CN110227725B - Roll gap self-learning deviation rectifying method and device - Google Patents
Roll gap self-learning deviation rectifying method and device Download PDFInfo
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- CN110227725B CN110227725B CN201910438242.1A CN201910438242A CN110227725B CN 110227725 B CN110227725 B CN 110227725B CN 201910438242 A CN201910438242 A CN 201910438242A CN 110227725 B CN110227725 B CN 110227725B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
- B21B—ROLLING OF METAL
- B21B37/00—Control devices or methods specially adapted for metal-rolling mills or the work produced thereby
- B21B37/58—Roll-force control; Roll-gap control
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
- B21B—ROLLING OF METAL
- B21B38/00—Methods or devices for measuring, detecting or monitoring specially adapted for metal-rolling mills, e.g. position detection, inspection of the product
- B21B38/08—Methods or devices for measuring, detecting or monitoring specially adapted for metal-rolling mills, e.g. position detection, inspection of the product for measuring roll-force
Abstract
The invention discloses a roll gap self-learning deviation rectifying method and device, relates to the technical field of steel rolling, is applied to a multi-frame continuous rolling process, and comprises the following steps: collecting current rolling data of each stand in a multi-stand continuous rolling mill when current coiled steel passes through a last stand of the multi-stand continuous rolling mill; determining the actual rolling force of each rack based on the current rolling data and the zone bit of the recalculation rolling force of each rack; obtaining the current values of the self-learning coefficients of the racks; judging whether the current value of the self-learning coefficient is within a preset range, if so, obtaining an updated value of the self-learning coefficient based on the current value of the self-learning coefficient and the old value of the self-learning coefficient; controlling the multi-frame continuous rolling mill to roll the next coil of strip steel according to the self-learning coefficient updating value; the problem of inaccurate roll gap setting caused by abnormal conditions can be solved, and the roll gap setting accuracy is improved, so that the rolling quality of the strip steel is improved.
Description
Technical Field
The invention relates to the technical field of steel rolling, in particular to a roll gap self-learning deviation rectifying method and device.
Background
In the hot rolling production process, the roll gap setting accuracy directly influences the thickness of a strip steel finished product, and the control precision of the strip steel thickness reflects the control level of the quality of the product.
At present, self-learning of roll gaps of a hot rolling production line mainly adopts a method for making difference between second flow thickness and thickness gauge thickness. The second flow thickness of each frame is calculated according to the actually measured thickness of the outlet by the principle of second flow equality; the thickness of each frame is obtained by back calculation through a roll gap equation according to data such as actually measured rolling force. In the production process, when the actually measured rolling force fluctuates due to external factors, the roll gap self-learning method cannot effectively improve the roll gap setting precision. For example, if the rolling force setting of a certain coil is too large, the situation that the rolling force setting is too large can not be corrected due to the fact that the actually measured rolling force and the recalculated rolling force are both too small when the tension between racks is abnormally increased in the threading process. The roll gap setting is smaller and the head thickness is thinner due to larger rolling force used when the roll gap is set in the next roll.
Disclosure of Invention
The embodiment of the application provides a roll gap self-learning deviation rectifying method and device, and solves the technical problem that the roll gap setting precision cannot be effectively improved when actually measured rolling force fluctuates in the related technology.
On one hand, the present application provides the following technical solutions through an embodiment of the present application:
a roll gap self-learning deviation rectifying method is applied to a multi-frame continuous rolling process and comprises the following steps:
collecting current rolling data of each stand in a multi-stand continuous rolling mill when current coiled steel passes through a last stand of the multi-stand continuous rolling mill;
calling the zone bit of each rack for recalculating the rolling force;
determining the actual rolling force of each rack based on the current rolling data and the zone bit of the recalculation rolling force of each rack; the actual rolling force is recalculated rolling force or actually measured rolling force;
obtaining the current values of the self-learning coefficients of the racks based on the actual rolling force and the rolling force set by the racks in the current rolling process of the coiled strip steel;
judging whether the current value of the self-learning coefficient is within a preset range, if so, obtaining an updated value of the self-learning coefficient based on the current value of the self-learning coefficient and the old value of the self-learning coefficient; the old value of the self-learning coefficient is a self-learning coefficient value set by all the racks in the current rolling process of the coiled strip steel;
and judging whether the self-learning coefficient updating value is in a preset range, if so, controlling the multi-stand continuous rolling mill to roll the next coil of strip steel based on the self-learning coefficient updating value.
Optionally, the obtaining an updated value of the self-learning coefficient based on the current value of the self-learning coefficient specifically includes:
and multiplying the difference value of the current value of the self-learning coefficient and the old value of the self-learning coefficient by the self-learning gain coefficient to obtain a product, and adding the product to the old value of the self-learning coefficient to obtain an updated value of the self-learning coefficient.
Optionally, when the flag bit of the recalculated rolling force is the first flag bit, the current rolling data includes the roll speed, the tension and the outlet thickness, the actual rolling force is the recalculated rolling force, and the recalculated rolling force is obtained through a rolling force calculation formula according to the current rolling data.
Optionally, when the flag bit of the recalculated rolling force is the second flag bit, the current rolling data includes an actually measured rolling force, and the actual rolling force is the actually measured rolling force.
Optionally, the obtaining the current values of the self-learning coefficients of the racks based on the actual rolling force and the rolling force set by each rack in the current rolling process of the coiled steel specifically includes:
and dividing the actual rolling force by the rolling force set by each rack in the current rolling process of the coiled strip steel to obtain the current value of the self-learning coefficient.
Optionally, the method further includes:
and if the self-learning coefficient updating value is not in the preset range, controlling the multi-frame continuous rolling mill to roll the next coil of strip steel based on the boundary value of the preset range.
Optionally, if the self-learning coefficient update value is not within the preset range, the multi-stand continuous rolling mill is controlled to roll the next strip steel based on the preset range value, and the method specifically includes:
when the updating value of the self-learning coefficient is larger than the upper limit boundary value of the preset range, controlling the multi-frame continuous rolling mill to roll the next coil of strip steel by using the upper limit boundary value;
and when the self-learning coefficient updating value is smaller than the lower limit boundary value of the preset range, controlling the multi-frame continuous rolling mill to roll the next coil of strip steel by using the lower limit boundary value.
Optionally, the controlling the multi-stand continuous rolling mill to roll the next coil of strip steel based on the updated self-learning coefficient specifically includes:
multiplying the set rolling force when the next strip steel is rolled by the self-learning coefficient updating value to obtain the corrected rolling force when the next strip steel is rolled;
and rolling the next coil of strip steel according to the corrected rolling force.
Optionally, the controlling the multi-stand continuous rolling mill to roll the next coil of strip steel based on the boundary value of the preset range specifically includes:
multiplying the set rolling force when the next coil of strip steel is rolled by the boundary value of the preset range to obtain the corrected rolling force when the next coil of strip steel is rolled;
and rolling the next coil of strip steel according to the corrected rolling force.
On the other hand, the present application provides a device for roll gap self-learning deviation rectification through another embodiment of the present application, which is applied to a multi-frame continuous rolling process, and includes:
the data acquisition module is used for acquiring current rolling data of each rack in the multi-rack continuous rolling mill when the current coiled steel passes through the last rack of the multi-rack continuous rolling mill;
the mark calling module is used for calling the mark position of each rack for recalculating the rolling force;
the first obtaining module is used for obtaining the actual rolling force of each rack based on the current rolling data and the zone bit of the recalculation rolling force of each rack; the actual rolling force is recalculated rolling force or actually measured rolling force;
the second obtaining module is used for obtaining the current values of the self-learning coefficients of the racks based on the actual rolling force and the rolling force set by the racks in the current rolling process of the coiled strip steel;
the third obtaining module is used for judging whether the current value of the self-learning coefficient is within a preset range, and if so, obtaining an updated value of the self-learning coefficient based on the current value of the self-learning coefficient and the old value of the self-learning coefficient; the old value of the self-learning coefficient is a self-learning coefficient value set by all the racks in the current rolling process of the coiled strip steel;
and the control module is used for judging whether the self-learning coefficient updating value is within a preset range, and controlling the multi-stand continuous rolling mill to roll the next coil of strip steel based on the self-learning coefficient updating value if the self-learning coefficient updating value is within the preset range.
One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:
the invention is applied to a multi-stand continuous rolling process, and firstly, the current rolling data of each stand when the current coiled steel passes through the last stand of a multi-stand continuous rolling mill is collected; then calling the zone bit of each rack for recalculating the rolling force; determining the actual rolling force of each rack based on the current rolling data and the zone bit of the recalculation rolling force of each rack; the actual rolling force is recalculated rolling force or actually measured rolling force; obtaining the current values of the self-learning coefficients of the racks based on the actual rolling force and the rolling force set by the racks in the current rolling process of the coiled strip steel; judging whether the current value of the self-learning coefficient is within a preset range, if so, obtaining an updated value of the self-learning coefficient based on the current value of the self-learning coefficient and the old value of the self-learning coefficient; the old value of the self-learning coefficient is a self-learning coefficient value set in the rolling process of the current coiled strip steel; controlling the multi-frame continuous rolling mill to roll the next strip steel according to the self-learning coefficient updating value, so as to realize the correction of the next strip steel; the self-learning coefficient value is corrected according to the set old self-learning coefficient value, so that the condition that the deviation of the rolling force cannot be corrected due to self-learning correction by utilizing the actually measured rolling force and the recalculated rolling force which have consistent variation trends in the prior art is avoided, the problem that the roll gap setting is not accurate due to the fact that the actual tension is larger, the actually measured rolling force and the recalculated rolling force are smaller and the roll gap self-learning cannot correct the next roll setting rolling force under the abnormal condition can be solved, and the rolling quality of the strip steel is improved; meanwhile, the recalculation rolling force or the actual measurement rolling force can be selected and used according to the mark bit of the recalculation rolling force to calculate the current value of the self-learning coefficient, so that the operation can be conveniently performed according to the selection of the working condition on site, the recalculated rolling force can avoid the condition of inaccurate detection possibly existing in the actual measurement rolling force, and the reliability of deviation correction is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
FIG. 1 is a flow chart of a method for roll gap self-learning deskewing in one embodiment of the present invention;
FIG. 2 is a centerline thickness deviation curve obtained using a prior art method in one embodiment of the present invention;
FIG. 3 is a center line thickness deviation curve obtained by a roll gap self-learning deviation correction method according to an embodiment of the present invention;
FIG. 4 is a diagram of a roll gap self-learning deviation rectification device in an embodiment of the present invention.
Detailed Description
The embodiment of the application provides a method and a device for roll gap self-learning deviation correction, and solves the problem that the roll force self-learning cannot effectively correct the set precision under the abnormal condition in the prior art.
In order to solve the technical problems, the general idea of the embodiment of the application is as follows:
a roll gap self-learning deviation rectifying method is applied to a hot continuous rolling process and comprises the following steps:
collecting current rolling data of each stand in a multi-stand continuous rolling mill when current coiled steel passes through a last stand of the multi-stand continuous rolling mill; calling the zone bit of each rack for recalculating the rolling force; determining the actual rolling force of each rack based on the current rolling data and the zone bit of the recalculation rolling force of each rack; the actual rolling force is recalculated rolling force or actually measured rolling force; obtaining the current values of the self-learning coefficients of the racks based on the actual rolling force and the rolling force set by the racks in the current rolling process of the coiled strip steel; judging whether the current value of the self-learning coefficient is within a preset range, if so, obtaining an updated value of the self-learning coefficient based on the current value of the self-learning coefficient and the old value of the self-learning coefficient; the old value of the self-learning coefficient is a self-learning coefficient value set by all the racks in the current rolling process of the coiled strip steel; and judging whether the self-learning coefficient updating value is in a preset range, if so, controlling the multi-stand continuous rolling mill to roll the next coil of strip steel based on the self-learning coefficient updating value.
In order to better understand the technical solution, the technical solution will be described in detail with reference to the drawings and the specific embodiments.
First, it is stated that the term "and/or" appearing herein is merely one type of associative relationship that describes an associated object, meaning that three types of relationships may exist, e.g., a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
Example one
In this embodiment, a roll gap self-learning deviation rectifying method is applied to a multi-frame continuous rolling process, and the method includes:
s101, collecting current rolling data of each frame in a multi-frame continuous rolling mill when current coiled steel passes through a last frame of the multi-frame continuous rolling mill;
s102, calling the zone bit of each rack for recalculating the rolling force;
s103, determining the actual rolling force of each rack based on the current rolling data and the zone bit of the recalculation rolling force of each rack; the actual rolling force is recalculated rolling force or actually measured rolling force;
s104, obtaining the current values of the self-learning coefficients of the racks based on the actual rolling force and the rolling force set by the racks in the current rolling process of the coiled strip steel;
s105, judging whether the current value of the self-learning coefficient is within a preset range, and if so, obtaining an updated value of the self-learning coefficient based on the current value of the self-learning coefficient and the old value of the self-learning coefficient; the old value of the self-learning coefficient is a self-learning coefficient value set by all the racks in the current rolling process of the coiled strip steel;
and S106, judging whether the self-learning coefficient updating value is in a preset range, and controlling the multi-stand continuous rolling mill to roll the next coil of strip steel based on the self-learning coefficient updating value if the self-learning coefficient updating value is in the preset range.
It should be noted that the method of the present invention is an improvement of a roll seam self-learning program logic method, and when the method is specifically implemented, the number of the stands is at least two, and the specific number is not limited, and taking a seven-stand continuous rolling process as an example, a self-learning parameter table as shown in table 1 below may be set to control the self-learning logic.
TABLE 1 SELF-LEARNING PARAMETERS TABLE
| Rack | F1 | F2 | F3 | F4 | F5 | F6 | F7 |
| Whether to be used for setting the zone bit of the roll gap | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Self-learning function input flag bit | 2 | 2 | 2 | 2 | 2 | 2 | 2 |
| Self-learning gain factor | 0.15 | 0.15 | 0.15 | 0.15 | 0.15 | 0.15 | 0.15 |
| Self-learning current value lower limit | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 |
| Self-learning current value upper limit | 1.08 | 1.08 | 1.08 | 1.08 | 1.08 | 1.08 | 1.08 |
| Self-learning update value lower bound | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 |
| Self-learning update value upper bound | 1.05 | 1.05 | 1.05 | 1.05 | 1.05 | 1.05 | 1.05 |
| Using recalculated rolling force flag bit | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
In table 1, F1 to F7 represent the 1 st to 7 th racks, and the setting parameters include a self-learning gain coefficient, upper and lower limits of self-learning current values, upper and lower limits of self-learning update values, a self-learning function input flag and a rolling force using recalculation flag. Whether the setting flag bit 0 is used for setting the roll gap represents that the setting flag bit is not used for setting the roll gap, 1 represents that the setting flag bit is used for setting the roll gap, and the settings in the table represent that the roll gap setting is applied to all of 7 frames; the self-learning function input flag bit item 0 represents that self-learning calculation is not carried out, and 1 represents that the self-learning calculation is carried out by applying the method of the invention but not updated (not applied to the next volume); 2 represents the self-learning calculation and updating (applied to the next volume) by applying the method of the present invention, and the settings in table 1 show that 7 frames are all self-learning calculation and updating (applied to the next volume) by applying the method of the present invention.
Also, it is understood that the use of the recalculated rolling force flag 1 represents the use of the recalculated rolling force for the current value calculation of the self-learning coefficient and 0 represents the use of the measured rolling force for the current value calculation of the self-learning coefficient, and in the present invention, the recalculated rolling force is used for the current value calculation of the self-learning coefficient, but may be used alternatively when the present invention is used.
The steps of the method of the present invention are described in detail below with reference to FIG. 1.
Referring to fig. 1, S101 is first performed to collect current rolling data of each stand in a multi-stand continuous rolling mill as a current coil steel passes through the last stand of the multi-stand continuous rolling mill.
Specifically, in the specific continuous rolling process, the method of the present invention first collects parameters of each stand of the rolling mill when the previous strip steel is rolled, i.e., when the previous strip steel passes through the last stand, so as to obtain actual rolling parameters in the rolling process, i.e., the current rolling data.
More specifically, in order to more accurately acquire real-time data, the current coiling steel head position reaches the tail frame outlet pyrometer, a self-learning time sequence is triggered, and current rolling data are acquired.
Next, executing S102, calling the mark of each rack for recalculating the rolling force;
as an alternative embodiment, the recalculation of the rolling force may be selected for use or not used when the method is performed, and therefore a flag for the recalculation of the rolling force is set for selection by the operator.
Next, S103 is executed to determine the actual rolling force of each stand based on the current rolling data and the flag of the recalculated rolling force of each stand.
In particular, the current rolling data includes, but is not limited to, actual rolling force, rolling speed and tension,
the actual rolling force is recalculated rolling force or actually measured rolling force;
and when the zone bit of the recalculating rolling force is a first zone bit, the current rolling data comprises basic parameters for calculating the rolling force, such as rolling speed, tension and the like, the actual rolling force is the recalculating rolling force, and the recalculating rolling force is obtained through a rolling force calculation formula according to the current rolling data. In a specific implementation process, when the flag bit of the recalculating rolling force is the first flag bit, the rolling speed and the tension are processed by using a rolling force formula to obtain the recalculating rolling force of each stand.
And when the zone bit of the recalculated rolling force is a second zone bit, the current rolling data comprise the actually measured rolling force, and the actual rolling force is the actually measured rolling force. In a specific implementation process, when the flag bit of the recalculated rolling force is the second flag bit, the actual rolling force is obtained based on the actually measured rolling force.
Specifically, the first flag bit and the second flag bit may be distinguishable from each other, for example, the first flag bit is 1, and the second flag bit is 0.
And executing S104, and obtaining the current value of the self-learning coefficient of each rack based on the actual rolling force and the rolling force set by each rack in the current rolling process of the coiled strip steel.
Specifically, the actual rolling force is divided by the rolling force set by each rack in the current rolling process of the coiled steel, so as to obtain the current value of the self-learning coefficient.
In some cases, the recalculation rolling force is inconsistent with the change trend of the rolling force set by each rack in the rolling process of the current coiled steel, for example, the actual tension is larger, the actual measurement rolling force is smaller than the recalculation rolling force, the trend correction can be performed, and the problem that the actual measurement rolling force and the recalculation rolling force with the same trend in the prior art cannot be corrected is solved.
Next, executing S105, judging whether the current value of the self-learning coefficient is within a preset range, if so, obtaining an updated value of the self-learning coefficient based on the current value of the self-learning coefficient and the old value of the self-learning coefficient; the old self-learning coefficient value is a self-learning coefficient value set by the multi-stand continuous rolling mill in the rolling process of the current coiled steel strip.
Specifically, the difference between the current value of the self-learning coefficient and the old value of the self-learning coefficient is multiplied by the self-learning gain coefficient to obtain a product, and the product is added with the old value of the self-learning coefficient to obtain an updated value of the self-learning coefficient.
The upper limit and the lower limit of the preset value can be set automatically after testing according to the requirement of deviation correction; the self-learning gain coefficient can also be set according to the same method, and the method of the method is not limited as long as the correction can be carried out.
It should be noted that, after determining whether the current value of the self-learning coefficient is within a preset range, the method further includes:
and if the current value of the self-learning coefficient is not in the preset range, returning an overrun flag bit. At the moment, self-learning updating is not carried out, and the rolling of the next coil of strip steel continues to use the self-learning parameters of the current coil of strip steel in the rolling process.
Specifically, if the current value of the self-learning coefficient is not within the preset range, returning an overrun flag bit, specifically including:
when the current value of the self-learning coefficient is larger than the upper limit boundary value of the preset range, returning to a first overrun zone bit;
and returning to a second overrun zone bit when the current value of the self-learning coefficient is smaller than the lower limit boundary value of the preset range.
