Optimization of cold rolling lubrication process for ultra-high strength steel based on PSO algorithm

ZHAN Bo; GUO Genfa; ZHU Jiahao

doi:10.3969/j.issn.1001-196X.2025.04.015

Abstract：Addressing the insufficient process lubrication and cooling capacity encountered during the production of ultra-high-strength steel using a six-stand tandem cold mill,a systematic investigation is conducted from the perspectives of emulsion parameter control mechanisms and multi-objective coordinated optimization.A dynamic adjustment method for process lubrication regimes based on a combination of mechanism modeling and intelligent optimization is proposed.First,a lubrication oil film thickness prediction model is constructed.This model comprehensively considers the non-linear effects of concentration,temperature,and flow rate parameters on the base oil's dynamic viscosity,quantitatively characterizes the mapping relationship between process parameters and the friction coefficient,and reveals the critical transition mechanism of oil film thickness with variations in rolling load and rolling speed.To address the multi-variable and strong-coupling problems in process parameter optimization for six-stand mills,the Particle Swarm Optimization(PSO)algorithm is introduced for global optimization.Industrial verification demonstrates that the optimized process lubrication regime significantly improves the consistency of lubrication conditions between the upper and lower surfaces of the strip,effectively suppressing thermal skid marks and slippage phenomena in critical stands.The cooling capacity of the emulsion system is systematically released,and rolling temperature rise and shape fluctuations are significantly reduced,providing technical support for the stable production of thin-gauge ultra-high-strength steel products.