Analysis of infrared temperature measurement model of molten steel based on infrared CCD

In order to quickly and accurately measure the molten steel temperature on line, infrared CCD camera temperature measurement technology was used to measure the surface temperature of molten steel. The image of molten steel at different temperatures was collected by an infrared CCD camera to calculate the average of the grayscale values in the region of the image near the temperature measured by the thermocouple. The golden section optimization method was introduced to determine the expansion coefficient in the generalized regression neural network. The nonlinear curve fitting between gray scale and temperature was compared by using the traditional least square method and the modified generalized regression neural network. The results show that the temperature measurement model established by the improved generalized regression neural network can effectively improve the on-line temperature measurement accuracy and make the measurement error of molten steel temperature within 0.1%. It meets the requirements of industrial design. This study provides a reference for the application of generalized regression neural network in the field of molten steel temperature measurement.