Investigation of Milling Parameters Effect on Material Removal Rate Using Taguchi and Artificial Neural Network Techniques - Haleel, Aseel J.; Abdulridha, Hind H.; Al-duroobi, Ahmed A. (Anbar Journal of Engineering Sciences, 2019)
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.