Modeling of Diffusion-Controlled Crystallization Kinetics in Al-Cu-Zr Metallic Glass

Abstract

Crystallization is a major challenge in metallic glass production, and predictive models may aid the development of controlled microstructures. This work describes a modeling strategy of nucleation, growth and the dissolution of crystals in a multicomponent glass-forming system. The numerical model is based on classical nucleation theory in combination with a multicomponent diffusion-controlled growth model that is valid for high supersaturation. The required thermodynamic properties are obtained by coupling the model to a CALPHAD database using the Al-Cu-Zr system as a demonstrator. The crystallization of intermetallic Al,CumZrn phases from the undercooled liquid phase were simulated under isothermal as well as rapid heating and cooling conditions (10−1-106Ks−1). The obtained time–temperature transformation and continuous-heating/cooling transformation diagrams agree satisfactorily with the experimental data over a wide temperature range, thereby, demonstrating the predictability of the modeling approach. A comparison of the simulation results and experimental data is discussed.

In Situ Mapping of Phase Evolutions in Rapidly Heated Zr‐Based Bulk Metallic Glass with Oxygen Impurities

“…[ 5 ] This phase was also mentioned to be one of the phases with the highest chemical driving force for the ternary Al 10 Cu 30 Zr 60 system. [ 30 ] However, because this was the only peak possibly correlating to that phase, it was deemed to be a to weak basis to confirm this phase and it was excluded from the refinement. The other reported phases (Al 2 Zr 3 [ 6 ] and α‐Zr [ 7 ] ) were also investigated as possible candidates, but did not overlap with the diffraction peaks of the pattern in such a way that it would improve the fit, and were thus excluded from further analysis.…”

“…[ 51 ] The scaling factor to the number of nucleation sites relating to the heterogeneous nucleation was set to Γ = 10 −3 and the diffusion rates of the constituent elements were scaled with the effective diffusion coefficient according to their atomic weight, setting Al as one. To overcome the non‐representative nucleation rate in the Al 10 Cu 30 Zr 60 observed from the Al 3 Zr 4 phase in the work by Ericsson and Fisk, [ 30 ] the factor Π = 0.83 was used to scale the interfacial energy for this phase. r core was taken as half of the smallest computed critical radius for the simulated phases, thus allowing the cores to grow during the simulations.…”

“…For this, a model based on CNGT coupled to the calculation of phase diagrams (CAL-PHAD) methodology is used. [39] The model originates from the work done by Ericsson and Fisk [30] but has been further developed to better represent the interfacial energy between the amorphous matrix and the precipitating phases, incorporate heterogeneous and homogeneous nucleation, and account for different diffusion rates of the constituent elements in the matrix. The devitrification in the samples is simulated using the thermodynamic database for the ternary Al-Cu-Zr system developed by Zhou et al [32] Allowing the intermixing of Cu and Al with fixed composition of Zr, the Cu-and Al-rich phases are modeled as line compounds; (Al,Cu)Zr 2 and (Al,Cu) 3 Zr 4 .…”

“…[28] A combination of these two scattering techniques (SAXS/WAXS) was applied to study phasetransformations in a Zr 46 Cu 46 Al 8 metallic glass in situ during low-temperature isothermal annealing by Wu et al [29] Supported by Kolmogorov-Johnson-Mehl-Avrami (KJMA) modeling, it was shown that the process is dominated by homogeneous nucleation and diffusion-controlled growth. With the other well-known continuum approach of modeling, classical nucleation and growth theory (CNGT), Ericsson et al [30] produced simulation results decoupled from data fits that showed good agreement with experimentally obtained continuous heating transformation (CHT) diagrams by using a thermodynamic description of the Zr-Cu-Al system to mimic AMLOY-ZR01. However, no consideration of phase fractions was taken, which is key to properly predict and understand a systems kinetic transformation behavior.…”

“…With the other well‐known continuum approach of modeling, classical nucleation and growth theory (CNGT), Ericsson et al. [ 30 ] produced simulation results decoupled from data fits that showed good agreement with experimentally obtained continuous heating transformation (CHT) diagrams by using a thermodynamic description of the Zr‐Cu‐Al system to mimic AMLOY‐ZR01. However, no consideration of phase fractions was taken, which is key to properly predict and understand a systems kinetic transformation behavior.…”