be automatically updated with the turbomachinery damage and repair data [88].”
MethodBalderas, D. C., Ortiz, A., Mendez, E., Ponce, P., & Molina, A. (2021) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-021-06649-8 · Cited by 43
A Bibliometric Analysis of Digital Twin in the Supply Chain
Siew, L. W., Hoe, L. W., & Lee, P. F. (2023) · Mathematics · doi:10.3390/math11153350 · Cited by 57
Digital twin applications in urban logistics: an overview
Abouelrous, A., Bliek, L., & Zhang, Y.-Q. (2023) · Urban, Planning and Transport Research · doi:10.1080/21650020.2023.2216768 · Cited by 42
“For the large part, DT applications have largely been manufacturing-based as in [27] and [38].”
Ante, L. (2021) · Manufacturing Letters · doi:10.1016/j.mfglet.2021.01.003 · Cited by 42
Revolution on digital twin technology—a patent research approach
Wang, K., Lee, T., & Hsu, Y. (2020) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-020-05314-w · Cited by 48
“Previous studies have provided different DT definitions for DT [1, 9, 37], digital shadow [38–40], and digital model [41–44].”
BackgroundMethodThe Rocky Road From Fed-Batch to Continuous Processing With E. coli
Kopp, J., Slouka, C., Oliver Spadiut, A., & Herwig, C. (2019) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2019.00328 · Cited by 45
“Within this monitoring strategy, first brought to public by NASA (Rosen et al., 2015), real processes are represented by virtual simulations and are continuously fed with all process parameters and sensor results monitored throughout the process (Zobel-Roos et al., 2019).”
MethodOn Digital Twin Condition Monitoring Approach for Drivetrains in Marine Applications
Johansen, S., & Nejad, A. (2019) · Volume 10: Ocean Renewable Energy · doi:10.1115/omae2019-95152 · Cited by 42
“The assets would be considered as a whole, all components included and implied faults could be predicted as the twin will have life cycle updates [8].”
BackgroundVirtual engineering of cyber-physical automation systems: The case of control logic
Schneider, G., Wicaksono, H., & Ovtcharova, J. (2019) · Advanced Engineering Informatics · doi:10.1016/j.aei.2018.11.009 · Cited by 42
Cameron, D., Waaler, A., & Komulainen, T. M. (2018) · Linköping electronic conference proceedings · doi:10.3384/ecp181539 · Cited by 45
“A wide variety of architectures have been proposed for digital twins (Tao and Zhang 2017, Alam and Saddik 2017, Uhlemann, Lehmann, and Steinhilper 2017, Yun, Park, and Kim 2017, Rosen et al. 2015, Parrott and Warshaw 2017).”
BackgroundProbabilistic machine learning based predictive and interpretable digital twin for dynamical systemsPreprint
Tripura, T., Desai, A., Adhikari, S., & Chakraborty, S. (2022) · Computers & Structures · doi:10.48550/arxiv.2212.09240 · Cited by 41
“Few of the main applications of DT include remote access, real-time monitoring, future prediction of remaining useful life, predictive maintenance, prolonging the life-cycle, and designing [44, 45, 13].”
BackgroundDigital Twin Application for Production Optimization
Jeon, S., & Schuesslbauer, S. (2020) · IEEE International Conference on Industrial Engineering and Engineering Management · doi:10.1109/ieem45057.2020.9309874 · Cited by 41
“It has often been described as a software model for the development and testing of different configurations [2,11,18].”
MethodWagner, R., & Kabalska, A. (2022) · Sustainable Development · doi:10.1002/sd.2474 · Cited by 40
Digital Twins Collaboration for Automatic Erratic Operational Data Detection in Industry 4.0
Sahal, R., Alsamhi, S. H., Breslin, J. G., Brown, K. N., & Ali, M. I. (2021) · Applied Sciences · doi:10.3390/app11073186 · Cited by 47
“The concept of using “Twins” originates from NASA’s Apollo program (National Aeronautics and Space Administration), where at least two identical spacecrafts were built to reflect the conditions of the spacecraft during a mission in the outer space [1].”
MethodClosed-loop digital twin system for air cargo load planning operations
Wong, E. Y. C., Mo, D. Y. W., & So, S. C. K. (2020) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2020.1775299 · Cited by 71
“Digital twin has been developed and applied mostly in product design, engineering and manufacturing (Tao et al. 2018a; Tao and Zhang 2017; Söderberg et al. 2017; Rosen et al. 2015).”
