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.
