Digital twin-driven rapid individualised designing of automated flow-shop manufacturing system
Abstract
Under a mass individualisation paradigm, the individualised design of manufacturing systems is difficult as it involves adaptive integrating both new and legacy machines for the formation of part families with uncertainty. A systematic virtual model mirroring the real world of manufacturing system is essential to bridge the gap between its design and operation. This paper presents a digital twin-driven methodology for rapid individualised designing of the automated flow-shop manufacturing system. The digital twin merges physics-based system modelling and distributed semi-physical simulation to provide engineering solution analysis capabilities and generates an authoritative digital design of the system at pre-production phase. An effective feedbacking of collected decision-support information from the intelligent multi-objective optimisation of the dynamic execution is presented to boost the applicability of the digital twin vision in the designing of AFMS. Finally, a bi-level iterative coordination mechanism is proposed to achieve optimal design performance for required functions of AFMS. A case study is conducted to prove the feasibility and effectiveness of the proposed methodology.
Integrity signals
Registry
Cites
Statistics
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?
Proposed improvements are reached by reducing energy consumption.
“The proposed improvements are reached by reducing the energy consumption [37,40]”
From Review of digital twin applications in manufacturing (2019) · cites this paper as [40]
Some reviewed papers mainly belong to the first service group but also offer services of the second group, and vice versa.
“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 [40]
Cites
Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems
Grieves, M., & Vickers, J. (2017) · Transdisciplinary Perspectives on Complex Systems · doi:10.1007/978-3-319-38756-7_4 · Cited by 3,727 · cited 1× · see citing works
“…the DT (Digital Twin), which was fi rstly adopted in spacecraft design by NASA (Boschert and Rosen 2016; Brenner and Hummel 2017; Ferguson, Bennett, and Ivashchenko 2017; Grieves 2014; Grieves and Vickers 2017), can be viewed as a combination of mod-elling-based method and optimisation-based method.”
MethodDigital twin-driven product design, manufacturing and service with big data
Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., & Sui, F. (2018) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-017-0233-1 · Cited by 2,920 · see citing works
Self-citationAtashpaz-Gargari, E., & Lucas, C. (2007) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2007.4425083 · Cited by 2,741 · see citing works
An overview of bilevel optimization
Colson, B., Marcotte, P., & Savard, G. (2007) · Annals of Operations Research · doi:10.1007/s10479-007-0176-2 · Cited by 1,452 · see citing works
The industrial management of SMEs in the era of Industry 4.0
Moeuf, A., Pellerin, R., Lamouri, S., Tamayo-Giraldo, S., & Barbaray, R. (2018) · International Journal of Production Research · doi:10.1080/00207543.2017.1372647 · Cited by 1,188 · see citing works
Digital Twin—The Simulation Aspect
Boschert, S., & Rosen, R. (2016) · Mechatronic Futures · doi:10.1007/978-3-319-32156-1_5 · Cited by 1,158 · see citing works
Reengineering aircraft structural life prediction using a digital twin
Tuegel, E. J., Ingraffea, A. R., Eason, T. G., & Spottswood, S. M. (2011) · International Journal of Aerospace Engineering · doi:10.1155/2011/154798 · Cited by 1,119 · see citing works
IEEE Transactions on Systems, Man, and Cybernetics: Systems
Institute of Electrical and Electronics Engineers (2023) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.6221021 · Cited by 1,099 · see citing works
Design of reconfigurable manufacturing systems
Koren, Y., & Shpitalni, M. (2010) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2011.01.001 · Cited by 781 · see citing works
C2PS: A Digital Twin Architecture Reference Model for the Cloud-Based Cyber-Physical Systems
Alam, K. M., & El Saddik, A. (2017) · IEEE Access · doi:10.1109/access.2017.2657006 · Cited by 741 · see citing works
