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The International Journal of Advanced Manufacturing Technology

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24-10-2023 | ORIGINAL ARTICLE

Digital twin connection model based on virtual sensor

Authors: Chongxin Wang, Xiaojun Liu, Minghao Zhu, Changbiao Zhu, Feng Lv, Zhonghua Ni

Published in: The International Journal of Advanced Manufacturing Technology | Issue 7-8/2023

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Abstract

With the development of the digital twin maturity model, higher requirements are put forward for data collection and transmission. From IoT (Internet of things) to an all-element information network architecture combining IoB (Internet of behavior), IoR (Internet of rule), and IoT, the existing data collection and transmission cannot meet the requirements. In order to achieve high real-time data collection and transmission of all-element data in the digital twin, a digital twin connection module framework for the production shop is proposed. The proposed framework uses the virtual sensor technology to complete the information collection of IoT and establishes the information network system of IoB and IoR. On this basis, a digital twin information collection model is constructed to complete the data integration and finally complete the data transmission. Based on OPC UA, a prototype framework of an all-element digital twin model connection module is developed, through which the construction of the all-element information network of IoT, IoB, and IoR has been completed, and a complete information path can be built. The effectiveness of the framework has been verified through the production line instance data collection and transmission network.

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Coronado PDU, Lynn R, Louhichi W, Parto M, Wescoat E, Kurfess T (2018) Part data integration in the shop floor digital twin: mobile and cloud technologies to enable a manufacturing execution system. J Manuf Syst 48:25–33. https://doi.org/10.1016/j.jmsy.2018.02.002CrossRef

Grieves MW, Vickers JH (2016) Digital twin: mitigating unpredictable, undesirable emergent behavior in complex systems 17 August 2016. Transdiscipl Perspect Compl Syst 85–113. https://doi.org/10.1007/978-3-319-38756-7_4

Lu Y, Xu XW (2018) Resource virtualization: a core technology for developing cyber-physical production systems. J Manuf Syst 47:128–140. https://doi.org/10.1016/j.jmsy.2018.05.003CrossRef

Rosen R, von Wichert G, Lo G, Bettenhausen KD (2015) About the importance of autonomy and digital twins for the future of manufacturing. IFAC-PapersOnLine 48:567–572. https://doi.org/10.1016/j.ifacol.2015.06.141CrossRef

Grieves M (2011) Virtually perfect: driving innovative and lean products through product lifecycle management. Space Coast Press,

Liu S, Bao J, Lu Y, Li J, Lu S, Sun X (2020) Digital twin modeling method based on biomimicry for machining aerospace components. J Manuf Syst. https://doi.org/10.1016/j.jmsy.2020.04.014CrossRef

Hamid G, Farbod K (2022) Construction of damage-free digital twin of damaged aero-engine blades for repair volume generation in remanufacturing. Robot Comput-Integr Manuf 77:102335. https://doi.org/10.1016/J.RCIM.2022.102335

Heidari M, Allameh E, de Vries B, Timmermans H, Jessurun J, Mozaffar F (2014) Smart-BIM virtual prototype implementation. Autom Constr 39:134–144. https://doi.org/10.1016/j.autcon.2013.07.004CrossRef

Patterson EA, Feligiotti M, Hack E (2013) On the integration of validation, quality assurance and non-destructive evaluation. J Strain Anal Eng Des 48:48–58. https://doi.org/10.1177/0309324712444681CrossRef

10.

Yun S, Herman F, Vyacheslav K, Luo Z, Ihor K, Oleg I, Olga M, Anatoliy S (2022) UAV and IoT-based systems for the monitoring of industrial facilities using digital twins: methodology, reliability models, and application. Sensors 22:6444–6444. https://doi.org/10.3390/S22176444CrossRef

11.

Anders S, Magnus Ö, Otto F, Constantin C, Emil G, Bengt L, Mats J (2023) Online geometry assurance in individualized production by feedback control and model calibration of digital twins. J Manuf Syst 66:71–78. https://doi.org/10.1016/J.JMSY.2022.11.011CrossRef

12.

Segura Á, Diez HV, Barandiaran I, Arbelaiz A, Álvarez H, Simões B, Posada J, García-Alonso A, Ugarte R (2020) Visual computing technologies to support the Operator 4.0. Comput Ind Eng 139:0360–8352. https://doi.org/10.1016/j.cie.2018.11.060CrossRef

13.

Zhiheng Z, Mengdi Z, Jian C, Ting Q, H GQ (2022) Digital twin-enabled dynamic spatial-temporal knowledge graph for production logistics resource allocation. Comput Ind Eng 171:0360–8352. https://doi.org/10.1016/j.cie.2022.108454CrossRef

14.

