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The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues

Lu, Yuqian, Liu, Chao, Wang, Kevin I-Kai, Huang, Huiyue, Xu, Xun·2020·Robotics and Computer-Integrated Manufacturing
DOI 10.1016/j.rcim.2019.101837
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Abstract

Abstract This paper reviews the recent development of Digital Twin technologies in manufacturing systems and processes, to analyze the connotation, application scenarios, and research issues of Digital Twin-driven smart manufacturing in the context of Industry 4.0. To understand Digital Twin and its future potential in manufacturing, we summarized the definition and state-of-the-art development outcomes of Digital Twin. Existing technologies for developing a Digital Twin for smart manufacturing are reviewed under a Digital Twin reference model to systematize the development methodology for Digital Twin. Representative applications are reviewed with a focus on the alignment with the proposed reference model. Outstanding research issues of developing Digital Twins for smart manufacturing are identified at the end of the paper.

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Research works printed but not linked: 42

  1. Söderberg, 2017, Toward a digital twin for real-time geometry assurance in individualized production, CIRP Ann. - Manuf. Technol., 66, 137, 10.1016/j.cirp.2017.04.038 · DOI · Paper page
  2. Lu, 2019, Cloud-based manufacturing equipment and big data analytics to enable on-demand manufacturing services, Robot. Comput. Integr. Manuf., 57, 92, 10.1016/j.rcim.2018.11.006 · DOI · Paper page
  3. Weyer, 2016, Future modeling and simulation of CPS-based Factories: an example from the automotive industry, IFAC-PapersOnLine, 49, 97, 10.1016/j.ifacol.2016.12.168 · DOI · Paper page
  4. Schleich, 2017, Shaping the digital twin for design and production engineering, CIRP Ann., 66, 141, 10.1016/j.cirp.2017.04.040 · DOI · Paper page
  5. Bagheri, 2015, Big future for cyber-physical manufacturing systems, Design World
  6. Uhlemann, 2017, The digital twin: realizing the cyber-physical production system for industry 4.0, Procedia CIRP, 335, 10.1016/j.procir.2016.11.152 · DOI · Paper page
  7. Davis, 2012, Smart manufacturing, manufacturing intelligence and demand-dynamic performance, Comput. Chem. Eng., 47, 145, 10.1016/j.compchemeng.2012.06.037 · DOI · Paper page
  8. Bauer, 2008, Human–robot collaboration: a survey, Int. J. Humanoid Rob., 5, 47, 10.1142/S0219843608001303 · DOI · Paper page
  9. Lu, 2019, Energy-efficient cyber-physical production network: architecture and technologies, Comput. Industr. Eng., 129, 56, 10.1016/j.cie.2019.01.025 · DOI
  10. Kumar, 2007, From mass customization to mass personalization: a strategic transformation, Int. J. Flexible Manuf. Syst., 19, 533, 10.1007/s10696-008-9048-6 · DOI · Paper page
  11. Angrish, 2017, A flexible data schema and system architecture for the virtualization of manufacturing machines (VMM), J. Manuf. Syst., 45, 236, 10.1016/j.jmsy.2017.10.003 · DOI
  12. Lu, 2018, Resource virtualization: a core technology for developing cyber-physical production systems, J. Manuf. Syst., 47, 128, 10.1016/j.jmsy.2018.05.003 · DOI · Paper page
  13. Bernstein, 2018, Contextualising manufacturing data for lifecycle decision-making, Int. J. Product Lifecycle Manag., 10, 326, 10.1504/IJPLM.2017.090328 · DOI
  14. 2014, TC 29, ISO 13399-3: 2014 cutting tool data representation and exchange - Part 3: reference dictionary for tool items, Int. Org. Standard.
  15. Lu, 2019, ManuService ontology: a product data model for service-oriented business interactions in a cloud manufacturing environment, J. Intell. Manuf., 30, 317, 10.1007/s10845-016-1250-x · DOI
  16. 2001, Performance evaluation of control networks: ethernet, ControlNet, and devicenet, IEEE Control Syst., 21, 66, 10.1109/37.898793 · doi:10.1109/37.898793
  17. Schlesinger, 2016, Automatic packing mechanism for simplification of the scheduling in profinet IRT, IEEE Trans. Industr. Inform., 12, 1822, 10.1109/TII.2015.2509450 · DOI
  18. Tramarin, 2016, On the use of IEEE 802.11n for industrial communications, IEEE Trans. Industr. Inf., 12, 1877, 10.1109/TII.2015.2504872 · DOI
  19. Toscano, 2012, Comparative assessments of IEEE 802.15.4/ZigBee and 6LoWPAN for low-power industrial WSNs in realistic scenarios, 115
  20. Pizoń, 2016, Perspectives for fog computing in manufacturing, Appl. Comput. Sci., 12
  21. Teng, 2004, Polishing blemishes: issues in data correction, IEEE Intell. Syst., 19, 34, 10.1109/MIS.2004.1274909 · doi:10.1109/mis.2004.1274909
