Delamination analysis in high speed drilling of carbon fiber reinforced plastics (CFRP) using artificial neural network model

Abstract

The carbon fiber reinforced plastics (CFRP) are highly promising materials for the applications in aeronautical and aerospace industries. The delamination is a major problem associated with the drilling fiber reinforced composite materials, which reduce the structural integrity of the material. The present work focuses on the analysis of delamination behavior as a function of drilling process parameters at the entrance of the CFRP plates. The delamination analysis in high speed drilling is performed by developing an artificial neural network (ANN) model with spindle speed, feed rate and point angle as the affecting parameters. A multilayer feed forward ANN architecture, trained using error-back propagation training algorithm (EBPTA) is employed for this purpose. Drilling experiments are conducted as per full factorial design using cemented carbide (grade K20) twist drills that serve as input–output patterns for ANN training. The ANN model so developed is validated by presenting training and new testing input patterns. The validated ANN model is then used to generate the direct and interaction effect plots to analyze the delamination behavior. The simulation results illustrate the effectiveness of the ANN models to analyze the effects of drilling process parameters on delamination factor. The analysis also demonstrates the advantage of employing higher speed in controlling the delamination during drilling.