基于鲸鱼优化算法的平整液关键参数优化OA

Addressing the issue of low flatness qualification rates during high-strength automotive sheet rolling in a single-stand leveling mill at a steel plant,this study investigates the influence mechanism of key leveling fluid parameters(operating temperature,spray flow rate,chemical concentration)on strip flatness and proposes a parameter optimization scheme to enhance sheet quality and production efficiency.First,a theoretical model linking leveling fluid parameters to strip flatness was established,with the objective function defined as the sum of squares of flatness deviations.Second,the Whale Optimization Algorithm was designed and applied to search for the optimal parameter combination that minimizes flatness deviation while satisfying equipment and process constraints.Finally,the optimized parameters were implemented on the actual production line for industrial validation.Results demonstrate that the optimized parameters significantly improve strip flatness.The average flatness qualification rate increased from 63.2%to 93.6%.Flatness deviation values markedly decreased,with the deviation distribution becoming smoother and more stable.This method effectively resolves flatness defects in high-strength automotive steel,substantially enhancing product quality and production efficiency,and provides a reliable optimization pathway for practical manufacturing.