dc.contributor
Universitat Politècnica de Catalunya. Departament d'Enginyeria Mecànica
dc.contributor
Domènech Mestres, Carles
dc.contributor.author
Dosset, Martin
dc.date.accessioned
2026-02-25T03:56:58Z
dc.date.available
2026-02-25T03:56:58Z
dc.date.issued
2026-01-27
dc.identifier
https://hdl.handle.net/2117/456132
dc.identifier
PRISMA-203339
dc.identifier.uri
https://hdl.handle.net/2117/456132
dc.description.abstract
This research assignment, conducted within the CASANDRA project, explores two key avenues for improving the manufacturing of large metal parts: the numerical modeling of welding processes and the use of Artificial Neural Networks for metal-sheet bending prediction. A bibliographic review provided insight into existing welding models and their limitations, particularly the need for experimentally calibrated parameters. The second part of the work focused on developing a simplified neural network based on theoretical bending equations to evaluate the potential of Machine Learning in forming operations. Although the results remained approximate, the study highlights the promise of AI-based methods for enhancing process accuracy and reducing operator dependency. Overall, the project emphasizes the value of combining simulation tools and data-driven approaches to support more efficient and modern industrial fabrication.
dc.description.abstract
Incoming
dc.format
application/pdf
dc.publisher
Universitat Politècnica de Catalunya
dc.subject
Àrees temàtiques de la UPC::Enginyeria mecànica
dc.subject
Neural networks (Computer science)
dc.subject
Strength of materials
dc.subject
Finite element method
dc.subject
Xarxes neuronals (Informàtica)
dc.subject
Resistència de materials
dc.subject
Elements finits, Mètode dels
dc.title
Use of AI and FEM finite elements method in the design of large-size parts