While additive manufacturing has seen rapid proliferation in recent years, process monitoring and quality assurance methods capable of detecting micro-scale flaws have seen little improvement and remain largely expensive and time-consuming. In this work we propose a pipeline for training two deep learning flaw formation detection techniques including convolutional neural networks and long short-term memory networks. We demonstrate that the flaw formation mechanisms of interest to this study, including keyhole porosity, lack of fusion, and bead up, are separable using these methods. Both approaches have yielded a classification accuracy over 99% on unseen test sets. The results suggest that the implementation of machine learning enabled acoustic process monitoring is potentially a viable replacement for traditional quality assurance methods as well as a tool to guide traditional quality assurance methods.