The Artificial Neural Network (ANN) and numerical methods are used widely for modeling andpredict the performance of manufacturing technologies. In this paper, the influence of millingparameters (spindle speed (rpm), feed rate (mm/min) and tool diameter (mm)) on material removalrate were studied based on Taguchi design of experiments method using (L16) orthogonalarray with 3 factor and 4 levels and Neural Network technique with two hidden layers and neurons.The experimental data were tested with analysis of variance and artificial neural networkmodel has been proposed to predict the responses. Analysis of variance result shows that tooldiameters were the most significant factors that effect on material removal rate. The predictedresults show a good agreement between experimental and predicted values with mean squarederror equal to (0.000001), (0.00003025), (0.002601) and (0.006889) respectively, which produceflexibility to the manufacturing industries to select the best setting based on applications.
The most common type of abrasive water jet is known as a valuable and advanced non-traditional machining operation due to its no heat-affected zone, best in removing material, very environmentally friendly, and no mechanical stresses. This paper gives an idea about Abrasive water jets in terms of applications, advantages, and limitations. Also illustrates the influence of the parameters on the material removal rate. The effect of feed rate, pressure, and stand-off distance were worked, at three levels for material removal rate (MRR) to machining Aluminium alloy type-5083 by using a tool consisting of a mixture of 70% water and 30% abrasives of red garnet. The distance of the standoff has the most significant impact on the rate of material removal, which is subsequently followed by the feed rate and finally the pressure. The findings demonstrated that the Taguchi model is capable of making accurate predictions regarding the machining reactions, with a rate of material removal of 93.3%.