MULTI-OBJECTIVE OPTIMIZATION OF SURFACE ROUGHNESS AND MATERIAL REMOVAL RATE IN CO2 LASER CUTTING USING ANN AND NSGA-II

The prediction of optimal laser cutting conditions for satisfying different requirements is of great importance in process planning. hi this paper, multi-objective optimization of CO2 laser cutting AISI 304 stainless steel using the non-dominated sorting genetic algorithm (NSGA-II), with surface roughness and material removal rate (MRR) as the objective functions was presented. The laser cutting experiments were conducted based on Taguchi's experimental design using L-27 orthogonal array by varying the laser power, cutting speed, assist gas pressure and focus position at three levels. Using these experimental data, the mathematical models of surface roughness and kerf width were developed using artificial neural network (ANN). The later ANN model was then used for calculating the MRR considering that the MRR is the function of cutting speed, workpiece thickness and kerf width. On the basis of a computer code written for ANN function models, the optimization problem was formulated and solved using NSGA-II. The obtained optimal solution set was plotted as Pareto optimal front. It was observed that the functional dependence between the surface roughness and material removal rate is nonlinear and can be expressed with a second degree polynomial.