Prediction fatigue life of aluminum alloy 7075 T73 using neural networks and neuro-fuzzy models

Abstract EN

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.

The classical power law formula is most 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.

For comparison the fatigue life curves of soft computing methods are plotted together with two conventional methods.

The neural network and neuro-fuzzy models give good results compared with two conventional methods.

Also it is shown that neural network model which is trained using Levenberg-Marquardt algorithm is best neural network models compared with other NNS models.

Also, it is found ANFIS 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.