MethodSupply Chains in the Era of Digital Twins – A Review
van der Valk, H., Strobel, G., Winkelmann, S., Hunker, J., & Tomczyk, M. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.08.019 · Cited by 40
Schützer, K., Bertazzi, J. D. A., Sallati, C., Anderl, R., & Zancul, E. D. S. (2019) · Procedia CIRP · doi:10.1016/j.procir.2019.03.212 · Cited by 40
Data enabling technology in digital twin and its frameworks in different industrial applications
Mohanraj, R., & Vaishnavi, B. K. (2025) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2025.100793 · Cited by 32
Digital twin-enabled smart industrial systems: recent developments and future perspectives
Kuo, Y., Pilati, F., Qu, T., & Huang, G. Q. (2021) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2021.1959710 · Cited by 48
“It leverages the Internet of Things (IoT) to facilitate learning, reasoning, and improving decision-making in complex systems (Rosen et al. 2015).”
BackgroundMostafa, F., Tao, L., & Yu, W. (2020) · Concurrency and Computation · doi:10.1002/cpe.6111 · Cited by 39
Real-Time Decision-Support System for High-Mix Low-Volume Production Scheduling in Industry 4.0
Kocsi, B., Matonya, M., Pusztai, L., & Budai, I. (2020) · Processes · doi:10.3390/pr8080912 · Cited by 39
“The most common and efficient way of analyzing manufacturing processes and complex production is to use a discrete-event simulation model [83].”
MethodTaxonomy of Industry 4.0 research: Mapping scholarship and industry insights
Nazarov, D., & Klarin, A. (2020) · Systems Research and Behavioral Science · doi:10.1002/sres.2700 · Cited by 39
Improving devices communication in Industry 4.0 wireless networks
Kunst, R., Avila, L., Binotto, A., Pignaton, E., Bampi, S., & Rochol, J. (2019) · Engineering applications of artificial intelligence · doi:10.1016/j.engappai.2019.04.014 · Cited by 39
The Italian Flagship Project: Factories of the Future
Terkaj, W., & Tolio, T. (2019) · Factories of the Future · doi:10.1007/978-3-319-94358-9_1 · Cited by 39
Digital twins in healthcare: Applications, technologies, simulations, and future trends
Elaziz, M. A., Al‐qaness, M. A. A., Dahou, A., Al‐Betar, M. A., Mohamed, M. M., El‐Shinawi, M., Ali, A., & Ewees, A. A. (2024) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1559 · Cited by 37
“Virtual representations of physical entities must accurately recreate their geometry, attributes, behaviors, and rules (Wärmefjord, Carlson, & Söderberg, 2016).”
Digital Twins in Type 1 Diabetes: A Systematic Review
Cappon, G., & Facchinetti, A. (2024) · Journal of Diabetes Science and Technology · doi:10.1177/19322968241262112 · Cited by 38
Digital twins for the designs of systems: a perspective
van Beek, A., Karkaria, V. N., & Chen, W. (2022) · Structural And Multidisciplinary Optimization · doi:10.1007/s00158-023-03488-x · Cited by 38
“, vehicles of the same model could benefit from different operation decisions as they are used by different drivers with different driving behavior under different usage contexts) (Rosen et al. 2015; Sharma et al. 2020; Söderberg et al. 2017).”
A digital twin-enabled value stream mapping approach for production process reengineering in SMEs
Lu, Y., Liu, Z., & Min, Q. (2021) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2021.1872099 · Cited by 70
“However, the past digital twin research has mostly focused on the product lifecycle management view and utilized in large manufacturing industry background (Grieves 2014; Rosen et al. 2015; Schleich et al. 2017).”
BackgroundMethodInfluentialCloud-Based Digital Twin for Industrial Robotics
Höbert, T., Lepuschitz, W., List, E., & Merdan, M. (2019) · Holonic and Multi-Agent Systems for Manufacturing · doi:10.1007/978-3-030-27878-6_9 · Cited by 38
Automating the Provisioning and Configuration of Devices in the Internet of Things
Hirmer, P., Breitenbücher, U., da Silva, A. C. F., Képes, K., Mitschang, B., & Wieland, M. (2016) · Complex Systems Informatics and Modeling Quarterly · doi:10.7250/csimq.2016-9.02 · Cited by 38
“Smart environments are divided into two parts: (i) the physical environment containing devices, sensors, and actuators, and (ii) its digital representation, which is referred to as digital twin [5], [6], [7] in the context of this article.”
