Effect of Tooth Profile Modification on Dynamic Tooth Load of Planetary Gear Train

Zhou, Jiaming; Yi, Fengyan; Xu, Xiangyang; Lai, J.; Liu, Yanfang; Dong, Peng

doi:10.1155/2019/8156971

Cited by 3 publications

(1 citation statement)

References 37 publications

(79 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By establishing the dynamic model of box transmission coupling, the actual meshing state of each gear pair is obtained by dynamic simulation, and then the actual meshing state is used as input to obtain the optimal modification parameters. Zhou et al [14] studied the influence of tooth profile modification on the dynamic response of the planetary gear system and calculated the time-varying mesh stiffness (TVMs) and transmission error (TES) by a numerical model. Then the dynamic deviation factor is introduced to describe the dynamic response of PGT.…”

Section: Introductionmentioning

confidence: 99%

Design of Multi-Stage Gear Modification for New Energy Vehicle Based on Optimized BP Neural Network

Tang

Wang

Chen³

et al. 2020

IEEE Access

View full text Add to dashboard Cite

The NVH (Noise, Vibration, and Harshness) characteristics of new energy vehicles are the key indexes to measure interior comfort. The multi-stage gear reducer in the transmission system is the primary source of vibration and noise. The parameterized 3D model of the multi-stage gear transmission system of the new energy vehicle was established through Romax software, and the comprehensive gear modification method of the tooth direction combined with the tooth profile was built. Then a complete simulation analysis process is established to solve the maximum vibration acceleration of the multi-stage gear transmission system under constant speed condition, to obtain the simulation data of two-stage gear set under different modification parameters. The traditional BP (Back Propagation) neural network is optimized and improved through the optimal selection of network parameters combined with Bayesian regularization. Based on the optimized BP neural network, a modified parameter-vibration noise prediction model is constructed. Finally, the GA (Genetic Algorithm) optimization algorithm is used to solve the prediction model to obtain the optimal combination of modification parameters aiming at the minimum vibration acceleration, the effectiveness and reliability of the modified design are verified through actual simulation. It provides ideas and a basis for the research on vibration and noise reduction of multi-stage gears.

show abstract

Section: Introductionmentioning

confidence: 99%