Forecasting FSW Material’s Behavior using an Artificial Intelligence-Driven Approach

Abdelhakim Dorbane; Fouzi Harrou; Ying Sun

doi:10.1109/DASA54658.2022.9765072

Forecasting FSW Material’s Behavior using an Artificial Intelligence-Driven Approach

Abdelhakim Dorbane, Fouzi Harrou, Ying Sun

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

A flexible data-driven methodology was developed to forecast the mechanical behavior of an aluminum alloy, namely Al6061-T6, in the case of friction stir welding. Specifically, Gated recurrent unit (GRU), a deep learning model, was investigated in this study. This is the first time the GRU model has been used to forecast the stress-strain curve of a material. The major features of the GRU consist in its ability to model time-series data and rely only on historical and actual data from the investigated material. The performance of the GRU model has been demonstrated based on actual data collected by conducting uniaxial tensile testing on the base material, and friction stirred welded, both tested at a deformation speed of 10 −3 s −1 . Forecasting tensile tests results showed promising and accurate results of the GRU-driven forecasting.

Original language	English (US)
Title of host publication	2022 International Conference on Decision Aid Sciences and Applications (DASA)
Publisher	IEEE
ISBN (Print)	978-1-6654-9502-8
DOIs	https://doi.org/10.1109/DASA54658.2022.9765072
State	Published - May 2 2022

Bibliographical note

KAUST Repository Item: Exported on 2022-05-10
Acknowledged KAUST grant number(s): OSR-2019-CRG7-3800
Acknowledgements: Supported by King Abdullah University of Science and Technology (KAUST), Office of Sponsored Research (OSR) under Award No: OSR-2019-CRG7-3800.

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10.1109/DASA54658.2022.9765072

Cite this

@inproceedings{72b0f330d33348ff84817f11e8325652,

title = "Forecasting FSW Material{\textquoteright}s Behavior using an Artificial Intelligence-Driven Approach",

abstract = "A flexible data-driven methodology was developed to forecast the mechanical behavior of an aluminum alloy, namely Al6061-T6, in the case of friction stir welding. Specifically, Gated recurrent unit (GRU), a deep learning model, was investigated in this study. This is the first time the GRU model has been used to forecast the stress-strain curve of a material. The major features of the GRU consist in its ability to model time-series data and rely only on historical and actual data from the investigated material. The performance of the GRU model has been demonstrated based on actual data collected by conducting uniaxial tensile testing on the base material, and friction stirred welded, both tested at a deformation speed of 10 −3 s −1 . Forecasting tensile tests results showed promising and accurate results of the GRU-driven forecasting.",

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

note = "KAUST Repository Item: Exported on 2022-05-10 Acknowledged KAUST grant number(s): OSR-2019-CRG7-3800 Acknowledgements: Supported by King Abdullah University of Science and Technology (KAUST), Office of Sponsored Research (OSR) under Award No: OSR-2019-CRG7-3800.",

year = "2022",

month = may,

day = "2",

doi = "10.1109/DASA54658.2022.9765072",

language = "English (US)",

isbn = "978-1-6654-9502-8",

booktitle = "2022 International Conference on Decision Aid Sciences and Applications (DASA)",

publisher = "IEEE",

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T1 - Forecasting FSW Material’s Behavior using an Artificial Intelligence-Driven Approach

AU - Dorbane, Abdelhakim

AU - Harrou, Fouzi

AU - Sun, Ying

N1 - KAUST Repository Item: Exported on 2022-05-10 Acknowledged KAUST grant number(s): OSR-2019-CRG7-3800 Acknowledgements: Supported by King Abdullah University of Science and Technology (KAUST), Office of Sponsored Research (OSR) under Award No: OSR-2019-CRG7-3800.

PY - 2022/5/2

Y1 - 2022/5/2

N2 - A flexible data-driven methodology was developed to forecast the mechanical behavior of an aluminum alloy, namely Al6061-T6, in the case of friction stir welding. Specifically, Gated recurrent unit (GRU), a deep learning model, was investigated in this study. This is the first time the GRU model has been used to forecast the stress-strain curve of a material. The major features of the GRU consist in its ability to model time-series data and rely only on historical and actual data from the investigated material. The performance of the GRU model has been demonstrated based on actual data collected by conducting uniaxial tensile testing on the base material, and friction stirred welded, both tested at a deformation speed of 10 −3 s −1 . Forecasting tensile tests results showed promising and accurate results of the GRU-driven forecasting.

AB - A flexible data-driven methodology was developed to forecast the mechanical behavior of an aluminum alloy, namely Al6061-T6, in the case of friction stir welding. Specifically, Gated recurrent unit (GRU), a deep learning model, was investigated in this study. This is the first time the GRU model has been used to forecast the stress-strain curve of a material. The major features of the GRU consist in its ability to model time-series data and rely only on historical and actual data from the investigated material. The performance of the GRU model has been demonstrated based on actual data collected by conducting uniaxial tensile testing on the base material, and friction stirred welded, both tested at a deformation speed of 10 −3 s −1 . Forecasting tensile tests results showed promising and accurate results of the GRU-driven forecasting.

UR - http://hdl.handle.net/10754/676711

UR - https://ieeexplore.ieee.org/document/9765072/

U2 - 10.1109/DASA54658.2022.9765072

DO - 10.1109/DASA54658.2022.9765072

M3 - Conference contribution

SN - 978-1-6654-9502-8

BT - 2022 International Conference on Decision Aid Sciences and Applications (DASA)

PB - IEEE

ER -

Forecasting FSW Material’s Behavior using an Artificial Intelligence-Driven Approach

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