| Summary: | The classical thermal cycle in a Hot Dip Galvanizing (HDG) line has four steps: heating, soaking, cooling, and aging. The furnace of an ArcelorMittal HDG line was revamped to increase its heating capacity. This new configuration without the soaking step led to the redefinition of the thermal cycles for all the steel grades, especially for High Strength Low Alloyed (HSLA) steels, where it was necessary to define a new control parameter based on time and temperature. This paper presents the work done to improve the control of the mechanical properties of HSLA steels in the HDG line. Four different types of numerical models (linear and polynomial regressions, artificial neural networks, and Multivariate Adaptive Regression Splines), are applied to predict the yield strength and the tensile strength of individual coils. It is concluded that the introduction of the time–temperature parameter improves the accuracy of the predictions over 10% in most of the cases. An additional improvement is obtained with the use of the process values corresponding to the sampling area instead of the coil average ones. The use of these models makes it possible, if necessary, to adjust individually the presets of the coils before processing them in the galvanizing line and reduce the scattering of the mechanical properties.
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