This review demonstrates the role of machine learning in improving continuum model accuracy for macrosegregation in continuous casting processes, indicating future industrial applications.
Macrosegregation is a common defect that occurs on a large‐scale range of 0.01–1 m, and cannot be eliminated during the rolling and homogenization heat treatment process. A comprehensive understanding of its formation mechanisms and accurate predictive modeling is significant for both scientific research and industrial practice. The review first outlines the fundamental mechanisms of macrosegregation formation, including negative segregation in the subsurface, positive segregation in the columnar‐to‐equiaxed transition zone, V‐shaped segregation, and center segregation. The two primary modeling approaches (the continuum model and the multiphase solidification model) are compared in terms of their governing equations and computational efficiency. Recent developments and applications of macrosegregation in both approaches are summarized. Continuum models are advantageous for large‐scale industrial simulations due to their lower computational demand, while multiphase solidification models are preferred when detailed mechanisms of solid–liquid interaction need to be captured. In future simulating studies on macrosegregation, macrosegregation modeling could incorporate machine learning approaches to enable real‐time forecasting. For precise predictions of macrosegregation, the coupling of macrosegregation modeling with formation and growth kinetics of inclusions should be established and used in the simulation of the continuous casting process.
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Wang et al. (2025) studied this question.
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