Utilization of Bayesian Optimization and KWN Modeling for Increased Efficiency of Al-Sc Precipitation Strengthening

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.