Evolution of MG AZ31 twin activation with strain: A machine learning study

Abstract

Complex relationships between microstructure and twin formation in AZ31 magnesium are investigated as a function of increasing strain using supervised machine learning. In one approach, strain is incorporated as an implicit attribute in a single predictive model, in a second method, separate decision trees are formed for each strain level. A comparison of the methods shows that the second better uncovers the underlying physics. The correlations revealed are found to exhibit similarities with parameters used in conventional modeling techniques, leading to the conclusion that machine learning has potential to assist in future microstructural modeling.