A multi-spectral radiation thermometry method based on MGACO-RFR emissivity model identification under the background of high temperature.

Multi-spectral radiation thermometry is extensively applied in high-temperature fields. Currently, multi-spectral radiation thermometry technology still faces issues such as inaccurate identification of target emissivity models due to high-temperature background reflection and significant temperature measurement errors. This paper proposes a multi-spectral radiation thermometry method based on a Mixed Genetic Algorithm and Ant Colony Optimization Random Forest (MGACO-RFR) for emissivity model identification. By combining high-temperature background radiation with different emissivity models, an MGACO-RFR classifier is established to identify the target emissivity model. The method achieves an accuracy rate of 97.1% without noise and 94.9% with 10% added noise. After the emissivity model is obtained, the black-winged kite optimization algorithm is used to solve the radiation thermometry equation considering high-temperature background targets, thereby deducing the target temperature. The experimental results of radiation thermometry under high-temperature backgrounds indicate that for three types of samples heated in a high-temperature furnace set at 1073, 1123, 1173, and 1223 K, a cooling device is used to create a temperature difference between the samples and their environment. Applying the aforementioned algorithms to GH3044(a high-temperature alloy model 3044), GH3128(a high-temperature alloy model 3128), and thermal barrier coating sample(a sample made of material similar to that of an engine turbine blade) results in average temperature measurement errors of 3.0, 3.5, and 3.1 K, respectively. This is of significant importance for the high-precision temperature inversion of common high-temperature alloys and engine turbine blades in industrial settings under high-temperature backgrounds.