Approach to multispectral thermometry with Planck formula and hybrid metaheuristic optimization algorithm

Abstract

Accurate temperature measurement has significant implications for product quality, industrial process control, and scientific research. As a non-contact temperature measurement method with broad application prospects, multispectral thermometry still poses significant challenges in data processing. Currently, most multispectral thermometry methods use the Wien approximation equation to construct the objective function. However, the use of the Wien approximation equation is conditional and generally applicable only to low temperatures or short wavelengths. In this paper, what we believe is a new data processing model of multispectral thermometry is established based on the Planck formula; Additionally, a feasible region constraint method is proposed to constrain the emissivity range; By utilizing a hybrid metaheuristic optimization algorithm based on differential evolution (DE) and multi-population genetic (MPG) algorithms, the simulation results of six different models and experimental results of silicon carbide demonstrate that the proposed algorithm achieves an average relative error in temperature measurement within 0.42% and a random relative error within 0.79%. The average computation time for each temperature inversion is approximately 0.26 seconds. The accuracy and efficiency of the algorithm ensure that it can be applied to real-time temperature measurement in industrial field.