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
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