The data generated during additive manufacturing (AM) practice can be used to train machine
learning (ML) tools to reduce defects, optimize mechanical properties, or increase efficiency. In
addition to the size of the repository, emerging research shows that other characteristics of the data
also impact suitability of the data for AM-ML application. What should be done in cases for which
the data in too small, too homogeneous, or otherwise insufficient? Data augmentation techniques
present a solution, offering automated methods for increasing the quality of data. However, many
of these techniques were developed for machine vision tasks, and hence their suitability for AM
data has not been verified. In this study, several data augmentation techniques are applied to
synthetic design repositories to characterize if and to what degree they enhance their performance
as ML training sets. We discuss the comparative advantage of these data augmentation techniques
across several canonical AM-ML tasks.Mechanical Engineerin
