Effect of Loading Pressure on Tribological Properties for Valve Plate Pair during the Running-In Process

Jiao, Wanxin; Liu, Wei; Zhu, Zongming; Xia, Yimin; Ma, Haoqin; Xia, Shiqi

doi:10.1080/10402004.2024.2320693

Cited by 1 publication

(2 citation statements)

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“…Researchers have conducted extensive research on the friction and wear characteristics of valve plate pairs in axial piston pumps [10][11][12]. In terms of numerical simulation, Jiao et al [13] simulated the movement and tribological properties of a valve plate pair during the running-in process using a ring block test. They also conducted mixed lubrication modeling of the valve plate pair and analyzed the impact of loading pressure on the tribological performance of the valve plate pair.…”

Section: Introductionmentioning

confidence: 99%

“…According to current numerical and experimental studies, each feature has a nonlinear relationship with the COF. However, when complex tribological relationships exist, studying the tribological performance of a valve plate pair interface through separate friction and wear experiments is often influenced by changes in multiple parameters and scales [13], and qualitative research is insufficient to meet practical needs and cannot effectively guide engineering practice. This process is time-consuming and labor-intensive.…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

confidence: 99%