Prediction of multiple characteristics of Friction-Stir welded joints by Levenberg Marquardt algorithm based artificial neural network

The present paper investigates the influence of process parameters of friction stir welding (FSW) technique on the resulting microstructure and mechanical characteristics of the fabricated joints. The material considered for FSW is AA1100 aluminium rolled plates that are joined by using a square pin tool. In this research, parametric characterization of surface properties has been carried out by simulation using an artificial neural network (ANN) that is designed with multi layer, multi neuron and logistic sigmoid activation function. Levenberg-Marquardt algorithm including second-order error optimization has been selected to train the ANN. The ANN simulation has been done to estimate the tensile and yield strength, elongation, flexure stress and grain size with respect to tool rotational speed (RS), travel speed (TS) and plunge depth (PD), which are the process parameters of FSW process and also find an optimum condition which is essential to save energy and resources. This process finds its application in various automotive industries as there is a wide use of aluminium in the automobiles and aircrafts. (C) 2019 Elsevier Ltd. All rights reserved.