Optimization and prediction of the optimal cutting conditions affecting the surface roughness of high carbon alloy steel

Abstract EN

In this research, optimization of turning cutting conditions and prediction of surface roughness has been satisfactorily accomplished.

Taguchi method was applied and second – order mathematical model used for prediction has been developed.

Standard LR9R Taguchi orthogonal array, S/N ratio through using the quality characteristic ‘the-lower-the-better’, and ANOVA technique were all adopted to determine the optimum cutting parameters and the more significant factor among them.

The parameters that have considered are Spindle speed S, feed rate f, and depth of cut a.

Nine specimens were machined according to the levels of parameters, and surface roughness (Ra) values were measured three times for each experiment.

The optimum conditions obtained were 1200 rpm spindle speed, 0.12 mm/rev feed rate, and 0.7 mm depth of cut.

Among them, the more significant factor is feed rate followed by spindle speed and depth of cut respectively.

Based on the results of prediction by the second-order model, it can be concluded that it is a very appropriate to predict the surface roughness of high carbon steel.

Confirmation results showed that the predicted values and measured values were dramatically close.

This indicates that the Taguchi method and multiple regression can be effectively used to optimize and predict the surface roughness such that the coefficient of determination was found to be 99.82 % with average error not exceed 1.12 %.