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Review of digital twin applications in manufacturing

Published: 01 December 2019 Publication History

Highlights

A deep literature review on DT applications in manufacturing is performed.
Rarely a DT environment offers a large set of services to the real system.
Almost never a DT share the elaborated analysis to the real counterpart.
A DT application is proposed in a Simulink environment to overcome these gaps.
The illustrated DT poses the basis for further improvements.

Abstract

In the Industry 4.0 era, the Digital Twin (DT), virtual copies of the system that are able to interact with the physical counterparts in a bi-directional way, seem to be promising enablers to replicate production systems in real time and analyse them. A DT should be capable to guarantee well-defined services to support various activities such as monitoring, maintenance, management, optimization and safety. Through an analysis of the current picture of manufacturing and a literature review about the already existing DT environment, this paper identifies what is still missing in the implemented DT to be compliant to their description in literature. Particular focuses of this paper are the degree of integration of the proposed DT with the control of the physical system, in particular with the Manufacturing Execution Systems (MES) when the production system is based on the Automation Pyramid, and the services offered from these environments, comparing them to the reference ones.
This paper proposes also a practical implementation of a DT in a MES equipped assembly laboratory line of the School of Management of the Politecnico di Milano. The application has been created to pose the basis to overcome the missing implementation aspects found in literature. In such a way, the developed DT paves the way for future research to close the loop between the MES and the DT taking into consideration the number of services that a DT could offer in a single environment.

