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ICT for in-situ monitoring of metal Additive Manufacturing

Antonio Giuseppe Varrella

ICT for in-situ monitoring of metal Additive Manufacturing.

Rel. Edoardo Patti, Davide Cannizzaro, Santa Di Cataldo, Massimo Poncino. Politecnico di Torino, Corso di laurea magistrale in Ict For Smart Societies (Ict Per La Società Del Futuro), 2020

Abstract:

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.

Relators: Edoardo Patti, Davide Cannizzaro, Santa Di Cataldo, Massimo Poncino
Academic year: 2020/21
Publication type: Electronic
Number of Pages: 69
Additional Information: Tesi secretata. Fulltext non presente
Subjects:
Corso di laurea: Corso di laurea magistrale in Ict For Smart Societies (Ict Per La Società Del Futuro)
Classe di laurea: New organization > Master science > LM-27 - TELECOMMUNICATIONS ENGINEERING
Aziende collaboratrici: FCA ITALY SPA
URI: http://webthesis.biblio.polito.it/id/eprint/15874
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