关键词: GCr15钢坯心部温度 , 黑匣子实验 , 神经网络 , 随机森林 , XGBoost

Abstract: The core temperature uniformity control of billet in heating furnace is very important to the stability of product quality,due to the high temperature environment in heating furnace, it is always a difficult problem to predict the core temperature of billet with high precision. In order to solve this problem, in this paper a temperature measurement method based on billet embedded thermocouple black box is established to effectively obtain the actual temperature distribution of billet at different positions in the heating furnace. Based on the experimental data of black box temperature measurement, the methods such as data cleaning,data smoothing and standardization areapplied, based on the data-driven neural network, random forest and XGBoost model, the unmeasured core temperature of billet is predicted by using the measurable gas temperature in the heating furnace. The prediction results of core temperature of GCr15 steel 150 mm x 150 mm billet show that the regression prediction effect of XGBoost model is the best, and the relative errors are mainly distributed in the range of 0%-5.4%. The absolute error of 97.1% of the sample points in the model is less than 10 °C , the RMSE error is 4. 1345 ℃ , and the MAPE error is 0.47%. The method of billet core temperature prediction based on billet embedded thermocouple black box temperature measurement + XGBoost model is proposed.

Key words: GCr15 Steel Billet Core Temperature , Black Box Experiment , Neural Network , Random Forest , XGBoost