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Prediction of Friction Torque and Temperature on Axial Angular Contact Ball Bearings for Threaded Spindle Using Artificial Neural Network

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Abstract

Purpose

As it is already known, modern industry is based on CNC machines, robots, actuators etc. Most of this equipment has in its structure threaded spindles. Installation of the threaded spindles is the most demanding task in mechanical engineering.

Methods

The bearings which are designed for this purpose have to meet many requirements regarding bigger number of revolutions, significant axial load, work accuracy, thermal stability etc.

Results

In order to ensure thermal stability of this kind of bearings, it is necessary to predict values of the main operating parameters of the bearing: total friction torque as well as temperature. This paper presents prediction of the total friction torque and temperature by axial angular contact ball bearings designed for threaded spindles using ANN (Artificial Neural Network).

Conclusion

As the main validating parameter was selected Mean Absolute Percentage Error (MAPE) which was in range 0,18- 1,32% for all ANNs.

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Acknowledgements

This research was financially supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia (Contract No. 451-03-68/2022-14/ 200109).

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Authors and Affiliations

  1. School of Engineering Management, Bulevar vojvode Mišića 43, 11000, Belgrade, Serbia

    Vladislav Krstić

  2. Faculty of Mechanical Engineering, University of Nis, Aleksandra Medvedeva 14, 18000, Niš, Serbia

    Dragan Milčić, Miloš Madić, Miodrag Milčić & Miloš Milovančević

Authors
  1. Vladislav Krstić
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  2. Dragan Milčić
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  3. Miloš Madić
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  4. Miodrag Milčić
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  5. Miloš Milovančević
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Correspondence to Miloš Milovančević.

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Krstić, V., Milčić, D., Madić, M. et al. Prediction of Friction Torque and Temperature on Axial Angular Contact Ball Bearings for Threaded Spindle Using Artificial Neural Network. J. Vib. Eng. Technol. 10, 1473–1480 (2022). https://doi.org/10.1007/s42417-022-00461-8

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  • DOI: https://doi.org/10.1007/s42417-022-00461-8

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