Modeling the flow behavior, recrystallization, and crystallographic texture in hot-deformed Fe-30 Wt Pct Ni Austenite

Abbod, M. F., Sellars, C. M., Cizek, Pavel, Linkens, D. and Mahfouf, M. 2007, Modeling the flow behavior, recrystallization, and crystallographic texture in hot-deformed Fe-30 Wt Pct Ni Austenite, Metallurgical and materials transactions A : physical metallurgy and materials science, vol. 38, no. 10, pp. 2400-2409.


Title Modeling the flow behavior, recrystallization, and crystallographic texture in hot-deformed Fe-30 Wt Pct Ni Austenite
Author(s) Abbod, M. F.
Sellars, C. M.
Cizek, Pavel
Linkens, D.
Mahfouf, M.
Journal name Metallurgical and materials transactions A : physical metallurgy and materials science
Volume number 38
Issue number 10
Start page 2400
End page 2409
Publisher Springer
Place of publication Berlin, Germany
Publication date 2007-10
ISSN 1073-5623
1543-1940
Summary The present work describes a hybrid modeling approach developed for predicting the flow behavior, recrystallization characteristics, and crystallographic texture evolution in a Fe-30 wt pct Ni austenitic model alloy subjected to hot plane strain compression. A series of compression tests were performed at temperatures between 850 °C and 1050 °C and strain rates between 0.1 and 10 s−1. The evolution of grain structure, crystallographic texture, and dislocation substructure was characterized in detail for a deformation temperature of 950 °C and strain rates of 0.1 and 10 s−1, using electron backscatter diffraction and transmission electron microscopy. The hybrid modeling method utilizes a combination of empirical, physically-based, and neuro-fuzzy models. The flow stress is described as a function of the applied variables of strain rate and temperature using an empirical model. The recrystallization behavior is predicted from the measured microstructural state variables of internal dislocation density, subgrain size, and misorientation between subgrains using a physically-based model. The texture evolution is modeled using artificial neural networks.
Language eng
Field of Research 091207 Metals and Alloy Materials
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2007, The Minerals, Metals & Materials Society and ASM International
Persistent URL http://hdl.handle.net/10536/DRO/DU:30007554

Document type: Journal Article
Collection: Centre for Material and Fibre Innovation
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