Optimization of Machining Parameter during the Laser Cutting of Inconel-718 Sheet Using Regression Analysis based Particle Swarm Optimization Method

Abstract

This experimental work describes the utilization of a hybrid approach of regression modeling and particle swarm optimization (PSO) for optimizing the process parameters during the laser cutting of the Inconel-718 sheet. The experiments have been performed by using four machining parameters such as assist gas pressure, standoff distance, cutting speed and laser power. The kerf width and kerf taper are used as an output quality characteristic. The experiments have been performed by using well planned orthogonal array L27.The second order regression models have been developed for kerf width and kerf taper by using the experimental data. The developed second order regression models have been utilized in optimization by particle swarm optimization. The comparison of the experimental result with optimum results confirms that the individual improvement in output quality characteristics kerf width and kerf taper is approximate 10% and 57%, respectively. The overall improvement of 46% has been observed during the optimization. Finally, the effects of different process parameters on different performances have also been discussed. The parametric effect analysis shows that minimum kerf taper may be obtained at lowest values of laser power and middle values of standoff distance