Abstract
In the hot strip rolling process, accurate prediction of bending force can improve the control accuracy of the strip flatness and further improve the quality of the strip. In this paper, based on the production data of 1300 pieces of strip collected from a hot rolling factory, a series of bending force prediction models based on an extreme learning machine (ELM) are proposed. To acquire the optimal model, the parameter settings of the models were investigated, including hidden layer nodes, activation function, population size, crossover probability, and hidden layer structure. Four models are established, one hidden layer ELM model, an optimized ELM model (GAELM) by genetic algorithm (GA), an optimized ELM model (SGELM) by hybrid simulated annealing (SA) and GA, and two-hidden layer optimized ELM model (SGITELM) optimized by SA and GA. The prediction performance is evaluated from the mean absolute error (MAE), root-mean-squared error (RMSE), and mean absolute percentage error (MAPE). The results show that the SGITELM has the highest prediction accuracy in the four models. The RMSE of the proposed SGITELM is 11.2678 kN, and 98.72% of the prediction data have an absolute error of less than 25 kN. This indicates that the proposed SGITELM with strong learning ability and generalization performance can be well applied to hot rolling production.
Highlights
In hot strip rolling (HSR) process, the product quality of the strip is mainly contributed by dimensional accuracy, mechanical properties, and surface properties
The dimensional accuracy of the strip requires two important indicators: strip thickness and surface profile [1]. e surface profile of the strip is defined as the difference of thickness between the center and a point of 40 mm from the edge of the strip, or in other words, this is the difference of thickness across the width of the strip [2]. ere are many factors that affect the surface profile of the strip, which are mainly related to the roller, strip, and rolling conditions in the HSR process [3]
Because the initialization of the weights and biases is randomly assigned in the extreme learning machine (ELM) algorithm, it will not lead to the optimal state of the network during the training process. erefore, in this study, we introduce the genetic algorithm (GA) algorithm to optimize the weights and biases of the ELM network
Summary
In hot strip rolling (HSR) process, the product quality of the strip is mainly contributed by dimensional accuracy, mechanical properties, and surface properties. Erefore, the mathematical model established by the traditional theory has the disadvantages of slow response speed and low control accuracy in production practice All these problems seriously restrict the further improvement of strip profile control accuracy. E ANN method improves the performance of prediction to a certain extent; there are still some problems, such as the slow learning speed of the algorithm for training models and the difficulty of adjusting numerous parameters. ELM has been widely used in metallurgy and metal processing fields, such as the mechanical properties of hot rolling products [20], rolling force prediction in HSR [21], silicon content prediction [22], alumina concentration detection [23], gas utilization ratio prediction in blast furnace [24], and tool fault diagnosis in numerical control machines [25].
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