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The use of ANN to predict the hot deformation behavior of AA7075 at low strain rates
Jenab, A ; Sharif University of Technology | 2013
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- Type of Document: Article
- DOI: 10.1007/s11665-012-0332-y
- Publisher: 2013
- Abstract:
- In this study, artificial neural network (ANN) was used to model the hot deformation behavior of 7075 aluminum alloy during compression test, in the strain rate range of 0.0003-1 s-1 and temperature range of 200-450 C. The inputs of the model were temperature, strain rate, and strain, while the output of the model was the flow stress. The feed-forward back-propagation network with two hidden layers was built and successfully trained at different deformation domains by Levenberg-Marquardt training algorithm. Comparative analysis of the results obtained from the hyperbolic sine, the power law constitutive equations, and the ANN shows that the newly developed ANN model has a better performance in predicting the hot deformation behavior of 7075 aluminum alloy
- Keywords:
- Artificial neural network ; Flow stress ; Hot deformation ; 7075 aluminum alloy ; Better performance ; Comparative analysis ; Deformation domain ; Feedforward backpropagation ; Hot deformation behaviors ; Levenberg-Marquardt training algorithm ; Power-law constitutive equation ; Compression testing ; Hot working ; Neural networks ; Plastic flow ; Strain rate
- Source: Journal of Materials Engineering and Performance ; Volume 22, Issue 3 , 2013 , Pages 903-910 ; 10599495 (ISSN)
- URL: http://link.springer.com/article/10.1007%2Fs11665-012-0332-y