Modeling the effect of variable work piece hardness on surface roughness in an end milling using multiple regression and adaptive Neuro fuzzy inference system

Abstract

The aim of this study is to correlate work piece material hardness with surface roughness in prediction studies. The proposed model is for prediction of surface roughness of tool steel materials of hardness 55 HRC to 62 HRC (±2 HRC). The machining experiments are performed under various cutting conditions using work piece of different hardness. The surface roughness of these specimens is measured. The result showed that the influence of work piece material hardness on surface finish is significant for cutting speed and feed in CNC end milling operation. It is also observed that the surface roughness prediction accuracy of Adaptive neuro fuzzy inference system using triangular membership function is better than Gaussian, bell shape membership function and regression analysis. Surface roughness prediction accuracy with material hardness as input parameter is 97.61%.

A Review of the Factors Influencing Surface Roughness in Machining and Their Impact on Sustainability

“…Özel et al [57] reported that a lower workpiece surface hardness results in better surface roughness when turning AISI H3 steels from 50 to 55 HRC. Similarly, Desale and Jahagirdar [84] studied the influences of different workpiece hardness values (from 55 HRC to 62 HRC) on the surface roughness of tool steels, showing a higher roughness value when hardness increases. Chen [85] studied surface roughness values when cutting hardened steel (45-55 HRC) with different CBN tools and stated that the harder the workpiece material, the lower the surface roughness obtained for a given set of operating parameters.…”