The COVID-19 pandemic struck the world by surprise and left everyone speechless. While the majority of the world was stuck in quarantine, the health care industry exemplified its importance as the frontline workers and scientists worked to keep everyone healthy and safe. In the early months of the pandemic people tried to return to our normal way of life with the exception of newly implemented social distancing rules and the mandatory use of face coverings. This caused a huge shortage of PPE, as companies struggled to manufacture and test masks in time to meet the demands of the world. Hence, the problem statement for our research project: How can we increase the speed and efficiency of the PPE quality assurance process by using advanced robotic systems and machine learning. There are three tests that need to be performed in order to certify a mask as medical grade: a flammability test, liquid barrier test, and fluid penetration test. Our project has thus far been researching how to create automated systems in order to conduct these tests with a robotic arm, as well as researching machine learning concepts which classify each test as a pass or fail.