The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Digital Twin—The Simulation Aspect
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“However, NASA had previously employed the same concept during the Apollo program as a”
From Digital Twins in the IIoT: Current Practices and Future Directions Toward Industry 5.0 (2025) · cites this paper as [18]
In the citing paper
“It enables the evolution of physical systems into more complex digital environments by integrating software functions, such as drive and sensor connectivity within cyber-physical systems (CPS) [121].”
From Analysis of Digital Twin Applications in Energy Efficiency: A Systematic Review (2025) · cites this paper as [121]
Citations
Cites
About The Importance of Autonomy and Digital Twins for the Future of Manufacturing
Rosen, R., von Wichert, G., Lo, G., & Bettenhausen, K. D. (2015) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2015.06.141 · Cited by 1,474
Self-citationFitzwater, L. M., Torng, Y. T., & Halbert, K. (2011) · doi:10.21236/ada586666 · Cited by 1
Cited by
Sorted by citations
Items 101–200 of 1,001
Digital twin-driven cyber-physical production system towards smart shop-floor
Zhang, H., Zhang, G., & Yan, Q. (2018) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-018-1125-4 · Cited by 126
IoT Open-Source Architecture for the Maintenance of Building Facilities
Villa, V., Naticchia, B., Bruno, G., Aliev, K., Piantanida, P., & Antonelli, D. (2021) · Applied Sciences · doi:10.3390/app11125374 · Cited by 128
Digital Twin Concepts with Uncertainty for Nuclear Power Applications
Kochunas, B. M., & Huan, X. (2021) · Energies · doi:10.3390/en14144235 · Cited by 137
Digital Twins for IoT-Driven Energy Systems: A Survey
Kabir, M. R., Halder, D., & Ray, S. (2024) · IEEE Access · doi:10.1109/access.2024.3506660 · Cited by 134
State-of-the-art survey on digital twin implementations
Liu, Y. K., Ong, S. K., & Nee, A. Y. C. (2022) · Advances in Manufacturing · doi:10.1007/s40436-021-00375-w · Cited by 126
Trauer, J., Schweigert-Recksiek, S., Engel, C., Spreitzer, K., & Zimmermann, M. (2020) · Proceedings of the Design Society: DESIGN Conference · doi:10.1017/dsd.2020.15 · Cited by 112
Digital twin-driven smart supply chain
Wang, L., Deng, T., Shen, Z. M., Hu, H., & Qi, Y. (2022) · Frontiers of Engineering Management · doi:10.1007/s42524-021-0186-9 · Cited by 135
Understanding Digital Transformation Initiatives: Case Studies Analysis
Furjan, M. T., Tomičić-Pupek, K., & Pihir, I. (2020) · Business Systems Research Journal · doi:10.2478/bsrj-2020-0009 · Cited by 122
Digital Twin: From Concept to Practice
Agrawal, A., Fischer, M. A., & Singh, V. (2022) · Journal of Management in Engineering · doi:10.1061/(asce)me.1943-5479.0001034 · Cited by 147
Predictive digital twin for offshore wind farms
Haghshenas, A., Hasan, A., Osen, O. L., & Mikalsen, E. T. (2023) · Energy Informatics · doi:10.1186/s42162-023-00257-4 · Cited by 126
Consistency check to synchronize the Digital Twin of manufacturing automation based on anchor points
Talkhestani, B. A., Jazdi, N., Schloegl, W., & Weyrich, M. (2018) · Procedia CIRP · doi:10.1016/j.procir.2018.03.166 · Cited by 119
A digital twin-driven production management system for production workshop
Ma, J., Chen, H., Zhang, Y., Guo, H., Ren, Y., Mo, R., & Liu, L. (2020) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-020-05977-5 · Cited by 119
Digital twins: Recent advances and future directions in engineering fields
Iranshahi, K., Brun, J., Arnold, T., Sergi, T., & Müller, U. C. (2025) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2025.200516 · Cited by 132
Post-Earthquake Building Evaluation Using UAVs: A BIM-Based Digital Twin Framework
LEVINE, N. M., & Spencer, B. F. (2022) · Sensors · doi:10.3390/s22030873 · Cited by 114
On the requirements of digital twin-driven autonomous maintenance
Khan, S., Farnsworth, M., McWilliam, R. P., & Erkoyuncu, J. A. (2020) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2020.08.003 · Cited by 123
Digital Twin: Where do humans fit in?
