Title: Damage prevention analysis of heavy-duty gear body based on finite element neural network

Authors: Weichi Pei; Jianwei Dong; Haiyang Long; Hongchao Ji; Wenming Zhang; Yaogang Li

Addresses: School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, China; College of Mechanical Engineering, North China University of Science and Technology, Tangshan, China ' College of Mechanical Engineering, North China University of Science and Technology, Tangshan, China ' College of Mechanical Engineering, North China University of Science and Technology, Tangshan, China ' College of Mechanical Engineering, North China University of Science and Technology, Tangshan, China ' School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, China ' College of Mechanical Engineering, North China University of Science and Technology, Tangshan, China

Abstract: The method of damage prevention analysis of heavy-duty gear body based on finite element neural network is proposed to improve the effectiveness of damage prevention analysis of heavy-duty gear body. Firstly, a design platform for gearbox gears of caterpillar tractors is developed based on finite element theory, the three-dimensional model of the gear is designed on this platform, and the bending and contact finite element analysis of the gear teeth is carried out, the bending stress and contact stress of the gears are obtained, which provides a basis for the parameter design and reliability of the gears. Secondly, a neural network algorithm is introduced to predict and analyse the impact of damage data of heavy-duty gear body. Finally, the effectiveness of the proposed algorithm is verified by simulation experiments.

Keywords: finite element; neural network; heavy-duty gear body; destruction prevention.

DOI: 10.1504/IJICA.2020.107116

International Journal of Innovative Computing and Applications, 2020 Vol.11 No.2/3, pp.73 - 78

Received: 08 Mar 2019
Accepted: 29 Apr 2019
Published online: 04 May 2020 *

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