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

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    Type
    Conference Paper
    Authors
    Dorbane, Abdelhakim
    Harrou, Fouzi cc
    Sun, Ying cc
    KAUST Department
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Statistics Program
    KAUST Grant Number
    OSR-2019-CRG7-3800
    Date
    2022-05-02
    Permanent link to this record
    http://hdl.handle.net/10754/676711
    
    Metadata
    Show full item record
    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.
    Citation
    Dorbane, A., Harrou, F., & Sun, Y. (2022). Forecasting FSW Material’s Behavior using an Artificial Intelligence-Driven Approach. 2022 International Conference on Decision Aid Sciences and Applications (DASA). https://doi.org/10.1109/dasa54658.2022.9765072
    Sponsors
    Supported by King Abdullah University of Science and Technology (KAUST), Office of Sponsored Research (OSR) under Award No: OSR-2019-CRG7-3800.
    Publisher
    IEEE
    Conference/Event name
    2022 International Conference on Decision Aid Sciences and Applications (DASA)
    ISBN
    978-1-6654-9502-8
    DOI
    10.1109/DASA54658.2022.9765072
    Additional Links
    https://ieeexplore.ieee.org/document/9765072/
    https://ieeexplore.ieee.org/document/9765072/
    https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9765072
    ae974a485f413a2113503eed53cd6c53
    10.1109/DASA54658.2022.9765072
    Scopus Count
    Collections
    Conference Papers; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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