Artificial neural network prediction to the hot compressive deformation behavior of Al–Cu–Mg–Ag heat-resistant aluminum alloy

Abstract The behavior of the flow stress of Al–Cu–Mg–Ag heat-resistant aluminum alloys during hot compression deformation was studied by thermal simulation test. The temperature and the strain rate during hot compression were 340–500 °C, 0.001 s−1 to 10 s−1, respectively. Constitutive equations and an artificial neural network (ANN) model were developed for the analysis and simulation of the flow behavior of the Al–Cu–Mg–Ag alloys. The inputs of the model are temperature, strain rate and strain. The output of the model is the flow stress. Comparison between constitutive equations and ANN results shows that ANN model has a better prediction power than the constitutive equations.

[1]  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..

[2]  H. Mcqueen,et al.  New formula for calculating flow curves from high temperature constitutive data for 300 austenitic steels , 1992 .

[3]  John G. Lenard,et al.  Using neural networks to predict parameters in the hot working of aluminum alloys , 1999 .

[4]  I. J. Polmear,et al.  Design and development of an experimental wrought aluminum alloy for use at elevated temperatures , 1988 .

[5]  M. P. Phaniraj,et al.  The applicability of neural network model to predict flow stress for carbon steels , 2003 .

[6]  Hitoshi Fujimoto,et al.  Modelling on flow stress of Mg–Al–Zn alloys at elevated temperatures , 1998 .

[7]  J. Jiménez,et al.  Microstructure and creep behaviour of an Osprey processed and extruded Al–Cu–Mg–Ti–Ag alloy , 2007 .

[8]  Kamineni Pitcheswara Rao,et al.  Assessment of simple flow-stress relationships using literature data for a range of steels , 1992 .

[9]  Ashu Jain,et al.  A comparative analysis of training methods for artificial neural network rainfall-runoff models , 2006, Appl. Soft Comput..

[10]  U. F. Kocks,et al.  A constitutive description of the deformation of copper based on the use of the mechanical threshold stress as an internal state variable , 1988 .

[11]  A. Taheri,et al.  The prediction of hot flow behavior of Al–6%Mg alloy , 2009 .

[12]  John J. Jonas,et al.  Prediction of steel flow stresses at high temperatures and strain rates , 1991 .

[13]  Xiaoming He,et al.  A method to predict flow stress considering dynamic recrystallization during hot deformation , 2008 .

[14]  J. Zhong,et al.  Application of neural networks to predict the elevated temperature flow behavior of a low alloy steel , 2008 .

[15]  Ahmad Mayyas,et al.  Prediction of density, porosity and hardness in aluminum–copper-based composite materials using artificial neural network , 2009 .