Coelho F, Relvas S, Barbosa-Póvoa AP (2020) Simulation-based decision support tool for in-house logistics: the basis for a digital twin. Comput Ind Eng 0360–8352 153:107094. https://doi.org/10.1016/J.CIE.2020.107094

15.

Zhaoshun L, Shuting W, Yili P, Xinyong M, Xing Y, Aodi Y, Ling Y (2022) The process correlation interaction construction of digital twin for dynamic characteristics of machine tool structures with multi-dimensional variables. J Manuf Syst 63:0278–6125. https://doi.org/10.1016/J.JMSY.2022.03.002CrossRef

16.

Chuting W, Ruifeng G, Haoyu Y, Yi H, Chao L, Changyi D (2023) Task offloading in cloud-edge collaboration-based cyber physical machine tool. Robot Comput-Integr Manuf 79:0736–5845. https://doi.org/10.1016/J.RCIM.2022.102439CrossRef

17.

Shang S, Jiang G, Sun Z, Tian W, Zhang D, Xu J, Cheung CF (2023) Roughness prediction of end milling surface for behavior mapping of digital twined machine tools. Digital Twin 3:4. https://doi.org/10.12688/DIGITALTWIN.17819.1

18.

He Z, Qinglin Q, Fei T (2022) A multi-scale modeling method for digital twin shop-floor. J Manuf Syst 62:0278–6125. https://doi.org/10.1016/J.JMSY.2021.12.011CrossRef

19.

Raza NSM, Mohammad G, Safa M, Christophe V, Jean-Marc N, Noureddine Z (2022) Human knowledge centered maintenance decision support in digital twin environment. J Manuf Syst 65:0278–6125. https://doi.org/10.1016/J.JMSY.2022.10.003CrossRef

20.

Liang G, Zhuyuxiu Z, Ruiqi Z, Hongli G, Guihao L, Zhe C (2023) Digital twin based condition monitoring approach for rolling bearings. Meas Sci Technol 34:0957–0233. https://doi.org/10.1088/1361-6501/AC9153CrossRef

21.

Yucheng W, Fei T, Meng Z, Lihui W, Ying Z (2021) Digital twin enhanced fault prediction for the autoclave with insufficient data. J Manuf Syst 60:0278–6125. https://doi.org/10.1016/J.JMSY.2021.05.015CrossRef

22.

Yujie W, Ruilong X, Caijie Z, Xu K, Zonghai C (2022) Digital twin and cloud-side-end collaboration for intelligent battery management system. J Manuf Syst 62:0278–6125. https://doi.org/10.1016/J.JMSY.2021.11.006CrossRef

23.

Haoqi W, Lindong L, Xupeng L, Hao L, Jiewu L, Yuyan Z, Vincent T, Gen L, Xiaoyu W, Chunya S, Guofu L (2023) A safety management approach for Industry 5.0′s human-centered manufacturing based on digital twin. J Manuf Syst 66:0278–6125. https://doi.org/10.1016/J.JMSY.2022.11.013CrossRef

24.

Yuan G, Liu X, Zhang C, Pham DT, Li Z (2023) A new heuristic algorithm based on multi-criteria resilience assessment of human–robot collaboration disassembly for supporting spent lithium-ion battery recycling. Eng Appl Artif Intell 126:106878. https://doi.org/10.1016/j.engappai.2023.106878CrossRef

25.

Fei T, Chenyuan Z, Qinglin Q, He Z (2022) Digital twin maturity model. Comput Integr Manuf Syst 28:1006–5911. https://doi.org/10.13196/j.cims.2022.05.001CrossRef

26.

Fei T, He Z, Qinglin Q, Jun X, Zheng S, Tianliang H (2021) Theory of digital twin modeling and its application. Comput Integr Manuf Syst 27:1006–5911. https://doi.org/10.13196/j.cims.2021.01.001CrossRef

27.

Qiu C, Li B, Liu H, He S, Hao C (2022) A novel method for machine tool structure condition monitoring based on knowledge graph. Int J Adv Manuf Technol 120:63–582. https://doi.org/10.1007/S00170-022-08757-5

28.

Xue Z, Chen X, He Y, Cao H, Tian S (2022) Gesture- and vision-based automatic grasping and flexible placement in teleoperation. Int J Adv Manuf Technol 122:117–132. https://doi.org/10.1007/S00170-021-08585-Z

29.

Grieves MW (2005) Product lifecycle management: the new paradigm for enterprises. Int J Prod Dev 2:1477–9056. https://doi.org/10.1504/IJPD.2005.006669CrossRef

30.

Githens G (2007) Product lifecycle management: driving the next generation of lean thinking by Michael Grieves. J Prod Innov Manag 24:0737–6782. https://doi.org/10.1111/j.1540-5885.2007.00250_2.xCrossRef

31.

Glaessgen EH, Stargel D (2012) The digital twin paradigm for future NASA and U.S. air force vehicles. Aerospace Res Cent 2012, J. https://doi.org/10.2514/6.2012-1818

32.

