Modelling of the hot deformation behaviour of a titanium alloy using constitutive equations and artificial neural network

Zhao, J; Ding, H; Zhao, W; Huang, M; Wei, D; Jiang, Z

Modelling of the hot deformation behaviour of a titanium alloy using constitutive equations and artificial neural network

Zhao, J Ding, H Zhao, W Huang, M Wei, D

Jiang, Z

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Publication Type:: Journal Article
Citation:: Computational Materials Science, 2014, 92 pp. 47 - 56
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Zhao, J	en_US
dc.contributor.author	Ding, H	en_US
dc.contributor.author	Zhao, W	en_US
dc.contributor.author	Huang, M	en_US
dc.contributor.author	Wei, D https://orcid.org/0000-0002-4247-8905	en_US
dc.contributor.author	Jiang, Z	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Computational Materials Science, 2014, 92 pp. 47 - 56	en_US
dc.identifier.issn	0927-0256	en_US
dc.identifier.uri	http://hdl.handle.net/10453/35326
dc.description.abstract	Hot deformation characteristics of a Ti600 titanium alloy were investigated by a Gleeble 1500D thermo-mechanical test simulator over the temperature range from 760 to 920 °C and strain rate range from 0.01 to 10 s-1. The flow behaviour and microstructural evolution were studied. Dynamic recrystallisation (DRX) grains exhibit different shapes at different deformation temperatures, and more severe distortion around the DRX grains exists in the matrix deformed at low temperature compared with that at relatively high temperature. Phenomenological and empirical constitutive equations were established based on the hyperbolic sine Arrhenius-type model and the multiple-linear regression, respectively. An artificial neural network (ANN) model was developed to predict the flow stress. A comparative study was made on the capability of the developed models to represent the hot deformation behaviour of this alloy. The results indicate that the flow stress of Ti600 alloy is sensitively dependent on the strain rate and deformation temperature. The multiple-linear model shows a higher accuracy in tracking the flow behaviour of Ti600 alloy than the Arrhenius-type model. The ANN model is much more efficient and has higher accuracy in predicting the hot deformation flow behaviour of Ti600 alloy than both the Arrhenius-type and multiple-linear models. © 2014 Elsevier B.V. All rights reserved.	en_US
dc.relation.ispartof	Computational Materials Science	en_US
dc.relation.isbasedon	10.1016/j.commatsci.2014.05.040	en_US
dc.subject.classification	Materials	en_US
dc.title	Modelling of the hot deformation behaviour of a titanium alloy using constitutive equations and artificial neural network	en_US
dc.type	Journal Article
utslib.citation.volume	92	en_US
utslib.for	0912 Materials Engineering	en_US
utslib.for	0913 Mechanical Engineering	en_US
utslib.for	0204 Condensed Matter Physics	en_US
utslib.for	0205 Optical Physics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	92	en_US

Abstract:

Hot deformation characteristics of a Ti600 titanium alloy were investigated by a Gleeble 1500D thermo-mechanical test simulator over the temperature range from 760 to 920 °C and strain rate range from 0.01 to 10 s-1. The flow behaviour and microstructural evolution were studied. Dynamic recrystallisation (DRX) grains exhibit different shapes at different deformation temperatures, and more severe distortion around the DRX grains exists in the matrix deformed at low temperature compared with that at relatively high temperature. Phenomenological and empirical constitutive equations were established based on the hyperbolic sine Arrhenius-type model and the multiple-linear regression, respectively. An artificial neural network (ANN) model was developed to predict the flow stress. A comparative study was made on the capability of the developed models to represent the hot deformation behaviour of this alloy. The results indicate that the flow stress of Ti600 alloy is sensitively dependent on the strain rate and deformation temperature. The multiple-linear model shows a higher accuracy in tracking the flow behaviour of Ti600 alloy than the Arrhenius-type model. The ANN model is much more efficient and has higher accuracy in predicting the hot deformation flow behaviour of Ti600 alloy than both the Arrhenius-type and multiple-linear models. © 2014 Elsevier B.V. All rights reserved.

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http://hdl.handle.net/10453/35326