Analysis of correlations of multiple - performance characteristics for optimization of CO2 laser nitrogen cutting of AISI 304 stainless steel

The identification of laser cutting conditions for satisfying different requirements such as improving cut quality characteristics and material removal rate is of great importance. In this paper, an attempt has been made to develop mathematical models in order to relate laser cutting parameters such as the laser power, cutting speed, assist gas pressure and focus position, and cut quality characteristics such as the surface roughness, kerf width and width of heat affected zone (HAZ). A laser cutting experiment was planned as per Taguchi’s L27 orthogonal array with three levels for each of laser cutting parameters considered. 3 mm thick AISI 304 stainless steel was used as workpiece material. Mathematical models were developed using a single hidden layer artificial neural network (ANN) trained with the Levenberg– Marquardt algorithm. On the basis of the developed ANN models the effects of the laser cutting parameters on the cut quality characteristics were presented. It was observed that laser cutting parameters variously affect cut quality characteristics. Also, for the range of operating conditions considered in the experiment, laser cut quality operating diagrams were shown. From these operating diagrams one can see the values of cut quality characteristics that can be achieved and subsequently select laser cutting parameter values. Furthermore, the analysis includes correlations between cut quality characteristics and material removal rate. To this aim, six trade-off operating diagrams for improving multiple responses at the same time were given.