20.500.12556/DKUM-92056 Predictive modelling of weld bead geometry in wire arc additive manufacturing This study investigates the predictive modelling of weld bead geometry in wire arc additive manufacturing (WAAM) through advanced machine learning methods. While WAAM is valued for its ability to produce large, complex metal parts with high deposition rates, precise control of the weld bead remains a critical challenge due to its influence on mechanical properties and dimensional accuracy. To address this problem, this study utilized machine learning approaches—Ridge regression, Lasso regression and Bayesian ridge regression, Random Forest and XGBoost—to predict the key weld bead characteristics, namely height, width and cross-sectional area. A Design of experiments (DOE) was used to systematically vary the welding current and travelling speed, with 3D weld bead geometries captured by laser scanning. Robust data pre-processing, including outlier detection and feature engineering, improved modelling accuracy. Among the models tested, XGBoost provided the highest prediction accuracy, emphasizing its potential for real-time control of WAAM processes. Overall, this study presents a comprehensive framework for predictive modelling and provides valuable insights for process optimization and the further development of intelligent manufacturing systems. wire arc additive manufacturing WA AM predictive modelling machine learning weld bead geometry XGBoost aditivna proizvodnja žičnih oblokov strojno učenje geometrija zvara true false true MDPI Angleški jezik Slovenski jezik https://www.mdpi.com/journal/jmmp © 2025 by the authors Članek v reviji 2025-03-13 13:19:48 2025-03-13 13:19:48 2026-01-09 08:44:52 0000-00-00 00:00:00 2025 0 0 21 str. iss. 2, [article no.] 67 Vol. 9 Feb. 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2025-02-01 2025-02-17 2025-02-19 621.74:669.715 228875523 10.3390/jmmp9020067 2504-4494 jmmp-09-00067.pdf jmmp-09-00067.pdf 1 5DED33BACF8C50A26729D5CB13CE6497 f97e365552734e05a886323bd3d710ad5cc61767c31216d6e9169923b317bd20 b8dbb90b-0005-11f0-82d0-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=189255 https://www.mdpi.com/2504-4494/9/2/67 0 15227c95-ed2f-11f0-8729-00155d580e02 https://dk.um.si/Dokument.php?lang=slv&id=201854 Fakulteta za strojništvo 0 0 0