Molecular-dynamics study of the α ↔ γ phase transition in Fe–C AITranslate
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Highlights • C stabilizes the high-temperature fcc phase, as in experiment. • Both austenitic and martensitic temperatures decrease with increasing C concentration. • High stresses occur during the austenitic transition, and are relieved by twinning. Using molecular dynamics simulation, we study the austenitic and the martensitic solid–solid phase transformation in the Fe–C system. Random alloys with C contents up to 1 at% are subjected to a heating/cooling cycle. The martensite and austenite phase transition temperatures can be determined from the hysteresis of the system volume with temperature. The martensite temperature decreases with C content, as in experiment. The influence of the C atom position on the phase transformation and the pathways of the transition are analyzed. The transformed austenite phase shows strong twinning.
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DOI:https://doi.org/10.1016/j.commatsci.2013.09.069
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Highlights • C stabilizes the high-temperature fcc phase, as in experiment. • Both austenitic and martensitic temperatures decrease with increasing C concentration. • High stresses occur during the austenitic transition, and are relieved by twinning. Using molecular dynamics simulation, we study the austenitic and the martensitic solid–solid phase transformation in the Fe–C system. Random alloys with C contents up to 1 at% are subjected to a heating/cooling cycle. The martensite and austenite phase transition temperatures can be determined from the hysteresis of the system volume with temperature. The martensite temperature decreases with C content, as in experiment. The influence of the C atom position on the phase transformation and the pathways of the transition are analyzed. The transformed austenite phase shows strong twinning.
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| GB/T 7714-2015 | [1] Binjun Wang, Emilia SakSaracino, Nina Gunkelmann, et al. Computational Materials Science, 2014(82). DOI:10.1016/j.commatsci.2013.09.069. |
| MLA | [1] Binjun Wang, et al., Computational Materials Science, no. 82, 2014, https://doi.org/10.1016/j.commatsci.2013.09.069. |
| APA | [1] Binjun Wang, Emilia SakSaracino, Nina Gunkelmann, & Herbert M. Urbassek. (2014). Computational Materials Science(82). https://doi.org/10.1016/j.commatsci.2013.09.069 |
| IEEE | [1] Binjun Wang, Emilia SakSaracino, Nina Gunkelmann, and Herbert M. Urbassek, Computational Materials Science, no. 82, 2014, doi: 10.1016/j.commatsci.2013.09.069. |
