Optimization of reaming parameters to improve surface roughness of En1A leaded material with the approach of particle swarm optimization

Abstract Surface roughness plays a major role in the manufacturing of mechanical components, an increase in wear and corrosion happen when the surface roughness value is high. To get a good surface finish many industries find it difficult in optimizing the correct parameters. In this paper a research work is carried out to find the optimal controllable parameters such as speed, feed and depth of cut influencing the surface roughness result. In machining, the surface roughness value is to be maintained as low as possible and it is attained by the value of optimal cutting parameters. So, in this paper with the application of RSM and PSO technique the optimal value of the cutting conditions for giving the minimum value of surface roughness is compared. The machining process taken for experiments is reaming in EN1A leaded material using TiAIN coated tool by vertical machining center. A total of 16 experiments were planned by the CCD method, on comparing the results of 16 experiments a regression model is developed to formulate the fitness function. Then PSO algorithm for the reaming process is developed to predict the surface roughness value for the reaming process and compared the same with experimental value and RSM predicted value. It is observed from this study that the PSO technique is capable of estimating the optimal cutting conditions than RSM that yield the minimum surface roughness value.