Artificial neural network chip serration frequency model in end milling of medium carbon steel

In this research, an Artificial Neural Network (ANN) model was developed for the investigation and prediction of the relationship between cutting parameters and chip serration frequency during high speed end milling of medium carbon steel (S45C). The input parameters of the ANN model are the cutting parameters: cutting speed, feed and axial depth of cut. The output parameter of the model was chip serration frequency. For this interpretation, advantages of statistical experimental design technique, experimental measurements, artificial neural network were exploited in an integrated manner. Cutting experiments are designed based on statistical central composite design experimental design technique. A predictive model for chip serration frequency was created using a feed-forward back-propagation neural network exploiting experimental data. The network was trained with pairs of inputs/outputs datasets generated, when end milling steel with TiN coated carbide inserts. A very good predicting performance of the neural network, in terms of concurrence with experimental data was attained. The model can be used for the analysis and prediction for the complex relationship between cutting conditions and the chip serration frequency in metal-cutting operation