Phase-field approach to simulate BCC-B2 phase separation in the AlnCrFe2Ni2 medium-entropy alloy

被引:10
作者
Coutinho, Yuri Amorim [1 ]
Kunwar, Anil [2 ]
Moelans, Nele [1 ]
机构
[1] Katholieke Univ Leuven, Dept Mat Engn, Kasteelpk Arenberg 44, B-3001 Leuven, Belgium
[2] Silesian Tech Univ, Fac Mech Engn, Sci & Didact Lab Nanotechnol & Mat Technol, Konarskiego 18A, PL-44100 Gliwice, Poland
基金
欧洲研究理事会;
关键词
SPINODAL DECOMPOSITION; FREE-ENERGY; MULTICOMPONENT; CALPHAD; MODEL; NI; FE; DIFFUSION; CR; SYSTEM;
D O I
10.1007/s10853-022-07058-2
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Phase separation is a relevant mode of transformation for microstructure development in multicomponent alloys. Its occurrence can drastically alter the composition landscape and lead to patterns, either beneficial or undesired to the alloy design. This phenomenon is reported commonly in the BCC phase region of AlCoCrFeNi high-entropy alloys and their cost-effective Co-free alternatives based on AlCrFeNi due to chemical ordering. To better understand this transformation, we employ a phase-field model with materials parameters obtained from the CALPHAD methodology. Microstructure simulations of phase separation are conducted for the BCC-B2 phase on the AlnCrFe2Ni2 alloy (n is the stoichiometric coefficient). The free energy density is obtained from a CALPHAD sublattice model, in which the disordered BCC and ordered B2 phases are formulated as a single Gibbs free energy expression. The relation between the site fraction variables in the CALPHAD sublattice model and the mole fractions in the phase-field equations is provided via neural networks. The acquisition of training data on the equilibrium value of the site fractions is presented along with the training and validation procedure. The trained neural networks are combined with a finite-element framework and Cahn-Hilliard model to simulate the BCC-B2 phase separation at different compositions. [GRAPHICS] .
引用
收藏
页码:10600 / 10612
页数:13
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