BackgroundExploiting Factory Telemetry to Support Virtual Reality Simulation in Robotics Cell
Kuts, V., Modoni, G. E., Terkaj, W., Tähemaa, T., Sacco, M., & Otto, T. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-60922-5_16 · Cited by 38
Digitization of Manufacturing Processes: From Sensing to Twining
Stavropoulos, P. (2022) · Technologies · doi:10.3390/technologies10050098 · Cited by 37
Digital twin for geometric feature online inspection system of car body-in-white
Zheng, Y., Chen, L., Lu, X., Sen, Y., & Cheng, H. (2020) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2020.1736637 · Cited by 43
“Much research has been focused on DT in smart manufacturing, and DT could revolutionize techniques of simulation, modeling and optimization (Rosen et al. 2015).”
BackgroundMethodVenkateswarlu, N., & Sathiyamoorthy, M. (2025) · Frontiers in Built Environment · doi:10.3389/fbuil.2025.1523464 · Cited by 49
The philosophical foundations of digital twinning
Wagg, D., Burr, C., Shepherd, J., Conti, Z. X., Enzer, M., & Niederer, S. (2025) · Data-Centric Engineering · doi:10.1017/dce.2025.4 · Cited by 42
“The origins of the twinning concept is usually attributed to the work of NASA during the Apollo programme, where physical duplicates were used (Rosen et al., 2015).”
Mutual information-enhanced digital twin promotes vision-guided robotic grasping
Hu, F.-W. (2022) · Advanced Engineering Informatics · doi:10.1016/j.aei.2022.101562 · Cited by 36
A survey on new trends of digital twin technology for power systems
Moghadam, H. M., Foroozan, H., Gheisarnejad, M., & Khooban, M. (2021) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-201885 · Cited by 36
Research on digital twin monitoring system during milling of large parts
Lu, Y., Yue, C., Liu, X.-L., Wang, L.-H., Liang, S. Y., Xia, W., & Dou, X.-P. (2024) · Journal of manufacturing systems · doi:10.1016/j.jmsy.2024.10.027 · Cited by 35
Dall’Ora, N., Alamin, K. S. S., Fraccaroli, E., Poncino, M., Quaglia, D., & Vinco, S. (2021) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2021.3132432 · Cited by 46
“Achieving communication with the physical plant and collecting real time data opens the door to smart manufacturing, as they allow manufacturers to better measure, understand, and optimize production and to leverage analytics to make decisions that are rooted in facts [19].”
BackgroundNewrzella, S. R., Franklin, D. W., & Haider, S. (2022) · IEEE Access · doi:10.1109/access.2022.3202941 · Cited by 48
“The Digital Twin is seen as a key concept for Industry 4.0 [37], [38], and Digital Twin applications are often found in manufacturing as part of Industry 4.0 [19], [26], [30], [31], [38].”
BackgroundInfluentialA Product Quality Monitor Model With the Digital Twin Model and the Stacked Auto Encoder
Zhang, S.-Z., Kang, C.-F., Liu, Z., Wu, J., & Ma, C.-M. (2020) · IEEE Access · doi:10.1109/access.2020.3003723 · Cited by 35
“promotes the digitalization in industry and provides a new path to solve problems in data-driven methods [14].”
