Forecasting FSW Material’s Behavior using an Artificial Intelligence-Driven Approach
Type
Conference PaperKAUST Department
Computer, Electrical and Mathematical Science and Engineering (CEMSE) DivisionStatistics Program
KAUST Grant Number
OSR-2019-CRG7-3800Date
2022-05-02Permanent link to this record
http://hdl.handle.net/10754/676711
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Show full item recordAbstract
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/dasa54658.2022.9765072Sponsors
Supported by King Abdullah University of Science and Technology (KAUST), Office of Sponsored Research (OSR) under Award No: OSR-2019-CRG7-3800.Publisher
IEEEConference/Event name
2022 International Conference on Decision Aid Sciences and Applications (DASA)ISBN
978-1-6654-9502-8Additional 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