KG Steel announced on the 20th that it has developed a prediction model for micro-component changes in the zinc-galvanized line integrated with artificial intelligence (AI) technology. This is expected to improve the production efficiency of zinc-coated steel plates, which are key materials for color-coated steel sheets.
Producing zinc-coated steel plates requires a coating process that involves applying molten zinc to the surface of the coils. For this, various alloy zinc ingots must continually be fed into the coating pot (coating bath) filled with molten zinc. Previously, workers had to frequently check the concentration of molten zinc to adjust the amount and timing of the feed.
However, with the introduction of the newly developed micro-component prediction model, AI can predict changes in the concentration of molten zinc in the pot in advance, providing workers with more precise guidance on ingot feeding. This effectively increases work efficiency.
A KG Steel official noted, "The management level of the coating bath concentration during the coating process is expected to improve by about 60%, significantly enhancing product quality, productivity, and cost competitiveness," adding, "We will enhance the micro-component prediction model, which is the first to be introduced in domestic cold rolling manufacturing processes, and expand its application scope."