|About this Abstract
||Materials Science & Technology 2019
||Ceramics and Glasses Simulations and Machine Learning
||Leveraging Machine Learning to Predict Microstructural and Macroscopic Properties of Alumina
||Russell Gleason, Branden Kappes, Geoff Brennecka, Aaron Stebner
|On-Site Speaker (Planned)
Alumina is one of the most widely used advanced ceramic materials, yet abnormal grain growth during production remains problematic. The difficulty in controlling the microstructural development of alumina leads to the commensurate difficulty in controlling its macroscopic properties. Since Coble, many different approaches have been used to frame the problem, from doping, to surface energy and bulk defect chemistry, and more recently complexions, but a complete understanding is still elusive. This work aims to take a new approach to the subject, namely machine learning. This approach leverages the power of data informatics to search for previously unrecognized phenomenological relationships among powder properties, chemical composition, manufacturing processes, and the resulting microstructural and macroscopic properties of alumina. Using neural networks and regression analysis, the properties of alumina samples produced by widely varying methods are compared to each other in order to extract relationships and elucidate methods of better controlling these properties.
||Definite: At-meeting proceedings