JP2022170101A - Rolling load prediction method, rolling control method and rolling load prediction device - Google Patents

Abstract

To provide a rolling load prediction method and rolling load prediction device which can accurately predict a rolling load in a short time.SOLUTION: In a rolling load prediction method according to the present invention, a step of calculating a learning coefficient uses only past data within a range of a prescribed Euclidean distance from a point in a data space corresponding to object rolled material data as a processing target, and a step of acquiring the plurality of pieces of past data as operation data includes a step of dividing a data space formed by the past data into a plurality of arbitrary meshes, searching for the oldest past data in the mesh storing the past data acquired at timing at which the new past data is acquired, and deleting the searched past data.SELECTED DRAWING: Figure 2

Description

The present invention relates to a rolling load prediction method, a rolling control method, and a rolling load prediction device.

In the process of manufacturing steel products such as thick steel plates, after heating a cast slab to a predetermined temperature in a heating furnace, the slab is rolled to the product thickness by rolling the slab multiple passes in a reversible rolling mill. . At this time, the rolling schedule, such as the amount of reduction in each pass, is automatically calculated and set by a computer. It is necessary to maximize the reduction amount.

Here, the reduction amount of each pass is restricted by the maximum normal load and maximum torque of the rolling mill. However, the reduction amount is restricted in the pass schedule. Therefore, if the prediction accuracy of the rolling load is poor, the constraint load within the pass schedule must be made smaller in consideration of the variation in prediction. Therefore, if the prediction accuracy of the rolling load (=actual rolling load/predicted rolling load) is improved, the constraint load can be increased to maximize the rolling efficiency.

Against this background, a method for reducing the prediction error of the rolling load by determining the learning value of the rolling load for this pass from the prediction accuracy of the rolling load for the previous pass and multiplying the predicted rolling load for this pass by the learning value. is proposed. However, in general, the steel plate temperature and the reduction ratio are different for each pass, and when the steel plate temperature is different, the deformation resistance, which is one of the explanatory variables of the rolling load, has an inflection point and becomes nonlinear with respect to the temperature near the transformation point. . Therefore, when the steel plate temperature straddles the transformation point between passes, simply reflecting the prediction accuracy of the rolling load of the previous pass in the current pass does not provide sufficient prediction accuracy of the rolling load. Therefore, in order to solve such problems, a method described in Japanese Patent Laid-Open Publication No. 2002-100001 has recently been proposed, which utilizes big data accumulated in a database.

JP 2015-66569 A

Theory and Practice of Plate Rolling / Edited by The Iron and Steel Institute of Japan, 1984

However, according to the method described in Patent Document 1, it is possible to explain the nonlinearity of the deformation resistance with respect to the steel plate temperature to some extent. is calculated and applied to learning, the calculation time in the database becomes enormous. Therefore, according to the method described in Patent Document 1, it is difficult to accurately predict the rolling load in a short period of time.

Further, according to the method described in Patent Document 1, it is possible to improve the prediction accuracy of the rolling load to some extent by utilizing big data. However, when data with a high occurrence frequency is used as a data set, the prediction accuracy of the rolling load is greatly improved, but when data with a low occurrence frequency is used, overfitting results in a decrease in the rolling load. Prediction accuracy may decrease.

SUMMARY OF THE INVENTION It is an object of the present invention to provide a rolling load prediction method and a rolling load prediction apparatus capable of accurately predicting a rolling load in a short period of time. Another object of the present invention is to provide a rolling control method capable of improving the rolling efficiency of a rolled material.

The rolling load prediction method according to the present invention is a rolling load prediction method for predicting the rolling load of each pass of a reversible rolling mill that rolls a target rolled material in multiple passes, and is a rolled material that is rolled in a past operation. a step of obtaining, as operation data, a plurality of past data for each operation including the set value or actual value of a predetermined operational factor and the actual value of the rolling load, and the value of the predetermined operational factor related to the target rolled material. a step of obtaining as rolled material data; a step of calculating the degree of similarity between the target rolled material data and the past data by using the predetermined operating factor as a condition item and comparing values for each of the condition items; The actual value of the rolling load of each past data is weighted by the similarity of the relevant past data, and the weighted value is used to apply the learning coefficient that each condition item exerts on the rolling load when rolling the target rolled material. obtaining the actual value of the predetermined operation factor after the previous pass rolling for the target rolled material, replacing the set value of the predetermined operation factor included in the target rolled material data with the obtained actual value, and predicting the rolling load of the next pass based on the calculated learning coefficient, wherein the step of calculating the learning coefficient is a range of a predetermined Euclidean distance from a point in the data space corresponding to the target rolled material data. The step of processing only the past data in the inside of the searching for the oldest past data in the mesh in which the past data acquired in step 1 is stored, and deleting the searched past data.

