College - Author 1
College of Engineering
Department - Author 1
Materials Engineering Department
College - Author 2
College of Engineering
Department - Author 2
Materials Engineering Department
College - Author 3
College of Engineering
Department - Author 3
Materials Engineering Department
Advisor
Dr. Mohsen Kivy, College of Engineering, Materials Engineering
Funding Source
The Spraque Family Foundation
Date
10-2023
Abstract/Summary
High-entropy alloys (HEA) are a very new development in the field of metallurgical materials. They are made up of multiple principle atoms unlike traditional alloys, which contributes to their high configurational entropy. The microstructure and properties of HEAs are are not well predicted with the models developed for more common engineering alloys, and there is not enough data available on HEAs to fully represent the complex behavior of these alloys. To that end, we explore how the use of machine learning models can be used to model the complex, high dimensional behavior in the HEA composition space. Based on our review and experimentation, the use of a variational autoencoder will allow future research to lower the dimensionality (and therefore complexity) of the HEA composition space and allow for novel alloy generation based on one or more desired properties.
October 1, 2023.
Included in
Artificial Intelligence and Robotics Commons, Data Science Commons, Metallurgy Commons, Other Materials Science and Engineering Commons
URL: https://digitalcommons.calpoly.edu/ceng_surp/2