A virtual sensing approach for quality and productivity optimization in laser flame cutting

Heat accumulation during laser flame cutting of thick mild steel plates is a problem well acknowledged both in industry and academia. If not considered carefully, elevated plate temperatures can lead to significant quality degradation or even cut failure. As a result, several approaches have been proposed in the literature to mitigate the negative effect of heat accumulation. However, those approaches cannot deliver a versatile solution without considerable productivity loss or substantial hardware complexity increase. To overcome these limitations, a virtual sensor, consisting of a heat transfer model and infrared thermography measurements, is presented in this work. During the process planning phase, the developed virtual sensor estimates the plate temperature for each contour in the nesting solution in order to assign optimized cutting parameters. For this purpose, an experimental campaign has been conducted to determine the cutting map that links each temperature level to its optimal cutting speed. This allows increasing both quality and productivity since the cutting speed in preheated regions can be set higher than the nominal value due to the complementary energy input. Finally, experimental validation of the proposed approach has been performed on 15 mm mild steel plates using an industrial fiber laser cutting machine. The results show that cut quality in the preheated regions can be improved while productivity is increased by 15-25%.

Automated summary

Heat accumulation during laser flame cutting of thick mild steel plates is a well-known problem that can lead to quality degradation and failure. To address this issue, a virtual sensor is proposed to estimate plate temperature and optimize cutting parameters. Experimental results show improved cut quality and increased productivity.