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Methodology of support modeling for additive process simulation

Tullio Laudadio

Methodology of support modeling for additive process simulation.

Rel. Christian Maria Firrone. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Meccanica, 2020


The main study field, where pages of my thesis work are set in, is additive manufacturing (AM). With this term engineers identify a range of technologies that allow to easily and quickly realize parts and prototypes, through a layer-by-layer approach, progressively adding material. These powerful, but very complex, tools, briefly described in following chapters, have already been applied in the aerospace, automotive and medical fields; despite popular knowledge, industrial technologies are clearly beyond the colloquially known as “3D printers” machines in term of quality level of quality and performances of built objects. As I discovered during my technical studies, it is common opinion that the direct manufacturing, from a virtual shape to physical part, is not as accurate, simple, but especially as repeatable as it could appear. Despite more than forty years of researches, the nature itself of additive processes does not lend to a reliable build, after simply pushing a “print” button. Additive manufacturing has its own rules, limits, and requirements, that are continuously updated after test, failures and discovers. In order to avoid unsuccessful (and expansive) prints, numerically modelling the additive phenomena gained more and more appeal through last years, as it has become computationally feasible to simulate the entire build by the introduction of optimized algorithms to solve with a multi-scale approach. Simulation software are quite recent, and their ability to approximate the thermo-mechanical behavior of a job, before printing it, can provide useful help and obvious benefits to industrial players who deal with metal AM. The main target of this thesis work is developing a simplified methodology of modeling supports, to fully take advantage of the opportunity of predicting distortion and fails, then mitigate them in design steps.

Relators: Christian Maria Firrone
Academic year: 2019/20
Publication type: Electronic
Number of Pages: 114
Additional Information: Tesi secretata. Fulltext non presente
Corso di laurea: Corso di laurea magistrale in Ingegneria Meccanica
Classe di laurea: New organization > Master science > LM-33 - MECHANICAL ENGINEERING
Aziende collaboratrici: Ge Avio Srl
URI: http://webthesis.biblio.polito.it/id/eprint/14201
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