Transient micro-elastohydrodynamic lubrication analysis of gear meshing interfaces with texture geometric parameters.

Area of Science:

• Tribology
• Mechanical Engineering
• Materials Science

Background:

• Gear performance is limited by anti-scuffing load-bearing capacity.
• Micro-convex-concave asperity (MCCA) interfaces and micro-element textures (MET) influence lubrication.
• Understanding transient thermal effects on lubrication is crucial.

Purpose of the Study:

• To develop a Thermal Elastohydrodynamic Lubrication (TEHL) model for gear pair anti-scuffing load-bearing capacity (ASLBC).
• To investigate the impact of Micro-Element Texture (MET) parameters on Interface Enriched Lubrication (IEL).
• To establish optimal MET features for enhancing ASLBC.

Main Methods:

• Established a TEHL model incorporating elastic deformation of MCCA interfaces.
• Utilized homogenization theory for numerical simulations of MCCA contact and sliding friction.
• Developed and solved a time-dependent micro-elastohydrodynamic IEL model using multi-level mesh refinement.
• Employed Univariate Sensitivity Analysis (USA) and Multivariate Linear Regression (MLR) for parameter optimization.

Main Results:

• MET parameters, including area ratio and depth-to-diameter ratio, significantly affect IEL.
• Autocorrelation length and MCCA amplitude influence IEL performance.
• MLR identified optimal MET parameters, achieving a 13.7% friction reduction compared to untextured surfaces.
• MLR-optimized configuration showed a 4.58% improvement over USA-optimized results.

Conclusions:

• The developed TEHL model effectively predicts ASLBC enhancement through MET.
• Multivariate Linear Regression (MLR) offers superior parameter optimization and captures parameter coupling effects compared to Univariate Sensitivity Analysis (USA).
• Optimized micro-textures, particularly Transverse Slit MET, significantly improve gear lubrication and reduce friction.