The orientations of these infinitesimally small separations between individual “grains” of a polycrystalline material have big effects. In a material such as aluminum, these collections of grains (called microstructures) determine properties such as hardness.
The orientations of these infinitesimally small separations between individual “grains” of a polycrystalline material have big effects. In a material such as aluminum, these collections of grains (called microstructures) determine properties such as hardness.
New research is helping scientists better understand how microstructures change, or undergo “grain growth,” at high temperatures.
A team of materials scientists and applied mathematicians developed a mathematical model that more accurately describes such microstructures by integrating data that can be identified from highly magnified images taken during experiments. Their findings are published in Nature: Computational Materials.
The research team included Jeffrey M. Rickman, Class of ’61 Professor of Materials Science & Engineering at Lehigh University; Katayun Barmak, Philips Electronics Professor of Applied Physics and Applied Mathematics at Columbia University; Yekaterina Epshteyn, Professor of Mathematics at the University of Utah; and Chun Liu, Professor of Applied Mathematics at the Illinois Institute of Technology.
