Modeling of dislocation density and strength on rheoforged A356 alloy during multi-directional forging AITranslate
Abstract AITranslate
Highlights • A combination of Shear Lag model and Nes model is presented. • The model is used for non-dendritic A356 alloy during multi-directional forging. • Modeling results of dislocation density and strength are acceptable. • The model in this case is less sensitive to variation of Si particles aspect ratio. In this study, a hybrid model is presented to predict the dislocation density and strength evolution of the rheoforged non-dendritic A356 alloy during multi-directional forging. Regarding the characteristics of non-dendritic A356 alloy, combination of Shear Lag and Nes models is used for the eutectic structure, and Nes model is used for the α-Al globular phase. The aspect ratio variations of Si particles in eutectic structure during 3 passes of multi-directional forging do not change the model predictions, significantly. Model predictions on shear stress are in good agreement with experimental results of shear punch test.
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DOI:https://doi.org/10.1016/j.commatsci.2013.08.029
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Highlights • A combination of Shear Lag model and Nes model is presented. • The model is used for non-dendritic A356 alloy during multi-directional forging. • Modeling results of dislocation density and strength are acceptable. • The model in this case is less sensitive to variation of Si particles aspect ratio. In this study, a hybrid model is presented to predict the dislocation density and strength evolution of the rheoforged non-dendritic A356 alloy during multi-directional forging. Regarding the characteristics of non-dendritic A356 alloy, combination of Shear Lag and Nes models is used for the eutectic structure, and Nes model is used for the α-Al globular phase. The aspect ratio variations of Si particles in eutectic structure during 3 passes of multi-directional forging do not change the model predictions, significantly. Model predictions on shear stress are in good agreement with experimental results of shear punch test.
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| GB/T 7714-2015 | [1] J. Kavosi, M. Saei, M. Kazeminezhad, et al. Computational Materials Science, 2014(81). DOI:10.1016/j.commatsci.2013.08.029. |
| MLA | [1] J. Kavosi, et al., Computational Materials Science, no. 81, 2014, https://doi.org/10.1016/j.commatsci.2013.08.029. |
| APA | [1] J. Kavosi, M. Saei, M. Kazeminezhad, & A. Dodangeh. (2014). Computational Materials Science(81). https://doi.org/10.1016/j.commatsci.2013.08.029 |
| IEEE | [1] J. Kavosi, M. Saei, M. Kazeminezhad, and A. Dodangeh, Computational Materials Science, no. 81, 2014, doi: 10.1016/j.commatsci.2013.08.029. |