A rolling control method according to the present invention includes a step of rolling a target strip to a target thickness by controlling a reversible rolling mill based on the rolling load predicted by the rolling load prediction method according to the present invention. characterized by comprising

A rolling load prediction device according to the present invention is a rolling load prediction device that predicts the rolling load of each pass of a reversible rolling mill that rolls a target rolling material in a plurality of passes, and is a rolling target rolled in past operations. Means for acquiring a plurality of past data for each operation including the set value or actual value of the predetermined operation factor and the actual value of the rolling load as operation data, and the value of the predetermined operation factor regarding the target rolled material means for obtaining rolling material data; means for calculating the degree of similarity between the target rolling material data and the past data by comparing the values for each of the condition items, with the predetermined operating factors as condition items; The actual value of the rolling load of each past data is weighted by the similarity of the relevant past data, and the weighted value is used to apply the learning coefficient that each condition item exerts on the rolling load when rolling the target rolled material. and acquiring actual values of predetermined operating factors after the previous pass rolling for the target rolled material, replacing the set values of the predetermined operating factors included in the target rolling material data with the acquired actual values, and means for predicting the rolling load of the next pass based on the calculated learning coefficient, wherein the means for calculating the learning coefficient is within a predetermined Euclidean distance range from a point in the data space corresponding to the target rolled material data. The means for processing only the past data in the inside and acquiring the plurality of past data as operation data divides the data space formed by the past data into a plurality of arbitrary meshes, and acquires the new past data. Searching for the oldest past data in the mesh in which the past data acquired in step 1 is stored, and erasing the searched past data.

According to the rolling load prediction method and the rolling load prediction device according to the present invention, the learning value of the rolling load can be accurately predicted in a short time. Further, according to the rolling control method of the present invention, it is possible to improve the rolling efficiency of the rolled material.

FIG. 1 is a schematic diagram showing the configuration of a rolling control device that is an embodiment of the present invention. FIG. 2 is a flow chart showing the flow of rolling force prediction processing, which is an embodiment of the present invention. FIG. 3 is a diagram for explaining an example of the processing of step S2 shown in FIG. FIG. 4 is a diagram for explaining a conventional method of managing operational data. FIG. 5 is a diagram for explaining an operational data management method according to an embodiment of the present invention.

DESCRIPTION OF THE PREFERRED EMBODIMENTS A rolling control apparatus, which is an embodiment of the present invention, will be described below with reference to the drawings.

〔Constitution〕
First, referring to FIG. 1, the configuration of a rolling control device according to an embodiment of the present invention will be described. FIG. 1 is a schematic diagram showing the configuration of a rolling control device that is an embodiment of the present invention. As shown in FIG. 1, a rolling control apparatus 1 according to an embodiment of the present invention includes a four-high reversible rolling mill 10 having a pair of upper and lower work rolls 11a and 11b and a pair of upper and lower support rolls 12a and 12b. It is a device for controlling the rolling process for rolling the thickness of the rolled material S to a target thickness by controlling the operation data management device 2, the rolling process computer (rolling process computer) 3, the load detection device 4, and the thickness control device. (Automatic Gauge Control Programmable Logic Controller: AGC PLC) 5 and a reduction device 6 are provided.

The operation data management device 2 is configured by an information processing device such as a computer, and stores a plurality of past data for each operation regarding the rolled material S rolled in past operations as operation data. In this embodiment, the operation data management device 2 stores, as past data, a set of data of the value of the actual rolling load/predicted rolling load and the actual value or set value of the explanatory variable (predetermined operational factor) of the rolling load. is doing. Examples of explanatory variables of the rolling load include the rolling reduction, the average temperature in the thickness direction of the rolled material S, the carbon equivalent, the thickness before and after rolling, and the like. Note that the predicted rolling load can be calculated by a well-known method using, for example, formula (1) shown below. In Equation (1), P is the predicted rolling load, ld is the contact arc length, Qp is the rolling force function, km is the deformation resistance of the rolled material S, and W is the strip width of the rolled material S. See Non-Patent Document 1, for example, for a method of calculating each factor on the right side.

The rolling process computer 3 uses the operation data stored in the operation data management device 2 to determine the pass schedule of the rolling process to be controlled, such as the thickness change amount of each pass, the advance rate, the predicted rolling load, the initial reduction position, etc. Calculate

The load detection device 4 detects the rolling load of the pair of upper and lower work rolls 11a and 11b, and outputs an electric signal indicating the detected rolling load to the plate thickness control device 5. FIG.

The strip thickness control device 5 controls the reduction device 6 based on the rolling load detected by the pass schedule calculated by the rolling process computer 3 and the load detection device 4 .

