Forecasting FSW Material’s Behavior using an Artificial Intelligence-Driven Approach
KAUST DepartmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
KAUST Grant NumberOSR-2019-CRG7-3800
Permanent link to this recordhttp://hdl.handle.net/10754/676711
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AbstractA 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.
CitationDorbane, 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
SponsorsSupported by King Abdullah University of Science and Technology (KAUST), Office of Sponsored Research (OSR) under Award No: OSR-2019-CRG7-3800.
Conference/Event name2022 International Conference on Decision Aid Sciences and Applications (DASA)