Advertisement

Cyber Physical Systems for Industry 4.0: Towards Real Time Virtual Reality in Smart Manufacturing

  • Emanuele Frontoni
  • Jelena Loncarski
  • Roberto Pierdicca
  • Michele Bernardini
  • Michele Sasso
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10851)

Abstract

Cyber Physical System (CPS) together with Internet of Things, Big Data, Cloud Computing and Industrial Wireless Networks are the core technologies allowing the introduction of the fourth industrial revolution, Industry 4.0. Along with the advances in new generation information technologies, smart manufacturing is becoming the focus of global manufacturing transformation. Considering the competitive nature of industry, it requires manufacturers to implement new methodologies. Realistic virtual models mirroring the real world are becoming essential to bridge the gap between design and manufacturing. In this paper model conceptualization, representation, and implementation of the digital twin is presented, on the real use case of manufacturing industry and in the cyber physical environment. A novel CPS architecture for real time visualization of complex industrial process is proposed. It essentially considers the Simulation technological pillar of Industry 4.0. The results from a real industrial environment show good performances in terms of real time behaviour, virtual reality and WebGL CPS visualization features, usability and readability.

References

  1. 1.
    Posada, J., Toro, C., Barandiaran, I., Oyarzun, D., Stricker, D., de Amicis, R., Pinto, E.B., Eisert, P., Döllner, J., Vallarino, I.: Visual computing as a key enabling technology for industrie 4.0 and industrial internet. IEEE Comput. Graph. Appl. 35(2), 26–40 (2015)CrossRefGoogle Scholar
  2. 2.
    Lee, J., Kao, H.A., Yang, S.: Service innovation and smart analytics for industry 4.0 and big data environment. Procedia Cirp 16, 3–8 (2014)CrossRefGoogle Scholar
  3. 3.
    Kagermann, H., Wahlster, W., Helbig, J.: Securing the future of German manufacturing industry. Recommendations for implementing the strategic initiative INDUSTRIE 4 (2013)Google Scholar
  4. 4.
    Khaitan, S.K., McCalley, J.D.: Design techniques and applications of cyberphysical systems: a survey. IEEE Syst. J. 9(2), 350–365 (2015)CrossRefGoogle Scholar
  5. 5.
    Lee, J., Bagheri, B., Kao, H.A.: A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manuf. Lett. 3, 18–23 (2015)CrossRefGoogle Scholar
  6. 6.
    National Institute of Standards and Technology: Workshop report on foundations for innovation in cyber-physical systems, January 2013Google Scholar
  7. 7.
    Lin, J., Yu, W., Zhang, N., Yang, X., Zhang, H., Zhao, W.: A survey on internet of things: architecture, enabling technologies, security and privacy, and applications. IEEE Internet Things J. 4(5), 1125–1142 (2017)CrossRefGoogle Scholar
  8. 8.
    Nitti, M., Pilloni, V., Colistra, G., Atzori, L.: The virtual object as a major element of the internet of things: a survey. IEEE Commun. Surv. Tutor. 18(2), 1228–1240 (2016)CrossRefGoogle Scholar
  9. 9.
    Grieves, M.: Digital twin: Manufacturing excellence through virtual factory replication. White paper (2014)Google Scholar
  10. 10.
    Qi, Q., Tao, F.: Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison. IEEE Access 6, 3585–3593 (2018)CrossRefGoogle Scholar
  11. 11.
    Pierdicca, R., Liciotti, D., Contigiani, M., Frontoni, E., Mancini, A., Zingaretti, P.: Low cost embedded system for increasing retail environment intelligence. In: 2015 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6. Turin (2015)Google Scholar
