Multiphysics coupling AI prediction method for thermomechanical behavior of steel ladle linings

Abstract

Predicting the thermomechanical responses of refractory linings in steel ladles is critical to optimizing production efficiency and ensuring safety in the iron and steel smelting industry. However, traditional numerical simulation methods suffer challenges of high computational costs and insufficient generalizability, while data-driven models are limited by a lack of physical rationality and poor interpretability. Aiming at overcoming these challenges, an artificial intelligence (AI) model, named the steel ladle Kolmogorov–Arnold network (SLKAN), is designed to predict the thermomechanical behavior of ladle linings. Based on the Kolmogorov–Arnold theorem and material constitutive equations, SLKAN precisely predicts the thermomechanical behavior of ladle linings. The model offers substantial advantages in predicting the maximum tensile stress in the steel shell and the maximum compressive stress at the working lining hot face: the coefficient of determination (R²) value for compressive stress prediction reaches 0.9942, with a mean absolute error (MAE) of 9.4136 and a root mean squared error (RMSE) of 0.0192; the R² value for tensile stress prediction is 0.9578, with an MAE of 41.4855 and an RMSE of 0.0385. Further analysis indicates that the function expressions of SLKAN hold clear physical significance. This study provides an interpretable, efficient AI solution for multiphysics coupling modeling in complex industrial scenarios and offers theoretical guidance for the application of AI in predicting the lifespan of steel-smelting equipment.