Prediction of Weld Geometry in Laser Overlap Welding of Low-Carbon Galvanized Steel
Accurate prediction of weld bead geometry is critical for optimizing laser overlap welding of low-carbon galvanized steel, as it directly affects joint quality and mechanical performance. Traditional finite element method (FEM)-based models provide reliable predictions but are computationally expens...
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MDPI AG
2025-04-01
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| Series: | Metals |
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| Online Access: | https://www.mdpi.com/2075-4701/15/4/447 |
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| author | Kamel Oussaid Narges Omidi Abderrazak El Ouafi Noureddine Barka |
| author_facet | Kamel Oussaid Narges Omidi Abderrazak El Ouafi Noureddine Barka |
| author_sort | Kamel Oussaid |
| collection | DOAJ |
| description | Accurate prediction of weld bead geometry is critical for optimizing laser overlap welding of low-carbon galvanized steel, as it directly affects joint quality and mechanical performance. Traditional finite element method (FEM)-based models provide reliable predictions but are computationally expensive and impractical for real-time applications. This study presents an artificial neural network (ANN)-based predictive model trained on a combination of experimental data and validated FEM simulations to estimate key weld characteristics, including depth of penetration (DOP), weld bead width at the surface (WS), and weld bead width at the interface (WI). The ANN model was evaluated using various improved statistical metrics. Results demonstrated a strong correlation between ANN predictions and experimental measurements, with R<sup>2</sup> values exceeding 95% for WS and DOP and 92% for WI, and mean errors below 7%. A comparative analysis between ANN, FEM, and experimental data confirmed the model’s reliability across different welding conditions. Additionally, ANN significantly reduced computational time compared to FEM while maintaining high accuracy, making it a practical tool for real-time process optimization. These findings highlight the potential of ANN models as efficient alternatives to conventional simulation techniques in laser overlap welding applications. Future improvements may involve integrating real-time sensor data and deep learning techniques to further enhance predictive performance. |
| format | Article |
| id | doaj-art-84688e81a3da4e58a3ae7eafa926bf4e |
| institution | DOAJ |
| issn | 2075-4701 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| series | Metals |
| spelling | doaj-art-84688e81a3da4e58a3ae7eafa926bf4e2025-08-20T03:13:52ZengMDPI AGMetals2075-47012025-04-0115444710.3390/met15040447Prediction of Weld Geometry in Laser Overlap Welding of Low-Carbon Galvanized SteelKamel Oussaid0Narges Omidi1Abderrazak El Ouafi2Noureddine Barka3Department of Mathematics, Computer Science and Engineering, Université du Québec à Rimouski, Rimouski, QC G5L 3A1, CanadaDepartment of Mathematics, Computer Science and Engineering, Université du Québec à Rimouski, Rimouski, QC G5L 3A1, CanadaDepartment of Mathematics, Computer Science and Engineering, Université du Québec à Rimouski, Rimouski, QC G5L 3A1, CanadaDepartment of Mathematics, Computer Science and Engineering, Université du Québec à Rimouski, Rimouski, QC G5L 3A1, CanadaAccurate prediction of weld bead geometry is critical for optimizing laser overlap welding of low-carbon galvanized steel, as it directly affects joint quality and mechanical performance. Traditional finite element method (FEM)-based models provide reliable predictions but are computationally expensive and impractical for real-time applications. This study presents an artificial neural network (ANN)-based predictive model trained on a combination of experimental data and validated FEM simulations to estimate key weld characteristics, including depth of penetration (DOP), weld bead width at the surface (WS), and weld bead width at the interface (WI). The ANN model was evaluated using various improved statistical metrics. Results demonstrated a strong correlation between ANN predictions and experimental measurements, with R<sup>2</sup> values exceeding 95% for WS and DOP and 92% for WI, and mean errors below 7%. A comparative analysis between ANN, FEM, and experimental data confirmed the model’s reliability across different welding conditions. Additionally, ANN significantly reduced computational time compared to FEM while maintaining high accuracy, making it a practical tool for real-time process optimization. These findings highlight the potential of ANN models as efficient alternatives to conventional simulation techniques in laser overlap welding applications. Future improvements may involve integrating real-time sensor data and deep learning techniques to further enhance predictive performance.https://www.mdpi.com/2075-4701/15/4/447laser overlap weldinglow-carbon galvanized steelweld bead geometryartificial neural networkpredictive modelingfinite element method |
| spellingShingle | Kamel Oussaid Narges Omidi Abderrazak El Ouafi Noureddine Barka Prediction of Weld Geometry in Laser Overlap Welding of Low-Carbon Galvanized Steel Metals laser overlap welding low-carbon galvanized steel weld bead geometry artificial neural network predictive modeling finite element method |
| title | Prediction of Weld Geometry in Laser Overlap Welding of Low-Carbon Galvanized Steel |
| title_full | Prediction of Weld Geometry in Laser Overlap Welding of Low-Carbon Galvanized Steel |
| title_fullStr | Prediction of Weld Geometry in Laser Overlap Welding of Low-Carbon Galvanized Steel |
| title_full_unstemmed | Prediction of Weld Geometry in Laser Overlap Welding of Low-Carbon Galvanized Steel |
| title_short | Prediction of Weld Geometry in Laser Overlap Welding of Low-Carbon Galvanized Steel |
| title_sort | prediction of weld geometry in laser overlap welding of low carbon galvanized steel |
| topic | laser overlap welding low-carbon galvanized steel weld bead geometry artificial neural network predictive modeling finite element method |
| url | https://www.mdpi.com/2075-4701/15/4/447 |
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