Simulation for manufacturing system design and operation: Literature review and analysis
Negahban, A., & Smith, J. S. (2014) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2013.12.007 · Cited by 584 · see citing works
Toward a Digital Twin for real-time geometry assurance in individualized production
Söderberg, R., Wärmefjord, K., Carlson, J. S., & Lindkvist, L. (2017) · CIRP Annals · doi:10.1016/j.cirp.2017.04.038 · Cited by 543 · see citing works
A big data approach for logistics trajectory discovery from RFID-enabled production data
Zhong, R. Y., Huang, G. Q., Lan, S., Dai, Q., Chen, X., & Zhang, T. (2015) · International Journal of Production Economics · doi:10.1016/j.ijpe.2015.02.014 · Cited by 465 · see citing works
Self-citationThe Digital Twin: Demonstrating the Potential of Real Time Data Acquisition in Production Systems ☆
Uhlemann, T., Schock, C., Lehmann, C., Freiberger, S., & Steinhilper, R. (2017) · Procedia Manufacturing · doi:10.1016/j.promfg.2017.04.043 · Cited by 426 · see citing works
Computational Difficulties of Bilevel Linear Programming
Ben‐Ayed, O., & Blair, C. (1990) · Operations Research · doi:10.1287/opre.38.3.556 · Cited by 414 · see citing works
Design for mass personalization
Tseng, M. M., Jiao, J. R., & Wang, C. (2010) · CIRP Annals · doi:10.1016/j.cirp.2010.03.097 · Cited by 217 · see citing works
Service-oriented manufacturing: a new product pattern and manufacturing paradigm
Gao, J., Yinliang, Y., Zhu, V., Sun, L., & Lin, L. (2009) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-009-0301-y · Cited by 214 · see citing works
Brenner, B., & Hummel, V. (2017) · Procedia Manufacturing · doi:10.1016/j.promfg.2017.04.039 · Cited by 212 · see citing works
Gu, P., Hashemian, M. S., & Nee, A. Y. C. (2004) · CIRP Annals · doi:10.1016/s0007-8506(07)60028-6 · Cited by 203 · see citing works
Stackelberg versus Cournot Equilibrium
Amir, R., & Grilo, I. (1999) · Games and Economic Behavior · doi:10.1006/game.1998.0650 · Cited by 202 · see citing works
Putnik, G. D., Sluga, A., ElMaraghy, H. A., Teti, R., Koren, Y., Tolio, T. A. M., & Hon, B. (2013) · CIRP Annals · doi:10.1016/j.cirp.2013.05.002 · Cited by 186 · see citing works
Leader-follower strategies for multilevel systems
Cruz, J. (1978) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1978.1101716 · Cited by 179 · see citing works
Liu, B. (1998) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(98)00174-6 · Cited by 166 · see citing works
Social manufacturing as a sustainable paradigm for mass individualization
Jiang, P., Leng, J., Ding, K., Gu, P., & Koren, Y. (2016) · Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture · doi:10.1177/0954405416666903 · Cited by 129 · see citing works
Self-citationLeng, J., & Jiang, P. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2016.03.008 · Cited by 126 · see citing works
Self-citationPandey, D., Kulkarni, M. S., & Vrat, P. (2011) · Computers & Industrial Engineering · doi:10.1016/j.cie.2011.06.023 · Cited by 115 · see citing works
Leng, J., Chen, Q., Mao, N., & Jiang, P. (2017) · Knowledge-Based Systems · doi:10.1016/j.knosys.2017.07.023 · Cited by 106 · see citing works
Self-citationMohapatra, P., Nayak, A., Kumar, S., & Tiwari, M. K. (2014) · International Journal of Production Research · doi:10.1080/00207543.2014.957872 · Cited by 73 · see citing works
Axiomatic Design of Mechanical Systems
Suh, N. P. (1995) · Journal of Mechanical Design · doi:10.1115/1.2836467 · Cited by 72 · see citing works
STEP-NC and function blocks for interoperable manufacturing
Xu, X. W., Wang, L., & Rong, Y. K. (2006) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2005.862147 · Cited by 71 · see citing works
Leng, J., & Jiang, P. (2017) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-017-1301-y · Cited by 70 · see citing works
Self-citationIDEF*: A comprehensive modelling methodology for the development of manufacturing enterprise systems
Cheng-Leong, A., Khoo, L. P., & Leng, G. R. K. (1999) · International Journal of Production Research · doi:10.1080/002075499189790 · Cited by 67 · see citing works
Mining and Matching Relationships From Interaction Contexts in a Social Manufacturing Paradigm
Leng, J., & Jiang, P. (2016) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2016.2623630 · Cited by 63 · see citing works