Agrawal, A., Thiel, R. W., Jain, P., Singh, V., & Fischer, M. A. (2023) · Automation in Construction · doi:10.1016/j.autcon.2023.104749 · Cited by 128
Das, O., Zafar, M. H., Sanfilippo, F., Rudra, S., & Kolhe, M. L. (2024) · Energy Conversion and Management X · doi:10.1016/j.ecmx.2024.100715 · Cited by 122
Liljaniemi, A., & Paavilainen, H. (2020) · Open Engineering · doi:10.1515/eng-2020-0040 · Cited by 84
Development of Digital Twin for Intelligent Maintenance of Civil Infrastructure
Mahmoodian, M., Shahrivar, F., Setunge, S., & Mazaheri, S. (2022) · Sustainability · doi:10.3390/su14148664 · Cited by 110
Data management for developing digital twin ontology model
Singh, S. K., Shehab, E., Higgins, N., Fowler, K., Reynolds, D., Erkoyuncu, J. A., & Gadd, P. (2020) · Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture · doi:10.1177/0954405420978117 · Cited by 103
Digital twin data: methods and key technologies
Zhang, M., Tao, F., Huang, B., Liu, A., Wang, L., Anwer, N., & Nee, A. Y. C. (2022) · Digital Twin · doi:10.12688/digitaltwin.17467.2 · Cited by 105
Methods for enabling real-time analysis in digital twins: A literature review
Eshaghi, M. S., Anitescu, C., & Rabczuk, T. (2024) · Computers & Structures · doi:10.1016/j.compstruc.2024.107342 · Cited by 120
Digital Twin for Smart Manufacturing: The Simulation Aspect
Shao, G., Jain, S., Laroque, C., Lee, L., Lendermann, P., & Rose, O. (2019) · Online World Conference on Soft Computing in Industrial Applications · doi:10.1109/wsc40007.2019.9004659 · Cited by 86
Lin, Y., Chen, L., Ali, A., Nugent, C., Cleland, I., Li, R., Ding, J., & Ning, H. (2024) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-024-00691-z · Cited by 126
Cognition Digital Twins for Personalized Information Systems of Smart Cities: Proof of Concept
Du, J., Zhu, Q., Shi, Y., Wang, Q., Lin, Y., & Zhao, D. Y. (2019) · Journal of Management in Engineering · doi:10.1061/(asce)me.1943-5479.0000740 · Cited by 103
Digital twin-enabled reconfigurable modeling for smart manufacturing systems
Zhang, C., Xu, W., Liu, J., Liu, Z., Zhou, Z., & Pham, D. T. (2019) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2019.1699256 · Cited by 101
Marcucci, E., Gatta, V., Pira, M. L., Hansson, L., & Bråthen, S. (2020) · Sustainability · doi:10.3390/su122410623 · Cited by 107
Construction method of shop-floor digital twin based on MBSE
Liu, J., Liu, J., Zhuang, C., Liu, Z., & Miao, T. (2021) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2021.05.004 · Cited by 98
Digital Twin for Human–Robot Collaboration in Manufacturing: Review and Outlook
Ramasubramanian, A. K., Mathew, R., Kelly, M., Hargaden, V., & Papakostas, N. (2022) · Applied Sciences · doi:10.3390/app12104811 · Cited by 108
Numerical simulation and evaluation of the world’s first metal additively manufactured bridge
Kyvelou, P., Buchanan, C., & Gardner, L. (2022) · Structures · doi:10.1016/j.istruc.2022.06.012 · Cited by 97
Digital Twin Providing New Opportunities for Value Co-Creation through Supporting Decision-Making
West, S., Stoll, O., Meierhofer, J., & Züst, S. (2021) · Applied Sciences · doi:10.3390/app11093750 · Cited by 112
Digital Twin conceptual framework for improving critical infrastructure resilience
Brucherseifer, E., Winter, H., Mentges, A., Mühlhäuser, M., & Hellmann, M. (2021) · at - Automatisierungstechnik · doi:10.1515/auto-2021-0104 · Cited by 69
Digital twin and its applications: A survey
