Kinetic Monte Carlo Simulation of Clustering in an Al-Mg-Si-Cu Alloy
Abstract
:1. Introduction
2. Materials and Methods
3. Results and Discussion
3.1. Early Aging Behavior Simulation
3.2. Reversion Ageing Treatment
4. Conclusions
- (1)
- The formation of many small clusters at the beginning of natural aging and artificial aging is mainly caused by the initial vacancies (quenching vacancies). The clusters containing capturing vacancies can merge with surrounding solute atoms and other clusters, and decompose before reaching a stable size.
- (2)
- After repeated merging and decomposition, the clusters reach stability. Small clusters are generally rich in Si or Mg, while large clusters have similar contents of Mg and Si. The decrease in the number of clusters in the alloy is attributed to the merging and growth of small clusters. The fluctuations and irregularities of the number of clusters are caused by the repeated merging and decomposition of clusters during aging.
- (3)
- In the process of reversion aging treatment, the baking hardening increases at first and then decreases, and the change of activation energy of β’’ phase is the opposite. There are some partial irregularities in the change of the decrease of in hardness and the activation energy of β″ phase with the extension of reversion aging time due to the interaction of decomposition and aggregation of clusters. The bake hardening of the sample after reversion aging in an oil bath at 523 K for 50 s is the best.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Element | Cohesive Energy (kJ/mol) | Diffusion Constant D0 (m2/s) | Activation Energy Q (kJ/mol) | Vacancy Formation Energy ΔHvf (kJ/mol) | Solute-Vacancy Binding Energy Ebi-v (eV) |
---|---|---|---|---|---|
Al | 327.279 | 1.76 × 10−5 | 126.4 | 67.54 | - |
Mg | 145.643 | 6.23 × 10−6 | 115.0 | 55.96 | 3.86 |
Si | 445.783 | 2.48 × 10−4 | 137.0 | 385.94 | 2.89 |
Cu | 336.236 | 6.54 × 10−5 | 136.0 | 119.64 | 4.34 |
Al | Mg | Cu | Si | Vacancy | |
---|---|---|---|---|---|
Al | −54.5 | −34.5 | −49.3 | −50.8 | −21.9 |
Mg | - | −16.8 | −32.2 | −38.1 | −10.5 |
Cu | - | - | −48.5 | −45.3 | −15.8 |
Si | - | - | - | −52.2 | −25.9 |
Vacancy | - | - | - | - | 0 |
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Ye, Q.; Wu, J.; Zhao, J.; Yang, G.; Yang, B. Kinetic Monte Carlo Simulation of Clustering in an Al-Mg-Si-Cu Alloy. Materials 2021, 14, 4523. https://doi.org/10.3390/ma14164523
Ye Q, Wu J, Zhao J, Yang G, Yang B. Kinetic Monte Carlo Simulation of Clustering in an Al-Mg-Si-Cu Alloy. Materials. 2021; 14(16):4523. https://doi.org/10.3390/ma14164523
Chicago/Turabian StyleYe, Qilu, Jianxin Wu, Jiqing Zhao, Gang Yang, and Bin Yang. 2021. "Kinetic Monte Carlo Simulation of Clustering in an Al-Mg-Si-Cu Alloy" Materials 14, no. 16: 4523. https://doi.org/10.3390/ma14164523
APA StyleYe, Q., Wu, J., Zhao, J., Yang, G., & Yang, B. (2021). Kinetic Monte Carlo Simulation of Clustering in an Al-Mg-Si-Cu Alloy. Materials, 14(16), 4523. https://doi.org/10.3390/ma14164523