Tensile strength prediction of dissimilar friction stir-welded AA6351–AA5083 using artificial neural network technique

Friction stir welding provides an alternative method of joining aluminum in a reliable way. Anticipation of the joint efficiency is then a necessary step to optimize the process of the welding operation. In the light of this, artificial neural network (ANN) technique may then be applied as a reliable method for simulating and predicting the durability of the joints for different process parameters. In the present work, an ANN model is presented that predicts the ultimate tensile strength of friction stir-welded dissimilar aluminum alloy joints. Four parameters were considered including tool pin profile (straight square, tapered square, straight hexagon, straight octagon and tapered octagon), rotational speed, welding speed and axial force. Experimental tests were conducted according to a four-parameter five level central composite design. A feed-forward back propagation ANN with a single hidden layer comprising 20 neurons was employed to simulate the ultimate tensile strength (UTS) of the joints. The neural network was trained using the data obtained from the experimental work. A comparison between the experimental and simulated data showed that the ANN model reliably predicted the UTS of dissimilar aluminum alloy friction stir-welded joints. The models developed were capable of predicting values with less than 5 % error. Furthermore, the effect of different process parameters on the tensile behavior of dissimilar joints was also investigated and reported upon.