A Wear Prediction Model for Spur Gears Based on the Dynamic Meshing Force and Tooth Profile Reconstruction

In this study, Archard's wear equation is combined with a nonlinear dynamic model and a reconstraction method for the wear tooth profile to predict the wear depth of gears. The dynamic model, which is used to determine the dynamic meshing force, and the reconstraction of the wear tooth profile serves as the basis of the sliding coefficient calculation. Next, the dynamic meshing force and sliding coefficient are used to calculate the surface wear in Archard's wear equation. Then, the dynamic meshing force and sliding coefficient would be recalculated according to the surface wear state. After multiple iterations of previous steps, the simulation results show that the non-uniform wear of the gear surface has a great influence on the distribution of dynamic meshing force, and will increase significantly the peak of dynamic meshing force. And in return, the changing dynamic meshing force would enhance the non-uniformity of wear. These two factors influence and exaggerate each other, but are limited by sliding coefficient. When the driving gear runs after 858 million cycles accumulatively, the maximum wear depth reaches 0.0737mm, and the peak value of dynamic meshing force is more than 4 times of the unweared, which exists the risk of overload. The proposed model can be used to predict gears wear life and design gears, which has a certain engineering significance.