References

[1]
S. Anand, O. Ghalsasi, B. Zhang, A. Goel, S. Reddy, S. Joshi, G. Morris, Additive manufacturing simulation tools in education, 2018 World Eng. Educ. Forum - Glob. Eng. Deans Counc. WEEF-GEDC 2018 (2019) 1–6,.
[2]
A. Angrish, B. Starly, Y.S. Lee, P.H. Cohen, A flexible data schema and system architecture for the virtualization of manufacturing machines (VMM), J. Manuf. Syst. 45 (2017) 236–247,.
[3]
A. Ardanza, A. Moreno, Á. Segura, M. de la Cruz, D. Aguinaga, Sustainable and flexible industrial human machine interfaces to support adaptable applications in the industry 4.0 paradigm, Int. J. Prod. Res. 7543 (2019) 0–15,.
[4]
G. Avventuroso, M. Silvestri, P. Pedrazzoli, A networked production system to implement virtual Enterprise and product lifecycle information loops, IFAC-PapersOnLine. 50 (2017) 7964–7969,.
[5]
M. Ayani, M. Ganebäck, A.H.C. Ng, Digital twin: applying emulation for machine reconditioning, Procedia CIRP, Elsevier B.V. (2018) 243–248,.
[6]
R. Beregi, Á. Szaller, B. Kádár, Synergy of multi-modelling for process control, IFAC-PapersOnLine. 51 (2018) 1023–1028,.
[7]
D. Botkina, M. Hedlind, B. Olsson, J. Henser, T. Lundholm, Digital twin of a cutting tool, Procedia CIRP. (2018),.
[8]
H. Brandtstaedter, C. Ludwig, L. Hubner, E. Tsouchnika, A. Jungiewicz, U. Wever, Digital twins for large electric Drive trains, Pet. Chem. Ind. Conf. Eur. Conf. Proceedings, PCIC Eur. 2018–June (2018),.
[9]
Y. Cai, B. Starly, P. Cohen, Y.S. Lee, Sensor data and information fusion to construct digital-twins virtual machine tools for cyber-physical manufacturing, Procedia Manuf. 10 (2017) 1031–1042,.
[10]
F. Caputo, A. Greco, M. Fera, R. Macchiaroli, Digital twins to enhance the integration of ergonomics in the workplace design, Int. J. Ind. Ergon. 71 (2019) 20–31,.
[11]
L. Cattaneo, L. Fumagalli, M. Macchi, E. Negri, Clarifying data analytics concepts for Industrial engineering, IFAC-PapersOnLine. 51 (2018) 820–825,.
[12]
A. da S. Barbosa, F.P. Silva, L.R.D.S. Crestani, R.B. Otto, Virtual assistant to real time training on industrial environment, Adv. Transdiscipl. Eng. 7 (2018) 33–42,.
[13]
J. David, A. Lobov, M. Lanz, Leveraging digital twins for assisted learning of flexible manufacturing systems, Proc. - IEEE 16th Int. Conf. Ind. Informatics, INDIN 2018. 589 (2018) 529–535,.
[14]
A. De Carolis, M. Macchi, E. Negri, S. Terzi, Guiding manufacturing companies towards digitalization, 23rd ICE/IEEE Int. Technol. Manag. Conf. (2017) 503–512,.
[15]
A. De Carolis, M. Macchi, E. Negri, S. Terzi, A maturity model for assessing the digital readiness of manufacturing companies, APMS 2017, Part I, IFIP AICT 513 (2017) 13–20,.
[16]
B.S. De Ugarte, A. Artiba, R. Pellerin, Manufacturing execution system - A literature review, Prod. Plan. Control. 20 (2009) 525–539,.
[17]
T. DebRoy, W. Zhang, J. Turner, S.S. Babu, Building digital twins of 3D printing machines, Scr. Mater. 135 (2017) 119–124,.
[18]
D. Dupláková, M. Flimel, J. Duplák, M. Hatala, S. Radchenko, F. Botko, Ergonomic rationalization of lighting in the working environment. Part I.: Proposal of rationalization algorithm for lighting redesign, Int. J. Ind. Ergon. 71 (2019) 92–102,.
[19]
E. Frontoni, J. Loncarski, R. Pierdicca, M. Bernardini, M. Sasso, Cyber physical systems for industry 4.0: towards Real time virtual reality in smart manufacturing, in: L.T. De Paolis, P. Bourdot (Eds.), Correct. to Augment. Reality, Virtual Reality, Comput. Graph., 2018,. pp. E1–E1.
[20]
L. Fumagalli, M. Macchi, A. Pozzetti, M. Taisch, G. Tavola, S. Terzi, New methodology for smart manufacturing research and education: The lab approach, Proc. Summer Sch. Fr. Turco. 13–15–Sept (2016) 42–47. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006043680&partnerID=40&md5=cdf822a3149be33e775b070189bad19d.
[21]
L. Fumagalli, A. Polenghi, E. Negri, I. Roda, Framework for simulation software selection, J. Simul. (2019) 1–18,.
[22]
