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

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.

[1]  K. P. N. Murthy,et al.  Constitutive flow behaviour of austenitic stainless steels under hot deformation: artificial neural network modelling to understand, evaluate and predict , 2006 .

[2]  N. Parson,et al.  Effect of homogenization treatment and silicon content on the microstructure and hot workability of dilute Al–Fe–Si alloys , 2014 .

[3]  G. David Garson,et al.  Interpreting neural-network connection weights , 1991 .

[4]  H. J. McQueen,et al.  Constitutive analysis in hot working , 2002 .

[5]  Mohammad Habibi Parsa,et al.  Constitutive equations for elevated temperature flow behavior of commercial purity aluminum , 2012 .

[6]  S. Venugopal,et al.  Artificial neural network modeling to evaluate and predict the deformation behavior of stainless steel type AISI 304L during hot torsion , 2009, Appl. Soft Comput..

[7]  N. Haghdadi,et al.  Artificial neural network modeling to predict the hot deformation behavior of an A356 aluminum alloy , 2013 .

[8]  J. Franklin,et al.  The elements of statistical learning: data mining, inference and prediction , 2005 .

[9]  M. E. Kassner,et al.  Energy dissipation efficiency in aluminum dependent on monotonic flow curves and dynamic recovery , 1995 .

[10]  C. M. Sellars,et al.  Development of constitutive equations for modelling of hot rolling , 2000 .

[11]  N. Parson,et al.  Hot deformation behavior and rate-controlling mechanism in dilute Al–Fe–Si alloys with minor additions of Mn and Cu , 2015 .

[12]  W. Blum,et al.  Dynamic recovery: sufficient mechanism in the hot deformation of Al (<99.99) , 2000 .

[13]  J. Jonas,et al.  Solute drag effects during the dynamic recrystallization of nickel , 1999 .

[14]  Qinglong Zhao,et al.  Comparison of the influence of Si and Fe in 99.999% purity aluminum and in commercial-purity aluminum , 2012 .

[15]  F. J. Humphreys,et al.  Recrystallization and Related Annealing Phenomena , 1995 .

[16]  H. J. McQueen,et al.  Metal forming: Industrial, mechanical computational and microstructural , 1993 .

[17]  Fahrettin Ozturk,et al.  Flow curve prediction of Al-Mg alloys under warm forming conditions at various strain rates by ANN , 2011, Appl. Soft Comput..

[18]  E. Evangelista.  Hot Deformation and Processing of Aluminum Alloys , 2011 .

[19]  Y. C. Lin,et al.  A phenomenological constitutive model for high temperature flow stress prediction of Al–Cu–Mg alloy , 2012 .

[20]  Amin H. Almasri,et al.  A physically based constitutive model for fcc metals with applications to dynamic hardness , 2008 .

[21]  V. Vemuri,et al.  Artificial neural networks: an introduction , 1988 .

[22]  G. R. Johnson,et al.  A CONSTITUTIVE MODEL AND DATA FOR METALS SUBJECTED TO LARGE STRAINS, HIGH STRAIN RATES AND HIGH TEMPERATURES , 2018 .

[23]  J. Kaufman.  Introduction to Aluminum Alloys and Tempers , 2000 .

[24]  Yunlian Qi,et al.  Development of constitutive relationship model of Ti600 alloy using artificial neural network , 2010 .

[25]  Yu Sun,et al.  A hybrid approach for processing parameters optimization of Ti-22Al-25Nb alloy during hot deformation using artificial neural network and genetic algorithm , 2011 .

[26]  P. Robi,et al.  High temperature deformation behavior of Al–Cu–Mg alloys micro-alloyed with Sn , 2010 .

[27]  R. Armstrong,et al.  Dislocation-mechanics-based constitutive relations for material dynamics calculations , 1987 .

[28]  Russell G. Death,et al.  An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data , 2004 .

[29]  N. Parson,et al.  Effect of Iron and Silicon Content on the Hot Compressive Deformation Behavior of Dilute Al-Fe-Si Alloys , 2014, Journal of Materials Engineering and Performance.

[30]  M. Spittel,et al.  Flow stress of light metal alloys , 2011 .

[31]  Julian D. Olden,et al.  Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks , 2002 .

[32]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[33]  I. Jalham.  Modeling capability of the artificial neural network (ANN) to predict the effect of the hot deformation parameters on the strength of Al-base metal matrix composites , 2003 .

[34]  Tzu-Tsung Wong,et al.  Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation , 2015, Pattern Recognit..

[35]  Y. Lin,et al.  A critical review of experimental results and constitutive descriptions for metals and alloys in hot working , 2011 .

[36]  Q. Pan,et al.  Artificial Neural Network Modeling to Evaluate and Predict the Deformation Behavior of ZK60 Magnesium Alloy During Hot Compression , 2010 .

[37]  E. Nes,et al.  Strengthening mechanisms in solid solution aluminum alloys , 2006 .

[38]  Jiao Luo,et al.  Constitutive model for high temperature deformation of titanium alloys using internal state variables , 2010 .

[39]  Q. Pan,et al.  Artificial neural network prediction to the hot compressive deformation behavior of Al–Cu–Mg–Ag heat-resistant aluminum alloy , 2011 .