Optimization and Finite Element Simulation of Wear Prediction Model for Hot Rolling Rolls
Xiaodong Zhang, Zizheng Li, Boda Zhang, Jiayin Wang, Sahal Ahmed Elmi, Zhenhua Bai
Abstract
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.