M. Garetti, P. Rosa, S. Terzi, Computers in industry life cycle simulation for the design of product – service systems, Comput. Ind. 63 (2012) 361–369,.
[23]
M. Garetti, L. Fumagalli, E. Negri, Role of ontologies for cps implementation in manufacturing, Manag. Prod. Eng. Rev. (2015),.
[24]
R. Govindaraju, K. Putra, A methodology for manufacturing execution systems (MES) implementation, IOP Conf. Ser. Mater. Sci. Eng. (2016),.
[25]
F. Guo, F. Zou, J. Liu, Z. Wang, Working mode in aircraft manufacturing based on digital coordination model, Int. J. Adv. Manuf. Technol. 98 (2018) 1547–1571,.
[26]
S. Haag, R. Anderl, Digital twin – proof of concept, Manuf. Lett. 15 (2018) 64–66,.
[27]
L. Hu, N.T. Nguyen, W. Tao, M.C. Leu, X.F. Liu, M.R. Shahriar, S.M.N. Al Sunny, Modeling of Cloud-based digital twins for smart manufacturing with MT connect, Procedia Manuf., Elsevier B.V. (2018) 1193–1203,.
[28]
S. Iarovyi, W.M. Mohammed, A. Lobov, B.R. Ferrer, J.L.M. Lastra, Cyber-physical systems for Open-knowledge-driven manufacturing execution systems, Proc. IEEE. 104 (2016) 1142–1154,.
[29]
ISO/IEC, ISO/IEC 62264-2, Enterprise-Control System – Part 2: Objects and Attributes for Enterprise-Control System Integration, 2012.
[30]
P. Janda, Mechatronic Concept of Heavy Machine Tools, 2018, pp. 0645–0652,.
[31]
N. Karanjkar, A. Joglekar, S. Mohanty, V. Prabhu, D. Raghunath, R. Sundaresan, Digital twin for energy optimization in an SMT-PCB assembly line, Proc. - 2018 IEEE Int. Conf. Internet Things Intell. Syst. IOTAIS 2018 (2019) 85–89,.
[32]
Z. Kemény, R.J. Beregi, G. Erdos, J. Nacsa, The MTA SZTAKI smart factory: platform for research and project-oriented skill development in Higher education, Procedia CIRP (2016) 53–58,.
[33]
G.L. Knapp, T. Mukherjee, J.S. Zuback, H.L. Wei, T.A. Palmer, A. De, T. Debroy, Acta Materialia Building Blocks for a Digital Twin of Additive Manufacturing, 135, 2017, pp. 390–399,.
[34]
S. Konstantinov, M. Ahmad, K. Ananthanarayan, R. Harrison, The cyber-physical E-machine manufacturing system: virtual engineering for complete lifecycle support, Procedia CIRP, The Author(S) (2017) 119–124,.
[35]
E.M. Kraft, The US air force digital thread / digital twin – life cycle integration and use of computational and experimental knowledge II, The Evolution Integr. Computational / Experimental Fluid. Dynamics (2016) 1–22,.
[36]
W. Kritzinger, M. Karner, G. Traar, J. Henjes, W. Sihn, Digital twin in manufacturing: A categorical literature review and classification, IFAC-PapersOnLine. 51 (2018) 1016–1022,.
[37]
A. Kusiak, Smart manufacturing, Int. J. Prod. Res. (2017).
[38]
V. Kuts, G.E. Modoni, W. Terkaj, T. Tähemaa, M. Sacco, T. Otto, Exploiting factory telemetry to support virtual reality simulation in robotics cell, in: L.T. De Paolis, P. Bourdot, A. Mongelli (Eds.), Augment. Reality, Virtual Reality, Comput. Graph. Fourth Int. Conf. Part I, 2017,.
[39]
J. Lee, B. Bagheri, H.A. Kao, A cyber-physical systems architecture for industry 4.0-based manufacturing systems, Manuf. Lett. 3 (2015) 18–23,.
[40]
Q. Liu, H. Zhang, J. Leng, X. Chen, Digital twin-driven rapid individualised designing of automated flow-shop manufacturing system, Int. J. Prod. Res. 7543 (2018) 1–17,.
[41]
J. Liu, H. Zhou, G. Tian, X. Liu, X. Jing, Digital twin-based process reuse and evaluation approach for smart process planning, Int. J. Adv. Manuf. Technol. (2019),.
[42]
E.A. Loeken, A. Trulsen, A.M. Holsaeter, E. Wiktorski, D. Sui, R. Ewald, Design principles behind the construction of an Autonomous laboratory-scale drilling rig, IFAC-PapersOnLine. 51 (2018) 62–69,.
[43]
F. Longo, L. Nicoletti, A. Padovano, Ubiquitous knowledge empowers the smart factory: The impacts of a service-oriented digital twin on enterprises’ performance, Annu. Rev. Control. (2019),.
[44]
W. Luo, T. Hu, W. Zhu, F. Tao, Digital Twin for CNC Machine Tool, 2018, pp. 2–5,.
[45]
M. Macchi, I. Roda, E. Negri, L. Fumagalli, Exploring the role of digital twin for asset lifecycle management, IFAC-PapersOnLine. 51 (2018) 790–795,.