Self-citationCochran, D. S., Foley, J. T., & Bi, Z. (2016) · International Journal of Production Research · doi:10.1080/00207543.2016.1218088 · Cited by 62 · see citing works
A Comprehensive Modelling Methodology for the Development of Manufacturing Enterprise Systems
Ang, C. L. (1997) · Springer eBooks · doi:10.1007/978-0-387-35291-6_19 · Cited by 56 · see citing works
Li, J., Dai, X., & Meng, Z. (2008) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2008.2006857 · Cited by 56 · see citing works
Modelling and design of flexible manufacturing systems using SADT and Petri nets tools
Santarek, K., & Buseif, I. M. (1998) · Journal of Materials Processing Technology · doi:10.1016/s0924-0136(97)00350-6 · Cited by 54 · see citing works
Digital twin tackles design challenges
Ferguson, S., Bennett, E., & Ivashchenko, A. (2017) · World Pumps · doi:10.1016/s0262-1762(17)30139-6 · Cited by 52 · see citing works
A dynamic Bayesian network approach for digital twin
Li, C., Mahadevan, S., Ling, Y., Wang, L., & Choze, S. (2017) · AIAA Non-Deterministic Approaches Conference · doi:10.2514/6.2017-1566 · Cited by 48 · see citing works
Implementing of a three-phase integrated decision support model for parts machining outsourcing
Leng, J., Jiang, P., & Ding, K. (2014) · International Journal of Production Research · doi:10.1080/00207543.2013.879344 · Cited by 46 · see citing works
Self-citationManufacturing system design with virtual factory tools
Yang, X., Malak, R. C., Lauer, C., Weidig, C., Hagen, H., Bernd Hamann, A., Aurich, J. C., & Oliver Kreylos, B. (2013) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2013.800948 · Cited by 46 · see citing works
Sharda, B., & Banerjee, A. (2013) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2013.01.001 · Cited by 43 · see citing works
Leng, J., & Jiang, P. (2018) · International Journal of Production Research · doi:10.1080/00207543.2018.1430903 · Cited by 43 · see citing works
Self-citationOutsourcer-supplier coordination for parts machining outsourcing under social manufacturing
Leng, J., Jiang, P., & Zheng, M. (2017) · Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture · doi:10.1177/0954405415583883 · Cited by 38 · see citing works
Self-citationCochran, D. S., Jafri, M. U., Chu, A. K., & Bi, Z. (2016) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2016.04.005 · Cited by 31 · see citing works
Leng, J., & Jiang, P. (2016) · Concurrent Engineering · doi:10.1177/1063293x16666312 · Cited by 31 · see citing works
Self-citationChattopadhyay, M., Sengupta, S., Ghosh, T., Dan, P. K., & Mazumdar, S. (2012) · Computers & Industrial Engineering · doi:10.1016/j.cie.2012.09.016 · Cited by 30 · see citing works
Cherkaoui, H., Huynh, K. T., & Grall, A. (2017) · International Journal of Production Research · doi:10.1080/00207543.2017.1370563 · Cited by 29 · see citing works
Advanced Information Technologies for Industrial Material Flow Systems
Nof, S. Y., & Moodie, C. L. (1989) · Springer eBooks · doi:10.1007/978-3-642-74575-1 · Cited by 28 · see citing works
A distributed configuration scheme for warehouse product service system
Zhang, F., Jiang, P., Li, J., Hui, J., & Zhu, B. (2017) · Advances in Mechanical Engineering · doi:10.1177/1687814017706434 · Cited by 13 · see citing works
The impact of corporate culture on manufacturing system design
Koren, Y., Gu, X., & Freiheit, T. I. (2016) · CIRP Annals · doi:10.1016/j.cirp.2016.04.061 · Cited by 12 · see citing works
Integration of manufacturing system design and quality management
Li, J., & Li, J. (2013) · IIE Transactions · doi:10.1080/0740817x.2013.774240 · Cited by 4 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Characterising the Digital Twin: A systematic literature review
Jones, D., Snider, C., Nassehi, A., Yon, J., & Hicks, B. (2020) · CIRP Journal of Manufacturing Science and Technology · doi:10.1016/j.cirpj.2020.02.002 · Cited by 2,422 · see citing works
Review of digital twin about concepts, technologies, and industrial applications
Liu, M., Fang, S., Dong, H., & Xu, C. (2020) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2020.06.017 · Cited by 1,965 · see citing works
Digital twin: origin to future
Singh, M., Fuenmayor, E., Hinchy, E., Qiao, Y., Murray, N., & Devine, D. (2021) · Applied System Innovation · doi:10.3390/asi4020036 · Cited by 1,038 · see citing works
