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June 3, 2026Lubricants0 citationsOpen Access

Multiscale Modeling of Micro-Textured Gear: Interface Enriched Lubrication and Anti-Scuffing Load-Bearing Capacity

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WZWeiqiang ZouXWXigui WangYWYue Wang

Key Points

  • This research aims to develop a multiscale model to understand the interplay between lubrication performance and load-bearing capacity in micro-textured gears.
  • Developed a contact model integrating MCCA characteristics
  • Utilized homogenization theory to analyze areal density effects
  • Employed univariate sensitivity analysis and multiple linear regression for parameter optimization.
  • Demonstrated enhanced interface enriched lubrication performance under TEHL conditions
  • Identified optimal microtexture parameters to improve anti-scuffing load-bearing capacity
  • Experimental data aligned with numerical predictions for dynamic friction coefficients.

Abstract

A multiscale contact model is developed for micro-textured gear interfaces incorporating Micro-Convex-Concave Asperity (MCCA) characteristics to elucidate the synergistic modulation between Interface Enriched Lubrication (IEL) performance and Anti-Scuffing Load-Bearing Capacity (ASLBC) of Micro-Textured Meshing Interfaces (MTMI) under transient Thermal Elastohydrodynamic Lubrication (TEHL) conditions. Homogenization theory is employed to quantify the effects of areal density and depth-to-diameter ratio on IEL characteristics. A time-resolved micro-elastohydrodynamic lubrication model, formulated through dimensionless discretization and adaptive mesh refinement, investigates the influences of autocorrelation length and MCCA amplitude on interfacial behavior. A correlation framework linking Micro-Element Texture (MET) geometric parameters to meshing ASLBC is established to identify optimal textures for simultaneous enhancement of IEL and ASLBC. Experimental observations demonstrate qualitative consistency with numerical predictions regarding the evolutionary trends of temperature fields and dynamic friction coefficients, providing preliminary physical validation for the proposed model. Univariate Sensitivity Analysis (USA) and Multiple Linear Regression (MLR) are further utilized to optimize microtexture parameters by elucidating the influences of MET sizes, area ratio, and configuration on meshing ASLBC and friction coefficients.

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Cite This Study

Zou et al. (2026) studied this question.

synapsesocial.com/papers/6a1fc49adee9eb8c0dce6104https://doi.org/10.3390/lubricants14060226
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