Wear Model of RV Reducer Based on Mixed Lubrication Analysis

Compared with traditional planetary gear reducers and harmonic drive reducers, the rotary vector (RV) reducer features higher power density and is widely used in fields such as aerospace and aviation special equipment, industrial robots, high-end CNC machine tools, etc. However, in special and extreme environments, particularly under severe operating conditions involving high/low temperatures, heavy loads, and high speeds, the components within RV reducers demand exceptionally high machining precision. Micron-level errors can cause multitooth meshing under load to evolve into multi-tooth interference. This compromises lubrication between components, ultimately leading to wear-induced failure. However, current research on the wear behavior of RV reducers under special extreme working conditions remains inadequate, and no effective wear prediction model has been established. Based on tribology theory and gear meshing principles, this study first conducted kinematic and force analyses of key components to determine the contact geometry, velocities, and loads of tribo-pairs. Subsequently, macroscopic contact geometry, microscopic surface topography, velocities, and loads were incorporated into the Reynolds equation and film thickness equation to establish a mixed lubrication analysis model for RV reducer components. Based on this, it solved the hybrid lubrication model for different components to obtain the interfacial film thickness of parts, thereby determining that the cycloidal-pin wheel and needle bearing exhibit the poorest lubrication state among RV reducer components and represent the most vulnerable weak links prone to wear failure. Using wear failure of these vulnerable components as the reducer’s wear failure criterion, an RV reducer wear model based on the hybrid lubrication model was established. Finally, equivalent friction-wear experiments were conducted to calibrate the wear model using experimental data. The ultimately established wear model enables prediction of RV reducer wear under special extreme working conditions, providing theoretical support for enhancing its service performance.