ICT for in-situ monitoring of metal Additive Manufacturing

In the Industry 4.0 scenario, early fault detection has gained a relevant importance. In this new digital context, Internet of Things (IoT) and machine learning have become fundamental. By collecting data from industrial processes and by analysing those, it’s possible to statistically detect, forecast and prevent faults. In this way, it’s possible to have a more secure working place and a more efficient working process in terms of times and costs. This approach has been widely applied to all kinds of processes, with attention to automatic ones. For this reason, a lot of approaches have been studied to early detect faults in the metal Additive Manufacturing (AM) technique. The latter was a breakthrough in the industrial sector since early years of the XXI century and its nowadays widely adopted. The main aim of this thesis is to retrieve useful information from pictures captured during the building process in a metal AM machine. Those data could be used in order to: stop preventively error-prone jobs; relate data from pictures with process parameters; set optimal parameters for the building process. In other words, to develop an algorithm for image acquisition and defects analysis of components produced with Selective Laser Melting machine during the process.