Optimization of Machining Parameters in Mild Steel Turning Operation by Response Surface Methodology

Abstract

A lot of process variables affect the surface roughness obtained in turned machine parts. One of these variables is bearing clearance. However, there is limited information on the influence of bearing clearance on surface integrity. This paper is an optimization study in which the surface roughness of AISI 1018 mild steel is minimized with the aid of the response surface methodology. In this paper, the effect of process parameters like cutting speed, feed, depth of cut, and bearing clearance is analyzed to ascertain how the surface finish properties of mild steel can be improved. The design of the experiment used for this study involves a rotatable central composite system. This design is used to find the experimental results of machining. The analysis of variance (ANOVA) was used to determine the statistical significance of the improved quadratic model developed. The numerical and graphical optimization carried out determined the optimum values of each of the parameters used in different ways. From the ANOVA, it was revealed that the most significant factor in the model was the depth of cut. This factor was closely followed by spindle speed, bearing clearance, and feed, respectively. Numerical optimization results employing the desirability function showed optimum values to be at bearing clearance of 70um, depth of cut of 2.5mm, feed of 0.01mm/rev, and a spindle speed of 450rpm. The result obtained using the graphical optimization option was similar to the results from the other options. The variation of surface roughness with the process parameters chosen for the experiment was mathematically modeled. The model developed used the response surface methodology, and it was validated with a set of experimental values. The result from the exercise undertaken revealed that the predicted values of the surface roughness were very close to measured values. The average percentage deviation of 6.20% for all sample data utilized showed that the model developed was in close agreement with the experimental results.