Abstract
A phase-field model is proposed to simulate coherency loss coupled with microstructure evolution. A special field variable is employed to describe the degree of coherency loss of each particle and its evolution is governed by a Ginzburg-Landau type kinetic equation. For the sake of computational efficiency, a flood-fill algorithm is introduced that can drastically reduce the required number of field variables, which allows the model to efficiently simulate a large number of particles sufficient for characterizing their statistical features during Ostwald ripening. The model can incorporate size dependence of coherency loss, metastability of coherent particles, and effectively incorporate the underlying mechanisms of coherency loss by introducing a so-called differential energy criterion. The model is applied to simulate coarsening of Al3Sc precipitates in aluminum alloy and comprehensively compared with experiments. Our results clearly show how the particle size distribution is changed during coherency loss and affects the coarsening rate.
Highlights
Loss of interfacial coherency is common during the growth of precipitates in a multiphase material system[1]
The simulation results agree with the experiments of IM in terms of the range of mean particle size as well as the evolution of particle size distribution (PSD) during coherency loss (CL)
It is clear that the case k = 2 matches best with the 15~40 nm observation in IM’s experiments
Summary
Loss of interfacial coherency is common during the growth of precipitates in a multiphase material system[1]. In an Al-Cu-Mg alloy, it is found that most precipitate particles remain coherent at 260 °C for 25 days, but they lose coherency within 20 min when a creep load is applied, which indicates the rate of CL highly depends on the availability of matrix dislocations in the Al-Cu-Mg system[11] Owing to these facts, and since CL is not a martensitic (diffusionless) process, it should be natural to consider CL as a continuous process instead of an abrupt transformation, which is the hypothesis of the current model. To facilitate large-scale simulation, a flood-fill algorithm is introduced to identify each individual particle from one phase-field profile, which can significantly reduce the number of field variables so as to drastically improve the computational efficiency
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