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

Abdelhakim Dorbane; Fouzi Harrou; Ying Sun

doi:10.1109/ICFSP55781.2022.9924883

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 proceeding › Conference contribution

7 Scopus citations

Abstract

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 language	English (US)
Title of host publication	2022 7th International Conference on Frontiers of Signal Processing (ICFSP)
Publisher	IEEE
ISBN (Print)	978-1-6654-8159-5
DOIs	https://doi.org/10.1109/ICFSP55781.2022.9924883
State	Published - 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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10.1109/ICFSP55781.2022.9924883

Cite this

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

abstract = "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.",

author = "Abdelhakim Dorbane and Fouzi Harrou and Ying Sun",

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.",

year = "2022",

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doi = "10.1109/ICFSP55781.2022.9924883",

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AU - Dorbane, Abdelhakim

AU - Harrou, Fouzi

AU - Sun, Ying

N1 - 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.

PY - 2022/10/28

Y1 - 2022/10/28

N2 - 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.

AB - 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.

UR - http://hdl.handle.net/10754/685367

UR - https://ieeexplore.ieee.org/document/9924883/

U2 - 10.1109/ICFSP55781.2022.9924883

DO - 10.1109/ICFSP55781.2022.9924883

M3 - Conference contribution

SN - 978-1-6654-8159-5

BT - 2022 7th International Conference on Frontiers of Signal Processing (ICFSP)

PB - IEEE

ER -

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

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