An adaptive network-based fuzzy approach for prediction of surface roughness in CNC end milling

Abstract

An Adaptive Network-based Fuzzy Inference System (ANFIS) has been designed for modeling and predicting the surface roughness in end milling operation for set of three given milling parameters (spindle speed, feed rate and depth of cut). Two different membership functions (triangular and bell shaped) were used during the hybrid-training process of ANFIS in order to compare the prediction accuracy of surface roughness by the two membership functions. The predicted surface roughness values obtained from ANFIS were compared with experimental data and multiple regression analysis. The comparison indicates that the adoption of both membership functions in ANFIS achieved better accuracy than multiple regression model.