Study of parameters for CO2 laser cutting process

In practical applications of laser cutting, the quality of the cut surfaces is the critical factor. Recently methods for studying the influence on quality of the main process variables have been developed, which seek to improve quality rather than explain the cutting mechanism. In the present study, CO2 laser cutting of stainless steel sheets is carried out at different combinations of levels of cutting parameters which include cutting speed, assisting gas pressure, workpiece thickness and laser pulsing frequency. Waviness (striations), flatness and metallurgical changes at the cut surface are considered as measurable variables in evaluating the overall cut quality. Factorial analysis is carried out to determine the parameters which affect cut quality while a neural network is introduced to classify the resulting striation patterns. The study includes an optical detection experiment, Micro-Particle Induced X-ray Emission (mu-PIXE), Nuclear Reaction Analysis (NRA), EDS and SEM microphotography of the resulting cut surfaces. It is found that the main effects of all the factors have significant affect on the quality measured variables. The neural network, which was developed, classifies the striation patterns successfully. Moreover, the cutting quality improves at certain combination of the levels of the cutting parameters.