Original Article

Multi-objective optimization of WAAM-CMT parameters: a comparative study of RSM and NSGA-II algorithm for bead geometry control

¹ University of Sousse, LMS, ENISo, BP 264 Erriadh, 4023 Sousse, Tunisia
² University of Monastir, LGM, ENIM, Avenue Ibn-Eljazzar. 5019 Monastir, Tunisia
³ University of Lyon, Ecole Centrale de Lyon, CNRS, LTDS, ENISE, 42023 Saint-Etienne, France

^* Corresponding author: oussamatrad01@gmail.com

Received: 25 August 2025
Accepted: 27 October 2025

Abstract

Wire-arc additive manufacturing (WAAM) with cold metal transfer (CMT) has attracted considerable attention owing to its ability to produce complex shapes with minimal geometric defects, particularly in the fabrication of high-value, large-scale components. This study aimed to optimize bead geometry variations in WAAM-CMT by employing a full factorial design of experiments to generate a comprehensive dataset for the training of machine learning algorithms. The RSM method and NSGA-II algorithm were utilized to optimize key process parameters, such as the wire feed rate, welding speed, and stand-off distance. The objective was to achieve different bead geometry configurations for testing and evaluating each optimization method. Two optimization settings were employed for each method: one aimed to achieve wetting angles close to 90°, maximizing the bead height and minimizing the bead width, which is crucial for printing straight, thin-walled structures with precise dimensions and minimal defects, while the second aimed to achieve a larger bead with minimal height and low angles suitable for thick printing parts. The results indicate that RSM outperforms NSGA-II in minimizing bead height and maximizing bead width, with deviations of 3.17% and 13.29%, respectively, compared to NSGA-II’s 3.70% and 38.58%, respectively. However, NSGA-II excels at minimizing bead width and controlling alpha 2 and maximum temperature, achieving lower deviations of 6.40%, 0.80%, and 4.0%, respectively. This study demonstrates the potential for integrating machine learning techniques to refine WAAM processes and enhance the quality and reliability of additive manufacturing.