Zhang, R., Wang, F., Cai, J., Wang, Y., Guo, H., & Zheng, J. (2022) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-022-10445-3 · Cited by 104
Analysis of Digital Twin Applications in Energy Efficiency: A Systematic Review
Ba, L., Tangour, F., El Abbassi, I., & Absi, R. (2025) · Sustainability · doi:10.3390/su17083560 · Cited by 96
Digital Post-Disaster Risk Management Twinning: A Review and Improved Conceptual Framework
Lagap, U., & Ghaffarian, S. (2024) · International Journal of Disaster Risk Reduction · doi:10.1016/j.ijdrr.2024.104629 · Cited by 82
Challenges when creating a cohesive digital twin ship: a data modelling perspective
Fonseca, Í. A., & Gaspar, H. M. (2020) · Ship Technology Research · doi:10.1080/09377255.2020.1815140 · Cited by 93
From Simulation to Autonomy: Reviews of the Integration of Artificial Intelligence and Digital Twins
Sajadieh, S. M. M., & Noh, S. D. (2025) · International Journal of Precision Engineering and Manufacturing-Green Technology · doi:10.1007/s40684-025-00750-z · Cited by 114
Newrzella, S. R., Franklin, D. W., & Haider, S. (2021) · IEEE Access · doi:10.1109/access.2021.3115055 · Cited by 100
Digital twins: a stepping stone to achieve ocean sustainability?
Tzachor, A., Hendel, O., & Richards, C. E. (2023) · npj Ocean Sustainability · doi:10.1038/s44183-023-00023-9 · Cited by 100
Abdelrahman, M., Macatulad, E., Lei, B., Quintana, M., Miller, C., & Biljecki, F. (2025) · Building and Environment · doi:10.1016/j.buildenv.2025.112748 · Cited by 120
Exploiting the Digital Twin in the Assessment and Optimization of Sustainability Performances
Barni, A., Fontana, A., Menato, S., Sorlini, M., & Canetta, L. (2018) · IEEE International Conference on Intelligent Systems · doi:10.1109/is.2018.8710554 · Cited by 94
Digital Twin-Based Optimization for Ultraprecision Motion Systems With Backlash and Friction
Guerra, R. H., Quiza, R., Villalonga, A., Arenas, J., & Castano, F. (2019) · IEEE Access · doi:10.1109/access.2019.2928141 · Cited by 89
Zhang, M., Zuo, Y. N., & Tao, F. (2018) · IEEE International Conference on Networking, Sensing and Control (ICNSC) · doi:10.1109/icnsc.2018.8361272 · Cited by 89
Zhang, X., & Zhu, W. (2019) · International Journal of Advanced Robotic Systems · doi:10.1177/1729881419880663 · Cited by 90
Healthy Operator 4.0: A Human Cyber–Physical System Architecture for Smart Workplaces
Sun, S., Zheng, X., Gong, B., Paredes, J. G., & Ordieres-Meré, J. B. (2020) · Sensors · doi:10.3390/s20072011 · Cited by 97
Data Handling in Industry 4.0: Interoperability Based on Distributed Ledger Technology
Sun, S., Zheng, X., Villalba-Díez, J., & Ordieres-Meré, J. B. (2020) · Sensors · doi:10.3390/s20113046 · Cited by 91
The ASSISTANT project: AI for high level decisions in manufacturing
Castañé, G. G., Dolgui, A. B., Kousi, N., Meyers, B., Thevenin, S., Vyhmeister, E., & Östberg, P.-O. (2022) · International Journal of Production Research · doi:10.1080/00207543.2022.2069525 · Cited by 87
A Digital Twins Approach to Smart Grid Security Testing and Standardization
Atalay, M., & Angın, P. (2020) · IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT) · doi:10.1109/metroind4.0iot48571.2020.9138264 · Cited by 84
Zheng, X., Psarommatis, F., Petrali, P., Turrin, C., Lu, J., & Kiritsis, D. (2020) · Procedia Manufacturing · doi:10.1016/j.promfg.2020.10.044 · Cited by 84
Steering Representations—Towards a Critical Understanding of Digital Twins
Korenhof, P., Blok, V., & Kloppenburg, S. (2021) · Philosophy & Technology · doi:10.1007/s13347-021-00484-1 · Cited by 86
