Abstract: Shape of the steel plate is a key quality indicator during the quenching process. In order to solve the problem of plate shape prediction of steel plates during the quenching process, a two-stage shape prediction method for steel plates in roller-hearth machine quenching process based on convolutional neural network and long short-term memory network (CNN-LSTM) was proposed. This method was divided into two stages. Firstly, the CNN was used to extract the plate shape features and capture the spatial information of the plate shape. Secondly, using quenching process parameters and historical plate shape characteristics as inputs, a plate shape prediction model was established through LSTM. Finally, by concatenating these two stages, both spatial and temporal information of the plate shape could be considered simultaneously. Based on the experiments with actual production data, the results show that the proposed method reduces the root mean squared error of the prediction is reduced from 0.0471 to 0.0264, which represents a 43.9% reduction in prediction error, achieving the goal of improving the plate shape prediction accuracy.

Key words: roller-hearth quenching machine, plate shape prediction, convolutional neural network, long short term memory network