A novel prediction method for gear friction coefficients based on a computational inverse technique

A novel prediction method for gear friction coefficients with or without lubrication is developed with a computational inverse technique. A surrogate model is introduced to describe the mapping between the friction coefficients and root stresses, according to an adaptive radial basis function. The convergence criterion for solving the friction coefficients is determined based on the calculated and measured root stresses. A nonzero friction coefficient at the pitch point, which is closely relative to the action of rolling friction, is detected using the proposed method. Results show that the gear friction coefficients decrease with an increase in rotation speed or a decline in applied torque. Moreover, the friction coefficients without lubrication are more than twice those with lubrication.