Delamination analysis of the helical milling of carbon fiber-reinforced plastics by using the artificial neural network model

As carbon fiber-reinforced plastics are widely used in aeronautical and aerospace industries, the improvement of their processing quality is a crucial task. In recent years, helical milling, a brand new machining process that results in better hole quality with one-time machining, has been attracting increasing attention. Based on full factor experimental design, helical milling experiments were performed by using a special cutter. Using the data obtained from the experiments, the correlation between the delamination and the process parameters was established by developing an artificial neural network (ANN) model. MATLAB ANN Toolbox was used for modeling. The effects of the process parameters on delamination at the exit of the machined holes were analyzed by using this model and the predicted results. The significance of the process parameters in the improvement of the hole quality in helical milling was also assessed.