Optimization of modification parameters of face gear pair based on TCA-NRBPNN-HYPE hybrid drive model

To enhance the meshing characteristics of the face gear pair and determine the optimal modification parameters, an optimization model of modification parameters, based on the TCA-NRBPNN-HYPE (Tooth Contact Analysis-Newton Raphson Back Propagation Neural Network-Hyperparameter Optimization) hybrid drive model is proposed. Firstly, a dual weight modification curve is introduced to modify the tooth surface of face gears, and the TCA model is employed to accurately obtain the meshing characteristics parameters of the modified gear pair, including contact position, transmission error, and contact stress. based on the modification parameters and TCA results, an NRBPNN prediction model is established to achieve mapping from modification parameters to meshing characteristics. Finally, the HYPE optimization model is applied to globally optimize the prediction results and obtain the optimal modification parameter combination. The results show that the optimal design reduces the contact position parameter from 4.15 to 1.50, the transmission error from 2.98″ to 0.314″, and the contact stress from 566.30 MPa to 292.33 MPa. These results indicate that the proposed method effectively improves the meshing characteristics and reliability of face gear pair.