The reduction device 6 controls the reduction amount of the rolled material S in each pass by controlling the gap (reduction position) between the pair of upper and lower work rolls 11a and 11b according to the control signal from the plate thickness control device 5. FIG.

In the rolling control apparatus 1 having such a configuration, the rolling process computer 3 executes the following rolling load prediction processing, thereby calculating the learned value of the rolling load in a short time and predicting the rolling load with high accuracy. making it possible. Hereinafter, with reference to FIG. 2, the flow of the rolling force prediction process, which is one embodiment of the present invention, will be described.

[Rolling load prediction processing]
FIG. 2 is a flow chart showing the flow of rolling force prediction processing, which is an embodiment of the present invention. The flowchart shown in FIG. 2 starts at the timing when the value of the explanatory variable of the rolling load (target rolling material data) for the rolled material S to be processed is input to the rolling process computer 3, and the rolling load prediction process is the process of step S1. proceed to

In the process of step S1, the rolling process computer 3 calculates the Euclidean distance from the point (data request point) in the operation data space corresponding to the target rolled material data for each past data stored in the operation data management device 2. calculate. Specifically, when the data request point and the past data point to be processed in the operation data space are points P and Q, respectively, and the number of explanatory variables of the rolling load is i (= 1 to n), the rolling process The computer 3 calculates the Euclidean distance d from the data request point P for the past data Q to be processed using the following formula (2). Here, in Expression (2), Q _i indicates the value of explanatory variable i in past data Q to be processed, and P _i indicates the value of explanatory variable i at data request point P. Thereby, the process of step S1 is completed, and the rolling force prediction process proceeds to the process of step S2.

In the process of step S2, the rolling process computer 3 gives an index to the past data whose Euclidean distance from the data request point is within a predetermined range of Euclidean distance based on the result of the process of step S1. For example, when the operation data space is a two-dimensional space consisting of explanatory variables 1 and 2 as shown in FIG. An index is given to the past data B' included in a circular area R1 having a radius of an arbitrary Euclidean distance from the center. Thereby, the processing of step S2 is completed, and the rolling force prediction processing proceeds to the processing of step S3.

In the process of step S3, the rolling process computer 3 sets the explanatory variable of the rolling load as a condition item, and compares the values of the condition items to obtain the respective past data and the target rolled material data indexed in the process of step S2. Calculate the similarity with Specifically, the rolling process computer 3 calculates the degree of similarity w(d|h) with the target rolled material data using the following formula (3) for each past data indexed in the process of step S2: Calculate Here, in Expression (3), h is a parameter for adjusting the spread of the degree of similarity w(d|h), and the smaller the value, the similarity w( d|h) can be calculated. Also, d indicates the Euclidean distance of each past data calculated by the formula (2). Thereby, the processing of step S3 is completed, and the rolling force prediction processing proceeds to the processing of step S4.

In the process of step S4, the rolling process computer 3 uses the similarity w(d|h) calculated by the process of step S3 for the k pieces of past data indexed in the process of step S2 as follows: Regression coefficients β _i (i=1 to n) of i explanatory variables x _i composing the past data are calculated by the following formula (4). Thereby, the processing of step S4 is completed, and the rolling force prediction processing proceeds to the processing of step S5.

In the process of step S5, the rolling process computer 3 calculates the i explanatory variables x i of the target rolled material data and the regression coefficients β _i of the _{i explanatory variables x i calculated} in the process of step S4 as follows: By substituting in (5), the learning coefficient of the rolling load is calculated. Then, the rolling load process computer 3 calculates a value obtained by multiplying the predicted rolling load calculated using the above formula (1) by the learning coefficient y' as the predicted rolling load after learning correction. As a result, the processing of step S5 is completed, and the series of rolling load prediction processing ends.

As is clear from the above description, in the rolling load prediction process, which is one embodiment of the present invention, the rolling process computer 3 detects a point within a predetermined Euclidean distance from a point in the operation data space corresponding to the target rolled material data. Since the learning coefficient of the rolling load is calculated using only the past data in , the rolling load can be accurately predicted. Moreover, as a result, the rolling efficiency of the rolled material can be improved. In the rolling load prediction process, the number of past data is limited at the stage of calculating the degree of similarity, but the number of past data may be limited at the stage of calculating the learning coefficient of the rolling load. That is, by limiting the number of past data before calculating the learning coefficient of the rolling load, the rolling load can be accurately predicted in a short time.

