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.
Highlights
The use of fiber-reinforced plastics (FRP’s) has increased to a great extent over the last few years due to their exceptional physical and mechanical properties such as, high strength to weight ratio, high impact resistance, excellent corrosion resistance and ease of manufacturing [1, 2]
The results revealed that the artificial neural network (ANN) models are better than the regression models in predicting thrust force and torque
In the present research initiative, a new ANN approach in context of drilling of composite laminates has been suggested to find the number of neurons in the hidden layer and the values of momentum factor and learning rate
Summary
The use of FRP’s has increased to a great extent over the last few years due to their exceptional physical and mechanical properties such as, high strength to weight ratio, high impact resistance, excellent corrosion resistance and ease of manufacturing [1, 2] The use of these materials has grown in the field of aerospace, aircrafts, automobiles etc. The mathematical models to predict the thrust force, torque and delamination during drilling of composite laminates have been developed [21, 22]. In the present research initiative, a new ANN approach in context of drilling of composite laminates has been suggested to find the number of neurons in the hidden layer and the values of momentum factor and learning rate
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