Rolling load prediction method, rolling load prediction device, and rolling control method
A rolling load prediction method predicts a rolling load of a rolling mill for rolling steel and includes predicting the rolling load of the rolling mill in a case where the steel is rolled under an operating condition for prediction, by inputting the operating condition for prediction into a rolling load prediction model that has been trained with operation record data including at least a factor related to a temperature of the steel as an input variable and an actual value of the rolling load of the rolling mill as an output variable.
1. A rolling load prediction device for predicting a rolling load of a rolling mill for rolling steel, the device comprising:
circuitry configured to:
predict the rolling load of the rolling mill in a case where the steel is rolled under an operating condition for prediction, by inputting the operating condition for prediction into a rolling load prediction model that has been trained with operation record data including at least a factor related to a temperature of the steel as an input variable and an actual value of the rolling load of the rolling mill as an output variable;
calculate a setting value of the rolling mill using the predicted rolling load of the rolling mill; and
control the rolling mill according to the calculated setting value, wherein
the factor related to the temperature of the steel includes a temperature of a skid rail that supports the steel in a heating furnace.
2. The rolling load prediction device according to claim 1 , wherein the rolling load prediction model is a learning model that has been trained using deep learning.
3. A rolling control method comprising:
calculating a setting value of a rolling mill using a rolling load of the rolling mill predicted using a rolling load prediction method; and
controlling the rolling mill according to the calculated setting value, wherein
the rolling load prediction method including
predicting the rolling load of the rolling mill in a case where the steel is rolled under an operating condition for prediction, by inputting the operating condition for prediction into a rolling load prediction model that has been trained with operation record data including at least a factor related to a temperature of the steel as an input variable and an actual value of the rolling load of the rolling mill as an output variable, and
the factor related to the temperature of the steel includes a temperature of a skid rail that supports the steel in a heating furnace.
4. The rolling control method according to claim 3 , wherein the rolling load prediction model is a learning model that has been trained using deep learning.