Title: Effect of multi-parameters interaction on transmission gear rattle based on RBF neural network

Authors: Dong Guo; Yawen Wang; Xiaohui Shi; Guangze Zheng; Xiuwen Xiong

Addresses: College of Automotive Engineering, Chongqing University of Technology, No. 69 Hongguang Avenue, Banan District, Chongqing, 400054, China ' Department of Mechanical and Aerospace Engineering, University of Texas at Arlington, Arlington, Texas 76019, USA ' College of Automotive Engineering, Chongqing University of Technology, No. 69 Hongguang Avenue, Banan District, Chongqing, 400054, China ' College of Automotive Engineering, Chongqing University of Technology, No. 69 Hongguang Avenue, Banan District, Chongqing, 400054, China ' College of Automotive Engineering, Chongqing University of Technology, No. 69 Hongguang Avenue, Banan District, Chongqing, 400054, China

Abstract: This paper proposes a method to analyse multi-parameters interaction on transmission gear rattle. Firstly, a simulation model of manual transmission was established, and the angular velocity of each loose gear as well as the mesh force were obtained. Then the loose gear angular velocity was measured on a manual transmission gearbox to verify the model. The derivative of gear mesh force was taken as the rattle index (jerk index), and was calculated using forward difference method. A radial basis function (RBF) neural network was applied to map the relationships between the selected input parameters and rattle index. The results show that gear backlash has the largest influence on gear rattle, followed by the inertia of the loose gear, the speed fluctuation and the drag torque. This study can be easily extended to other types of transmission systems to control the gear noise and improve sound quality.

Keywords: manual transmission; gear rattle; RBF neural network; weight analysis; drag torque.

DOI: 10.1504/IJVNV.2018.097208

International Journal of Vehicle Noise and Vibration, 2018 Vol.14 No.3, pp.219 - 237

Available online: 26 Dec 2018 *

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