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ADOPTING ARTIFICIAL NEURAL NETWORK FOR WEAR INVESTIGATION OF BALL BEARING MATERIALS UNDER PURE SLIDING CONDITION
Authors:
Atul A. Patil1
, Sumit S. Desai1
, Lalit N. Patil2
, Sarika A. Patil1
1Dr. D. Y. Patil Institute of Technology, Pimpri, Pune, India
2Mechanical Engineering Department, JSPM’s Rajarshi Shahu College of Engineering, Pune, India
Received: 25.11.2021.
Accepted: 07.02.2022.
Available: 30.06.2022
Abstract:
In the industry, ball bearings are the most widely used machine element. The ball materials may differ in various bearing applications. Wear of the ball and recess after a period of use is the most common cause of ball bearing failure. The present study aims to develop the artificial neural network model for assessing the wear of different ball bearing materials. A wear test method has been followed as suggested by the ASTM-G99 standard. The pin on disc apparatus was selected to conduct numerous trials. L9 array was considered to design the experiments. The factors considered for this study were load, time, and sliding speed. Based on the results obtained, ANN code was proposed to evaluate wear using numerous test parameters. The results obtained from the proposed model are nearly similar to experimental results, which would be evidence for the correctness of the model. The proposed neural network model can be used in numerous applications with given parameters.
Keywords:
ANOVA, Artificial Neural Network, Design of Experiments Wear Analysis
References:
[1] F. Lu, R. Zhu, Q. Fu, Wear depth calculation and influence factor analysis for groove ball bearing. MATEC Web Conf., Vol.211, 2018:08002. https://doi.org/10.1051/matecconf/201821108002
[2] L. Patil, A.V. Patil, R.B. Barjibhe, A Wear Analysis of Composite Ball Materials using Tribometer, Int. Journal of Engineering Research and Applications, 6 (1), 2016: 79-78.
[3] L.N. Patil, A.V. Patil, Wear Prediction Model for Composite Bearing Balls under Pure Sliding Contact Condition, International Journal of Engineering Research & Technology, 4 (12), 2015: 547-550.
http://dx.doi.org/10.17577/IJERTV4IS120620
[4] R. K. Upadhyay, L. A. Kumaraswamidhas, Md. S. Azam, Rolling element bearing failure analysis: A case study. Case Studies in Engineering Failure Analysis, 1 (1), 2013: 15-17. https://doi.org/10.1016/j.csefa.2012.11.003
[5] L. Wang, R. W. Snidle, L. Gu, Rolling contact silicon nitride bearing technology: a review of recent research. Wear, 246 (1–2),2000: 159-173. https://doi.org/10.1016/S0043-1648(00)00504-4
[6] C. Piconi, S. G. Condo, T. Kosmač, Alumina – and Zirconia-based Ceramics for Load – bearing Applications. in Advanced Ceramics for Dentistry, Chapter 11, J.Z. Shen and T. Kosmač. Butterworth-Heinemann, 2014: 219-253.
[7] H. Taplak, İ. Uzmay, Ş. Yıldırım, An artificial neural network application to fault detection of a rotor bearing system. Industrial Lubrication and Tribology, 58 (1), 2006: 32-44. https://doi.org/10.1108/00368790610640082
[8] J.P. Patel, S.H. Upadhyay, Comparison between Artificial Neural Network and Support Vector Method for a Fault Diagnostics in Rolling Element Bearings. Procedia Engineering, 144, 2016: 390-397.
https://doi.org/10.1016/j.proeng.2016.05.148
[9] B.K.N. Rao, P.S. Pai, T.N. Nagabhushana, Failure Diagnosis and Prognosis of Rolling – Element Bearings using Artificial Neural Networks: A Critical Overview. J. Phys.: Conf. Ser., 364, 2012: 012023. http://doi: 10.1088/1742-6596/364/1/012023
[10] S. Veličković, B. Stojanović, M. Babić, A.Vencl, I. Bobić, G. Vadászné Bognár, F. Vučetić, Parametric optimization of the aluminium nanocomposites wear rate. J Braz. Soc. Mech. Sci. Eng., 41(1), 2019: 19. https://doi.org/10.1007/s40430-018-1531-8
[11] B. Stojanovic, J. Blagojevic, M. Babic, S. Velickovic, and S. Miladinovic, Optimization of hybrid aluminum composites wear using Taguchi method and artificial neural network. Industrial Lubrication and Tribology, 69 (6), 2017: 1005-1015. https://doi: 10.1108/ILT-02-2017-0043
[12] D. Mikic, E. Desnica, A. Asonja, B. Stojanovic, and V. Epifanic-Pajic, Reliability Analysis of Ball Bearing on The Crankshaft of Piston Compressors. Journal of the Balkan Tribological Association, 22 (4), 2016: 5060- 5070.
