Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : Fatigue life


Prediction Fatigue Life of Aluminum Alloy 7075 T73 Using Neural Networks and Neuro-Fuzzy Models

Mustafa S. Abdullatef; Nazhat . AlRazzaq; Mustafa M. Hasan

Engineering and Technology Journal, 2016, Volume 34, Issue 2, Pages 272-283

In present paper the fatigue life of aluminum alloy 7075 T73 under constant amplitude loading is predicted using ANN and ANFIS models. Many neural networks models are used for this purpose and also different neuro-fuzzy models are built for predict fatigue life.Theclassical power law formula ismost common used to find fatigue behaviors of materials. In present study, two techniques are used to find coefficients of the formula linear and nonlinear regression. Forcomparison the fatigue life curves of soft computing methods are plotted together with two conventionalmethods. The neural network and neuro-fuzzy models give good results compared with two conventional methods. Also it is shown thatneural network model which is trained using Levenberg-Marquardt algorithm is best neural network modelscompared with other NNS models.Also, it is foundANFIS models with input trapezoidal membership function is best performance from other membership function types to predict fatigue life. It can be stated that neuro-fuzzy models are better models than neural network and conventional methods to predict fatigue life of the maintained alloy.

Analysis the Effects of Shot Peening Upon the Mechanical and Fatigue Properties of 2024-T351 Al-Alloy

Alalkawi H. J. M; Talal Abed-Aljabar; Safaa H. Alokaidi

Engineering and Technology Journal, 2012, Volume 30, Issue 1, Pages 1-12

This paper presents an experimental study on the effect of shot peening on
mechanical properties and residual stresses of 2024-T351 Aluminum alloy. Under the
effects of shot peening time SPT the results show that the existence of SPT can
improve the mechanical properties and fatigue life up to a limit value of SPT. The 15
minutes SPT gave the highest value of (σu, σy) which is about 6.7 % for (σu) and 11.7
% for (σy). Empirical equations were proposed to evaluate the SPT with the
endurance limit stress and the residual stresses.

Estimation of Fatigue Life Components By Proposed Mathematical Model

Engineering and Technology Journal, 2010, Volume 28, Issue 19, Pages 922-932

In this study the fatigue behavior of an aluminum alloy designated 2024 – T3
under constant and variable amplitude of stresses is considered. The applied load
adopted is a rotating bending one, the cross Section of the laboratory samples is
circular with a diameter of (6.74mm). All tests were carried out under a stress ratio
of R = - 1 and at room temperature condition. The study consists of two parts
experimental and theoretical. The experimental part includes carrying out
laboratory tests on two groups of specimens the first group was tested under
constant stress amplitude to establish the S-N curve of the specimen's material,
while the second group was tested under variable amplitude of stress to assess the
effects of the accumulated fatigue damage. The theoretical part of the study
includes a review of previous literature adopted to derive a theoretical and
mathematical model depending upon the variation of the stresses obtainedby
some previous theories, taking into consideration low and high stress levels, and
even post yield.
The derived model is denoted as elastic-plastic model for the evaluation of life
time of machinery parts. The linear theory of Miner and the theory of Elastic
Cracks Propagation are also studied throughout the theoretical part of the study.
In order to assess the capability of the two theories with the derived model: a
comparison is held between the experimented results and the results obtained by
applying the two theories.
It is noted that results obtained by applying the two theories are lower
(underestimates) than those obtained from the experimental study and that results
obtained by the suggested derived model are in better agreement than those
obtained by the two theories.