Bayesian approach to pyrometry during intercritical annealing of advanced high strength steel

Abstract

Precise thermal control during intercritical annealing is essential to the production of advanced high strength steels (AHSS). Measuring the temperature of the steel strip through pyrometry requires detailed knowledge of the spectral emissivity of the steel strip, which is imperfectly known since it varies with wavelength, direction, temperature, surface roughness, and oxidation, the latter depending on alloy composition and processing conditions. This study presents a Bayesian pyrometry methodology in which temperature and spectral emissivity are described as unknown stochastic variables that are inferred simultaneously. Additional information about the spectral emissivity obtained through ex situ characterization are incorporated into the inference through maximum likelihood priors. While standard pyrometry methods provide a point estimate of surface temperature, the Bayesian framework infers the posterior probability density, which will allow galvanizers to better assess the reliability of the pyrometrically-inferred temperature.