University of Waterloo
Assembly Line
AI In-situ Monitoring Detects Fusion Flaws in L-PBF Metal 3D Printing
In-situ process monitoring is the key for validating the quality of AM-made parts and minimizing the need for post quality control. In this collaborative research, in-situ datasets collected from a co-axial photodiode installed in an EOS M 290 were subject to a set of correction factors to remove chromatic and monochromatic distortions from the signal. The corrected datasets were then analyzed using statistical and machine learning algorithms. These algorithms were systematically tuned and customized to detect lack of fusion flaws.