Predicting of mechanical properties of Fe–Mn–(Al, Si) TRIP/TWIP steels using neural network modeling

In this work, an artificial neural network (ANN) model was established in order to predict the mechanical properties of transformation induced plasticity/twinning induced plasticity (TRIP/TWIP) steels. The model developed in this study was consider the contents of Mn (15–30 wt%), Si (2–4 wt%) and Al...

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Bibliographic Details
Published in:Computational materials science 2009-06, Vol.45 (4), p.959-965
Main Authors: Dini, G., Najafizadeh, A., Monir-Vaghefi, S.M., Ebnonnasir, A.
Format: Article
Language:English
Subjects:
Artificial neural network (ANN)
Condensed matter: structure, mechanical and thermal properties
Deformation and plasticity (including yield, ductility, and superplasticity)
Exact sciences and technology
Mechanical and acoustical properties of condensed matter
Mechanical properties
Mechanical properties of solids
Physics
Steel
TRIP/TWIP
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Summary:In this work, an artificial neural network (ANN) model was established in order to predict the mechanical properties of transformation induced plasticity/twinning induced plasticity (TRIP/TWIP) steels. The model developed in this study was consider the contents of Mn (15–30 wt%), Si (2–4 wt%) and Al (2–4 wt%) as inputs, while, the total elongation, yield strength and tensile strength are presented as outputs. The optimal ANN architecture and training algorithm were determined. Comparing the predicted values by ANN with the experimental data indicates that trained neural network model provides accurate results.
ISSN:0927-0256
1879-0801
DOI:10.1016/j.commatsci.2008.12.015