[13] E. Desnica, A. Asonja, D. Mikic, B. Stojanovic, Reliability Model of Bearing Assembly on An Agricultural Cardan Shaft. Journal of the Balkan Tribological Association, 21 (1), 2015: 38-48.
[14] B. Stojanovic, M. Babic, N. Miloradovic, S. Mitrovic, Tribological Behaviour of A356/10SiC/3Gr Hybrid Composite In DrySliding Conditions. Materials and Technologies, 49 (1), 2015: 117-121.
[15] J. Halme P. Andersson, Rolling contact fatigue and wear fundamentals for rolling bearing diagnostics – state of the art, Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology, 224 (4), 2010: 377-393.
[16] E. V. Zaretsky, A Basics of Bearing Life Prediction. Lewis Research Center Cleveland, Ohio, 1997: p.12.
[17] S. Veličković, B. Stojanović, M. Babić, I. Bobić, Optimization of tribological properties of aluminum hybrid composites using Taguchi design. Journal of Composite Materials, 51(17), 2017: 2505-2515.
https://doi:10.1177/0021998316672294
[18] R K. Roy, A primer on the Taguchi method, 2. ed. Dearborn, Society of Manufacturing Engineers, 2010.
[19] T. Zhaoping, S. Jianping, The Contact Analysis for Deep Groove Ball Bearing Based on ANSYS. Procedia Engineering, 23, 2011: 423-428. https://doi:10.1016/j.proeng.2011.11.2524
[20] V.G. Pavlov, Wear calculations for radial ball bearings. Russ. Engin. Res. 28 (7), 2008: 643-650.
https://doi.org/10.3103/S1068798X08070058
[21] T. Kolodziejczyk, R. Toscano, S. Fouvry, G. Morales-Espejel, Artificial intelligence as efficient technique for ball bearing fretting wear damage prediction. Wear, 268 (1-2), 2010: 309-315. https://doi:10.1016/j.wear.2009.08.016
[22] X. Zhang, H. Xu, W. Chang, H.Xi, S. Pei, W. Meng, H. Li, S. Xu, A dynamic contact wear model of ball bearings without or with distributed defects. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 234 (24), 2020:4827-4843. https://doi: 10.1177/0954406220931544
[23] H. Li, H. Li, Y. Liu, H. Liu, Dynamic characteristics of ball bearing with flexible cage lintel and wear. Engineering Failure Analysis, 117, 2020: 104956. https://doi:10.1016/j.engfailanal.2020.104956
[24] Z. Yang, Y. Zhang, K. Zhang, S. Li, Wear Analysis of Angular Contact Ball Bearing in Multiple-Bearing Spindle System Subjected to Uncertain Initial Angular Misalignment. Journal of Tribology, 143 (9), 2021: 091703.
https://doi:10.1115/1.4049258
[25] A.A. Torrance, J.E. Morgan, G.T Y. Wan, An additive’s influence on the pitting and wear of ball bearing steel. Wear, 192 (1-2), 1996: 66-73. https://doi: 10.1016/0043-1648(95)06751-5
[26] B. Hanrahan, S. Misra, C.M. Waits, R. Ghodssi, Wear mechanisms in microfabricated ball bearing systems. Wear, 326-327, 2015: 1-9. https://doi:10.1016/j.wear.2014.12.032
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)
Volume 9
Number 3
September 2024
Last Edition
Volume 9
Number 3
September 2024
How to Cite
A.A. Patil, S.S. Desai, L.N. Patil, S.A. Patil, Adopting Artificial Neural Network for Wear Investigation of Ball Bearing Materials Under Pure Sliding Condition. Applied Engineering Letters, 7(2), 2022: 81–88.
