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Machine Learning Methods to predict the Fatigue Life of Selectively Laser Melted Ti6Al4V Components

Alessio Centola

Machine Learning Methods to predict the Fatigue Life of Selectively Laser Melted Ti6Al4V Components.

Rel. Davide Salvatore Paolino, Andrea Tridello, Alberto Ciampaglia. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Meccanica, 2023

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Abstract:

The aim of the following work is to search a relation between the different process parameters and the fatigue life of Ti6Al4V components, produced via Selective Laser Melting technologies, using Machine Learning techniques and their several types of Neural Networks, as an attempt to reduce the cost of further fatigue testing, as well as to have an idea of the life of an hypothetical component, in relation to the inputted process parameters, thermal treatments and surface treatments.

Relators: Davide Salvatore Paolino, Andrea Tridello, Alberto Ciampaglia
Academic year: 2022/23
Publication type: Electronic
Number of Pages: 112
Subjects:
Corso di laurea: Corso di laurea magistrale in Ingegneria Meccanica
Classe di laurea: New organization > Master science > LM-33 - MECHANICAL ENGINEERING
Aziende collaboratrici: UNSPECIFIED
URI: http://webthesis.biblio.polito.it/id/eprint/27034
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