A Mechanism-Data Fusion Prediction Model Based on LightGBM for Hot Rolling Force

Accurate and interpretable prediction of hot rolling force is essential for optimizing material deformation, improving product quality, and reducing energy consumption in the hot rolling process. However, due to idealized assumptions and dynamic process variations, the in-field mechanistic models often lack sufficient accuracy in practical settings, which may lead to issues such as excessive thickness tolerance deviations and plate shape defects. To address this limitation, the authors propose a mechanism–data fusion model based on the LightGBM (LGBM) framework, which incorporates both mechanistic parameters and key process variables as structured input features, aiming to enhance predictive performance. To further optimize the model, Particle Swarm Optimization (PSO) is employed for hyperparameter tuning within the LGBM framework. Comparative experiments demonstrate the proposed model outperforms alternatives on the test set, with 94.05% of predictions having relative errors within ±6% and a coefficient of determination (R2) of 0.977—indicating strong engineering reliability.