Skip to main content

Digital Twin of Experimental Workplace for Quality Control with Cloud Platform Support

  • Conference paper
  • First Online:
4th EAI International Conference on Management of Manufacturing Systems

Abstract

This chapter deals with implementation of digital twin for experimental quality control system to remote monitoring, simulation, and optimization of real process. The main area of study is problem of connection and transformation of digital data from quality control process and product to digital twins and synchronization with Cloud Platform. Actual status of experimental quality control system is synchronized with digital twin for online interaction. Digitalized data must be stored in long-term horizon, which is performed by Cloud Platform, and it provides Big Data processing techniques. Digital twin of quality control system is transferred from 3D model to simulation software Tecnomatix. Interconnection between cloud control system and simulated Tecnomatix model (digital twin) is realized by OPC server. The technologies selected for data collection from experimental system are vision systems, RFID, and MEMS devices.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Židek, K., et al. (2018). Data optimization for communication between wireless IoT devices and Cloud platforms in production process. In: MMS 2018, Dubrovnik, November 06–08, 2018, 8 p.

    Google Scholar 

  2. Xu, L. D., & Duan, L. (2019). Big data for cyber physical systems in industry 4.0: A survey. Enterprise Information Systems, 13(2), 148–169.

    Article  MathSciNet  Google Scholar 

  3. Li, G., Tan, J., & Chaudhry, S. S. (2019). Industry 4.0 and big data innovations. Enterprise Information Systems, 13(2), 145–147.

    Article  Google Scholar 

  4. Sanin, C., et al. (2019). Experience based knowledge representation for Internet of Things and Cyber Physical Systems with case studies. Future Generation Computer Systems, 92, 604–616.

    Article  Google Scholar 

  5. Panda, A., et al. (2011). Optimalization of heat treatment bearings rings with goal to eliminate deformation of material. Chemické listy, 105(S), 459–461.

    Google Scholar 

  6. Han, W., Liu, W., Zhang, K., Li, Z., & Liu, Z. (2019). A protocol for detecting missing target tags in RFID systems. Journal of Network and Computer Applications, 132, 40–48.

    Article  Google Scholar 

  7. Lee, C.-C., Chen, S.-D., Li, C.-T., Cheng, C.-L., & Lai, Y.-M. (2019). Security enhancement on an RFID ownership transfer protocol based on cloud. Future Generation Computer Systems, 93, 266–277.

    Article  Google Scholar 

  8. Liu, C.-G., Liu, I.-H., Lin, C.-D., & Li, J.-S. (2019). A novel tag searching protocol with time efficiency and searching accuracy in RFID systems. Computer Networks, 150, 201–216.

    Article  Google Scholar 

  9. Židek, K., & Hošovský, A. (2013). Wireless device based on MEMS Sensors and Bluetooth Low Energy (LE/Smart) technology for diagnostics of mechatronic systems. Applied Mechanics and Materials, 460, 13–21.

    Article  Google Scholar 

  10. Varanis, M., et al. (2018). MEMS accelerometers for mechanical vibrations analysis: A comprehensive review with applications. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 40(11), 527.

    Article  Google Scholar 

  11. Dumont, M., &Wolf, D. (2019). Usage of MEMS capacitive acceleration sensors for structural monitoring. In Dynamics of Civil Structures, Vol 2. Conference Proceedings of the Society for Experimental Mechanics Series. Cham: Springer.

    Google Scholar 

  12. Caputo, F., Greco, A., Fera, M., & Macchiaroli, R. (2019). Digital twins to enhance the integration of ergonomics in the workplace design. International Journal of Industrial Ergonomics, 71, 20–31.

    Article  Google Scholar 

  13. Tomko, M., & Winter, S. (2019). Beyond digital twins – A commentary. Environment and Planning B: Urban Analytics and City Science, 46(2), 395–399.

    Google Scholar 

  14. David, J., Lobov, A., & Lanz, M. (2018). Learning experiences involving digital twins. In IECON 2018 (pp. 3681–3686).

    Google Scholar 

  15. Martinez, G. S., et al. (2018). Automatic generation of a simulation-based digital twin of an industrial process plant. In IECON 2018 (pp. 3084–3089).

    Google Scholar 

  16. Khan, A., Dahl, M., Falkman, P., & Fabian, M. (2018). Digital twin for legacy systems: Simulation model testing and validation. In 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE). (pp. 421–426). IEEE.

    Google Scholar 

  17. Shubenkova, K., et al. (2018). Possibility of digital twins technology for improving efficiency of the branded service system. In 2018 Global Smart Industry Conference (GloSIC) (pp. 1–7). IEEE.

    Google Scholar 

  18. Lu, Y., & Xu, X. (2019). Cloud-based manufacturing equipment and big data analytics to enable on-demand manufacturing services. Robotics and Computer-Integrated Manufacturing, 57, 92–102.

    Article  Google Scholar 

  19. Aissam, M., Benbrahim, M., Kabbaj, M. N. (2019). Cloud robotic: Opening a new road to the industry 4.0. In New Bioprocessing Strategies: Development and Manufacturing of Recombinant Antibodies and Proteins (pp.1–20).

    Google Scholar 

  20. Mahmoud, M. S. (2019). Architecture for cloud-based industrial automation. In Third International Congress on Information and Communication Technology. Advances in Intelligent Systems and Computing, vol 797. Singapore: Springer.

    Google Scholar 

  21. Hu, Y., Zhu, F., Zhang, L., Lui, Y., & Wang, Z. (2019). Scheduling of manufacturers based on chaos optimization algorithm in cloud manufacturing. Robotics and Computer-Integrated Manufacturing, 58, 13–20.

    Article  Google Scholar 

  22. Hrehova, S., & Vagaska, A. (2018). Design of study support to get skills in plant simulation tecnomatix environment. In: INTED 2018, Valencia, 5th–7th of March (pp. 7942–7945).

    Google Scholar 

  23. Balog, M., Husár, J., Knapčíková, L., & Šoltýsová, Z. (2015). Automation monitoring of railway transir by using RFID technology. Acta Tecnologia, 1(1), 9–12.

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the Slovak Research and Development Agency under contract No. APVV-15-0602 and also by the Project of the Structural Funds of the EU, ITMS code: 26220220103.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kamil Zidek .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zidek, K., Pitel, J., Pavlenko, I., Lazorik, P., Hosovsky, A. (2020). Digital Twin of Experimental Workplace for Quality Control with Cloud Platform Support. In: Knapcikova, L., Balog, M., Perakovic, D., Perisa, M. (eds) 4th EAI International Conference on Management of Manufacturing Systems. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-34272-2_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-34272-2_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34271-5

  • Online ISBN: 978-3-030-34272-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics