JPH0371907A - Adjusting device for metal rolling shape - Google Patents

Abstract

PURPOSE:To perform the adjustment of a shape with good accuracy after predicting the future variation of the shape by deciding the latest target shape data, control gain, etc., based on the shape variation tendency and latest actual shape information and performing the shape adjustment based thereon. CONSTITUTION:In a metal rolling shape adjusting device 1, the actual shape data of elongation and tense detected from the sensor of a roll rolling mill 2 side is stored as well as collected periodically by a sampling means. The variation tendency of the actual shape and latest actual shape information are operated from the actual shape data stored in the sampling means by a shape variation tendency arithmetic means. Moreover the shape is adjusted with good accuracy by adjusting the target shape data to be given to the control gain or shape control part of the case of setting the target shape, based on the variation tendency and latest actual shape information.

Description

[Detailed Description of the Invention] [Field of Industrial Application] The present invention relates to a metal roll rolling mill, and more specifically, to a shape control section that controls the surface shape of metal, adjusting control gain or target shape data. In particular, the present invention relates to a metal rolling shape adjusting device for adjusting the surface shape.

[Prior art]

FIG. 19 shows a roll rolling mill 2 for rolling aluminum foil, which is an example of the background of the present invention. In aluminum foil rolling, the raw aluminum foil 51 with a width of about 700 to 1700 mm and a thickness of several μm to several hundred μm wound around the entrance coil 50 has a width of about 300 to 12
It is rolled by a pair of rolling rolls 52 at a speed of 0.00 m/min to reduce its thickness to about 'A-Vs. Then, the rolled aluminum 7!53 is transferred to the outlet coil 64 (
It is conveyed in the direction of the arrow by a constant tension generated by the rotational drive of the drive shaft in FIG. 1) and wound around the output coil 64.

For example, when rolling raw aluminum foil 51 with a thickness of several hundred μm into aluminum foil 53 with a thickness of several μm, the rolling process is repeated several times, and the number of times of rolling can be referred to as the number of buses. It will be done.

In the above-mentioned metal rolling process in micron units, the Al-4 foil 53 is stretched in the foil width direction (arrow L), as shown in FIG. 20, even though its thickness is the same. There are clearly areas that are ``tight'' and areas that are ``tight''.

That is, the stretched portion 54 has a peak portion 56 and a valley portion 57 formed along the conveying direction (arrow K) of the aluminum foil 53, and the stretched portion 55 has a generally flat shape. Therefore, the aluminum foil 53 shown in the figure is in a state in which the central portion in the foil width direction (arrow L) is elongated and the end portions are stretched.

The distribution of elongation and tension in the foil width direction (arrow L) will hereinafter be referred to as the surface shape or actual shape of the aluminum foil 53. The surface shape has a great influence on the quality of the foil product, and in some cases, a large tension is applied to the stretched portion 55, causing the foil to break. Further, the stretched portion 54 causes wrinkles.

Regarding the aluminum foil 53 as a final product, it is natural that a flat shape with uniform elongation and tension is desired, but it is not necessarily a flat actual shape for each pass, and The aluminum foil 53 in the pass has a wide variety of shapes.

The surface shape of the aluminum foil 53 as described above is similar to that of the rolling roll 5.
can be controlled by changing the shape of 2. As shown in FIGS. 19 to 21, the rolling roll 52 is
Due to the heat generation during rolling and its heat conduction properties, a bulge called thermal crown occurs. The example shown in FIG. 21 is a case where the quarter portion a is swollen. Such a bulge, ie, a thermal crown, changes the surface shape of the aluminum foil 53 depending on its appearance location and degree of swell. That is, the aluminum foil 53 that has been rolled at a portion of the rolling roll 52 where the degree of expansion of the thermal crown is large is in an elongated state at that rolled portion. Therefore, the surface shape of the aluminum foil 53 is similar to that of the rolling roll 52.
The temperature or injection amount of the coolant 58 (Fig. 1) that is injected toward the rolling roll 52 to cool
It can be controlled by changing the width of the foil 53 (arrow L (FIG. 20)).

Such shape control of the aluminum foil 53 is performed by roll rolling.
This is done by the shape control section 3 installed adjacent to a2. That is,
The shape control section 3 is rotatably provided on the exit side of the rolling roll 52, and controls the aluminum foil 5 from the inspection roll 4, which is made up of elements 48 divided into 36 pieces in the foil width direction (arrow L).
The actual shape data for elongation and tension in step 3 is manually generated. One piezoelectric element (not shown) is embedded in each element 4e, and serves as a sensor for detecting the pressure force applied to the outer peripheral surface of the element 4.

The aluminum foil 53, which is pressed onto the element 4° and pulled in the transport direction (arrow K) with a constant tension, has a pressing force against the element 4° when its stretched portion 54 passes over the element 4°. is small, and conversely, when the tensioned part 55 passes, it is detected large.

Therefore, as shown in FIG. 21, the actual shape of the aluminum foil 53 is expressed as a distribution in the width direction of the elongation rate (actual shape data) obtained by converting the pressure contact force data detected from each element 4e. In the illustrated case, the quarter portion a of the rolling roll 52 is too swollen, so cooling of that portion is promoted and heat is stored in the central portion of the rolling roll 52 and both ends thereof.
A target shape has been set.

The shape control unit 3 compares and calculates the actual shape data with the target shape data input in advance, and injects the actual shape data toward the part of the rolling roll corresponding to the element 4e having a higher elongation rate. Increase the amount of coolant 58 used. The coolant 58 is disposed on the inlet side of the rolling roll 52 and is injected from an injection pipe 59 which is divided and injected in the foil width direction (arrow L).

Thereby, the thermal crown of the rolling roll 52 is alleviated,
The portion of the aluminum foil 53 corresponding to the quarter portion a deforms toward the tensioned state. Furthermore, if the elongation rate of the actual shape data is lower than that of the actual shape data, the opposite operation is performed. In addition, in the target shape data, the elongation rate obtained from the element 46 corresponding to the quarter part a of the rolling roll 52 is often set to O so as to promote cooling of the quarter part a of the rolling roll 52.

As described above, in foil rolling, the final product is obtained by repeating rolling (baths) multiple times. The plan of how many passes and what thickness or surface shape to obtain the final product is called an operation policy. This operation policy defines the target shape of elongation and tension in intermediate buses along with the target thickness in order to finish the final product into a flat shape with elongation and tension.

In actual operation, the distribution of elongation and tension in buses en route as described above differs. This is because, as described above, operating conditions such as thermal deformation (thermal crown) of the rolling rolls differ for each bus. Therefore, the above-mentioned operational policy is one that determines the target shape of elongation and tension for each bus by taking into account the expected operating conditions to some extent among such actual operating conditions.

However, the target shape of elongation and tension based on the operation policy as described above and the actual target shape of elongation and tension by the shape control section 3, that is, the target shape data, often do not match. For example, in a certain bus roll mill, a certain material is
An example of this is when rolling with a target shape in the operation policy as indicated by the broken line in Figure 2, and obtaining good results by setting the actual target shape data in the shape control section 3 as indicated by the solid line. It is.

Therefore, in order to solve these problems, a method of adjusting the target shape has been developed as shown in the flowchart of FIG. 23. According to this, first, the actual shape data of the aluminum N53 at the present time is individually collected from each element 4° of the inspection port 4 (step 530).

Then, in step S31, the current aluminum foil 53
The rolling state, in other words, the actual shape of the aluminum N53, is classified into actual shape classification items that have been pattern-classified in advance, and the extent of the classification is determined at the same time.

For example, as shown in FIGS. 24(a) to 24(f),
It is specified by actual shape classification items such as "edge elongation", "edge tension", "quarter elongation", and "medium elongation", and the degree thereof is expressed by the number of levels. Incidentally, the larger the value of the level number, the greater the tendency.

Subsequently, in step S32 of FIG. 23, the target shape data is changed and adjusted according to the specified real shape classification item data and its number of levels so as to obtain the desired real shape, and is output to the shape control unit 3. [Problem to be Solved by the Invention] In roll rolling I2 as described above, the surface shape of the aluminum foil 53 may change with a certain tendency even during rolling under the same conditions. be. For example, when both ends of the rolling roll 52 begin to heat up, a swollen region a called a thermal crown appears at both ends of the rolling roll 52, or the degree of swelling increases, as shown in FIG. As a result, the surface shape of the aluminum foil 53 may tend to elongate at the edges.

Table 1 shows the tendency of changes in surface shape.

Table 1 For example, if the current surface shape is identified as "edge elongation" and its degree is level 3, the same (2) ), the method of adjusting the target shape that should be handled in the future should be different from the case where the tendency is shown in item (3). It is necessary to reduce the control gain when setting the target shape data given to the shape control section 3.

However, in the conventional shape control section 3, the target shape and control gain are adjusted based only on the judgment result of the actual shape at a certain point in time, so misplaced control may be performed in some cases, and as described above. It has not always been possible to perform appropriate control that takes into account the tendency of changes in surface shape.

Therefore, the first object of the present invention is to be able to grasp the tendency of change in the shape of the metal caused by this change even if the rolling characteristics of a roll rolling mill change significantly, and to be able to predict the change tendency. An object of the present invention is to provide a metal rolling shape adjusting device that can accurately adjust the shape based on the change tendency and the latest actual shape information.

On the other hand, there are cases where it is not possible to express all the rolling states only by the current actual shape data and the shape change tendency derived by calculation from the actual shape data. That is, statistical characteristic information that cannot be obtained unless data is collected periodically and actual shape data at several past times is used, such as the above-mentioned change trends or averages of the collected data.

There are many types of variance, correlation between data, three-dimensional pattern recognition, etc. Such statistical characteristic information is also important in adjusting the surface shape of the aluminum foil 5a.

Therefore, a second object of the present invention is a metal rolling shape adjusting device capable of calculating past statistical characteristic information of a metal shape and adjusting the shape with high precision based on the statistical characteristic information. Our goal is to provide the following.

[Means to solve the problem]

In order to achieve the above-mentioned first object, the first means adopted by the present invention is based on a shape control section that controls the shape in the width direction of a strip-shaped metal stretched by a roll mill. A metal rolling shape adjustment device that adjusts the shape by giving target shape data, comprising: a sensor that detects actual shape data of rolled metal; and a sampling means that periodically collects and stores the actual shape data. , a shape change tendency calculating means for calculating a change tendency of the shape within a predetermined time from the actual shape data, and determining the latest target shape data, control gain, etc. based on the change tendency and the latest actual shape information, This is a metal rolling shape adjusting device comprising a first shape adjusting means that adjusts the shape based on the first shape adjusting means.

In addition, in order to achieve the above-mentioned second object, the second means adopted by the present invention is a shape control method that controls the shape in the width direction of a strip-shaped metal stretched by a roll mill. A metal rolling shape adjustment device that adjusts the shape by supplying target shape data to a part, which includes a sensor that detects actual shape data of rolled metal, and a sampling device that periodically collects and stores the actual shape data. means and
statistical characteristic information calculating means for calculating statistical characteristic information of the shape within a predetermined time from the actual shape data stored in the sampling means; This metal rolling shape adjusting device comprises a second shape adjusting means that determines shape data, control gain, etc., and adjusts the shape based on the determined shape data.

Here, the actual shape data may be an output value from a sensor, or may be a concept representing the shape of elongation/tension as shown in the examples below.

[Effect]

According to the first invention, when rolling a metal with a roll mill, the shape of the metal in the width direction is determined by the shape control section based on target shape data automatically or manually given from the metal rolling shape adjusting device. The control is performed, for example, by controlling the coolant that cools the rolling rolls. At this time, in the metal rolling shape adjusting device, real shape (shape) data such as elongation and tension detected by a sensor on the roll rolling mill is periodically collected by a sampling means and stored. Then, the shape change tendency calculating means calculates the change tendency of the actual shape within a predetermined time and the latest real shape information from the real shape data stored in the sampling means. Furthermore, the first shape adjusting means adjusts the shape to be accurate by adjusting the control gain when setting the target shape or the target shape data given to the shape control section based on the change tendency and the latest actual shape information. Adjust well.

Furthermore, according to the second invention, after the actual shape data is stored in the sampling means in the first invention, the statistical characteristic information calculation means calculates the past predetermined time period related to the shape from the actual shape data. Statistical characteristic information, e.g. mean value 1
Calculates variance, correlation, etc. Of course, the tendency of the shape to change can also be calculated. Then, the second shape adjusting means adjusts the shape of the metal based on the statistical characteristic information.

Thereby, even if the shape cannot be properly adjusted using only the latest actual shape information, the shape can be adjusted with high accuracy.

〔Example〕

Next, embodiments embodying the present invention will be described with reference to the accompanying drawings to provide an understanding of the present invention.

Here, FIG. 1 is a schematic diagram showing a system arrangement of an aluminum foil rolling shape adjusting device according to an embodiment of the present invention, and FIG. 2 is a schematic diagram showing a processing flow of the aluminum foil rolling shape adjusting device.
Figure 3 (a) is an explanatory diagram showing the main parts of the actual shape data expressed by the elongation rate distribution in the foil width direction, and Figure 3 (middle) is an explanatory diagram showing the actual shape classification items of pattern-classified aluminum foil. , FIG. 4(a) is a flowchart showing the processing procedure for determining the change tendency of actual shape data, and FIG. The explanatory drawings shown in Fig. 5(a) and the same figure (
bl is a graph showing the correlation between the number of levels of two real shape classification items, Figure 6 is a three-dimensional graph showing a three-dimensional pattern due to changes in real shape data of aluminum foil over time, and Figure 7 is a conceptual diagram of a neural network. Figure 8 is an explanatory diagram showing an example of the relationship between shape change goals and corresponding action candidates for real shape classification items, and Figure 9 shows the importance of shape change goals and the relationship between real shape classification items. A graph showing the relationship with confidence when FIG. 12(a) is an explanatory diagram showing the processing procedure of a routine for checking the validity of the axigon to be modified using a check tree; FIG. 12(a) is an explanatory diagram showing target shape adjustment parameters used to change the target shape; (b) is a state diagram showing a change in the situation of the parameter a3; FIG. ) is a state diagram showing a situation where the central part has a reverse pattern, the first
Figure 3 is a schematic explanatory diagram showing an example of inference execution for changing the target shape, Figure 14 is a flow sheet showing the process flow for adjusting the target shape, and Figure 15 is the input displayed on the screen of the terminal on the rolling mill side. A display diagram showing the menu, FIG. 16 is a display diagram showing an example of the target shape displayed on the screen, FIG. 17 is a flow sheet showing a method for correcting the actual shape obtained asymmetrically, and FIG. 18 is a display diagram showing the example of the target shape displayed on the screen. FIG. 2 is a schematic explanatory diagram showing a situation in which an asymmetric actual shape is corrected.

In the following description, the same reference numerals will be used for the elements common to the roll rolling mill 2 shown in FIGS. 19 to 21, and the description thereof will be omitted.

Further, the following embodiments are merely examples embodying the present invention, and are not intended to limit the technical scope of the present invention.

The aluminum foil rolling shape adjusting device 1 according to this embodiment is applied to process online control as an expert system that systematizes operator control know-how. Prior to a detailed explanation of this system, the outline of the system configuration of the aluminum foil rolling target shape adjusting apparatus 1 will be explained using FIG. 2.

As shown in the figure, the aluminum foil rolling shape adjustment device 1 includes a rolling data collection section 7, a rolling situation analysis section 8, a control target generation section 9, an axilan candidate inference section 11, and a target shape generation section 1.
2 and the action effect evaluation unit 10, the rolling situation analysis knowledge base D1 stores operational knowledge.
．． A control goal setting knowledge base Dt, an action inference knowledge base DS (including knowledge for maintaining consistency of rules and validity of actions), and working memories M+, Mz, Ms, M4. that temporarily store various data. It is equipped with Ms. An overview of the processing contents in each of the above sections will be explained below.

In the aluminum foil rolling shape adjustment device 1, first, inference processing is activated by a key input from the terminal device 6 on the aluminum foil rolling machine 41!2 side, and the operating condition data from the aluminum foil rolling mill 2 is transferred to the shape control unit 3. Input via .

■Rolling data collection unit The rolling data collection unit 7 receives operating condition data including actual shape data from the shape control unit 3 and writes it into the working memory M1.

■Rolling condition analysis section The rolling condition analysis section 8 analyzes the actual shape data and determines the rolling condition of the aluminum foil 53. That is, it is determined to what extent the actual shape data matches each of the actual shape patterns that have been classified in advance into several types of patterns and stored in the rolling situation analysis knowledge base D1.

At the same time, the current target shape data is analyzed.

■Control target production section The control target generation section 9 changes the actual shape of the aluminum foil 53 in what direction based on the analysis result of the actual shape data by the rolling situation analysis section 8 and the input from the terminal device 6 by the operator 5. Set control objectives.

■Action candidate inference unit■-(1) Rule inference The action candidate inference unit 11 is an IF-THEN type in which the control objective, operating conditions, etc. are the condition part, and the action etc. to realize the control objective are the conclusion part. Actions determined to be appropriate are stored in the working memory M by rule inference applying rules stored in the Axilan inference knowledge base D.

write to. During this writing, the following processing is executed.

(2) Elimination of contradictions and redundancies When contradictory action candidates are listed, the action of the control objective that is considered more important is applied.

■-(3) Learning invalid actions In the above rules, if there are multiple action candidates for a certain control objective, give priority to the action candidates and select only the action with the highest priority. Apply. An ineffective action that is executed to achieve a certain control goal will not be repeatedly applied even if the same control goal is set next time.

■Target Shape Data The target shape data 12 generates new target shape data based on the applied action and outputs it to the shape control section 3. Based on this target shape data, the shape control unit 3
controls the aluminum foil rolling mill 2.

■Action Effect Evaluation Unit The Axilan effect evaluation unit 10 evaluates whether or not the control of the aluminum foil rolling mill 2 based on the applied action was effective based on the results of data analysis and the arrangements made with the operator 5. At this time, the action evaluated to be invalid is stored in the working memory M4, and is referred to during the next action candidate inference by the action candidate inference section 11.

This example will be described in detail below.

As shown in FIGS. 1 and 2, the Al ξ foil rolling shape adjusting device 1 includes a shape control section 3 that controls the injection amount or temperature of the coolant 58 so as to adjust the actual shape of the Al 5 foil 53. At the same time, target shape data serving as a guideline for the control is outputted, and operating condition data is inputted from the shape control section 3.

Another possible method for adjusting the actual shape of the aluminum foil 53 is to control the push-up force of the push-up roll 60 that urges the lower roll 52 upward toward the upper roll 52. In this embodiment, only the control of the coolant 58 will be described below.

■ Collection of operating condition data In the aluminum foil rolling shape adjustment device 1, the inspection roll 4 is equipped with a sensor that detects actual shape data indicating the elongated portion 54 and tensioned portion 55 (Fig. 20) of the aluminum foil 53 at the time of rolling. The element 4 is provided downstream of the rolling roll 52 in the conveying direction (arrow K), and
The operating condition data including the actual shape data is outputted to the rolling data collection section 7 (FIG. 2) every minute via the shape control section 3. The rolling data collection unit 7 receives operating condition data transferred from the shape control unit 3 at predetermined time intervals (Table 2).
is written in the working memory M1 historically to update Table-2, and the rolling situation analysis section 8 is activated. That is, the rolling data collecting section 7 and the work memo 'JM+ are an example of the sampling means.

■ Analysis of actual shape data From the actual shape data detected by the element 4e (sensor), the elongation and tension state of the aluminum foil 53 during rolling (
As shown in FIG. 3(b), the real shape classification items (respectively actual shape classification items) and the rolling situation analysis knowledge for calculating their degrees are real shape classification items such as "edge tension" to "touch elongation". and a specific program for each item for specifying the real shape classification item detected from the element 46 and corresponding to the real shape data indicating the real shape.

First, a method for identifying actual shape classification items will be described. The actual shape data detected as a tension distribution from the element 4° is obtained in the form of an elongation distribution in the foil width direction, as shown in FIG. 3(a).

The actual shape data shown in the figure consists of an end portion, a quarter portion, a center portion A, and a center portion B from the outside, and the entire center portion is further composed of the center portion A and the center portion B. In this case, the tension portion 55 is located in the center portion B, and the extension portions 54 are located in the quarter portions on both sides.

The actual shape classification items stored in the rolling situation analysis knowledge base D are mainly classified into five types as shown below, as shown in FIG. 3(b).

(1) "Edge tension"...When the elongation rate at the edge shows a low value at the end, it is considered to be edge tension. ■Whether the value at the end is the minimum value of the whole.

■Judged by the extent of the difference in elongation rate between the end and quarter parts.

(2) “Edge elongation”: Contrary to the case of edge stretching, this refers to a case where the value at the edge is significantly larger than other parts.

In many cases, it is preferable to have a real shape where the edges extend to some extent, but if the edges extend too much, it is considered a problematic shape.

(3) "Quarter elongation"...Judged based on how large the elongation rate value of the most elongated part near the part corresponding to the set zero point in the target shape is compared to that at the end. . As mentioned above, the actual shape in the quarter portion is the easiest to stretch, and most of these types of shapes appear in actual actual shapes.

(4) "Medium tension": The tension state (low elongation rate) at the center is compared with that at the ends.

(5) "Medium elongation"...The difference between the elongation rate value of the most elongated part in the center and the part that elongated the most overall (in most cases, the edge or quarter part) is small. Is it?
Or, if it is negative, it is determined that there is medium growth.

There are two types of mid-stretch: ■ General mid-stretch where the central part is stretched; and ■ Lam stretch where the quarter to mid-section is stretched and the central part is stretched.

Other unique real shape classification items include "non-target" below.
, there is "zero point inappropriateness".

"Asymmetrical"...Usually, the target shape is symmetrical in the width direction of the foil, and the actual shape is generally symmetrical, but a shape in which this symmetry is disrupted is called an asymmetrical shape. The criteria for this judgment are: (1) The difference in elongation rate between the maximum value and the elongation rate between the minimum value is large between the left and right ends.

■One end is specified as end tension and the other end as end extension.

It is considered asymmetric if either of the following holds true.

Of course, either of these may hold true.

"Inappropriate zero point": refers to a case where the location of the zero point set in the target shape and the location showing the maximum elongation rate in the actual shape do not match. Normally, heat tends to accumulate in the quarter part a of the rolling roll 52, and the quarter part of the actual shape corresponding to the quarter part a is most easily expanded. Therefore, when setting the target shape, the zero point is set at the most elongated portion of the quarter portion of the actual shape. If these parts are out of alignment, it is necessary to make adjustments to make them lose.

Based on the detected actual shape data, it is determined which actual shape classification item the current shape state corresponds to according to the method described in the "Identification Method" section of Part 3). As described above, this method is stored as a program in the rolling condition analysis knowledge base D.

As described above, one or more actual shape classification items that are the cause of the actual shape data inputted from the work memory M are calculated by the specific program in the rolling situation analysis section 8.

Normally, only one real shape classification item is not always selected for which it is determined that a certain real shape is in that state. Since actual shape data is obtained as a result of complexly intertwined operating conditions, it is often determined that the actual shape data falls under multiple actual shape classification items. In that case, there are real shape classification items that have a strong causal relationship with the real shape data and classification items that have a weak causal relationship. The strength of such a causal relationship, that is, the degree of the shape state, is called certainty.

The rolling situation analysis unit 8 narrows down the actual shape data into l or 2 or more actual shape classification items with a certain degree of certainty derived by an appropriate function, and stores the actual shape classification items and their certainty factors in the working memory M2. Make me remember. For example, four elements 4 from the end where Aldan F553 is placed.
The ratio β2/α between the elongation rate difference α2 between the part with the highest elongation rate and the end within the range of the actual shape data input from 6 and the difference β2 between the maximum and minimum elongation rates in the entire actual shape data.
2 exceeds a predetermined setting value, the actual shape at this time is interpreted as including the actual shape classification item "edge elongation", and is set from 0 to 1 according to the value of the ratio. Confidence is added. The same applies to other real shape classification items.

In this case, based on the actual shape data within a predetermined past time from the working memory M+, calculate statistical characteristic information of the roll rolling mill 2, such as the average, change trend 1 variance, correlation, three-dimensional pattern recognition, etc. Based on the statistical characteristic information,
That is, using the statistical characteristic information as a variable for the confidence level,
The certainty factor may be calculated for each of the actual shape classification items.

For example, the actual shape at a certain point in time may be extremely different from the actual shape at points before and after that for some reason. Specifically, due to an abnormality in the shape of the raw material sheet before rolling, a state of "edge elongation" is detected momentarily while the actual shape of "edge tension" continues, and the subsequent "edge tension" is detected. This is a case where the condition continues.

Therefore, by applying the average value of the statistical characteristic information of the actual shape data at several points in the past up to the above-mentioned certain point, it is possible to judge the rolling state of the trend without being influenced by the above-mentioned noise elements. .

Next, a case where a change tendency of actual shape data within a past predetermined time is employed as the statistical characteristic information will be described in detail below. This tendency is calculated, for example, according to the processing procedure of the flowchart shown in FIG. 4(a). During the rolling operation of Al2Fjf53, the actual shape data of the aluminum foil 53 obtained from the inspection rolls 4 of two roll mills is collected at predetermined time intervals by the rolling data collection unit 7 (S40).

Next, the actual shape data is compared with the actual shape classification item in the rolling condition analysis section 8, and is specified as a certain actual shape classification item, and the degree thereof is expressed by a level number represented by a natural number O to 5. That is, the rolling state of the aluminum foil 53 is determined (S41).

For example, from the end where the aluminum foil 53 is pressed,
Within the range of the actual shape data input from one element 4゜, the elongation rate difference α between the part with the highest elongation rate and the end, and the difference β between the maximum and minimum elongation rates in the entire actual shape data
When the ratio β2/α exceeds a predetermined setting value, it is determined that the actual shape at this time includes the actual shape classification item "edge elongation", and the The degree of "edge elongation" is expressed by a level number represented by a natural number 0 to 5.

Further, in step S42, the number H1 of shape points from -5 to +5 is determined for the actual shape data at time T, and is stored in a storage section (not shown) that stores the number Ht of shape points within a predetermined time.

This embodies an empirical handling method when the operator 5 specifies real shape data into an arbitrary real shape classification item, and makes it easier for the operator 5 to understand when displayed on the terminal 6, for example. , the confidence of the real shape classification item to which a certain real shape data fits and the certainty of the real shape classification item whose elongation/tension state is contradictory to the real shape classification item are expressed as both positive and negative natural numbers centered on O. It is converted as coordinates.

For example, if the real shape classification item is "edge elongation" and the degree is level 5, the shape score is +5 points, and if it is level 1, it is +1 point. Also, if you are level 5 in "edge tension", you will be given 15 points, and if it is level 1, you will be given 11 points. "
0 points will be given if the item does not fall into the category of ``edge elongation'' or ``edge tension.''

Then, if it is necessary to change or adjust the target shape by input from the terminal device 6 on the roll rolling mill 2 side in response to a request from the operator 5 or by input from the rolling situation analysis section 8, (
S43), and in step S44, the change tendency of the actual shape within a predetermined time up to the present is calculated. That is, the shape change tendency calculating means realizes the function of calculating the shape change tendency shown in step S44.

For example, the shape change tendency at time T is calculated from the past number of shape points Ht-i*+, . . . , H4. Here, the past 10 shape points Ht-q.

... shows an example calculated from HL. First, a shape point difference Hd expressed by the following equation is determined from the maximum and minimum values of the shape points L-9+, . . . , HL.

H4=max(Ht-q, -, Ht)-IIIi
n(Hz-q, -, H-) Then, the shape point difference H
When d is 2 or less, it is determined that the actual shape of the aluminum foil 53 is stable and no tendency to change shape is observed.

If Hd is 3 or more, it is first determined whether the actual shape is in a periodic state of change. For example, shape score HL-9+
..., HL, the number of times the number of shape points changes in the direction of increasing is Hl, and the number of times the number of shape points changes in the direction of decreasing is H-
Then, as shown in the following formula, IH, -H-1≦3. and 3≦Hd≦4 That is, when the absolute value of the difference between Ho and H- is 3 or less and the shape point difference Hd is 3 or more and 4 or less, it is determined that the actual shape tends to change periodically.

If Hd is 2 or more and the above formula is not satisfied, it is determined that a shape change tendency is observed, and it is determined what kind of tendency there is. For example, the current number of shape points H
t and the shape score HL-9 of the past 10 days are compared.

In other words, as shown in Table 3, when Ht shows a larger value than Ht-9, it indicates an elongation tendency where the edges are growing, and conversely, when it shows a small value, the edges are becoming stretched. It is judged that there is a tendency to be tense. Further, if H5 and Ht-9 are equal, it is determined that the actual shape is stable, and the step of correcting the number of levels of the actual shape classification item (S45), which will be described later.
will be processed by bypassing.

Therefore, for example, if the shape change tendency at time T is "edge elongation tendency" and the actual shape classification item at that time is identified as "edge elongation J" and the number of levels is 3, then As shown in (c), a correction action is taken to increase the number of levels by 2 for the actual shape classification item at that time (S45).This is important for future shape control. Considering both the shape judgment and shape change tendency, level 3 in the above example is inappropriate, and realistically the degree of "edge elongation" is almost level 5 (@
This is because it is considered to be comparable to a state in which elongation is progressing rapidly.

Therefore, in step S47 in FIG. It is adjusted and output to the shape control section 3 (34B).

On the other hand, if it is determined in step 344 that the actual shape tends to change periodically, the control gain for changing and adjusting the target shape data is reduced by 30% (
S46). This suppresses the tendency of periodic changes in the actual shape. That is, step S45. S46. S47. S4
The means for realizing the function No. 8 is the shape adjusting means.

On the other hand, if dispersion is applied as the statistical characteristic information, the rolling state can be determined from the degree of dispersion of the actual shape data regarding the shape of the end of the aluminum foil 53 due to temporal changes, for example. That is, when the degree of dispersion is large, it indicates that the rolling condition is unstable. Therefore, in such a case, the shape control section 3 executes the control actions shown in (i) and (ii) below to stabilize the rolling state.

At this time, (i) change and set the coolant amount control gain to a lower value.

(ii) Since it is possible that the rolling roll 52 is not sufficiently heated, the amount of coolant is decreased overall or the elongation rate of the target shape is increased overall.

Further, as an example where the characteristic information is a correlation, the correlation between the level of quarter elongation and the level of edge tension is shown in FIG. 5(a).
Shown below. At this time, it can be said that the relationship between the two is a positive correlation, indicating that quarter elongation and edge tension are likely to occur simultaneously.

In such a case, it is empirical knowledge that a shape adjustment method such as reducing the amount of coolant at the end in order to eliminate the end tension is not effective. On the other hand,
If the relationship between the two is uncorrelated, as shown in FIG. The method adjusts the actual shape.

Furthermore, as the statistical characteristic information, 3 related to the actual shape data
It can also be by recognition of dimensional patterns.

The three-dimensional pattern P is shown in FIG. In the figure,
An arrow M indicated by a two-dot chain line indicates the temporal position transition of the element 4° whose elongation rate has detected the maximum value at each time t0 to 1°. As shown in the figure, element 4 with the maximum elongation rate. is several elements in the width direction of the foil 4. It meanders within the width of. If this is the case, the coolant 58 is injected not only to the part that is currently at its maximum elongation, but also equally intensively to the rolling rolls 52 corresponding to the several elements within the width of 4 degrees, including that part. There must be.

This applies coolant 5 only to the area that is currently experiencing maximum growth.
This is because even if 8 is injected, the elongated area only moves to the adjacent area. The statistical characteristic information expressed by such a three-dimensional pattern P, such as "the elongated portion of the real shape meanders depending on the time," can of course be determined by the numerical calculation algorithm described above.
A recognition method using a neural network that performs pattern il Q of actual shape data of the aluminum foil 53 is effective.

As shown in FIG. 7, in the neural network 20, a plurality of neurons 15 that calculate and output input data through dark value processing are conceptually arranged as an input layer, an intermediate layer, and an output layer, and the spaces between the eyebrows of each are connected. They are connected via a section 16. Then, the neural network 20
The pattern data of the actual shape data of the aluminum foil 53 is used as input data, the actual shape classification items and their degrees are used as output data, and the correspondence relationship between the two is learned by changing the connection weight of the connection section 16. Therefore, when new real shape data is input to the trained neural network 20, the real shape data is specified in one of the real shape classification items along with its degree. Such a neural network 20 may be applied in the rolling situation analysis section 8.

Here, as a calculation method for determining the actual shape state of the Al 5 foil 53, examples of numerical calculations using specific methods determined for each actual shape classification item have been mainly shown. It goes without saying that the same calculation effect can be obtained by making a judgment based on a rule base (not shown) that stores judgment knowledge. That is,
The statistical characteristic information calculating means realizes the function of calculating statistical characteristic information of the actual shape data in the rolling condition analysis section 8.

The actual shape data input from the working memory M to the rolling situation analysis unit 8 and subjected to calculation may be one type of data value at one point in time within the predetermined time, or several types of data values at one point in time. , or the value of one type of data at several points in time, or the value of several types of data at several points in time, and the data necessary for the calculation may be used as appropriate.

In this manner, the rolling situation analysis unit 8 determines the actual shape classification items and their reliability for the current actual shape data of the aluminum foil 53, and writes them into the working memory M2.

■Generation of control target The control target data (shape change target (Fig. 8) and its importance), which is the key to appropriately setting or changing the target shape, is generated by the operator 5 in the control target generation unit 9. It is input from the terminal device 6 on the roll rolling mill 2 side, or is automatically generated based on rolling status data such as actual shape classification items and their reliability in the working memory M2.

In this automatic generation, "the operational policy ('for a given bus, the edges are thickened (stretched and rolled, etc.)' is reflected"
, or a rule such as "If there is a conflict between the information input by the operator 5 and the automatically generated information, the operator's input information is given priority" is applied with reference to the control target setting knowledge base D2.

For example, if the detected actual shape data includes "edge elongation" as in the example above, and the confidence level at that time is 0.8, in order to eliminate the "edge elongation", five "I want to stretch the edges" is selected from among the shape change targets, and a degree of importance corresponding to the confidence level (0, 8) is assigned to the selected shape change target.

Then, the shape change target and its importance are stored in the working memory M3.

The situation in which the importance of the shape change target is calculated in the control target generation unit 9 from the certainty of the actual shape classification item specified as described above will be described in detail below.

The degree of importance is a measure of whether or not it is necessary to change the target shape data, and is shown in the graph (Fig. 9) showing the relationship between the actual shape data and the confidence level of the actual shape classification item indicating the degree of edge tension, for example. shown.

In the figure, an example of "edge tension" among the actual shape classification items is shown.

An importance calculation function (f(x)) that gives the degree (importance) of the necessity of changing the target shape is defined for each of the actual shape classification items such as edge tension and edge elongation. For example, the importance calculation function f,() regarding "edge tension" is defined as follows.

That is, the degree of edge tightness (confidence) is compared with a first threshold L+, and as shown in FIG. 9, if it exceeds the first threshold L1, the actual shape classification item is selected for target shape modification. , the degree of importance corresponding to the degree of certainty shown in the figure is calculated.

On the other hand, if the confidence level is less than or equal to the first threshold L1, the importance level is O
is given, and the actual shape classification item is not used when changing the target shape.A first threshold value is individually set for each of the real shape classification items, and the respective confidence and the first threshold value are set individually. A comparison calculation is made with the threshold value L1, and it is determined for each item whether or not the target shape should be changed.

Whether or not it is necessary to change the target shape is determined for each actual shape classification item as described above, and also based on whether the average of the combined importance of each item exceeds the darkness value. There is also.

Next, a method for determining whether or not it is necessary to change the target shape from the actual shape classification items selected for changing the target shape based on the synthesized importance levels or their averages will be described in detail.

Letting the actual shape classification items be S, , S, .
is defined as below.

g (rs + (x+),”・,rs = (
x+ )) The function g() is expressed as gmi(Σf−r(xi))/i or gmiΣf
, , (x,) However, 0≦fat(Xl)≦1°fat(Xi) is expressed in the form of a summation average or a summation, such that X is strongly monotonically increasing.

Here, f (L, ) is strongly monotonically increasing with respect to (Ll), when l,, <L, f(Ll) < f(Ll)
It means that.

In this way, the composite importance indicating the degree of necessity for starting the inference process is calculated from the importance of each item in the action candidate inference section 11, and the composite importance is set to a predetermined second dark value L!
(not shown), inference processing starts,
Target shape data is changed appropriately.

A specific example is given below: The importance of "edge tension" is 0.4 The importance of "quarter elongation" is 0.6 The importance of other ("edge elongation", "medium elongation", "middle tension") When , the composite importance is 0, 4 + 0.6 + 0 = 1.0 in the case of summation. If the second threshold value RI at this time is 0.9, the combined importance is greater, so the inference process is started. As a method for determining the degree of importance serving as the trigger, the above-described total average method may be employed.

In this embodiment, as mentioned above, when the confidence level for each item exceeds the threshold value given for each item, for example,
The inference process is also started when the degree of the difference exceeds the threshold L2 shown in FIG.

Furthermore, when the composite importance exceeds the second threshold 4a L3, a start signal may be simultaneously output to an alarm device (not shown) to drive the alarm device.

Next, the target shape inference process will be explained.

■Application of actions■-(1) Rule inference Rolling process including operating condition data including current target shape data and current actual shape data processed as described above, extracted actual shape classification items and their confidence levels situational data,
The control target data including the shape change target and its importance are stored in the working memories M, , M2 . From M3, each is transferred to the action candidate inference unit 11. The action candidate inference unit 11 compares each transferred data with the condition part of the rule stored in the action inference knowledge base D3, and as a result of the comparison, extracts a rule that satisfies all condition parts. , the target shape change data (FIG. 8, hereinafter referred to as axine) in the conclusion part of the rule is selected.

As mentioned above, the inference process for drawing a conclusion (target shape to be adopted) corresponding to the conditions such as operating condition data, rolling status data, and control target data must rely on the knowledge (know-how) of experienced people. I don't get it. In the present invention, such inference processing is automated, and rules for automatic inference are accumulated and stored in the action inference knowledge base D3. Such a rule is expressed in the form "if [condition part], then [conclusion part]" and is expressed in the form of a logical product as shown below.

If [control target data] and [operating condition data 1 rolling status data], [designation of target shape adjustment parameter and its degree of change (action and degree)] Here, the target shape adjustment parameter is As shown in -1, this is an element that determines the target shape data (target elongation rate distribution for the bus).

Not all of the parameters shown in Table 6 are necessarily listed as the target shape adjustment parameters constituting the conclusion part of each rule. In many cases, only some target shape adjustment parameters necessary to satisfy the condition part are described together with the degree of change thereof.

For example, as shown in FIG. 10, in rule example 1,
If the actual shape of the aluminum foil 53 is determined to have quarter elongation, and there is no zero point under the part with the largest elongation near the quarter part, then A rule that specifies an action such as "Bring it to" is stored in the axigon inference knowledge base D3.

■=(2) Elimination of contradictions and redundancies On the other hand, as shown in rule example 4 shown in Table 4, additional conditions may be added to the rules.

For example, in a real shape, when edge tension and quota elongation occur at the same time, rule example 2 and rule example 3 may be selected. The unit 11 is unable to resolve these contradictions and an error occurs. Therefore, for example, by setting an additional condition in the condition part of rule example 2 and making it rule example 4, this problem can be solved. That is, in rule example 4, if the importance of wanting to stretch the edges and the importance of wanting to stretch the quota part are both greater than the first darkness value, but the importance of wanting to stretch the quota part is less than 0.4. In order to do this, the target value for the edge level (the elongation rate at the edges in the width direction) is increased. As a method for resolving such contradictions or redundancies, it is also possible to give priority to the shape change target with the higher degree of importance.

On the other hand, there are cases where two or more types of shape change targets are simultaneously selected for certain actual shape data, and the content of the action corresponding to each shape change target is the same.

For example, the actual shape classification items (Figure 3 (bl)) of the current actual shape data are simultaneously specified as "edge tension" and "medium elongation", and the degree of difference is determined as an action derived from each actual shape classification item. However, this is the case when "Raise the edge level" (Figure 8), which has the same content, is selected at the same time.As a rule to be applied in such a case, the and setting the degree of action is stored in advance in the axilan inference knowledge base D.

Therefore, when three actors with the same content but with different degrees are selected at the same time as described above, instead of executing these actions at the same time, only the one with the highest degree of action is executed. On the contrary, only actions with a small degree may be executed, or an axicon commensurate with the average value of the degree of each action may be selected or generated.

■-(3) Learning of invalid axicons Furthermore, as shown in Fig. 8, for the shape change goal of 1,
There are several types of action candidates with priorities added. Then, when a certain shape change target is selected, the action with the highest priority is executed.

The priority is not fixed, but is checked for each inference. For example, when inference is executed in the action candidate inference unit 11, which axicon was adopted for which shape change target is stored in the working memory M5, and during the next inference, the axigon effect evaluation unit 10 uses the previous shape change target Whether the goals have been achieved (evaluation of effectiveness) is determined by comparing the importance of the previous and current times.

As a result, if the current target shape data that has been changed based on the previous shape change target and its importance is determined to be valid by the action effect evaluation unit 10, that is, the importance is lower than the previous value. If so, it is assumed that the adopted action is valid, and the action inference knowledge base D
The priority stored in , is incremented. Conversely, if it is determined that the action is invalid, the previously applied action that was invalid and the rule that inferred the selection of the axicon are stored in the working memory M4.

For example, the shape change target shown in FIG. 8 is based on the rolling status data from the rolling status analysis unit 8 or the input data from the operator 5, and the previous time "I want to lengthen the edges" is determined to be 0 in importance.
If the decision is made in step 6, "Raise the level of the edge (priority l)" with the highest priority among the associated actions is selected, and the level of the edge is increased according to the importance level of 0.6. The target shape data with increased (elongation rate) is output to the shape control unit 3, and the target shape data and shape change target are applied at the same time.

its importance (0,6), its action, and its priority (
1) is stored in the accitane inference knowledge base D. Then, when determining the current shape change target, if "I want to extend the shape change target edge" is selected with the previous importance level of 0.6 or higher, the edge tension state of the actual shape in question will be improved. In many cases, the axicons that were applied last time were invalid.On the other hand, this time
is selected with an importance level of less than 0.6, it is determined that the previous axicon was valid.

Therefore, the invalidated action and the rule that inferred the action are written into the working memory M4. Then, even if the same shape change target is selected and the invalid action is selected at the next inference, the invalid axicon will not be applied, and the axicon with the next highest priority that is judged to be appropriate will be applied. This data is then used to change the appropriate target shape data. As a result, if the next axicon applied to achieve the shape change target is determined to be effective, the priority of the action is raised and the priority of the invalid action is lowered. The priority changed as described above is written in the priority section in the action reasoning knowledge base D3.

In this way, even if the target shape obtained by the aluminum foil rolling shape adjustment device l has no effect on the actual shape and the problematic actual shape of the aluminum foil 53 continues, In the current ti theory, even if the shape change goal is the same as the previous inference, an action different from the previous action is selected. As a result, invalid rules are not repeatedly applied, and the actual shape is appropriately changed.

The action candidate inference unit 11 checks in detail whether or not the action proposed as a candidate is appropriate according to the check tree shown in FIG. be registered.

As mentioned above, in the Accitane inference knowledge base D3, a rule set consisting of a plurality of rules having the same shape change target in the condition part is set for each shape change target, and a master that stores the rule set is set. A save area is secured to store the area and the rules removed from the rule set. Note that the rules for which inference processing has been completed are sequentially removed from the main area to the save area.

In the check tree shown in the figure, first, check whether a rule that matches the currently selected shape change target and the shape change target exists in the rule set to which the rule belongs. It is checked in case C1 whether there are any left. If Case CI is applicable, proceed to Case C1 to check additional conditions related to operating conditions, etc. in the condition section of the rule.

