TY - GEN
T1 - Desarrollo de técnicas de Inteligencia Artificial (IA) para su implementación en los procesos de fabricación aditiva
AU - Fernández-Zabalza, Aitor
AU - Veiga, Fernando
AU - Suarez, Alfredo
AU - Alfaro, José Ramón
N1 - Publisher Copyright:
© 2024 by the authors.
PY - 2024
Y1 - 2024
N2 - This work focuses on the development of Artificial Intelligence (AI) techniques applied to directed energy deposition (DED) processes in additive manufacturing. The research aims to find correlations through AI models between process parameters and the geometry of the additive layer. By implementing machine learning algorithms, complex relationships are explored, linking factors such as deposition speed, temperature, and other process parameters to the geometric characteristics of the deposited layers. This approach optimizes the quality and efficiency of additive manufacturing by precisely understanding how adjustments in process parameters impact the final structure. Promising results indicate that AI plays a crucial role in the continuous improvement of DED processes, paving the way for a more efficient and precise additive manufacturing.
AB - This work focuses on the development of Artificial Intelligence (AI) techniques applied to directed energy deposition (DED) processes in additive manufacturing. The research aims to find correlations through AI models between process parameters and the geometry of the additive layer. By implementing machine learning algorithms, complex relationships are explored, linking factors such as deposition speed, temperature, and other process parameters to the geometric characteristics of the deposited layers. This approach optimizes the quality and efficiency of additive manufacturing by precisely understanding how adjustments in process parameters impact the final structure. Promising results indicate that AI plays a crucial role in the continuous improvement of DED processes, paving the way for a more efficient and precise additive manufacturing.
KW - Additive Manufacturing
KW - Artificial Intelligence (AI)
KW - Directed Energy Deposition (DED)
KW - Machine Learning
KW - Optimization
KW - Process Parameters
UR - http://www.scopus.com/inward/record.url?scp=85212950601&partnerID=8YFLogxK
M3 - Contribución a la conferencia
AN - SCOPUS:85212950601
T3 - Proceedings from the International Congress on Project Management and Engineering
SP - 714
EP - 725
BT - Proceedings from the 28th International Congress on Project Management and Engineering, CIDIP 2024
PB - Asociacion Espanola de Direccion e Ingenieria de Proyectos (AEIPRO)
T2 - 28th International Congress on Project Management and Engineering, CIDIP 2024
Y2 - 3 July 2024 through 4 July 2024
ER -
