Prediction of Surface Roughness in Turning of EN 353 Using Response Surface Methodology

Abstract

In the present work, an attempt has been made to investigate the influence of cutting speed, feed, depth of cut and nose radius on surface roughness during wet turning of EN 353 steel using tungsten carbide inserts. Surface roughness prediction models in terms of speed, feed, depth of cut and nose radius is developed by using response surface methodology based on center composite rotatable design. A comparison of first order models with quadratic model was carried out on the basis of percentage mean absolute error and mean square error. The results clearly reveals that the predicted data using quadratic model is in close agreement to the experimental surface roughness values as compared to predicted data using first order model. In addition to this, it has been revealed that the speed is the main influencing factor affecting the surface roughness. The depth of cut has no significant influence on the roughness. Mathematical model for surface roughness shows that surface roughness decreases with increase in speed and nose radius, but increases with increase in feed. The percentage variation between the predicted and experimental values of the surface roughness during the confirmation experiments was found within 5 %. An attempt has also been made to obtain optimum cutting conditions for minimum surface roughness.