A Tree-Driven Ensemble Learning Approach to Predict FS Welded Al-6061-T6 Material Behavior

Abdelhakim Dorbane, Fouzi Harrou, Ying Sun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


This paper proposes a machine learning approach to forecast the mechanical behavior of an aluminum alloy, Al6061-T6, in the case of friction stir welding. Essentially, we investigate the performance of the bagged trees regression (BT) in forecasting the stress-strain curve of an aluminum alloy. This choice's motivation is due to BT's ability to improve the performance of machine learning models by combining multiple learners versus single regressors. Actual data was gathered by performing uniaxial tensile testing on both base material and joined using FSW at a deformation speed of 10−3s−1. Then, the performance of the BT model is compared to that of the Support Vector regression, and it proved to be more accurate.
Original languageEnglish (US)
Title of host publication2022 7th International Conference on Frontiers of Signal Processing (ICFSP)
ISBN (Print)978-1-6654-8159-5
StatePublished - Oct 28 2022

Bibliographical note

KAUST Repository Item: Exported on 2022-11-02
Acknowledged KAUST grant number(s): OSR2019-CRG7-3800
Acknowledgements: This publication is based upon work supported by King Abdullah University of Science and Technology (KAUST), Office of Sponsored Research (OSR) under Award No: OSR2019-CRG7-3800.


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