Development of an artificial neural network algorithm for predicting the surface roughness in end milling of Inconel 718 alloy

Abstract

In this work, an artificial neural network (ANN) model was developed for the investigation and prediction of the relationship between cutting parameters and surface roughness during high speed end milling of nickel-based Inconel 718 alloy. 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 surface roughness. 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 three-level full factorial experimental design technique. A predictive model for surface roughness 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 Inconel 718 alloy with single-layer PVD TiAlN 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 surface roughness in metal-cutting operations and for the optimization of the surface roughness for efficient and economic production.