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

Abstract

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.

Keywords

• axial piston pump
• valve plate pair
• machine learning model
• friction coefficient