Desarrollo de técnicas de Inteligencia Artificial (IA) para su implementación en los procesos de fabricación aditiva

Aitor Fernández-Zabalza; Fernando Veiga; Alfredo Suarez; José Ramón Alfaro

Desarrollo de técnicas de Inteligencia Artificial (IA) para su implementación en los procesos de fabricación aditiva

Translated title of the contribution: Development of Artificial Intelligence (AI) Techniques for Implementation in Directed Energy Deposition (DED) Processes for Additive Manufacturing

Aitor Fernández-Zabalza^*
, Fernando Veiga
, Alfredo Suarez
, José Ramón Alfaro

^*Corresponding author for this work

Smart manufacturing technologies

Public University of Navarre

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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.

Translated title of the contribution	Development of Artificial Intelligence (AI) Techniques for Implementation in Directed Energy Deposition (DED) Processes for Additive Manufacturing
Original language	Spanish
Title of host publication	Proceedings from the 28th International Congress on Project Management and Engineering, CIDIP 2024
Publisher	Asociacion Espanola de Direccion e Ingenieria de Proyectos (AEIPRO)
Pages	714-725
Number of pages	12
ISBN (Electronic)	9788409638772
Publication status	Published - 2024
Event	28th International Congress on Project Management and Engineering, CIDIP 2024 - Jaen, Spain Duration: 3 Jul 2024 → 4 Jul 2024

Publication series

Name	Proceedings from the International Congress on Project Management and Engineering
ISSN (Electronic)	2695-5067

Conference

Conference	28th International Congress on Project Management and Engineering, CIDIP 2024
Country/Territory	Spain
City	Jaen
Period	3/07/24 → 4/07/24

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Cite this

Fernández-Zabalza, A., Veiga, F., Suarez, A., & Alfaro, J. R. (2024). Desarrollo de técnicas de Inteligencia Artificial (IA) para su implementación en los procesos de fabricación aditiva. In Proceedings from the 28th International Congress on Project Management and Engineering, CIDIP 2024 (pp. 714-725). (Proceedings from the International Congress on Project Management and Engineering). Asociacion Espanola de Direccion e Ingenieria de Proyectos (AEIPRO).

Fernández-Zabalza, Aitor ; Veiga, Fernando ; Suarez, Alfredo et al. / Desarrollo de técnicas de Inteligencia Artificial (IA) para su implementación en los procesos de fabricación aditiva. Proceedings from the 28th International Congress on Project Management and Engineering, CIDIP 2024. Asociacion Espanola de Direccion e Ingenieria de Proyectos (AEIPRO), 2024. pp. 714-725 (Proceedings from the International Congress on Project Management and Engineering).

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title = "Desarrollo de t{\'e}cnicas de Inteligencia Artificial (IA) para su implementaci{\'o}n en los procesos de fabricaci{\'o}n aditiva",

abstract = "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.",

keywords = "Additive Manufacturing, Artificial Intelligence (AI), Directed Energy Deposition (DED), Machine Learning, Optimization, Process Parameters",

author = "Aitor Fern{\'a}ndez-Zabalza and Fernando Veiga and Alfredo Suarez and Alfaro, \{Jos{\'e} Ram{\'o}n\}",

note = "Publisher Copyright: {\textcopyright} 2024 by the authors.; 28th International Congress on Project Management and Engineering, CIDIP 2024 ; Conference date: 03-07-2024 Through 04-07-2024",

year = "2024",

language = "Espa{\~n}ol",

series = "Proceedings from the International Congress on Project Management and Engineering",

publisher = "Asociacion Espanola de Direccion e Ingenieria de Proyectos (AEIPRO)",

pages = "714--725",

booktitle = "Proceedings from the 28th International Congress on Project Management and Engineering, CIDIP 2024",

address = "Espana",

}

Fernández-Zabalza, A, Veiga, F, Suarez, A & Alfaro, JR 2024, Desarrollo de técnicas de Inteligencia Artificial (IA) para su implementación en los procesos de fabricación aditiva. in Proceedings from the 28th International Congress on Project Management and Engineering, CIDIP 2024. Proceedings from the International Congress on Project Management and Engineering, Asociacion Espanola de Direccion e Ingenieria de Proyectos (AEIPRO), pp. 714-725, 28th International Congress on Project Management and Engineering, CIDIP 2024, Jaen, Spain, 3/07/24.

Desarrollo de técnicas de Inteligencia Artificial (IA) para su implementación en los procesos de fabricación aditiva. / Fernández-Zabalza, Aitor; Veiga, Fernando; Suarez, Alfredo et al.
Proceedings from the 28th International Congress on Project Management and Engineering, CIDIP 2024. Asociacion Espanola de Direccion e Ingenieria de Proyectos (AEIPRO), 2024. p. 714-725 (Proceedings from the International Congress on Project Management and Engineering).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

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 - https://www.scopus.com/pages/publications/85212950601

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 -

Fernández-Zabalza A, Veiga F, Suarez A, Alfaro JR. Desarrollo de técnicas de Inteligencia Artificial (IA) para su implementación en los procesos de fabricación aditiva. In Proceedings from the 28th International Congress on Project Management and Engineering, CIDIP 2024. Asociacion Espanola de Direccion e Ingenieria de Proyectos (AEIPRO). 2024. p. 714-725. (Proceedings from the International Congress on Project Management and Engineering).

Desarrollo de técnicas de Inteligencia Artificial (IA) para su implementación en los procesos de fabricación aditiva

Abstract

Publication series

Conference

UN SDGs

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