Title of article :
ANN constitutive model for high strain-rate deformation of Al 7075-T6
Author/Authors :
Jamal Sheikh-Ahmad، نويسنده , , Janet Twomey، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2007
Pages :
7
From page :
339
To page :
345
Abstract :
An artificial neural network (ANN) constitutive model was developed for Al 7075-T6 based on flow data found in the literature and orthogonal machining tests. The use of orthogonal machining data allowed the ANN network to be trained and tested at high strain-rates of deformation common in machining operations. A new ANN method of network construction (training and validation) was successfully applied to the sparse high strain-rate regime. The method of training and validation, 0.632e stop training method, requires less experimentation to determine network parameters and makes the most efficient use of scarce data. The ANN predictions at high strain-rates where compared with and shown to be superior to a parametric constitutive model.
Keywords :
Constitutive model , Neural networks , 0.632e error , Al 7075 , High strain-rate
Journal title :
Journal of Materials Processing Technology
Serial Year :
2007
Journal title :
Journal of Materials Processing Technology
Record number :
1180825
Link To Document :
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