Optimization of milling parameters using artificial neural network and artificial immune system

The present paper is an attempt to predict the effective milling parameters on the final surface roughness of the work-piece made of Ti-6Al-4V using a multi-perceptron artificial neural network. The required data were collected during the experiments conducted on the mentioned material. These parameters include cutting speed, feed per tooth and depth of cut. A relatively newly discovered optimization algorithm entitled, artificial immune system is used to find the best cutting conditions resulting in minimum surface roughness. Finally, the process of validation of the optimum condition is presented.