In the conventional operational data management method, as shown in FIG. 4, the oldest performance data is searched at the timing when new performance data is acquired, and the searched oldest performance data is deleted. The number of performance data was kept constant. For this reason, according to the conventional method of managing operation data, the distribution state of the actual data is uneven, and in the region where the distribution state is coarse (the number of data is small), the prediction accuracy of the rolling load is lowered. For this reason, in the rolling control device 1 which is one embodiment of the present invention, the operation data management device 2 divides the data space into a plurality of arbitrary meshes as shown in FIG. Search for the oldest data in the mesh where new performance data is stored, and delete the searched performance data. According to such a process, since the actual data in each mesh becomes a fixed number, it is possible to suppress the decrease in prediction accuracy of the rolling load in the area where the actual data is rough.

In order to evaluate the effect of the present invention, a comparison test was conducted on the rolling load prediction accuracy and calculation speed in the rolling of thick plates. In this example, a four-high reversible rolling mill with an average work roll diameter of 1200 mm and a backup roll diameter of 2200 mm was used. Comparative Example 1 is the case where the rolling load is not applied using the learning coefficient. Comparative Example 2 is the method described in Prior Document 1 in which the learning coefficient is calculated using data accumulated in the past. Using the operation data management method of Example 1, the rolling load prediction accuracy (actual rolling load/predicted rolling load) was evaluated using 10,000 points of data.

The results of Example 1 and Comparative Examples 1 and 2 are shown in Table 1 below. First, in Comparative Example 1 that does not use data accumulated in the past, the standard deviation of the rolling load prediction accuracy was 6.3%, whereas in Comparative Example 2, the standard deviation of the rolling load prediction accuracy was 5. A large improvement of 0.6% was confirmed. On the other hand, in Example 1, the standard deviation of the rolling load prediction accuracy was further improved to 3.7%, and it can be said that the present invention has a beneficial effect on industrial production.

1 rolling control device 2 operation data management device 3 rolling process computer (rolling process computer)
4 Load detector 5 Plate thickness controller (AGC PLC)
6 reduction device 10 reversible rolling mill

Claims (3)

A rolling load prediction method for predicting the rolling load of each pass of a reversible rolling mill that rolls a target rolling material in multiple passes,
a step of acquiring, as operation data, a plurality of pieces of past data for each operation, including the set value or actual value of a predetermined operation factor and the actual value of the rolling load regarding the rolled material to be rolled in the past operation;
a step of acquiring the value of the predetermined operation factor related to the target rolled material as target rolled material data;
a step of calculating the degree of similarity between the target rolled material data and the past data by using the predetermined operating factors as condition items and comparing values for each of the condition items;
The actual value of the rolling load of each past data is weighted by the similarity of the relevant past data, and the weighted value is used to apply the learning coefficient that each condition item exerts on the rolling load when rolling the target rolled material. and calculating
Acquire the actual value of the predetermined operation factor after the previous pass rolling for the target rolled material, replace the set value of the predetermined operation factor included in the target rolled material data with the obtained actual value, and use the calculated learning coefficient predicting the rolling load for the next pass based on
including
In the step of calculating the learning coefficient, only past data within a range of a predetermined Euclidean distance from a point in the data space corresponding to the target rolled material data is processed,
The step of acquiring a plurality of past data as operation data includes dividing a data space formed by the past data into a plurality of arbitrary meshes, and storing the past data acquired at the timing when new past data is acquired. A rolling load prediction method, comprising searching for the oldest past data of and deleting the searched past data. A rolling comprising a step of rolling the thickness of the target rolled material to a target thickness by controlling the reversible rolling mill based on the rolling load predicted by the rolling load prediction method according to claim 1. control method. A rolling load prediction device that predicts the rolling load of each pass of a reversible rolling mill that rolls a target rolling material in multiple passes,
Means for acquiring, as operation data, a plurality of pieces of past data for each operation, including set values or actual values of predetermined operation factors and actual values of the rolling load, regarding the rolled material to be rolled in past operations;
means for acquiring the value of the predetermined operation factor related to the target rolled material as target rolled material data;
means for calculating the degree of similarity between the target rolled material data and the past data by comparing the values for each of the condition items, with the predetermined operating factor as a condition item;
The actual value of the rolling load of each past data is weighted by the similarity of the relevant past data, and the weighted value is used to apply the learning coefficient that each condition item exerts on the rolling load when rolling the target rolled material. a means for calculating
Acquire the actual value of the predetermined operation factor after the previous pass rolling for the target rolled material, replace the set value of the predetermined operation factor included in the target rolled material data with the obtained actual value, and use the calculated learning coefficient means for predicting the rolling load for the next pass based on
with
The means for calculating the learning coefficient processes only past data within a range of a predetermined Euclidean distance from a point in the data space corresponding to the target rolled material data,
The means for acquiring the plurality of past data as operation data divides the data space formed by the past data into a plurality of arbitrary meshes, and the mesh where the past data acquired at the timing when new past data is acquired is stored. and searching for the oldest past data of and erasing the searched past data.

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