EXPERIMENTAL ANALYSIS OF CUT QUALITY ON SS347 MATERIAL USING CO2 ASSISTED LASER BEAM CUTTING AND PARAMETRIC OPTIMIZATION USING GENETIC ALGORITHM

Laser Beam cutting is a type of non-conventional machining process in which the removal of materials takes place due to the melting and vaporization of material when the laser beam comes in contact with it. This work examines the impact of the cut quality characteristics of the SS347 material and to find reduced surface roughness, machining time and heat affected zone by laser beam cutting. The cutting process was assisted by CO 2 gas pressure. Power, standoff distance, speed and CO 2 gas pressure are the cutting parameters considered for this study and the output parameters measured are machining time, heat affected zone and surface roughness. In accordance with L-9 orthogonal arrays, the experiments were planned. Analysis of Variance has been used to study how input functions influence output functions which revealed that speed (59.75% and 89.75%) is the significant factor for machining time and surface roughness while power (91.27%) was the dominant factor for heat affected zone. Gas pressure did not have much influence in the output parameters. The mathematical expressions of the output are used as input for the multi-objective function of the genetic algorithm. Optimal solutions are compared to hybrid and without-hybrid functions. It is found that the hybrid function shows a higher performance compared to the without hybrid function.