One of the main important aspect of friction stir welded parts is the different hardness values reached in the characteristic welding zone, as a function of the maximum temperature derived from the welding process. Indeed, these differences affect the mechanical properties and the service quality of component. For these reasons, a hybrid model for predicting the final hardness of the single points of the welding as a function of the maximum reached temperature is developed. Specifically, the hybrid approach takes into account the finite element method (FEM) and the artificial neural network (ANN). The FEM model was set-up and the temperature map output was introduced into the ANN together with experimental results for the ANN training. The hybrid approach FEM-ANN provides a robust framework for forecasting aluminium hardness after the FSW process without experimentally investigating each welding.

(2022). Hybrid finite elements method-artificial neural network approach for hardness prediction of AA6082 friction stir welded joints [journal article - articolo]. In INTERNATIONAL JOURNAL OF MECHATRONICS AND MANUFACTURING SYSTEMS. Retrieved from http://hdl.handle.net/10446/227350

Hybrid finite elements method-artificial neural network approach for hardness prediction of AA6082 friction stir welded joints

Quarto, Mariangela;Bocchi, Sara;D'Urso, Gianluca;Giardini, Claudio
2022

Abstract

One of the main important aspect of friction stir welded parts is the different hardness values reached in the characteristic welding zone, as a function of the maximum temperature derived from the welding process. Indeed, these differences affect the mechanical properties and the service quality of component. For these reasons, a hybrid model for predicting the final hardness of the single points of the welding as a function of the maximum reached temperature is developed. Specifically, the hybrid approach takes into account the finite element method (FEM) and the artificial neural network (ANN). The FEM model was set-up and the temperature map output was introduced into the ANN together with experimental results for the ANN training. The hybrid approach FEM-ANN provides a robust framework for forecasting aluminium hardness after the FSW process without experimentally investigating each welding.
articolo
Quarto, Mariangela; Bocchi, Sara; D'Urso, Gianluca; Giardini, Claudio
(2022). Hybrid finite elements method-artificial neural network approach for hardness prediction of AA6082 friction stir welded joints [journal article - articolo]. In INTERNATIONAL JOURNAL OF MECHATRONICS AND MANUFACTURING SYSTEMS. Retrieved from http://hdl.handle.net/10446/227350
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10446/227350
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