The Impact of Surface Roughness on the Friction and Wear Performance of GCr15 Bearing Steel

Surface roughness plays a crucial role in determining surface quality, influencing factors such as vibration, noise, assembly precision, lubrication, and wear resistance in bearings. This research examines how surface roughness (Sa) affects the friction and wear characteristics of GCr15 steel under conditions with adequate oil lubrication while varying the applied load. The findings indicate that with an increase in Sa, the friction coefficient of GCr15 steel also increases. As the load rises from 15 N to 35 N, the friction coefficient remains relatively constant. However, higher loads lead to more severe wear of the microprotrusions on the surface of GCr15 steel. The wear area first decreases and then increases as Sa increases. The minimum wear area occurs when Sa is 0.5 μm. Additionally, a back propagation neural network (BPNN) model has been developed to predict the wear performance of GCr15 steel. Validation experiments show that the average prediction error for the BPNN model is 10.64%.

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Publication Details

Journal
Lubricants
Published
2025-04-18
DOI
https://doi.org/10.3390/lubricants13040187
Citations
15
Primary Topic
Adhesion, Friction, and Surface Interactions
Type
article
Field-Weighted Citation Impact
12.95

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article

The Impact of Surface Roughness on the Friction and Wear Performance of GCr15 Bearing Steel

Zhipeng Gong, Wen-Bo Chen, Zeyuan Liu, Sanming Du et al.
15 citations
Lubricants
Adhesion, Friction, and Surface Interactions
12.95
article

The Impact of Surface Roughness on the Friction and Wear Performance of GCr15 Bearing Steel

Zhipeng Gong, Wen-Bo Chen, Zeyuan Liu, Sanming Du, Tiantian He, Yongzhen Zhang
article en
15 citations

Abstract

Surface roughness plays a crucial role in determining surface quality, influencing factors such as vibration, noise, assembly precision, lubrication, and wear resistance in bearings. This research examines how surface roughness (Sa) affects the friction and wear characteristics of GCr15 steel under conditions with adequate oil lubrication while varying the applied load. The findings indicate that with an increase in Sa, the friction coefficient of GCr15 steel also increases. As the load rises from 15 N to 35 N, the friction coefficient remains relatively constant. However, higher loads lead to more severe wear of the microprotrusions on the surface of GCr15 steel. The wear area first decreases and then increases as Sa increases. The minimum wear area occurs when Sa is 0.5 μm. Additionally, a back propagation neural network (BPNN) model has been developed to predict the wear performance of GCr15 steel. Validation experiments show that the average prediction error for the BPNN model is 10.64%.

LubricantsVol. 13(4)
Henan University of Science and Technology (CN)
National Natural Science Foundation of China, Henan Provincial Science and Technology Research Project
Openalex Percentile: Top 1%
Adhesion, Friction, and Surface Interactions
12.95
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