A strategy for model establishment and predictive control of the grinding process of a leadscrew by two artificial neural networks is proposed. The first ANN is used to model the relationship of thermal deformation and force deformation error with the pitch error of the workpiece

and the second ANN outputs the final modified control value according to the output of the first ANN and the theoretical control value

and the modified control value can be applied for compensation control at the beginning of the next sampling period. The experimental results show that the pitch error of the workpiece is decreased by more than 80%

thus significantly improving the grinding precision of the leadscrew.