Surface Roughness Optimization of Stainless Steel using ABC (Artificial Bee Colony) Algorithm

Abstract

In any machining operation surface roughness results inaccuracy and inefficiency. So it is always desirable to ensure minimum level of surface roughness. Both of them depend on some parameters like feed, spindle speed and depth of cut. Optimization of these parameters ensures existence surface roughness under the tolerance limit. In this project, our aim is to devise a way of predicting surface roughness for a given set of parameters. To do that, we collected experimental results of surface roughness for twenty sets of parameters which were selected by Central Composite Design (CCD). Surface roughness was measured by taking microscopic images of tool edge and job piece surface after each machining operation and then by using Image Processing Tool of MATLAB. The obtained results were then used for developing ABC (Artificial Bee Colony) Algorithm which was then used for the prediction of tool wear and surface roughness for a given set of parameters. The prediction and actual result were then compared and it was seen that both results coincide with each other.