AccScience Publishing / IJAMD / Volume 1 / Issue 3 / DOI: 10.36922/ijamd.4285
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ORIGINAL RESEARCH ARTICLE

Prediction of wall geometry for cold-metal-transfer-based wire-arc additive manufacturing

Robin Kromer1* ,  Eric Lacoste1
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1 Univ. Bordeaux, CNRS, Bordeaux INP, I2M, UMR 5295, F-33400, Talence, France
IJAMD 2024, 1(3), 20–32; https://doi.org/10.36922/ijamd.4285
Submitted: 19 July 2024 | Accepted: 2 September 2024 | Published: 10 October 2024
(This article belongs to the Special Issue AI Usage in the Analysis of the Additive Manufacturing Process)
© 2024 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Wire-arc additive manufacturing (WAAM) is an advanced technique for fabricating large metal components through layer-by-layer material deposition using arc welding methods. This study focused on optimizing the WAAM process by employing machine learning models to predict and control bead geometries, specifically bead height (BH) and bead width (BW), while ensuring consistent height increments in multibead walls. Based on CMT technology in cold metal transfer experiments, linear regression models achieved high accuracy in predicting BH and BW. Analysis of variance results highlighted the considerable influence of voltage (V) and travel speed (TS) on bead geometries. For multibead wall characteristics, polynomial regression models incorporating non-linear terms, such as travel speed (TS²) and dwell time (Dt²), were developed to predict height (H) and waviness (W). Various optimization metrics were employed to balance the trade-offs between H and W for identifying optimal welding conditions that achieved the target H while minimizing W. A notable innovation of this research is the optimization of dwell time (Dt) for each layer to achieve a linear incremental H profile, minimizing W and ensuring consistent layer quality.

Keywords
Machine learning
Dwell time
Bead geometry
Process modeling
Wire-arc additive manufacturing
Funding
None.
Conflict of interest
The authors declare no competing interests.
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International Journal of AI for Materials and Design, Electronic ISSN: 3029-2573 Print ISSN: 3041-0746, Published by AccScience Publishing
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