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Modelling of the Superplastic Deformation of the Near-α Titanium Alloy (Ti-2.5 Al-1.8 Mn) Using Arrhenius-Type Constitutive Model and Artificial Neural Network
Metals • 2017
Publication Information
Authors
Ahmed Mosleh, Anastasia Mikhaylovskaya, Anton Kotov, Theo Pourcelot, Sergey Aksenov, James Kwame, Vladimir Portnoy
Keywords
superplasticity; titanium alloys; constitutive modelling; arrhenius-type constitutive equation; artificial neural network; activation energy
Journal
Metals
Publisher
Multidisciplinary Digital Publishing Institute
Volume
7
Issue
12
Pages
568
publication.type
International
Paper Link
Open Link
Supplementary Materials
Not Available
Abstract
The paper focuses on developing constitutive models for superplastic deformation
behaviour of near-α titanium alloy (Ti-2.5 Al-1.8 Mn) at elevated temperatures in a range
from 840 to 890 C and in a strain rate range from 2× 10− 4 to 8× 10− 4 s− 1. Stress–strain
experimental tensile tests data were used to develop the mathematical models. Both,
hyperbolic sine Arrhenius-type constitutive model and artificial neural-network model were
constructed. A comparative study on the competence of the developed models to predict the
superplastic deformation behaviour of this alloy was made. The fitting results suggest that
the artificial neural-network model has higher accuracy and is more efficient in fitting the
superplastic deformation flow behaviour of near-α Titanium alloy (Ti-2.5 Al-1.8 Mn) at
superplastic forming than the Arrhenius-type constitutive model. However, the tested
behaviour of near-α titanium alloy (Ti-2.5 Al-1.8 Mn) at elevated temperatures in a range
from 840 to 890 C and in a strain rate range from 2× 10− 4 to 8× 10− 4 s− 1. Stress–strain
experimental tensile tests data were used to develop the mathematical models. Both,
hyperbolic sine Arrhenius-type constitutive model and artificial neural-network model were
constructed. A comparative study on the competence of the developed models to predict the
superplastic deformation behaviour of this alloy was made. The fitting results suggest that
the artificial neural-network model has higher accuracy and is more efficient in fitting the
superplastic deformation flow behaviour of near-α Titanium alloy (Ti-2.5 Al-1.8 Mn) at
superplastic forming than the Arrhenius-type constitutive model. However, the tested