Width prediction method of rough rolled material, width control method of rough rolled material, manufacturing method of hot-rolled steel sheet, and generation method of width prediction model of rough rolled material

A width prediction method of a rough rolled material, which predicts a width of the rough rolled material in a hot rolling line including a heating furnace configured to heat a slab, a width reduction pressing device configured to intermittently perform width reduction on the heated slab, a rough rolling mill configured to manufacture the rough rolled material by performing rough rolling on the slab after the width reduction, and a finish rolling mill configured to manufacture a hot-rolled steel sheet by performing finish rolling on the rough rolled material, the width prediction method comprising:

• a step of predicting a width distribution in a longitudinal direction of the rough rolled material by using a width prediction model learned by machine learning, the width prediction model for which
• an input data is data including one or more operational parameters selected from operational parameters of the width reduction pressing device and one or more operational parameters selected from operational parameters of the rough rolling mill, and
• an output data is information on the width distribution in the longitudinal direction of the rough rolled material.

A generation method of a width prediction model of a rough rolled material, which generates a width prediction model of predicting a width of the rough rolled material in a hot rolling line including a heating furnace configured to heat a slab, a width reduction pressing device configured to intermittently perform width reduction on the heated slab, a rough rolling mill configured to manufacture the rough rolled material by performing rough rolling on the slab after the width reduction, and a finish rolling mill configured to manufacture a hot-rolled steel sheet by performing finish rolling on the rough rolled material, the generation method comprising:

• a width prediction model generating step of
• acquiring a plurality of pieces of learning data, in which one or more pieces of operational performance data selected from operational parameters of the width reduction pressing device and one or more pieces of operational performance data selected from operational parameters of the rough rolling mill are included as input performance data, and information on a width distribution in a longitudinal direction of the rough rolled material is output performance data, and
• generating the width prediction model by machine learning using the plurality of pieces of acquired learning data.