Digital twin paradigm: A systematic literature review
Semeraro, C., Lezoche, M., Panetto, H., & Dassisti, M. (2021) · Computers in industry (Print) · doi:10.1016/j.compind.2021.103469 · Cited by 852 · see citing works
Review of digital twin applications in manufacturing
Cimino, C., Negri, E., & Fumagalli, L. (2019) · Computers in Industry · doi:10.1016/j.compind.2019.103130 · Cited by 874 · see citing works
Digital twins-based smart manufacturing system design in Industry 4.0: A review
Leng, J., Wang, D., Shen, W., Li, X., Liu, Q., & Chen, X. (2021) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2021.05.011 · Cited by 801 · see citing works
Self-citationLim, K. Y. H., Zheng, P., & Chen, C.-H. (2020) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-019-01512-w · Cited by 700 · see citing works
Digital twin-based sustainable intelligent manufacturing: a review
He, B., & Bai, K.-J. (2020) · Advances in Manufacturing · doi:10.1007/s40436-020-00302-5 · Cited by 642 · see citing works
Digital Twins: State of the art theory and practice, challenges, and open research questions
Sharma, A., Kosasih, E., Zhang, J., Brintrup, A., & Calinescu, A. (2022) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2022.100383 · Cited by 587 · see citing works
Leng, J., Ruan, G., Jiang, P., Xu, K., Liu, Q., Zhou, X., & Liu, C. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110112 · Cited by 520 · see citing works
Self-citationMachine Learning based Digital Twin Framework for Production Optimization in Petrochemical Industry
Min, Q., Lu, Y., Liu, Z., Su, C., & Wang, B. (2019) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.05.020 · Cited by 436 · see citing works
Knowledge-driven digital twin manufacturing cell towards intelligent manufacturing
Zhou, G., Zhang, C., Li, Z., Ding, K., & Wang, C. (2019) · International Journal of Production Research · doi:10.1080/00207543.2019.1607978 · Cited by 433 · see citing works
A survey on AI-driven digital twins in Industry 4.0: smart manufacturing and advanced robotics
Huang, Z., Shen, Y., Li, J., Fey, M., & Brecher, C. (2021) · Sensors · doi:10.3390/s21196340 · Cited by 418 · see citing works
Kamble, S., Gunasekaran, A., Parekh, H., Mani, V., Belhadi, A., & Sharma, R. (2021) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2021.121448 · Cited by 414 · see citing works
Digital twin in smart manufacturing
Li, L., Lei, B., & Mao, C. (2022) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2021.100289 · Cited by 374 · see citing works
Applications of Digital Twin across Industries: A Review
Singh, M., Srivastava, R., Fuenmayor, E. A., Kuts, V., Qiao, Y., Murray, N., & Devine, D. M. (2022) · Applied Sciences · doi:10.3390/app12115727 · Cited by 376 · see citing works
Leng, J., Liu, Q., Ye, S., Jing, J., Wang, Y., Zhang, C., Zhang, D., & Chen, X. (2019) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2019.101895 · Cited by 343 · see citing works
Self-citationDigital twin for smart manufacturing, A review
Soori, M., Behrooz Arezoo, B., & Dastres, R. (2023) · Sustainable Manufacturing and Service Economics · doi:10.1016/j.smse.2023.100017 · Cited by 411 · see citing works
Simulation in industry 4.0: A state-of-the-art review
Ferreira, W. D. P., Armellini, F., & de Santa-Eulália, L. A. (2020) · Computers & Industrial Engineering · doi:10.1016/j.cie.2020.106868 · Cited by 351 · see citing works
Industry 4.0 smart reconfigurable manufacturing machines
Morgan, J., Halton, M., Qiao, Y., & Breslin, J. G. (2021) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2021.03.001 · Cited by 336 · see citing works
Liu, Q., Leng, J., Yan, D., Zhang, D., Wei, L., Yu, A., Zhao, R., Zhang, H., & Chen, X. (2020) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2020.04.012 · Cited by 315 · see citing works
Self-citationDigital twin modeling method based on biomimicry for machining aerospace components
Liu, S., Bao, J., Lu, Y., Li, J., Lu, S., & Sun, X. (2020) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2020.04.014 · Cited by 310 · see citing works
Recent advances on industrial data-driven energy savings: Digital twins and infrastructures
