Hot Forging Process Optimization of Gear Shaft Based on RBF Neural Network

The RBF (radial basis function)neural network model for the optimization of gear shaft hot forging forming process was established, and the model calculation process, convergence characteristics and fitting results were analyzed.Based on RBF neural network, the hot forging forming process of the gear shaft was optimized.The results show that the yield strength of the gear shaft hot forged parts optimized based on the RBF neural network model is increased from 425 MPa to 456 MPa, the maximum forming force is reduced from 565 kN to 508 kN, the yield strength increase rate is 7.3%, and the maximum forming force reduction rate is 10.1%.The best production process parameters for gear shaft hot forging forming are die preheating temperature of 300℃, blank heating temperature of 1150℃, friction coefficient of 0.3, and hot forging speed of 40 mm/s.