Optimization of surface texture and lubrication performance for friction pairs in continuous rotary electro-hydraulic servo motor

Purpose This paper aims to improve the friction performance of the tribo-pair in a continuous rotary electro-hydraulic servo motor through optimizing surface texture design. Design/methodology/approach By establishing an oil film thickness equation for the textured surface and numerically solving the Reynolds equation, the effects of various texture geometric parameters on oil film pressure distribution, load-carrying capacity and leakage were analyzed. A hybrid optimization approach combining Particle Swarm Optimization (PSO) and the Sparrow Search Algorithm (SSA) was employed to determine the optimal geometric parameters. Findings The results show that texture depth and size significantly influence both the load-carrying capacity and leakage. Fluid simulation analysis revealed that square textures demonstrated superior load-carrying performance. In fluid-structure interaction simulations, when the texture spacing was 1.5a, the oil film load-carrying capacity at the blade tip reached its maximum and shear stress was minimized. Further calculations confirmed that the friction coefficient was significantly reduced under the optimized design. Originality/value The optimization strategy effectively enhances lubrication performance, reduces friction and provides an important reference for improving equipment performance and extending service life. Peer review The peer review history for this article is available at: Link to the cited website

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Publication Details

Journal
Industrial Lubrication and Tribology
Published
2025-11-20
DOI
https://doi.org/10.1108/ilt-05-2025-0211
Primary Topic
Hydraulic and Pneumatic Systems
Type
article
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article

Optimization of surface texture and lubrication performance for friction pairs in continuous rotary electro-hydraulic servo motor

Xiaojing Wang, Xiaolong Wang, Shiyuan Zhang, Peng Ziqin
Industrial Lubrication and Tribology
Hydraulic and Pneumatic Systems
article

Optimization of surface texture and lubrication performance for friction pairs in continuous rotary electro-hydraulic servo motor

Xiaojing Wang, Xiaolong Wang, Shiyuan Zhang, Peng Ziqin
article en

Abstract

Purpose This paper aims to improve the friction performance of the tribo-pair in a continuous rotary electro-hydraulic servo motor through optimizing surface texture design. Design/methodology/approach By establishing an oil film thickness equation for the textured surface and numerically solving the Reynolds equation, the effects of various texture geometric parameters on oil film pressure distribution, load-carrying capacity and leakage were analyzed. A hybrid optimization approach combining Particle Swarm Optimization (PSO) and the Sparrow Search Algorithm (SSA) was employed to determine the optimal geometric parameters. Findings The results show that texture depth and size significantly influence both the load-carrying capacity and leakage. Fluid simulation analysis revealed that square textures demonstrated superior load-carrying performance. In fluid-structure interaction simulations, when the texture spacing was 1.5a, the oil film load-carrying capacity at the blade tip reached its maximum and shear stress was minimized. Further calculations confirmed that the friction coefficient was significantly reduced under the optimized design. Originality/value The optimization strategy effectively enhances lubrication performance, reduces friction and provides an important reference for improving equipment performance and extending service life. Peer review The peer review history for this article is available at: Link to the cited website

Industrial Lubrication and TribologyVol. 78(1)
Beijing University of Civil Engineering and Architecture (CN)
Openalex Percentile: Top 63%
Hydraulic and Pneumatic Systems
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Optimization of surface texture and lubrication performance for friction pairs in continuous rotary electro-hydraulic servo motor — Xiaojing Wang, Xiaolong Wang, et al. · Industrial Lubrication and Tribology (2025) | TGRS Research Map | TGRS