Prediction of forces during drilling of composite laminates using artificial neural network: A new approach

Abstract

Drilling of fiber-reinforced plastics (FRP's) is an inevitable machining operation, because it facilitates assembly of several components by means of mechanical fastening. But, drilling of FRP leads to delamination which results in reduced life and efficiency of the FRP part. The delamination that induced during drilling is directly affected by the thrust force and torque. In the present research endeavour, four different types of drill point geometries have been used for making of holes in two different types of composite laminates. The drilling of composite laminate has been conducted at three different levels of spindle speed and feed rate. A new artificial neural network (ANN) approach has been proposed to predict the drilling-induced thrust force and torque. The values of thrust force and torque predicted by the proposed ANN models are in close agreement with the experimental values.