If the case C1 does not apply, that is, if all the condition parts including the incidental conditions do not match, the rule is removed from the rule set in the main area to the save area. If the above-mentioned supplementary conditions are not satisfied, in case C4, whether the action of the rule in which all the above-mentioned condition parts were satisfied has been applied in the past and its effect was not recognized;
That is, it is checked whether it is currently registered in the working memory M4. If case C4 is applicable, that rule is removed from the rule set (main area). If case C4 does not apply, the existence of mutually contradictory actions is checked in case C5. As described above, not only one shape change target is set, but a plurality of shape change targets may be selected. In this case, the actions associated with each shape change target often conflict with each other.

Therefore, as a result of the check, if there is an action that is inconsistent with the currently checked action among the actions that have been determined to be valid and have already been registered in the working memory M, then the rule that includes one of the actions , if the importance of the currently selected shape change target is greater, the action of that rule is registered in the work memory M5, and the already registered contradictory action is removed to the save area.

On the other hand, if the importance of the shape target with the already registered action is greater, the registered action is left in the working memory MS, and the rule with the currently checked action is added to the rule set. (main area) to the save area.

If case C5 does not apply, that is, if the above-mentioned actions do not contradict each other, it is checked in case C4 whether the shape change targets of these actions are the same. If 5 cases apply to case C6, it is checked whether there is a difference in the priority of each of the above actions (
In case Ct), if there is a difference in priority, only the action with the higher priority remains in the working memory M5, and the action with the lower priority is deleted from the working memory M. If there is no difference in priority, both actions are left together in the working memory M.

As described above, if case C does not apply, it is determined that there is no rule in the rule set that matches the shape change target selected this time, and the process proceeds to check case C2.

If case C8 applies, that is, all rules in each rule set have been checked, but if it is determined that all action candidates are nonconforming due to the checks in cases C5 to C1, past invalid information in working memory M is At the same time, since it is not possible to select an appropriate action candidate under the current priority, the priority is reset and each rule consisting of the action candidates with the reset priority is After restoring the rule set, the action check reasoning is restarted from the beginning.

On the other hand, if case C2 does not apply, an action candidate that is determined to be appropriate as a result of verifying all the rules in each rule set is selected and stored in the working memory M.

Since it should be stored in , we end the inference related to the relevant axis calculation.

In this way, rules in a rule set consisting of rules that have a certain shape change goal in common are sequentially removed from the rule set (main area) after checking the validity of their actions one by one. Stored in the evacuation area.

Then, the above rule check is executed until the rule set of the main area related to the shape change target becomes empty. In other words, the case where the rule set is empty does not correspond to case C1.

If two or more shape change targets are selected,
The validity check of the above action is performed for another shape change target set in the same manner as above (C1 to Ct).

In this way, the above-mentioned check routine checks the inconsistency, priority, effectiveness record, etc. between the action candidates already registered in the working memory M5 and the action candidates to be newly registered, and then checks the target shape data. The validity of the actions to be applied to the changes and the consistency of the rules are maintained.

■Generation of target shape Next, the action and its degree registered in the work memory M5, ie, "the degree of raising the edge level is 0.8
” is transferred to the target shape data 12.

The target shape generation unit 12 changes the values of the target shape adjustment parameters shown in Table 5 and FIGS. 12(a) to 12(d) based on the target shape change data. For example, if the above-mentioned action is "Raise the end level. The degree is 0.8", the value of the target shape adjustment parameter a related to the elongation rate of the end will be the same as the degree of the action. The settings are changed accordingly, and the target shape data changes. Furthermore, the shape control section 3 is given an output signal that changes the control gain based on the input changed target shape data, and controls the coolant 58 of the roll rolling mill 2 .

■Evaluation of applied action Then, the operating condition data including the newly obtained actual shape data is input to the rolling data collection section 7, and the same process as the previous time is repeated. That is, the shape change target and its importance for the current actual shape are calculated by the control target generation unit 9, and compared with the previous one by the action candidate inference unit 11.

A specific example in which the above reasoning is repeated is shown in FIG. For example, if the shape change target E calculated from actual shape data included in certain operating condition data is "I want to increase the quota" and its importance is 0.8,
The second inference C2 is executed, and the action candidates at that time are (AI) widening the width of the zero point and (A2) moving the zero point outward. Here, action A with high priority
1 was applied, and the target shape data before and after being changed thereby were as shown in the inference result E.

However, when evaluating the effect of action A1, E
The importance of l did not become smaller than 0.8, and its effect was not recognized. Therefore, the rule that derived the above-mentioned action A1 is stored in the working memory M4 as being invalid.

Next, apply action A2 with the next highest priority and perform 2
The second inference E4 is executed. In this case, the rule including the shape change target E is continuously applied to continue the rolling process, and if the operating conditions are unchanged from the first inference, the actions A1 and A2 are obtained as candidates. Therefore, as a result of verifying the history of action A1 using this second inference E, the above action A1
is stored in the working memory M as being invalid last time, so it is not applied this time (case C4 in Figure 11).
, action A2 with the next highest priority is applied to change the target shape data.

As a result, if it is determined that the quota growth has been improved, the priority of action A2 is upgraded over that of action A1 and is stored in the action inference knowledge base D.

In this way, when certain target shape data is given to the real shape of Al-:Fi53, the change in the actual shape and the corresponding change in the target shape data are determined by the action candidate inference unit 11
The target shape data changed each time the inference is made, the actual shape data obtained as a result, the operating condition data, rolling status data, control target data, etc. used to set the target shape data. The experience value is stored in the action inference knowledge base D as an inference rule. Then, the axis candidate inference unit 11 performs
Appropriate target shape data is calculated based on the respective experience values, and the target shape data is automatically generated via the target shape generation section 12 and output to the shape control section 3.

The aluminum foil rolling shape adjusting device 1 as described above is the 14th
As shown in the figure, the rolling mill side terminal 6 is operated by the operator 5.
It is activated by pressing a key from (Ste.

B21). Subsequently, the operator 5 inputs the shape change target information along with the degree of importance according to the t input menu (FIG. 15) displayed on the screen of the terminal 6 (the steno knob 22
).

Accordingly, the aluminum foil rolling shape adjusting device 1 analyzes the rolling data transferred from the shape control unit 3 (step 23), creates an appropriate target shape by inference (step 24), and performs the target shape before and after modification (step 24). 16) on the screen, and outputs the corrected target shape data to the roll mill 2 via the shape control section 3 (step 2).
5) Then, in step 26, evaluate whether the corrected target shape was effective for the actual shape that had the problem.
), the system waits for input from the operator 5.

At this time, the shape adjustment device 1 also automatically generates a shape change target, but if it contradicts the shape change target input by the operator 5, the shape change target is The shape change target may be generated based on a rule that gives priority to either the object or the shape determination result from the actual shape classification item.

The aluminum foil rolling shape adjusting device 1 constantly adjusts the aluminum foil 53.
Although the operator 5 starts the inference when it is determined that it is necessary to monitor the actual shape of the target shape data and change the target shape data, it is also possible to automatically start the process of changing the target shape data. For example, in the condition part of the rule, a comparison condition between the certainty factor of the actual shape classification item and a certain threshold value α is set.

A specific example of the rule is given below.

If [actual shape classification item and its confidence level] and [operating conditions], then [designation of shape change target and its degree],
Specifically, if the confidence of edge extension is α and the bus is the second bus, then the importance of wanting to extend the edge is expressed as 1.0. That is, when the actual shape changes and the degree of edge extension becomes equal to or less than the dark value α, the above rule is applied and inference is started.

