New analytical leakage models for tribological interfaces in axial piston pumps

Chao, Qun; Shao, Yuechen; Liu, Chengliang; Zhao, Jingcheng

doi:10.1177/09544062221149304

Cited by 3 publications

(2 citation statements)

References 35 publications

(58 reference statements)

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“…The working fluid of hydraulic systems is typically provided by axial piston pumps, which convert mechanical energy into hydraulic energy to transmit the fluid power. The efficiency of energy conversion depends on the mechanical and volumetric losses of the axial piston pumps [3]. Specifically, the mechanical loss in an axial piston pump is primarily attributed to the wear of important friction pairs.…”

Section: Introductionmentioning

confidence: 99%

Tribological Behavior Analysis of Valve Plate Pair Materials in Aircraft Piston Pumps and Friction Coefficient Prediction Using Machine Learning

Wang,

Nie,

Liu

et al. 2024

Metals

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To address the problem of tribological failure in an aircraft piston pump valve plate pair, the friction and wear properties of the valve plate pair materials (W9Mo3Cr4V-HAl61-4-3-1) of an axial piston pump at a certain speed and load were studied using a disc-ring tester under lubrication with No. 15 aviation hydraulic oil. The results show that the friction coefficient (COF) fluctuated in the range of 0.019~0.120 when the load (L) increased from 30 N to 120 N, and the speed increased from 100 r/min to 500 r/min. With the increase in the rotational speed, the COF of the valve plate pair decreased first and then increased. When the rotation speed (V) was 300 r/min, the relative COF was the smallest. Under L lower than 60 N, abrasive wear was the main wear mechanism. Under L higher than 90 N, the main wear mechanism was adhesive wear but mild oxidation wear also occurred. In addition, based on the V, L, radius (R), and test duration (T), which affected COF, the random forest regression (RFR) algorithm, the bagging regression (BR) algorithm, and the extra trees regression (ETR) algorithm were used as machine learning methods to predict the COF of the valve plate pair. Mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) were used to evaluate its performance, with the results showing that the ETR prediction model was the best method for predicting COF. The results of the machine learning also showed that the contributions of V, L, R, and T were 43.56%, 36.76%, 13.13%, and 6.55%, respectively, indicating that V had the greatest influence on the COF of the W9Mo3Cr4V/HAl61-4-3-1 friction pair. This study is expected to provide support for the rapid development of new valve plate pair materials.

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Section: Introductionmentioning

confidence: 99%

Tribological Behavior Analysis of Valve Plate Pair Materials in Aircraft Piston Pumps and Friction Coefficient Prediction Using Machine Learning

Wang,

Nie,

Liu

et al. 2024

Metals

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“…The volumetric losses mainly arise from leakage of tribological interfaces. 17 The main friction pairs exist for sealing, lubricating, and bearing, 18 which isolate high pressure oil and bear external load to guarantee the normal operation of the EHA pump. 19,20 Compared with the other two friction pairs, the cylinder block/ valve plate interface has the maximal contact region, which makes the leakage 21 and friction effect evident.…”

Section: Introductionmentioning

confidence: 99%

Tribological characteristics of hard-to-hard matching materials of cylinder block/valve plate interface in electro-hydrostatic actuator pumps

Fu,

Lyu,

Kang

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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Axial piston pumps, serving as the essential power component in electro-hydrostatic actuators (EHA), play a crucial role in achieving advanced actuation by ensuring high efficiency and reliability. The cylinder block/valve plate interface, which is the greatest contact area inside the axial piston pump, significantly affects both leakage and energy dissipation. The interface experiences significant friction loss and severe wear in aerospace applications, primarily due to the rapidly changing mixed and boundary lubrication conditions under a wide range of speed and pressure. Therefore, to improve the efficiency and reliability of electro-hydrostatic actuators, strengthening the cylinder block/valve plate interface is a key problem to overcome. Previous studies have demonstrated that the utilization of soft-to-hard matching materials enhances the friction characteristics of the interface, but it also results in relatively severe wear on the soft material. Therefore, in this study, we propose the concept of employing hard-to-hard matching materials as a strategy to mitigate wear in axial piston pumps. An optimized friction simulation test apparatus is adopted, which contains a simulated piston structure to take the piston stirring effect into consideration. Experimental results show that the implementation of hard-to-hard materials leads to a substantial improvement in the wear resistance of the interface under a wide range of rotational speed (300–6000 rpm). The wear depths of the TiAlN/TiAlN (hard-to-hard) matching material are only 28.6%~34.7% of the tin bronze/TiAlN (soft-to-hard) matching material. Besides, compared with traditional tin bronze/nitrided (soft-to-hard) matching material, the hard-to-hard matching materials improve both friction and wear performance. The hard-to-hard matching materials provide a novel approach to reinforce the cylinder block/valve plate interface, and thus to promote volumetric efficiency and extend the service life of the axial piston pump.

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Physics informed neural networks for fault severity identification of axial piston pumps

Wang,

Zhou,

et al. 2023

Journal of Manufacturing Systems

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New analytical leakage models for tribological interfaces in axial piston pumps

Cited by 3 publications

References 35 publications

Tribological Behavior Analysis of Valve Plate Pair Materials in Aircraft Piston Pumps and Friction Coefficient Prediction Using Machine Learning

Tribological Behavior Analysis of Valve Plate Pair Materials in Aircraft Piston Pumps and Friction Coefficient Prediction Using Machine Learning

Tribological characteristics of hard-to-hard matching materials of cylinder block/valve plate interface in electro-hydrostatic actuator pumps

Physics informed neural networks for fault severity identification of axial piston pumps

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