Abstract
This work outlines a reference guideline for the realization and the optimization of a vision-based monitoring system for metal Additive Manufacturing (AM) processes. We perform the selection of equipment and image processing methods in the spirit of providing a monitoring solution that could be cost affordable and easily integrated into any laser-based AM machine. By experimental tests conducted on a commercial Direct Energy Deposition machine, we assess the equipment configuration and camera acquisition settings that, in addition to the proposed image-processing algorithm, could optimize the anomaly detection during the deposition.
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