| Title: | Predictive modelling of weld bead geometry in wire arc additive manufacturing |
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| Authors: | ID Šket, Kristijan (Author)
ID Brezočnik, Miran (Author)
ID Karner, Timi (Author)
ID Belšak, Rok (Author)
ID Ficko, Mirko (Author)
ID Vuherer, Tomaž (Author)
ID Gotlih, Janez (Author) |
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| Files: |  jmmp-09-00067.pdf (3,54 MB)
MD5: 5DED33BACF8C50A26729D5CB13CE6497
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| Language: | English |
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| Work type: | Unknown |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FS - Faculty of Mechanical Engineering
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| Abstract: | 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. |
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| Keywords: | wire arc additive manufacturing, WA AM, predictive modelling, machine learning, weld bead geometry, XGBoost |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 13.02.2025 |
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| Article acceptance date: | 17.02.2025 |
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| Publication date: | 19.02.2025 |
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| Year of publishing: | 2025 |
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| Number of pages: | 21 str. |
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| Numbering: | Vol. 9, iss. 2, [article no.] 67 |
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| PID: | 20.500.12556/DKUM-92056  |
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| UDC: | 621.74:669.715 |
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| ISSN on article: | 2504-4494 |
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| COBISS.SI-ID: | 228875523 |
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| DOI: | 10.3390/jmmp9020067 |
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| Copyright: | © 2025 by the authors |
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| Publication date in DKUM: | 13.03.2025 |
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| Views: | 0 |
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| Downloads: | 6 |
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| Metadata: |    |
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| Categories: | Misc.
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