Reliable Analysis and Power Control for the Nontraditional Cutting Methodology on the CO₂ Laser Machine

This paper is dedicated to the research work of the nontraditional cutting methodology on the CO₂ Laser machine. The reliable analysis and the effective power control can be considered as an effective method for a modifying digital pulse-width modulation (DPWM) power control for a CO₂ laser cutting machine. The power controller uses software embedded on a field-programmable gate array (FPGA) chip to control laser power, and thus laser cutting depth. To develop the power control system, an adaptive neural fuzzy inference system (ANFIS) was trained to model the relationship between feed rate, laser power and cutting depth. The model was implemented by reproducing a bitmap image in a 2.5 D engraving. Laser power output was determined by the grayscale level in the bitmap image. The maximum laser cutting depth was 4 mm, and the feed rate varied between 10 mm/s and 80 mm/s. Laser power was less than 48 W. The precision for the cutting depth in the model was less than 0.036 mm. The precision for the actual cutting depth of the laser was within 0.044 mm. Implementation of the model improved the precision of the laser cutting machine to within the range ±0.05 mm.