Teng, S. Y., Touš, M., Leong, W. D., How, B. S., Lam, H. L., & Máša, V. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110208 · Cited by 315 · see citing works
A Survey on Digital Twin for Industrial Internet of Things: Applications, Technologies and Tools
Xu, H., Wu, J., Pan, Q., Guan, X., & Guizani, M. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3297395 · Cited by 304 · see citing works
Jiang, H., Qin, S., Fu, J., Zhang, J., & Ding, G. (2020) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2020.05.012 · Cited by 286 · see citing works
Blockchain-Secured Smart Manufacturing in Industry 4.0: A Survey
Leng, J., Ye, S., Zhou, M., Zhao, J. L., Liu, Q., Guo, W., Cao, W., & Fu, L. (2021) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2020.3040789 · Cited by 291 · see citing works
Self-citationBlockchain Security: A Survey of Techniques and Research Directions
Leng, J., Zhou, M., Zhao, J. L., Huang, Y., & Bian, Y. (2020) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2020.3038641 · Cited by 289 · see citing works
Self-citationHuman Digital Twin for Fitness Management
Barricelli, B. R., Casiraghi, E., Gliozzo, J., Petrini, A., & Valtolina, S. (2020) · IEEE Access · doi:10.1109/access.2020.2971576 · Cited by 259 · see citing works
The adoption of urban digital twins
Ferré-Bigorra, J., Casals, M., & Gangolells, M. (2022) · Cities · doi:10.1016/j.cities.2022.103905 · Cited by 254 · see citing works
Leng, J., Yan, D., Liu, Q., Xu, K., Zhao, J. L., Shi, R., Wei, L., Zhang, D., & Chen, X. (2019) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2019.2930418 · Cited by 247 · see citing works
Self-citationMakerchain: A blockchain with chemical signature for self-organizing process in social manufacturing
Leng, J., Jiang, P., Xu, K., Liu, Q., Zhao, J. L., Bian, Y., & Shi, R. (2019) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.06.265 · Cited by 243 · see citing works
Self-citationDigital twins-based remote semi-physical commissioning of flow-type smart manufacturing systems
Leng, J., Zhou, M., Xiao, Y., Zhang, H., Liu, Q., Shen, W., Su, Q., & Li, L. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.127278 · Cited by 227 · see citing works
Self-citationSustainability Assessment of Intelligent Manufacturing Supported by Digital Twin
Li, L., Qu, T., Liu, Y., Zhong, R. Y., Xu, G., Sun, H., Gao, Y., Lei, B., Mao, C., Pan, Y., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3026541 · Cited by 215 · see citing works
Digital twin: a state-of-the-art review of its enabling technologies, applications and challenges
Hu, W., Zhang, T., Deng, X., Liu, Z., & Tan, J. (2021) · Journal of Intelligent Manufacturing and Special Equipment · doi:10.1108/jimse-12-2020-010 · Cited by 228 · see citing works
Implementing Industry 4.0 principles
Cañas, H., Mula, J., Díaz-Madroñero, M., & Campuzano-Bolarín, F. (2021) · Computers & industrial engineering · doi:10.1016/j.cie.2021.107379 · Cited by 173 · see citing works
A digital-twin visualized architecture for Flexible Manufacturing System
Fan, Y., Yang, J.-Z., Chen, J.-H., Hu, P., Wang, X., Xu, J.-C., & Zhou, B. (2021) · Journal of manufacturing systems · doi:10.1016/j.jmsy.2021.05.010 · Cited by 181 · see citing works
Yelles-Chaouche, A. R., Гуревский, Е. Е., Brahimi, N., & Dolgui, A. B. (2020) · International Journal of Production Research · doi:10.1080/00207543.2020.1813913 · Cited by 204 · see citing works
Zhuang, C., Miao, T., Liu, J., & Xiong, H. (2020) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2020.102075 · Cited by 202 · see citing works
Data-Model Combined Driven Digital Twin of Life-Cycle Rolling Bearing
Qin, Y., Wu, X., & Luo, J. T. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3089340 · Cited by 204 · see citing works
Wang, P., & Luo, M. (2020) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2020.11.012 · Cited by 184 · see citing works
Digital Twin for Machining Tool Condition Prediction
Qiao, Q., Wang, J., Ye, L.-K., & Gao, R. (2019) · Procedia CIRP · doi:10.1016/j.procir.2019.04.049 · Cited by 157 · see citing works
Park, K. T., Lee, J.-H., Kim, H.-J., & Noh, S. D. (2019) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-019-04653-7 · Cited by 178 · see citing works
Nguyen, T. D., Duong, Q. H., Nguyen, T. V., Zhu, Y., & Zhou, L. (2021) · International Journal of Production Economics · doi:10.1016/j.ijpe.2021.108381 · Cited by 179 · see citing works
