In the present paper, the effectiveness of the use of Machine Learning techniques, in particular Deep Learning algorithms, in the analysis of Ti-6Al-4V (Ti64) manufacture is studied; relationships between the values of physical parameters used during the production and mechanical characteristics are defined by means of the analysis of images taken from sections of the specimens, where defects and microstructural discontinuities can be observed. The Deep Learning approach, widely used for image classification and features extraction, also in this case shows promising possibilities,
Machine learning for mechanical properties classification in Additive Manufacturing / Di Giamberardino, Paolo; Iacoviello, Daniela; Berto, Filippo; Fiorillo, Rossella; Natali, Stefano; Pilone, Daniela; Schillaci, Carolina; Bellini, Costanzo; Di Cocco, Vittorio. - (2025). (Intervento presentato al convegno International Conference on Control, Decision and Information Technologies (CODIT) tenutosi a Split (Croazia)).
Machine learning for mechanical properties classification in Additive Manufacturing
Paolo Di Giamberardino
;Daniela Iacoviello;Filippo Berto;Rossella Fiorillo;Stefano Natali;Daniela Pilone;Carolina Schillaci;Vittorio Di Cocco
2025
Abstract
In the present paper, the effectiveness of the use of Machine Learning techniques, in particular Deep Learning algorithms, in the analysis of Ti-6Al-4V (Ti64) manufacture is studied; relationships between the values of physical parameters used during the production and mechanical characteristics are defined by means of the analysis of images taken from sections of the specimens, where defects and microstructural discontinuities can be observed. The Deep Learning approach, widely used for image classification and features extraction, also in this case shows promising possibilities,I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
