Using Industry 4.0 Technologies for Teaching and Learning in Education Process

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 644)

Abstract

It is a well-known fact that if the teaching of theoretical knowledge is supplemented by practical exercises, greater learning efficiency is achieved. This contribution shows in this regard the modernized task of measuring asynchronous motor characteristics using Industry 4.0 advanced technologies. The introduced modernization consists in the improvement of obsolete measurement by automated data collection with the possibility of subsequent processing, archiving, visualization and creation of a measurement report. The whole solution is implemented through DAQ devices from National Instruments and Microsoft SQL Server.

Keywords

Industry 4.0 Teaching and learning Networks Internet laboratories Internet of things IoT DAQ Database 

Notes

Acknowledgements

This work is supported from research project FSI-S-17-4477 “Zvysovani technicke vyspelosti vyrobnich stroju a zarizeni”.

References

  1. 1.
    Hadas, Z., Brezina, T., Andrs, O., et al.: Simulation modelling of mechatronic system with flexible parts. In: 2012 15th International Power Electronics and Motion Control Conference (2012)Google Scholar
  2. 2.
    Kovář, J., Andrš, O.: Particle swarm optimization technique applied to image recognition using delta robot. In: Proceedings of the 17th International Conference on Soft Computing, Brno, CR, pp. 67–72 (2011)Google Scholar
  3. 3.
    Richert, A., Shehadeh, M., Plumanns, L., et al.: Educating engineers for industry 4.0: virtual worlds and human-robot-teams: empirical studies towards a new educational age. In: 2016 IEEE Global Engineering Education Conference, pp. 142–149. IEEE (2016)Google Scholar
  4. 4.
    Andrs, O., Hadas, Z., Kovar, J., et al.: Model-based design of mobile platform with integrated actuator – design with respect to mechatronic education (2014). doi: 10.1007/978-3-319-02294-9
  5. 5.
    Mortl, A., Lawitzky, M., Kucukyilmaz, A., et al.: The role of roles: physical cooperation between humans and robots. Int. J. Robot. Res. 31, 1656–1674 (2012). doi: 10.1177/0278364912455366 CrossRefGoogle Scholar
  6. 6.
    Kovar, J., Mouralova, K., Ksica, F., et al.: Virtual reality in context of industry 4.0. In: Maga, D., Stefek, A., Brezina, T. (eds.) 2016 Proceedings of the 17th International Conference on Mechatronics – Mechatronika 2016, pp. 1–7 (2016)Google Scholar
  7. 7.
    Prieto-Blazquez, J., Arnedo-Moreno, J., Herrera-Joancomarti, J.: An integrated structure for a virtual networking laboratory. IEEE Trans. Ind. Electron. 55, 2334–2342 (2008). doi: 10.1109/TIE.2008.921231 CrossRefGoogle Scholar
  8. 8.
    Casini, M., Prattichizzo, D., Vicino, A.: Operating remote laboratories through a bootable device. IEEE Trans. Ind. Electron. 54, 3134–3140 (2007). doi: 10.1109/TIE.2007.907026 CrossRefGoogle Scholar
  9. 9.
    Donzellini, G., Ponta, D.: A simulation environment for e-learning in digital design. IEEE Trans. Ind. Electron. 54, 3078–3085 (2007). doi: 10.1109/TIE.2007.907011 CrossRefGoogle Scholar
  10. 10.
    Andrš, O., Březina, T.: Design of fuzzy logic controller for DC motor. In: Mechatronics Recent Technological and Scientific Advances, pp. 9–18. Springer, Varšava (2011)Google Scholar
  11. 11.
    Andrs, O., Hadas, Z., Kovar, J.: Introduction to design of speed controller for fuel pump. In: Brezina, T., Maga, D., Stefek, A. (eds.) 2014 Proceedings of the 16th International Conference on Mechatronics (Mechatronika 2014), pp. 672–676 (2014)Google Scholar
  12. 12.
    Andrs, O., Kovar, J., Rucka, J.: Design of test device for automatic pressure sewerage control unit. In: Maga, D., Stefek, A., Brezina, T. (eds.) 2016 Proceedings of the 17th International Conference on Mechatronics—Mechatronika 2016, pp. 55–60 (2016)Google Scholar
  13. 13.
    Hadas, Z., Vetiska, V., Smilek, J. et al.: Efficiency of electromagnetic vibration energy harvesting system. In: Smart Sensors, Actuators, MEMS VII; Cyber Physical Systems (2015). doi: 10.1117/12.2178448
  14. 14.
    Hadas, Z., Vetiska, V., Singule, V., et al.: Energy harvesting from mechanical shocks using a sensitive vibration energy harvester. Int J. Adv. Robot. Syst. (2012). doi: 10.5772/53948 Google Scholar
  15. 15.
    Kovar, J., Rucka, J., Andrs, O.: Simulation modelling of water-supply network as mechatronic system. In: Brezina, T., Maga, D., Stefek, A. (eds.) Proceedings of the 2014 16th International Conference on Mechatronics (Mechatronika 2014), pp. 697–700 (2014)Google Scholar
  16. 16.
    Rucka, J., Andrs, O., Kovar, J.: Design of the pump controller of the low pressure sewer network. MM Sci. J. 2016, 1654–1658 (2016). doi: 10.17973/MMSJ.2016_12_2016205 CrossRefGoogle Scholar
  17. 17.
    Holub, M., Michalíček, M., Vetiška, J., Marek, J.: Prediction of machining accuracy for vertical lathes. In: Mechatronics 2013 Recent Technological and Scientific Advances, pp. 41–48. Springer (2013)Google Scholar
  18. 18.
    Augste, J., Holub, M., Knoflíček, R. et al.: Monitoring of energy flows in the production machines. In: Mechatronics 2013, pp. 1–7. Springer, Cham (2014)Google Scholar
  19. 19.
    Liu, Y., Xu, X.: Industry 4.0 and cloud manufacturing: a comparative analysis. J. Manuf. Sci. Eng. 139(3), 34701 (2016). doi: 10.1115/1.4034667 MathSciNetCrossRefGoogle Scholar
  20. 20.
    Vasić, V.S., Lazarevic, M.P.: Standard industrial guideline for mechatronic product design. FME Trans. 36, 103–108 (2008)Google Scholar
  21. 21.
    Sipsas, K., Alexopoulos, K., Xanthakis, V., Chryssolouris, G.: Collaborative maintenance in flow-line manufacturing environments: an industry 4.0 approach. Procedia CIRP 55, 236–241 (2016). doi: 10.1016/j.procir.2016.09.013 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Faculty of Mechanical EngineeringBrno University of TechnologyBrnoCzech Republic

Personalised recommendations