Mapping of Microstructure Transitions during Rapid Alloy Solidification Using Bayesian-Guided Phase-Field Simulations

TL;DR

This paper combines phase-field simulations with Bayesian active learning to efficiently map microstructure transitions during rapid alloy solidification, providing insights for process optimization in additive manufacturing.

Contribution

It introduces a Bayesian-guided phase-field simulation framework to systematically explore microstructure transitions in rapid solidification across multiple parameters.

Findings

Classical KGT model accurately predicts the planar interface threshold.

Microstructures transition from dendrites to cells near the critical G value.

Unstable intermediate microstructures are identified at low G without banding instability.

Abstract

This study addresses microstructure selection mechanisms in rapid solidification, specifically targeting the transition from cellular/dendritic to planar interface morphologies under conditions relevant to additive manufacturing. We use a phase-field model that quantitatively captures solute trapping, kinetic undercooling, and morphological instabilities across a broad range of growth velocities ($V$) and thermal gradients ($G$), and apply it to a binary Fe-Cr alloy, as a surrogate for 316L stainless steel. By combining high-fidelity phase-field simulations with a Gaussian Process-based Bayesian active learning approach, we efficiently map the microstructure transitions in the multi-dimensional space of composition, growth velocity, and temperature gradient. We compare our PF results to classical theories for rapid solidification. The classical KGT model yields an accurate prediction of the value of $G$ above which the interface is planar for any growth velocity. Microstructures transition from dendrites to cells as the temperature gradient increases close to this value of $G$. We also identify the occurrence of unstable "intermediate" microstructures at the border between dendritic and planar at low $G$, in the absence of banding instability in this Fe-Cr alloy. Our results highlight the capabilities of Bayesian-guided PF approaches in exploring complex microstructural transitions in multidimensional parameters spaces, thereby providing a robust computational tool for designing process parameters to achieve targeted microstructures and properties in rapidly solidified metallic alloys.