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.

A method for operating an annealing furnace to anneal a metal strip provides that, initially, at least one target material property (MP.sub.Target) is specified for a point or a section of the metal strip after passing through the annealing furnace. In addition, information (E) on the metal strip is provided before or in the annealing furnace. A calculation of a target temperature distribution (T.sub.Target) and/or a target speed (V.sub.Target) of the metal strip in the annealing furnace is then carried out with the assistance of a computer-aided model as a function of the target material properties and the specified information. The target temperature distribution and/or target speed calculated in this manner is/are subsequently set in the annealing furnace in order to transfer the material property of the metal strip behind the annealing furnace to the desired target material property MP.sub.Target.

A hot rolling line control system includes a rolling condition setting unit, an operation data collection unit that collects rolling conditions and operation data of a line during rolling, an operation data storage unit, a material measurement data storage unit that stores material actual measurement data obtained by measuring a material of a rolled steel sheet, a material prediction unit that predicts material of rolled steel sheet, and a material prediction data storage unit that stores material prediction data in the material prediction unit, and the material prediction unit includes a classification criteria creation and material model regression unit that creates classification criteria using operation data and the material actual measurement data, classifies the operation data and the material actual measurement data according to the created classification criteria, and regresses the classified operation data and material actual measurement data to create a material model for each classification.

A hot rolling line control system includes a rolling condition setting unit, an operation data collection unit that collects rolling conditions and operation data of a line during rolling, an operation data storage unit, a material measurement data storage unit that stores material actual measurement data obtained by measuring a material of a rolled steel sheet, a material prediction unit that predicts material of rolled steel sheet, and a material prediction data storage unit that stores material prediction data in the material prediction unit, and the material prediction unit includes a classification criteria creation and material model regression unit that creates classification criteria using operation data and the material actual measurement data, classifies the operation data and the material actual measurement data according to the created classification criteria, and regresses the classified operation data and material actual measurement data to create a material model for each classification.

A method of controlling a rolling mill production system for production of a coil-shaped end product from a slab, the production including processing the slab by sequentially arranged production units, the processing by the production units resulting in a respective strip-shaped product having physical data, the method including modeling, under consideration of the physical data, the processing of a testing product by a plurality of production units arranged downstream from a given production unit while taking into account the physical data. If the modelling shows that, under consideration of the physical data, one of the products resulting from processing by the downstream production units does not meet a predetermined quality criterion, the intended manufacture of the product is interrupted and a signal relating to the interrupting is outputted.

A method of controlling a rolling mill production system for production of a coil-shaped end product from a slab, the production including processing the slab by sequentially arranged production units, the processing by the production units resulting in a respective strip-shaped product having physical data, the method including modeling, under consideration of the physical data, the processing of a testing product by a plurality of production units arranged downstream from a given production unit while taking into account the physical data. If the modelling shows that, under consideration of the physical data, one of the products resulting from processing by the downstream production units does not meet a predetermined quality criterion, the intended manufacture of the product is interrupted and a signal relating to the interrupting is outputted.

Hot rolling stock (1) made of metal which is rolled in at least one roll stand (2) and then cooled in a cooling section (5) arranged downstream of the at least one roll stand (2). Sound generated by means of a sound generator arrangement (8) is coupled into the rolling stock (1) by a coupling device (1) so that a standing sound wave is formed at least in the region of the rolling stock (1) which is located in the vicinity of the coupling device (10).

Disclosed is a method of emulsion concentration optimization for a cold continuous rolling mill set for achieving vibration suppression, the method comprising: defining the process parameters involved in the process of emulsion concentration optimization; setting an initial set value of an emulsion concentration comprehensive optimization target function for a cold continuous rolling mill set for achieving vibration suppression; calculating a bite angle of each stand; calculating a vibration determination index reference value of each stand; setting the emulsion concentration of each stand; calculating the outlet temperature of a strip steel of each stand; calculating the dynamic viscosity of an emulsion in a roll gap of each stand; calculating the oil film thickness in the roll gap of each stand; calculating the emulsion concentration comprehensive optimization target function; determining whether the inequation F(X)<F.sub.0 is established; determining whether the concentration of the emulsion exceeds a feasible region range, and outputting the optimal emulsion concentration set value.

A chattering detection method for a cold rolling mill, the method including: measuring a vibration of a cold rolling mill; calculating a time waveform of vibration intensity by performing frequency analysis on a time waveform of the measured vibration of the cold rolling mill for a predetermined period equal to or shorter than a time in which a periodic vibration continues without converging; and detecting a chattering sign vibration of the cold rolling mill based on a number of points having vibration intensity values that exceed a predetermined threshold, the points being included in the calculated time waveform of the vibration intensity.

The present disclosure provides a strip flatness prediction method considering lateral spread during rolling. The method includes: step 1: acquiring strip parameters, roll parameters and rolling process parameters; step 2: introducing a change factor of a lateral thickness difference before and after rolling and a lateral spread factor by considering lateral metal flow, and constructing a strip flatness prediction model based on the coupling of flatness, crown and lateral spread; step 3: constructing a three-dimensional (3D) finite element model (FEM) of a rolling mill and a strip, simulating strip rolling by the 3D FEM, extracting lateral displacement and thickness data of the strip during a stable rolling stage, calculating parameters of the strip flatness prediction model based on the coupling of flatness, crown and lateral spread; and step 4: predicting the flatness of the strip by the strip flatness prediction model based on the coupling of flatness, crown and lateral spread.