Intelligent self-evolving design method of high-load-bearing hydrostatic oil groove

Hydrostatic oil grooves in the friction pair are responsible for guiding, storing, and distributing lubricating oil, widely applied to ultra-high-power hydraulic motors of tunnel boring machines, aerospace variable pumps, and hydrostatic precision guideways/spindles of high-end industrial mother machines, etc. The traditional design methods for oil groove patterns highly rely on the designer's experience and size optimization of preset shapes, making it difficult to achieve optimal lubrication. For that, this study proposes the intelligent self-evolving design method of oil grooves, in which the AI-assisted generalized pattern search algorithm (GPS+AI) is designed to make an oil groove pattern self-evolve towards maximizing load-bearing capacity according to the friction pair’s contact force feedback from a lubrication model. The designed oil groove pattern is machined onto the piston of a hydraulic motor and is experimentally evaluated for its lubrication load-bearing capacity through the home-made quasi-actual roller-piston pair testing rig. Comparing two traditional oil grooves, the new oil groove can reduce the friction torque (contact force) by a maximum of 88%, which is very significant for improving efficiency and lifespan of ultra-high power hydraulic motors (power > 10⁶ W), especially under the dual-carbon target.