Multi‐Strategy Synergistic Enhancement of Self‐Lubricating Fabric Liners and Their Tribological Mechanisms

Self‐lubricating polytetrafluoroethylene (PTFE)/Kevlar fabric liners are critical for bearing reliability, yet simultaneous optimization of friction, wear, and stability remains challenging. This study proposes a multi‐dimensional synergistic modification strategy integrating fiber‐surface functionalization with resin‐matrix doping. Specifically, a stable interphase was engineered via layer‐by‐layer (LbL) self‐assembly of boron nitride (BN) nanosheets, while a heterogeneous structure was developed by incorporating carbon nanotubes (CNT) and graphene oxide (Go) into the resin matrices. To optimize filler concentrations, a machine learning‐aided inverse design framework was employed, achieving a low prediction error of approximately 6%. Tribological characterization and molecular dynamics simulations elucidated that the optimized LbL‐Go‐CNT liner achieves a stable friction coefficient of 0.132 and a 55% reduction in wear rate at 200 MPa. Mechanistically, Go enables low‐shear sliding while CNT provides structural reinforcement. Together with BN and PTFE, these components promote the formation of a robust, dense transfer film. This experimental‐computational co‐design offers a novel paradigm for developing next‐generation self‐lubricating composites.