Estimation of the Dominant Process Parameters on Coating Thickness in a Continuous Galvanizing Line With Computational Fluid Dynamics and Machine Learning Approaches

Abstract

In this study, the dominant process parameters in the air-jet continuous galvanizing line on coating thickness were estimated by computational fluid dynamics and machine learning approaches. First, 128 different cases consisting of different levels of process parameters were created with the Taguchi method. Then, numerical analyses were performed for each case, calculating the maximum pressure gradient and maximum shear stress values on the strip, which were then used in the analytical model developed based on one-dimensional lubrication theory to obtain coating thickness values. Lastly, artificial intelligence techniques based on different machine learning algorithms such as K-Nearest Neighbors, linear regression, random forest and Adaboost, the relative effects of the process parameters influencing the coating thickness were compared through the feature importance values. It was observed that the dominant process parameters differ in low and high jet pressure cases. Accordingly, in the case of low jet pressure, air jet pressure, nozzle slot opening and velocity of the steel strip stand out as the dominant parameters, while in the case of high jet pressure, the most effective parameters influencing the coating thickness are air jet pressure and nozzle slot opening. In addition to this, the effect of the distance between the nozzle and the zinc pot influencing the coating thickness can also be neglected in both low and high pressure cases. Moreover, it was also noticed that the effects of nozzle angle and the distance between the nozzle and the steel strip influencing the coating thickness increase with increasing jet pressure.