Application of an artificial neural network model to predict the ultimate tensile strength of friction-welded titanium tubes

This paper presents an investigation to establish the link between friction welding process parameters and the ultimate tensile strength (UTS) of friction-welded titanium joints by application of an artificial neural network (ANN) technique. The experimental matrix is based on a central composite design with parameters varied at five levels. The UTS of the joints was modeled by the application of the response surface method (RSM). The joint UTS was also simulated by the application of a feed-forward back-propagation ANN with a single hidden layer composing of 20 neurons. The ANN was tested against and trained with the experimental data. The influence of the various parameters on the UTS was assessed by performing a sensitivity analysis. Lastly, the predictions of both the RSM model and the ANN were compared with one another. The results indicate that ANN is indeed a feasible technique for modeling and predicting the effect of process parameters on the UTS for friction welding of titanium tubes. When compared to RSM, ANN displayed a closer agreement with the data. In both cases, however, prediction errors were within 5%. Moreover, the link between the various process parameters and the UTS of the weld joints was also examined and commented upon.