Application of Symmetric Neural Networks for Bead Geometry Determination in Wire and Arc Additive Manufacturing (WAAM)

Fernández-Zabalza, Aitor; Veiga, Fernando; Suárez, Alfredo; Uralde, Virginia; Sandua, Xabier; Alfaro, José Ramón

doi:10.3390/sym17030326

Open AccessArticle

Application of Symmetric Neural Networks for Bead Geometry Determination in Wire and Arc Additive Manufacturing (WAAM)

by

Aitor Fernández-Zabalza

¹,

Fernando Veiga

^1,*

,

Alfredo Suárez

²

,

Virginia Uralde

¹

,

Xabier Sandua

¹

and

José Ramón Alfaro

¹

Department of Engineering, Public University of Navarre, Los Pinos Building, Campus Arrosadía, E31006 Pamplona, Spain

²

TECNALIA, Basque Research and Technology Alliance (BRTA), Parque Científico, Parque Científico Tecnológico de Gipuzkoa, E20009 Saint Sebastian, Spain

^*

Author to whom correspondence should be addressed.

Symmetry 2025, 17(3), 326; https://doi.org/10.3390/sym17030326

Submission received: 30 December 2024 / Revised: 21 January 2025 / Accepted: 19 February 2025 / Published: 21 February 2025

(This article belongs to the Section Engineering and Materials)

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Abstract

The accurate prediction of weld bead geometry is crucial for ensuring the quality and consistency of wire and arc additive manufacturing (WAAM), a specific form of directed energy deposition (DED) that utilizes arc welding. Despite advancements in process control, predicting the shape and dimensions of weld beads remains challenging due to the complex interactions between process parameters and material behavior. This paper addresses this challenge by exploring the application of symmetrical neural networks to enhance the accuracy and reliability of geometric predictions in WAAM. By leveraging advanced machine learning techniques and incorporating the inherent symmetry of the welding process, the proposed models aim to precisely forecast weld bead geometry. The use of neuronal networks and experimental validation demonstrate the potential of symmetrical neural networks to improve prediction precision, contributing to more consistent and optimized WAAM outcomes.

Keywords: weld bead geometry prediction; machine learning in additive manufacturing; process parameter optimization in arc-DED

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MDPI and ACS Style

Fernández-Zabalza, A.; Veiga, F.; Suárez, A.; Uralde, V.; Sandua, X.; Alfaro, J.R. Application of Symmetric Neural Networks for Bead Geometry Determination in Wire and Arc Additive Manufacturing (WAAM). Symmetry 2025, 17, 326. https://doi.org/10.3390/sym17030326

AMA Style

Fernández-Zabalza A, Veiga F, Suárez A, Uralde V, Sandua X, Alfaro JR. Application of Symmetric Neural Networks for Bead Geometry Determination in Wire and Arc Additive Manufacturing (WAAM). Symmetry. 2025; 17(3):326. https://doi.org/10.3390/sym17030326

Chicago/Turabian Style

Fernández-Zabalza, Aitor, Fernando Veiga, Alfredo Suárez, Virginia Uralde, Xabier Sandua, and José Ramón Alfaro. 2025. "Application of Symmetric Neural Networks for Bead Geometry Determination in Wire and Arc Additive Manufacturing (WAAM)" Symmetry 17, no. 3: 326. https://doi.org/10.3390/sym17030326

APA Style

Fernández-Zabalza, A., Veiga, F., Suárez, A., Uralde, V., Sandua, X., & Alfaro, J. R. (2025). Application of Symmetric Neural Networks for Bead Geometry Determination in Wire and Arc Additive Manufacturing (WAAM). Symmetry, 17(3), 326. https://doi.org/10.3390/sym17030326

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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Application of Symmetric Neural Networks for Bead Geometry Determination in Wire and Arc Additive Manufacturing (WAAM)

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