Parametric optimization of friction stir welding process of age hardenable aluminum alloys−ANFIS modeling

Abstract

A comparative approach was performed between the response surface method (RSM) and the adaptive neuro-fuzzy inference system (ANFIS) to enhance the tensile properties, including the ultimate tensile strength and the tensile elongation, of friction stir welded age hardenable AA6061 and AA2024 aluminum alloys. The effects of the welding parameters, namely the tool rotational speed, welding speed, axial load and pin profile, on the ultimate tensile strength and the tensile elongation were analyzed using a three-level, four-factor Box-Behnken experimental design. The developed design was utilized to train the ANFIS models. The predictive capabilities of RSM and ANFIS were compared based on the root mean square error, the mean absolute error, and the correlation coefficient based on the obtained data set. The results demonstrate that the developed ANFIS models are more effective than the RSM model.

Keywords

aluminum alloys / response surface method (RSM) / adaptive neuro-fuzzy inference system (ANFIS) / friction stir welding / Box-Behnken design / neuro fuzzy

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