Chen, G., Wang, P., Feng, B., Li, Y., & Liu, D. (2019) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2019.1699254 · Cited by 164 · see citing works
Digital Twins in Product Lifecycle for Sustainability in Manufacturing and Maintenance
Rojek, I., Mikołajewski, D., & Dostatni, E. (2020) · Applied Sciences · doi:10.3390/app11010031 · Cited by 166 · see citing works
Digital Twin-driven machining process for thin-walled part manufacturing
Zhu, Z., Xi, X., Xu, X. W., & Cai, Y. (2021) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2021.03.015 · Cited by 155 · see citing works
Digital twin for the structural health management of reusable spacecraft: A case study
Ye, Y., Yang, Q., Yang, F., Huo, Y., & Meng, S. (2020) · Engineering Fracture Mechanics · doi:10.1016/j.engfracmech.2020.107076 · Cited by 144 · see citing works
Wang, J., Yang, C., Shen, H., Cao, J., & Rutkowski, L. (2020) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2020.2979860 · Cited by 138 · see citing works
Machine learning techniques in emerging cloud computing integrated paradigms: A survey and taxonomy
Soni, D., & Kumar, N. (2022) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2022.103419 · Cited by 144 · see citing works
Consistency retention method for CNC machine tool digital twin model
Wei, Y., Hu, T., Zhou, T., Ye, Y., & Luo, W. (2020) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2020.06.002 · Cited by 134 · see citing works
Meng, F., & Zhao, Y. L. (2022) · Environmental Science and Pollution Research · doi:10.1007/s11356-022-21434-0 · Cited by 134 · see citing works
Wang, Y., Wang, S., Yang, B., Zhu, L., & Liu, F. (2019) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.119299 · Cited by 131 · see citing works
Digital twin for ship life-cycle: A critical systematic review
Mauro, F., & Kana, A. A. (2022) · Ocean Engineering · doi:10.1016/j.oceaneng.2022.113479 · Cited by 134 · see citing works
Digital twin of composite assembly manufacturing process
Polini, W., & Corrado, A. (2020) · International Journal of Production Research · doi:10.1080/00207543.2020.1714091 · Cited by 125 · see citing works
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 · see citing works
A Cross-Domain Systematic Mapping Study on Software Engineering for Digital Twins
Dalibor, M., Jansen, N., Rumpe⋆, B., Schmalzing, D., Wachtmeister, L., Wimmer, M., & Wortmann, A. (2022) · Journal of Systems and Software · doi:10.1016/j.jss.2022.111361 · Cited by 126 · see citing works
The architectural design and implementation of a digital platform for Industry 4.0 SME collaboration
Liu, Z., Sampaio, P., Pishchulov, G., Mehandjiev, N., Cisneros-Cabrera, S., Schirrmann, A., Jiru, F., & Bnouhanna, N. (2022) · Computers in Industry · doi:10.1016/j.compind.2022.103623 · Cited by 119 · see citing works
dos Santos, C. H., Montevechi, J. A. B., de Queiroz, J. A., Miranda, R. D. C., & Leal, F. (2021) · International Journal of Production Research · doi:10.1080/00207543.2021.1898691 · Cited by 122 · see citing works
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 · see citing works
Su, S., Zhong, R. Y., Jiang, Y., Song, J., Yang, F., & Cao, H. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2023.102030 · Cited by 130 · see citing works
Leng, J., Guo, J. F., Xie, J., Zhou, X., Liu, A., Gu, X., Mourtzis, D., Qi, Q., Liu, Q., Shen, W., et al. (2024) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2024.07.012 · Cited by 115 · see citing works
Self-citationChen, C., Zhao, K., Leng, J., Liu, C., Fan, J., & Zheng, P. (2025) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2025.102982 · Cited by 113 · see citing works
Self-citationA Reconfigurable Modeling Approach for Digital Twin-based Manufacturing System
Zhang, C., Xu, W., Liu, J., Liu, Z., Zhou, Z., & Pham, D. T. (2019) · Procedia CIRP · doi:10.1016/j.procir.2019.03.141 · Cited by 107 · see citing works
Digital‐twin assisted: Fault diagnosis using deep transfer learning for machining tool condition
Deebak, B. D., & Al-Turjman, F. M. (2021) · International Journal of Intelligent Systems · doi:10.1002/int.22493 · Cited by 108 · see citing works
Digital twin for integration of design-manufacturing-maintenance: an overview
