Reliable roll force prediction in cold mill using multiple neural networks

Cold rolling mill process in steel works uses stands of rolls to flatten a strip to a desired thickness. The accurate prediction of roll farce is essential for product quality. Currently, a suboptimal mathematical model is used. We trained two multilayer perceptrons, one to directly predict the roll force and the other to compute a corrective coefficient to be multiplied to the prediction made by the mathematical model. Both networks were shown to improve the accuracy by 30-50%. Combining the two networks and the mathematical model results in systems with an improved reliability.