Abstract
This paper focuses on the introduction of digitization in the production process. When deploying Industry-Oriented 4.0 components in the plant, emphasis is placed on HMI and workplace visualization. Designing a suitable way to visualize data obtained from the production process can have a significant impact on the workplace response. Timely and properly conducted responses to potential changes in the production process have a positive impact on the resulting quality of the production process. Continuous development of the elements of virtual and augmented reality also increases their usability in the field of data visualization from the production process. These technologies make it possible to meet the high demands on the clarity of a great deal of information.
This paper introduces the creation of a production cell into virtual and augmented reality. Particular emphasis is placed on the way of data visualization, including the environment, geometric accuracy of the CNC machine tool, and information from the safety parts. For efficient information handling, access to their display in virtual and augmented reality has been chosen.
The first part of the publication introduces methods for creating a virtual model based on photogrammetry. In the second part of the publication, procedures are presented for collecting and visualizing information on the geometric accuracy of the machine. Finally, procedures related to the risk analysis and functional safety of CNC machine tools are presented. In conclusion, the advantages, disadvantages, and recommendations of the presented solution, the critical places and the difficulty with the realization of the virtual workplace are referred to.
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References
Blecha, P., Durakbasa, N., Holub, M.: Digitized production – its potentials and hazards. In: Proceedings of the International Symposium Production Research 2018, pp. 402–411. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-92267-6_35
Ruzarovsky, R., Holubek, R., Sobrino, D.R.D., Janicek, M.: The simulation of conveyor control system using the virtual commissioning and virtual reality. Adv. Sci. Technol. J. 12, 164–171 (2018). https://doi.org/10.12913/22998624/100349
Kroupa, J., Tuma, Z., Kovar, J., Singule, V.: Virtual laboratory for study of construction of machine tools. MM Sci. J. 2018, 2503–2506 (2018). https://doi.org/10.17973/MMSJ.2018_11_2017100
Kovar, J., Mouralova, K., Ksica, F., Kroupa, J., Andrs, O., Hadas, Z.: Virtual reality in context of Industry 4.0 proposed projects at Brno University of Technology. In: Proceedings of 2016 17th International Conference Mechatronics - Mechatronika, ME 2016 (2017)
Holub, M., Bradac, F., Pokorny, Z., Jelinek, A.: Application of a ballbar fordiagnostics of cnc machine tools. MM Sci. J. 12, 2601–2605 (2018). https://doi.org/10.17973/MMSJ.2018_12_2018032
Archenti, A.: Prediction of machined part accuracy from machining system capability. CIRP Ann. 63, 505–508 (2014). https://doi.org/10.1016/J.CIRP.2014.03.040
Szipka, K., Laspas, T., Archenti, A.: Measurement and analysis of machine tool errors under quasi-static and loaded conditions. Precis. Eng. 51, 59–67 (2018). https://doi.org/10.1016/J.PRECISIONENG.2017.07.011
Vichare, P., Nassehi, A., Flynn, J.M., Newman, S.T.: Through life machine tool capability modelling. Procedia Manuf. 16, 171–178 (2018). https://doi.org/10.1016/J.PROMFG.2018.10.163
Wąsik, M., Kolka, A.: Machining accuracy improvement by compensation of machine and workpiece deformation. Procedia Manuf. 11, 2187–2194 (2017). https://doi.org/10.1016/J.PROMFG.2017.07.365
Belforte, G., Bona, B., Canuto, E., Donati, F., Ferraris, F., Gorini, I., Morei, S., Peisino, M., Sartori, S., Levi, R.: Coordinate measuring machines and machine tools self calibration and error correction. CIRP Ann. 36, 359–364 (1987). https://doi.org/10.1016/S0007-8506(07)62622-5
Mendikute, A., Leizea, I., Yagüe-Fabra, J.A., Zatarain, M.: Self-calibration technique for on-machine spindle-mounted vision systems. Measurement 113, 71–81 (2018). https://doi.org/10.1016/J.MEASUREMENT.2017.08.029
Givi, M., Mayer, J.R.R.: Validation of volumetric error compensation for a five-axis machine using surface mismatch producing tests and on-machine touch probing. Int. J. Mach. Tools Manuf 87, 89–95 (2014). https://doi.org/10.1016/J.IJMACHTOOLS.2014.08.001
Mutilba, U., Gomez-Acedo, E., Sandá, A., Vega, I., Yagüe-Fabra, J.A.: Uncertainty assessment for on-machine tool measurement: an alternative approach to the ISO 15530-3 technical specification. Precis. Eng. (2019). https://doi.org/10.1016/J.PRECISIONENG.2019.03.005
Florussen, G.H.J., Spaan, H.A.M., Spaan-Burke, T.M.: Verifying the accuracy of five-axis machine tool focused on kinematic ISO tests using a torus-shaped test work piece. Procedia Manuf. 14, 58–65 (2017). https://doi.org/10.1016/J.PROMFG.2017.11.007
Florussen, G.H.J., Spaan, H.A.M.: Dynamic R-test for rotary tables on 5-axes machine tools. Procedia CIRP 1, 536–539 (2012). https://doi.org/10.1016/J.PROCIR.2012.04.095
Brecher, C., Behrens, J., Klatte, M., Lee, T.H., Tzanetos, F.: Measurement and analysis of thermo-elastic deviation of five-axis machine tool using dynamic R-test. Procedia CIRP 77, 521–524 (2018). https://doi.org/10.1016/J.PROCIR.2018.08.244
Theissen, N., Laspas, T., Szipka, K., Archenti, A.: Virtual machining system simulator: analysis of machine tool accuracy. Procedia Manuf. 25, 338–343 (2018). https://doi.org/10.1016/J.PROMFG.2018.06.101
Fujishima, M., Ohno, K., Nishikawa, S., Nishimura, K., Sakamoto, M., Kawai, K.: Study of sensing technologies for machine tools. CIRP J. Manuf. Sci. Technol. 14, 71–75 (2016). https://doi.org/10.1016/j.cirpj.2016.05.005
Augste, J., Holub, M., Knoflíček, R., Novotny, T., Vyroubal, J.: Monitoring of energy flows in the production machines. In: Mechatronics 2013, pp. 1–7. Springer, Berlin (2014). https://doi.org/10.1007/978-3-319-02294-9-1. Recent Technol. Sci. Adv
Acknowledgment
These results were obtained with financial support of the Faculty of Mechanical Engineering, Brno University of Technology (grant no. FSI-S-17-4477).
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Holub, M., Tuma, Z., Kroupa, J., Kovar, J., Blecha, P. (2020). Case Study of Digitization of the Production Cell. In: Durakbasa, N., Gençyılmaz, M. (eds) Proceedings of the International Symposium for Production Research 2019. ISPR ISPR 2019 2019. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-31343-2_22
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