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Journal of Materials Engineering and Performance
Erschienen in: Journal of Materials Engineering and Performance 7/2012

01.07.2012

Inverse Identification of the Dynamic Recrystallization Parameters for AZ31 Magnesium Alloy Using BP Neural Network

verfasst von: Yan Lou, Wenhua Wu, Luoxing Li

Erschienen in: Journal of Materials Engineering and Performance | Ausgabe 7/2012

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Abstract

The effect of the dynamic recrystallization (DRX) parameters are of prime importance to improve the accuracy of the numerical simulation of hot forming processes for metals. However, it is difficult to determine the values of DRX parameters from experiments because of the influence of various factors, such as temperature, etc. In the present study, the DRX parameters for AZ31 magnesium alloy are identified by using the method of inverse analysis based on measured stress, BP neural network algorithm, genetic algorithm (GA), orthogonal experiment, and numerical simulation. Then, by applying the identified parameters in finite element analysis, the comparison between the numerically calculated and the experimental results is made to verify the correctness of the method. The results show that the numerically calculated stress, strain, recrystallized fraction, and average grain size valus are in good agreement with the experimental ones. These results demonstrate that the method of inverse analysis is a feasible and an effective tool for determination of the AZ31 DRX parameters.
Vorheriger Artikel Dielectric Response of High Explosives at THz Frequencies Calculated Using Density Functional Theory
Nächster Artikel Modeling the Effects of Coolant Application in Friction Stir Processing on Material Microstructure Using 3D CFD Analysis

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Metadaten
Titel
Inverse Identification of the Dynamic Recrystallization Parameters for AZ31 Magnesium Alloy Using BP Neural Network
verfasst von
Yan Lou
Wenhua Wu
Luoxing Li
Publikationsdatum
01.07.2012
Verlag
Springer US
Erschienen in
Journal of Materials Engineering and Performance / Ausgabe 7/2012
Print ISSN: 1059-9495
Elektronische ISSN: 1544-1024
DOI
https://doi.org/10.1007/s11665-011-0015-0

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