Temperature Regimes and Chemistry for Stabilizing Precipitation Hardening Phases in Al–Sc Alloys: Combined CALPHAD–Deep Machine Learning

Abstract

In this work, CALPHAD-based calculations provided with data for various stable and metastable phases in 2XXX, 6XXX, and 7XXX classes of aluminum-based alloys. These data were scaled and then used to develop Deep Learning Artificial Neural Network (DLANN) models for all these phases as a function of composition and temperature. Code was written in the python programming language using TensorFlow/Keras libraries. DLANN models were used for determining the amount of various phases for new compositions and temperatures. The resulting data were further analyzed through the concept of Self-organizing Maps (SOM) and a few candidates were chosen for studying the precipitation kinetics of Al3Sc phase under the framework of CALPHAD approach. This work reports on heat-treatment simulation for one case of 6XXX alloy where the nucleation site was on dislocation, while a detailed study for other alloys is reported in a previously published work. Grain-growth simulations presented in this work are valid for single crystals only.