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.