eesti teaduste
akadeemia kirjastus
SINCE 1952
Proceeding cover
of the estonian academy of sciences
ISSN 1736-7530 (Electronic)
ISSN 1736-6046 (Print)
Impact Factor (2022): 0.9
Production monitoring system design and implementation; pp. 10-16

Tanel Eiskop, Aleksei Snatkin, Kristo Karjust, Ernst Tungel

The aim of the current study was to develop a highly flexible and scalable modular real-time monitoring system with predictive capabilities. The main focus was on forecasting tool/component life-span. A two-stage model is proposed for predicting tool life-span. Based on real-time monitoring data a back-propagation artificial neural network model was developed and validated. The obtained response surfaces for vibrations, current, and temperature are utilized in an analytical tool wear forecast model.


 1.        Eiskop, T., Snatkin, A., Kõrgesaar, K., and Søren, J. Develop­ment and application of a holistic production monitoring system. In Proceedings of the 9th International Conference of DAAAM Baltic Industrial Engineering (Otto, T., ed.). Tallinn University of Technology, Tallinn, 2014, 85–91.

 2.        Snatkin, A., Eiskop, T., Karjust, K., and Majak, J. Production monitoring system development and modifi­cation. In Proc. Estonian Acad. Sci., 2015, 64, 567–580.

 3.        Sahno, J., Shevtshenko, E., and Karaulova, T. Framework for continuous improvement of production processes. Inzinerine Ekonomika : Engineering Economics, 2015, 26(2), 169–180.

 4.        Karaulova, T., Poljantshikov, I., Shevtshenko, E., and Kramarenko, S. Fractal approach for manufacturing project managements. Mechanics, 2014, 20, 352–359.

 5.        Vagnorius, Z. Reliability of Metal Cutting Tools: Stochastic Tool Life Modelling and Optimization of Tool Replace­ment Time. PhD Thesis, Norwegian University of Science and Technology, Trondheim, 2010.

 6.        Suresh, R. and Basavarajappa, S. Effect of process parameters on tool wear and surface roughness during turning of hardened steel with coated ceramic tool. Procedia Material Science, 2014, 5, 1450–1459.

 7.        Attanasio, A., Ceretti, E., and Giardini, C. Analytical models for tool wear prediction during AISI 1045 turning operations. Procedia CIRP, 2013, 8, 218–223.

 8.        Li, B. A review of tool wear estimation using theoretical analysis and numerical simulation technologies. Int. J. Refract. Met. H., 2012, 35,143–151.

 9.        Rao, K. V., Murthy, B. S. N., and Rao N. M. Prediction of cutting tool wear, surface roughness and vibration of work piece in boring of AISI 316 steel with artificial neural network. Measurement, 2014, 51, 63–70.

10.        D’Addona, D. M., Genna, S., Leone, C., and Matarazzo, D. Prediction of poly-methyl-methacrylate laser milling process characteristics based on neural networks and fuzzy data. Procedia Cirp, 2016, 41, 981–986.

11.        Xu, C., Xu, T., Zhu, Q., and Zhang, H. Study of adaptive model parameter estimation for milling tool wear. J. Mech. Eng., 2011, 57(7–8), 568–578.

12.        Leone, C., D’Addona, D., and Teti, R. Tool wear modelling through regression analysis and intelligent methods for nickel base alloy machining. CIRP-JMST, 2011, 4, 327–331.

13.        Davidson, L. and Moss, J. M. Pro SQL Server 2012: Relational Database Implementation. Apress, Berkely, CA, USA, 2012.

14.        Leo, M., Distante, C., Bernabei, M., and Persaud, K. An efficient approach for preprocessing data from a large-scale chemical sensor array. Sensors (Basel), 2014, 14, 17786–17806.

15.        Majak, J., Pohlak, M., Eerme, M., and Velsker, T. Design of car frontal protection system using neural networks and genetic algorithm. Mechanika, 2012, 4, 453–460.

16.        Karjust, K., Pohlak, M., and Majak, J. Technology route planning of large composite parts. International Journal of Material Forming, 2010, 3, 631–634.

17.        Majak, J. and Pohlak, M. Decomposition method for solving optimal material orientation problems. Compos. Struct., 2010, 92, 1839–1845.

18.       Herranen, H., Pabut, O., Eerme, M., Majak, J., Polak, M., Kers, J., et al. Design and testing of sandwich structures with different core materials. Mater. Sci. – Medzg., 2011, 18, 45–50.

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