  Artificial Intelligence and Machine Learning algorithms have considerable
potential to influence the prediction of material properties. Additive
materials have a unique property prediction challenge in the form of surface
roughness effects on fatigue behavior of structural components. Traditional
approaches using finite element methods to calculate stress risers associated
with additively built surfaces have been challenging due to the computational
resources required, often taking over a day to calculate a single sample
prediction. To address this performance challenge, Deep Learning has been
employed to enable low cycle fatigue life prediction in additive materials in a
matter of seconds.
