Slag removal path estimation by slag distribution and deep learning

In the steel manufacturing process, de-slagging machine is used to remove slag floating on molten metal in a ladle. In general, temperature of floating slag on the surface of the molten metal is above 1,500℃. The process of removing such slag at high temperatures is dangerous and is only performed by trained human operators. In this paper, we propose a deep learning method for estimating the slag removal path to automate slag removal task. We propose an idea of developing a slag distribution image structure(SDIS); combined with a deep learning model to estimate the removal path in an environment in which the flow of molten metal cannot be controlled. The SDIS is given as the input into to the proposed deep learning model, which we train by imitating the removal task of experienced operators. We use both quantitative and qualitative analyses to evaluate the accuracy of the proposed method with the experienced operators.