https://doi.org/10.18485/aeletters.2022.7.2.5
More Citation Formats
Patil, A. A., Desai, S. S., Patil, L. N., & Patil, S. A. (2022). Adopting Artificial Neural Network for Wear Investigation of Ball Bearing Materials Under Pure Sliding Condition. Applied Engineering Letters, 7(2), 81–88. https://doi.org/10.18485/aeletters.2022.7.2.5
Patil, Atul A., et al. “Adopting Artificial Neural Network for Wear Investigation of Ball Bearing Materials under Pure Sliding Condition.” Applied Engineering Letters, vol. 7, no. 2, 2022, pp. 81–88, https://doi.org/10.18485/aeletters.2022.7.2.5
Patil, Atul A., Sumit S. Desai, Lalit N. Patil, and Sarika A. Patil. 2022. “Adopting Artificial Neural Network for Wear Investigation of Ball Bearing Materials under Pure Sliding Condition.” Applied Engineering Letters 7 (2): 81–88. https://doi.org/10.18485/aeletters.2022.7.2.5
Patil, A.A., Desai, S.S., Patil, L.N. and Patil, S.A. (2022). Adopting Artificial Neural Network for Wear Investigation of Ball Bearing Materials Under Pure Sliding Condition. Applied Engineering Letters, 7(2), pp.81–88.
doi: 10.18485/aeletters.2022.7.2.5
Journal Menu
Archive
ADOPTING ARTIFICIAL NEURAL NETWORK FOR WEAR INVESTIGATION OF BALL BEARING MATERIALS UNDER PURE SLIDING CONDITION
Authors:
Atul A. Patil1
, Sumit S. Desai1
, Lalit N. Patil2
, Sarika A. Patil1
1Dr. D. Y. Patil Institute of Technology, Pimpri, Pune, India
2Mechanical Engineering Department, JSPM’s Rajarshi Shahu College of Engineering, Pune, India
Received: 25.11.2021.
Accepted: 07.02.2022.
Available: 30.06.2022
Abstract:
In the industry, ball bearings are the most widely used machine element. The ball materials may differ in various bearing applications. Wear of the ball and recess after a period of use is the most common cause of ball bearing failure. The present study aims to develop the artificial neural network model for assessing the wear of different ball bearing materials. A wear test method has been followed as suggested by the ASTM-G99 standard. The pin on disc apparatus was selected to conduct numerous trials. L9 array was considered to design the experiments. The factors considered for this study were load, time, and sliding speed. Based on the results obtained, ANN code was proposed to evaluate wear using numerous test parameters. The results obtained from the proposed model are nearly similar to experimental results, which would be evidence for the correctness of the model. The proposed neural network model can be used in numerous applications with given parameters.
Keywords:
ANOVA, Artificial Neural Network, Design of Experiments Wear Analysis
References:
[1] F. Lu, R. Zhu, Q. Fu, Wear depth calculation and influence factor analysis for groove ball bearing. MATEC Web Conf., Vol.211, 2018:08002. https://doi.org/10.1051/matecconf/201821108002
[2] L. Patil, A.V. Patil, R.B. Barjibhe, A Wear Analysis of Composite Ball Materials using Tribometer, Int. Journal of Engineering Research and Applications, 6 (1), 2016: 79-78.
[3] L.N. Patil, A.V. Patil, Wear Prediction Model for Composite Bearing Balls under Pure Sliding Contact Condition, International Journal of Engineering Research & Technology, 4 (12), 2015: 547-550. http://dx.doi.org/10.17577/IJERTV4IS120620
[4] R. K. Upadhyay, L. A. Kumaraswamidhas, Md. S. Azam, Rolling element bearing failure analysis: A case study. Case Studies in Engineering Failure Analysis, 1 (1), 2013: 15-17. https://doi.org/10.1016/j.csefa.2012.11.003
[5] L. Wang, R. W. Snidle, L. Gu, Rolling contact silicon nitride bearing technology: a review of recent research. Wear, 246 (1–2),2000: 159-173. https://doi.org/10.1016/S0043-1648(00)00504-4
[6] C. Piconi, S. G. Condo, T. Kosmač, Alumina – and Zirconia-based Ceramics for Load – bearing Applications. in Advanced Ceramics for Dentistry, Chapter 11, J.Z. Shen and T. Kosmač. Butterworth-Heinemann, 2014: 219-253.
