Optimizing the Cutting of Inconel 718 Sheets with CO2 Laser by Particle Swarm Algorithm

In this paper, the impact of different operative variables on the quality of cutting of Inconel material 718 is studied. Utilizing Taguchi test design, the input variables including carbon dioxide laser power and the cutting speed for cutting three different thicknesses of Inconel 718 alloy were investigated in order to achieve the optimal conditions. After obtaining experimental test results, dataset was modeled using artificial neural networks. The neural network model is then used for evaluating candidate solutions in particle swarm optimization (PSO) algorithm which is employed for optimization of cutting conditions. The results indicated that when the laser power of is 1714 (W), the cutting speed is 1382 (mm/min) and the thickness of the material is 0.8 (mm), The best quality for cutting Inconel 718 is achieved with a carbon dioxide laser cutting machine. The results of optimal cutting parameters of Inconel alloy with carbon dioxide laser which were obtained by PSO were verified through an experimental test and similar papers. The results of this experimental test were very close to the optimal values of the PSO, which demonstrates the efficiency of neural network model in predicting the quality of cutting and the efficiency of PSO in finding optimal conditions.