Modeling and Prediction of Surface Roughness in Ball End Millingof Oxygen-Free High Conductivity Copper Using Adaptive NeuroFuzzy Inference System

Abstract

Oxygen Free High Conducting Copper (OFHC) is one of the reasons materials for numerous applications due to its high thermal conductivity, great
machinability, and great quality In this paper, an adaptive neuro-fuzzy inference system (ANFIS) is developed as a predicting model of the machining
performance of OFHC measured for surface roughness in terms of process parameters, namely, the cutting feed rate or feed per tooth, axial depth of cut, radial depth of cut, and the cutting speed. The validation of the model was performed based on both Nash-Sutcliffe model efficiency coefficient (NSE) and Root mean square error (RMSE), the values of (NSE) and (RMSE) were 1.00 and 0.003 respectively, which indicates the model is reliable and has very good accuracy and performance.