Lifecycle wear prediction of spur gears with time-dependent Archard model and surface topography evolution

• Closed-loop model predicts 3D gear wear under dry friction conditions • Time-dependent wear coefficient describes nonlinear wear evolution • Dynamic geometry update captures tooth-shape evolution during service • Simulation results show strong agreement with experimental observations • Proposed model improves prediction accuracy and computational efficiency Gear wear degrades transmission accuracy by altering tooth geometry, contact pressure, and dynamic response. Many prediction approaches under dry/solid-lubricated conditions still under-represent the tribo-dynamic coupling among a time-dependent wear coefficient (TDWC), evolving surface morphology, and contact kinematics. This study proposes a lifecycle wear-prediction scheme that integrates a profile-alignment/correction (error) model, a lumped-parameter dynamic model, and a wear module. The framework introduces a spatio-temporal TDWC model that maps wear-coefficient variation along the tooth profile and over time, while incorporating surface-morphology feedback into the dynamic analysis to achieve closed-loop wear evolution. Tooth morphology is measured by 3D profilometry and refined via mathematical processing, then fed to the dynamic model with time-varying meshing stiffness (TVMS) computed by the potential-energy method (PEM). Contact is evaluated with Hertzian theory, and wear depth is updated by the Archard law using friction and wear-coefficient parameters calibrated from equivalent wear tests (EWTs), iteratively refreshing tooth geometry. Simulations and experiments show strong agreement across tooth positions, with tooth-level wear-mass deviations < 1 mg, indicating high predictive accuracy at modest computational cost under dry friction.