And finally, S106 is executed, whether the self-learning coefficient updating value is within a preset range or not is judged, and if yes, the multi-stand continuous rolling mill is controlled to roll the next coil of strip steel based on the self-learning coefficient updating value.
At the moment, in specific implementation, the set rolling force during rolling of the next coil of strip steel is multiplied by the updated value of the self-learning coefficient to obtain the corrected rolling force during rolling of the next coil of strip steel;
and rolling the next coil of strip steel according to the corrected rolling force.
More specifically, the roll gap setting bounce amount is calculated when the corrected rolling force is used for rolling the next coil of strip steel.
As another case, when the self-learning coefficient update value is not within the preset range, the method further includes:
and if the self-learning coefficient updating value is not in the preset range, controlling the multi-frame continuous rolling mill to roll the next coil of strip steel based on the boundary value of the preset range.
In particular, the method comprises the following steps of,
when the updating value of the self-learning coefficient is larger than the upper limit boundary value of the preset range, controlling the multi-frame continuous rolling mill to roll the next coil of strip steel by using the upper limit boundary value;
and when the self-learning coefficient updating value is smaller than the lower limit boundary value of the preset range, controlling the multi-frame continuous rolling mill to roll the next coil of strip steel by using the lower limit boundary value.
In specific implementation, the set rolling force during rolling of the next coil of strip steel is multiplied by the boundary value of the preset range to obtain the corrected rolling force during rolling of the next coil of strip steel;
and rolling the next coil of strip steel according to the corrected rolling force.
When the next coil of strip steel is controlled to be rolled, the self-learning update coefficients of the stands obtained when the current coil of strip steel is rolled respectively correspond to the correction of the rolling force of each stand when the next coil of strip steel is controlled to be rolled.
The technical solution of the present embodiment is further illustrated by a typical application example as follows: when a strip steel is rolled on a 1580mm hot rolling production line for STC1 with the specification of 2.75mm x 1208mm, after the strip steel is threaded through an F6 frame, the tension between the frames is increased, the actually measured rolling force of F7 is reduced, the thickness of the head is thinner as shown in figure 2, the thickness is influenced by the actually measured tension increase, and the recalculated rolling force of the F7 frame is smaller. Specific numerical values are as follows. If the roll gap self-learning method is not arranged, the set rolling force of the next roll cannot be corrected to the direction of reduction through the rolling force self-learning, so that the set rolling force is larger, the roll gap calculated by the rolling force is smaller, and the thickness of the head is still thinner. By adopting the roll gap self-learning method, the current value of the self-learning coefficient is firstly calculated, the flag bit which is set in the model table and adopts the recalculation rolling force is 1, the recalculation rolling force (8743T) is adopted for actually measuring the rolling force, the set rolling force is 9546T, and the current value of the self-learning coefficient is the ratio of 8743 to 9546, namely 0.9159. An updated value of the self-learning coefficient is then calculated as the difference between the current value of the self-learning coefficient (0.9159) and the old value of the self-learning coefficient (0.9612) multiplied by the self-learning gain coefficient (0.15) plus the old value of the self-learning coefficient (0.9612), 0.9544. When setting the roll gap of the next roll, the roll force used for bounce calculation is calculated by multiplying the set roll force by the self-learning coefficient (0.9544), so that the roll force for roll gap calculation is reduced, the roll gap setting is increased, and the tendency of the head thickness to be thinner is improved, as shown in fig. 3.