A 3D functional plant modelling framework for agricultural digital twins
Mitsanis, C., Hurst, W., & Teki̇nerdoğan, B. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.108733 · Cited by 86
Seven Metaphors to Understand Digital Twins of Built Assets
Camposano, J. C., Smolander, K., & Ruippo, T. (2021) · IEEE Access · doi:10.1109/access.2021.3058009 · Cited by 94
Barenji, A. V., Liu, X., Guo, H., & Li, Z. (2020) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2020.1775297 · Cited by 85
Stavropoulos, P., & Mourtzis, D. (2022) · Design and Operation of Production Networks for Mass Personalization in the Era of Cloud Technology · doi:10.1016/b978-0-12-823657-4.00010-5 · Cited by 80
Generic development methodology for flexible robotic pick-and-place workcells based on Digital Twin
Tipary, B., & Erdös, G. (2021) · Robotics Comput. Integr. Manuf. · doi:10.1016/j.rcim.2021.102140 · Cited by 72
Far, S. B., Rad, A. I., Bamakan, S. M. H., & Asaar, M. R. (2023) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2023.103675 · Cited by 79
Advancing digital twin implementation: a toolbox for modelling and simulation
Singh, S., Weeber, M., & Birke, K.-P. (2021) · Procedia CIRP · doi:10.1016/j.procir.2021.03.078 · Cited by 86
Enhancing Digital Twins through Reinforcement Learning
Cronrath, C., Aderiani, A. R., & Lennartson, B. (2019) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/coase.2019.8842888 · Cited by 77
Ante, L. (2021) · Manufacturing Letters · doi:10.1016/j.mfglet.2021.01.003 · Cited by 42
Papyshev, G., & Yarime, M. (2021) · Data & Policy · doi:10.1017/dap.2021.17 · Cited by 81
Past, present, and future research of digital twin for smart manufacturing
Son, Y. H., Kim, G.-Y., Kim, H. C., Jun, C., & Noh, S. D. (2021) · Journal of Computational Design and Engineering · doi:10.1093/jcde/qwab067 · Cited by 79
Shop Floor Digital Twin in Smart Manufacturing: A Systematic Literature Review
Corallo, A., Vecchio, V. D., Lezzi, M., & Morciano, P. (2021) · Sustainability · doi:10.3390/su132312987 · Cited by 78
The Role of a Digital Industry 4.0 in a Renewable Energy System
Scharl, S., & Praktiknjo, A. (2019) · International Journal of Energy Research · doi:10.1002/er.4462 · Cited by 75
Digital Twins in Architecture, Engineering, Construction and Operations. A Brief Review and Analysis
Al-Sehrawy, R., & Kumar, B. (2020) · Lecture notes in civil engineering · doi:10.1007/978-3-030-51295-8_64 · Cited by 59
Hartmann, D., & der Auweraer, H. D. V. (2020) · SEMA SIMAI Springer series · doi:10.1007/978-3-030-61844-5_1 · Cited by 74
He, X., Ai, Q., Wang, J.-B., Tao, F., Pan, B., Qiu, R., & Yang, B. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3203823 · Cited by 62
Digital Transformation of Learning Organizations
Ifenthaler, D., Hofhues, S., Egloffstein, M., & Helbig, C. (2021) · Springer eBooks · doi:10.1007/978-3-030-55878-9 · Cited by 96
Extended Reality Application Framework for a Digital-Twin-Based Smart Crane
Yang, C., Tu, X., Autiosalo, J., Ala-Laurinaho, R., Mattila, J., Salminen, P., & Tammi, K. (2022) · Applied Sciences · doi:10.3390/app12126030 · Cited by 75
Perno, M., Hvam, L., & Haug, A. (2023) · Computers in Industry · doi:10.1016/j.compind.2023.103987 · Cited by 71
Digital Twins in the Context of Seaports and Terminal Facilities
Neugebauer, J., Heilig, L., & Voß, S. (2024) · Flexible Services and Manufacturing Journal · doi:10.1007/s10696-023-09515-9 · Cited by 88
Towards the Development of Digital Twins for the Bio-manufacturing Industry.
