Abstract
Surface temperature is a critical operating parameter that influences the phase transition time and rolling quality of U75V rail steel plates in the rolling process. There is still no extensive online detection system for the surface temperature of rail steel plates due to the hazardous environment, incorrect surface emissivity, and complex backgrounds. In this paper, online surface temperature detection equipment based on multi-spectral photography was built for high-temperature rail steel plates in the rolling processes. Then, the emissivity model for a high-temperature environment, colorimetric thermometry, and noise filtering methods were investigated to improve the accuracy of the temperature detection results of rail steel plates. Finally, the surface temperature of the U75V rail steel plate during three rolling passes could be calculated online point by point, and the greatest error was approximately 0.82% due to the blackbody calibration experiments. The results not only have a positive effect on understanding the temperature declination process of low-alloy rail steel plates during the rolling process, but could also benefit the control of the cooling rate and optimize the rolling model during rolling passes.
Subject
General Materials Science,Metals and Alloys
Cited by
3 articles.
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