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Ahamed, H.
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article
Prediction of flow stress during hot deformation of MA'ed hybrid aluminium nanocomposite employing artificial neural network and Arrhenius constitutive model
Abstract
<jats:sec><jats:title content-type="abstract-heading">Purpose</jats:title><jats:p>The aim of this paper is to develop a suitable artificial neural network (ANN) model that fits best in predicting the experimental flow stress values to the closet proximity for mechanically alloyed Al6063/0.75Al2O3/0.75Y2O3 hybrid nanocomposite.</jats:p></jats:sec><jats:sec><jats:title content-type="abstract-heading">Design/methodology/approach</jats:title><jats:p>The ANN model is implemented on neural network toolbox of MATLAB<jats:sup>®</jats:sup> using feed‐forward back propagation network and logsig functions. A set of 80 training data and 20 testing data were used in the ANN model. The layout of the network is arranged with three input parameters that include temperature, strain and strain rate, one hidden layer with 22 neurons and one output parameter consisting of flow stress. Flow stress was also predicted using Arrhenius constitutive model.</jats:p></jats:sec><jats:sec><jats:title content-type="abstract-heading">Findings</jats:title><jats:p>Based on the comparison of the predicted results using ANN model and Arrhenius constitutive model, it was observed that the ANN model has higher accuracy and could be used to estimate the flow stress values during hot deformation of Al6063/0.75Al2O3/0.75Y2O3 hybrid nanocomposite.</jats:p></jats:sec><jats:sec><jats:title content-type="abstract-heading">Originality/value</jats:title><jats:p>The ANN trained with feed forward back propagation algorithm developed, presents the excellent performance of flow stress prediction of Al6063/0.75Al2O3/0.75Y2O3 hybrid nanocomposite with minimum error rates.</jats:p></jats:sec>