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Modeling the Effects of Cu content and deformation variables on the high-temperature flow behavior of dilute Al-Fe-Si alloys using an artificial neural network

Shakiba Mohammad, Parson Nick et Chen X-Grant. (2016). Modeling the Effects of Cu content and deformation variables on the high-temperature flow behavior of dilute Al-Fe-Si alloys using an artificial neural network. Materials, 9, (7), p. 536.

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URL officielle: http://dx.doi.org/doi:10.3390/ma9070536

Résumé

The hot deformation behavior of Al-0.12Fe-0.1Si alloys with varied amounts of Cu (0.002–0.31 wt %) was investigated by uniaxial compression tests conducted at different températures (400 oC–550 oC) and strain rates (0.01–10 s -1). The results demonstrated that flow stress decreased with increasing deformation temperature and decreasing strain rate, while flow stress increased with increasing Cu content for all deformation conditions studied due to the solute drag effect. Based on the experimental data, an artificial neural network (ANN) model was developed to study the relationship between chemical composition, deformation variables and high-temperature flow behavior. A three-layer feed-forward back-propagation artificial neural network with 20 neurons in a hidden layer was established in this study. The input parameters were Cu content, temperature, strain rate and strain, while the flow stress was the output. The performance of the proposed model was evaluated using the K-fold cross-validation method. The results showed excellent generalization capability of the developed model. Sensitivity analysis indicated that the strain rate is the most important parameter, while the Cu content exhibited a modest but significant influence on the flow stress.

Type de document:Article publié dans une revue avec comité d'évaluation
Volume:9
Numéro:7
Pages:p. 536
Version évaluée par les pairs:Oui
Date:2016
Sujets:Sciences naturelles et génie > Génie > Génie des matériaux et génie métallurgique
Département, module, service et unité de recherche:Départements et modules > Département des sciences appliquées > Module d'ingénierie
Mots-clés:1xxx aluminum alloys, hot deformation, flow stress prediction, artificial neural network modeling, sensitivity analysis, Alliages d'aluminium 1xxx, déformation à chaud, prévision de contraintes d'écoulement, réseau neuronal artificiel, modélisation, analyse de sensibilité
Déposé le:24 août 2017 13:49
Dernière modification:24 août 2017 13:49
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