Optimization of Surface Roughness from Different Aspects in High-Power CO2 Laser Cutting of AA5754 Aluminum Alloy

Abstract

Surface roughness is one of the main indicators of quality assessment of finished parts processed by laser cutting. This paper presents the experimental results regarding surface roughness of laser cuts in high-power CO2 laser cutting of AA5754 aluminum alloy by using nitrogen as the assist gas. Based on carrying out the full factorial design experiment, the collected data were used for the development of an artificial neural network prediction model of surface roughness in terms of laser cutting parameters such as cutting speed, laser power and assist gas pressure. In addition to the modeling and analysis of the interdependencies between the considered process inputs and surface roughness, this paper presents results regarding single- and multi-objective optimization, determined by the use of a genetic algorithm, by considering the surface roughness as the main criterion, as well as other criteria such as kerf width, assist gas consumption and material removal rate.