Gargalo, C. L., Heras, S. C. D. L., Jones, M., Udugama, I. A., Mansouri, S., Krühne, U., & Gernaey, K. (2020) · Advances in Biochemical Engineering/Biotechnology · doi:10.1007/10_2020_142 · Cited by 55
Bi-level dynamic scheduling architecture based on service unit digital twin agents
Zhang, J., Deng, T., Jiang, H., Chen, H., Qin, S., & Ding, G. (2021) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2021.05.007 · Cited by 71
Implementation of Battery Digital Twin: Approach, Functionalities and Benefits
Singh, S., Weeber, M., & Birke, K. (2021) · Batteries · doi:10.3390/batteries7040078 · Cited by 58
Langås, E. F., Zafar, M. H., & Sanfilippo, F. (2025) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-025-02580-x · Cited by 70
Probabilistic methods for risk assessment of airframe digital twin structures
Millwater, H. R., Ocampo, J., & Crosby, N. (2019) · Engineering Fracture Mechanics · doi:10.1016/j.engfracmech.2019.106674 · Cited by 69
Λαμπρόπουλος, Γ., & Siakas, K. V. (2022) · Journal of Software Evolution and Process · doi:10.1002/smr.2494 · Cited by 78
Fotland, G., Haskins, C., & Rølvåg, T. (2019) · Systems Engineering · doi:10.1002/sys.21503 · Cited by 68
Food Supply Chains as Cyber-Physical Systems: a Path for More Sustainable Personalized Nutrition
Smetana, S. M., Aganovic, K., & Heinz, V. (2020) · Food Engineering Reviews · doi:10.1007/s12393-020-09243-y · Cited by 70
Cloud-Based Cyber-Physical Robotic Mobile Fulfillment Systems: A Case Study of Collision Avoidance
Keung, K. L., Lee, C. K. M., Ji, P., & Ng, K. K. H. (2020) · IEEE Access · doi:10.1109/access.2020.2992475 · Cited by 68
Next generation DES simulation: A research agenda for human centric manufacturing systems
Turner, C. J., & Garn, W. (2022) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2022.100354 · Cited by 70
Toward Data-Driven Optimal Control: A Systematic Review of the Landscape
Prag, K., Woolway, M., & Çelik, T. (2022) · IEEE Access · doi:10.1109/access.2022.3160709 · Cited by 70
A conceptual framework for smart production planning and control in Industry 4.0
Cañas, H., Mula, J., Campuzano, F., & Escoto, R. P. (2022) · Computers & industrial engineering · doi:10.1016/j.cie.2022.108659 · Cited by 60
Virtual reality-based digital twins for greenhouses: A focus on human interaction
Slob, N., Hurst, W., van de Zedde, R., & Tekinerdoğan, B. (2023) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2023.107815 · Cited by 57
Digital twins for cutting processes
Bergs, T., Biermann, D., Erkorkmaz, K., & M'saoubi, R. (2023) · CIRP annals · doi:10.1016/j.cirp.2023.05.006 · Cited by 62
Digital 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
Synchronizing physical and digital factory: benefits and technical challenges
Modoni, G. E., Caldarola, E. G., Sacco, M., & Terkaj, W. (2019) · Procedia CIRP · doi:10.1016/j.procir.2019.02.125 · Cited by 62
Shkundalov, D., & Vilutienė, T. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103757 · Cited by 65
Knowledge Graphs as Enhancers of Intelligent Digital Twins
Sahlab, N., Kamm, S., Müller, T., Jazdi, N., & Weyrich, M. (2021) · IEEE International Conference on Industrial Cyber-Physical Systems (ICPS) · doi:10.1109/icps49255.2021.9468219 · Cited by 64
Shaping future low-carbon energy and transportation systems: Digital technologies and applications
Song, J., He, G., Wang, J., & Zhang, P. (2022) · iEnergy · doi:10.23919/ien.2022.0040 · Cited by 73
