Prevention of Thermal Crack in Steel Slab Using Neural Networks Model to Predict Impact Absorption Energy

To prevent thermal cracks on steel slabs during the stacking, scarfing, and grinding processes after continuous casting, an optimized machine learning (ML) model to predict the impact absorption energy (EIA) of steel slabs is developed. A total of 1,421 experimental EIA data are collected from Charpy impact tests at slab temperatures 25 ≤ TS ≤ 400 °C, then ML models that considered 15 steel‐component variables and temperatures are developed. Four ML models are developed and their accuracies are compared. The optimized deep neural network algorithm predicts EIA most accurately with root mean squared error of 10.82 J and coefficient of determination (R2) of 0.992. Then the predicted EIA is used as the criterion to predict the occurrence of thermal cracks in slabs that are actually produced by a continuous casting process. At TS = 250 °C, cracking does not occur when the steel has predicted EIA > 175 J. The use of the developed model to predict EIA can prevent the formation of thermal cracks in slabs produced by continuous casting, and enable optimization of the cooling method and scarfing methods that precede the next process after continuous casting of slabs. An optimized deep neural network model is developed to predict the impact absorption energy of steel slabs. Then the predicted impact absorption energy is used as the criterion to prevent thermal cracks on steel slabs. The use of the developed model can enable the optimization of cooling methods and scarfing methods that precede the next process after the continuous casting of slabs.