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Naji, M. |
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Motta, Antonella |
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Aletan, Dirar |
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Mohamed, Tarek |
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Ertürk, Emre |
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Taccardi, Nicola |
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Kononenko, Denys |
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Petrov, R. H. | Madrid |
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Alshaaer, Mazen | Brussels |
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Bih, L. |
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Casati, R. |
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Muller, Hermance |
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Kočí, Jan | Prague |
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Šuljagić, Marija |
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Kalteremidou, Kalliopi-Artemi | Brussels |
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Azam, Siraj |
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Ospanova, Alyiya |
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Blanpain, Bart |
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Ali, M. A. |
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Popa, V. |
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Rančić, M. |
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Ollier, Nadège |
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Azevedo, Nuno Monteiro |
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Landes, Michael |
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Rignanese, Gian-Marco |
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Senthilkumar, V.
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (10/10 displayed)
- 2023Thermal Adsorption and Corrosion Characteristic Study of Copper Hybrid Nanocomposite Synthesized by Powder Metallurgy Routecitations
- 2021EFFECTS OF PARTICLE SIZE AND SINTERING TEMPERATURE ON SUPERELASTICITY BEHAVIOR OF NiTi SHAPE MEMORY ALLOY USING NANOINDENTATIONcitations
- 2021Generative Design and Topology Optimization of Analysis and Repair Work of Industrial Robot Arm Manufactured Using Additive Manufacturing Technologycitations
- 2014Modelling and Analysis of Electrical Discharge Alloying through Taguchi Techniquecitations
- 2014Development of carbide intermetallic layer by electric discharge alloying on AISI-D2 tool steel and its wear resistancecitations
- 2012Mathematical Modeling of Machining Parameters in Electrical Discharge Machining with Cu-B<sub>4</sub>C Composite Electrodecitations
- 2012Prediction of flow stress during hot deformation of MA'ed hybrid aluminium nanocomposite employing artificial neural network and Arrhenius constitutive modelcitations
- 2011Constitutive Modeling for the Prediction of Peak Stress in Hot Deformation Processing of Al Alloy Based Nanocompositecitations
- 2008Influence of titanium carbide particles addition on the forging behaviour of powder metallurgy composite steelscitations
- 2007Some Aspects on Hot Forging Features of P/M Sintered High-Strength Titanium Carbide Composite Steel Preforms Under Different Stress State Conditionscitations
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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>