Application of Cuckoo Search Algorithm for Surface Roughness Optimization in Co2 Laser Cutting

In this paper, empirical modeling of surface roughness in CO2 laser cutting of stainless steel using was presented. Mathematical modeling was based on using feed forward neural network by exploiting experimental measurements obtained from the Taguchi’s L27 experimental design. The mathematical models of surface roughness was expressed as explicit nonlinear functions of the selected input parameters such as laser power, cutting speed, assist gas pressure and focus position. Training of the feed forward neural network was based on Levenberg-Marquardt algorithm and the average absolute percentage errors on training and testing data were 8.71 % and 9.66%, respectively. In addition to modeling, through ANN integration with cuckoo search algorithm optimal laser cutting conditions with minimal surface roughness were identified.