Performance Assessment and Failure Mechanisms of Roller Bearings Using Dual Material Comparison

Authors

  • Jaswant Sharma
  • Indrakant Singh

DOI:

https://doi.org/10.24113/ijoscience.v11i1.502

Abstract

This study presented the performance and failure mechanisms of roller bearings for structural steel and AISI 52100 alloy steel under various operating conditions. It emphasizes how material properties affect stress, deformation, and thermal performance according to simulations carried out in ANSYS Workbench. Results clearly present that AISI 52100 alloy steel exhibits better strength against stress, dimensional stability, and thermal efficiency compared with structural steel. Key factors contributing to the bearing's failure included high tightening axial force, vibration, and temperature, with spalling and wear as the dominant modes of failure. The improvement in performance further came through dimensional changes reducing stress and deformation. The whole research highlights the need for attention to material selection and axial force control while designing roller bearings to be reliable and efficient. Findings of the present study may thus advocate for the use of AISI 52100 alloy steel in applications requiring high performance and durability. Future studies might look at advanced materials and optimized designs to further advance bearing reliability under demanding conditions.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Author Biographies

Jaswant Sharma

M.Tech Scholar 

Sagar Institute of Research & Technology

Bhopal, Madhya Pradesh, India

Indrakant Singh

Assistant Professor

Sagar Institute of Research & Technology

Bhopal, Madhya Pradesh, India

References

Zheng, Z., Wang, C., Wang, L., Ji, Z., Song, X., Mak, P. I., ... & Wang, Y. (2024). Micro-Electro-Mechanical Systems Microphones: A Brief Review Emphasizing Recent Advances in Audible Spectrum Applications. Micromachines, 15(3), 352. https://doi.org/10.3390/mi15030352 DOI: https://doi.org/10.3390/mi15030352

Wahid, A., Breslin, J. G., & Intizar, M. A. (2024). TCRSCANet: Harnessing Temporal Convolutions and Recurrent Skip Component for Enhanced RUL Estimation in Mechanical Systems. Human-Centric Intelligent Systems, 1-24. https://doi.org/10.1007/s44230-023-00060-0 DOI: https://doi.org/10.1007/s44230-023-00060-0

Kannan, V., Zhang, T., & Li, H. (2024). A review of intelligent condition monitoring of rolling element bearings. Machines, 12(7), 484. https://doi.org/10.3390/machines12070484 DOI: https://doi.org/10.3390/machines12070484

Gbashi, S. M., Olatunji, O. O., Adedeji, P. A., & Madushele, N. (2024). From academic to industrial research: A comparative review of advances in rolling element bearings for wind turbine main shaft. Engineering Failure Analysis, 108510. https://doi.org/10.1016/j.engfailanal.2024.108510

Graf, S., & Koch, O. (2024). Changes in Surface Topography and Light Load Hardness in Thrust Bearings as a Reason of Tribo-Electric Loads. Lubricants, 12(9), 303. https://doi.org/10.3390/lubricants12090303 DOI: https://doi.org/10.3390/lubricants12090303

Kamat, Pooja & Sugandhi, Rekha. (2020). Bibliometric Analysis of Bearing Fault Detection using Artificial Intelligence. Library Philosophy and Practice. 2020.

Hiltunen, S., Sapkota, J., Ioannou, E., Haddad Momeni, M., Master, E., & Ristolainen, M. (2024). Comparative assessment of chemical and biochemical approaches for the activation of lignocellulosic materials and emerging opportunities for expansin-related proteins. Cellulose, 31(1), 147-168. https://doi.org/10.1007/s10570-023-05637-3 DOI: https://doi.org/10.1007/s10570-023-05637-3

ALI, S. H., Yu, Y. A. O., Bangfu, W. U., Biao, Z. H. A. O., Wenfeng, D. I. N. G., JAMIL, M., ... & Dongdong, X. U. (2024). Recent developments in MQL machining of aeronautical materials: a comparative review. Chinese Journal of Aeronautics. https://doi.org/10.1016/j.cja.2024.01.018 DOI: https://doi.org/10.1016/j.cja.2024.01.018

Gbashi, S. M., Olatunji, O. O., Adedeji, P. A., & Madushele, N. (2024). From academic to industrial research: A comparative review of advances in rolling element bearings for wind turbine main shaft. Engineering Failure Analysis, 108510. https://doi.org/10.1016/j.engfailanal.2024.108510 DOI: https://doi.org/10.1016/j.engfailanal.2024.108510

Harling, J. C., Jacobs, G., & Lehmann, B. (2024, June). Chemical factors for premature failures of roller bearings in wind turbines due to white etching cracks: A review from literature and industry experience. In Journal of Physics: Conference Series (Vol. 2767, No. 4, p. 042015). IOP Publishing. DOI 10.1088/1742-6596/2767/4/042015 DOI: https://doi.org/10.1088/1742-6596/2767/4/042015

Graf, S., Koch, O., & Sauer, B. (2023). Influence of Parasitic Electric Currents on an Exemplary Mineral-Oil-Based Lubricant and the Raceway Surfaces of Thrust Bearings. Lubricants, 11(8), 313. https://doi.org/10.3390/lubricants11080313 DOI: https://doi.org/10.3390/lubricants11080313

Su, B., Lu, C., & Li, C. (2024). Current Status of Research on Hybrid Ceramic Ball Bearings. Machines, 12(8), 510. https://doi.org/10.3390/machines12080510 DOI: https://doi.org/10.3390/machines12080510

Hakim, M., Omran, A. A. B., Ahmed, A. N., Al-Waily, M., & Abdellatif, A. (2023). A systematic review of rolling bearing fault diagnoses based on deep learning and transfer learning: Taxonomy, overview, application, open challenges, weaknesses and recommendations. Ain Shams Engineering Journal, 14(4), 101945. https://doi.org/10.1016/j.asej.2022.101945 DOI: https://doi.org/10.1016/j.asej.2022.101945

Kumar, N., & Satapathy, R. K. (2023). Bearings in aerospace, application, distress, and life: a review. Journal of Failure Analysis and Prevention, 23(3), 915-947. https://doi.org/10.1007/s11668-023-01658-z DOI: https://doi.org/10.1007/s11668-023-01658-z

Pan, C., Wang, C., Zhao, Y., Bao, Y., & Chang, J. (2023). Recent Patents on Roller Bearing. Recent Patents on Engineering, 17(3), 72-88. https://doi.org/10.2174/1872212117666220620103329 DOI: https://doi.org/10.2174/1872212117666220620103329

Li, J., Luo, W., & Bai, M. (2024). Review of research on signal decomposition and fault diagnosis of rolling bearing based on vibration signal. Measurement Science and Technology. DOI 10.1088/1361-6501/ad4eff DOI: https://doi.org/10.1088/1361-6501/ad4eff

Peng, B., Bi, Y., Xue, B., Zhang, M., & Wan, S. (2022). A survey on fault diagnosis of rolling bearings. Algorithms, 15(10), 347. DOI: https://doi.org/10.3390/a15100347

Downloads

Published

01/15/2025

How to Cite

Sharma, J., & Singh, I. (2025). Performance Assessment and Failure Mechanisms of Roller Bearings Using Dual Material Comparison. SMART MOVES JOURNAL IJOSCIENCE, 11(1), 28–35. https://doi.org/10.24113/ijoscience.v11i1.502

Issue

Section

Articles