Finite element analysis-based flatness control and bending force optimization strategy for high rolling force conditions

To address severe flatness control failure of cold rolled strips under ultra-high rolling force, a collaborative bending force multi-objective optimization strategy based on surrogate models is proposed for a 1450 six-high tandem cold mill. Latin hypercube sampling (LHS) is adopted to generate process sample points, and Gaussian process regression (GPR) and radial basis function neural network (RBFNN) are constructed as fast surrogate models to replace time-consuming ABAQUS finite element simulation. NSGA-II and particle swarm optimization (PSO) are combined to perform global constrained optimization of work roll bending (WRB) and intermediate roll bending (IRB) forces, aiming to minimize transverse thickness deviation, quadratic/quartic strip crown, flatness residual error and rolling force unevenness simultaneously. Finite element verification shows that under high rolling force of 25000 kN, the optimized collaborative bending force combination reduces strip transverse thickness difference from 18 μm to 6 μm, quadratic crown from +11 μm to −5 μm, quartic crown from 3 μm to 1 μm, rolling pressure unevenness by 75.9%, and almost eliminates edge wave flatness defects. Mechanism analysis reveals that high rolling force significantly shifts the optimal single bending force operating window and increases quartic crown proportion, while intermediate roll bending force presents stronger anti-interference performance for quarter-wave defects. The proposed surrogate model-based optimization framework effectively overcomes the compensation limit of single bending force control under extreme heavy load, providing theoretical and technical support for high-precision flatness control of ultra-thin high-strength cold rolled strips.