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B-SPLINE BASED METAMODEL OF THE THERMAL ANALYSIS OF THE WIRE ARC ADDITIVE MANUFACTURING PROCESS

Published online by Cambridge University Press:  19 June 2023

Mathilde Zani*
Affiliation:
Arts et Métiers Institute of Technology, Université de Bordeaux, CNRS, INRA, Bordeaux INP, HESAM Université, I2M UMR, F-33405 Talence, France;
Marco Montemurro
Affiliation:
Arts et Métiers Institute of Technology, Université de Bordeaux, CNRS, INRA, Bordeaux INP, HESAM Université, I2M UMR, F-33405 Talence, France;
Enrico Panettieri
Affiliation:
Arts et Métiers Institute of Technology, Université de Bordeaux, CNRS, INRA, Bordeaux INP, HESAM Université, I2M UMR, F-33405 Talence, France;
Philippe Marin
Affiliation:
Grenoble Alpes - Laboratoire G-SCOP UMR 5272, F-38000 Grenoble, France
*
Zani, Mathilde, I2M laboratory, France, mathilde.zani@ensam.eu

Abstract

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Among additive manufacturing processes, wire arc additive manufacturing (WAAM) is one of the most promising methods for manufacturing complex near-net-shape parts, as it allows the layer-by-layer deposition of welded material at a high deposition rate. However, this technology is highly dependent on deposition conditions and thermomechanical phenomena during the process. Therefore, process simulation could be used to analyse the effects of different deposition parameters on the thermomechanical results to optimise the process. However, as the computing time required for this study may become prohibitive, a dedicated strategy is needed to reduce it while maintaining a good level of accuracy. In this study, only the thermal analysis of the process is investigated. An efficient metamodel based on B-spline entities is developed to emulate the thermal response of the WAAM process when building a mild steel four-layer wall structure. Thanks to B-spline entities, the temperature profile at different locations is approximated as a function of a subset of deposition parameters of WAAM process, and the results are compared with the simulated temperature profile resulting from a validation dataset.

Type
Article
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

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