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Journal of Materials Engineering and Performance
Erschienen in: Journal of Materials Engineering and Performance 9/2009

01.12.2009

A Model to Predict Recrystallization Kinetics in Hot Strip Rolling Using Combined Artificial Neural Network and Finite Elements

verfasst von: M. Seyed Salehi, S. Serajzadeh

Erschienen in: Journal of Materials Engineering and Performance | Ausgabe 9/2009

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Abstract

A thermo-mechanical model has been developed to establish a coupled heat conduction and plastic flow analysis in hot-rolling process. This model is capable of predicting temperature, strain, and strain rate distributions during hot rolling as well as the subsequent static recrystallization fraction and grain size changes after hot deformation. Finite element and neural network models are coupled to assess recrystallization kinetics after hot rolling. A new algorithm has been suggested to create differential data sets to train the neural network. The model is then used to predict histories of various deformation variables and recrystallization kinetics in hot rolling of AA5083. Comparison between the theoretical and the experimental data shows the validity of the model.
Vorheriger Artikel Investigation of Process Parameters for the Backward Extrusion of Arbitrary-Shaped Tubes from Round Billets Using Finite Element Analysis
Nächster Artikel Transverse-Weld Tensile Properties of a New Al-4Cu-2Si Alloy as Filler Metal

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Metadaten
Titel
A Model to Predict Recrystallization Kinetics in Hot Strip Rolling Using Combined Artificial Neural Network and Finite Elements
verfasst von
M. Seyed Salehi
S. Serajzadeh
Publikationsdatum
01.12.2009
Verlag
Springer US
Erschienen in
Journal of Materials Engineering and Performance / Ausgabe 9/2009
Print ISSN: 1059-9495
Elektronische ISSN: 1544-1024
DOI
https://doi.org/10.1007/s11665-009-9359-0

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