A Neuro-fuzzy Approach to Select Cutting Parameters for Commercial Die Manufacturing

AbstractSurface roughness is a quality index for machined surfaces. In this study an algorithm has been developed to determine the feasible solutions for cutting parameters in order to obtain desired surface roughness for three dimensional dies. Here the average surface roughness values for a commercial die material EN24 after ball end milling operation have been measured after experiments with different cutting parameters. These datasets have been used for training and testing different prediction models like artificial neural network (ANN), response surface methodology (RSM), adaptive neuro-fuzzy inference system (ANFIS) and mathematical equation based on machining theories. ANFIS model has been selected as better prediction model because it has shown minimum value of root mean square error (RMSE) and mean absolute percentage error (MAPE) for training and testing datasets. This ANFIS model has been used further for predicting surface roughness of a typical die made of EN24 after ball end milling operation