Published Paper Details:

In recent years, aluminum-matrix composites (AMCs) have been widely used to replace cast iron in aerospace and automotive industries. Machining of these composite materials requires better understanding of cutting processes regarding accuracy and efficiency. This study addresses the modeling of the match inability of self-lubricating aluminum /alumina/graphite hybrid composites synthesizing by the powder metallurgy method. In this study, multiple regression analysis (MRA) and artificial neural networks (ANN) were using to investigate the influence of parameters on the thrust force and torque in the drilling processes of self-lubricating hybrid composite materials. The models are identify by using cutting speed, feed, and volume fraction of the reinforcement particles as input data and the thrust force and torque as the output data. A comparison between two prediction methods is developing to compare the prediction accuracy. Here we are finding the spindle speed is insignificant or significant, it directing us to set it either at the highest spindle speed to obtain high material removal rate or at the lowest spindle speed to prolong the tool life depending on the need for the application