MILČIĆ, Dragan ;ALSAMMARRAIE, Amir ;MADIĆ, Miloš ;KRSTIĆ, Vladislav ;MILČIĆ, Miodrag . Predictions of Friction Coefficient in Hydrodynamic Journal Bearing Using Artificial Neural Networks. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 67, n.9, p. 411-420, september 2021. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/sl/article/predictions-of-friction-coefficient-in-hydrodynamic-journal-bearing-using-artificial-neural-networks/>. Date accessed: 26 dec. 2024. doi:http://dx.doi.org/10.5545/sv-jme.2021.7230.
Milčić, D., Alsammarraie, A., Madić, M., Krstić, V., & Milčić, M. (2021). Predictions of Friction Coefficient in Hydrodynamic Journal Bearing Using Artificial Neural Networks. Strojniški vestnik - Journal of Mechanical Engineering, 67(9), 411-420. doi:http://dx.doi.org/10.5545/sv-jme.2021.7230
@article{sv-jmesv-jme.2021.7230,
author = {Dragan Milčić and Amir Alsammarraie and Miloš Madić and Vladislav Krstić and Miodrag Milčić},
title = {Predictions of Friction Coefficient in Hydrodynamic Journal Bearing Using Artificial Neural Networks},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {67},
number = {9},
year = {2021},
keywords = {artificial neural network, hydrodynamic journal bearing, babbitt metal tin-based alloy, friction coefficient},
abstract = {This paper explores the influence of the frequency of shaft sleeve rotation and radial load on a journal bearing made of tin-babbitt alloy (Tegotenax V840) under hydrodynamic lubrication conditions. An experimental test of the frictional behaviour of a radial plain bearing was performed on an originally developed device for testing rotating elements: radial and plain bearings. Using the back-propagation neural network, based on experimental data, artificial neural network models were developed to predict the dependence of the friction coefficient and bearing temperature in relation to the radial load and speed. Using experimental data of the measured friction coefficient with which the artificial neural network was trained, well-trained networks with a mean absolute percentage error on training and testing of 0.0054 % and 0.0085 %, respectively, were obtained. Thus, a well-trained neural network model can predict the friction coefficient depending on the radial load and the speed.},
issn = {0039-2480}, pages = {411-420}, doi = {10.5545/sv-jme.2021.7230},
url = {https://www.sv-jme.eu/sl/article/predictions-of-friction-coefficient-in-hydrodynamic-journal-bearing-using-artificial-neural-networks/}
}
Milčić, D.,Alsammarraie, A.,Madić, M.,Krstić, V.,Milčić, M. 2021 September 67. Predictions of Friction Coefficient in Hydrodynamic Journal Bearing Using Artificial Neural Networks. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 67:9
%A Milčić, Dragan %A Alsammarraie, Amir %A Madić, Miloš %A Krstić, Vladislav %A Milčić, Miodrag %D 2021 %T Predictions of Friction Coefficient in Hydrodynamic Journal Bearing Using Artificial Neural Networks %B 2021 %9 artificial neural network, hydrodynamic journal bearing, babbitt metal tin-based alloy, friction coefficient %! Predictions of Friction Coefficient in Hydrodynamic Journal Bearing Using Artificial Neural Networks %K artificial neural network, hydrodynamic journal bearing, babbitt metal tin-based alloy, friction coefficient %X This paper explores the influence of the frequency of shaft sleeve rotation and radial load on a journal bearing made of tin-babbitt alloy (Tegotenax V840) under hydrodynamic lubrication conditions. An experimental test of the frictional behaviour of a radial plain bearing was performed on an originally developed device for testing rotating elements: radial and plain bearings. Using the back-propagation neural network, based on experimental data, artificial neural network models were developed to predict the dependence of the friction coefficient and bearing temperature in relation to the radial load and speed. Using experimental data of the measured friction coefficient with which the artificial neural network was trained, well-trained networks with a mean absolute percentage error on training and testing of 0.0054 % and 0.0085 %, respectively, were obtained. Thus, a well-trained neural network model can predict the friction coefficient depending on the radial load and the speed. %U https://www.sv-jme.eu/sl/article/predictions-of-friction-coefficient-in-hydrodynamic-journal-bearing-using-artificial-neural-networks/ %0 Journal Article %R 10.5545/sv-jme.2021.7230 %& 411 %P 10 %J Strojniški vestnik - Journal of Mechanical Engineering %V 67 %N 9 %@ 0039-2480 %8 2021-09-28 %7 2021-09-28
Milčić, Dragan, Amir Alsammarraie, Miloš Madić, Vladislav Krstić, & Miodrag Milčić. "Predictions of Friction Coefficient in Hydrodynamic Journal Bearing Using Artificial Neural Networks." Strojniški vestnik - Journal of Mechanical Engineering [Online], 67.9 (2021): 411-420. Web. 26 Dec. 2024
