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Author Ji, Guoliang ♦ Yang, Guang ♦ Li, Lei ♦ Li, Qiang
Source SpringerLink
Content type Text
Publisher Springer US
File Format PDF
Copyright Year ©2014
Language English
Subject Domain (in DDC) Technology ♦ Engineering & allied operations
Subject Keyword artificial neural network ♦ constitutive model ♦ Cu-0.4 Mg alloy ♦ deformation behavior ♦ Characterization and Evaluation of Materials ♦ Tribology, Corrosion and Coatings ♦ Quality Control, Reliability, Safety and Risk ♦ Engineering Design
Abstract For predicting the high-temperature deformation behavior in a Cu-0.4 Mg alloy, the true stress-strain data from isothermal hot compression tests on a Gleeble-1500 thermo-mechanical simulator, in a wide range of temperatures (500, 600, 700, 750, and 800 °C) and strain rates (0.005, 0.01, 0.1, 1, 5, and 10 s$^{−1}$), were employed to develop the Arrhenius-type constitutive model and the artificial neural network (ANN) constitutive model. Furthermore, prediction ability of the two models for high-temperature deformation behavior was evaluated. Correlation coefficients (R) between the experimental and predicted flow stress for the Arrhenius-type constitutive model and the ANN constitutive model are 0.9860 and 0.9998, respectively, and average absolute relative errors between the experimental and predicted flow stress for these two models are 5.3967% and 0.7401%, respectively. Results show that the ANN constitutive model can accurately predict the high-temperature deformation behavior over a wider range of temperatures and strain rates, while for the Arrhenius-type constitutive model there is greater divergence in the regime of high strain rates and low temperatures.
ISSN 10599495
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2014-02-27
Publisher Place Boston
e-ISSN 15441024
Journal Journal of Materials Engineering and Performance
Volume Number 23
Issue Number 5
Page Count 10
Starting Page 1770
Ending Page 1779

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Source: SpringerLink