Argon gas injection in slab continuous casting is common practice to counter SEN clogging phenomena. Bubble characteristics determine the probability of bubble-driven defects such as steel cleanliness, liquid steel reoxidation, and sliver and blister defects. 1:2 scaled water model studies were performed with the help of an advanced high-speed-high-resolution camera shadowgraph imaging technique. Bubble Sauter mean diameter and count were calculated using Trainable Weka segmentation, a machine learning image-based segmentation in the ImageJ platform for different processing conditions such as gas flow rate, liquid flow rate, mold width, and submerged entry nozzle (SEN) depth. A predictive model was developed on the experimental data using an artificial neural network (ANN) algorithm to optimize the bubble mean diameter and count sampling strategy. The model performance is optimized based on the cross-validated adjusted R2. The model shows significant promise with bootstrapping aggregation, five-fold cross-validation, and improved accuracy.

Continuous cooling transformation (CCT) diagrams for two thermo-mechanically controlled processed (TMCP) steels were produced using a modified Johnson–Mehl–Avrami–Kolmogorov (JMAK) model, which accounted for the simultaneous transformation of multiple phases under non-isothermal conditions. A basin hopping algorithm was used to sequentially optimize the model parameters for each phase. Samples were prepared using a dilatometer which replicated the deformation and cooling rates experienced during TMCP. Scanning electron microscopy (SEM) and electron back-scattered diffraction (EBSD) were used to identify and quantify the phases present in each steel. CCT diagrams illustrating the start and stop temperatures of each phase were constructed for both steel samples. Through inclusion of the stop temperatures of each phase transformation, the utility of the CCT diagrams were expanded. This was done by introducing the possibility of applying the Scheil additive principle with respect to the beginning and end of each phase transformation. With this modification, the CCT diagrams are now more appropriately suited to predict the phase transformations that occur on the ROT, where non-continuous cooling occurs.