[46]
MESA, MES Explained: A High Level Vision - MESA White Paper Number 6, 1997, http://www.cpdee.ufmg.br/-seixas/PaginaII/Download/DownloadFiles/pap6.pdf.
[47]
MESA INTERNATIONAL, MES Explained: A High Level Vision, White Pap., 1997, pp. 1–25.
[48]
L. Monostori, Cyber-physical production systems: roots from manufacturing science and technology, At-Automatisierungstechnik. (2015),.
[49]
A. Moreno, G. Velez, A. Ardanza, I. Barandiaran, Á.R. de Infante, R. Chopitea, Virtualisation process of a sheet metal punching machine within the industry 4.0 vision, Int. J. Interact. Des. Manuf. 11 (2017) 365–373,.
[50]
S. Mousavi, S. Thiede, W. Li, S. Kara, C. Herrmann, An integrated approach for improving energy efficiency of manufacturing process chains, Int. J. Sustain. Eng. 9 (2016) 11–24,.
[51]
E. Negri, L. Fumagalli, M. Garetti, L. Tanca, Requirements and languages for the semantic representation of manufacturing systems, Comput. Ind. 81 (2016) 55–66,.
[52]
E. Negri, L. Fumagalli, M. Macchi, A review of the roles of digital twin in CPS-based production systems, Procedia Manuf. 11 (2017) 939–948,.
[53]
E. Negri, L. Fumagalli, C. Cimino, M. Macchi, FMU-supported simulation for CPS digital twin, Procedia Manuf. CARV Int. Conf. Chang. Agil. Reconfigurable Virtual Prod. Nantes, 8th – 10th Oct. 2018. 00 (2018).
[54]
E. Negri, L. Fumagalli, C. Cimino, M. Macchi, FMU-supported simulation for CPS digital twin, Procedia Manuf. 28 (2019) 201–206,.
[55]
D. Olivotti, S. Dreyer, B. Lebek, M.H. Breitner, Creating the foundation for digital twins in the manufacturing industry: an integrated installed base management system, Inf. Syst. E-Bus. Manag. 17 (2019) 89–116,.
[56]
J.O. Oyekan, W. Hutabarat, A. Tiwari, R. Grech, M.H. Aung, M.P. Mariani, L. López-Dávalos, T. Ricaud, S. Singh, C. Dupuis, The effectiveness of virtual environments in developing collaborative strategies between industrial robots and humans, Robot. Comput. Integr. Manuf. 55 (2019) 41–54,.
[57]
A. Padovano, F. Longo, L. Nicoletti, G. Mirabelli, A digital twin based service oriented application for a 4.0 knowledge navigation in the smart factory, IFAC-PapersOnLine. 51 (2018) 631–636,.
[58]
K.T. Park, S.J. Im, Y.S. Kang, S. Do Noh, Y.T. Kang, S.G. Yang, Service-oriented platform for smart operation of dyeing and finishing industry, Int. J. Comput. Integr. Manuf. 32 (2019) 307–326,.
[59]
K.T. Park, Y.W. Nam, H.S. Lee, S.J. Im, S. Do Noh, J.Y. Son, H. Kim, Design and implementation of a digital twin application for a connected micro smart factory, Int. J. Comput. Integr. Manuf. 00 (2019) 1–19,.
[60]
S. Rabah, A. Assila, E. Khouri, F. Maier, F. Ababsa, V. Bourny, P. Maier, F. Mérienne, Towards improving the future of manufacturing through digital twin and augmented reality technologies, Procedia Manuf. 17 (2018) 460–467,.
[61]
V. Roblek, M. Meško, A. Krapež, A complex View of industry 4.0, SAGE Open. 6 (2016),.
[62]
R. Rosen, G. Von Wichert, G. Lo, K.D. Bettenhausen, About the importance of autonomy and digital twins for the future of manufacturing, IFAC-PapersOnLine. 28 (2015) 567–572,.
[63]
M.R. Shahriar, S.M.N. Al Sunny, X. Liu, M.C. Leu, L. Hu, N.T. Nguyen, MTComm based virtualization and integration of physical machine operations with digital-twins in cyber-physical manufacturing cloud, in: Proc. - 5th IEEE Int. Conf. Cyber Secur. Cloud Comput. 4th IEEE Int. Conf. Edge Comput. Scalable Cloud, CSCloud/EdgeCom 2018, IEEE, 2018, pp. 46–51,.
[64]
S. Sierla, V. Kyrki, P. Aarnio, V. Vyatkin, Automatic assembly planning based on digital product descriptions, Comput. Ind. 97 (2018) 34–46,.
[65]
V. Souza, R. Cruz, W. Silva, S. Lins, V. Lucena, A digital twin architecture based on the Industrial internet of things technologies, 2019 IEEE Int. Conf. Consum. Electron. ICCE 2019 (2019) 1–2,.
[66]
J. Spranger, R. Buzatoiu, A. Polydoros, L. Nalpantidis, E. Boukas, Human-machine interface for remote training of robot tasks., IST 2018 - IEEE Int. Conf. Imaging Syst. Tech. Proc. (2018) 1–5,.