The technical scheme in the embodiment of the application at least has the following technical effects or advantages:
the invention is applied to a multi-stand continuous rolling process, and firstly, the current rolling data of each stand when the current coiled steel passes through the last stand of a multi-stand continuous rolling mill is collected; then calling the zone bit of each rack for recalculating the rolling force; determining the actual rolling force of each rack based on the current rolling data and the zone bit of the recalculation rolling force of each rack; the actual rolling force is recalculated rolling force or actually measured rolling force; obtaining the current values of the self-learning coefficients of the racks based on the actual rolling force and the rolling force set by the racks in the current rolling process of the coiled strip steel; judging whether the current value of the self-learning coefficient is within a preset range, if so, obtaining an updated value of the self-learning coefficient based on the current value of the self-learning coefficient and the old value of the self-learning coefficient; the old value of the self-learning coefficient is a self-learning coefficient value set in the rolling process of the current coiled strip steel; controlling the multi-frame continuous rolling mill to roll the next strip steel according to the self-learning coefficient updating value, so as to realize the correction of the next strip steel; the self-learning coefficient value is corrected according to the set old self-learning coefficient value, so that the condition that the deviation of the rolling force cannot be corrected due to self-learning correction by utilizing the actually measured rolling force and the recalculated rolling force which have consistent variation trends in the prior art is avoided, the problem that the roll gap setting is not accurate due to the fact that the actual tension is larger, the actually measured rolling force and the recalculated rolling force are smaller and the roll gap self-learning cannot correct the next roll setting rolling force under the abnormal condition can be solved, and the rolling quality of the strip steel is improved; meanwhile, the recalculation rolling force or the actual measurement rolling force can be selected and used according to the mark bit of the recalculation rolling force to calculate the current value of the self-learning coefficient, so that the operation can be conveniently performed according to the selection of the working condition on site, the recalculated rolling force can avoid the condition of inaccurate detection possibly existing in the actual measurement rolling force, and the reliability of deviation correction is improved.
Example two
In this embodiment, a device for roll gap self-learning deviation rectification is applied to a multi-frame continuous rolling process, which is shown in fig. 4 and includes:
the data acquisition module is used for acquiring current rolling data of each rack in the multi-rack continuous rolling mill when the current coiled steel passes through the last rack of the multi-rack continuous rolling mill;
the mark calling module is used for calling the mark position of each rack for recalculating the rolling force;
the first obtaining module is used for obtaining the actual rolling force of each rack based on the current rolling data and the zone bit of the recalculation rolling force of each rack; the actual rolling force is recalculated rolling force or actually measured rolling force;
the second obtaining module is used for obtaining the current values of the self-learning coefficients of the racks based on the actual rolling force and the rolling force set by the racks in the current rolling process of the coiled strip steel;
the third obtaining module is used for judging whether the current value of the self-learning coefficient is within a preset range, and if so, obtaining an updated value of the self-learning coefficient based on the current value of the self-learning coefficient and the old value of the self-learning coefficient; the old value of the self-learning coefficient is a self-learning coefficient value set by all the racks in the current rolling process of the coiled strip steel;
and the control module is used for judging whether the self-learning coefficient updating value is within a preset range, and controlling the multi-stand continuous rolling mill to roll the next coil of strip steel based on the self-learning coefficient updating value if the self-learning coefficient updating value is within the preset range.
Since the roll gap self-learning deviation rectifying device described in this embodiment is a device used for implementing the roll gap self-learning deviation rectifying method in the first embodiment of the present application, based on the roll gap self-learning deviation rectifying method described in the first embodiment of the present application, those skilled in the art can understand the specific implementation manner of the device of this embodiment and various variations thereof, and therefore, how to implement the method in the embodiment of the present application by the system is not described in detail herein. The scope of the present application is intended to be covered by the claims so long as those skilled in the art can implement the method for self-learning roll gap deviation correction in the embodiments of the present application.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (9)
1. A roll gap self-learning deviation rectifying method is applied to a multi-frame continuous rolling process and is characterized by comprising the following steps:
collecting current rolling data of each stand in a multi-stand continuous rolling mill when current coiled steel passes through a last stand of the multi-stand continuous rolling mill;
calling the zone bit of each rack for recalculating the rolling force;
determining the actual rolling force of each rack based on the current rolling data and the zone bit of the recalculation rolling force of each rack; the actual rolling force is recalculated rolling force or actually measured rolling force;
obtaining the current values of the self-learning coefficients of the racks based on the actual rolling force and the rolling force set by each rack in the current rolling process of the coiled strip steel, wherein the actual rolling force is divided by the rolling force set by each rack in the current rolling process of the coiled strip steel;
judging whether the current value of the self-learning coefficient is within a preset range, if so, obtaining an updated value of the self-learning coefficient based on the current value of the self-learning coefficient and the old value of the self-learning coefficient; the old value of the self-learning coefficient is a self-learning coefficient value set by each rack in the current rolling process of the coiled strip steel;
and judging whether the self-learning coefficient updating value is in a preset range, if so, controlling the multi-stand continuous rolling mill to roll the next coil of strip steel based on the self-learning coefficient updating value.
