A strategy for contact fatigue life prediction of polymer gears via an experimental-simulated hybrid data-driven model

A timely trend in gear transmission involves the replacement of steel with polymers. Nevertheless, the absence of fundamental durability data for polymer gears impediments to their reliable applications during power transmissions. The expensive and time-consuming gear fatigue experiments make it impossible to rely merely on experimental data. In this study, a strategy for contact fatigue life prediction of polymer gears via an experimental-simulated hybrid data-driven model is presented. The hybrid data is established with a certain mixture ratio of experiment and simulation data, augmented by the CTAB-GAN algorithm. This specific algorithm was combined with the XGBoost algorithm to predict the contact fatigue life of gears made out of different polymer materials, with prediction accuracy controlled within the 3-fold scatter band. Moreover, an empirical predictive formula for contact fatigue life was developed. The hybrid data-driven model, merging experimental and simulated data, allows for efficient estimation of fatigue life and material selection strategies, generating insight into the anti-fatigue design of polymer gears.