Artificial Neural Network for Predicting machining performance of Uncoated Carbide (WC-Co) in Milling Machining Operation

Surface roughness (R-a) is one of the most common responses in machining and an effective parameter to represent the quality of a machined surface. This paper presents the capability of an Artificial Neural Network (ANN) technique to develop a model to predict the R value of milling process. The model, presented as a network structure, is developed using the MATLAB ANN toolbox. Four different network structures were developed and assessed. The result of the modeling shows that a 3-7-1 network structure is the best model for end milling a titanium alloy using an uncoated carbide (WC-Co) cutting tool. The result of the ANN model has been compared to the experimental result, and ANN gave a good agreement between predicted and experimentally measured process parameters. The ANN technique has decreased the minimum surface roughness value of the experimental sample data by about 0.0126 mu m, or 5.33%.