BackgroundOkita, T., Kawabata, T., Murayama, H., Nishino, N., & Aichi, M. (2019) · Procedia CIRP · doi:10.1016/j.procir.2019.02.048 · Cited by 35
A Digital Twin use cases classification and definition framework based on Industrial feedback
Abisset‐Chavanne, E., Coupaye, T., Golra, F. R., Lamy, D., Piel, A., Scart, O., & Primet, P. V.-B. (2024) · Computers in Industry · doi:10.1016/j.compind.2024.104113 · Cited by 42
Digital Twins in Manufacturing: A RAMI 4.0 Compliant Concept
Lindner, M. S., Bank, L., Schilp, J., & Weigold, M. (2023) · Sci · doi:10.3390/sci5040040 · Cited by 38
Digital twins for electro-physical, chemical, and photonic processes
Guo, Y. B., Klink, A., Bártolo, P., & Guo, W. (2023) · CIRP Annals · doi:10.1016/j.cirp.2023.05.007 · Cited by 45
Underwater Drone Architecture for Marine Digital Twin: Lessons Learned from SUSHI DROP Project
Lambertini, A., Menghini, M., Cimini, J., Odetti, A., Bruzzone, G., Bibuli, M., Mandanici, E., Vittuari, L., Castaldi, P., Caccia, M., et al. (2022) · Italian National Conference on Sensors · doi:10.3390/s22030744 · Cited by 34
Li, Y., Chang, D., Gao, Y., Zou, Y., & Bao, C. (2021) · Journal of Advanced Transportation · doi:10.1155/2021/1936764 · Cited by 33
“It can be used to simulate, optimize, and control the behaviors of physical entities. concept of “twins” can be traced back to NASA’s Apollo program, which was built to allow mirroring the conditions of the space vehicle during the mission [16].”
BackgroundPortela, R. M. C., Varsakelis, C., Richelle, A., Giannelos, N., Pence, J., Dessoy, S., & von Stosch, M. (2020) · Advances in biochemical engineering, biotechnology · doi:10.1007/10_2020_138 · Cited by 50
Comparison of Digital Twin Development in Manufacturing and Maritime Domains
Taylor, N., Human, C., Kruger, K., Bekker, A., & Basson, A. (2019) · Service Orientation in Holonic and Multi-Agent Manufacturing · doi:10.1007/978-3-030-27477-1_12 · Cited by 34
Sensor Control in Anti-Submarine Warfare—A Digital Twin and Random Finite Sets Based Approach
Wang, P., Yang, M., Peng, Y., Zhu, J., Ju, R.-S., & Yin, Q.-J. (2019) · Entropy · doi:10.3390/e21080767 · Cited by 34
Brundage, M. P., Sexton, T., Hodkiewicz, M., Morris, K. C., Arinez, J., Ameri, F., Ni, J., & Xiao, G. (2019) · Journal of manufacturing science and engineering · doi:10.1115/1.4044105 · Cited by 34
“However, AI and other Smart Manufacturing (SM) technologies are not one-sizefits-all solutions for all data types or problems, especially when there are many human-centered aspects in the workflow.”
BackgroundInfluentialCioroaica, E., Kuhn, T., & Buhnova, B. (2019) · 2019 IEEE/ACM 41st International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER) · doi:10.1109/icse-nier.2019.00011 · Cited by 34
“Since then, the notion of DT has also been adopted in the emerging Industry 4.0 [12] for representing the status of production devices and to enable forecast of change impacts.”
BackgroundInfluentialTomin, N., Kurbatsky, V., Borisov, V., & Musalev, S. (2020) · E3S Web of Conferences · doi:10.1051/e3sconf/202020902029 · Cited by 34
Gesture Control of Cyber Physical Systems
Horváth, G., & Erdős, G. (2017) · doi:10.1016/j.procir.2017.03.312 · Cited by 34
Walton, R. B., Ciarallo, F. W., & Champagne, L. E. (2024) · Computers & Industrial Engineering · doi:10.1016/j.cie.2024.110241 · Cited by 33
MADTwin: a framework for multi-agent digital twin development: smart warehouse case study
Marah, H., & Challenger, M. (2023) · Annals of Mathematics and Artificial Intelligence · doi:10.1007/s10472-023-09872-z · Cited by 36
“This data stream can be quite valuable in the product life-cycle [39].”
Towards Metaverse: Utilizing Extended Reality and Digital Twins to Control Robotic Systems
Kaarlela, T., Pitkäaho, T., Pieskä, S., Padrão, P., Bobadilla, L., Tikanmäki, M., Haavisto, T., Bataller, V. B., Laivuori, N., & Luimula, M. (2023) · Actuators · doi:10.3390/act12060219 · Cited by 39
Digital twins in the green life sciences
Knibbe, W., Afman, L., Boersma, S., Bogaardt, M., Evers, J., van Evert, F. V., van der Heide, J., Hoving, I., van Mourik, S., de Ridder, D., et al. (2022) · NJAS Impact in Agricultural and Life Sciences · doi:10.1080/27685241.2022.2150571 · Cited by 33
“In NASA Technology Roadmaps, their definition explicitly links them to physical systems (Rosen et al., 2015).”
