Prediction of Surface Roughness in End Milling Process Using Intelligent Systems: A Comparative Study

Abstract

A study is presented to model surface roughness in end milling process. Three types of intelligent networks have been considered. They are (i) radial basis function neural networks (RBFNs), (ii) adaptive neurofuzzy inference systems (ANFISs), and (iii) genetically evolved fuzzy inference systems (G‐FISs). The machining parameters, namely, the spindle speed, feed rate, and depth of cut have been used as inputs to model the workpiece surface roughness. The goal is to get the best prediction accuracy. The procedure is illustrated using experimental data of end milling 6061 aluminum alloy. The three networks have been trained using experimental training data. After training, they have been examined using another set of data, that is, validation data. Results are compared with previously published results. It is concluded that ANFIS networks may suffer the local minima problem, and genetic tuning of fuzzy networks cannot insure perfect optimality unless suitable parameter setting (population size, number of generations etc.) and tuning range for the FIS, parameters are used which can be hardly satisfied. It is shown that the RBFN model has the best performance (prediction accuracy) in this particular case.