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HomeKey Engineering MaterialsKey Engineering Materials Vol. 1050Parameter Optimization and Data-Driven Soft-Sensor...

Parameter Optimization and Data-Driven Soft-Sensor Framework for Torque Prediction in Bobbin-Tool Friction Stir Welding of AA2024

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Abstract:

Bobbin-Tool Friction Stir Welding (BT-FSW) is a solid-state joining process in which axial forces are internally balanced by the tool, eliminating the need for a backing plate and enabling the joining of hollow aerospace structures. Owing to the coupled thermo-mechanical nature of the process, weld stability and quality are governed by the interaction between process parameters and the resulting torque response, which is difficult to assess in situ using conventional sensing alone. BT-FSW experiments were performed on AA2024-T351 sheets with thicknesses of 2.4, 2.8 and 3.6 mm using a structured Design of Experiments (DoE). The 3.6 mm joints achieved approximately 90 % of the base-material strength, while the 2.4 and 2.8 mm joints reached about 80 % and 85 %, respectively. These mechanical results were used as ground truth to train machine learning regression models for steady-state torque predictions. By augmenting nominal process parameters with force-derived features, the proposed soft-sensing framework achieved strong agreement between predicted and measured torque, demonstrating that compact, physics-based feature engineering enables reliable prediction under limited experimental data conditions.

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Periodical:

Key Engineering Materials (Volume 1050)

Pages:

69-80

DOI:

https://doi.org/10.4028/p-1eNlcu

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Cite this paper

Online since:

April 2026

Authors:

Fares Nouira*, Luciano Bergmann, Frederic E. Bock, Ghada Bouattour, Marco Pacchione, Benjamin Klusemann

Keywords:

Aerospace Aluminum Alloys, Bobbin-Tool Friction Stir Welding, Machine Learning, Process Monitoring, Process Parameter Optimization, Soft-Sensor Framework, Torque Prediction

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Creative Commons CC BY 4.0

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* - Corresponding Author

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