[67]
M. Stopper, B. Katalinic, Service-oriented architecture design aspects of OPC UA for Industrial applications, Int. MultiConference Eng. Comput. Sci. II (2009) 18–21. doi:10.1.1.148.5549.
[68]
E. Sujová, H. Čierna, I. Zabińska, Application of digitization procedures of production in practice, Manag. Syst. Prod. Eng. 27 (2019) 23–28,.
[69]
M. Taisch, B. Stahl, F. Vaccari, A. Cataldo, A Production-State Based Approach for Energy Flow Simulation in Manufacturing Systems, 2013, pp. 227–234,.
[70]
F. Tao, J. Cheng, Q. Qi, M. Zhang, H. Zhang, F. Sui, Digital twin-driven product design, manufacturing and service with big data, Int. J. Adv. Manuf. Technol. 94 (2018) 3563–3576,.
[71]
V. Toivonen, M. Lanz, H. Nylund, H. Nieminen, The FMS training center - A versatile learning environment for engineering education, Procedia Manuf., Elsevier B.V. (2018) 135–140,.
[72]
J. Um, S. Weyer, F. Quint, Plug-and-simulate within modular assembly line enabled by digital twins and the use of AutomationML, IFAC-PapersOnLine. 50 (2017) 15904–15909,.
[73]
J. Um, J. Popper, M. Ruskowski, Modular augmented reality platform for smart operator in production environment, Proc. - 2018 IEEE Ind. Cyber-Physical Syst. ICPS 2018 (2018) 720–725,.
[74]
P.D. Urbina Coronado, R. Lynn, W. Louhichi, M. Parto, E. Wescoat, T. Kurfess, Part data integration in the shop floor digital twin: Mobile and cloud technologies to enable a manufacturing execution system, J. Manuf. Syst. 48 (2018) 25–33,.
[75]
J. Vachalek, L. Bartalsky, O. Rovny, D. Sismisova, M. Morhac, M. Loksik, The digital twin of an industrial production line within the industry 4.0 concept, Proc. 2017 21st Int. Conf. Process Control. PC 2017 (2017),.
[76]
X.V. Wang, L. Wang, Digital twin-based WEEE recycling, recovery and remanufacturing in the background of industry 4.0, Int. J. Prod. Res. 0 (2018) 1–11,.
[77]
X. Wang, Y. Guo, Y. Wang, Automatic detection of regions of interest in breast ultrasound images based on local phase information, Biomed. Mater. Eng. 26 (2015) S1265–S1273,.
[78]
C.C. Wei, Y.T. Lee, K.S. Cao, W.C. Lee, Implementation of a data acquisition system for heterogeneous machines, SII 2017 - 2017 IEEE/SICE Int. Symp. Syst. Integr. 2018–Janua (2018) 232–235,.
[79]
S. Weyer, M. Schmitt, M. Ohmer, D. Gorecky, Towards industry 4.0 - standardization as the crucial challenge for highly modular, multi-vendor production systems, IFAC-PapersOnLine. 48 (2015) 579–584,.
[80]
J. Xie, X. Wang, Z. Yang, S. Hao, Virtual monitoring method for hydraulic supports based on digital twin theory, Min. Technol. Trans. Inst. Min. Metall. 128 (2019) 77–87,.
[81]
F. Yao, A. Keller, M. Ahmad, B. Ahmad, R. Harrison, A.W. Colombo, Optimizing the scheduling of Autonomous guided vehicle in a manufacturing process, Proc. - IEEE 16th Int. Conf. Ind. Informatics, INDIN 2018 (2018) 264–269,.
[82]
S. Zambal, C. Eitzinger, M. Clarke, J. Klintworth, P.Y. Mechin, A digital twin for composite parts manufacturing : EEffects of defects analysis based on manufacturing data, in: Proc. - IEEE 16th Int. Conf. Ind. Informatics, INDIN 2018, IEEE, 2018, pp. 803–808,.
[83]
H. Zhang, Q. Liu, X. Chen, D. Zhang, J. Leng, A digital twin-based approach for designing and multi-objective optimization of hollow glass production line, IEEE Access. 5 (2017) 26901–26911,.
[84]
P. Zheng, T.J. Lin, C.H. Chen, X. Xu, A systematic design approach for service innovation of smart product-service systems, J. Clean. Prod. 201 (2018) 657–667,.
[85]
D. Zuehlke, SmartFactory-towards a factory-of-things, Annu. Rev. Control. (2010),.

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cover image Computers in Industry
Computers in Industry  Volume 113, Issue C
Dec 2019
84 pages

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 December 2019

Author Tags

  1. Industry 4.0
  2. Manufacturing
  3. Digital twin
  4. Simulation
  5. MES
  6. CPS
  7. Cyber-physical systems

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