Document Type
Article
Publication Date
6-6-2022
Department
Department of Materials Science and Engineering
Abstract
The Kampmann and Wagner numerical model was adapted in MATLAB to predict the precipitation and growth of Al3Sc precipitates as a function of starting concentration and heat‐treatment steps. This model was then expanded to predict the strengthening in alloys using calculated average precipitate number density, radius, etc. The calibration of this model was achieved with Bayesian optimization, and the model was verified against experimentally gathered hardness data. An analysis of the outputs from this code allowed the development of optimal heat treatments, which were validated experimentally and proven to result in higher final strengths than were previously observed. Bayesian optimization was also used to predict the optimal heat‐treatment temperatures in the case of limited heat‐treatment times.
Publication Title
Metals
Recommended Citation
Deane, K.,
Yang, Y.,
Licavoli, J.,
Nguyen, V.,
Rana, S.,
Gupta, S.,
Venkatesh, S.,
&
Sanders, P. G.
(2022).
Utilization of Bayesian Optimization and KWN Modeling for Increased Efficiency of Al‐Sc Precipitation Strengthening.
Metals,
12(6).
http://doi.org/10.3390/met12060975
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/16227
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Version
Publisher's PDF
Publisher's Statement
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Publisher’s version of record: https://doi.org/10.3390/met12060975