Prediction of Surface Roughness in End Milling Process Using Intelligent Systems: A Comparative Study
A study is presented to model surface roughness in end milling process. Three types of intelligent networks have been considered. They are Radial Basis Function Neural networks (RBFNs), Adaptive NeuroFuzzy Inference Systems (ANFISs), and 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.