The mechanical strength of aluminum alloys which are joined with friction stir welding modelling with artificial neural networks

Abstract

Artificial neural networks (ANN) is increasingly being used to solve engineering problems. Successful welding operations are at the top of engineering problems. The friction stir welding method, which is used to combine the materials under the melting temperature, is also a method that can be analyzed with ANN. In this study, Al-7075 workpieces (with and without aging treatment) were joined by the friction stir welding method. Tension tests were applied to the joined workpieces and the resulting values were modeled by ANN. In ANN model; The tensile strength of the assembled parts was tried to be estimated by input parameters (the type of the material, the number of revolutions and the feed rate). In the created ANN model; Different nerve numbers, different transfer functions, using different layer numbers, the models created by alternative network structures have been tested. In these experiments, prediction performance was measured using neuron and algorithm type variables. At the end of this study, it was seen that the ANN model for the parts joined with the FSW process made predictions with high consistency. The best estimates were obtained with the Levenberg Marquardt (LM) algorithm and the ANN model established with 3 neurons. This ANN model is seen as a suitable solution for this problem with 98% success.