(Nanowerk News) The orientation of the tiny separations between the individual “grains” of this polycrystalline material has a large effect. In a material like aluminum, this collection of grains (called microstructure) determines properties such as hardness.
The new research is helping scientists better understand how microstructure changes, or undergoes “grain growth”, at high temperatures.
A team of materials scientists and applied mathematicians developed a mathematical model that more accurately describes the microstructure by integrating data identifiable from magnified images taken during the experiment.
Their findings are published in Properties: Computing Material (“Point process microstructural model of metallic thin films with implications for roughening”).
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.
“Our model is novel because it is rendered in the form of identifiable features from experimental micrographs, or photographs that reveal microstructural details on the nanometer to micron length scale,” said Rickman. “Because our model can be related to these experimental features, it is a more precise representation of the actual grain growing process.”
The researchers mapped the crystal orientations on aluminum thin films with columnar grains and used a stochastic process to mark points to represent triple junctions, the points where three grains and grain boundaries meet in the structure. Their model is the first to integrate data on the interaction and disorientation of these three junctions to predict grain growth.
Predicting grain growth is key to the creation of new materials and is an important area of study in materials science. Consequently, many grain growth models have been developed. However, the project’s direct relationship between the mathematical model and the experimental micrograph is very distinctive.
According to Rickman, connecting the model directly to features that can be tracked during an experiment will benefit computational materials scientists who model grain growth kinetics.
“Ultimately, this research provides a way to better understand how grain growing works and how it can be used to inform the development of new materials,” said Rickman.