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Analyses of Full-load, Modal, and Fatigue Life of Electric Motorcycle Frame Using Finite Element Software ANSYS.

Authors :
Hong-Mei Dai
Bo-Hao Chen
Chao-Ming Hsu
Chiang-Lung Lin
Cheng-Fu Yang
Source :
Sensors & Materials; 2023, Vol. 35 Issue 8, Part 1, p2817-2829, 13p
Publication Year :
2023

Abstract

There are two methods of analyzing the effects of various loads on the full-load, modal, and fatigue life of an electric motorcycle frame. The first method involves attaching stress sensors to the frame, and the second method utilizes finite element method (FEM) software for simulation. In this study, the simulation method was employed, and this paper details the creation of an analysis system for an electric motorcycle frame using FEM software tools such as Visual Basic and ANSYS parametric design languages. By inputting geometric models and parameters, the system generated LOG files to create FEM models of the motorcycle frame. These LOG files were then imported into ANSYS for structural analysis, which included full-load analysis, modal analysis, and fatigue life analysis. By utilizing the aforementioned models, users were able to easily perform full-load, modal, and fatigue life analyses of the electric motorcycle frame by inputting the necessary parameters into function tables. During the full-load analysis, various forces were applied at different positions to determine the maximum stress, deformation, and strain values. The modal analysis of the electric motorcycle frame involved determining the first through sixth orders of the natural vibration frequencies and modes (mode shape), as well as the vibration frequencies and modes under full load. In the fatigue life analysis, a fatigue tool was used to apply forces ranging from 50 to 200% of the full-load boundary condition. Additionally, the relationship between the number of cycles to failure (N) and the applied stress amplitude was examined. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09144935
Volume :
35
Issue :
8, Part 1
Database :
Complementary Index
Journal :
Sensors & Materials
Publication Type :
Academic Journal
Accession number :
170378215
Full Text :
https://doi.org/10.18494/SAM4461