Optimization of Machine Tool Settings on Hypoid Gear Dynamics

Abstract

Abstract Hypoid gears are widely used to transmit torque on cross axis shafts in a vehicle rear axle system. The dynamic responses of these hypoid geared rotor system have a significant effect on the performance of noise, vibration, and harshness (NVH) for the vehicle design. From past studies, the main source of excitation for this vibration energy comes from hypoid gear transmission error (TE). Thus, the design of hypoid gear pair with minimization of TE is one way to control the dynamic behavior of the vehicle axle system. In this paper, an approach to obtain minimum TE and improved dynamic response with optimal machine tool setting parameters for manufacturing hypoid gears is discussed. A neural network, named Feed-Forward Back Propagation (FFBP), with Particle Swarm Optimization (PSO) and Gradient Descent (GD) training algorithms are used to predict the TE. With the optimal machine tool setting parameters, a 14 degrees of freedom geared rotor system analysis is performed to verify the improvement on dynamic response aiming at minimizing the TE. A case study of a hypoid gear pair with specified design parameters and working condition is presented to validate the proposed method. The results conclude that minimization of TE, the main excitation of vehicle axle gear whine noise and vibration, with optimal machine tool setting parameters can improve the overall dynamic response. The proposed approach provides a better understanding of an optimal design hypoid gear set to minimize TE and effect on vehicle axle system dynamics.