Experimental Technology and Management

[Objective] The precise estimation of rolling force during the process of cold continuous rolling is of paramount importance for ensuring product quality, enhancing automation levels, improving production efficiency, and optimizing process settings. However, the conventional cold rolling force mechanism model often relies solely on process parameters during the cold-rolling stage. It disregards the genetic effects of the hot rolling process on the material's structure and properties, and it cannot effectively capture the complex, nonlinear impact of cross-process parameters on the rolling force. This results in limited prediction accuracy and generalization ability. This study proposes a cold rolling force prediction model based on Bayesian optimization and an improved light gradient boosting machine(BO-LightGBM), aiming to comprehensively explore the process coupling between hot and cold rolling. The model aims to enhance adaptability and accuracy in predicting force during cold rolling across various steel grades and production scenarios. [Methods] The modeling process involves the development of a multi-source feature system, incorporating 7-dimensional hot rolling parameters(e.g., finish rolling temperature, coiling temperature, final thickness, etc.), 11-dimensional cold rolling parameters(e.g., strip width, rolling speed, deformation resistance coefficient, etc.), and the predicted output from the traditional mechanism-based model. This comprehensive feature set enables the model to represent a cross-process fusion of variables that collectively influence rolling force. It is acknowledged that there is variability and coupling across different rolling stands in a tandem cold rolling mill. Therefore, a stand-specific modeling strategy is employed. The development of independent prediction models tailored to each stand facilitates the capture of local process characteristics, nonlinear interactions, and contextual dependencies. Furthermore, a Bayesian optimization algorithm is employed to automatically fine-tune the hyperparameters of the BO-LightGBM model for each stand. This approach effectively reduces human intervention, avoids suboptimal manual tuning, and enhances the efficiency and robustness of the learning process. [Results] The impact of incorporating upstream process data was evaluated by training rolling force prediction models on two distinct datasets. The first dataset contained only cold rolling parameters, while the second dataset included both hot and cold rolling parameters. A series of comparative experiments have demonstrated that 1) following the implementation of the hot rolling process parameters, the mean absolute error in rolling force prediction for each stand has been shown to decrease by an average of 1.803 t, whereas the root mean square error has been demonstrated to decrease by an average of 2.573 t, thereby indicating a substantial enhancement in accuracy. 2) In a real industrial cold rolling setting system, the model has been found to enhance the rolling force setpoint accuracy for MR T-4 CA and MR T-5 CA steel grades by 2.024% and 1.962%, respectively, thereby underscoring its practical engineering value and operational significance. [Conclusions] The proposed BO-LightGBM rolling force prediction model demonstrates excellent performance in terms of accuracy, robustness, and generalization. The model effectively incorporates upstream hot rolling data and employs stand-specific learning with automated hyperparameter optimization, thereby capturing the hereditary influence of the hot rolling stage and overcoming the limitations of traditional mechanism models in cross-process modeling. The model offers a promising data-driven solution for intelligent process control in modern steel rolling operations and supports the advancement of smart manufacturing in the metallurgical industry.