Phase-field method (PFM) has become a mainstream computational method for predicting
the evolution of nano and mesoscopic microstructures and properties during materials …

This paper presents the first comprehensive literature review of deep learning (DL)
applications in additive manufacturing (AM). It addresses the need for a thorough analysis in …

Phase-field (PF) modeling is a versatile physics-based computational method that has been
used to simulate the evolution of microstructures. The PF method can produce accurate …

Programming the shape of soft smart materials is a challenging task due to the enormous
design space involved. In this study, we propose a novel approach to determine applied …

Surrogate models driven by sizeable datasets and scientific machine-learning methods
have emerged as an attractive microstructure simulation tool with the potential to deliver …

Cavitation and acoustic streaming created by in situ high-intensity ultrasound have been
exploited for microstructural refinement during directed energy deposition (DED) additive …

Laser powder bed fusion (LPBF) Ti-6Al-4V is widely studied for use in structural applications
in aerospace and medical industries, but mechanical anisotropy and microstructural …

We propose GrainGNN, a surrogate model for the evolution of polycrystalline grain structure
under rapid solidification conditions in metal additive manufacturing. High fidelity …

Abstract Anisotropic Physics-Regularized Interpretable Machine Learning Microstructure
Evolution (APRIMME) is a general-purpose machine learning solution for grain growth …

This study introduces a novel microstructure prediction model (polycrystalline grain
transformers: PGTs) for metal solidification processes, leveraging the capabilities of …