Alimam, H., Mazzuto, G., Tozzi, N., Ciarapica, F. E., & Bevilacqua, M. (2023) · Journal of King Saud University: Computer and Information Sciences · doi:10.1016/j.jksuci.2023.101846 · Cited by 60
Chhetri, S. R., Faezi, S., Canedo, A., & Faruque, M. A. A. (2019) · IEEE/ACM International Conference on Internet-of-Things Design and Implementation (IoTDI) · doi:10.1145/3302505.3310085 · Cited by 63
Quantifying the unknown impact of segmentation uncertainty on image-based simulations
Krygier, M., LaBonte, T., Martinez, C., Norris, C., Sharma, K., Collins, L. N., Mukherjee, P. P., & Roberts, S. A. (2021) · Nature Communications · doi:10.1038/s41467-021-25493-8 · Cited by 71
Digital Twins for Clinical and Operational Decision-Making: Scoping Review
Riahi, V., Diouf, I., Khanna, S., Boyle, J., & Hassanzadeh, H. (2024) · Journal of Medical Internet Research · doi:10.2196/55015 · Cited by 63
Digital twin-enabled smart industrial systems: a bibliometric review
Ciano, M. P., Pozzi, R., Rossi, T., & Strozzi, F. (2020) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2020.1852600 · Cited by 63
A Geospatial Platform to Manage Large-Scale Individual Mobility for an Urban Digital Twin Platform
Lee, A., Lee, K., Kim, K.-H., & Shin, S.-W. (2022) · Remote Sensing · doi:10.3390/rs14030723 · Cited by 62
Operation Procedures of a Work-Center-Level Digital Twin for Sustainable and Smart Manufacturing
Park, K., Lee, D.-G., & Noh, S. (2020) · International Journal of Precision Engineering and Manufacturing - Green Technology · doi:10.1007/s40684-020-00227-1 · Cited by 52
Maintenance optimization for a multi-unit system with digital twin simulation
Savolainen, J., & Urbani, M. (2021) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-021-01740-z · Cited by 56
Geometrical Variations Management 4.0: towards next Generation Geometry Assurance
Schleich, B., Wärmefjord, K., Söderberg, R., & Wartzack, S. (2018) · Procedia CIRP · doi:10.1016/j.procir.2018.04.078 · Cited by 63
The Concept of Enterprise BIM: Current Research Practice and Future Trends
Godager, B. A., Onstein, E., & Huang, L. (2021) · IEEE Access · doi:10.1109/access.2021.3065116 · Cited by 69
Keung, K. L., Lee, C. K. M., & Ji, P. (2022) · Advanced Engineering Informatics · doi:10.1016/j.aei.2022.101540 · Cited by 62
Metadata
- Authors
- Boschert, S., & Rosen, R.
- Affiliations
- Siemens (Germany) (DE)
- Corresponding author
- Boschert, Stefan
- ORCID
- Boschert, Stefan
- Venue
- Mechatronic Futures
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 59-74
- OpenAlex
- W2482127302
- Cited by
- 1,158
- References
- 2
- Citations by year
- 2026: 78 · 2025: 165 · 2024: 165 · 2023: 175 · 2022: 162 · 2021: 143 · 2020: 128 · 2019: 77 · 2018: 48 · 2017: 13 · 2016: 4
- FWCI
- 107.09
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Springer eBooks (publishedVersion)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Physical Sciences › Mathematics › Modeling and Simulation › Modeling and Simulation Systems
- Physical Sciences › Engineering › Control and Systems Engineering › Systems Engineering Methodologies and Applications
- Keywords
- digital twin, simulation, modeling and simulation, mechatronic systems, system development, design validation, failure prediction, virtual-physical integration, product life cycle
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review