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International Journal of AI for
                                                                            Materials and Design





                                        ORIGINAL RESEARCH ARTICLE
                                        Prediction of wall geometry for

                                        cold-metal-transfer-based wire-arc additive
                                        manufacturing



                                        Robin Kromer* and Eric Lacoste

                                        Univ. Bordeaux, CNRS, Bordeaux INP, I2M, UMR 5295, F-33400, Talence, France
                                        (This article belongs to the Special Issue: AI Usage in the Analysis of the Additive Manufacturing
                                        Process)





                                        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
            *Corresponding author:      regression models incorporating non-linear terms, such as travel speed (TS²) and
            Robin Kromer                dwell time (Dt²), were developed to predict height (H) and waviness (W). Various
            (robin.kromer@u-bordeaux.fr)  optimization metrics were employed to balance the trade-offs between H and W for
            Citation: Kromer R, Lacoste E.   identifying optimal welding conditions that achieved the target H while minimizing
            Prediction of wall geometry for   W. A notable innovation of this research is the optimization of dwell time (Dt) for each
            cold-metal-transfer-based wire-arc
            additive manufacturing.     layer to achieve a linear incremental H profile, minimizing W and ensuring consistent
            Int J AI Mater Design. 2024;1(3):   layer quality.
            20-32.
            doi: 10.36922/ijamd.4285
            Received: July 19, 2024     Keywords: Machine learning; Dwell time; Bead geometry; Process modeling; Wire-arc
                                        additive manufacturing
            Accepted: September 2, 2024
            Published Online: October 10,
            2024
            Copyright: © 2024 Author(s).   1. Introduction
            This is an Open-Access article
            distributed under the terms of the   Wire-arc additive manufacturing (WAAM) is a process that creates large metal parts
            Creative Commons Attribution   by depositing the associated material in successive layers based on arc metal transfer.
            License, permitting distribution,   This technique offers several advantages, including optimized raw part designs, reduced
            and reproduction in any medium,
                                                                                1
            provided the original work is   material waste, and high production efficiency.  WAAM employs various welding
            properly cited.             techniques to melt metal wires, including gas metal arc welding (GMAW), gas tungsten
            Publisher’s Note: AccScience   arc welding, and plasma arc welding. Among these, GMAW is particularly preferred
            Publishing remains neutral with   for its high material deposition rate.  A variation of GMAW, called cold metal transfer
                                                                     2
            regard to jurisdictional claims in
            published maps and institutional   (CMT), features a controlled dip transfer mode, making it popular in WAAM owing to
            affiliations.               its low heat input and reduced spatter. 3


            Volume 1 Issue 3 (2024)                         20                             doi: 10.36922/ijamd.4285
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