Stephan W, Torben M, Moritz O, Dominic G, Detlef Z (2016) Future modeling and simulation of CPS-based factories: an example from the automotive industry. IFAC-PapersOnLine 49:2405–8963. https://doi.org/10.1016/j.ifacol.2016.12.168CrossRef

33.

Fei T, Meng Z (2017) Digital twin shop-floor: a new shop-floor paradigm towards smart manufacturing. IEEE Access 5:20418–20427. https://doi.org/10.1109/access.2017.2756069

34.

Lu Y, Liu C, Wang KI-K, Huang H, Xu X (2020) Digital twin-driven smart manufacturing: connotation, reference model, applications and research issues. Robot Comput-Integr Manuf 61:0736–5845. https://doi.org/10.1016/j.rcim.2019.101837CrossRef

35.

Liu C, Jiang P, Jiang W (2020) Web-based digital twin modeling and remote control of cyber-physical production systems. Robot Comput-Integr Manuf 64:0736–5845. https://doi.org/10.1016/j.rcim.2020.101956CrossRef

36.

Fei T, Bin X, Qinglin Q, Jiangfeng C, Ping J (2022) Digital twin modeling. J Manuf Syst 64:0278–6125. https://doi.org/10.1016/J.JMSY.2022.06.015CrossRef

37.

Qiangwei B, Gang Z, Yong Y, Sheng D, Wei W (2021) The ontology-based modeling and evolution of digital twin for assembly workshop. Int J Adv Manuf Technol 117:0268–3768. https://doi.org/10.1007/S00170-021-07773-1CrossRef

38.

Luchang B, Youtong Z, Hongqian W, Junbo D, Wei T (2021) Digital twin modeling of a solar car based on the hybrid model method with data-driven and mechanistic. Appl Sci 11(14):6399–6399. https://doi.org/10.3390/APP11146399CrossRef

39.

Xiaochen Z, Foivos P, Pierluigi P, Claudio T, Jinzhi L, Dimitris K (2020) A quality-oriented digital twin modelling method for manufacturing processes based on a multi-agent architecture. Procedia Manuf 51:2351–9789. https://doi.org/10.1016/j.promfg.2020.10.044CrossRef

40.

Pavol D, Vladislav K, Kathryn B (2022) Digital twin modeling, multi-sensor fusion technology, and data mining algorithms in cloud and edge computing-based smart city environments. Geopolit Hist Int Relat 14:91–106. https://www.jstor.org/stable/48679655

41.

Nghia NT, Ponciroli R, Bruck P, Esselman TC, Rigatti JA, Vilim RB (2022) A digital twin approach to system-level fault detection and diagnosis for improved equipment health monitoring. Ann Nucl Energy 170:0306–4549. https://doi.org/10.1016/J.ANUCENE.2022.109002CrossRef

42.

Peter P, Karol R, Alžbeta K, Emília B (2022) Simulation of virtual redundant sensor models for safety-related applications. Sensors 22:778–778. https://doi.org/10.3390/S22030778CrossRef

43.

Jabeom K, Sungmin Y (2022) In-situ sensor virtualization and calibration in building systems. Appl Energy 325:0306–2619. https://doi.org/10.1016/J.APENERGY.2022.119864CrossRef

44.

Paepae T, Bokoro PN, Bokoro PN (2022) A virtual sensing concept for nitrogen and phosphorus monitoring using machine learning techniques. Sensors 22:7338–7338. https://doi.org/10.3390/S22197338CrossRef

45.

Eduardo G, Nicolás M, Javier L, Antonio L (2022) Miniterm, a novel virtual sensor for predictive maintenance for the Industry 4.0 era. Sensors 22:6222–6222. https://doi.org/10.3390/S22166222CrossRef

46.

Hasan MZ, Al-Rizzo H (2020) Beamforming optimization in Internet of things applications using robust swarm algorithm in conjunction with connectable and collaborative sensors. Sensors 20:2048–2048. https://doi.org/10.3390/s20072048CrossRef

47.

Jiang H, Qin S, Fu J, Zhang J, Ding G (2020) How to model and implement connections between physical and virtual models for digital twin application. J Manuf Syst 58:36–51. https://doi.org/10.1016/j.jmsy.2020.05.012

Title: Digital twin connection model based on virtual sensor
Authors: Chongxin Wang
Xiaojun Liu
Minghao Zhu
Changbiao Zhu
Feng Lv
Zhonghua Ni
Publication date: 24-10-2023
Publisher: Springer London
Published in: The International Journal of Advanced Manufacturing Technology / Issue 7-8/2023
Print ISSN: 0268-3768
Electronic ISSN: 1433-3015
DOI: https://doi.org/10.1007/s00170-023-12438-2

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