TY - JOUR AU - Milčić, Dragan AU - Alsammarraie, Amir AU - Madić, Miloš AU - Krstić, Vladislav AU - Milčić, Miodrag PY - 2021 TI - Predictions of Friction Coefficient in Hydrodynamic Journal Bearing Using Artificial Neural Networks JF - Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2021.7230 KW - artificial neural network, hydrodynamic journal bearing, babbitt metal tin-based alloy, friction coefficient N2 - This paper explores the influence of the frequency of shaft sleeve rotation and radial load on a journal bearing made of tin-babbitt alloy (Tegotenax V840) under hydrodynamic lubrication conditions. An experimental test of the frictional behaviour of a radial plain bearing was performed on an originally developed device for testing rotating elements: radial and plain bearings. Using the back-propagation neural network, based on experimental data, artificial neural network models were developed to predict the dependence of the friction coefficient and bearing temperature in relation to the radial load and speed. Using experimental data of the measured friction coefficient with which the artificial neural network was trained, well-trained networks with a mean absolute percentage error on training and testing of 0.0054 % and 0.0085 %, respectively, were obtained. Thus, a well-trained neural network model can predict the friction coefficient depending on the radial load and the speed. UR - https://www.sv-jme.eu/sl/article/predictions-of-friction-coefficient-in-hydrodynamic-journal-bearing-using-artificial-neural-networks/
@article{{sv-jme}{sv-jme.2021.7230},
author = {Milčić, D., Alsammarraie, A., Madić, M., Krstić, V., Milčić, M.},
title = {Predictions of Friction Coefficient in Hydrodynamic Journal Bearing Using Artificial Neural Networks},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {67},
number = {9},
year = {2021},
doi = {10.5545/sv-jme.2021.7230},
url = {https://www.sv-jme.eu/sl/article/predictions-of-friction-coefficient-in-hydrodynamic-journal-bearing-using-artificial-neural-networks/}
}
TY - JOUR AU - Milčić, Dragan AU - Alsammarraie, Amir AU - Madić, Miloš AU - Krstić, Vladislav AU - Milčić, Miodrag PY - 2021/09/28 TI - Predictions of Friction Coefficient in Hydrodynamic Journal Bearing Using Artificial Neural Networks JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 67, No 9 (2021): Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2021.7230 KW - artificial neural network, hydrodynamic journal bearing, babbitt metal tin-based alloy, friction coefficient N2 - This paper explores the influence of the frequency of shaft sleeve rotation and radial load on a journal bearing made of tin-babbitt alloy (Tegotenax V840) under hydrodynamic lubrication conditions. An experimental test of the frictional behaviour of a radial plain bearing was performed on an originally developed device for testing rotating elements: radial and plain bearings. Using the back-propagation neural network, based on experimental data, artificial neural network models were developed to predict the dependence of the friction coefficient and bearing temperature in relation to the radial load and speed. Using experimental data of the measured friction coefficient with which the artificial neural network was trained, well-trained networks with a mean absolute percentage error on training and testing of 0.0054 % and 0.0085 %, respectively, were obtained. Thus, a well-trained neural network model can predict the friction coefficient depending on the radial load and the speed. UR - https://www.sv-jme.eu/sl/article/predictions-of-friction-coefficient-in-hydrodynamic-journal-bearing-using-artificial-neural-networks/
Milčić, Dragan, Alsammarraie, Amir, Madić, Miloš, Krstić, Vladislav, AND Milčić, Miodrag. "Predictions of Friction Coefficient in Hydrodynamic Journal Bearing Using Artificial Neural Networks" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 67 Number 9 (28 September 2021)
Strojniški vestnik - Journal of Mechanical Engineering 67(2021)9, 411-420
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
This paper explores the influence of the frequency of shaft sleeve rotation and radial load on a journal bearing made of tin-babbitt alloy (Tegotenax V840) under hydrodynamic lubrication conditions. An experimental test of the frictional behaviour of a radial plain bearing was performed on an originally developed device for testing rotating elements: radial and plain bearings. Using the back-propagation neural network, based on experimental data, artificial neural network models were developed to predict the dependence of the friction coefficient and bearing temperature in relation to the radial load and speed. Using experimental data of the measured friction coefficient with which the artificial neural network was trained, well-trained networks with a mean absolute percentage error on training and testing of 0.0054 % and 0.0085 %, respectively, were obtained. Thus, a well-trained neural network model can predict the friction coefficient depending on the radial load and the speed.