The frictional performance of water-lubricated UHMWPE is influenced by the combination of structural parameters and operating conditions. To improve the efficiency of optimal design of surface texture aimed at improving frictional performance, a novel integration of the Orthogonal Array method (OAM), machine learning (ML) prediction, and Particle Swarm Optimization (PSO) is proposed for predicting and optimizing the coefficient of friction (COF) of copper ball-textured UHMWPE surfaces using a small dataset. In order to reduce manufacturing and testing cost, decrease required training samples for ML algorithm, OAM which could efficiently acquire data set with comprehensive feature information is used to determine the parameters of test samples to generate a small but effective dataset. 25 textured samples based on L16 (4⁴) and L9 (3⁴) are fabricated, with the parameter set determined using OAM. COFs of the samples are tested using RTEC tribo-tester. Trend analysis is conducted to investigate the influence of force, frequency, depth and ellipse axis ratio on COF. Multi-linear Regression (MLR) and Gaussian Process Regression are employed. MLR exhibits better prediction accuracy and is integrated with PSO to minimize COF. The error between the experimental and the theoretical results obtained by the integration method of MLR and PSO is only 1.04%, demonstrating the feasibility of predicting COF and optimizing surface texture using the integrated method with a limited dataset determined by OAM.

This paper aims to investigate the combined effects of working condition and structural parameters of groove texture on the dynamic characteristics, stability and unbalance response of a water-lubricated hydrodynamic bearing–rotor system to avoid instability and excessive vibration of the rotor. The Navier–Stokes equation, standard K-ε model with enhanced wall treatment and Zwart–Gerber–Belamri cavitation model are considered using the commercial software Fluent to calculate the stiffness and damping coefficients of a groove-textured, water-lubricated bearing based on the dynamic mesh method; the critical mass to express the stability and the unbalance response solved by the fourth order Runge–Kutta method of the rotor are calculated based on dynamic equations. The results indicate that shallower and longer groove textures can improve the direct stiffness along the load direction (Formula presented.), weaken the stiffness in the orthogonal direction (Formula presented.), improve stability and decrease the unbalance response amplitude of the water-lubricated bearing–rotor system at a greater rotational speed and smaller eccentricity ratio; however, the impact of grooves on damping parameters is not as great as it is on stiffness—there exists an optimum groove width to achieve a best dynamic performance.