Abstract：Aiming at the problem of low precision of hot rolled strip head thickness, a hit prediction method of hot rolled strip head thickness based on deep learning was proposed. In the process of finish rolling, the tension of the head end of the steel is small, and the temperature is usually lower. At the same time, the process parameters of the rolling mill are complex, and it is difficult to set accurately. The thickness of the rolled strip head is often unqualified. This study intends to use the nonlinear fitting ability of the deep neural network to design the prediction model of strip head thickness, to provide a reference for the parameter setting of rolling mill, improve the hit rate of head thickness and reduce the waste of steel. The deep neural network (DNN) consists of the input layer, hidden layer, and output layer structure. The prediction model is designed by TensorFlow open-source machine learning framework and implemented by program. By adjusting the parameters of the neural network and studying their effects on the model performance, the prediction model is optimized. Finally, the model of head thickness prediction was trained and tested with the test data of various thicknesses of strip steel, and the result showed that the accuracy of classification prediction was more than 80%.
于加学, 孙杰, 张殿华. 基于深度学习的热轧带钢头部厚度的命中预测[J]. 钢铁, 2021, 56(9): 19-25.
YU Jia-xue, SUN Jie, ZHANG Dian-hua. Accurate prediction of head thickness of hot-rolled strip based on deep learning[J]. Iron and Steel, 2021, 56(9): 19-25.
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2021, Vol. 56 
