Addressing the challenge of roll loss and strip deformation arising from the lack of precise prediction of the roll temperature field in hot tandem rolling mills, this study employs numerical analysis via the finite difference method. Based on the roll temperature field and hot roll crown model, an intelligent support cooling control system for the roll cooling water of hot rolls is established. This system comprehensively considers the direct impact of specific parameters on the roll temperature field in the intricate context of cooling water dynamics. The study focuses on the cyclic superposition effect of rolling coil quantities on the roll temperature field and the resulting hot roll shape, and theoretical calculations along with simulation analyses were conducted using finite element software. Through the integration of field-measured values, the study achieves accurate predictions of the temperature field and hot roll profile for both work rolls and backup rolls.

Roll wear significantly affects production efficiency and product quality in hot-rolled strip steel manufacturing by reducing roll lifespan and impeding the control of strip shape. This study addresses these challenges through a comprehensive analysis of the roll wear mechanism and the integration of an elastic deformation model. We propose an optimized wear prediction model for work and backup rolls in a hot continuous rolling finishing mill, dynamically accounting for variations in strip specifications and cumulative wear effects. A three-dimensional elastic–plastic thermo-mechanical coupled finite element model was established using MARC 2020 software, with experimental calibration of wear coefficients under specific production conditions. The developed dynamic simulation software achieved high-precision wear prediction, validated by field measurements. The optimized model reduced prediction deviations for work and backup rolls to 0.012 and 0.004, respectively, improving accuracy by 5.3% and 3.25% for uniform and mixed strip specifications. This research provides a robust theoretical framework and practical tool for precision roll wear management in industrial hot rolling processes.