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

Authors: Mariangela Quarto; Sara Bocchi; Gianluca D'Urso; Claudio Giardini

Addresses: Department of Management, Information and Production Engineering, University of Bergamo, Via Pasubio7/b, 24044, Dalmine (Bergamo) – Italy ' Department of Management, Information and Production Engineering, University of Bergamo, Via Pasubio7/b, 24044, Dalmine (Bergamo) – Italy ' Department of Management, Information and Production Engineering, University of Bergamo, Via Pasubio7/b, 24044, Dalmine (Bergamo) – Italy ' Department of Management, Information and Production Engineering, University of Bergamo, Via Pasubio7/b, 24044, Dalmine (Bergamo) – Italy

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.

Keywords: hybrid approach; FEM; finite element analysis; neural network; process sustainability; FSW; friction stir welding; aluminium alloy; artificial intelligence; forecasting model; hardness prediction.

DOI: 10.1504/IJMMS.2022.124919

International Journal of Mechatronics and Manufacturing Systems, 2022 Vol.15 No.2/3, pp.149 - 166

Received: 29 Sep 2021
Accepted: 12 Jan 2022
Published online: 16 Aug 2022 *