[7] H. Taplak, İ. Uzmay, Ş. Yıldırım, An artificial neural network application to fault detection of a rotor bearing system. Industrial Lubrication and Tribology, 58 (1), 2006: 32-44. https://doi.org/10.1108/00368790610640082
[8] J.P. Patel, S.H. Upadhyay, Comparison between Artificial Neural Network and Support Vector Method for a Fault Diagnostics in Rolling Element Bearings. Procedia Engineering, 144, 2016: 390-397. https://doi.org/10.1016/j.proeng.2016.05.148
[9] B.K.N. Rao, P.S. Pai, T.N. Nagabhushana, Failure Diagnosis and Prognosis of Rolling – Element Bearings using Artificial Neural Networks: A Critical Overview. J. Phys.: Conf. Ser., 364, 2012: 012023. http://doi: 10.1088/1742-6596/364/1/012023
[10] S. Veličković, B. Stojanović, M. Babić, A.Vencl, I. Bobić, G. Vadászné Bognár, F. Vučetić, Parametric optimization of the aluminium nanocomposites wear rate. J Braz. Soc. Mech. Sci. Eng., 41(1), 2019: 19. https://doi.org/10.1007/s40430-018-1531-8
[11] B. Stojanovic, J. Blagojevic, M. Babic, S. Velickovic, and S. Miladinovic, Optimization of hybrid aluminum composites wear using Taguchi method and artificial neural network. Industrial Lubrication and Tribology, 69 (6), 2017: 1005-1015. https://doi: 10.1108/ILT-02-2017-0043
[12] D. Mikic, E. Desnica, A. Asonja, B. Stojanovic, and V. Epifanic-Pajic, Reliability Analysis of Ball Bearing on The Crankshaft of Piston Compressors. Journal of the Balkan Tribological Association, 22 (4), 2016: 5060- 5070.
[13] E. Desnica, A. Asonja, D. Mikic, B. Stojanovic, Reliability Model of Bearing Assembly on An Agricultural Cardan Shaft. Journal of the Balkan Tribological Association, 21 (1), 2015: 38-48.
[14] B. Stojanovic, M. Babic, N. Miloradovic, S. Mitrovic, Tribological Behaviour of A356/10SiC/3Gr Hybrid Composite In DrySliding Conditions. Materials and Technologies, 49 (1), 2015: 117-121.
[15] J. Halme P. Andersson, Rolling contact fatigue and wear fundamentals for rolling bearing diagnostics – state of the art, Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology, 224 (4), 2010: 377-393.
[16] E. V. Zaretsky, A Basics of Bearing Life Prediction. Lewis Research Center Cleveland, Ohio, 1997: p.12.
[17] S. Veličković, B. Stojanović, M. Babić, I. Bobić, Optimization of tribological properties of aluminum hybrid composites using Taguchi design. Journal of Composite Materials, 51(17), 2017: 2505-2515. https://doi:10.1177/0021998316672294
[18] R K. Roy, A primer on the Taguchi method, 2. ed. Dearborn, Society of Manufacturing Engineers, 2010.
[19] T. Zhaoping, S. Jianping, The Contact Analysis for Deep Groove Ball Bearing Based on ANSYS. Procedia Engineering, 23, 2011: 423-428. https://doi:10.1016/j.proeng.2011.11.2524
[20] V.G. Pavlov, Wear calculations for radial ball bearings. Russ. Engin. Res. 28 (7), 2008: 643-650. https://doi.org/10.3103/S1068798X08070058
[21] T. Kolodziejczyk, R. Toscano, S. Fouvry, G. Morales-Espejel, Artificial intelligence as efficient technique for ball bearing fretting wear damage prediction. Wear, 268 (1-2), 2010: 309-315. https://doi:10.1016/j.wear.2009.08.016
[22] X. Zhang, H. Xu, W. Chang, H.Xi, S. Pei, W. Meng, H. Li, S. Xu, A dynamic contact wear model of ball bearings without or with distributed defects. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 234 (24), 2020:4827-4843. https://doi: 10.1177/0954406220931544
[23] H. Li, H. Li, Y. Liu, H. Liu, Dynamic characteristics of ball bearing with flexible cage lintel and wear. Engineering Failure Analysis, 117, 2020: 104956. https://doi:10.1016/j.engfailanal.2020.104956
[24] Z. Yang, Y. Zhang, K. Zhang, S. Li, Wear Analysis of Angular Contact Ball Bearing in Multiple-Bearing Spindle System Subjected to Uncertain Initial Angular Misalignment. Journal of Tribology, 143 (9), 2021: 091703. https://doi:10.1115/1.4049258
[25] A.A. Torrance, J.E. Morgan, G.T Y. Wan, An additive’s influence on the pitting and wear of ball bearing steel. Wear, 192 (1-2), 1996: 66-73. https://doi: 10.1016/0043-1648(95)06751-5
[26] B. Hanrahan, S. Misra, C.M. Waits, R. Ghodssi, Wear mechanisms in microfabricated ball bearing systems. Wear, 326-327, 2015: 1-9. https://doi:10.1016/j.wear.2014.12.032
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)
Volume 9
Number 3
September 2024
Last Edition
Volume 9
Number 3
September 2024