Modelling of the Superplastic Deformation of the Near-α Titanium Alloy (Ti-2.5Al-1.8Mn) Using Arrhenius-Type Constitutive Model and Artificial Neural Network

Metals

James Kwame¹  Anastasia Mikhaylovskaya²  Vladimir Portnoy²  Ahmed Mosleh²  Theo Pourcelot²  Anton Kotov²  Sergey Aksenov³ 

[1] Advanced Forming Research Centre, University of Strathclyde, 85 Inchinnan Dr, Inchinnan, Renfrew PA4 9LJ, UK;Department of Physical Metallurgy of Non-Ferrous Metals, National University of Science and Technology “MISiS”, Leninsky Prospekt, 4, 119049 Moscow, Russia;Moscow Institute of Electronics and Mathematics, National Research University Higher School of Economics, Tallinskaya 34, 123458 Moscow, Russia;

关键词: superplasticity;    titanium alloys;    constitutive modelling;    arrhenius-type constitutive equation;    artificial neural network;    activation energy;   

DOI  :  10.3390/met7120568

来源: DOAJ

【 摘 要 】

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-α Titanium 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.

【 授权许可】

Unknown