MODELING OF ABRASIVE WATER JET MACHINING USING TAGUCHI METHOD AND ARTIFICIAL NEURAL NETWORKS

This work presents a hybrid approach based on the Taguchi method and the Artificial Neural Networks (ANNs) for the modeling of surface quality characteristics in Abrasive Water Jet Machining (AWJM). The selected inputs of the ANN model are the thickness of steel sheet, the nozzle diameter, the stand-off distance and the traverse speed. The outputs of the ANN model are the surface quality characteristics, namely the kerf geometry and the surface roughness. The data used to train the ANN model was selected according to the Taguchi's design of experiments. The acquired results indicate that the proposed modelling approach could be effectively used to predict the kerf geometry and the surface roughness in AWJM, thus supporting the decision making during process planning.