Surface roughness is an index which determines the quality of machined products and is influenced by the cutting parameters. In this study, the average surface roughness R_a(value) for aluminum after ball end milling operation has been measured. 84 experiments have been conducted using varying cutter axis inclination angle (φ degree), spindle speed (S rpm), feed rate (f_y mm/min), feed (f_x mm), and depth of cut (t mm) in order to find R_a. This data has been divided into two sets on a random basis; 68 training data set and 16 testing data set. The training data set has been used to train different adaptive neuro-fuzzy inference system (ANFIS) models for R_a prediction. And testing data set has been used to validate the models. Better ANFIS model has been selected based on the minimum value of root mean square error (RMSE) which is constructed with three Gaussian membership functions (gaussmf) for each input variables and linear membership function for output. The selected ANFIS model has been compared with theoretical model and response surface model (RSM). This comparison is done based on RMSE and absolute average percentage error. The comparison shows that the selected ANFIS model gives better result for training and testing data. So, this ANFIS model can be used further for predicting surface roughness of aluminum for ball end milling operation.

Key words: Ball end mill, adaptive neuro-fuzzy inference system (ANFIS), roughness prediction.