A Review of Thermal Spectral Imaging Methods for Monitoring High-Temperature Molten Material Streams

Abstract

Real-time closed-loop control of metallurgical processes is still in its infancy, mostly based on simple models and limited sensor data and challenged by extreme temperature and harsh process conditions. Contact-free thermal imaging-based measurement approaches thus appear to be particularly suitable for process monitoring. With the potential to generate vast amounts of accurate data in real time and combined with artificial intelligence methods to enable real-time analysis and integration of expert knowledge, thermal spectral imaging is identified as a promising method offering more robust and accurate identification of key parameters, such as surface temperature, morphology, composition, and flow rate.

Theoretical-Numerical Investigation of a New Approach to Reconstruct the Temperature Field in PBF-LB/M Using Multispectral Process Monitoring

“…For this, a linear system of equations (LSoE) based on Equation ( 15) is defined and solved for a user-defined number of superimposed Planck's radiation spectra. Afterwards, the success of the approximation and the fulfillment of Equation (13) and Inequality (14) are checked. A graphical representation of the described methodology is given in Figure 3.…”

“…Equation (13) and Inequality ( 14) are satisfied. For the case of n = 3, the approximation is not considered successful since the values of c 2 = 1.9% and c 3 = 1.4% are above the threshold of 0.5%.…”

“…The emissivity lies between zero and one, depending on the material composition, the surface condition, the temperature of the measurement object, and the direction and wavelength of the emitted radiation. The correct estimation of this parameter is one known difficulty in deriving absolute temperature values of surfaces with radiation measurement [13]. Other factors, such as the spectral characteristics of the transmission path and stray radiation, which are present in practical conditions, also affect the correct determination of the real object surface temperature and must therefore be taken into account.…”

“…The values and/or number of the temperatures T i considered in the LSoE are changed iteratively until the error Err, see Equation (15), is small enough and the constraints for the weighting factors, see Equation (13) and Inequality (14), are fulfilled. If the error Err is small enough, the approximation is considered successful.…”

Guided Co-operative Waderhunt Neural Network for Measuring the Temperature of Visible Heat Source

“…The pyrometers fall into the noncontact temperature measurement category and are only used to measure the temperature at specific, targeted locations. It is a highly risky and difficult task to detect the temperature of visible sources of heat, such as incandescent lamps, furnaces, burners, boilers, etc., with the support of sensors [4]. In addition, the installation of conventional sensors like resistance temperature detectors (RTD) close to hightemperature heat sources is remarkably challenging [5,6].…”