2. The method of claim 1, wherein obtaining an updated value of a self-learning coefficient based on the current value of the self-learning coefficient and the old value of the self-learning coefficient comprises:
and multiplying the difference value of the current value of the self-learning coefficient and the old value of the self-learning coefficient by the self-learning gain coefficient to obtain a product, and adding the product to the old value of the self-learning coefficient to obtain an updated value of the self-learning coefficient.
3. The method of claim 1, wherein the current rolling data includes a roll speed, a tension and an exit thickness when the flag of the recalculated rolling force is the first flag, the actual rolling force is the recalculated rolling force, and the recalculated rolling force is obtained by a rolling force calculation formula according to the current rolling data.
4. The method of claim 1, wherein the current rolling data comprises a measured rolling force when the flag of the recalculated rolling force is the second flag, and wherein the actual rolling force is the measured rolling force.
5. The method of claim 1, wherein the method further comprises:
and if the self-learning coefficient updating value is not in the preset range, controlling the multi-frame continuous rolling mill to roll the next coil of strip steel based on the boundary value of the preset range.
6. The method of claim 5, wherein if the self-learning coefficient update value is not within the preset range, controlling the multi-stand continuous rolling mill to roll the next strip based on the preset range value comprises:
when the updating value of the self-learning coefficient is larger than the upper limit boundary value of the preset range, controlling the multi-frame continuous rolling mill to roll the next coil of strip steel by using the upper limit boundary value;
and when the self-learning coefficient updating value is smaller than the lower limit boundary value of the preset range, controlling the multi-frame continuous rolling mill to roll the next coil of strip steel by using the lower limit boundary value.
7. The method of claim 1, wherein the controlling the multi-stand continuous rolling mill to roll the next strip based on the updated values of the self-learning coefficients comprises:
multiplying the set rolling force when the next strip steel is rolled by the self-learning coefficient updating value to obtain the corrected rolling force when the next strip steel is rolled;
and rolling the next coil of strip steel according to the corrected rolling force.
8. The method of claim 5, wherein controlling the multi-stand continuous rolling mill to roll a next coil of strip steel based on the boundary value of the preset range comprises:
multiplying the set rolling force when the next coil of strip steel is rolled by the boundary value of the preset range to obtain the corrected rolling force when the next coil of strip steel is rolled;
and rolling the next coil of strip steel according to the corrected rolling force.
9. The utility model provides a device of roll gap self-learning rectifying, is applied to multimachine frame tandem rolling technology, its characterized in that includes:
the data acquisition module is used for acquiring current rolling data of each rack in the multi-rack continuous rolling mill when the current coiled steel passes through the last rack of the multi-rack continuous rolling mill;
the mark calling module is used for calling the mark position of each rack for recalculating the rolling force;
the first obtaining module is used for obtaining the actual rolling force of each rack based on the current rolling data and the zone bit of the recalculation rolling force of each rack; the actual rolling force is recalculated rolling force or actually measured rolling force;
the second obtaining module is used for obtaining the current value of the self-learning coefficient of each rack based on the actual rolling force and the rolling force set by each rack in the current rolling process of the coiled strip steel, wherein the actual rolling force is divided by the rolling force set by each rack in the current rolling process of the coiled strip steel;
the third obtaining module is used for judging whether the current value of the self-learning coefficient is within a preset range, and if so, obtaining an updated value of the self-learning coefficient based on the current value of the self-learning coefficient and the old value of the self-learning coefficient; the old value of the self-learning coefficient is a self-learning coefficient value set by each rack in the current rolling process of the coiled strip steel;
and the control module is used for judging whether the self-learning coefficient updating value is within a preset range, and controlling the multi-stand continuous rolling mill to roll the next coil of strip steel based on the self-learning coefficient updating value if the self-learning coefficient updating value is within the preset range.
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