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Title

Fatigue life prediction under random loading conditions in 7475-T7351 aluminum alloy using the RMS model
Authors

S. T. Kim
D Tadjiev
H. T. Yang
Journal / Anthology

INTERNATIONAL JOURNAL OF DAMAGE MECHANICS
Year: 2006
Volume: 15
Issue: 1
Page range: 89-102
Description

This article is concerned with the fatigue life prediction in specimens of 7475-T7351 high strength aluminum alloy subjected to random fatigue loading. Fatigue life predictions are made using the root mean square model. This model is chosen because it has been defined as the most simple and effective one for fatigue life prediction in the components subjected to random loading by the authors of this article. The analysis procedure used in this study is relatively simple. The loading history for each specimen is analyzed to determine the root mean square maximum and minimum stresses and predictions are then made by assuming that the tests have been conducted under constant amplitude loading at the root mean square maximum and minimum stresses. The ratios of the predicted lives range from 3.22 to 1.52. These ratios are fairly good considering that the normal scatter in fatigue crack growth rates may range from a factor of two to four under identical load conditions. Moreover, an attempt has been made to improve prediction procedure using Forman's equation applied in the root mean square model. While using the improved prediction procedure, the ratios of the predicted lives range from 1.35 to 0.62 (e.g., error bound is reduced almost five times: from 222 to 48). Only relatively simple computer programs (Microsoft Excel for load history analysis and Mathematica for performing calculations) and a desktop computer are employed to make predictions. Improved prediction procedure allows more precise prediction of fatigue life as well as helps to obtain better prediction ratios but further experimental work should be performed to verify the validity of the attempt.
Subject

*Engineering > Mechanical and Structural Engineering
Keywords

fatigue crack grown prediction, RMS model, root mean square, aluminum alloy, fatigue of materials