Furthermore, the aluminum foil rolling shape adjusting device 1 has a function of improving even when the obtained actual shape data is asymmetrical in the foil width direction. As shown in FIGS. 17 and 18, if it is determined that the actual shape data is asymmetric (step 31), the operator 5 is temporarily set higher (step 32). This is for temporarily deflecting the injection amount distribution of the coolant 58 injected onto the rolling roll 52 in the foil width direction and making the heat distribution on the rolling roll 52 uniform, especially at the start of operation (
This is effective during roll temperature rise) or when restarting (after roll reshuffling). The processing in the steno knob 32 is repeated until the actual shape data becomes symmetrical.

The aluminum foil rolling shape adjusting device 1 employs an element 4° in which a piezoelectric element is embedded as a sensor for detecting the actual shape data of elongation and tension at the time of rolling, but its appearance is similar to that of the element 4e. It is also possible to use a plurality of air bearing type elements as the sensor, and to detect the actual shape data based on changes in the air pressure.

In this example, the control target is Alz 7F353.
is used, but the material is not limited thereto, and may be made of copper or other metals, and its thickness is not limited.

In the above-mentioned embodiment, in order to facilitate understanding, the change in the actual shape at the end of the aluminum foil 53 was described; It should be noted that the process (located between the end and the central part) is carried out at the same time and in the same way as the end.

Furthermore, in this embodiment, the aluminum foil rolling shape adjustment device 1 is arranged independently of the shape control section 3, but it is a part of a mini-computer (not shown) that realizes the shape control section 3. If you configure it as , you can increase the data processing speed and use the actual shape system of aluminum FF153? 11 further improve accuracy.

〔Effect of the invention〕

According to a first aspect of the present invention, there is provided a metal rolling shape adjusting device that adjusts the shape by supplying target shape data to a shape control section that controls the shape in the width direction of a strip-like metal stretched by a roll rolling machine. , a sensor for detecting actual shape data of rolled metal, a sampling means for periodically collecting and storing the actual shape data, and a shape change for calculating a change tendency of the shape within a predetermined time from the actual shape data. determining the latest target shape data, control gain, etc. based on the trend calculation means and the change trend and the latest actual shape information;
There is provided a metal rolling shape adjusting device characterized by comprising a first shape adjusting means that adjusts the shape based on this. As a result, even if the rolling characteristics of the roll mill change significantly, it is possible to understand the tendency of the metal shape to change at that time, predict future changes in the shape, and adjust the shape with precision. can do well.

According to a second aspect of the present invention, there is provided a metal rolling shape adjusting device that adjusts the shape by giving target shape data to a shape control section that controls the shape in the width direction of a strip-shaped metal stretched by a roll rolling machine. a sensor for detecting actual shape data of rolled metal; a sampling means for periodically collecting and storing the actual shape data; and a sensor for detecting the actual shape data of the rolled metal; a statistical feature information calculation means for calculating statistical property information of the shape; and a statistical feature information calculation means that determines the latest target shape data, control gain, etc. based on the statistical property information and the latest actual shape information, and determines the latest target shape data, control gain, etc. of the shape based on this. There is provided a metal rolling shape adjusting device characterized by comprising a second shape adjusting means for performing adjustment. Thereby, appropriate shape adjustment can be performed in accordance with a wider variety of shape change modes than in the first invention.

[Brief explanation of drawings]

FIG. 1 is a schematic diagram showing the system layout of an aluminum foil rolling shape adjusting device according to an embodiment of the present invention, FIG. 2 is a configuration diagram showing the processing flow of the aluminum foil rolling shape adjusting device, and FIG.
a) is an explanatory diagram showing the main parts of the actual shape data expressed by the elongation weight distribution in the foil width direction; FIG. 3 (+)) is an explanatory diagram showing the actual shape classification items of pattern-classified aluminum foil;
Figure 4 (a) is a flowchart showing the processing procedure for determining the first change in actual shape data, and Figure 4 (b) is an action for correcting the number of levels of actual shape classification items by taking shape change trends into account. Explanatory diagrams showing FIGS. 5(a) and 5(b)
) is a graph showing the correlation between the number of levels of two real shape classification items, Figure 6 is a three-dimensional graph showing a three-dimensional pattern due to changes over time in real shape data of aluminum foil, and Figure 7 is a neural network 1a Figure 8 is an explanatory diagram showing an example of the relationship between shape change targets and corresponding action candidates for real shape classification items, and Figure 9 shows the importance of shape change targets and actual shape data of 0. 1. A graph showing the relationship between the confidence level and the confidence level when the edge is tight. FIG. Figure 11 is an explanatory diagram showing the processing procedure of a routine that checks the validity of the action to be applied using a Chenok tree, and Figure 12 (a) is a target shape used to change the target shape. An explanatory diagram showing adjustment parameters, the same figure (bl is a state diagram showing 33 status changes of the parameters,
) is a state diagram showing a situation where the center part of the target shape adjusted by the parameter 34 is 1ll (pattern),
FIG. 13(d) is a state diagram showing a situation where the center part is a reverse pattern, FIG. 13 is a schematic explanatory diagram showing an example of inference execution for changing the target shape, and FIG. 14 is a processing flow for adjusting the target shape. Fig. 15 is a flow sheet showing the manual menu displayed on the screen of the terminal on the rolling mill side, Fig. 16 is a flow sheet showing the
Figure 17 is a display diagram showing an example of the target shape displayed on the screen, Figure 17 is a flow sheet showing a method for correcting the asymmetrically obtained actual shape, and Figure 18 is a situation in which the asymmetrical actual shape is corrected. 19 is a schematic perspective view showing a roll rolling mill which is an example of the background of the present invention, FIG. 20 is an external view showing the surface shape of aluminum foil after rolling, and FIG. 21 is a rolling An explanatory diagram showing the correlation between the cross-sectional shape of the roll, the actual shape of the aluminum foil, and the target shape for controlling the actual shape, Fig. 22 Target shape for operation of Haal ξF5 and target shape set for control 23 is a flowchart showing the processing procedure of the conventional aluminum foil rolling shape adjustment device which is the background of the present invention, and FIGS. 24(a) to 24(f) show the actual shape classification items and It is an explanatory view showing an example of the number of levels as a graph of elongation rate versus foil width direction. [Explanation of symbols] 1... Aluminum foil rolling shape adjustment device

Claims (2)

[Claims] (1) A metal rolling shape adjustment device that adjusts the shape by supplying target shape data to a shape control section that controls the shape in the width direction of a strip of metal stretched by a roll mill, the rolled metal a sensor for detecting actual shape data; a sampling means for periodically collecting and storing the actual shape data; a shape change tendency calculation means for calculating a change tendency of the shape within a predetermined time from the actual shape data; A first shape adjustment means that determines the latest target shape data, control gain, etc. based on the change tendency and the latest actual shape information, and adjusts the shape based on this. Metal rolling shape adjustment equipment. (2) A metal rolling shape adjustment device that adjusts the shape by supplying target shape data to a shape control section that controls the shape in the width direction of a strip-shaped metal stretched by a roll mill, the rolled metal a sensor for detecting actual shape data; a sampling means for periodically collecting and storing the actual shape data; and calculating statistical characteristic information of the shape within a predetermined time from the actual shape data stored in the sampling means. a second shape that determines the latest target shape data, control gain, etc. based on the statistical characteristic information and the latest actual shape information, and adjusts the shape based on this; 1. A metal rolling shape adjusting device, comprising: adjusting means.