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Materials 2016, 9(7), 536; doi:10.3390/ma9070536

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

1
Department of Applied Science, University of Québec at Chicoutimi, Saguenay, QC G7H 2B1, Canada
2
Arvida Research and Development Centre, Rio Tinto Alcan, Saguenay, QC G7S 4K8, Canada
*
Author to whom correspondence should be addressed.
Academic Editor: Javier Narciso
Received: 28 March 2016 / Revised: 24 June 2016 / Accepted: 28 June 2016 / Published: 30 June 2016
View Full-Text   |   Download PDF [4487 KB, uploaded 30 June 2016]   |  

Abstract

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 temperatures (400 °C–550 °C) 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. View Full-Text
Keywords: 1xxx aluminum alloys; hot deformation; flow stress prediction; artificial neural network modeling; sensitivity analysis 1xxx aluminum alloys; hot deformation; flow stress prediction; artificial neural network modeling; sensitivity analysis
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Shakiba, M.; Parson, N.; Chen, X.-G. 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 2016, 9, 536.

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