Experimental investigation of the effect of processing parameters on the surface roughness operation for using as expert system database

Abstract

In this paper, the effect of processing parameters including feed rate (f), cutting depth (d), nose radius (r e ), cutting speed (V) and also cooling condition such as dry condition, wet condition, and minimum quantity lubrication, on the surface quality of copper, as a low machinability material, was investigated. It was observed that the best surface quality and the lowest tool wear are achieved by minimum quantity lubrication, and the highest tool wear and the lowest surface quality are obtained under dry machining. However, there are no significant differences in surface quality and tool wear under dry machining when compared to minimum quantity lubrication and wet machining. Therefore, due to cost and environmental considerations, dry machining is recommended for turning commercially pure copper. According to the results, feed rate and nose radius are the most important factors affecting the surface roughness, respectively. At low feed rate, 1.2 mm nose radius is a good option to achieve the best surface roughness. Besides, at high feed rate, 0.8 mm nose radius is recommended. To achieve an acceptable surface roughness with suitable material removal rate, the combination of the lowest feed rate and the highest cutting speed, along with the moderate cutting depth and nose radius is suggested. The best surface roughness of 0.381 µm has been achieved at r e = 1.2 mm, V = 220 m/min, d = 0.5 mm, and f = 0.08 mm/rev, which is comparable with the surface quality obtained by the conventional grinding operation. The results also revealed that the existed empirical model can predict the surface roughness only at high feed rate and low cutting speed, and therefore, it cannot be recommended for predicting the surface finish of the materials with low machinability. Hence, a full quadratic model was developed for the prediction of the surface roughness, which can be used for the database of expert systems.