Work roll temperature field calculation and analysis based on multi-objective genetic algorithm

Equivalent heat transfer coefficients are key input factors for work roll temperature field analysis models in hot strip rolling, and the coefficients are usually calculated with genetic algorithms, but the rolling process of alternating material and width in a work roll service period for a 1800 mm hot strip rolling mill makes the coefficients difficult to be calculated accurately. The surface temperature distributions of a serviced work roll under multiple rolling schedules were taken as the optimization goals, and the equivalent heat transfer coefficients with a higher adaptability were obtained by using a multi-objective genetic algorithm. The optimizing process was improved by changing the genetic operators, which avoided the disadvantages of premature convergence and local convergence. The optimization model was proved effectively by comparing the simulation results with measured data. The effects of rolling parameters on the thermal crown were analyzed with the optimization model. It is predicted that under alternating width rolling, the thermal crown exponentially increases; but under alternating material and width rolling schedules, the thermal crown has a fluctuation of 6 to 21.8 μm between the continuous two strips, and towards stability gradually.