Data-Driven Prediction of Tensile Strength and Hardness in Ultrasonic Vibration-Assisted Friction Stir Welding of AA6082-T6

Shrief, Eman El, Fadel, Omnia O, Baraya, Mohamed, El-Asfoury, Mohamed S and Abass, Ahmed ORCID: 0000-0002-8622-4632 (2026) Data-Driven Prediction of Tensile Strength and Hardness in Ultrasonic Vibration-Assisted Friction Stir Welding of AA6082-T6 JOURNAL OF MANUFACTURING AND MATERIALS PROCESSING, 10 (4). p. 123. ISSN 2504-4494, 2504-4494

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Abstract

This work investigates how ultrasonic vibration can enhance friction stir welding (FSW) of an AA6082-T6 aluminium alloy and develops a data-driven tool to predict joint performance from process settings. A custom ultrasonic transducer and horn were designed and tuned using finite element modal and harmonic analyses, confirming a strong longitudinal resonance near 27.9 kHz with a tip amplitude of about 46 µm. A 27-run factorial experiment varied tool rotation (600–900 rpm), welding speed (45–55 mm/min), and plunge depth (0.10–0.25 mm). Welded joints were assessed using tensile strength and Vickers hardness. Four predictive models, support vector regression (SVR), Gaussian process regression (GPR), artificial neural networks (ANNs), and multiple linear regression (MLR) were trained and compared under five-fold cross-validation. The best joint quality was obtained at 900 rpm, 55 mm/min, and a 0.25 mm plunge depth, yielding a tensile strength of 188.7 MPa and a hardness of 102 HV. Overall, MLR provided the strongest predictive performance while remaining interpretable (UTS R² = 0.81, RMSE = 11.84 MPa; hardness R² = 0.67, RMSE = 2.36 HV), matching the ANN for UTS prediction and outperforming the ANN, GPR, and SVR for hardness. A coupling physics-based ultrasonic design with an interpretable predictive model offers a practical route to reduce trial and error, improve parameter selection, and accelerate the process development for ultrasonic vibration-assisted FSW of aluminium alloys; however, modest models can outperform complex ones when the dataset is limited.

Item Type:	Article
Uncontrolled Keywords:	friction stir welding, aluminium alloy, ultrasonic vibration, mechanical characterisation, numerical simulation, artificial neural network, factorial experimental design
Divisions:	Faculty of Science & Engineering Faculty of Science & Engineering > School of Engineering Faculty of Science & Engineering > School of Engineering > Materials, Design and Manufacturing Eng
Depositing User:	Symplectic Admin
Date Deposited:	01 Apr 2026 13:24
Last Modified:	23 May 2026 11:17
DOI:	10.3390/jmmp10040123
Open Access URL:	https://www.mdpi.com/2504-4494/10/4/123
Related Websites:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3197810
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