TCHEA1: A Thermodynamic Database Not Limited for “High Entropy” Alloys
By Huahai Mao, Hai-lin Chen, Qing Chen. 2017
160 citations recorded by the source corpus.
In this paper we report a thermodynamic database which was developed by using the CALPHAD approach. The TCHEA1 database includes 15 chemical elements (Al, Co, Cr, Cu, Fe, Hf, Mn, Mo, Nb, Ni, Ta, Ti, V, W and Zr). It is suitable for the study of Bcc and Fcc HEA systems. The database is constructed based on the thermodynamic assessment of all binary systems and many key ternary systems where almost all possible metastable and stable phases are considered. It is extensively demonstrated in the present work that TCHEA1 gives satisfactory prediction on the phase equilibria in various HEA systems (quaternary to ennead) and wide temperature ranges (liquidus to subsolidus). Thermodynamic stability calculations of simple solid solutions (Bcc and Fcc) and intermetallics (sigma, Laves, μ-phase etc.) are validated against the available experimental information in as-cast or as-annealed state. Such CALPHAD database focusing on the modelling of Gibbs energy rather than entropy makes reliable predictions of thermodynamic equilibrium and phase transformation, no matter whether the alloy/system has high entropy or not. Cases with miscibility gap in liquid and solid solutions and second-order phase transition at low temperatures are demonstrated. With the volume data included, TCHEA1 is capable to predict the density and thermal expansion coefficient of HEAs as well. This thermodynamic database is also applicable in process simulations when used together with compatible kinetic databases.
Subfields: Aerospace Engineering, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/546c7b19a9830ba39089f66ea07f9d5aa2b0246f
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In Aerospace Engineering
- Zhiming Li
- Fritz Körmann
- Dierk Raabe
- Blazej Grabowski
- R. Ritchie
- P. K. Liaw
- Easo P. George
- Brian Cantor
In Mechanical Engineering
- Dierk Raabe
- Zhiming Li
- D. Ponge
- Fritz Körmann
- A. Kwiatkowski da Silva
- Oliver Gutfleisch
- C. Tasan
- O. A. Zambrano
Citations in the corpus
- Machine learning–enabled high-entropy alloy discovery (2022, methodology): “These conventional methods for designing alloys, which have been applied to small subspaces of the HEAs composition realm, include the CALculation of PHAse Diagrams (CALPHAD) and Density-Functional Theory (DFT)Mao et al. (2017)K¨ormann et al. (2015)Rao et al. (2019).”