  22. Weber, 2017, M2DDM - a maturity model for data-driven manufacturing, Procedia CIRP, 173, 10.1016/j.procir.2017.03.309 · DOI
  23. Lin, 2013, Mapreduce is good enough? If all you have is a hammer, throw away everything that's not a nail!, Big Data, 1, 28, 10.1089/big.2012.1501 · DOI
  24. Kannan, 2019, A digital twin for grinding wheel: an information sharing platform for sustainable grinding process, J. Manuf. Sci. Eng., 141, 21015, 10.1115/1.4042076 · DOI
  25. Zhuang, 2018, Digital twin-based smart production management and control framework for the complex product assembly shop-floor, Int. J. Adv. Manuf. Technol., 96, 1149, 10.1007/s00170-018-1617-6 · DOI · Paper page
  26. Schluse, 2018, Experimentable digital twins-streamlining simulation-based systems engineering for industry 4.0, IEEE Trans. Industr. Inf., 14, 1722, 10.1109/TII.2018.2804917 · DOI · Paper page
  27. Zheng, 2018, An application framework of digital twin and its case study, J. Ambient Intell. Humaniz. Comput., 1
  28. Liu, 2018, Research on modelling and optimization of hot rolling scheduling, J. Ambient Intell. Humaniz. Comput.
  29. Padovano, 2018, A digital twin based service oriented application for a 4.0 knowledge navigation in the smart factory, IFAC-PapersOnLine, 51, 631, 10.1016/j.ifacol.2018.08.389 · DOI · Paper page
  30. Guo, 2018, Modular based flexible digital twin for factory design, J. Ambient Intell. Humaniz. Comput.
  31. Wang, 2018, Digital twin for rotating machinery fault diagnosis in smart manufacturing, Int. J. Prod. Res., 1
  32. Wang, 2018, Digital twin-based WEEE recycling, recovery and remanufacturing in the background of industry 4.0, Int. J. Prod. Res.
  33. Liu, 2018, A systematic development method for cyber-physical machine tools, J. Manuf. Syst., 48, 13, 10.1016/j.jmsy.2018.02.001 · DOI
  34. Cai, 2017, Sensor data and information fusion to construct Digital-Twins virtual machine tools for cyber-physical manufacturing, Procedia Manuf., 10, 1031, 10.1016/j.promfg.2017.07.094 · DOI · Paper page
  35. Choi, 2017, A case study of development for manufacturing industry, Int. J. Comput. Appl. Technol., 55, 289, 10.1504/IJCAT.2017.086018 · DOI · Paper page
  36. Schroeder, 2016, Digital twin data modeling with AutomationML and a communication methodology for data exchange, IFAC-PapersOnLine, 49, 12, 10.1016/j.ifacol.2016.11.115 · DOI · Paper page
  37. Helu, 2018, A standards-based approach for linking as-planned to as-fabricated product data, CIRP Ann., 67, 487, 10.1016/j.cirp.2018.04.039 · DOI
  38. Zhang, 2017, A digital twin-based approach for designing and multi-objective optimization of hollow glass production line, IEEE Access, 5, 26901, 10.1109/ACCESS.2017.2766453 · DOI · Paper page
  39. Petković, 2019, Human intention estimation based on hidden markov model motion validation for safe flexible robotized warehouses, Robot. Comput. Integr. Manuf., 57, 182, 10.1016/j.rcim.2018.11.004 · DOI
  40. Graessler, 2018, Intelligent control of an assembly station by integration of a digital twin for employees into the decentralized control system, Procedia Manuf., 185, 10.1016/j.promfg.2018.06.041 · DOI
  41. Xu, 2017, Machine tool 4.0 for the new era of manufacturing, Int. J. Adv. Manuf. Technol., 92, 1893, 10.1007/s00170-017-0300-7 · DOI · Paper page
  42. Liu, 2018, MTConnect-based cyber-physical machine Tool: a case study, Procedia CIRP, 72, 492, 10.1016/j.procir.2018.03.059 · doi:10.1016/j.procir.2018.03.059

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Metadata

Authors
Lu, Y., Liu, C., Wang, K. I.-K., Huang, H., & Xu, X.
Venue
Robotics and Computer-Integrated Manufacturing
Publisher
Elsevier BV
Published
2020-02
Volume
61
Pages
101837
ISSN
0736-5845 (ISSN-L)
DOI
10.1016/j.rcim.2019.101837
OpenAlex
W2966906878
Cited by
1,624
References
9
FWCI
102.40
Type
journal article
Language
en
Open access
closed
Licence
  • www.elsevier.com/tdm/userlicense/1.0/ (tdm)
  • www.elsevier.com/legal/tdmrep-license (tdm)

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CoChat. (2026). Citation check: Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues. https://app.cochat.ai/papers/10.1016/j.rcim.2019.101837
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Lu, Y., Liu, C., Wang, K. I.-K., Huang, H., & Xu, X. (2020). Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues. Robotics and Computer-Integrated Manufacturing. https://doi.org/10.1016/j.rcim.2019.101837

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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