On the prediction of surface roughness in the hard turning based on cutting parameters and tool vibrations

This research work concerns the elaboration of a surface roughness model in the case of hard turning by exploiting the response surface methodology (RSM). The main input parameters of this model are the cutting parameters such as cutting speed, feed rate, depth of cut and tool vibration in radial and in main cutting force directions. The machined material tested is the 42CrMo4 hardened steel by Al2O3/TiC mixed ceramic cutting tool under different conditions. The model is able to predict surface roughness of Ra and Rt using an experimental data when machining steels. The combined effects of cutting parameters and tool vibration on surface roughness were investigated while employing the analysis of variance (ANOVA). The quadratic model of RSM associated with response optimization technique and composite desirability was used to find optimum values of cutting parameters and tool vibration with respect to announced objectives which are the prediction of surface roughness. The adequacy of the model was verified when plotting the residuals values. The results indicate that the feed rate is the dominant factor affecting the surface roughness, whereas vibrations on both pre-cited directions have a low effect on it. Moreover, a good agreement was observed between the predicted and the experimental surface roughness. Optimal cutting condition and tool vibrations leading to the minimum surface roughness were highlighted.

► Study the influence of the following parameters: feed rate, cutting speed and depth of cut on surface roughness. ► Optimal cutting conditions for each performance level are established. ► The ranges of best cutting conditions adapted, were given. ► The model predicting equations for surface roughness of Ra and Rt were developed using an experimental data.