Delamination prediction in drilling of CFRP composites using artificial neural network

Carbon fibre reinforced plastic (cfrp) materials play a major role in the applications of aeronautic,aerospace,sporting and transportation industries. machining is indispensible and hence drilling of cfrp materials is considered in this present study with respect to spindle speed in rpm,drill size in mm and feed in mm/min. delamination is one of the major defects to be dealt with. the experiments are carried out using computer numerical control machine and the results are applied to an artificial neural network (ann) for the prediction of delamination factor at the exit plane of the cfrp material. it is found that ann model predicts the delamination for any given set of machining parameters with a maximum error of 0.81% and a minimum error of 0.03%. thus an ann model is highly suitable for the prediction of delamination in cfrp materials. © school of engineering,taylor's university.