In the hot continuous rolling process, the main factor affecting the actual thickness of strip is the rolling force. The precision of rolling force calculation is the key to realize accurate on-line control. However, because of the complexity and nonlinearity of the rolling process, as well as many influencing factors, the theoretical analysis of the traditional rolling force prediction model often needs to be simplified and hypothesized. This leads to the incompleteness of the mathematical model and the deviation between the calculated results and the actual working conditions. In this paper, a rolling force prediction method based on genetic algorithm (GA), particle swarm optimization algorithm (PSO), and multiple hidden layer extreme learning machine (MELM) is proposed, namely, PSO-GA-MELM algorithm, which takes MELM as the basic model for rolling force prediction. In the modeling process, GA is used to determine the optimal number of hidden layers and the optimal number of hidden nodes, and PSO is used to search for the optimal input weights and biases. This method avoids the influence of human intervention on the model and saves the modeling time. This paper takes the actual production data of BaoSteel 2050 production line as experimental data, and the experimental results indicate that the algorithm can be effectively used to determine the optimal network structure of MELM. The rolling force prediction model trained by the algorithm has excellent performance in prediction accuracy, computational stability, and the number of hidden nodes and is applicable to the prediction of rolling force in hot continuous rolling process.
Flow Stress Calculation of IN718 Alloy for Hot Continuous Rolling Process
