MULTI-OBJECTIVE OPTIMIZATION OF MICROTURNING PROCESS PARAMETERS USING PARTICLE SWARM TECHNIQUE

In this work, for the first time an attempt has been made to carry out multi-objective optimization for tool based microturning process parameters using particle swarm optimization (PSO) technique. The input microtuming process parameters considered are speed, feed and depth of cut. The output parameters considered are material removal rate (MRR), surface roughness (Ra) and tool wear (TW). The significant parameters are identified individually using ANOVA and main effect plots. However, it is observed that the main goal of the manufacturers is to produce high quality products in shorter interval of time. In order to meet the above objective, multiobjective optimization is carried out to achieve simultaneously higher MRR, low Ra and low TW using PSO. From the PSO analysis, it is observed that the combination of microturning parameters such as speed (18.25 m/min), feed (9.31 mu m/rev) and depth of cut (14.61 mu m) results in high MRR, low Ra and low tool wear. The PSO analysis indicates that it is a promising optimization algorithm due to its simplicity, low computational cost and good performance. A confirmation test was carried out to validate the predicted results.