Prediction and optimization of tensile properties of 2219-T8 aluminum alloy TIG welding joint by machine learning

Abstract

The tensile properties of 2219-T8 aluminum alloy TIG welding joint were significantly affected by the microstructure, local mechanical properties and weld geometry. This paper proposed a machine learning model to predict and optimize the tensile properties of 2219-T8 aluminum alloy TIG welding joint. The relationship between tensile strength of joint and weld geometry, weld zone and partially melted zone (PMZ) properties was developed by Kriging model combining whale optimization algorithm (WOA). This surrogate model demonstrated a high precision, with R2 = 0.952 and RMSE=3.77 MPa. The surrogate model, which also served as a welding process guide, was utilized to determine the ideal weld geometry corresponding to various weak zone properties. By applying the optimization process based on the surrogate model, the optimized joint strength coefficient reached 70 %, and elongation exceeded 4 %. The collaborative regulation mechanism of geometry and property was also discussed.