Tool Life Prediction in Face Milling Machining of 7075 Al by Using Artificial Neural Networks (ANN) and Taguchi Design of Experiment (DOE)

Abstract—Tool life is an important indicator of the milling operation in manufacturing process. Studies and analyses of milling process are usually based on three main parameters composed of cutting speed, feed rate and depth of cut. The aim of this study is to discover the role of these parameters in tool life prediction in milling operations by using artificial neural networks and Taguchi design of experiment. Machining experiments were performed under various cutting conditions by using sample specimens. A very good agreement between predicted model and experimental results was obtained. The correlation between the estimated and experimental data was 0.96966 for train and 0.94966 for test.