Patent ID: 8185232

Claim:
A learning method of rolling load prediction for hot rolling, using a prediction error of a rolling load at an actual pass of a stock to correct a predicted value of rolling load at a rolling pass of said stock to be performed subsequent to said actual pass, said learning method of rolling load prediction for hot rolling characterized by, when setting a learning coefficient for rolling load prediction, making a gain for multiplying with the prediction error of the rolling load at said actual pass smaller, the smaller a thickness of the stock, wherein C P denotes said prediction error rate of rolling load at said actual rolling pass, C F denotes said learning coefficient, α denotes said gain, said method comprising: a) determining said prediction error rate C P of rolling load at said actual rolling pass; b) determining fro said subsequently performed rolling pass a calculated rolling load P cal using a rolling load model; c) determining said gain α according to a thickness of the stock; d) determining said learning coefficient C F using said gain α obtained in step c) and said prediction error rate C P obtained in step a), said learning coefficient C F of the rolling load at said subsequently performed rolling pass is calculated using formula (2) C F =α·C P +(1−α)· C F′ (2) wherein C F′ is a learning coefficient of said rolling load at said actual rolling pass; and e) obtaining a predicted rolling load P set for said subsequently performed rolling pass based on said calculated rolling load P cal obtained in step b) and said learning coefficient C F obtained in step d).