Title of article :
Finite element and artificial neural network analysis of ECAP
Author/Authors :
Esmailzadeh، نويسنده , , M. and Aghaie-Khafri، نويسنده , , M.، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2012
Pages :
7
From page :
127
To page :
133
Abstract :
Equal channel angular pressing (ECAP) is the most promising among the developed severe plastic deformation (SPD) techniques to induce strain in bulk metals. In this study finite element method (FEM) and artificial neural network (ANN) were used to simulate ECAP deformation of AA2024 aluminum alloy. The results show that the equivalent plastic strains are not uniform and the deformation inhomogeneity indexes and the location of maximum equivalent plastic strain are varied with the increasing friction coefficient. Moreover, the area over which friction acts and hence the total accumulated friction force is reduced when the billet length is reduced. The FEM and ANN results were in good agreement with experimental measurements.
Keywords :
Artificial neural network , Equal channel angular pressing , Strain measurements , Finite element model , Aluminum alloys
Journal title :
Computational Materials Science
Serial Year :
2012
Journal title :
Computational Materials Science
Record number :
1689818
Link To Document :
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