Journal of Undergraduate Research


statistical magnesium models, machine learning, manufacturing


Ira A. Fulton College of Engineering and Technology


Mechanical Engineering


Magnesium is a potential replacement for steels and aluminum in strength applications. Despite desirable strength and weight properties, magnesium is costly to manufacture. To reduce manufacturing costs, extensive research has been done on is a phenomenon called twinning, where a large group of magnesium atoms collectively reorient from a base orientation to a new orientation. This reorientation caused by twinning has the potential to enable easier material deformation, allowing for less costly manufacturing. Our research group pursued a novel approach to twinning research by using data mining and machine learning algorithms. Data collected from samples of magnesium using a scanning electron microscope (SEM) method called electron back-scatter detection (EBSD) is used to train machine learning models. This approach was shown to have potential to assist the materials science community in their quest to understand the twinning phenomenon [1].