Modelling of the superplastic deformation of the near-a titanium alloy (Ti-2.5AL-1.8MN) using arrhenius-type constitutive model and artificial neural network

Ahmed Mosleh, Anastasia Mikhaylovskaya, Anton Kotov, Theo Pourcelot, Sergey Aksenov, James Kwame, Vladimir Portnoy

Research output: Contribution to journalArticle

25 Citations (Scopus)
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Abstract

The paper focuses on developing constitutive models for superplastic deformation behaviour of near-α titanium alloy (Ti-2.5Al-1.8Mn) 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-αTitanium alloy (Ti-2.5Al-1.8Mn) at superplastic forming than the Arrhenius-type constitutive model. However, the tested results revealed that the error for the artificial neural-network is higher than the case of Arrhenius-type constitutive model for predicting the unmodelled conditions.

Original languageEnglish
Article number568
Number of pages15
JournalMetals
Volume7
Issue number12
DOIs
Publication statusPublished - 15 Dec 2017

Keywords

  • activation energy
  • arrhenius-type constitutive equation
  • artificial neural network
  • constitutive modelling
  • superplasticity
  • titanium alloy

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