Fu, Y., Zhu, G., Zhu, M., & Xuan, F. (2022) · Chinese Journal of Mechanical Engineering · doi:10.1186/s10033-022-00760-x · Cited by 98 · see citing works
Digital Twin-Driven Cyber-Physical System for Autonomously Controlling of Micro Punching System
Zhao, R., Yan, D., Liu, Q., Leng, J., Wan, J., Chen, X., & Zhang, X. (2019) · IEEE Access · doi:10.1109/access.2019.2891060 · Cited by 95 · see citing works
Self-citationWang, M., Feng, S., İncecik, A., Królczyk, G., & Li, Z. (2022) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2021.114512 · Cited by 96 · see citing works
Assessment of Industry 4.0 Maturity Models by Design Principles
Dikhanbayeva, D., Shaikholla, S., Suleiman, Z., & Türkyılmaz, A. (2020) · Sustainability · doi:10.3390/su12239927 · Cited by 91 · see citing works
Improved multi-fidelity simulation-based optimisation: application in a digital twin shop floor
Zhang, Z., Guan, Z., Gong, Y. Y., Luo, D., & Yue, L. (2020) · International Journal of Production Research · doi:10.1080/00207543.2020.1849846 · Cited by 89 · see citing works
A digital twin-based layout optimization method for discrete manufacturing workshop
Guo, H., Zhu, Y., Zhang, Y., Ren, Y., Chen, M., & Zhang, R. (2021) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-020-06568-0 · Cited by 90 · see citing works
Multi-scale evolution mechanism and knowledge construction of a digital twin mimic model
Liu, S., Lu, Y., Li, J., Song, D., Sun, X., & Bao, J. (2021) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2021.102123 · Cited by 89 · see citing works
Toward digital validation for rapid product development based on digital twin: a framework
Huang, S., Wang, G., Lei, D., & Yan, Y. (2022) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-021-08475-4 · Cited by 93 · see citing works
Zhang, X., & Zhu, W. (2019) · International Journal of Advanced Robotic Systems · doi:10.1177/1729881419880663 · Cited by 90 · see citing works
Integration of real-time locating systems into digital twins
Ruppert, T., & Abonyi, J. (2020) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2020.100174 · Cited by 83 · see citing works
Digital Twin for Fault Detection and Diagnosis of Building Operations: A Systematic Review
Hodavand, F., Ramaji, I. J., & Sadeghi, N. (2023) · Buildings · doi:10.3390/buildings13061426 · Cited by 93 · see citing works
A Methodology to Develop and Implement Digital Twin Solutions for Manufacturing Systems
Qamsane, Y., Moyne, J. R., Toothman, M., Kovalenko, I., Balta, E. C., Faris, J., Tilbury, D. M., & Barton, K. L. (2021) · IEEE Access · doi:10.1109/access.2021.3065971 · Cited by 94 · see citing works
Jia, W., Wang, W., & Zhang, Z. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2023.101915 · Cited by 89 · see citing works
Federated learning-empowered smart manufacturing and product lifecycle management: A review
Leng, J., Li, R. C., Xie, J., Zhou, X., Li, X., Liu, Q., Chen, X., Shen, W., & Wang, L. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103179 · Cited by 88 · see citing works
Self-citationBridge management through digital twin-based anomaly detection systems: A systematic review
Ríos, A. J., Plevris, V., & Nogal, M. (2023) · Frontiers in Built Environment · doi:10.3389/fbuil.2023.1176621 · Cited by 83 · see citing works
Digital Twin Applications in the Water Sector: A Review
Bam, P. G., Rezaei, N., Roubanis, A., Austin, D. M., Austin, E. S., Tarroja, B., Takacs, I., Villez, K., & Rosso, D. (2025) · Water · doi:10.3390/w17202957 · Cited by 98 · see citing works
Machining process-oriented monitoring method based on digital twin via augmented reality
Liu, S., Lu, S., Li, J., Sun, X., Lu, Y., & Bao, J. (2021) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-021-06838-5 · Cited by 80 · see citing works
Towards Digital Twin Implementation for Assessing Production Line Performance and Balancing
Fera, M., Greco, A., Caterino, M., Gerbino, S., Caputo, F., Macchiaroli, R., & D’Amato, E. (2019) · Sensors · doi:10.3390/s20010097 · Cited by 79 · see citing works
Ante, L. (2021) · Manufacturing Letters · doi:10.1016/j.mfglet.2021.01.003 · Cited by 42 · see citing works
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 · see citing works
Digital Twin-based manufacturing system: a survey based on a novel reference model