BackgroundEmmert‐Streib, F., & Yli-Harja, O. P. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms232113149 · Cited by 38
“In industry, the idea of a digital twin was met with great interest and notable early adoptions thereof were in astronautics and aerospace by NASA [4] and in manufacturing [5–8].”
BackgroundMPC-Based Process Control of Deep Drawing: An Industry 4.0 Case Study in Automotive
Cavone, G., Bozza, A., Carli, R., & Dotoli, M. (2022) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2022.3177362 · Cited by 33
Kender, R., Kaufmann, F., Rößler, F., Wunderlich, B., Golubev, D., Thomas, I., Ecker, A., Rehfeldt, S., & Klein, H. (2021) · Computers and Chemical Engineering · doi:10.1016/j.compchemeng.2021.107349 · Cited by 33
Yuan, Z., Pan, Y., Wang, H., Wang, S., Peng, Y. L., Jin, C., Xu, C., Feng, X., Shen, K., Zheng, Y., et al. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.107113 · Cited by 37
Synthetic datasets for Deep Learning in computer-vision assisted tasks in manufacturing
Manettas, C., Nikolakis, N., & Alexopoulos, K. (2021) · Procedia CIRP · doi:10.1016/j.procir.2021.10.038 · Cited by 43
From modeling and simulation to Digital Twin: evolution or revolution?
Ali, Z., Biglari, R., Denil, J., Mertens, J., Poursoltan, M., & Traoré, M. (2024) · International Conference on Advances in System Simulation · doi:10.1177/00375497241234680 · Cited by 32
“49 Consequently, the space of possible initial conditions to explore is larger with a traditional M&S model than with the DT model.”
Malik, M., Raziq, M. M., Sarwar, N., & Gohar, M. (2024) · Benchmarking An International Journal · doi:10.1108/bij-06-2023-0376 · Cited by 37
Fang, Q., Chen, X., Castro-Lacouture, D., & Li, C. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2023.102182 · Cited by 32
A Review on Recent Trends and Applications of IoT in Additive Manufacturing
Chigilipalli, B. K., Karri, T., Chetti, S. N., Bhiogade, G., Kottala, R. K., & Cheepu, M. (2023) · Applied System Innovation · doi:10.3390/asi6020050 · Cited by 37
“…multi-scale, probabilistic simulation of a vehicle or a system that uses the best available physical models, sensor updates, fl eet history, etc., to mirror the life of its fl ying twin” [33,34]. available physical models, sensor updates, fleet history, etc., to mirror the life of twin” [33,34].”
BackgroundLee, J., Lee, Y., & Hong, C. (2023) · Applied Sciences · doi:10.3390/app13074343 · Cited by 32
“…spaceship used for the moon exploration mission were input from the spaceship near the moon to the simulator on the ground through communications, and then the causes of failure and the restoration method of normal operation were investigated via a virtual experiment using the simulator [13,14].”
Method
Metadata
- Authors
- Rosen, R., von Wichert, G., Lo, G., & Bettenhausen, K. D.
- Affiliations
- Siemens (Germany) (DE)
- Siemens (United States) (US)
- Corresponding author
- Rosen, Roland
- Lo, George
- Bettenhausen, Kurt D.
- Venue
- IFAC-PapersOnLine
- Publisher
- Elsevier BV
- Published
- 2015
- Volume
- 48
- Issue
- 3
- Pages
- 567-572
- ISSN
- 2405-8963 (ISSN-L)
- OpenAlex
- W2346505350
- Cited by
- 1,474
- References
- 2
- Citations by year
- 2026: 113 · 2025: 163 · 2024: 202 · 2023: 226 · 2022: 200 · 2021: 183 · 2020: 138 · 2019: 142 · 2018: 73 · 2017: 22 · 2016: 11
- FWCI
- 61.97
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- IFAC-PapersOnLine (publishedVersion)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Flexible and Reconfigurable Manufacturing Systems
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Keywords
- Industry 4.0, digital twin, future of manufacturing, future of work, industrial automation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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