  12. 12.
    Mancini, A., Clini, P., Bozzi, C.A., Malinverni, E.S., Pierdicca, R., Nespeca, R.: Remote touch interaction with high quality models using an autostereoscopic 3D display. In: De Paolis, L.T., Bourdot, P., Mongelli, A. (eds.) AVR 2017. LNCS, vol. 10325, pp. 478–489. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-60928-7_40CrossRefGoogle Scholar
  13. 13.
    Pierdicca, R., Frontoni, E., Pollini, R., Trani, M., Verdini, L.: The use of augmented reality glasses for the application in industry 4.0. In: De Paolis, L.T., Bourdot, P., Mongelli, A. (eds.) AVR 2017. LNCS, vol. 10324, pp. 389–401. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-60922-5_30CrossRefGoogle Scholar
  14. 14.
    Sturari, M., Liciotti, D., Pierdicca, R., Frontoni, E., Mancini, A., Contigiani, M., Zingaretti, P.: Robust and affordable retail customer profiling by vision and radio beacon sensor fusion. Pattern Recogn. Lett. 81, 30–40 (2018)CrossRefGoogle Scholar
  15. 15.
    Frontoni, E., Marinelli, F., Rosetti, R., Zingaretti, P.: Shelf space re-allocation for out of stock reduction. Comput. Ind. Eng. 106, 32–40 (2017)CrossRefGoogle Scholar
  16. 16.
    Frontoni, E., Marinelli, F., Paolanti, M., Rosetti, R., Zingaretti, P.: Optimal production planning by reusing components. In: 2016 24th Mediterranean Conference on Control and Automation (MED), pp. 1272–1277. Athens, June 2016Google Scholar
  17. 17.
    Coronado, P.D.U., Lynn, R., Louhichi, W., Parto, M., Wescoat, E., Kurfess, T.: Part data integration in the shop floor digital twin: mobile and cloud technologies to enable a manufacturing execution system. J. Manuf. Syst. (2018)Google Scholar
  18. 18.
    Giraldo, J., Sarkar, E., Cardenas, A.A., Maniatakos, M., Kantarcioglu, M.: Security and privacy in cyber-physical systems: a survey of surveys. IEEE Des. Test 34(4), 7–17 (2017)CrossRefGoogle Scholar
  19. 19.
    Torrisi, N.M.: Monitoring services for industrial. IEEE Ind. Electron. Mag. 5(1), 49–60 (2011)CrossRefGoogle Scholar
  20. 20.
    Iarovyi, S., Mohammed, W.M., Lobov, A., Ferrer, B.R., Lastra, J.L.M.: Cyber-physical systems for open-knowledge-driven manufacturing execution systems. Proc. IEEE 104(5), 1142–1154 (2016)CrossRefGoogle Scholar
  21. 21.
    Almada-Lobo, F.: The industry 4.0 revolution and the future of manufacturing execution systems (mes). J. Innov. Manag. 3(4), 16–21 (2016)Google Scholar
  22. 22.
    Buckholtz, B., Ragai, I., Wang, L.: Cloud manufacturing: current trends and future implementations. J. Manuf. Sci. Eng. 137(4), 040902 (2015)CrossRefGoogle Scholar
  23. 23.
    Schleich, B., Anwer, N., Mathieu, L., Wartzack, S.: Shaping the digital twin for design and production engineering. CIRP Ann. 66(1), 141–144 (2017)CrossRefGoogle Scholar
  24. 24.
    Huang, J., Zhu, Y., Cheng, B., Lin, C., Chen, J.: A petrinet-based approach for supporting traceability in cyber-physical manufacturing systems. Sensors 16(3), 382 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Emanuele Frontoni
    • 1
  • Jelena Loncarski
    • 2
  • Roberto Pierdicca
    • 3
  • Michele Bernardini
    • 1
  • Michele Sasso
    • 4
  1. 1.Department of Information EngineeringUniversità Politecnica delle MarcheAnconaItaly
  2. 2.Department of Engineering Sciences, Division for Electricity ResearchUppsala UniversityUppsalaSweden
  3. 3.Dipartimento di Ingegneria CivileUniversitá Politecnica delle MarcheAnconaItaly
  4. 4.UBISIVE s.r.l.FermoItaly

Personalised recommendations