Liu, S., Zheng, P., & Bao, J. (2023) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-023-02172-7 · Cited by 84 · see citing works
Edge intelligence-driven digital twin of CNC system: Architecture and deployment
Yu, H., Yu, D. I., Wang, C., Hu, Y., & Li, Y. (2022) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2022.102418 · Cited by 72 · see citing works
Leng, J., Chen, Z., Huang, Z., Zhu, X., Su, H., Lin, Z., & Zhang, D. (2022) · Machines · doi:10.3390/machines10100858 · Cited by 76 · see citing works
Self-citationLeveraging digital twin into dynamic production scheduling: A review
Ouahabi, N., Chebak, A., Kamach, O., Laayati, O., & Zegrari, M. (2024) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2024.102778 · Cited by 84 · see citing works
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 · see citing works
Wang, J., Li, X., Wang, P., & Liu, Q. (2022) · Technology Analysis and Strategic Management · doi:10.1080/09537325.2022.2026320 · Cited by 77 · see citing works
Self-citationPatel, D., Sahu, C. K., & Rai, R. (2023) · International Journal of Production Research · doi:10.1080/00207543.2023.2262050 · Cited by 74 · see citing works
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 66 · see citing works
Badakhshan, E., & Ball, P. D. (2023) · International Journal of Production Research · doi:10.1080/00207543.2023.2244604 · Cited by 66 · see citing works
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 · see citing works
Cutting Parameter Optimization for Reducing Carbon Emissions Using Digital Twin
Zhao, L., Fang, Y., Lou, P., Yan, J., & Xiao, A. (2021) · International Journal of Precision Engineering and Manufacturing · doi:10.1007/s12541-021-00486-1 · Cited by 65 · see citing works
A Middleware Platform for Intelligent Automation: An Industrial Prototype Implementation
Coito, T., Martins, M. S. E., Viegas, J. L., Firme, B., Figueiredo, J. M. G., Vieira, S. M., & Sousa, J. M. C. (2020) · Computers in Industry · doi:10.1016/j.compind.2020.103329 · Cited by 64 · see citing works
Digital twin monitoring and simulation integrated platform for reconfigurable manufacturing systems
Leng, B., Gao, S., Xia, T., Pan, E., Seidelmann, J., Wang, H., & Xi, L. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2023.102141 · Cited by 64 · see citing works
Adaptive sway reduction for tower crane systems with varying cable lengths
Zhang, M., Zhang, Y., Ji, B., Ma, C., & Cheng, X. (2020) · Automation in Construction · doi:10.1016/j.autcon.2020.103342 · Cited by 61 · see citing works
Maheshwari, P., Kamble, S., Belhadi, A., Mani, V., & Pundir, A. K. (2022) · International Journal of Production Research · doi:10.1080/00207543.2022.2104181 · Cited by 61 · see citing works
A Digital Twin-Based Operation Status Monitoring System for Port Cranes
Zhou, Y. Q., Fu, Z., Zhang, J., Li, W., & Gao, C. (2022) · Sensors · doi:10.3390/s22093216 · Cited by 61 · see citing works
- Authors
- Liu, Q., Zhang, H., Leng, J., & Chen, X.
- Affiliations
- Guangdong University of Technology (CN)
- Corresponding author
- Leng, Jiewu
- Venue
- International Journal of Production Research
- Publisher
- Informa UK Limited
- Published
- 2018-05-08
- Volume
- 57
- Issue
- 12
- Pages
- 3903-3919
- ISSN
- 0020-7543 (ISSN-L)
- 1366-588X
- OpenAlex
- W2799347500
- Cited by
- 370
- References
- 52
- Citations by year
- 2026: 21 · 2025: 41 · 2024: 37 · 2023: 65 · 2022: 67 · 2021: 60 · 2020: 57 · 2019: 21 · 2018: 1
- FWCI
- 28.72
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Production Research (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Natural Science Foundation of China (51675108, 51705091)
- Science and Technology Plan Project of Guangzhou (201804020092)
- Science and Technology Planning Project of Guangdong Province of China (2015B010128007, 2016A010106006)
- Fundamental Research Funds for the Central Universities (2015ZZ079)
- Science and Technology Plan Project of Guangdong Province of China (2015B010128007, 2016A010106006)
- 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 › Manufacturing Process and Optimization
- Keywords
- digital twin, part families, semi-physical simulation, multi-objective optimization, decision support
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