Switching of control mechanisms during the rapid solidification of alloys

Dr. Yijia Gu
Physics Room 223A
Prof. Andrew Meng

Switching of control mechanisms during the rapid solidification of alloys


The formation of complex solidification patterns is an intrinsic non-equilibrium phenomenon. It is the interplay between capillary and kinetic effects at the solidification front (solid-liquid interface) that produces the complex growth patterns we see in nature. In general, the solidification growth is solely controlled by diffusion. Pure metals are controlled by thermal diffusion, while alloys are controlled by solute diffusion.  However, in the rapid solidification of alloys, the solidification growth may undergo a change from solute diffusion-controlled to thermal diffusion-controlled. The switching of control mechanisms is found to cause the velocity jump and disrupt the microstructure development. In this work, we will investigate two rapid solidification processes, additive manufacturing (AM) and melt spinning (MS), using phase-field modeling. Specifically, the nucleation or the onset of the solidification of AM and MS will be explored. The resulting solidification pathway and the development of inhomogeneous microstructures will be elucidated.


Dr. Yijia Gu obtained his Ph.D. in Materials Science and Engineering (MSE) with a minor in Computational Science from the Pennsylvania State University in 2014. During his Ph.D., Dr. Gu performed thermodynamic and kinetic modeling work on semiconductor, metal, and ferroelectric materials mostly using the phase-field method. Then, he launched his career at Alcoa Technical Center (ATC, now Arconic Technology Center), where he was first a Senior Engineer and then Staff Engineer. At ATC, he did CALPHAD and kinetic modeling work for alloy design and processing optimization, including both conventional route and additive manufacturing. In 2019, he joined Missouri S&T as an Assistant Professor in the MSE department, where he has been collaborating with colleagues and continues to apply computational materials modeling to the studies of advanced steels, machine learning-assisted alloy design, as well as metal additive manufacturing