The current status of large-scale phase-field (PF) simulations for dendrite growth is reviewed
by focusing on the study conducted by our group. The discussion includes the competitive …

Experimental grain growth observations often deviate from grain growth simulations,
revealing that the governing rules for grain boundary motion are not fully understood. A …

Integrating phase-field simulations and in situ observation experiments is a promising
approach to better understand dendrite growth during alloy solidification. To integrate …

In this paper, a new methodology is presented to inlude anisotropic grain boundary
properties in a phase-field model. The new approach is thermodynamically consistent and …

In phase-field simulations, accurate material parameters are required to quantitatively
predict microstructural evolutions. Non-sequential data assimilations enable the estimation …

The phase-field (PF) method can effectively predict the formation of microstructures of
eutectic alloys. However, numerous simulation parameters must be determined correctly for …

This paper presents the use of a Gaussian neighborhood mode filter for predicting grain
growth in a manner similar to the solutions obtained by a Monte Carlo Potts model. This …

In this study, we developed a Pareto optimal driven automation framework for quantitative
Cahn–Hilliard simulation of spinodal decomposition processes exploiting the scarce …

Accurately predicting dendrite growth during alloy solidification is crucial for enhancing the
quality of metallic products. Recently, data assimilation has emerged as a promising tool for …

Grain growth during static recrystallization has been modeled by the multi-phase-field
method. However, its predictive accuracy is insufficient because grain boundary properties …