Proceedings of the 4th 2016 International Conference on Material Science and Engineering (ICMSE 2016)

Structural Fatigue Life Prediction Based on ANSYS Random Vibration Analysis

Authors
Song Tao, Bin Chen, Xing-Jun Fan
Corresponding Author
Song Tao
Available Online December 2016.
DOI
10.2991/icmse-16.2016.39How to use a DOI?
Keywords
Life prediction, ANSYS, Random vibration, Linear cumulative damage theory, Three interval method
Abstract

Fatigue failure is one of the main reasons for the engineering structure's failure, and it is of great significance to predict fatigue life of structure. This paper briefly introduced the principle of stochastic fatigue failure calculation analysis, andÿanalyzed random vibration of aÿT-shaped structure by using ANSYS software. Under a given load combined with linear cumulative damage theory and three interval method proposed by Steinberg, the fatigue life of the structure is predicted.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

Volume Title
Proceedings of the 4th 2016 International Conference on Material Science and Engineering (ICMSE 2016)
Series
Advances in Engineering Research
Publication Date
December 2016
ISBN
10.2991/icmse-16.2016.39
ISSN
2352-5401
DOI
10.2991/icmse-16.2016.39How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Song Tao
AU  - Bin Chen
AU  - Xing-Jun Fan
PY  - 2016/12
DA  - 2016/12
TI  - Structural Fatigue Life Prediction Based on ANSYS Random Vibration Analysis
BT  - Proceedings of the 4th 2016 International Conference on Material Science and Engineering (ICMSE 2016)
PB  - Atlantis Press
SP  - 235
EP  - 238
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmse-16.2016.39
DO  - 10.2991/icmse-16.2016.39
ID  - Tao2016/12
ER  -