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A framework for fatigue life prediction of materials under the multi-level cyclic loading.

Authors :
Gao, Jianxiong
Yuan, Yiping
Xu, Rongxia
Source :
Engineering Failure Analysis. Sep2021, Vol. 127, pN.PAG-N.PAG. 1p.
Publication Year :
2021

Abstract

• A conditional PDF model is presented to characterize fatigue life distribution under CAC loading. • A fatigue damage accumulation model is developed to account for the loading sequence effects. • The fatigue damage accumulation rule of material under the multi-level cyclic loading is elaborated. • A general framework is proposed to predict fatigue life under the multi-level cyclic loading. Fatigue life prediction of materials under the cyclic loading is still a particularly challenging task remains to be resolved. This study aims to develop a generic framework for fatigue life prediction under the multi-level cyclic (MLC) loading with consideration of the loading sequence effects. Firstly, a conditional probability density function (PDF) model is presented to quantify the fatigue life distributions of materials under any constant amplitude cyclic (CAC) loading. Subsequently, a fatigue damage accumulation model is proposed from the perspective of cumulative failure probability, which is capable of accounting for the nonlinear characteristics of damage accumulation and the loading sequence effects. Finally, a generic framework for fatigue life prediction of materials under the MLC loading is developed based on the proposed models. The fatigue life data of fiber reinforced composites under the two-level and three-level cyclic loading are utilized to verify the proposed framework. The results show that the predicted fatigue lives agree well with the fatigue test data of fiber reinforced composites. Moreover, the proposed framework can be easily extended to calculate fatigue life of materials under any MLC loading. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
13506307
Volume :
127
Database :
Academic Search Index
Journal :
Engineering Failure Analysis
Publication Type :
Academic Journal
Accession number :
151718833
Full Text :
https://doi.org/10.1016/j.engfailanal.2021.105496